Eclipse Community Forums
Forum Search:

Search      Help    Register    Login    Home
Home » Eclipse Projects » BIRT » How to define the scale of an X Axis (Chart Data)
How to define the scale of an X Axis (Chart Data) [message #73186] Mon, 12 September 2005 09:31 Go to next message
Eclipse User
Originally posted by: tobi.home.de

hi all,

i want to plot a graph with a lot of input-data and i don't want to group
the results. problem: the labels on my x-axis overlaps each other.

i need to define a scale for x-axis but it is not possible because the
responsible option-field is not active/grey, you can't enter a value
(under: chart dialog-> data -> x-axis -> scale).

this field is only active for the y-axis .. (like the screenshot in the
help-document: working with data on a chart axis -> defining the scale of
an axis). how can i enter data in this grey fields? the help-document
seems to be wrong in this section?

how can i define the scale for x axis??

please help, urgent!

thanks,
tobi
Re: How to define the scale of an X Axis (Chart Data) [message #73224 is a reply to message #73186] Mon, 12 September 2005 10:27 Go to previous messageGo to next message
Eclipse User
Originally posted by: tobi.home.de

This is a multi-part message in MIME format.
--------------070505020301090205030409
Content-Type: text/plain; charset=ISO-8859-15; format=flowed
Content-Transfer-Encoding: 7bit

additional information: the screenshot makes my problem clear (see
attachment)

> hi all,
> i want to plot a graph with a lot of input-data and i don't want to
> group the results. problem: the labels on my x-axis overlaps each other.
> i need to define a scale for x-axis but it is not possible because the
> responsible option-field is not active/grey, you can't enter a value
> (under: chart dialog-> data -> x-axis -> scale).
> this field is only active for the y-axis .. (like the screenshot in the
> help-document: working with data on a chart axis -> defining the scale
> of an axis). how can i enter data in this grey fields? the help-document
> seems to be wrong in this section?
>
> how can i define the scale for x axis??
>
> please help, urgent!
>
> thanks,
> tobi
>


--------------070505020301090205030409
Content-Type: image/png;
name="run.png"
Content-Transfer-Encoding: base64
Content-Disposition: inline;
filename="run.png"

iVBORw0KGgoAAAANSUhEUgAABBcAAALFCAYAAACYmgsvAABgtklEQVR42uzd b2gc+WH4/+Qr
FtW5bsOCcUIo5jBdlmCKqhqMEMey3IMeYlmWwxxNg3HDYQrBPUrIg8JRwg8T iiH0gQjmMLTm
EMIE50EpizAmpYhlWUS5J0YYIYyLCcIUsWwQIgQjls+vn0l2T7v6t7bmbEvz esGQ2ZnRn5GV
1X7e99mZrwUAAACAY/iaHwEAAABwHOICAAAAcCziAgAAAHAs4gIAAABwLOIC AAAAcCziAgAA
AHAs4gIAAABwLOICAAAAcCziAgAAAHAs4gIAAABwLOICAAAAcCziAgAAAHAs 4gIAAABwLOIC
AJwy5XI55PN53xMA8NqICwBw2v64f+1ryeJ7AgBe2996PwIAOGV/3MUFAOB1 /633IwCAU/JH
/Q8D+P2W/fzv//5v+OEPfxjOnj0bJicnQ7FYDD/96U/3HPe73/0uTE9PJ5/n 5z//+YFf/9e/
/nXyuS5evJh8zKt8TwCAuAAAnJC48OzZs/Cd73xn32O///3v7xsOCoVCsv/B gwf7Boipqanw
zjvvhPX19Vf6ngAAcQEAeMsiw2H6MxGq1Wr47//+72Rb/N9SqZRs/6//+q89 H/OrX/0q2Rcv
zPj48eOhfdeuXUv2LSwsvPL3BACc4NcffgQAcMr+uB8xkF9cXEz2X7lyZc++ VquV7Pve9763
78fevHkz2X/+/PmwubmZbPvss8+SbT/4wQ9e+XsCAE746w8/AgA4ZX/cjxjI 12q1ZH+73d6z
r9frJfv+9E//9MiPf++995IYkcvlkuss/Pa3v33l7wkAOOGvP/wIAOCU/XE/ YiAf39Zw2LUQ
4hIv8HiQbrebXPyxf+yZM2eGrrPwKt8TAHDCX3/4EQDAKfvjfsRAfmJi4si4 EGcjHObOnTuD
Y3/84x8f+3sCAE746w8/AgA4ZX/cx5y5sL29/UqfP97C8ty5c+Hb3/724AKP T548Odb3BACc
8NcffgQAcMr+uB8xkJ+bm0v2371796U/d7wmw+zsbPK2iXh3iZ/85CfJ54q3 oYy3o3zV7wkA
OOGvP/wIAOB06b/t4SD9uzvE6ybE6ye8jPgWiPix8XP0vf/++8m269evv/L3 BACcbP7KA8Ap
8+677yYD+Z/97GfJTINRcVv/gozf/e53wy9+8Yvw4sWLZF+8vWSc0RDvBDHq P/7jP5KP+f73
vz+0vdPpJLemjPvu3bv3St8TACAuAABvkZs3b+65QOOox48fh0KhcOhFHXeL 11T45je/GUql
0r7XaohvkYhvlTjo+gvjfE8AgLgAALxF4rUQ4kUXDxvIP336NHzve99LokA8 Jr514eLFi+FH
P/pRePTo0eC4OKshXlPhnXfeOfSWk//6r/+afJ74Ofa7/sI43xMAIC4AAAAA GSQuAAAAAMci
LgAAAADHIi4AAAAAxyIuAAAAAMciLgAAAADHIi4AAAAAxyIuAAAAAMciLgAA AADHIi4AAAAA
xyIuAAAAAMciLgAAAADHIi4AAAAAxyIuAAAAAMciLgAAAADHIi4AAAAAx/JG 48LXvva1Pcuo
lZWVUC6Xw+Tk5L7HNBqNUCqVkiWuAwAAAK95fP9Gv/jXDv/yjx8/DtPT02F1 dXXf/e12OwkP
nU4nWSqVSrINAAAAeI3j+zf6xY+IC9euXQvNZvPA/fV6PSwvLw8ex2NrtZp/ VQAAAHid4/s3
+sW/9rWQy+XC1NRU+Pzzz/fsP3v2bFhcXAznz58PxWIx3L9/f2h/Pp8PvV5v 8Diux20AAADA
axzfv+lvIAaBL774Irz//vthYWFhaN/ExES4fv162NraCi9evEjW7969++U3 v8/Mhxgr+uL1
Glqt1pFLPA4AAAA4oXGhL14z4eLFi0Pb4myF3TMTtre3k1kMfaMzF3Z2do6c uXDUWzEAAACA
l/PWjLTjzIQYE3aLMxW63e7QMZcuXRo8Hr3mQlyvVquHn7C4AAAAAKl6K0ba 8W0PH3/88Z7r
Ljx69Ci5qGPcH2cl3LhxIzx48GCwP94ZIt4hIgaIzc3N5M4RS0tLh5+wuAAA AACpeuMXdIzX
VZiZmQn37t3b95h4QccLFy6Ec+fO7bmgY9RoNJIZD4VCIczPz4/1NY/a319e dtth2wEAAEBc
OC0nPE5c+P/C3pAwxrbDtgMAAMCpHWtn7oTFBQAAAEh3rJ25ExYXAAAAIN2x duZOWFwAAACA
dMfamTthcQEAAADSHWtn7oTFBQAAAEh3rJ25ExYXAAAAIN2xduZOWFwAAACA dMfamTthcQEA
AADSHWtn7oTFBQAAAEh3rJ25ExYXAAAAIN2x9pse6I8uB1lcXNx3f6PRCKVS KVniurgAAAAA
GYsL41hdXQ2zs7N7jm+326FcLodOp5MslUol2SYuAAAAgLgw0O12w8zMTNjY 2NhzfL1eD8vL
y4PHzWYz1Go1cQEAAACyFBdyuVyYmpoKn3/++b7HXLlyZTAbYXTAns/nQ6/X GzyO63GbuAAA
AAAZiQv9IPDFF1+E999/PywsLAztu3Xr1tC2/Qbyo2Ks6FtZWQmtVuvIJR4n LgAAAMAJjQt9
8ZoJFy9e3DtQP+Sij6MzF3Z2dsxcAAAAgKzGhRcvXoRisfhSYWD0mgtxvVqt vtTn2G+/uAAA
AADjeytGwFtbW+Hjjz8+8LoLB4WBeC2GeIeIeNHHzc3N5M4RS0tL4gIAAABk JS7EAfjExERy
N4h79+6NdfyoRqORzHgoFAphfn7+lT6HuAAAAAAnNC68kRMWFwAAACDdsXbm TlhcAAAAgHTH
2pk7YXEBAAAA0h1rZ+6ExQUAAABId6yduRMWFwAAACDdsXbmTlhcAAAAgHTH 2pk7YXEBAAAA
0h1rZ+6ExQUAAABId6yduRMWFwAAACDdsXbmTlhcAAAAgHTH2pk7YXEBAAAA 0h1rv+mB/uiy
3/5cLhdmZ2fDkydP9nyORqMRSqVSssR1cQEAAAAyFhfG0ev1wu3bt8P09PTQ 9na7Hcrlcuh0
OslSqVSSbeICAAAAiAv7mpycHHpcr9fD8vLy4HGz2Qy1Wu1YX1NcAAAAgJcc 37/RL/6HtzxM
TU2Fzz///NBjY0SIMxN2y+fzyayGvrget4kLAAAAkJG40A8CX3zxRXj//ffD wsLCvsc8f/48
XL58OWxsbBwZCmKs6FtZWQmtVuvIJR4nLgAAAMAJjQt98ZoJFy9e3LN9fX09 eatDDAyjRmcu
7OzsmLkAAAAAWY0LL168CMVicWhbDArVajVsb2/v+zGj11yI6/F4cQEAAAAy Fhe2trbCxx9/
vOe6C3Nzc2Ftbe3Aj4t3hojXYeh2u2FzczO5c8TS0pK4AAAAAFmJC3EAPjEx EWZmZsK9e/f2
H+iPLKMajUYy46FQKIT5+fmxvqa4AAAAAKckLryRExYXAAAAIN2xduZOWFwA AACAdMfamTth
cQEAAADSHWtn7oTFBQAAAEh3rJ25ExYXAAAAIN2xduZOWFwAAACAdMfamTth cQEAAADSHWtn
7oTFBQAAAEh3rJ25ExYXAAAAIN2xduZOWFwAAACAdMfamTthcQEAAADSHWtn 7oTFBQAAAEh3
rP2mB/qjy6hGoxFKpVKyxPWX3S8uAAAAwCmPC4dpt9uhXC6HTqeTLJVKJdk2 7n5xAQAAADIe
F+r1elheXh48bjaboVarjb1fXAAAAIAMxIVcLhempqbC559/vmd/Pp8PvV5v 8Diux23j7hcX
AAAA4JTHhX4Q+OKLL8L7778fFhYWjgwBMUaMu39lZSW0Wq0jl3icuAAAAAAn NC70xWsmXLx4
cWjb6MyEnZ2dQ2cujO7f94TNXAAAAIDTGRdevHgRisXi0LbRayrE9Wq1OvZ+ cQEAAAAyEhe2
trbCxx9/vOe6C/HOD/EOEN1uN2xubiZ3hlhaWhp7v7gAAAAApzwuxAH4xMRE mJmZCffu3dv3
mEajkcxoKBQKYX5+/qX3iwsAAABwiuPCGzlhcQEAAADSHWtn7oTFBQAAAEh3 rJ25ExYXAAAA
IN2xduZOWFwAAACAdMfamTthcQEAAADSHWtn7oTFBQAAAEh3rJ25ExYXAAAA IN2xduZOWFwA
AACAdMfamTthcQEAAADSHWtn7oTFBQAAAEh3rJ25ExYXAAAAIN2x9tvyjSwu Lg4NyJ8+fRo+
/PDDcObMmZDL5cJ7770Xms3m0Mc0Go1QKpWSJa6LCwAAAJDRuLC6uhpmZ2eH BuQXL14M9+7d
C71eL1ni+tmzZwf72+12KJfLodPpJEulUkm2iQsAAACQsbjQ7XbDzMxM2NjY GBqQx5Cwvr4+
ePzs2bMkOPTV6/WwvLw8eBxnNdRqNXEBAAAAshYXrly5MphxsHtAHt/m8O67 7yYB4dGjR6Fa
rSZvlejL5/PJjIa+uB63iQsAAACQobhw69atsLCwsO/Af3NzM5nRcPXq1fDR Rx+FTz75JGxv
bx8aCeK1GXZbWVkJrVbryCUeJy4AAADACYwLyUB8nyWKF3N8+PDh4Ni1tbUk MvSNzlzY2dkx
cwEAAACyFhcOG/hPTEzs2T85OTlYH73mQlyPb50QFwAAAEBcSExNTQ29XeHB gwfJ7Sj74nUa
4h0i4gUh41so4p0jlpaWxAUAAAAQF34v3h1ibm4uma0Ql/iWiOfPnw8dHy/6 WCwWQ6FQCPPz
8y/9NcQFAAAAOGVx4bWcsLgAAAAA6Y61M3fC4gIAAACkO9bO3AmLCwAAAJDu WDtzJywuAAAA
QLpj7cydsLgAAAAA6Y61M3fC4gIAAACkO9bO3AmLCwAAAJDuWDtzJywuAAAA QLpj7cydsLgA
AAAA6Y61M3fC4gIAAACkO9bO3AmLCwAAAJDuWPtt+UYWFxf3DMhXVlZCuVwO k5OTvx+0j+xv
NBqhVColS1wXFwAAACCjcWF1dTXMzs4ODcgfP34cpqenk337abfbSXjodDrJ UqlUkm3iAgAA
AGQsLnS73TAzMxM2NjaGBuTXrl0LzWbzwI+r1+theXl58DgeW6vVxAUAAADI Wly4cuXKYMbB
7gH52bNnk7dKnD9/PhSLxXD//v2hj8vn86HX6w0ex/W4TVwAAACADMWFW7du hYWFhX0H/hMT
E+H69etha2srvHjxIlm/e/fuoZEgl8sNPY7XbGi1Wkcu8ThxAQAAAE5gXOhf pHF0ieJshd0z
E7a3t5NZDH2jMxd2dnbMXAAAAICsxYXDBv5xpkK8HkNfnL1w6dKlwePRay7E 9Wq1Ki4AAACA
uPB7jx49Si7qGN8WEWcl3LhxIzx48GCwP16nId4hIgaIzc3N5M4RS0tL4gIA AACIC1+KF3S8
cOFCOHfu3J4LOkaNRiN5+0ShUAjz8/Ov9DXEBQAAADhFceG1nLC4AAAAAOmO tTN3wuICAAAA
pDvWztwJiwsAAACQ7lg7cycsLgAAAEC6Y+3MnbC4AAAAAOmOtTN3wuICAAAA pDvWztwJiwsA
AACQ7lg7cycsLgAAAEC6Y+3MnbC4AAAAAOmOtTN3wuICAAAApDvWztwJiwsA AACQ7lj7bflG
FhcXDxyQH7Sv0WiEUqmULHFdXAAAAICMxoXV1dUwOzu774D8oH3tdjuUy+XQ 6XSSpVKpJNvE
BQAAAMhYXOh2u2FmZiZsbGzsGZAftq9er4fl5eXB42azGWq1mrgAAAAAWYsL V65cGcw4GB2Q
H7Yvn8+HXq83eBzX4zZxAQAAADIUF27duhUWFhb2Hfgftu+gSJDL5YYer6ys hFardeQSjxMX
AAAA4ATGhWQgvs9y1L5odObCzs6OmQsAAACQtbjwMgP/o665ENer1eqxvsbL hARxAQAAAP4w
Fn6rvpmXiAvxWgzxDhHxoo+bm5vJnSOWlpbEBQAAABAXxt/XaDRCsVgMhUIh zM/PH/trvExI
EBcAAADgD2PhzJ2wuAAAAADpjrUzd8LiAgAAAKQ71s7cCYsLAAAAkO5YO3Mn LC4AAABAumPt
zJ2wuAAAAADpjrUzd8LiAgAAAKQ71s7cCYsLAAAAkO5YO3MnLC4AAABAumPt zJ2wuAAAAADp
jrUzd8LiAgAAAKQ71s7cCYsLAAAAkO5Y+235RhYXF/cO3v9vyeVyYXZ2Njx5 8mTPxzQajVAq
lZIlrosLAAAAkNG4sLq6mgSE/QbkvV4v3L59O0xPTw9tb7fboVwuh06nkyyV SiXZJi4AAABA
xuJCt9sNMzMzYWNj49AB+eTk5NDjer0elpeXB4+bzWao1WriAgAAAGQtLly5 cmUw4+CgAXmM
CHFmwm75fD6Z1dAX1+M2cQEAAAAyFBdu3boVFhYWDh34P3/+PFy+fDmZ2XBU JIjXZ9htZWUl
tFqtI5d4nLgAAAAAJzAu9C/aOLr0ra+vJ291iIFh1OjMhZ2dHTMXAAAAIGtx 4bCBfwwK1Wo1
bG9v73vs6DUX4no8XlwAAAAAcSExNzcX1tbWDjw2XqchXochXhByc3MzuXPE 0tKSuAAAAADi
wq5B+gFvl+hrNBqhWCyGQqEQ5ufnX/priAsAAABwyuLCazlhcQEAAADSHWtn 7oTFBQAAAEh3
rJ25ExYXAAAAIN2xduZOWFwAAACAdMfamTthcQEAAADSHWtn7oTFBQAAAEh3 rJ25ExYXAAAA
IN2xduZOWFwAAACAdMfamTthcQEAAADSHWtn7oTFBQAAAEh3rJ25ExYXAAAA IN2xduZOWFwA
AACAdMfab8s3sri4uGdA3mg0QqlUSpa4Puqo/eICAAAAZCQurK6uhtnZ2aEB ebvdDuVyOXQ6
nWSpVCrJtnH3iwsAAACQkbjQ7XbDzMxM2NjYGBqQ1+v1sLy8PHjcbDZDrVYb e7+4AAAAABmJ
C1euXBnMONg9IM/n86HX6w0ex/W4bdz94gIAAABkIC7cunUrLCws7Dvw329w nsvlxt4frays
hFardeQSjxMXAAAA4ATGhWQgvs8Sjc5M2NnZOXTmwuj+w77mkd+TuAAAAAAn Iy4cNvAfvaZC
XK9Wq2PvFxcAAAAg43EhXoch3gEiXvBxc3MzuTPE0tLS2PvFBQAAAMh4XIga jUYoFouhUCiE
+fn5PccftV9cAAAAgIzFhddywuICAAAApDvWztwJiwsAAACQ7lg7cycsLgAA AEC6Y+3MnbC4
AAAAAOmOtTN3wuICAAAApDvWztwJiwsAAACQ7lg7cycsLgAAAEC6Y+3MnbC4 AAAAAOmOtTN3
wuICAAAApDvWztwJiwsAAACQ7lg7cycsLgAAAEC6Y+03PdCPSy6XC1NTU+Hh w4dD+58+fRo+
/PDDcObMmeSY9957LzSbzcH+RqMRSqVSssR1cQEAAAAyFhd2W1tbC+fPnx/a dvHixXDv3r3Q
6/WSJa6fPXs22ddut0O5XA6dTidZKpVKsk1cAAAAgIzGhRgPCoXC0LYYEtbX 1wePnz17lgSH
qF6vh+Xl5cG+OKOhVquJCwAAAJDFuBBnHty4cSPcvXt3aHt8q8O7776bRIRH jx6FarWavFUi
yufzSZDoi+txm7gAAAAAGYsL/esuLCws7Nm3ubkZZmZmwtWrV8NHH30UPvnk k7C9vX1gJIjX
ZdhtZWUltFqtI5d4nLgAAAAAJzQuRFtbW+HTTz8Nd+7cGdoeL+a4+yKP8boM MTJEozMXdnZ2
zFwAAACArMaFvsnJyaHHExMTBx4zes2FuB7fNiEuAAAAQEbjQrwg46VLl4a2 xdtT7n7LwoMH
D5LbUUbxzhDxDhHdbjd5+0S8c8TS0pK4AAAAAFmKC/fv3w8XLlxIZiN88MEH yd0gdouP5+bm
kv1xiW+JeP78+WB/vOBjsVhM7jIxPz8/3gmLCwAAAHB64sL169eTWQev9YTF BQAAAEh3rJ25
ExYXAAAAIN2xduZOWFwAAACAdMfamTthcQEAAADSHWtn7oTFBQAAAEh3rJ25 ExYXAAAAIN2x
duZOWFwAAACAdMfamTthcQEAAADSHWtn7oTFBQAAAEh3rJ25ExYXAAAAIN2x duZOWFwAAACA
dMfab3qgH5dcLhempqbCw4cP9xyzsrISyuVymJycHBzf12g0QqlUSpa4Li4A AABAxuLCbmtr
a+H8+fND2x4/fhymp6fD6urqnuPb7XYSHTqdTrJUKpVkm7gAAAAAGY0LvV4v FAqFoW3Xrl0L
zWZz3+Pr9XpYXl4ePI7H1Wo1cQEAAACyGBfizIMbN26Eu3fvDm0/e/ZsWFxc TGY0FIvFcP/+
/cG+fD6fBIm+uB63iQsAAACQsbjQv47CwsLCnn0TExPh+vXrYWtrK7x48SJZ 7weI/Qbv8doN
u8XrNbRarSOXeJy4AAAAACc0LkQxHnz66afhzp07Q9vjbIXdsxO2t7cH12UY nbmws7Nj5gIA
AABkNS70xTtC7BZnKnS73cHjOHvh0qVLyfroNRfierVaFRcAAAAgq3EhXpCx Hw76Hj16lFzU
Mc5siDMT4nUZHjx4kOyLd4aId4iI8WFzczO5c8TS0pK4AAAAAFmKC/ECjRcu XEhmLHzwwQfh
2bNne46JF3SMx5w7d27ogo5Ro9FI3joR7zIxPz8/3gmLCwAAAHB64kJ820Oc dfBaT1hcAAAA
gHTH2pk7YXEBAAAA0h1rZ+6ExQUAAABId6yduRMWFwAAACDdsXbmTlhcAAAA gHTH2pk7YXEB
AAAA0h1rZ+6ExQUAAABId6yduRMWFwAAACDdsXbmTlhcAAAAgHTH2pk7YXEB AAAA0h1rZ+6E
xQUAAABId6z9pgf6ccnlcmFqaio8fPjwwGMXFxf3DNgbjUYolUrJEtfFBQAA AMhYXNhtbW0t
nD9/ft99q6urYXZ2dmjA3m63Q7lcDp1OJ1kqlUqyTVwAAACAjMaFXq8XCoXC nu3dbjfMzMyE
jY2NoQF7vV4Py8vLg8fNZjPUajVxAQAAALIYF+LMgxs3boS7d+/u2XflypXB jITdA/Z8Pp8E
ib64HreJCwAAAJCxuNC/7sLCwsKefbdu3RraPjq4HxWv3bDbyspKaLVaRy7x OHEBAAAATmhc
iLa2tsKnn34a7ty5s294GF2i0ZkLOzs7Zi4AAABAVuNC3+Tk5NhhYPSaC3G9 Wq0efcLiAgAA
AKTqrRkBxwsyXrp0aewwEK/DEO8QES/4uLm5mdw5YmlpSVwAAACALMWF+/fv hwsXLiQzFj74
4IPw7NmzlwoDjUYjFIvF5C4T8/Pz452wuAAAAACnJy5cv349mXXwWk9YXAAA AIB0x9qZO2Fx
AQAAANIda2fuhMUFAAAASHesnbkTFhcAAAAg3bF25k5YXAAAAIB0x9qZO2Fx AQAAANIda2fu
hMUFAAAASHesnbkTFhcAAAAg3bF25k5YXAAAAIB0x9qZO2FxAQAAANIda2fu hMUFAAAASHes
/aYH+nHJ5XJhamoqPHz48MD9s7Oz4cmTJ0P7G41GKJVKyRLXxQUAAADIWFzY bW1tLZw/f37f
fb1eL9y+fTtMT08PtrXb7VAul0On00mWSqWSbBMXAAAAIKNxIQaEQqFw6DGT k5OD9Xq9HpaX
lwePm81mqNVqR5+wuAAAAACpeitGwHHmwY0bN8Ldu3cPPCaGhDg7oS+fzydB oi+ux23iAgAA
AGQsLvSvq7CwsHDgMc+fPw+XL18OGxsbh0aCeG2G3VZWVkKr1TpyiceJCwAA AHBC40K0tbUV
Pv3003Dnzp09+9bX15O3O8TAsNvozIWdnR0zFwAAACCrcaFv9zUVohgUqtVq 2N7e3nPs6DUX
4no8VlwAAACAjMaFeEHGS5cuDW2bm5tL7iKxn3hniHgNhm63GzY3N5M7Rywt LYkLAAAAkKW4
cP/+/XDhwoVkxsIHH3wQnj17tnegPrLs1mg0QrFYTO4yMT8/P94JiwsAAABw euLC9evXk1kH
r/WExQUAAABId6yduRMWFwAAACDdsXbmTlhcAAAAgHTH2pk7YXEBAAAA0h1r Z+6ExQUAAABI
d6yduRMWFwAAACDdsXbmTlhcAAAAgHTH2pk7YXEBAAAA0h1rZ+6ExQUAAABI d6yduRMWFwAA
ACDdsXbmTlhcAAAAgHTH2pk7YXEBAAAA0h1rv+mBflxyuVyYmpoKDx8+3HNM o9EIpVIpWeL6
uPvEBQAAAMhAXNhtbW0tnD9/fmhbu90O5XI5dDqdZKlUKsm2o/aJCwAAAJDB uNDr9UKhUBja
Vq/Xw/Ly8uBxs9kMtVrtyH3iAgAAAGQsLsSZBzdu3Ah3794d2p7P55Po0BfX 47aj9okLAAAA
kKG40L/uwsLCwlghIF6f4ah9fSsrK6HVah25xOPEBQAAADihcSHa2toKn376 abhz587Q9tHZ
CTs7OwfOXNi979ATNnMBAAAATl9c6JucnBx6PHpdhbherVaP3CcuAAAAQAbj Qrwg46VLl4a2
xbs/xLtAdLvdsLm5mdwdYmlp6ch94gIAAABkJC7cv38/XLhwIZmx8MEHH4Rn z57tOabRaIRi
sZjcSWJ+fn7sfeICAAAAZCAuXL9+PZl18FpPWFwAAACAdMfaxxmkHzSAPmzf Gz9hcQEAAADS
HWsfZ5C+3wD6s88+ExfEBQAAADLkpUfAX//61wfx4LDlnXfeERcAAABAXNjr W9/61pFh4Rvf
+Eb45S9/KS4AAACAuHDEIPwEDqDFBQAAAEh5rP2qH/jd7343/OY3v9l33/b2 diiVSm/nCYsL
AAAAkO5Y+ziD9MnJyfDw4cOh7f/5n/8Zzpw544KO4gIAAAAZ8coj4O985zuD t0b87d/+bbLt
7//+7wcXfIzXXRAXAAAAQFw41F//9V8PAkN/tkJc/uqv/urtPWFxAQAAANId ax/3E/zoRz8a
ulPEP/zDP7zdJywuAAAAQLpj7eN8cHwbRD8q/PEf//Fg/W/+5m/GHujHJZfL hdnZ2fDkyZOh
/U+fPg0ffvhhMisiHvPee++FZrM52N9oNJILR8YlrosLAAAAcILiQrxbRBxA x2ssxMjQjw39
ay7EazKMq9frhdu3b4fp6emh7RcvXgz37t1L9sclrp89ezbZ1263Q7lcDp1O J1kqlUqyTVwA
AACAExIX4uD5j/7oj5K7Q+wWH8ftrzK4jnef2C2GhPX19cHjZ8+eJcEhqtfr YXl5ebAvzmio
1WriAgAAAJyUuBBnLvzmN7/Zd9/29nbyVoWXEUNBnH2wW3yrw7vvvpvse/To UahWq8lbJaJ8
Pp/MZuiL63GbuAAAAAAnJC6k6fnz5+Hy5cthY2NjaPvm5maYmZkJV69eDR99 9FH45JNPknBx
UCSI12XYbWVlJbRarSOXeJy4AAAAAG8wLsQB/+9+97tX+tj4tof4doYYGEbF izk+fPhw8Hht
bS2JDNHozIWdnR0zFwAAAOAkxYU4mP/Lv/zLodtQDg2wxxhcx6AQ3+rQn40w amJiYs+2/nUZ
Rq+5ENfj5xIXAAAA4ITEhXjryN1h4VXiwtzcXDIb4SBTU1NDb1l48OBBcjvK KN4ZIl6jodvt
Jm+fiHeOWFpaEhcAAADgpMSF//f//l8ygI7XQ4izGF4lLozGidGPiXeHiAEi zlaIS3xLxO63
T8QLPhaLxVAoFML8/Px4JywuAAAAwNsRF/oxIIaF3YP2ONNg3LjwRk5YXAAA AIB0x9qv+oF/
8id/kgyg/+Iv/iL8z//8T7K+uroavvWtbyXr3/nOd8QFAAAAEBcO9m//9m/7 vq0hLl//+teH
7vIgLgAAAIC4sK9///d/D+fPn09iQj8q/Nmf/dnQXRzeuhMWFwAAACDdsXbm TlhcAAAAgHTH
2pk7YXEBAAAA0h1rH2eQftAA2t0ixAUAAACyI/W48Nlnn4kL4gIAAAAZ8tIj 4P7FG49a3nnn
HXEBAAAAxIW9vvWtbx0ZFr7xjW+EX/7yl+ICAAAAiAtHDMJP4ABaXAAAAICU x9pveqAfl1wu
F2ZnZ8OTJ0/2HLOyshLK5XKYnJzcEzQajUYolUrJEtfFBQAAAMhYXOjr9Xrh 9u3bYXp6emj7
48ePk22rq6t7PqbdbifRodPpJEulUkm2iQsAAACQwbjQF2cn7Hbt2rXQbDb3 PbZer4fl5eXB
43hcrVY7+oTFBQAAAEjVWzMCjqEgzj7Y7ezZs2FxcTGcP38+FIvFcP/+/cG+ fD6fzHjoi+tx
m7gAAAAAGYwLz58/D5cvXw4bGxtD2ycmJsL169fD1tZWePHiRbJ+9+7dAyNB vHbDbvF6Da1W
68glHicuAAAAwAmNC+vr68nbGWJgGBVnK+yenbC9vZ3MYohGZy7s7OyYuQAA AABZiwsxKFSr
1SQa7CfOVOh2u4PHcfbCpUuXkvXRay7E9fi5xAUAAADIUFyYm5sLa2trB+5/ 9OhRclHH+LaI
ODPhxo0b4cGDB8m+eGeIeI2GGB82NzeTO0csLS2JCwAAAJCluJAMxEeWUfGC jhcuXAjnzp0b
uqBj1Gg0krdOFAqFMD8/P/bXFBcAAADglMSFN3LC4gIAAACkO9bO3AmLCwAA AJDuWDtzJywu
AAAAQLpj7cydsLgAAAAA6Y61M3fC4gIAAACkO9bO3AmLC6/t5zx6B5CD7gry MscCAAAgLogL
WYoLx/iZ+TkCAACIC+KCuCAuAADASX0t/xUtiAvigrggLgAAQJZey6e9eG0v LogL4oK4AAAA
4oK4gLggLogLAADAqY4L43yNr+L7GOdz/uIXvwjnz5/fM+5J82tkNi7033eT y+XC7OxsePLk
yYHHLi4u7vlhNhqNUCqVkiWuiwviAgAAkN24kMaY8MyZM19JXIjj3t/+9rdv 5Jxf5ZxOVFzo
6/V64fbt22F6enrf/aurq0l82P0P1m63Q7lcDp1OJ1kqlUqyTVwQFwAAgNMR F54+fRrq9frQ
97a8vBwuXLgQ3nnnnXD37t0Dx3vdbjfMzc0lg/rRscZBn2Pci0/++Mc/Dt/8 5jfDxYsXw/37
94eO/93vfheuXr2afO44xn3+/PmBn3fc72v02P2+xujnmZqaChMTEwd+7Vqt lvx8T1Vc6Juc
nNyzLf5CzMzMhI2NjaEfRvwFiz+wvmazmfxwxAVxAQAAONlxIf4X/p/85CfJ AHn3uC9+jitX
riTjw/X19XDu3LkDx3s//OEPw89//vPkP2aPHjPu59jPT3/60/DP//zPyfr2 9nb42c9+NvQx
//iP/5j8x/Pos88+Cz/4wQ8O/LyjY6Bxv6+Dvkb/uO9973vh17/+9aHnFH+u f/7nf578nON5
nJq4EE8szj4YFX+4/RkJu38g+Xx+6Jckrsdt4oK4AAAAnNy4EN8S/93vfjf8 y7/8y75hYPdA
+LCZAIVCYc/Hv+zn2E+xWAxbW1sHfsy3v/3twf749eMMh3Hjwrjf10Ffo3/c zs7OWOcUPzb+
nOMMjPhzP/FxIU7huHz5clJodrt161ZYWFg48Ac/Kk532W1lZSW0Wq0jl3ic uCAuAAAAbzYu
fPLJJ8l/sV9bWzvwexv3cXxLwHE/x372+7x7xjK7lv7x48SFcb+vg77GOF9n P/2ZEvHnf2Lj
QjyJ+HaG3e8ROegHtvt9IqMzF2KZMXNBXAAAAE5uXIju3buXzFyIbzfYb+bC uI/jf90f/S/4
acSFeJ2Dw76veEeIeE2EccairxoXDvoaLxsX4nnEn3P8ecef+7F+b97kL20M CtVqdez3dxx2
zYW4Hj+XuCAuAAAAJzcuRP1rLsRrAvzqV796pQF4vOhi/9oILxMTYjzY7z9+ 9/3d3/3dYIZ9
PO7GjRtDnyN+zXgdhK8yLhz0NQ76OvudU/y5xmta/NM//VMqd7F4oyO3eOXO g6a7HPVDitdh
iNdoiBd83NzcTO4csbS0JC6ICwAAwAmPC3373S1i3AF4/I/YcczZv2PCuJ8j jisPmxUfx6Bx
9n0csMc7Ozx48GDP5/j000+T2QW7v3aaceGgr3HQ19nvnOLP9dTcLeKgtzyM GwYajUZyMY14
oY75+fmXDhTigrgAAAC8vXGBE/R7k8X/o4gL4gIAAHDIa/mvaEFcEBfEBXEB AAAAcUFcEBcA
AAAQF8QFcQEAAABxQVwQF8QFAAAAcUFcEBfEBQAAAMQFcUFcAAAAQFwQF8QF AAAAxAVxQVwQ
FwAAAMQFcUFcEBcAAADEBXFBXBAXAAAAEBfEBXEBAACArMWFZAD5f0sulwuz s7PhyZMnL7W/
0WiEUqmULHFdXBAXAAAAyFhc6Ov1euH27dthenp67P3tdjuUy+XQ6XSSpVKp JNvEBXEBAACA
DMaFvsnJybH31+v1sLy8PHjcbDZDrVYTF8QFAAAAshoXYiiIsw/G3Z/P55MZ DX1xPW4TF8QF
AAAAMhgXnj9/Hi5fvhw2NjbG3r/foDNem2G3lZWV0Gq1jlziceKCuAAAAMAJ jQvr6+vJ2xli
QHiZ/aMzF3Z2dsxcEBcAAADIWlyIwaBarYbt7e2X3j96zYW4Ho8VF8QFAAAA MhQX5ubmwtra
2ivtj3eGiNdg6Ha7YXNzM7lzxNLSkrggLgAAAJCluJAMIEeWl9nfaDRCsVgM hUIhzM/Pj/01
xQVxAQAAgFMSF97UoFdcEBcAAAAQF8QFcQEAAABxQVwQF8QFAAAAcUFcGH+g PHLNiONsExcA
AAAQF7IYF1LcJi4AAAAgLogL4oK4AAAAIC6IC+KCuAAAAIC4IC6ICwAAAIgL 4oK4AAAAgLgg
LogL4gIAAIC4IC6IC+ICAACAuPCmBqD/t+RyuTA7OxuePHmy55hGoxFKpVKy xPVx94kL4gIA
AAAZiAt9vV4v3L59O0xPTw9tb7fboVwuh06nkyyVSiXZdtQ+cUFcAAAAIGNx oW9ycnLocb1e
D8vLy4PHzWYz1Gq1I/eJC+ICAAAAGYwLMRTE2Qe75fP5ZFZDX1yP247aJy6I CwAAAGQsLjx/
/jxcvnw5bGxsHBkC4vUZjtrXt7KyElqt1pFLPE5cEBcAAAA4oXFhfX09eTtD DAyjRmcn7Ozs
HDhzYfe+owa9aQyKxQVxAQAAgLcgLsSgUK1Ww/b29r77R6+rENfj8UftExfE BQAAADISF+bm
5sLa2tqB++PdH+J1GLrdbtjc3EzuDrG0tHTkPnFBXAAAACAjcSEZQI4soxqN RigWi6FQKIT5
+fmx94kL4gIAAAAZiAtvatArLogLAAAAiAvigrgAAACAuCAuiAviAgAAgLgg LogL4gIAAIC4
IC6IC+ICAAAA4oK4IC4AAAAgLogL4oK4AAAAIC6IC+KCuAAAACAuiAvigrgA AACAuCAuiAsA
AACIC+KCuCAuAAAAiAsvYX19Pdy8eTNMTU0NbX/69Gn48MMPw5kzZ0Iulwvv vfdeaDabQ8c0
Go1QKpWSJa6LC+ICAAAAGYwLV69eDXfu3NkzkLx48WK4d+9e6PV6yRLXz549 O9jfbrdDuVwO
nU4nWSqVSrJNXBAXAAAAyFhcOGjgH0NCnNXQ9+zZsyQ49NXr9bC8vDx4HGc1 1Go1cUFcAAAA
QFz4vfg2h3fffTcJCI8ePQrVajV5q0RfPp9PZjT0xfW4TVwQFwAAABAXEpub m2FmZiZ528RH
H30UPvnkk7C9vX1oKIjXZuhbWVkJrVbryCUeJy6ICwAAAJzCuBAv5vjw4cPB 47W1tSQy9I3O
XNjZ2TFzQVwAAABAXPjSxMTEnmMmJycH66PXXIjr8a0T4oK4AAAAgLiQiLem 3P2WhQcPHiS3
o+yLd4aId4jodrvJWyjinSOWlpbEBXEBAACArMWFZCA5skTx7hBzc3PJbIW4 xLdEPH/+fOhj
40Ufi8ViKBQKYX5+/qUDhrggLgAAAHAK4sLrHvSKC+ICAAAA4oK4IC4AAAAg LogL4oK4AAAA
IC6IC+KCuAAAACAuiAvigrgAAACAuCAuiAsAAACIC+KCuCAuAAAAiAvigrgg LgAAAIgL4oK4
IC4AAAAgLogL4gIAAADigrggLogLAAAA4sJLWF9fDzdv3gxTU1N79q2srIRy uRwmJyd/P+Ac
GWw2Go1QKpWSJa6LC+ICcMKeK0ae2/fb9rLHAgCQwbhw9erVcOfOnT0vDh8/ fhymp6fD6urq
vh/XbreT8NDpdJKlUqkk28QFcQHwXAEAQMbiwkED/2vXroVms3ng8fV6PSwv Lw8ex2NrtZq4
YMAAeK4AAEBc+L2zZ8+GxcXFcP78+VAsFsP9+/eH9ufz+dDr9QaP43rcJi4Y MACeKwAAEBcS
ExMT4fr162Frayu8ePEiWb979+6hoSCXyw3W4/UaWq3WkUs8TlwwYAA8VwAA cArjQpytsHtm
wvb2djKLoW905sLOzo6ZCwYMgOcKAADEhS/FmQrdbnfwOM5euHTp0uDx6DUX 4nq1WhUXDBgA
zxUAAIgLv/fo0aPkoo7xbRFxVsKNGzfCgwcPBvvjnSHiHSJigNjc3EzuHLG0 tCQuGDAAnisA
AMhaXNh9n/LR+5XHCzpeuHAhnDt3bs8FHaNGo5G8faJQKIT5+fmXDhjiggED 4LkCAIBTEBde
9wtZccGAAfBcAQCAuCAuGDAA4oLnCgAAcUFcEBcMGEBc8FwBACAuiAviggED 4LkCAMDrOnFB
XDBgADxXAAAgLogLBgyAuAAAgLggLogLBgwgLniuAAAQF8QFccGAAfBcAQAg LogL4oIBA+C5
AgAAcUFcMGAAPFcAACAuiAviggEDiAueKwAAxIWXsL6+Hm7evBmmpqYOPGZx cXHfF4+NRiOU
SqVkieviggED4LkCAIAMxoWrV6+GO3fuHPjicHV1NczOzu7Z3263Q7lcDp1O J1kqlUqyTVww
YAC+wv9v/2FJbZvnCgAAcSHtF62jut1umJmZCRsbG3v21+v1sLy8PHjcbDZD rVYTF8QF4A3/
f/ureN7zXAEAIC68cly4cuXKYDbC6P58Ph96vd7gcVyP28QFcQEQFwAAEBcS t27dCgsLCwfu
3+/FZC6XG6yvrKyEVqt15BKPExfEBUBcAADgFMaF3e/P3e+9uqMzF3Z2dsxc EBcAcQEAAHFh
/P2j11yI69VqVVwQFwBxAQAAcWG8/fFaDPEOEfGij5ubm8mdI5aWlsQFcQEQ FwAAyFpcOOyt
D0eFgUajEYrFYigUCmF+fj6VgCEuiAuAuAAAwAmLC6/7hbG4IC58pec9EsiO sw3EBXEBAEBc
EBfEhSzGhYz9WyMuiAsAAIgL4oK4IC6AuAAAgLggLogL4gKICwAAiAvigrgg LhgkIS6ICwAA
4oK4IC6IC+IC4oK4AAAgLogL4oK4IC6AuAAAgLggLogL4gKICwAAiAvigrgg LoC4IC4AAIgL
4oK4IC6IC4gL4gIAgLggLogL4oK4AOICAADigrggLogLIC4AAJCBuLC+vh5u 3rwZpqam9r6Q
/L8ll8uF2dnZ8OTJkz0f22g0QqlUSpa4Li6IC+ICiAsAAGQwLly9ejXcuXPn wBeHvV4v3L59
O0xPTw9tb7fboVwuh06nkyyVSiXZJi6IC+ICiAsAAGQsLow78J+cnBx6XK/X w/Ly8uBxs9kM
tVpNXBAXxAUQFwAAEBf2ihEhzkzYLZ/PJ7Ma+uJ63CYuiAviAogLAACIC0Oe P38eLl++HDY2
No48Pl6foW9lZSW0Wq0jl3icuCAuiAsgLgAAcErjQrzYY3yrQwwMo0ZnLuzs 7Ji5IC6ICyAu
AAAgLnwpBoVqtRq2t7f3PX70mgtxPR4vLogL4gKICwAAiAuJubm5sLa2duDx 8c4Q8ToM3W43
bG5uJneOWFpaEhfEBXEBxAUAALIWF5IXhyPLYdt3azQaoVgshkKhEObn5186 YIgL4oK4AOIC
AACnIC687hfG4oK4IC6AuAAAgLggLogL4sJxvseRmUDH2Ya4IC4AACAuiAvi QhbjgtkViAsA
AIgL4oK4IC6IC4gLficBAMQFcUFcEBfEBcQFcQEAQFwQF8QFcUFcQFwQFwAA EBfEBXFBXBAX
EBcAABAXxAVxQVwQFxAXAAAQF8QFcUFcEBcQF8QFAABxQVwQF8QFcQFxQVwA ABAXxAVxQVwQ
FwzkEBcAADiJcWF9fT3cvHkzTE1N7dnXaDRCqVRKlrj+svvFBXFBXBAXEBd4 S353/rActX3c
bQCAuDDk6tWr4c6dO3teMLTb7VAul0On00mWSqWSbBt3v7ggLogL4gLiAm/v 747nHwAQF76y
Fx671ev1sLy8PHjcbDZDrVYbe7+4IC6IC+IC4gLiAgCQ8biQz+dDr9cbPI7r cdu4+8UFcUFc
EBcQFxAXAICMx4X9XkDkcrmx96+srIRWq3XkEo8TF8QFccGLe8QFxAUA4BTG hdGZCTs7O4fO
XBjdP87XSPOFs7ggLogLiAviAuICAIgLb1lcGL2mQlyvVqtj7xcXxAVxQVxA XEBcAAAyHhfi
nR/iHSC63W7Y3NxM7gyxtLQ09n5xQVwQF8QFxAXEBQAgI3Fh972rR+9h3Wg0 QrFYDIVCIczP
z+/52KP2iwvigrggLiAuIC4AABmIC6/7xY24IC6IC+IC4gLiAgAgLogL4oK4 IC4gLvidFBcA
AHFBXBAXxAVxAXFBXBAXPP8AgLggLogL4oK4gLggLiAuAIC/9eKCuCAuiAte 3CMuIC4AAOKC
uCAuiAviAuIC4gIAIC6IC+KCuCAuIC6IC+KCf2sAEBfEBXFBXBAXEBfEBcQF ABAXxAVxQVwQ
F7y4R1xAXAAAxAVxQVwQF8QFxAXEBQBAXBAXxAVxQVxAXBAXxAX/1gAgLqTg 6dOn4cMPPwxn
zpwJuVwuvPfee6HZbA72NxqNUCqVkiWuiwvigrggLiAuIC4AAOLCkIsXL4Z7 9+6FXq+XLHH9
7Nmzyb52ux3K5XLodDrJUqlUkm3igrggLogLiAuICwCAuDAQQ8L6+vrg8bNn z5LgENXr9bC8
vDzYF2c01Go1cUFcEBfEBcQFxAUAQFz4Unyrw7vvvptEhEePHoVqtZq8VSLK 5/PJbIa+uB63
iQvigrggLiAuIC4AAOLCwObmZpiZmQlXr14NH330Ufjkk0/C9vb2gZEgXpdh t5WVldBqtY5c
4nHigrggLnhxj7iAuAAAnMK4EC/m+PDhw8HjtbW1JDJEozMXdnZ2zFwQF8QF cQFxAXEBABAX
hk1MTOzZNjk5mfzv6DUX4np824S4IC6IC+IC4gLiAgAgLgxMTU0NvWXhwYMH ye0oo3hniHiH
iG63m7x9It45YmlpSVwQF8QFcQFxAXEBABAXvhTvDjE3N5fMVohLfEvE8+fP B/vjBR+LxWIo
FAphfn5+7Bc34oK4IC6IC4gLiAsAQEbiwlf14kZcEBfEBXEBcQFxAQAQF8QF cUFcEBcQF/xO
igsAgLggLogL4oK4gLggLogLnn8AQFwQF8QFcUFcQFwQFxAXAMDfenFBXBAX xAUv7hEXEBcA
AHEhY3Eh2faH5bBtL3usuCAueHGPuIC4AACIC1mKC2/5i3FxQVzgFf9txoiB byoaiguICwCA
uCAuiAvigrjg90dcQFwAAMQFcUFcMDgUF8QFccHvpLgAAIgL4oK4IC6IC/j9 ERf8fnv+AQBx
QVwQF8QFg0Mv7v3+iAuICwDACYkLKysroVwuh8nJyX0vVNZoNEKpVEqWuC4u iAsGh+KCuCAu
IC4AAOLCwOPHj8P09HRYXV3dd3+73U7CQ6fTSZZKpZJsExfEBYNDcUFcEBcQ FwAAcSFx7dq1
0Gw2D9xfr9fD8vLy4HE8tlariQvigsGhuCAuiAuICwCAuPB7Z8+eDYuLi+H8 +fOhWCyG+/fv
D+3P5/Oh1+sNHsf1uE1cEBcMDsUFcUFcQFzgJf9tRt5+Ou62w7YDIC68FSYm JsL169fD1tZW
ePHiRbJ+9+7dQyNBLpcbehyv2dBqtY5c4nHigrhgcOjFvd8fcQFxwb9NOv/f BEBceGvE2Qq7
ZyZsb28nsxj6Rmcu7OzsmLkgLhgcigvigriAuIC4AIC48KU4U6Hb7Q4ex9kL ly5dGjweveZC
XK9Wq+KCuGBwKC6IC+IC4gLiAgDiwu89evQouahjfFtEnJVw48aN8ODBg8H+ eGeIeIeIGCA2
NzeTO0csLS2JC+KCwaG4IC6IC4gLiAsAiAtfihd0vHDhQjh37tyeCzpGjUYj eftEoVAI8/Pz
Y/8BFRfEBYNDccHvj7iAuIC4AEBG4sJX9QdUXBAXDA7FBb8/4gLiAuICAOKC uCAuGByKC17g
iwt+J8UFxAUAxAVxQVwQF8QFxAVxQVzw/CMuACAuiAvigrggLuD3R1xAXBAX /NsA+HsiLogL
4oLBoRf3fn/EBcQF/zbiAgDigrggLhgcigvigriAuIC4wOn4ffzDctT2cbcB 4oK4IC6IC+LC
iXrx8zIvcg48VlxI5/n1Ff9tvCgVF/zbiAt4rgDEBXFBXBAXxIUT/WJcXHg7 n18xYPB8Ji7g
uQIQF8QFcUFcEBfEBXHB77gBA+ICnisAcUFcEBfEBXFBXBAXxAUDBv824gKe KzxXgLggLogL
4oK44MW4uCAu+P+MAYO4AJ4rQFx4kxYXF/d9gmg0GqFUKiVLXBcXxAVxQVwQ F8QFv+MGDIgL
eK7w+wjiwh6rq6thdnZ2zxNEu90O5XI5dDqdZKlUKsk2cUFcEBfEBXFBXPA7 bsBwrO8zY3cb
yWpccGcZzxVAhuJCt9sNMzMzYWNjY88TRL1eD8vLy4PHzWYz1Go1cUFcEBfE BXFBXPA7bsDg
OVJc8G/tucIPE8SFL125cmUwG2H0CSKfz4derzd4HNfjNnFBXPBCxYspcUFc 8DtuwOA5Ulzw
b+25AhAXErdu3QoLCwsHRoH9njByudzQ45WVldBqtY5c4nHigrggLviDLS6I C37HDRjEBXHB
c4XnCuCUxYXd72Xb731tozMXdnZ2zFwQF7xQ8WJKXBAXvpptY77f2nuzxQVx wd9sTv9zxXH/
Bvi7gLjwFjzR7DZ6zYW4Xq1WxYXjfD8pXtRIXBAXxAVx4VTFhQw+n4kL4oK4 4P/XnitO9vMZ
iAtjRoF4LYZ4h4h40cfNzc3kzhFLS0vighfjXqi8ge/R1dXFBc9nXkSKC+KC v9mIC35/EBdO
ZFyIGo1GKBaLoVAohPn5+Vf+POKCF+Piwtv1eyYuiAuez96S3/GveEabuCAu +JuNuADiwol+
whIXvBg/6MXvV/HiWVzwYlxc8Hxm4CUuiAviwqmOARkLkeICiAvighfjX/kf BnHBi3FxwfOZ
33FxwfOZuJC5uJCx5wpxAcQFccGLcXFBXBAXxAVxwYDBgFNcEBc8V4gLIC6I C16Miwvigrjw
Ou4Cc+ixns/EBXHBv7W4YHAoLogLIC6IC16MiwviQobiguczccGAwYBTXHh9 v+PHjbcZu3vS
a/0bkMEL1IK4IC54MS4uiAvigucPz2cGDOKCuHAKfsc9V/gbIC4gLogLXoyL C+LCm/49S/G/
MIgLns8MGMQFceENPJ+d4udxzxXigrgA4oK44MW4uJDR3zNxwfOZAYO4IC6c 3OczccFzhbgA
4oK44MW4uCAu+DmKC35mBgzigrggLniu8DdAXEBcEBe8GBcXDIrFBXHB92PA IC6IC+KC5wpx
QVwAcUFcOC1PxG/5lX7FBXHBiyTPZwYM4oK4IC681a99Ur6jhb8B4gKIC+KC 71FcEBfEBf/f
FBfEBXFBXPB85m+AuACnPy70y2gulwuzs7PhyZMnQ/sbjUYolUrJEtfFBX+8 TuXP7Lj/NeEr
/i8MJ+r37Cue+eL/m37PDDjFBXFBXPA3wM9MXEBceIv1er1w+/btMD09PdjW brdDuVwOnU4n
WSqVSrJNXPDHy8/Mz8zPzM9MXMhGXPjKpoKLC+KC5zPfj7gApzMu9E1OTg7W 6/V6WF5eHjxu
NpuhVquJC/54+Zn5mfmZ+Zll8wK1b/ksp6z+nokL4oK/AX5m4gLiwlsmhoQ4 O6Evn///2zvz
EDuqLIyPOhq3ZIxJRh3FPcZ9iSKOS4wLaowSFEUFN8QVlbiggqh/zKAirtEZ XFBxRNS4ok0j
QZEgQdoFRUUlLigaRIJEJEiQJtzhu3getyv1Xle9e2t5nd8PmnROV70673u3 7r11XtX9Jvs7
Ggz9rhjFBQYvNEMzNEMz3G9oZxQXKC5QXODcpLgAQHFhHX766Sd36KGHuhUr VvQsEmhthpCR
kRG3bNmycX+0HR0xOaIZmqEZmlFcoJ3VsXZOrav4T6Q1ZKrQjHbPGDCBH/MC oLiQYfny5f5x
BxUYQrJ3LoyOjnLnAoMXmpEPmqEZxQXaGe0MzdAMzdAMgOLCWFRQmD9/vlu9 evU6f8uuuaDf
tS3FBTpiNEMzNEOz9dK7nnZGOyMfNEMzcgSguJDPvHnz3Jdffpn7NzlDaA2G VatWuZUrV3rn
iOHhYYoLdMRohmZohmbkiGZohmZohmYUFwAoLrjcb2fyniMaGhpyM2fOdFOn TnWLFi0q/JoU
F8gRzdCMfNAMzcgRzdCMfNCM4gLAelJcqKpgQXGBHNEMzcgHzdCMHNEMzcgH zSguAFBcoLhA
R4xm5INmaEaOaIZm5INmaEaOFBeA4gLFBTpi8kEzNEMzNEMz8kEzNCNHNKO4 ABQXKC7QEaMZ
mqEZmqEZ+aAZmqEZmqEZxQWguEBxgeICOaIZmqEZmqEZOaIZmqEZmlFcAKC4 QHGBjhjNyAfN
0Iwc0QzN0AzN0IwcASguUFygIyYfNEMzckQzNCMfNEMzckQzigtAcYHiAp0c mqEZOaIZmpEP
mqEZOaIZmlFcAIoLFBfoiOmI0QzN0Ix80AzN0AzN0Ix8KC4AUFyguEBHjGZo hmZohmZohmZo
Rj5oRnEBgOICxQU6YvJBMzQjRzRDMzRDMzQjRzSjuAAUF2pgaGjIzZo1y//o d4oLdMRoRj5o
hmbkiGZohmZohmZoBkBxoTDvvvuumzNnjvvll1/8z9y5c32M4gIdMZqhGZqh GTmiGZqhGZqh
GcUFAIoLhViwYIFbunRp5//vvPOOO/XUUyku0BGjGZqhGZqRI5qhGZqhGZpR XACguFCMyZMn
u7Vr13b+r98Vo7hAR4xmaIZmaEaOaIZmaIZmaEZxAYDiQrHEc06ejTfeeMz/ R0ZG3LJly8b9
0XZjTs4/f8rGYvdPHWtbPmiGZuSIZmiGZmiGZmiGZmhWTz4AFBcKkr1zYXR0 tNCdC70Iiwxl
Y7H7p461LR80QzNyRDM0QzPyQTM0QzM0ay4fAIoLXciuuaDf58+fH/Wauouh 31js/qljbcsH
zdCMHNEMzdCMfNAMzdAMzZrLB4DiQhfkDCGHiFWrVrmVK1d654jh4WGKC3TE aIZm5INmaIZm
aIZm5INmaAZAcaE4Q0NDbubMmW7q1Klu0aJF0a9HR0xHjGZohmbkg2ZoRo5o hmbkQ3EBYD0r
LqSGjpiOGM3QDM3IB83QjBzRDM3Ih+ICAMWF6OJCuNqqforGymxbR6xt+aAZ mpEjmqEZmpEP
mqEZmqFZffkAUFxouLhAlZcqLzmiGZqRD5qhGTmiGZqRD3cuAFBcoLhAR4xm aEY+aIZmaIZm
aEY+aIZmABQXKC7QEZMPmqEZOaIZmqEZ+aAZmqEZxQWguEBxgU4OzdCMHNEM zcgHzdCMHNEM
zSguAMWF9Y2RkZG+Y7H7p461LR80QzNyRDM0QzPyQTM0QzM0ay4fAIoLAAAA AAAAANBqKC4A
AAAAAAAAQBQUFwAAAAAAAAAgCooLAAAAAAAAABAFxQUAAAAAAAAAiILigkT4 y1gZ3n77bR87
4IAD3AsvvNAztt9++/m4xcruX+Y4YQwAAMb248cff7z76aefcmPWz6eOhfG8 WJXHHoR80AzN
yBHN0KzefLpd3wBQXGiguPDqq6+6Sy+91MfWrl3rrrzySnfTTTd1ja1Zs8bH FXv55ZdL799P
TMcJ8z733HPdl19+Oeb95MWy2zYVa1uOaIZmaDYxNNPvO+ywg3vjjTfGxE49 9dQxk7GUsTCu
f5cvX17bsQchHzRDM3JEMzSrNx+KC0BxoWE222yzzu+77rqr++OPPzqx0dFR t/HGG3eN2f6K
7bXXXqX3LxqzDsKOE3Yajz/+uPvXv/41pjPJi2W3bSrWthzRDM3QbOJo9t// /tfNmjXLrVix
ohP77bff3IYbblhJzI6t4q/6a03y6jr2IOSDZmhGjmiGZvXmIywfAIoLDXDa aaf5Rw/Ejjvu
6E9Si61cudKfnN1itv8rr7ziH10ou3/RmDqQSZMmuU022cRtsMEG/nfF7Mdi YTwvFsabirUt
RzRDMzSbGJrZ3/fff3936KGHdiZ8KjgcdthhlcTsUTbF5syZ429PrevYg5AP mqEZOaIZmtWb
j8WUDwDFhQb4+uuv/XoGr732mnv++ef9Ywi6rWnvvfd2Bx98sLvmmmu6xnQC v/POO27y5Mnu
xhtvLL1/0Zg6kB9++MHNmzevs+5Ct9udit4a1VSMfNAMzcgxdT4qKoToGxx9 k6O4+lL181XE
jj32WJ+Pxeo89iDkg2ZoRo5ohmb15hPGACguNIQqjVdffbV/rEEnpR5NUBXw 5JNPHjemyqBu
z+13/yIxdVw6jq23kDeZ7hVvU4x80AzNyDF1PgAAAABAcQEAAAAAAAAABhiK C3/y/vvvu4su
usjtvPPObqONNnJTp071jyCcfvrp48a0YvnixYvX2V/xzz//PF/4krcKt90S 54orrhhjk9kt
JsJ4Xiz7vvNi4bPXKWIWzx4v9XGKxrS2htqRfh+vnRVtp6ljvdp3P228iti3 337bsYvVe/j5
55/dI4884t+DntU/+uija4vJSUaPT+mz/utf/9p4PmVylH5/+9vf/GKy0rQK u91Yq19tu/vu
u/tbQXX3l96b/j388MPdkUce2Vds/vz57t13323vAJ5jv9nUGDAI41S3WNEx qalY23KsK5+8
9h4u7FomFrt/6ljdx+6m5yDMh9qWTxU55s35qpyfAVBcqJinn37anX/++X7h RK2hcPHFF7vL
L7/cn7CaqGpNhW6xZ555xt12221u+vTpbsmSJZ39NRHXtlo7YdmyZf442267 rT/OyMhIp1Mp
Gmu7JY4Wn9QjHGEO+smLhduGr5e3rTrKMCZ9s+81NqZFM+049vcqjlM0plys PSrWq50Vbaep
Y8pn2rRpPp/HHnus8x5i2njqmFDeH374oY/pAtQuPLUAq9ZHOeKII2qLabFX Pdp0xx13uAUL
FjSeT5kcrS/Rtlowqmq73X6sfvX3Y445xp1xxhnupZde8p//66+/7vOfPXu2 /71MTMdQYWHu
3LluaGhoTP/QJvvOrP1mU2PAIIxT3WJlxqSmYm3Lsa58shfDmq9p0etVq1aV isXunzpW97FV
RA6PPSjzobblU0WOeXO+2PmZxi6N1Xb9AUBxoWbM4cHQ7zrZ9a8qf/o2q1tM kzrt/9577/mY
7f+Pf/zDPfXUU36yqoswfdunjl0xbacOpEzMOqTVq1f739toiWPPQduxrRpr WOyXX37pbBu6
YeRte8ghh3Q+G8V0obj55pt3YtomNqZvQLXGheUhqjhO0Zh0CGO92lnRdpo6 pnzUTnUROmXK
FH8hGtvGU8eEtbPwwsysmdROdYFdV0wDv/KxWNP5lMkxPLc1Sa3CbjfW6nfm zJm+bYZ2vRZT
nyN7yjIxazN5sbbZd4b2m02OAYNs3WbtfrwxqalYmXFzIuWj42TPKbtw+uST TwrHYvdPHas7
n3322cdfgNqxB2U+1LZ8qsgxb84XOz8TFgOguNAA+tbTJrJCv+uEtZgG0G4x TUptfxtoLR4O
kjrBb775Zl8csA6kTEz/hrE2WuLY8f/zn//4zk3V05122snH1MnNmDHDx/Rt 40knneSr6eqU
r7/++q7bnnDCCe6CCy5wn376qR8ctQquKrUXXnih11l5xMZ0Ufrss8/6uE1m qjhO0ZjuerF2
plivdla0naaOhe1bMd0xENvGq4hliwtnnnlmZwJrFxJ1xT7++OMxFzBN51Mm R7VLs7dScaEK
u91Yq1/dCqr/60fbCouFBYKisUGx/83ab2Ld1l8+GpPkzqR8eo1JTcXKjJsT KR8dR5+X3eVi
6HEtfVZFYxrnY/ZPHas7H81xzjrrLHfUUUf5OdqgzIfalk8VOebN+VLOzwAo LjSAnvPT8346
KXWBotuKNFBazG5TyovpVlzb3woAFs9W29X5aBAOB5WiMU2gw1gbLXH0zLIm vfrbv//9b3/s
Bx54wD+7rbgmbkKTEXWmNil+9NFHe267xRZb+Pe/yy67eG1MI10EaNsUMYsr pguTqo5TJBa2
R+nTq50VbaepY3ntO7aNVxHbaqutfFtSTBPWhQsXuu2228498cQTfuKqO4vq ip144on+G1rp
qeJH0/mUyVETUt0NcN1117k999yzErvdWKtf9R96H7pYtHPrvvvu89vpwluP VJSJqc0sXbrU
T9Czj0WsM5A2tL5Inv0m1m395aMiksaaTTfdtOeY1FSszLg5kfLR/62gFj7X Hv50i5XZto5Y
08fWPEo6DtJ8qI35pM4xb85XxfwMgOJCzaharm8xdNvtk08+OSamDqBXzOKa tIT7500Adfu2
qpapYrD+tMdu7axoO00d63aBk7qNx8S++uort8022/gB9uyzz3a///67++ij j/ytiXovxx13
XG2x77//3k8odBGjSV7T+ZTJ0eLaXo98VWG3m8Lqd9999/WPUtmilPpX26lQ UjamNqPHfcIF
Hdtm3wmwPoE1Mf0G1yBx8zMAigsAAAAAAAAA0HooLgAAAAAAAABAFBQXBghZ yuh56CLe7PKw
L7ptNx/31M8H99VAc3zcYz3Oszmm9lG2xcq0BoD5dpeJ6Xm9MJb8pG+xX/f7 77/vLrroor58
nfNieu1//vOfSdtPv7EwntcOs20ylQ932baneNj2sm1yUDzXY8/DbKyKHFPE 9PiKHpUy696y
Y0CVMd2mq0dssuOMnhE2Xe29hDGL58WqPGfMoSCVFva+U/a5qftIucFobZVU c4Ds7ynHw7aM
UzFjl/RevHjxuNuava19Nil03H333Ts2xLFzyLxzu+g5XOZcj+0rUs9p5UQl La2v0MKZWqfH
HqfTGiH6LBWXhtJOWh199NG5MT3KecMNN/j1S7SukV4/VrMq5uIAFBf6uNjq FhvPZ9ziVXuc
a0ExLTql1ZltsMnzZtcCaHfffbcfrO655x7//27byn1AnaBitmCZYvLb1er/ lkfRWLcJX6xm
WR/3GD9z8yK2v1fho6zF5exvWun6pptu6juWXZin33ZmKNZWv261Tz3jr5X/ 9f5TeD3rtbVI
3xFHHOEXAYxtPzGxMK5/LZ+8NpnSh7tM21uzZo2PW9vTpMnitu2geK7Hnoc6 bngettGHXRNZ
jQthP1xmDKg6JgcOPUOsi4+5c+f6cUZ5Wjw7VlgsLNrkxVKfM/oMbYy1WKwW eg1736n63Icf
ftiPu6n6SF3QKKbFTPXlRZnxPoz3Ki7Enoep+p5U41TM2KUx6bbbbnPTp093 S5Ys6bmtXtss
Je+8885oHdWHHXPMMe6MM85wL730UvQcstu5XfQcLhqL7Sti5rR5MX1Oso+0 Y0gTuRhpbSBZ
c2udHhVzrIgjpyMtRKw5SF5MRSD145oPKKb9YzWjuAAUF1peXBjPZ9ziVXuc mw97Xiy0T7P4
d999t04su60GSK2ur4VgttxyS18xtZjZX5aJ9SouxGoW+rjH+JmbF7FNKFN4 FOu1w/egVXs1
gIjR0VF/nDIxy0ExVbJTtDNDsbb6dWvhxdCHO4XXs7lE6O4FXejHtp+YWPZi xvLJa5Mxntsx
7VEobm1PE5fQplfxtnqupz4P7bimRRt92PVNWRgrOwbUEbMFxRTT382qzeJm Jaq42aNavFtM
i3ba+05xzugztNyzOvb7vg3FUvW50iK8AyS2j9TirBrHdcE4bdq0UuN9OIew xU8Vt3Mp1XmY
qu9JNU7FjF0ak3Qh+t577/lYr21VgBWKycEkVkdro+G8InYOmXduFz2Hi8Zi +4pvvvmm03Y1
RsTOc8MFEu0LHxWEzMLZtFfc7JwtXjSWQrMwBkBxoaHigt0mWdajvE6Pc3Ui ZqEl8rzZLa4q
aDaWt60NDPKXVgd68803e5tD06VMLHu7aSrNsj7uMX7mt9xyi7vgggvcokWL /AVnCo9ifR6q
VJsWGvz07YZi0twGxKIxe8/6vFO1s2zbNtrk161vCX788cfOa6bwerbXVkyP KMS2n5iY3a6q
mN6j5ZPXJmM8t2Pao1Bc36zo1k9NhC0utG1bPddTn4d2XBWA9M1uG33YZZ2o nLPFhaJjQB0x
fdMnS0/Ft956a//trb5R1V14+tZXF/ayU1Nc45A+B11QzZgxo2vshBNO8OfM p59+6gtVKXzq
Lfesjv2+77ziQmyf+/e//92fM8pXpOgjw4uQmDmAvt1VXO9/eHg42XmYqu9J NU7FjF3SW9ua
5WCvz1Bf+Fh/pnE8Vkdro/pR355iDpl3bhc9h4vGYvsK7a+xWHcMSv/Yea4+ qzB25plnuo8/
/rhTXLCLecXtwt/i3WK6g0E6qvCjAkgKzew9SzMAigstu3OhyO1FdfiZ68JD FynqRGwQyvNm
199k2TZlyhR3++23j7tt1sddk2R1RuFAXDRmz/llYzGa5fm4x/qZq0PXNwEa IFN4FOu19b5f
e+01/9q6BVEDs/TWseXXXSYmHX744Qc/cKd6hjBbXGijX7fdXm6vn8Lr2dqP aRLbfmLbnt1G
qZjl061N9uu5HdMeNSHRtzB6fvTGG2/0t2kqbsUue7a/jZ7rqc9DafHQQw/5 C3gVWdrowx6O
C+HFStExoI6Yvu3TN6VqO5r0Cl0o6JZ8awuPPvqojz/wwAP+FmG9P30O3WLa X5+LPudddtkl
ibamZfiNecz7tjae+pyxPlIXOCn6yOy4ETMHEPfff7//9nXhwoWdxyJizsOY vqeKcSpm7JLe
GtMVV6zXZ6gitPVnKXTUeaQ7VFTQtjYfO4fMO7eLnsNlzvXYvkKFEa1l8eab b0a3cX0BoD7H
YmrnJ554or8TUZ+jChC60Fd8u+22c0888YS/4Jf23WJae0HHUTvXuhgpNMu+ ZwCKCzUzCF7F
Qt8GqAMbz69dtyfee++9hbfNLuioW8H0zU6YR5GY/s2LxWg2KOhbaA3Iuo1c 70OTK+l98skn
l45pwFLBJ5z4pWg/VbbNVK+J13Pz7VFtT4/PZPe3Ntlmz/WU52H4nttM3rhQ ZgyoI5Y3zgyK
lined8pzpo4+st85gKFvTHXRaXfOpToP2zxXK/q5WFwX5ONta/1ZKh333Xdf /0hR1XPIQaDf
Nq67NLSwpYpJ+gx///139/333/tipYoDKjRp/Q3FP/roI/8Ylj6/4447rmtM ++sOLN3VouI+
AMWFAUadZHbBu6Kx2P3LHAcAAAAAAACgzazXxQVdyOt217BKWTQWu3+Z4xhV WFHqWa9TTjkl
ia2Vbl3baqutxrXiKWPbo23NoqdKC7xYKzlVrM0+KqU9WBWWWrkdQZ+PXMh+ S7EU7SdrvVX2
M+hmEWi304avGWMnGatZGKvCumu8Y/dDaL9VxmaryHlti8NFD2aZ99qEBV6/ /U8/bdxurc5u
11abx2z/2kZS2jzqveoWaD16mLLvSdnnjmdF2YpJaoQddRjP01GfdYoxu8zY 1aRtbZXnYZvn
Z1VaFPeycC6zv9aDkJvH+nJeA8UFaAFmk6VFk3rZZJWxIfvf//7nOyUVHBSL tbVSh6nb+fRM
34MPPtjViqeMbY+2NYueqizwYq3ktK/Zg9nzkrEWirYidSpLLT3Pr1v11H6M GDsmodc1G7IU
tmgqAtj7ViEthb1cnvVjjJ1krGZZ67bQZiucSMXEilrClrGJNfstxfWsaVGb rW4x+zzMeqts
PuO9Hy0S2YQFXj/9T79tXEUaW1E+/PzbZvPYy6awagvnMu1en4/0+OCDD5L0 ZzrOc8895wvu
+je276mizw3tDs2Ksm023DF21GE8HANMx1RjdtGxq0nb2iqswo1UFtdVzM+q tAofz8K56P5a
g+G8885zl1xyibcJTTmHtPMagOICrIPZAOXF+rURspXgLRZraxVaJynWzXan jG2PPX8WDhKp
LfBireR010IYS2GhaDqmstSSp7YmzgcddFDHMizGjilsP6HtYsz7Nh0tlsJe LpxEpbCiDLUo
a2sl8qzbstZWsTZU4WTKzqlYa13L0eIxNls2EQ1jZfPJ29ZeRzEVV5uwwBvv 3CxqwVk0lldc
aJvNY6/iQtUWzmXavfozLeBpsdj+zI6jYoVW0k9hZascf/311845lSpHi6Xo K6r6vPqxo85e
2NsYYGOXxesau5q0ra3CKtxIZXFdxfwstUVxGQvnovtbe7T9U84h7bwGoLgA 62A2QHmxfm2E
zBLJYrG2VuFEV7FutjtlbHu0bV6lOqUFXqyVnG7vNi1s8EplD1aFpdatt94a bcekmLy9TQcR
+77VHk1btYUU9nLhJCqFFaXusrGYCjUprNt22203/42ctfFYG6o8K9MU1rr2 uyZ/ZWy28mJV
2NZqEqnXt/fehAXeeOdmUQvOojGt3K7b7sO20zabx7zPuk4L56Lt3n43q+fY /ixsB2qPKfoe
9ZFytVCbSpVjkfOwaRvufu2ou9n/qtijb5stXtfY1aRtbRV9bmqL6yrmZ6kt istYOBfd3+bi
ZhOacg65vi1mDRQXWkXR9Qxi1zjoN6ZOSLdXqsNNZUWpDlvfnqojf/HFF6Nt rZSvWVqaRU+e
7U4Z2x5dMNgKvFVa4Jke/VjJmbWUeVfH6mgXSN0m4/2+7/DH3ke/dkyK6cLE Ji2pbNFMW8VS
2MtlXUtS2EmGsVgdhb4t1TcuaucWj7Gh0mtkbahirXX1SI2d22VttvJiOoZW yFYs+41pkXzy
YqEVpVlo1mGBV5UFZ5GYLGt1saRxQu1AtM3mMfyssnfp9PtZp46JsB9P0Z9Z 4dyOmcpGWUWg
L774IkmOvb7N7revSB2LsaPuZv8r1wTNq9Qf6VvyOseumLlG6vMwhVV4aovr 1POz1BbFZSyc
i+6/ePFi/1iE5sbqf6uwmAWguFAzRdcziF3jICam15U10LRp0/xEMpWNkJ6B 1rdTKWytVHVV
Fdq+Rexlu1PUtie06Akna72qsk1Y4FVlD9ZtYEj1vmMsxxTTN6RFLLUmunVk rHVbt3g/VGHX
Fp7bZW228mI697PWWylsa6uyoqzC7m59oUkL57KfxSBY4abMMcyrl3100zbc gzRmt/Vcr7oP
W58sistYOBfdX1/87bHHHthwA8WFiULR9Qxi1ziIiRl5MQAAAAAAAACKCw1T dD2D2DUO+omp
ohmuzJsXM3RXQjbephj5oBmakSOaoRn5oBmakSOapc8HgOJCS9ACSbqlSLcW 9VqjIHaNg35i
WmNBi8Po1iYt3KVbrx5++OFOLEQdTTbephj5oBmaoRmaoRn5oBmakSOapc8H gOJCixgeHi60
RkHsGgcxMR1H9j0AAAAAAAAAFBcAAAAAAAAAYMJAcQEAAAAAAAAAoqC48CfL li3zjzVosUT5
H+vfww8/3D+aMF5s/vz57pFHHul7/9SxtuWDZmhGjmiGZmiGZmiGZmiGZvXk w2PUQHGhQYaG
htyxxx7rRkZG3Nq1a33s9ddfdwcffLCbPXu2/71bTNvffffd3uHhnnvuKb1/ 6ljb8kEzNCNH
NEMzNEMzNEMzNEOzevJRYWHu3Ln++gaA4kIDzJw50/3222+5sdAiMi9m8e++ +26dWJH9U8fa
lg+aoRk5ohmaoRmaoRmaoRma1ZOPyIsBUFyoCVUbV65cmRsLT868mMWXL1++ TqzI/qljbcsH
zdCMHNEMzdAMzdAMzdAMzerJh+ICUFxomEWLFnnrx7ffftv98ccfPnbfffe5 vffe2+2///7u
1Vdf7RrT9ldffbWbMmWKu/3220vvnzrWtnzQDM3IEc3QDM3QDM3QDM3QrJ58 li5d6ubMmcNj
EUBxoUmGh4f9QiyTJ092kyZN8v+q4KBFUsaLLViwwN17771975861rZ80AzN yBHN0AzN0AzN
0AzN0KyefFjQESguAAAAAAAAAMBAQnFhgChql4ltD1ZHaIZmaIZmaIZmaIZm aIYVJQDFBViH
onaZ2PZgdYRmaIZmaIZmaIZmaIZmWFECUFyAXIraZVoc2x6sjtAMzdAMzdAM zdAMzdAMK0oA
igswhqJ2mRbHtgerIzRDMzRDMzRDMzRDMzTDihKA4gKMoahdJrY9WB2hGZqh GZqhGZqhGZqh
GVaUABQXoCtF7TKx7cHqCM3QDM3QDM3QDM3QDM2wogSguAAAAAAAAAAAAwPF BQAAAAAAAACI
guICAAAAAAAAAERBcQEAAAAAAAAAoqC4AAAAAAAAAABRUFwAAAAAAAAAgCgo LgAAAAAAAABA
FBQXAAAAAAAAACAKigsAAAAlmT59uttggw0mzHEAAAAAYqG4AAAAUHbw/Mtf /M9EOQ4AAABA
9LwFCQAAAMpf8Ic/WR577DG3+eab+79tuOGG7rDDDnOjo6P+b7NmzfJx/Ruy 7bbbjokXOQ4A
AAAAxQUAAIAJWFx48cUXc7eZPXu2//vq1avdZptt5mNXXXWVj51++un+/5Mm TXK//vorxQUA
AACguAAAALC+FBjy2HHHHf3fDjzwQH+3wrnnnrvO9m+99ZZfS0Gxa6+9tvP3 JUuWFD4OAAAA
QKvmR0gAAABQcvDscdFvRYMVK1b4/6vAYNvrrgVDdy2EdyVcdtllpY4DAAAA 0Kr5ERIAAACU
HDx7XPTnPc5gP2vWrBmz7RZbbOHjekyi7HEAAAAAWjU/QgIAAICSg2eBOxc+ ++yznq+hOxXC
woOtv1D0OAAAAACtmh8hAQAAQMnB88+L/rvuumudv+2xxx7+bzNmzHBLly7N 3V9rK9hrLFy4
0P+rooTWYih6HAAAAIBWzY+QAAAAoBz2OEPenQUqKNjdC3luD3KDkCuE/n/O Oef42Lx58/z/
N910045bxHjHAQAAAKC4AAAAMMAMDw+PufDPojsTtt9++9ziwqxZs/zvO+yw w5h97PX096LH
AQAAAGgLzFQAAAAAAAAAIAqKCwAAAAAAAAAQBcUFAAAAAAAAAIiC4gIAAAAA AAAAREFxAQAA
AAAAAACioLgAAAAAAAAAAFFQXAAAAAAAAACAKCguAAAAAAAAAEAUFBcAAAAA AAAAIAqKCwAA
AAAAAAAQxf8Bb9xkMgfOmyYAAAAASUVORK5CYII=
--------------070505020301090205030409--
Re: How to define the scale of an X Axis (Chart Data) [message #73261 is a reply to message #73224] Mon, 12 September 2005 15:08 Go to previous messageGo to next message
Eclipse User
Originally posted by: statineni.xxxx.com

Hey Tobi,

I had similar problem with data overlapping on xaxis. what i did was to
just slide the angle so that data on the chart will be set in an angle.TO
do that go to Chart Dialog-> Attirbutes ->Font editor[click on the little
button]->Rotate it an angle which you like.

Thanks,
Sudha

Tobi wrote:

> additional information: the screenshot makes my problem clear (see
> attachment)

>> hi all,
>> i want to plot a graph with a lot of input-data and i don't want to
>> group the results. problem: the labels on my x-axis overlaps each other.
>> i need to define a scale for x-axis but it is not possible because the
>> responsible option-field is not active/grey, you can't enter a value
>> (under: chart dialog-> data -> x-axis -> scale).
>> this field is only active for the y-axis .. (like the screenshot in the
>> help-document: working with data on a chart axis -> defining the scale
>> of an axis). how can i enter data in this grey fields? the help-document
>> seems to be wrong in this section?
>>
>> how can i define the scale for x axis??
>>
>> please help, urgent!
>>
>> thanks,
>> tobi
>>
Re: How to define the scale of an X Axis (Chart Data) [message #73282 is a reply to message #73261] Mon, 12 September 2005 15:52 Go to previous messageGo to next message
Eclipse User
Originally posted by: tobi.home.de

hi sudha, hi all,

thanks for the quick reply!

i allready tryed this (maybe you can see it in my screenshot, the lables
are rotated -90). the problem is, that there are many values on x-axis,
for every value i get a label.

how can i skip most of the labels, so that i get only few labels (e.g.
30 labels on x-axis like on y-axis) for hundredreds of values?

thanks in advance,
felix


> Hey Tobi,
>
> I had similar problem with data overlapping on xaxis. what i did was to
> just slide the angle so that data on the chart will be set in an
> angle.TO do that go to Chart Dialog-> Attirbutes ->Font editor[click on
> the little button]->Rotate it an angle which you like.
>
> Thanks,
> Sudha
>
> Tobi wrote:
>
>> additional information: the screenshot makes my problem clear (see
>> attachment)
>
>
>>> hi all,
>>> i want to plot a graph with a lot of input-data and i don't want to
>>> group the results. problem: the labels on my x-axis overlaps each other.
>>> i need to define a scale for x-axis but it is not possible because
>>> the responsible option-field is not active/grey, you can't enter a
>>> value (under: chart dialog-> data -> x-axis -> scale).
>>> this field is only active for the y-axis .. (like the screenshot in
>>> the help-document: working with data on a chart axis -> defining the
>>> scale of an axis). how can i enter data in this grey fields? the
>>> help-document seems to be wrong in this section?
>>>
>>> how can i define the scale for x axis??
>>>
>>> please help, urgent!
>>>
>>> thanks,
>>> tobi
>>>
>
>
Re: How to define the scale of an X Axis (Chart Data) [message #73319 is a reply to message #73282] Mon, 12 September 2005 14:36 Go to previous messageGo to next message
Eclipse User
Originally posted by: mpadhye.actuate.com

Hi Tobi,

Unfortunately, in the current version of BIRT, there is no way to have
some of the labels not appear in the X Axis. This is something we are
looking at in v 2.0.

As for a Scale for X-Axis, this is possible, but only if the X-Axis is
a Numerical (linear) type. It is not possible to set a scale for a Text
axis.

One thing you might look into is using a script in the chart to set the
labels as trasnsparent for all except say every 20th entry. This way you
can still have the chart show up correctly. Of course, this is more of a
workaround, but it should work. Check the Chart FAQ and the BIRT help
for more info on scripting for charts.

Thanks,
Milind

Tobi wrote:
> hi sudha, hi all,
>
> thanks for the quick reply!
>
> i allready tryed this (maybe you can see it in my screenshot, the lables
> are rotated -90). the problem is, that there are many values on x-axis,
> for every value i get a label.
>
> how can i skip most of the labels, so that i get only few labels (e.g.
> 30 labels on x-axis like on y-axis) for hundredreds of values?
>
> thanks in advance,
> felix
>
>
>> Hey Tobi,
>>
>> I had similar problem with data overlapping on xaxis. what i did was
>> to just slide the angle so that data on the chart will be set in an
>> angle.TO do that go to Chart Dialog-> Attirbutes ->Font editor[click
>> on the little button]->Rotate it an angle which you like.
>>
>> Thanks,
>> Sudha
>>
>> Tobi wrote:
>>
>>> additional information: the screenshot makes my problem clear (see
>>> attachment)
>>
>>
>>
>>>> hi all,
>>>> i want to plot a graph with a lot of input-data and i don't want to
>>>> group the results. problem: the labels on my x-axis overlaps each
>>>> other.
>>>> i need to define a scale for x-axis but it is not possible because
>>>> the responsible option-field is not active/grey, you can't enter a
>>>> value (under: chart dialog-> data -> x-axis -> scale).
>>>> this field is only active for the y-axis .. (like the screenshot in
>>>> the help-document: working with data on a chart axis -> defining the
>>>> scale of an axis). how can i enter data in this grey fields? the
>>>> help-document seems to be wrong in this section?
>>>>
>>>> how can i define the scale for x axis??
>>>>
>>>> please help, urgent!
>>>>
>>>> thanks,
>>>> tobi
>>>>
>>
>>
Re: How to define the scale of an X Axis (Chart Data) [message #73472 is a reply to message #73319] Tue, 13 September 2005 14:10 Go to previous messageGo to next message
Eclipse User
Originally posted by: tobi.home.de

This is a multi-part message in MIME format.
--------------030100070704080205010807
Content-Type: text/plain; charset=ISO-8859-15; format=flowed
Content-Transfer-Encoding: 7bit

hi milind, hi all,

> As for a Scale for X-Axis, this is possible, but only if the X-Axis is
> a Numerical (linear) type. It is not possible to set a scale for a
> Text axis.

this is exactly what i need. how can i set the datatype to "numerical"?
the original db-data is oracle "number", the dataset-data in birt shows
"decimal".

pls look at my screenshot, the chart-data is set to linear but i can't
enter a scale, the fields are grey. where is my failure .. ?

thanks in advance,
tobi

>Hi Tobi,
>
> Unfortunately, in the current version of BIRT, there is no way to have some of the labels not appear in the X Axis. This is something we are looking at in v 2.0.
>
> As for a Scale for X-Axis, this is possible, but only if the X-Axis is a Numerical (linear) type. It is not possible to set a scale for a Text axis.
>
> One thing you might look into is using a script in the chart to set the labels as trasnsparent for all except say every 20th entry. This way you can still have the chart show up correctly. Of course, this is more of a workaround, but it should work. Check the Chart FAQ and the BIRT help for more info on scripting for charts.
>
>Thanks,
>Milind


>> Tobi wrote:
>>
>>> hi sudha, hi all,
>>>
>>> thanks for the quick reply!
>>>
>>> i allready tryed this (maybe you can see it in my screenshot, the
>>> lables are rotated -90). the problem is, that there are many values
>>> on x-axis, for every value i get a label.
>>>
>>> how can i skip most of the labels, so that i get only few labels
>>> (e.g. 30 labels on x-axis like on y-axis) for hundredreds of values?
>>>
>>> thanks in advance
>>>
>>>
>>>> Hey Tobi,
>>>>
>>>> I had similar problem with data overlapping on xaxis. what i did was
>>>> to just slide the angle so that data on the chart will be set in an
>>>> angle.TO do that go to Chart Dialog-> Attirbutes ->Font editor[click
>>>> on the little button]->Rotate it an angle which you like.
>>>>
>>>> Thanks,
>>>> Sudha
>>>>
>>>> Tobi wrote:
>>>>
>>>>> additional information: the screenshot makes my problem clear (see
>>>>> attachment)
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>>> hi all,
>>>>>> i want to plot a graph with a lot of input-data and i don't want
>>>>>> to group the results. problem: the labels on my x-axis overlaps
>>>>>> each other.
>>>>>> i need to define a scale for x-axis but it is not possible because
>>>>>> the responsible option-field is not active/grey, you can't enter a
>>>>>> value (under: chart dialog-> data -> x-axis -> scale).
>>>>>> this field is only active for the y-axis .. (like the screenshot
>>>>>> in the help-document: working with data on a chart axis ->
>>>>>> defining the scale of an axis). how can i enter data in this grey
>>>>>> fields? the help-document seems to be wrong in this section?
>>>>>>
>>>>>> how can i define the scale for x axis??
>>>>>>
>>>>>> please help, urgent!
>>>>>>
>>>>>> thanks,
>>>>>> tobi
>>>>>>
>>>>
>>>>
>
>
> ------------------------------------------------------------ ------------
>
>
> ------------------------------------------------------------ ------------
>


--------------030100070704080205010807
Content-Type: image/jpeg;
name="screenshot.jpg"
Content-Transfer-Encoding: base64
Content-Disposition: inline;
filename="screenshot.jpg"

/9j/4AAQSkZJRgABAQEAXwBgAAD/4QAWRXhpZgAATU0AKgAAAAgAAAAAAAD/ /gAXQ3JlYXRl
ZCB3aXRoIFRoZSBHSU1Q/9sAQwAFAwQEBAMFBAQEBQUFBgcMCAcHBwcPCwsJ DBEPEhIRDxER
ExYcFxMUGhURERghGBodHR8fHxMXIiQiHiQcHh8e/9sAQwEFBQUHBgcOCAgO HhQRFB4eHh4e
Hh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4e /8AAEQgChwJc
AwEiAAIRAQMRAf/EABwAAQACAwEBAQAAAAAAAAAAAAAGBwIEBQMIAf/EAGgQ AAEDAwICAQoO
DAoIBAQEBwECAwQABREGEhMhBxQVFyIxUVeV0dMWMjdBU1VWYXWRkrPS1Ag1 UmVxcpOUlqK0
tSMzNjh0doGjprIkQkeFoaSxwSVixeEmQ1RzNFiC4vBERWOEwvH/xAAaAQEB AQEBAQEAAAAA
AAAAAAAAAwQBBQIG/8QAPREBAAEBAggLBwQCAgMAAAAAAAECAxEEFFFxobHR 4RMVFiExMjNi
cpGSBRI0QVJhsgZjgfBTwUJDIiPx/9oADAMBAAIRAxEAPwD6H1TfVWG3x3WY gklx4MpQXdgA
2KVnOD9z3vXqONdIsl2D1c1Z47kYjIdRPKkq54AGG+ZJ5ADmTyrV6ZguTpVl hEgx98oAuA8w
nhuZH9oyP7agMa1N6dt5tMe7sypDMhQXEfe2tNBbYVxk4wcnKEgZ7hd7mTVs Hw3A7O2iwwij
/jNUzfPRfddER0y8nDqsNor9+y6kXXzzdPP/ACtbRWqrxf7tK41uYYtrTfp0 KKi292v8GV5w
s4JJwBt5DnkE9jWGqrLpHT8i/wCoZph22MUB14NLc2lawhPaoBUcqUByHr1E +iHexpyS04tl
a0yyCtrG1WGmxnlyzy5+/muT9ktFuFz6HLtEtlvmXCUX4a0x4jCnXVhMppSt qUgk4AJ/spbW
tnbVe/ZU+7TMRdHz6Pn98rdgs1TZRVVN8zzpho3pH0hq+RKi6evAlS4qAt6M 4w6w+lJ7iuG6
lKiO5zxjmK7Gm7117scW6i33G29UI3dS3BjgyGuZGFoydp5d+qet71z1v0zW nVsTTF6sVrsl
rlR35N0iGK9NW8AEtJbV2xQjBVk8sk+9ms7jZb0voY0Ppmfom4Ovt2iceK5a pMh6PI39o0Gk
LQGlq5KDjmQkA4HdqLQ+oG9X29zX72iw1K64M2xFzU4Up4JaU6psAHdndlJ5 Yxj167vG9+vm
CNaNfTWH5EKHd496e6MIkJMp9pbSzMDqitouKAw9jPdIIJB5d2ufZ9O6iEXU Z6OtM6k0rEc0
kqNJZuO9hUq5bwdzYWrJXs3jiDAyffzQfU12usW1WqXc5rhbiw2FvvLxnahC SpRx+AGuVoLV
rGsNNR7/ABbZdLdGkgLYRcGktuONkBSXAEqUNqgQQc/2V8s2iwWKdqa/2O0a KvVsbc6OHyu3
XKO4HXpaX0KStLalFRPEQnBAG5ScgHuntaw0beYug+jKLE0/KNhi20qvFuat LkxTcxxlB4js
VLja1q3cTnntFEnGTQfRrer7e5r97RYaldcGbYi5qcKU8EtKdU2ADuzuyk8s Yx69Dq+3jpBT
ongyuuKrUbqHdqeDwg6Gtud27fuOcbcY9f1q+c7bp3XSY11btg1A9cD0fdQx J86EqI844Jjx
S1zWoIc4RSACsqwUk4zy6/QbZY0DpgZn2TROpNOWpOkTGfVc4jraVy+qG1Kw VkjJAz62cE49
egujVHSVo7TE2bCvl4MR+FFamSE9SvObGXXQyhWUIIOXCE4GSO6RjnXP1D0x 9Hun9QTLDd7+
uLOgqbTKSYEhTbJWkKTudS2UDIUDndXTvGndPXeQ/IudoiS3ZDDcd5bqMlbT bnEQg+8F9sPf
qoJN4uOm+krpQS7oXU19bvphpgiLa1uRZO2IEKSt0jYE5VgnJxg97FBeC9Rx zdLTDixJ01i5
suPNT4rXEiNpSlKhvcBwneFdr3d2DXW43v1876V0rqXS9y6JYkq3yZ71lst1 RMW0kqaaccQ2
pDSnPSp59oCTg7eVQHTFn1quTf5Gm9PXSwPXTSEhC2m7dKiNJmB1BDZdfWou PBBWA6duSTju
Gg+xuN79ON79fHdrtkDr9qe1aX0hqCzOPdGMtpUCaw4H35KnUpKkNqJUdx5Z AG4gkA909fXO
jUWzQem9OQNGT5a5dqemSpi4Mqc+LgpllOzYhxIacO0YWvkgIISBzoPq3je/ Tje/XyXedGXj
UsJc2+2C+y5sbovjpYLjL6VG5tlwhJAxveB57Tk5Occ66aNI3a1RdWx7bZr2 UXjo3SuUVoed
Mu6KDgUCVZKnyD6Xu8+5QfUPG9+orqjpK0dpibNhXy8GI/CitTJCepXnNjLr oZQrKEEHLhCc
DJHdIxzr5yuXRcEs3dlnTl8W0ej9qY2jMlSXbwkLAJGe2fSMYRzIzyAq+rJp 21XfS9skajs7
Uq4yLREjzVS2yXVhvDgQvPPtXMqwfXoJ7xvfpxvfrn8b36cb36Docb36cb36 5/G9+nG9+g6H
G9+nG9+ufxvfpxvfoOhxvfpxvfrn8b36cb36Docb36cb365/G9+nG9+g6HG9 +nG9+ufxvfpx
vfoOhxvfpxvfrn8b36cb36Docb36cb365/G9+nG9+g6HG9+nG9+ufxvfpxvf oOhxvfpxvfrn
8b36cb36Docb36cb365/G9+nG9+g6HG9+nG9+ufxvfpxvfoOhxvfpxvfrn8b 36cb36Docb36
cb365/G9+nG9+g6HG9+nG9+ufxvfpxvfoOhxvfpxvfrn8b36cb36Docb36cb 365/G9+nG9+g
6HG9+nG9+ufxvfpxvfoOhxvfpxvfrn8b36cb36Docb36cb365/G9+nG9+g6H G9+nG9+ufxvf
pxvfoOhxvfpxvfrn8b36cb36Docb36cb365/G9+nG9+g6HG9+vxpaXpK0OKX tQhJASop5knv
fgrQ43v15syMTnhn/wCUj/qug4PZS6NuLwvRCvdu254cnGfw7cY9/uVLZbgY ebbaWsJd5HKi
ojtkjIz+N/wFV9xL32OfQt6Grl1V1o638bjxuFv4PD3Z427bnn6XOPW9apZc pP8ApMfn3vnG
6DpLn2Zu8t2ZyWUT3WS+2wp9YUtsHBKefPBIzjuZFecO52m4wJUy0zOqUxnH GlOIdUpHER6Z
PM4ODyOPf9cVCukzT8/WS4VnQ4m3QGiZDl0aX/pTS8FIbZ+5yCdyj/q9rjnk dPSyZ9u0WLZc
YUKG5CZXHQIfJlxCRhLiU91GRz2nuHPM92gkaHS3rCxutHat5b0Zw93c2WlO bfe7ZtBz3eXe
JqbVAI69+q9P+9Ld/Znqn9BXMaM5c4SltMpfjrK2lBWMK2qKFAg+tkEe/XpE sz8RrgxYDTDe
c7GtiRn8Arc0P/J4f0uV+0OV2657sX33c7t83XI71vn+wfrp8tOt8/2D9dPl qRUrriO9b5/s
H66fLTrfP9g/XT5akVKCO9b5/sH66fLWtdLC5dLbJts+CmRElNKZfaUsYWhQ wpJ59wgkVK6U
Fc6O6NLJpB+TI0/YhFkSkpS88uUt5xSU9xO9xalBI7wOKknW+f7B+uny1IqU Ed63z/YP10+W
nW+f7B+uny1IqUEd63z/AGD9dPlp1vn+wfrp8tSKlBHet8/2D9dPlrWulhcu ltk22fBTIiSm
lMvtKWMLQoYUk8+4QSKldKCudHdGlk0g/JkafsQiyJSUpeeXKW84pKe4ne4t Sgkd4HFSTrfP
9g/XT5akVKCO9b5/sH66fLTrfP8AYP10+WpFSgjvW+f7B+uny063z/YP10+W pFSgjvW+f7B+
uny063z/AGD9dPlqRUoI71vn+wfrp8tOt8/2D9dPlqRUoI71vn+wfrp8tOt8 /wBg/XT5akVK
CO9b5/sH66fLTrfP9g/XT5akVKCO9b5/sH66fLTrfP8AYP10+WpFSgjvW+f7 B+uny063z/YP
10+WpFSgjvW+f7B+uny063z/AGD9dPlqRUoI71vn+wfrp8tOt8/2D9dPlqRU oI71vn+wfrp8
tOt8/wBg/XT5akVKCO9b5/sH66fLTrfP9g/XT5akVKCO9b5/sH66fLTrfP8A YP10+WpFSgjv
W+f7B+uny063z/YP10+WpFSgjvW+f7B+uny063z/AGD9dPlqRUoI71vn+wfr p8tOt8/2D9dP
lqRUoI71vn+wfrp8tOt8/wBg/XT5akVKCO9b5/sH66fLTrfP9g/XT5akVKCO 9b5/sH66fLTr
fP8AYP10+WpFSgjvW+f7B+uny063z/YP10+WpO4GUOKRsWdpIzvHkrHLP3C/ ljyUEa63z/YP
10+WnW+f7B+uny1Jcs/cL+WPJTLP3C/ljyUEa63z/YP10+WnW+f7B+uny1Jc s/cL+WPJTLP3
C/ljyUEa63z/AGD9dPlp1vn+wfrp8tSXLP3C/ljyUyz9wv5Y8lBGut8/2D9d Plp1vn+wfrp8
tSXLP3C/ljyUyz9wv5Y8lBGut8/2D9dPlp1vn+wfrp8tSXLP3C/ljyUyz9wv 5Y8lBGut8/2D
9dPlryctM9TnEShba8bSUrTzH9uRUqyz9wv5Y8lMs/cL+WPJQRTrTdPZHvja 8lfhss1eS6hx
xRGAouJBTzzyxjHMD4hUsyz9wv5Y8lMs/cL+WPJQRTrTdPZHvja8lfirPcVp KXC8tB7qSpsA
+9yANSzLP3C/ljyVGZuon2+lC26UajtiLJssu4OuqJLm9p+M2hKe4AMOrJyC SduMYOQ1rcv/
AOM7RHUf4ViatDifuSYjqgPf5KB/tqx6rSB6piPhU/u6rLoITof+Tw/pcr9o crt1xND/AMnh
/S5X7Q5XboFKUoFKUoFKUoFKUoFKUoFKUoFKUoFKUoFKUoFKUoFKUoFKUoFK UoFKUoFKUoFK
UoFKUoFKUoFKUoFKUoFKUoFKUoFKUoFKUoFKUoFKUoFKUoOD0m6if0rpuXeY tvauEhMuPHaj
uSCwhSn5DbIKlhKykAuZOEnuVy7fq28x9SRNP6ssEW0ybi06u3yYU8zIzym0 7ltkqbaWlYTl
QBTghKsHIxW70t2K56j0lLtlm6j6u6uiSWhLdU20rgS2nilSkpWU5DZGQk8y K5bFg1TfNVWy
+6qbs9vas6H1QYVulOSd7zrZbLjjq22+QQpYCQjurJJ5AUHtH6QbFF0xaLpc 5z8oTra1PMm3
2eW4yWlICuKUpQssoPM/whBA7vcNbWoOkHSFiUgXG7EBUMTiuPGdkIbjHOHn FNpUG0HBwpRA
ODjNQNvo91yzpqzacMyHJhRNORLZhq+S4TcWS22UOu7GUAyUq7TAWpHJOMDJ NRzXDMrQemrj
aDeNNquN00XFtcmFJkupfW8wy80kxEhvMneVqTw+0IIST6bFBckPXOl5l1m2 uJcXH5cBviS0
oiPFLCeEh0FStm0ZQ4kjn23bAZKVAeDXSJpNUO6S3Z0yI3a4Sp8tMy2yY7iY yQSXUocbSpxH
I80BXPl3SK41g0jqO26Z1gLdNhwLxe1NvW+RgqMdSYEdhPEynkQ40s8twAIP M5FRif0Zatur
eoHHjBhquelZtmabkX+bcVJfdKClxTjyO1RyVkISMYHJWe1CaPdK+g2RI4t3 ko6mSlx/Nslf
wbSu4+r+D5MH2b+L/wDNWxdekvRVrukq2zrypl+HIRGkq6jfU0y6tCFtpW6E FCdyVp25V2xO
BkggaGsdF3S8zNaPRX4SE33SbdljBxagUPpMzKl4ScI/0hHMZPJXLkM6j+gb w5aL/EEmBxLj
qW13Zkla8JaiiBxEq7Xks9SOYAyDlOSMnAe186UbPHdsMi3PLcgyr25a7kl6 3yUymFCG8+lC
WCkOBwqQ1gFB3JXyHMGu8dd6VGnmb6LktUR+SYjSExXlSFyASCyGAji8QbVZ Rt3DBOOVRW+6
I1SNaOamsrlmdWnUbd1aZlvuoC2hajDUhRShW1ZWSQQCAOfMjadO4dGd8mQG Lq9Kidfk35+8
uxo9wkxI54rHALKJDQDqcICTxAnmQrKMKwAmg1haZiLDKtt4iCJcp7sXD8Z3 etTbLy1tY5Fl
xJZUVcQcghScBRFecXpD0pJs8+8omzUW6BDVOelO2yS00qOkZLralNgOpxzy 3uz62a4No0Hc
I3oeeEO3QVwb9Ius1nrpJncUOQn4/wDHPp3uLKnUE5CRgH1+7EdY6Sv9g6Nd dSHGoFnsg0pP
ZFqg3WRMYW7w8pdQh5tAjBKUrTsbyDv5+lGQsSX0hadkafv022XhuM9areua 4udb5CUoaCVF
L/DKUrea7U9s3kHGAc1vT9daXt976yzLmUzEuNNOqTFdUyy47jhoddCS20pe U7UrUCdwxnIq
Ial0Rq/VcLUb93csUSdM0xJsVvRFfdW0ovc1POqUgFA3JRhCQvaN3bKzWvee jC4u6ivbrUaL
dLXepiJjzcnUM+EGFhttCgY7OWnxloKBVsPrHIANBO9J3yXdr5quDJbYQ3Z7 siFHLaSFKQYc
Z8leScq3PKHLAwByzklbtZ6fmXpdkclrgXVK1ITCntKjuvYONzQWAHU+vuQV DnTSdjl2m+ar
nSXGFt3i7Imxw2olSUCHGYIXkDCtzKjyyMEc85A01aGiT7y1dtS3Offno0oS oMd9QbixFpVl
BQygBKlJ5YW5vUDzBHcoNy3az0/MvS7I5LXAuqVqQmFPaVHdewcbmgsAOp9f cgqHOuFP/nD2
X+qdw/a4VdFWhok+8tXbUtzn356NKEqDHfUG4sRaVZQUMoASpSeWFub1A8wR 3K50/wDnD2X+
qdw/a4VBvQPVMR8Kn93VZdVpA9UxHwqf3dVl0EJ0P/J4f0uV+0OV26jNgecj 6Hlvsq2uNrnL
QrGcEPOkGpT1jc9u7l8ljzdBhSs+sbnt3cvksebp1jc9u7l8ljzdBhSs+sbn t3cvksebp1jc
9u7l8ljzdBhSs+sbnt3cvksebp1jc9u7l8ljzdBhSs+sbnt3cvksebp1jc9u 7l8ljzdBhSs+
sbnt3cvksebp1jc9u7l8ljzdBhSs+sbnt3cvksebp1jc9u7l8ljzdBhSs+sb nt3cvksebp1j
c9u7l8ljzdBhSs+sbnt3cvksebp1jc9u7l8ljzdBhSs+sbnt3cvksebrVbZd i3KVFXMfkoQy
w4kuhAKSpToPpUjl2iaD3pWMGIua5KUqbIaDbwQlLYRjGxJ9dJPdJra60H2y m/E19Cg16Vsd
aD7ZTfia+hTrQfbKb8TX0KDXpWx1oPtlN+Jr6FOtB9spvxNfQoNelbHWg+2U 34mvoU60H2ym
/E19Cg16VsdaD7ZTfia+hTrQfbKb8TX0KDXpWx1oPtlN+Jr6FOtB9spvxNfQ oNelbHWg+2U3
4mvoU60H2ym/E19Cg16VsdaD7ZTfia+hTrQfbKb8TX0KDXpWx1oPtlN+Jr6F OtB9spvxNfQo
NelbHWg+2U34mvoU60H2ym/E19Cg16VsdaD7ZTfia+hTrQfbKb8TX0KDXpWx 1oPtlN+Jr6FO
tB9spvxNfQoNelbHWg+2U34mvoU60H2ym/E19Cg16VsdaD7ZTfia+hTrQfbK b8TX0KDXpWx1
oPtlN+Jr6FOtB9spvxNfQoNelbHWg+2U34mvoU60H2ym/E19Cg16VsdaD7ZT fia+hTrQfbKb
8TX0KDXpWx1oPtlN+Jr6FOtB9spvxNfQoK9kTumJyQ4tvTmg0IUolKVX6Wog Z5AnqQZ/DgV5
9WdMvue0D48l/VKsbrQfbKb8TX0KdaD7ZTfia+hQVz1Z0y+57QPjyX9Up1Z0 y+57QPjyX9Uq
xutB9spvxNfQp1oPtlN+Jr6FBXPVnTL7ntA+PJf1SnVnTL7ntA+PJf1SrG60 H2ym/E19CnWg
+2U34mvoUFc9WdMvue0D48l/VKdWdMvue0D48l/VKsbrQfbKb8TX0KdaD7ZT fia+hQVz1Z0y
+57QPjyX9Up1Z0y+57QPjyX9UqxutB9spvxNfQp1oPtlN+Jr6FBXPVnTL7nt A+PJf1SnVnTL
7ntA+PJf1SrG60H2ym/E19CnWg+2U34mvoUFc9WdMvue0D48l/VKdWdMvue0 D48l/VKsbrQf
bKb8TX0KdaD7ZTfia+hQVz1Z0y+57QPjyX9Up1Z0y+57QPjyX9UqxutB9spv xNfQp1oPtlN+
Jr6FBXPVnTL7ntA+PJf1SvPTlk1zK6TI2qtVQ9OQ2Itmk29tu23B6QpanXo7 gJDjLYAAZVzy
e6OVWV1oPtlN+Jr6FakmOuHPYa6qeeQ404ohwI5FJRjG1I+6NBEoHqmI+FT+ 7qsuq0geqYj4
VP7uqy6CurR6n8/8M/552rFqurR6n8/8M/552rFoFR3pF1KrSumjcWIXV85+ SxBgxS5ww9If
dS00lSsHancsEnBwAeR7lSKuLrXTkPVWnnrPNefjhTjbzEmOoJdjvNLS406g kEbkrSlQyCDj
BBBIoI7a77qK3anj2vVOrNCOqdbUt2DGS5ElsjaVApC3nOMOXM7W+WT62K99 MdJFpv02Cwi1
XmAxdIzku0y5jKENXFpABKm9qypPaqCglxKFFJyAQDjnSej2+Xy8WmVq/Utr uca1yFSG0Q7J
1I++osuM4ddLywU7XVckJRzx3K1uj3omjaOnxVRfQuuNCYVHjvs6aaYuK0lB QOLKS525weZS
hBV6/dNB72jpk03Mtka6z7XfbJbZdlcvcSXcI7aUSI7aUKc2hC1K3J4iORA3 ZygqGDW650n2
yJar5NvFhv1nfs1qVd3YUxlkPvxEhRK29jikE5SQUqUlSSRuCciuZL6IINw0 hpnTNyu7j8Sy
6besLqm44QqQHGmG+MMqIQUljcEkKHbczy568foh26W1JY1P6TgqvVnftYlW fSzcFxIcTt4j
hS6riEd3aNiSfWHLASz0YSnbLEucHRepp3ValFqO2iM27wxgpdVxXkJQlQII ClBffSCCK5Nk
1qrUOutLC0vvN2a6WG5y3ozzKUuB9iTDaAV3SlSC48kgHBJ9fANe3SPoWVqq 62ua1NtC2IbD
zLkC8Wsz4iysoIeDfFQA6nYQCrcMLVyFeHR90ajSUjT7qbwmWmywLnCShMJL IcEyW1IBwlWE
bA1twBg5yNuNtBYNKUoFKUoFKUoFKUoFcOX9v5n9Fjf5367lcOX9v5n9Fjf5 36D2syihq4rG
MpfyM/8A2kVQenOmXpRjdFdm6WtUW3SErSMt1CZ7FubkMzYjan+BxRvcWhYC sEp5HmOfdIvW
2y4TPXBiTMjsLW9yDjgScFpAzgmqW010KuxtJ2XQ+pOluNeNG2p9L3WmNbWo hlFLhdSl13ir
UpG85KRjPLnyBAWLrnpYs+jL4mBfNP6lagdUMx13hMJJgoW7tCe3KwpQyoAl KVAHkedcOz9M
D6OkzpHsmo7O/bdP6RjsSE3EsDtEFsqWXSHFFW/G5oIRkpB3YOBUY6SOhSLr HUl/ua9f2Nlu
7So8ptyTam5UyJwtn8C2+p0FDJ2E7UBJyrmSMhUnv/RvZbxq/WVykayjps2s ba1DuttCGyvi
NNltp1t7d2m0HO0pIJ7veoN6z9N2lrhab5Odtd+t7tnsyr4uHMjtofkwglR4 rQDhSfS4wpSS
CRkCtvo46XbBrjUKbFEs+obVMdtaLtFF1hBhMqKpQTxGyFKyApQHPGc5GRzq EaW6GrbZdNai
si9ZaVUm7WF+ytSYWm4cJ9tLqQniOuNq3vEYBIykKPM88ES7TGi7VZdcWDU3 oshP9aNJo051
PsSni7XG18bdvO30mNmD3fTUG9r3pJOhrys6i0pek6ZDKVnUEJsSWGDz3B5t H8I2kcu2woHN
Stm4i+aWTddLzob3VsTjW6U6hS2FFSctrUkFKinmCQCDjvVENVaF0BqzVLd9 1TN69JZQhMe2
y7jugMqTntxHyEKUc8yoK7g71SS8PRV6ak2vT1+tdlkmMWIcgIQ6iKcYSoNB SQdvrDIHIety
oK36IelLV+vNYLsT9hgWhOm2Fs6tcdClf6dvUhDUUhfJB2KcKlbuRCe72xsL pD1hbtIaBvOr
HnG5DNujqWlCFZ4rvINtDHrqWpKR+MKrvSvRFpXStztE2w6pjs4t7tv1Gh8h 3r+hzKlOOniA
od3qWoL7YgK29wV73voh6PJNmtGmLRKtVn0pHu/Xa6WhpXETcnA2EISpZcyl IISSMK3bR3O7
QdDoz6UXpXR7fbv0jNQrFetLPOtX5mOFcJoBIcbWhJKlFK21JxzJJyB3qjHS n0z3XsPXrUem
9P6t0xJi9RPRZ11tTaW3mnZbLatgJWCShZ7VQCsHIFbkjoW6P2519j2S6wLJ pzUFl623K0RN
oSt1KytqUhZX2q0biMFJBrG9dHV+1DoKdo/UnTHCuUN1EVEVXWhhpTXAfadC l7XcuKIb25yk
dsTgmg7g6ddItWPU9zuls1FZ3NNLjJnQp8ENST1ScMKQncRhZ+6Kcf62K6jn StZYegZus73Z
r7ZYEVxDSGpbDanpS1kBAZDTi0ublKAGFYznOACa4950NGl6k1ve4Ws7Oy5q piAwpiXb2pbL
KYyVBSVoW5h0LCu5hOPWJ7tRi3dBelU9HmpdJ3HV8JwXyazOQYMZqNFhOtY2 FqMVrSASO3BJ
CgccuVBaXR3r20a265sQolxt1wtTyGp0Ce2hD7BWnehR2KUkpUnmCFHuGpQh 9hbymUPNqcT6
ZAUCofhFVv0O6Rt+gWbkH9QaWlOTi1k2qxxbUgBAVjcGiSsneeajgesBk12r Lpfo7s2srhrG
2RbXGv1yQpuZNTJyt1KlJUoEFWOZQk8h61BFNa6v6SnumtzQGhvQky0zp1u8 LdvMaQsrUqQt
ooCmnBgckn0p9fu9ytrRnS1JumgpV5uOkLu/erXeH7JdLbZ2xJLUpnmtSVKU kBrGDuUQBuAy
eROrrfRd1ufSirXmk+lC2abluWVFodbctjU3KEvLd3gqdSAcqH+qfS+vnFci 59D1k9Atp07a
taQlvxbw5eLi9eGUTWbvIcSoLVIZDiArmQUjOBtGc92gz1j09cPS+lL/AKP0 7crgi6anRZbh
FkRU8eOUqIcYCeKkCQrls5qRyOSOVSTUXTbpCxakl2aZGvC27c7HYulwajpV Ft7j2NiHVbwr
PbJzsSoJzzIqIwOhqDB6P06cidIcBifG1YNT2+e1b2UNx3gQUtmOHNqkDnyS UjmMAAYOOpuh
OxXTW1w1PF1ZphDt2LTlxbuWnodxJeQkJU4wp45Z3YyU9sMnNBJdY9POldLa kv8AZrhYtUPI
087GRdZ8WAlyLFS+hKkLUvfkJ7cAjG7IOAQM1vfZLa3vGgOhy76k0/FdeuCA hph5DSHERStW
OM4FEdqO4MBXbKTkEZxwtZdF9q1FG6TGfRtCjejkwefASvqLqZtCPZBxN2zP +rjPr1K+l7T1
q6QOjG7aK9E0K29cGm2+q+1d4exxC87N6c52Y7o7tBDX9X39Ecafk6r1Jbb5 atISL9cXpNig
lx1KnClslCVlCXW+GsbE9ooKGVZrtwum/TkvUGmdPw7HqedP1DaId3jCPCQs NR5CykLeIc7T
ZjKzzSB3CTyrR6UOjxOrNXydR2TpHY069PsC7DcGxFZkh6OpalgpKljYrKyC efLuYPOtvo70
DatI6qtN99GEKb1u0fF0zwuGlvicBzfx93EON3c2YOPujQdbps1peej202/V rUGNO01EkhF/
bCFGU0wshKHmTuCTtWRuSQcg8iME1u9FGo79f9ERNSatYttqcuqlSoMRokFm IvmylxSlELcK
MKURtHbYwMVz+kjS1n13drEi9akgr03bZBly7NtSU3B4D+C4q9/8Wg8+HtIU e6eWK4Fn6JNB
ehZGlNV3G3aosVvmuv2KPLWUOW5lf/yOIHMuBPcBIBA5esKD06d+le66Evdr t1gtMe6liOq7
6gSsKK4trQ4hta0YI/hCVKIzkYbVyNSbW3SdYdMyrPAZg3fUNzvLSpEGDZYw kPOspAJd5qSl
KOY5lQznlnnUXldEvR9ftYai1NrqTaNUy7q8gREvgNpt8ZtG1DKP4Q5PdUVc sk9wVzLf0VO2
m36WfsnSqxD1BpuI9bY1ychNPIfgrXuQw6ypzmUAJAUlSe53O8Gtfumu42Lp YcE2x6re096D
kXZVnZtKDLiuiU6lx53JBQkNo5grx3MAk1Lrp02aXYudotlqtd9v8y7Wlm7x 2bcw0ViK76RR
DriCScHtU7lDHMVzzoBMu/3O+3jpDiXGfctIr04+6YjTWVKdcc4+EuAYHE27 AP8AVzu51HtX
dDMPUOjrDpZ/XWmzCtFmj2xL0ixMvSkFtG0vMPcYKZKsA4yoAgd2gnWvemHT Wj7ubTMt97ny
2beLlObgxkL6gjZxxHdy0949qncrAJxiuzr7V3WfopuutbFGdu4ZtZmw0x0h XESUbkOYUpPa
AELVzztBwCcA1trzoZ07f9RQNQW/VdlbnsWtq2STe7VFu6JCGvSO7XSNjvfW Dz9cd3NjXC22
mV0ZyNFpvtuZS9ZlWsSG0NIQjLJa3hpBSlIHdCE4A7gwKCuOjrpdl2ro30tK 1n6I9Ram1Kyq
bGhsW2K09wA2hSlpShaW+CM5SpSgtQUO1zyEouvTfo6LaNL3C3sXS8q1O267 bI0JpsOrS1gO
buKtCUlJO0p3ZznAOKjOrehvTt809oiMjVdlTddJ2tu2NyZ9sjzostpLSEHi RnVYByjcCFZT
k8zWzqPoxi3bQdm0knVmjRFt6Hg6h/TERbC1uLKt7TSHEBhQyR2h55ycnnQd LV3SperV0k9H
WnoekboqDqhuQ5MS/GSJLO1GUpT/AAoCVNntnAQrtCNuTyrlaP6X1Wuwajna vkTLrKGubhYr
PDhRm+O6htf8G0kDYntUgkrWR76u5Wy30ZQoNu6O0WbpDSxcNEcZDMuW23J6 qaeRsdQpJWNu
U8kkE7RjkcVy750J6bu+nJ9ukawt6prmq5WpIUh2Gy+yyt882HWHFqS8jHI5 25wDy7hCVv8A
TdpFGlYV6YhXyZKm3Rdoas8eIFz+rEZ4jRRu2gpHMndtwRz5iteN08aSe0/I uhtOo2ZLV9Nh
btb0NCJj8xKErUhKCvAACualqSBg59bMD1voyHpDRVglQL3vvlqv6p8WZpXS 0YNx+IzsXxIL
SxvQUoCSrcV5KeeBy5Gg+i30cdHl1kawuybZd5OsZF/tr90gx1rWhTbbeZMN R2AL2qJaJ5dr
z74WxO6cdJxdJW3USbbf5Qn3w2HqCPDSuWxNAWS0tvf3e0/1SrO5OMg5qT9G eurVr20TZ9sh
3KA5b57tvmw7iwGpEd9vBUhSQpQ7iknIJ7tQC39F1tYsemre5q7TrLll1S1q FRttmjwWXyhs
t8INNLASSCDxCVq5Y5jGJf0c2G1aPl6qf9EsKb6IL8/eMdq3wOIhtPD9Od2N md3LOe5QTqla
fXa1e2cL8uny067Wr2zhfl0+Wg3KVp9drV7Zwvy6fLTrtavbOF+XT5aDcpWn 12tXtnC/Lp8t
Ou1q9s4X5dPloNylafXa1e2cL8uny067Wr2zhfl0+Wg3KVp9drV7Zwvy6fLT rtavbOF+XT5a
DcpWn12tXtnC/Lp8tOu1q9s4X5dPloNyuReftpD/APsPf5m62+u1q9s4X5dP lrnT5cWVdIvU
0ll/aw7u4bgVjKm8ZxQRGB6piPhU/u6rLqtIHqmI+FT+7qsugrq0ep/P/DP+ edqxarq0ep/P
/DP+edrs9JVs1VNgxJeldQ3O2uw3SuVDgtw+JPZIwUIXJacSh1PJSM4So5Qo pCg42EspUG6N
bfqozpd6vOqtQTrQ+0G7bb7tEhsvgZyZLoZjtKbUrkEsq5oTkuYWvhs49PL0 lno1kdSTZkJx
25WxhT0SSth0Icnx0LCXGyFJylSk5BBwTQTulVk5Gd0R0maWttqvV4k2u+iY 1MhXO5PTuFwW
S6mQhx9SnEYICFDdtPETyBANaNj6Q7/P1fabQ1cLVcId9RJTClsafnMMMLQy p1tYfdc4ctsh
JzwygnkQcc6C26VSuiNW6mjdDug3JWqLe9erpaW5Slu2KbcpLzYbbO7gsO8R ZBWAt0qCcqHa
jOKsPos1JI1doS3X+ZGRGkv8Vt5tCVJSFtOraUQlfbJBKCdquYzg8xQSelKU ClKUClKUClKU
ClKUCuHL+38z+ixv879dyuHL+38z+ixv879B+0pSgUpSgUpSgUpSgUpUCGu9 QzL1erfp/o4v
N8atE4wZEqPPhNNl0NtuYAedQr0jqD3MZOMnFBPaVCPRTr/wN6h8b2z6zT0U 6/8AA3qHxvbP
rNBN6VCPRTr/AMDeofG9s+s09FOv/A3qHxvbPrNBN6VCPRTr/wADeofG9s+s 09FOv/A3qHxv
bPrNBN6VCPRTr/wN6h8b2z6zT0U6/wDA3qHxvbPrNBN6VCPRTr/wN6h8b2z6 zT0U6/8AA3qH
xvbPrNBN6Vp6ekXCdZmZt4s8iySnVLBhOutPONhKsdsptRRk4yNqjyIzg5A3 8M/dr+QPLQYU
rPDP3a/kDy1D7rr202rUd9tlwaktRrPGtzi5DbDj7jzs151ltpDLSVLUdzaA MZJLmMDbkhLa
VE+yBZParWf6HXX6tTsgWT2q1n+h11+rUEspUc0lrbTuqbpdbXaJE3q+0cHq +LMt0iG7H4qS
pvch9tB7ZKSRgdzB9cZkdApSlApSlApSlApSlApUQ1ZrG42nVcLTVm0jcdRT 5cF2cERJUdnh
tNuNoUVF5aB6Z1GMEnmeXKtf0U6/8DeofG9s+s0E3pUI9FOv/A3qHxvbPrNP RTr/AMDeofG9
s+s0E3pUI9FOv/A3qHxvbPrNPRTr/wADeofG9s+s0E3pUI9FOv8AwN6h8b2z 6zT0U6/8Deof
G9s+s0E3pUI9FOv/AAN6h8b2z6zT0U6/8DeofG9s+s0E3pUI9FOv/A3qHxvb PrNPRTr/AMDe
ofG9s+s0E3pUI9FOv/A3qHxvbPrNSPSk2+XSE8/edMSdOuodKEx5Uth1awAk 78srUgJ7bA7b
OQeQGCQ6lKz4Su+j5Y8tOErvo+WPLQYUrSt11t9xlXGLClIfdtkrqSYEg4be 4Tbu3PcPaOoP
LIGcd0EDdoIfA9UxHwqf3dVl1WkD1TEfCp/d1WXQV1aPU/n/AIZ/zztWLVdW j1P5/wCGf887
VBdIH8vdQ/Ckn51Vel7O9n47VVT71132v/3DHhmF4tTE3X3vsKuXqvT9q1RY nrJemHX4Ly21
rS1IcYWFNuJcQpK21JWkhaEkEEdyvjWletyb/d0b2Djnuadz660/obTNjlvz IcKTImSGTHcl
XCc/OfLROS2HH1rUEE8ykEA4HKtOw9GmkLJcbbcIUOeuRatwtypN0lSExEFt TZbbS44pKUbF
EbQMck8spTj5Rr1ix5Ep4MxWHX3SCQhtBUogDJOB3gCf7KT+m7v+3RvOOO5p 3PqVHRdo5qNB
jxItzhIgIcajGLeZbKm2nCkqZCkug8LKE4bztTgYAqQ6XsNp0zZGbLY4Yh29 hTimmQtSggrW
pasFRJxuUo47gzgYAAr44EeQYplBh3qcL4Zd2HYFYztz3M4GcV+sRZL7TzrE d11thIW8tCCQ
2nIGVEdwZIHPv1zk5H+XRvOOJ+jTufbNK+Ia9X47rLbLjmza8jejatKjjcU8 wDyOUnkcH1+4
RXeTf7ujeccdzTufbNK+IaU5N/u6N5xz3NO59vUr4hpTk3+7o3nHPc07n29S viGlOTf7ujec
c9zTufb1K+IaU5N/u6N5xz3NO59vVw5f2/mf0WN/nfr49q7/ALG/7Q3r+lNf 5VVjw/2Niljw
nv3/AMXf7aMF9o4xae57t387lqVDelPVGotIafm6gtmn7Vdbdb4i5Mrqi6uR nu15lKEJYcCu
XrlSf+9TKo30oWGZqjo8v2nbe4w1LuMFyOyt9RDaVKGAVEAkD8ANeG9N4WzV wYgX1zVibZaZ
VibEie3ElPSm2o5bK0ub1sNFWQlfJKVelxknIGL/AEj6Oj3d61P3N9uTHkNR pBVAkBuO46hC
20uubNje4OIwVEAkkd0ECJ9INvi6k6WrPZrTcY7q1NBGqIjZ3qTEZcRJjhzH pNzmUAK9Mh9w
jIBrQdsepNT3jpL03B60tWa46gZamyX3XBJYT1vglfDQEFKyU4AypG08+27g C3YlxiSp82Cw
twvwVIS+FNLSkFSQpOFEAK5EelJx3Dg1t1qRDcjPmiW3ETDCkdRqaWouKTtG /iAgAHdnGCeW
M4PKtugUpSgVWekdR+hSD0k3nqPqzGu48XhcXh/x7NtY3ZwfS8XdjHPGOWci zKqe12C6al09
0n2qy9RdX+juLKZEt5TTSuA3bHylS0oWU5DZGQk8yOVBbmrrqLNa2JapsaGH J8SLxJDDjqVF
6Q20EAIIIUor2pUe1SSCrkDXJtnSPo25Xxuyw7s4uW7LkQkbob6GlSGCsOsh 1SA3xE8NZ2bs
lI3AEEGudfrVrfVVhTAvNr07aXY93tc5kxLu9LS43HmtPuhW6M1tVtawnG4E nmUgZPO7HN0d
07ZrTIlQx1Jqu43eQptxYzHkuTVJSg7f4wJlIyDgAhWCcDISnTWvdKajuYt1 nuan31treYK4
rrTcltCglS2HFpCHkgkAqbKgMjnzFLVrvTNzvCbTHlTW5biHFsiVbZMdEhKP Tlpx1tKHcDmd
hVy59yoR0cdGl503Lszc2PBfFjhriwrmrUNwkq/iuGFJhO/wLORjKUrUB3E4 5Y19M9H2uRqn
TF61BJiOPWnjie+vUEyYZq3IzjRdbZWhLLA3qB2JTyBOD2uFBZOi9W2DWVqF 103McmwVBCkS
DGdaQsKSFDaVpTuxnBxnaQUnBBA7lRzowsEjSvRvprTUsx1SrXao0R9UfJbU 420lK1JJAJBU
CckAnPMVI6BSlKBSlKDnTvSI/GX/ANapzpf6eNOdFt/atOptNaqKZDfEizIs ZhcaSBjcEKLw
OUkgKSQCMg4wpJNxzvSI/GX/ANaiPSRonT3SDpOTprUsPqiG92yFoIDsd0A7 XW1YO1YyefME
EggpJBCOdDPS/Zulbq17TundSRYMLtXZ0+Oy2wp04PCSUuqKl4O4gDAGMkbk 7o5q31UdUf03
RP74XVsaYsVo0xYIdgsEBm32yE3w48dockjuk5PMkkklRJKiSSSSTVT6t9VH VH9N0T++F0F0
FxAdS0VpDiklSU55kDGTjvDI+MVow75ZZiGVxLxb5CX5K4rJakoUHHkBSltJ wea0htZKRzAQ
rPcNaF3s8t3WFq1HD4Di4UKTBUy64WwUSH4i1rCglXNKI6iE47YkAlIJUK+6 POiu/afvunZk
25wVRbU4+6+228+svLFviQGFgdojcW2HHFFaV7S4UpzkrAaXQf8Azhumv+lW r5l6p90o63To
m2RJLdqeu0iQ6r/RmXNq0sNoU6+93DkIbSTj/WUUp5FQqA9B/wDOG6a/6Vav mXqmd90C3qfX
Mm9ajfkGBHgoh2pmBcpMVaAslUhThaUgneQ0nbkjDYPdPIO7fdXWCyswXJkx 1w3AFUNqHFdl
vSEgBRUhtlKlqSAQSoDAyMnmKjM3pRs1r1bcId3kGPaGrNBuUd9MCQt0Jeck pcW6EpJbbSGm
u2UlISVHceYxz7DofVumHbPLtDtnuTtmjS7TGjzZbrSXLct1DjGXQ2sodbDa EEbVBQGcg10b
xo+/3b0YyJLtsbk6g0tHtLaW3FlDchAmbiSU54eZKMHmeSuQ5ZCRDWWn1X9V jakyn5aHUsuK
YgPusNuKSFJQt9KC0lRSpJwVA4UO/XnG1zpeTqHrCzc1KmGQuKlXUzoYW+gE rZS+U8JTiQFZ
QFFQweXI1FXdEaib1TAn2lm2WgNvxVzbjFushLktttKEuNuxOHwXStKSgOKV uSCCOacVp6f6
MrhaL5HacjRrja413cuUeS9qGelTRW8p0DqIZYK0lZG7cAe6U5JoJZH6SdHS Libexc5Dsjjy
YwCLfIKVvx9/FZSoN7VOjhrIbBKlAZSCCCY0Ol6GvQMTWYiOtod03KvJtC4c jqhwshnOx3YE
8IF0JK9hBCgsHalRrfgaGuzFtsEZciCV27Vk69PELVhTDzsxaUp7X04ElGQc DkrmeWeBF6L9
SPaOt+np0q0s9Q6NuemUvMvOOcQviMll4gtpxyZUVJycEgAq5kBNp/SLpS3M RXLjMlxVPxBM
U2q3SSuOwTjivpDeWEZBG5wJHI8+Rr8c1giLrO926aWxaoFrt0tp5hlx1xS5 T8loghGcp/gm
8YTyyok47kRvnR7qKfeXL45EgSH7lbGIVygI1NPgstqaLgCkuMIHHQpLhBQ4 hOCDg9sakdq0
xe7JfJ10tLdpw5ZLVbY0d190IQYzsgu5O0q27HxsOSSU9tjukP2Q+iL9kHbJ LqXVIZ0bcnFB
ppTiyBLhE7UJBUo94AEnuAGvEdMsJfR5D1umI60h7TEu9mzOQpPVDpZDGdj2 wJ4SS8ElzYQQ
sLB2pUa2m/5x1m/qhcf2yDXEi9FOp3tF27Tk+VZ2OoNFXTSyXmH3HOIZCYqW XiktpxyYWVJy
cEgAqySAnVw6StI22PEduU2XFVIiCappdtklcZgkjivpDeWG8gje6EDkefI1 t2rUMmb0j3nT
u2MqBCtFvnMOoBK1qkOy0qyc4KcR0EYA7quZyMQK+9HGpbje3b85Dt8l+6Wq PBudvRqi4QGG
1MlwBSXI6Bx0KS4QUONpwQcK7Y1MdIaUlWTV8y6HqNuC7YLZa2WGXFqLa4q5 RV6YZ2YfQEkk
k4OceuExpSlApSlApSlApSlArSmf/N//AI+4rdrSmf8Azf8A+PuKDRpSlBAe iX7fdI39bF/s
EOp9UB6Jft90jf1sX+wQ6n1BD4HqmI+FT+7qsuq0geqYj4VP7uqy6CurR6n8 /wDDP+edqguk
D+XuofhST86qr9tHqfz/AMM/552qy1h0Y64n6tvM+JZOJHkz33ml9VMjchTi iDgryORHdr3/
AGBbWdlaVzaVRHN85ueV7Vs666Kfdi9WdKm/Ym6QPaD/AJxj6dOxN0ge0H/O MfTr9Pj2Df5K
fOHiYtbfRPlKEV09LxrhKv0VFse6nkoXxQ+VbUsBPbFxR9YJAJP4KknYm6QP aD/nGPp07E3S
B7Qf84x9OvmrDcGmmYi0p842vqnBraJv9yfKWHSE9FusKPc7CEpszDq2VsIb 4fCfUSouKTk4
DgAI7wTt/wBWt7TEqy6cskKLeJM1ld2y/MaYjJcC4ykraQlRK0lPIrWMBXdQ cchWp2JukD2g
/wCcY+nTsTdIHtB/zjH06zTaYLNnFnw0XZ4vW923iv3+Dm/NLctFhRAYmwLl ChO3KPKdbs5c
SP8ATHQntgRg8RGNik55FRCeYUaws0VhDVmDVhcmSH7MtRVGgtyXG19WOjiF pY2uHakJ58wM
c+Va3Ym6QPaD/nGPp07E3SB7Qf8AOMfTrk2thN99tT5xt/kii1jos58tyPaw hqg6jlRlOMLU
nYollkNJG5AVgoHJChnBSO4QRXJqb9ibpA9oP+cY+nTsTdIHtB/zjH061UYZ g1NMRNrT5wjV
g9tMzMUT5ShFKm/Ym6QPaD/nGPp07E3SB7Qf84x9OvrHsG/yU+cPnFrb6J8p QilTfsTdIHtB
/wA4x9OnYm6QPaD/AJxj6dMewb/JT5wYtbfRPlKEUqb9ibpA9oP+cY+nTsTd IHtB/wA4x9Om
PYN/kp84MWtvonylCKu/7G/7Q3r+lNf5VVB+xN0ge0H/ADjH06s3oW09eNNQ btAvcPqWQ46y
8lHEQvKCHADlJI7qT8VeT7awqxtMFmmiuJm+OiYb/ZtjaUW99VMxGZP6UpX4 9+hKUpQKUpQK
UpQK5uk7NG06/e34z0l1y83JVxkb1J2pcLTbQSgbcgbGUd0nnuPIEAdKqx0r p+9az1HrV57p
C1ZaGbZqBUCLFtzkUNJbEWO7/wDNYWrO55f+tjGAAMUFsdWK76/jHkp1Yrvr +MeSoR2Lbj4W
ukH8rA+qU7Ftx8LXSD+VgfVKCb9WK76/jHkp1Yrvr+MeSoR2Lbj4WukH8rA+ qU7Ftx8LXSD+
VgfVKCb9WK76/jHkp1Yrvr+MeSoR2Lbj4WukH8rA+qU7Ftx8LXSD+VgfVKCb 9WK76/jHkp1Y
rvr+MeSoR2Lbj4WukH8rA+qU7Ftx8LXSD+VgfVKCb9WK76/jHkp1Yrvr+MeS oR2Lbj4WukH8
rA+qU7Ftx8LXSD+VgfVKDd1nD1tcZjB01qi02aI22eI3KsqpjjjhUSVbw+2A nGAE7c5ycnIA
4PWHpX8I+nv0UV9bro9i24+FrpB/KwPqlOxbcfC10g/lYH1Sg53WHpX8I+nv 0UV9brkTejHV
dwY1FKm68hIvt161GLcIti2JhrgSlSG1lpb6w4SpWMEgDAyDzqUdi24+FrpB /KwPqlSWx2pd
hsrEBd1uF3dStwrmXEtrfd7blnYhKBgYACUgYHPJJJCmuxX0yf8A5hp36OM+ dp2K+mT/APMN
O/RxnztXhxVd5HyB5KcVXeR8geSgq/oX6MbtoS/aovt91krVFx1CYqn31W5M UpLCXEgkJWoK
yFj1h6X181ZtQywXKfJ6ZdX29+U6uHEtNpVHYzhttS1zd6gkcgpW1OT3SEpB 5AYmdApSlApS
lApSlApSlByk2OL6OI+rVPSOq49tdtzTaSkN8N11txalZBJOWUAYIwN2c5GO /wBWK76/jHkq
rNbs3i89Llo01D1Xe7DCcsMuc6bapkKccbkR0JzxW1jGHl9wAnlz5Vt9ju7+ FfX35WB9VoLI
6sV31/GPJTqxXfX8Y8lVv2O7v4V9fflYH1WnY7u/hX19+VgfVaCyOrFd9fxj yU6sV31/GPJV
b9ju7+FfX35WB9Vp2O7v4V9fflYH1WgsjqxXfX8Y8lOrFd9fxjyVW/Y7u/hX 19+VgfVadju7
+FfX35WB9VoLI6sV31/GPJTqxXfX8Y8lVv2O7v4V9fflYH1WnY7u/hX19+Vg fVaCyOrFd9fx
jyU6sV31/GPJVb9ju7+FfX35WB9Vp2O7v4V9fflYH1WgsjqxXfX8Y8lcbVyN QTrXwNN3aFaZ
inAVyJkAzE7Mc0pQlxvByE8ySMZ5ZIIiHY7u/hX19+VgfVadju7+FfX35WB9 VoHWHpX8I+nv
0UV9bp1h6V/CPp79FFfW6dju7+FfX35WB9Vp2O7v4V9fflYH1Wg6fRxpe5aZ Zvbt3vMe7Trx
dDcHno8ExW0ksMs7QguOHuMg53d1XcGKldaGn7UuzWaPCeu1xu7oClLmT1oU 84So4zsSlAwM
ABKRyHPJyTv0EPgeqYj4VP7uqy6rSB6piPhU/u6rLoK6tHqfz/wz/nnasWq6 tHqfz/wz/nna
sWgUpSgVx9Yaih6WsxvFxYkrgtutokvMpChGbUoJLzgJB4acgqIyQMnGASOx XnJZZkx3I8hp
DzLqChxtaQpK0kYIIPdBHrUHKVqOCdXNaZYbfkzFQzMfW0EluM1u2oLiiRgr IUEgAk7FnkBm
tDV+ubJpa/WKzXMSjIvT5ZZUy2FIYG5CAt05G1BcdabB59s4kd8jmdCWkntJ aUkxZsRxic9c
ZRUp2RxnFRkPLbiAr3K7VMZDACc8h3QDmo7qXozvGur7qu5Xq/3jT7cttNrt 0eGYriVRGgFp
eUVtrUlSn1LX2ikKAS3nBAwFnS71ZoapiZd2gRzBaS/MDslCep21Z2rcye0S dqsE4B2nvVzp
Gq7Rbk3aRfrtY7VCt8xMXqh65thIKmm3AHd20NLO/kgkkp2qzhQArPUGmtd6 nl6a1Jc9MxmL
np6Ew7OhqlNkXp/iJUtgFC9oQ2W+K2XOXEU36UBed29aRuqrnqO7OWG/PSXN UIuVpfs82I3K
ZT1rjx1OpD6w0RuS6goX+HBGCQte3ToVygszrdLjzIjydzT7DgcbcT30qGQR +Ctio90cNahZ
0bCb1S203dQp3ipQltJ2F1Zb3hvtOJw9m/Z2u/djlipDQKUpQKUpQKUpQK4c v7fzP6LG/wA7
9dyuHL+38z+ixv8AO/QftQ3UE2Wz0waRgolvtxJNquqnWA4Q26tC4ewqT3FK AUvGeYBVjump
lXH1RpmyamjsM3iGp4xnOLHdafcYeYXgjc262pK0HBIO0jIoK/1fqu/2HVeq 3bRw5zrS9PxI
sSW6vgJXKlLacxg9oopWk595JIIGK3NV6r1hpy7R2bs9aoNqaiNOSbubLJfi vOlSg4klt7/R
EpAQd7m9J3d0YqUQdDaWh2o2xi2HgLnM3BxTkl1x12Qy4hxtxx1SitZSptHp lHkkA8uVZ6k0
Zp/UUov3dia+FNhp1lFxkNMPIBJ2usoWG3E8zyWkg5oIZqDXGr7e/rG7sN2J dj0vcmo7kdbD
plSmjGjPObXA5tQscc7e1UFchhONx/dR641fBi9JN0ix7Km3aRbdEUOsuLck uiCzIAWQ4AlK
VOHOB2wUANpSSrqQOjW1Pat1Ffr9GTLVcLuzOitty3ktlDUaO2gPNAhtwpca WpIUFgZBBBJA
lJ05ZFMXuOuA24zfVqcubbilKTIKmUMnIJwAW20JwMDlnukkhDOkPXN/seop FptEW2OqDNoL
JlJXgrmXExV7ilXIBIBGByPM7hyrSGt9YjUC9Fq6wm/G9CA3cepHREDHUQll ZY4u8rxlG0OA
H02QBipPA6NdGQllxm1vrdUYpU6/cJDziupXuMxlS3CSELAIGcYAScp5V4a6 0WxcY78m1WWB
LuEqe1NkGVc5MNRW2zwkuNPshS2VhISnKBzG4H0xNBn0f3vU9+ZU/cOs7SIV ynwJqWGnMuFl
3Y2pslXa5woqBz3RjGOcHuguK9CdKka1XebZ5kvX0CIibDeU08yHetTailSS CO1WR74JFTTo
y0MjTVni9cOGq5NS5soCNIdLLXVLhWpA3EF3A2jesZJBPLJrx6MLZa7zJ6Rr fdmkvRxrVEgI
LhR/CMxYDraspIPJaEnHcOMHIyKCKat1zqDUMPQrUK4SrZMt92tjmpkRHlN/ w6rk3BVFVjGW
1rEslJ5ENJ5YNd/TWqtVTrnA07paJpi1Ga9qR91x+G8tCFRLolhKghDqdynO KVL7YdsoqGAN
hnitE6NMi4SOtcdLtyuUe6y1JeWkuyo6kKacOFctqm0naMJJySDuVnYtOl9M Wq5M3GBEQzKY
E0Nr6oWrb1W+mRI5FRB3upSr3sYTgcqD86NdQPar0BY9RyYzcZ+4wm33Wm1E pQsp7YJJ54zn
GfWqQ1zdP2202CyRLLaUIjQIbQajtcUr2IHcGVEk/wBpNb/GZ9lR8oUGdKw4 zPsqPlCnGZ9l
R8oUGdKw4zPsqPlCnGZ9lR8oUGdKw4zPsqPlCnGZ9lR8oUGdKw4zPsqPlCnG Z9lR8oUGdc6d
6RH4y/8ArW9xmfZUfKFcq8TYcVtkypcdgLU5s4jgTu5+tnu90UHz/wDZTyOm rSsJzWfR3ql5
VjYbBuNs63RXXIYSOb6FKaKlt8srBJKDlXNGeHJPscYnS4/YDqHpW1G89Imt jqOzmDHYMZBw
eI8UNpUHD6zecJBO4FRwiy+vNo9tYP5wjy0682j21g/nCPLQQ/S3q6a5+B7N /mnVMZK3EPut
IUre+hIaye4rO04+Uk/2VC9HvMyOm/XDzDrbrarPZtq0KCge3nDuip+UNl5t 1SApbeSgn/VJ
GM0Gqwv+FQ0VrUIyXC4c81Y7VOe/kKBrKI9Id4alNKKHGyvIZWkI5ZA3Hkrl 64rYQ22hbi0o
AW7jer1zjuVi0ww2cobwcYHbqwPwDOB8VBrNqkuogOLdbQXlBRSlBPItlXPt ufc7n4O9W4kK
AO5SScnGBjA9b+2sS00UNIKO1aCQgBRGMDA5g57lZJSlIISnGSSeZPM/hoP2 lKUClKUEBn/z
h7L/AFTuH7XCqfVAZ/8AOHsv9U7h+1wqn1ApSlApSlApSlApSlApSlApSlAp SlBm56Rr8X/u
awrNz0jX4v8A3NYUEPgeqYj4VP7uqy6rSB6piPhU/u6rLoK6tHqfz/wz/nna sWq6tHqfz/wz
/nnasWgUpSgUpXM1Sq+N6fmOabbhO3ZDe+M1MCuE6oHOxRSQU7gCkK57SQSC Bgh06VBOi/XM
rX8iVd7bBEPTcdAjJ6pbIlOzRgvJ7uEoaP8ABnkdywoghKQVafSTrO92LVce 1M3CxabtS4Qk
G9XqE8/GdeK1JLAUhxpDSkhKVFS1894wDg0Fj0qFydbLtkazQX7e5qK/XGK5 KTG0/sW2tlsp
Cnwp5xCUt9ugDKskqwndXn2TrG9J03Gtdvu91d1HFelQkxmEApQ0tpLod4i0 8Mp4uSD9woem
2pUE4pVeW7pFhojMxIkPUGpLpJm3NLcRhiMh9LUSWtlxRytDaWkK2oSSresF PIqKsbL/AEn2
Vbdh6z2263uRfIrsuJGipZadDTZQlzdx3GwFJUsJKASrOeXI0E6pXnFdL8Zp 4tONFxAVw3Bh
SMjOCO+K9KBSlKBSlKBXDl/b+Z/RY3+d+u5XDl/b+Z/RY3+d+g/aUrkXzU1i sdztNsutxbiy
7w+Y8BtSVEvOAZIyBgesMnAyQO6QCHXpSlApSuVpu/Q79aXbnDbfbZalyoik upAVvjvuMLIw
SMFTaiOfcIyAeVB1aVzNKXqLqTS9q1DBbebi3OG1MZQ8AHEocQFpCgCQDgjO Cfw1+N3yIvVj
2mg2/wBVswW5yllI4ZbW4tAAOc7stn1sYxzoOpX60SyjY0S2nJVhPIZJyT/a ST/bX5WlfrrB
sdnlXe5urZhRGy6+4lpTmxA7qilIJwO6TjkASeQoOhxnvZV/KNOM97Kv5Rrl zr5aob9sYfmJ
Ll0d4UJLaS4XjsKyRtB7UJBJUe1A7p5ivzU98tWmrDLvt7liJboaN77xQpW0 ZAGEpBJJJAAA
JJPKg6vGe9lX8o04z3sq/lGteFJjzYbMyK6h6O+2l1pxBylaFDII94g1oSL5 EY1XC02tt8y5
sGRNbWEjhhDK2UKBOc7iX0Y5EYCuY5ZDr8Z72VfyjTjPeyr+UawpQZ8Z72Vf yjTjPeyr+Uaw
pQRG9dLPR/ZbrItV31xaYE6MvY9HkSwhxs93BB59wg/gNafZt6LfCNYfz9Pl rPopuVus7vSf
c7tOjQIUfVi1PSJDobbbHUEIZUo8hU/0tqC0aosbN7sUwTLe8txDbwbUjcW3 FNr5KAPJSFDu
c8ZHKgr3s29FvhGsP5+ny07NvRb4RrD+fp8tWrSgrnT/AEqaE1DeI9msWtLZ crjJUUsxo0ri
OLIBJwkc8AAkn1gCTyFdLUmidJ6lnInaj0tp+8y22gyh+fCYkOJQCSEBSwSE 5Uo47mSe/Uxc
9O1+N/2NcGQqUl2OI7LLjanCJClulBbRsUQpICTvO4IGCUjBUc5ASoITbtA9 ENxlXGLC0Ho1
922SupJgTZI+G3uE27tzswe0dQeWQM47oIG52LOjHwc6P8SRvoVo9Ev2+6Rv 62L/AGCHU+oO
PpvS2mNNdUehzTlns3VO3j9QQm4/F2527tgG7G5WM9zJ79dilKBSlKBSlKBS lKBSlKAj+DcU
4jtVqASpQ5EgZwCfeyfjNZ8Z72VfyjWFKDPjPeyr+UacZ72VfyjWFKDPjPey r+UacZ72Vfyj
WFKDPjPeyr+UacZ72VfyjWFKDPjPeyr+UacZ72VfyjWFKDPjPeyr+UacZ72V fyjWFKDPjPey
r+UacZ72VfyjWFKDPjPeyr+UacZ72VfyjWFKD9UpSjlSio++c1+UpQQ+B6pi PhU/u6rLqtIH
qmI+FT+7qsugrq0ep/P/AAz/AJ52rFqurR6n8/8ADP8AnnasWgUpSgVx9Z2m ffdOybRb7w5Z
1ytrbsppvc6lkkcQNnI2LKcgL57Sc4OK7FKCI6X0NB0rqJczTLjdss8iGhiV aW2ctKdbCUNP
oO7tFBsbFcjvCUE4Kcnb1PbNXy54e09qi222OWQ25GnWcy0lWTlaSl5pQJBA wSochy7uZHSg
qSX0I2sW2xoiv2iXOtSJaCq92RudFfEl7juf6OFN8MhzOzYpO1JKe2FSrTuh 02m92C5plQWx
aLTNt3U0K3JisL6oeju70ISohsJ4GNvPO/JORzmNKCprn0Lw5IhyhJsk24RZ dzdSbxY0zoq2
psxckoLJcSQtBUEhaVpz22RhWB1r90dSbho+3abZd0imLFQ4l1mTphLsbcpW QtlpLyOCpOTg
gq7uTk86sOlBz9NWzrLpy2WbqyTO6giNRuqZKtzr2xATvWfXUcZJ75roUpQK UpQKUpQK4cv7
fzP6LG/zv13K4cv7fzP6LG/zv0H7VNay0dq7XupNRXGHPiWRiI2i2WnrhanH HSppaJBktK4q
NgL6WwFbVghhJAx3blpQUPf7DI1vH1bqK86Pnt3F3REIwWX4jgWxcEmeVJZB HN5Cy3tUntgF
JIOF8/3qDS906U4M++aDnQ3bSth4Tm9ISVvXK4FI/hlyW2CA02T66+2X2xwl Cd170oPn64sX
Cdru33eFpIW66s6obEtUfTUrqpMXqgtqccuBUG1tLbO4oQlSQlXeSVDoWXR7 kFVgvsfT8pm9
K1pdjLkiOvjCE49P27jjIYUC0oD0hKkqHNWTeNKCsLXatRD7F232W2x5cS/p 0kxHQwcsvoeE
ZILfPBQvOU88YPexVfXzTq5cu8L6PtIXax2ldstiJTDtmfjiQhExapLYaJbU 4rhkbwlSS4Mg
KO4E/SFKCgkaXflaHultt7VwMCdqGzOJhQtNyrO1EQmYxx1sNOrUtI2J3qUn alJBV65NXnbr
bb7dbGrXAhR4sFlvhtx2mwltCPuQkcgPerapQVboDSVztlz1SqG44w9bJPWv TK7nGW7HiwVN
svlLaQpBWjiOKbyFZww2nPaV6axtWt9QXHTenFzbal2EtV3uNy6zvGA6tpeI zAaL+SrKuIRx
eRZSruKAqzqUFEtWW+22wWrSepbRNvWn9N3tSJqIdvdU1PgLjrVGKWQVl1tp xaW1NArI4SSQ
rHPtjSemtQ6x0u03o11rSzNkuyBElWxyOw24uTDKUraWkBO7DikoUB6XIAKe Vt0oPm/rHPia
b0zdJVguN71HHsMVhu13bTsiWgrbK8JbkgDqN45AUtZxyQSOVdLV2nb2/wBI V9k3LqhiY9cW
H7LcmNLyrg+ywhDWEMyWnQ3HG9LgWhaUhW5RO4K5X9SgpLU2kHH52tdQt2CU 5em9XWx+1yks
LLqWUt25Li2T9zgPJWpPIhKgrITytfTLdtbani22x+AlVwfU+l2OpovPFfbu jd6ZKjzChyPr
V1qUES6EwDdukjIz/wDF6/2CFXU6FIUy36BTGnxJER/rtdHOG+2UK2ruEhaF YPPCkqSoH1wQ
RyNefRxaJmn5mrZM1cYpvN+XcI6ULUVJa6mjsgL7XAJLKjgE8in18gS7qxPf R8Z8lBtUrV6s
T30fGfJTqxPfR8Z8lAucZubEXDeU8lp9KmlqZeWy4ApCgSlaCFIVz5KSQQeY INVp2JNC/wD0
t+/Si6fWasSfcCxEdktxnJa2EKcRHYUOI8Qk4QneUpCieQ3KSMnmQOdVjI1T q6Q7HekdC17e
ciuF2Otdytii0soUgqSTI7U7VrTkc8KUO4TQYdCECHapmvrdAbebjR9UFttL sp2QvAgQu646
pS1f2qOO4MAACx6gvRDBvkdWrblfbHIsrl3v6pzEWQ+y64GupYzeSWVrT6Zp Y7ucDOBmp1QK
UpQKUpQKUpQKUpQKUpQKUpQKUpQKUpQKUpQKUpQKUpQKUpQKUpQKUpQQ+B6p iPhU/u6rLqtI
HqmI+FT+7qsugrq0ep/P/DP+edqxarq0ep/P/DP+edqxaBSlKBXD1zableNO ux7LdXrXdWlp
kQpCFqCA8g7kpdSD27SsbVJPdST6+CO5Sgrvoyu161xclawntS7PbYaXLdFt XVGQuSheyU67
sOFhLiFNNg9wIWvHbjHn0l2TUs3VMe4ttX26aebghs26yXxVtktyd6ip3IW2 HgUlACVOAJKT
yVu5Tmw2e22K3qgWqN1PGVIfklG9Sv4R51brisqJPNa1HHcGcDAwK5epdFaf 1DcE3C4t3FEp
LQZ4sK6yoalNgkhKuA4jcMqPI57poK8k9JTzDOnbHpJ+4P8AVUOY+/OullmX OQyYz4YUy4zG
wsuBxSklaiEp2f65UM9W3621reLvoy2RLNCsz95tUyddEXOM9xIpjvRmyEIJ bUQriqACgCNy
FH0pSqUy+j/ST9rtttRa1wmbWFpgrgS3ojzAXzWEusrS4Ao81dt2x5nJrftu l7Jb5tvmxYjg
k2+I9DjOuSXHFJaeW2twEqUdxUpps7lZPLu8zkKmi9I90iGJYbHbINtfkz76 +8+xYZtxaSiN
cnGB/ARjvK3FHctZUEg55HclNdu6681v6HNOXdGn3LGzMivOXV+TZJU8w3kK SlLZjtqbeShf
bqDhBwlIykZqXTOj7ScmIxHNvkR+p5UmUy/EnyI8hpyS6p18pebWlxIWtRJS Fbe4MYAAzuGh
dOzbdDgOpuzbENC0NdT3mYwspWcqC1tupU4Ce7vKqDtWOa1crLBuLMmNKalR 23kPxjlp0KSC
FIP3Jzke9W5WvbIMO2W2NbbfGbjQ4rKGI7LacIbbSAlKQPWAAArYoFKUoFKU oFcOX9v5n9Fj
f5367lcOX9v5n9Fjf536D9r8UQkFSiABzJPrV+1Hr3Fh3DR17Rfpss26VGmx 5SWwBw2NziFF
GxO/cEDvnu9zOKD2s+r9J3mW9Ds+p7LcpLAJdZiTmnnEAd3KUqJFb9jukG92 WDebW/1RAnx2
5MZ3YpO9taQpKsKAIyCDggGqn0jqOGnUll0/Zr9p7WkIMPMiRChoRMtLaWVE LdU2S2EqKUt7
drRysYB5gcDoh1JMsXRq0/q1y4Idj6MiyrPFt8hXCegIjI3LbBwOqQvG8qzs Cm9p2kkhe19u
sCx2Sdero/1PAgR1yZLuxStjaElSlYSCTgA8gCa3AQQCO4a+Zb3flv2TXlph 3dmbbX9CXGUp
DGpn703xkBKch11ICFbXe2Qgkc0kgcs9npi1kiHO1A/aro7Au1lU0ltt7Uz8 dxSkttuFbVvb
SpDzRSrClOYBwvmAM0H0FStd6Mt1wrTPmsg47RrhbRy9bc2T7/drDqJz21uX 9x5qg26VqdRO
e2ty/uPNU6ic9tbl/ceaoNulanUTntrcv7jzVOonPbW5f3HmqDbpWp1E57a3 L+481TqJz21u
X9x5qg26VqdROe2ty/uPNU6ic9tbl/ceaoNulanUTntrcv7jzVOonPbW5f3H mqDbpWp1E57a
3L+481TqJz21uX9x5qg26VqdROe2ty/uPNU6ic9tbl/ceaoNulanUTntrcv7 jzVOonPbW5f3
HmqDbpWp1E57a3L+481TqJz21uX9x5qg26VqdROe2ty/uPNU6ic9tbl/ceao NulanUTntrcv
7jzVOonPbW5f3HmqDbpWp1E57a3L+481TqJz21uX9x5qg26VqdROe2ty/uPN U6ic9tbl/cea
oNulanUTntrcv7jzVOonPbW5f3HmqDbpWp1E57a3L+481TqJz21uX9x5qg26 VqdROe2ty/uP
NU6ic9tbl/ceaoNulanUTntrcv7jzVOonPbW5f3HmqDbpWp1E57a3L+481Tq Jz21uX9x5qg2
6VqdROe2ty/uPNU6ic9tbl/ceaoNulanUTntrcv7jzVOonPbW5f3HmqDbpWp 1E57a3L+481T
qJz21uX9x5qg26VqdROe2ty/uPNU6ic9tbl/ceaoNulacHiony465T8hCGmV pLoRkFRdB9Kl
PLtE1uUEPgeqYj4VP7uqy6rSB6piPhU/u6rLoK6tHqfz/wAM/wCedqxarq0e p/P/AAz/AJ52
rFoFKUoFeUuTHhxXZUt9qPHaSVuOurCUISOZJJ5ADv161FeliFYbjoG4RNSz 3bfbFKZW5KbS
FFlSHkLbUQUqBSFpSSFApxndyzQdPTmqdMakDp07qOz3kM/xpgTW39n4dhOP 7a69UlbdfsMP
ageTcLRrG32/Tz8t2+6cihp9jhlIEZawpbZcUCpaSlSdvDJKAMGobO1bPjQN cR7FqPdHGhbh
Pacg6sk3jhSmtoS4h9xKeE4A5zS2ogdqeXLIfT9KoLXdruFpmdIDEPV+rgmz 6OZvsIqvT5KJ
xM0Fw9tzQepm/wCC/iuau05jHNv+pFm761cb1jeW9Xx7xCTpy0N3F0NPFUKE otojA7HUKWte
8EKCASrtCSoh9A2y7265TLlEhSOK/a5IiTE7FJ4TpabdCckAHtHWzkZHbY7o IG9XzxrS5Xi3
ag1T1JJTDs72uW0XiSq5u25CGhZYhQFymkLWwhTgQCsAc8J3JCs1Z3QjLnS9 HOqmX233ttE5
5MSTCuK56QxkKQ2qStCC8pO4p345gDJJyaCc0pSgUpSgUpSgVw5f2/mf0WN/ nfruVw5f2/mf
0WN/nfoP2tS0fa8f0iT+0OVt1qWj7Xj+kSf2hyg26V49UI6s6lwrfs359bGe 5+Hka/ESkLed
bQAC0tCVKWcJ7Y45H+w/20HshKUJCUJCUjuADAptTv37RuxjOOeO9WCX2VOc NLzZXz7UKGeX
d5VmVJCgkqAUe4M8zQftKUoIbqHpGsll1HIsDtt1FPnRmWn3k2yzSJgbQ5u2 FRaSoJyULABw
e1NafZUtHuW19+ic/wA1TS3q6a5+B7N/mnVPqCA9lS0e5bX36Jz/ADVOypaP ctr79E5/mqn1
KCA9lS0e5bX36Jz/ADVOypaPctr79E5/mqn1KCA9lS0e5bX36Jz/ADVOypaP ctr79E5/mqn1
KCA9lS0e5bX36Jz/ADVOypaPctr79E5/mqn1KCA9lS0e5bX36Jz/ADVOypaP ctr79E5/mqn1
KCL6V1rD1HclQYli1RDUhourduNkkQ2gkED07qUgnKh2oye6cYBIlFZt+kd/ F/7itZ2QhuQy
woK3O52kdwYx3fjoPaleIkIMtUYA7kt8RR9YDIHx8xX6iRHWlSkPtKSj0xCw QPw0HrSvwKSV
FIUCocyM8xQKSVFIUCU90A9yg/a4mtNT23SVnTdbqiWthUhqMlMWOp91Tjig hCUoSCpRKiAA
ATkiu3UB6cPtDp7+tll/b2aB2VLR7ltffonP81TsqWj3La+/ROf5qp9SggPZ UtHuW19+ic/z
VOypaPctr79E5/mqn1KCA9lS0e5bX36Jz/NU7Klo9y2vv0Tn+aqfUoID2VLR 7ltffonP81Ts
qWj3La+/ROf5qp9SggPZUtHuW19+ic/zVOypaPctr79E5/mqn1KCA9lS0e5b X36Jz/NU7Klo
9y2vv0Tn+aqfUoID2VLR7ltffonP81TsqWj3La+/ROf5qp9SggPZUtHuW19+ ic/zVOypaPct
r79E5/mqn1KCA9lS0e5bX36Jz/NU7Klo9y2vv0Tn+aqfUoID2VLR7ltffonP 81TsqWj3La+/
ROf5qp9SggPZUtHuW19+ic/zVOypaPctr79E5/mqn1KCA9lS0e5bX36Jz/NV JtJ6gZ1HAdmx
rZeYDTbpaxc7e7DcUoAE7UOgKIwodtjBOQCSDjsVmr+IR+Mr/oKDRj/bid/R 43+Z+tutSP8A
bid/R43+Z+tugh8D1TEfCp/d1WXVaQPVMR8Kn93VZdBXVo9T+f8Ahn/PO1Yt V1aPU/n/AIZ/
zztSXV+sbHpR63M3g3Pi3J1bMREK0ypqnVoQVqRhhteFbApQBwSELIyEqwEg pUZ0xrrT+or5
IsluTem58aMmU81Osc2FsaUopQol9pA7ZSVgDOTsXjO1WJKtSUIK1qCUpGSS cACg/aVArZ0s
6Tn9G9516lUuParOl1UhEhCW3lBKEuN7ElWDxULbU3kjcHEdwnFbUDpR0JNv 0qzsaltZci2p
q6uPmazwep18TJCt/wDqJb3K9ZKVoOe2oJnX4hKUJCUJCUjuADAqFa06QoFj sN1vNsctN6Yt
tpuE9bbN0QHVuRNm5pKQlXLKtq1//LO0EHcMdyFq3S0yDcJ0TUtmfi2wHrg8 1OaUiJtBKuKo
KwjABJ3Y7hoO1XKsVhh2e43ydGcfW5ep4nSQ4oFKXAwyxhGAMJ2sIPPJyTzx gDna615pbRUH
qq/3eJG/hozRaVIbS4A+9wkOFKlDtAd6ir1ktuHntNb7up9NNXaJaXdQ2hu4 zUByJEVNbDz6
T3FIRncoHvgGg69K5EjVGmo9/b09I1FaGby4AW7eua2mSvPMYbJ3HP4K69Ap SlApSlApSlAr
hy/t/M/osb/O/Xcrhy/t/M/osb/O/Qftalo+14/pEn9ocrbrUtH2vH9Ik/tD lB+vsul119tA
UtCWy2NwG4gryP7QrH9tG4jjato2qwuMVK3DtiHFKWf+NbVKDXaYWlEUbEgp fccXzHcJXgn+
wivZBcygloJJSSrtgdvvf2+9WVKBSlKCA6W9XTXPwPZv806p9XAs2nBC13f9 TuzdwusWFFTH
S1/FiPxjuKs8yovkYxy2A5OcCRYZ+7X8geWgwpWeGfu1/IHlphn7tfyB5aDC lZ4Z+7X8geWm
Gfu1/IHloMKVnhn7tfyB5aYZ+7X8geWgwpWeGfu1/IHlphn7tfyB5aDClZ4Z +7X8geWmGfu1
/IHloDfpHfxf+4rUksLedynA2sr2qz3F7kFP/Ef8K3MtpQsJUslQxzTj1we/ 71edBqJZkJCn
g2jjLYcUoFQIC1KRtT7+Ep/4V+huRxnHVNuuJ4BQkOrbyTuHLCe4O73/AF62 qUGvEZdisKY2
pdIUDvCgN5PL1z63/TFe6d2VgpASFcjkdty7tftKBUB6cPtDp7+tll/b2an1 R/XGnFamh2yI
JYiph3eFcVr4e8qEd5L2wDI9MUBOc8sk88YISClZ4Z+7X8geWmGfu1/IHloM KVnhn7tfyB5a
YZ+7X8geWgwpWeGfu1/IHlphn7tfyB5aDClZ4Z+7X8geWmGfu1/IHloMKVnh n7tfyB5aYZ+7
X8geWgwpWeGfu1/IHlphn7tfyB5aDClZ4Z+7X8geWmGfu1/IHloMKVnhn7tf yB5aYZ+7X8ge
WgwpWeGfu1/IHlphn7tfyB5aDClZ4Z+7X8geWmGfu1/IHloMKVnhn7tfyB5a YZ+7X8geWgwr
NX8Qj8ZX/QUwz92v5A8tFlHDShBUcEkkjHdx5KDRj/bid/R43+Z+tutSP9uJ 39Hjf5n626CH
wPVMR8Kn93VZdVpA9UxHwqf3dVl0FdWj1P5/4Z/zztTi92uBerW9bbkxxozu 0kBakKSpKgpC
0LSQpC0qCVJWkhSVJCkkEA1B7R6n8/8ADP8AnnasWg5mm7JFscFbDDj0h990 vzJkghT8t4gB
TrigACrCUpAACUpSlCEpQlKRx+li23u+aMkaesaXEO3dxuDKlNuJQqJFcVh9 0bjzUG94SBk7
lJOMA1K6UFKX/oz1LbY2qLVYrlPv0LVGlpVsfXPVFZ6kktsluIQGm2gUKS4t BISSNqMnA5fu
uNPawvkTVi4uk5rbmotBptLLbkuKFRpbRmENOEO47fqhG1SSpPI7imrqpQVD 0vaEu1zhvQdL
2dtUVGhr1Z47TbrbaUvv9SBhoBSh3Q0vtu4NvMjIzp6j0bqXV0bVCmdLtaX4 +jZFgix3ZLKh
KfXzQocEqCWW8bUlW1X8IrtE+vdVKCkNV2PXOp7pdb8rRcm3nqKydTw3bhFW 8+5DuZlPIBS4
UJOz0pKgDkZKTkDwv2g9SytSajMqDqmTbtQXGNckdbpFqQlgttshKH1PpLyF Nra5FlS04wU4
JOb2pQVXdrHqWH0jrmaVs94jMTrrGlXN2RIhu2uS2Etodd2KUZLbwbRhPDAT uSknkTVqUpQK
UpQKUpQKUpQK4cv7fzP6LG/zv13K4cv7fzP6LG/zv0H7WpaPteP6RJ/aHK26 1LR9rx/SJP7Q
5QbdK1ny7x/TPpZ291lAUoqz6+QTjudwV+oeKy23HKXlFBWVrOwAA454B9c4 xig2KVpuOvNO
yV8MLDbLa1DiYCfT5A5czy/4V+vvr4MtXCw2wCCriYKjtCsDA9+g26VpvuS0 rkBIaGyUhpI3
nuHZy9L7/d9/3ue2N+VbgkAHAIVnPL8FBW9yna7vPSdftPae1LarNCtUCDJ/ 0qzmYpxUgvgg
EPN7QOAO/nce5itvrD0r+EfT36KK+t00t6umufgezf5p1T6ggPWHpX8I+nv0 UV9bp1h6V/CP
p79FFfW6n1KCA9Yelfwj6e/RRX1unWHpX8I+nv0UV9bqfUoID1h6V/CPp79F FfW6dYelfwj6
e/RRX1up9SggPWHpX8I+nv0UV9bp1h6V/CPp79FFfW6n1KCA9Yelfwj6e/RR X1unWHpX8I+n
v0UV9bqfUoIzpO0a3i3Fx/UmsLZdoaWTsjxLIYit5UkAqWXnMpA3cgAc4OcA gyas2/SO/i/9
xWlKWoS2WwuQlKkLJDKNyiRtx/qnlzNBtUrwB2qjgiSorWrm92hGEKPcCRkc q1pEp5UBSi0G
i5FU8gpcyRyHvcu6KDoUrWkyiw46l1lLZSU7RxAR2xIAJPcxjnWKJSXRgBCy l9tB4buUncoY
7YDueSg26hvS9eb1ZdNwXbBKjxJ0y8wbel6RH4zbYkSEMlRRuSVYC84ChzHd qVw1SHGQpxLe
S6tPp+4kKI73vY9+oR04faHT39bLL+3s0DrD0r+EfT36KK+t06w9K/hH09+i ivrdTCSt9L69
7rrTQxsU22FA8ue7kT3fwUkXBDKtgLS9qAskuBO4H7nvmgh/WHpX8I+nv0UV 9bp1h6V/CPp7
9FFfW6l5lgTihBKy602W0FWBzKsn3uVey5W1Lx4eeE6lvu93O3n+t/woIV1h 6V/CPp79FFfW
6dYelfwj6e/RRX1upkmarckrZCWlOKb3b8nKc88Y7nKvFc1xLjMhxstslpaw AvJUOWMjv+Wg
ifWHpX8I+nv0UV9bp1h6V/CPp79FFfW6l6biktvEpbUppouYbdCgR3s45Gth h9a31suNBtQS
FDCs5Bz/AMeVBCOsPSv4R9Pfoor63TrD0r+EfT36KK+t1PqUEB6w9K/hH09+ iivrdOsPSv4R
9Pfoor63U+pQQHrD0r+EfT36KK+t06w9K/hH09+iivrdT6lBAesPSv4R9Pfo or63TrD0r+Ef
T36KK+t1PqUEB6w9K/hH09+iivrdOsPSv4R9Pfoor63U+pQQHrD0r+EfT36K K+t06w9K/hH0
9+iivrdT6lBqadiXSDYYyL7co92uKlLLslqKYzZG7tQlveopAGM5UrJyeQIA 3t6fYUfGfLXo
9/8AhGP/ANX/AFriXqXPhfwrLbLjB5ElJyk+/wA+57//APBDr70+wo+M+Woj Zb5cJvSvqixu
rQLfbrZbHo7SUDtXHly+Ioq7pJDbYxnA2jABJJ79pXOkRRJmJaaQsZaQlJ3K H3RyeQ73f/6w
3S3q6a5+B7N/mnUEzj/bid/R43+Z+tutSP8Abid/R43+Z+tugh8D1TEfCp/d 1WXVaQPVMR8K
n93VZdBXVo9T+f8Ahn/PO1YtV1aPU/n/AIZ/zztWLQKifSVqO/6YgxLhabFb LjALpTcZU66L
iN29GO1eXsYdJazyWvADYIUrtAtaJZSghmhNTaqvt8ucW6acssO2wMs9cbfe XZaHZQUUuMIC
4zW7h4IWsEpSvtBlaXA3INUqvjen5jmm24Tt2Q3vjNTArhOqBzsUUkFO4ApC ue0kEggYO7Ai
RYEGPBgxmYsSM0llhhlsIbaQkYShKRySkAAADkAK5us7TPvunZNot94cs65W 1t2U03udSySO
IGzkbFlOQF89pOcHFBHOi/XMrX8iVd7bBEPTcdAjJ6pbIlOzRgvJ7uEoaP8A BnkdywoghKQV
afSf0hzdLamt0GBEiSIEVpE7UbzpO6JCW8llC0YON24uOHORsYc9ciuzpfQ0 HSuolzNMuN2y
zyIaGJVpbZy0p1sJQ0+g7u0UGxsVyO8JQTgpydB3op0ndrrfrrq+0WnUs27S SpD0y3NqVEjh
tLbbDZVuKQkJKtwIypSlYHrB0NVa/s+m7yLPcYtwM59lty3NNNoUbktTgbLT GVDK0lSCoK2h
KVhWdoURy53SJb7NdbtDdRebrM6/ItUWEhuM3/CmC1JKGlrWhBRsUVZdWFbi pIyAmtI9Ftzk
Q7QbhrORJuen4TTFjmpiFHUzyD2z7qOIeMpxCUNqBKRs4gGN5I6N00DMkN6j DFxsj4vt2RcH
2LrZOrYwSmIxH4Zb4qN3NgLCtwxuxg4yQmdomLn25mW7BlQFuJyqNKCQ62c4 wrapSc/gJHv1
tVwOj3TnoS0jCsHVypvUxcPFKNiRvcUvYhOTsbTu2oTk7UpSMnGa79ApSlAp SlApSlArhy/t
/M/osb/O/Xcrhy/t/M/osb/O/Qftalo+14/pEn9ocrbrUtH2vH9Ik/tDlB7u Mha93GfRyxhC
gB/xBr8MdoBsNl1rYCkKSobiCcnJIIOTz7letKDzUw2W3kZcPFQlCiVAnA3e 93e2NHWG1sPs
9uEvElRzzGUhPLl3k16UoPN5lDi3lb3U8RwOYBGEqGMEcu7yHvcu5WYSAVEK WckemIOOX4K/
aUEB0t6umufgezf5p1T6vCLChxp8q4MRI6JksITIf4SS44lAOxJURkpTuVgd wFSiOZOdriq7
yPkDyUGFKz4qu8j5A8lOKrvI+QPJQYUrPiq7yPkDyU4qu8j5A8lBhSs+KrvI +QPJTiq7yPkD
yUGFKz4qu8j5A8lOKrvI+QPJQYUrPiq7yPkDyU4qu8j5A8lAb9I7+L/3FeDj IcdS5xXW1JSU
gtlPMHGe6D3q9y6opKe1APdwkCsKDBLKAptRcecKFKUCtSfXSU+skd81g5FZ WylolzaljgDm
M4wBnud3lXtSgwfabdecdO8KXtwQr0uCSCPfya/SjcEhx6Q7tcS526weaSCB yAHrVlXnIeZj
tKefdbabT6Za1BIHrd00BtlKBgLdxvK8bhjnkkdzuZOagvTh9odPf1ssv7ez Uw682j21g/nC
PLWtPlabnhgTpVqlJjvokNJedbWlLqDlC8E4yk4IPrEAjmAaDoOx1LWoiS+g K7qUkY/4jI/s
r86kQkpLLjjOEBHaY5gdzug1h1+tXtpbfyrdOv1q9tLb+VboM3IjbhUpSl7l JSnOeY2kkEe/
zrFyEha1KLroC1JUpIIwSMYPc94V+dfrV7aW38q3Tr9avbS2/lW6D0EVvYhO VEJcLg98knP9
nM15pgNYCVOOrQlBQlCiMBJ9buZ9anX61e2lt/Kt06/Wr20tv5VugzMXcy40 5IecStBR2xHI
H8A/616BpIfL2TuKAnHrYBJ/714dfrV7aW38q3Tr9avbS2/lW6DbpWp1+tXt pbfyrdOv1q9t
Lb+VboNulanX61e2lt/Kt06/Wr20tv5Vug26VqdfrV7aW38q3Tr9avbS2/lW 6DbpWp1+tXtp
bfyrdOv1q9tLb+VboNulanX61e2lt/Kt06/Wr20tv5Vug26VqdfrV7aW38q3 Tr9avbS2/lW6
DpPf/hGP/wBX/WtdSUqSUqAUkjBBGQRUP1fY9Farmsy71dpClsNcJtMXUMmI 2kZJzw2XkJ3E
nmrGSAATgDHE7HPRh7YXP9L7h9ZoLNWpS1FSjknumoBpb1dNc/A9m/zTq9Ow 3o3/AOm1L+kt
z8/XS0dpHSuk7vcmrImQi5SY8dUxMq6Py3uCFPcEkPOLUhG4v4xgEhXdxyDu x/txO/o8b/M/
W3WpH+3E7+jxv8z9bdBD4HqmI+FT+7qsuq0geqYj4VP7uqy6CurR6n8/8M/5 52rFqurR6n8/
8M/552rFoFKUoFKUoFKVCem6bMt/R+qTAlyIj/Xa1t8VhwoVtXcI6FpyOeFJ UpJHrgkHkaCb
Uri62u3WTTUq4pnQoTiShDTstpbre9awlKdjZC1qJOAhPNRIA7tU5qnWN/1B py6WaSmI9cbT
qLTjkSSu0y7Y29xriyUBbD5U4kBTZBUCQQeWDyoL9pVR3/pJ1JpiJqC2XaLb rje7fMt0eLIg
w3ww4masoQpTCVOO7kFKyUIUorwnGCrlzr/qW+37SDsa+QHkKg6s0+I8/rPK trUxtdxinKWZ
OVpUlQUk9soelOeeAF20qt9L6x1G/r1dk1N1FZw9JktQbe7aZCVyENlZQtqb xCy8pTaeIUBK
VJGeXak1ZFApSlApSlArhy/t/M/osb/O/Xcrhy/t/M/osb/O/Qftalo+14/p En9ocrbrUtH2
vH9Ik/tDlB6uyozS9jshpCu8pYBr1QpK0haFBSSMgg5BrWkS2uMYi5aWEY3P EubSR9yPfP8A
wFeUuU2EylB4JCo6epwlXLkF524//TQb9K0ipKJ4QXA8tbiUpbDygpOQB6Xu EeuSa8w8lUlo
oWEqVICFAvqKsbtpyjuAfh59yg6CVJUMpIIzjkfXr9rmcSKxE4al/wAKX3Eg LkrSASs4yQoY
GOf/AP2vdlLK5DDbUlx5ptlalKS6TxFBSO6c+/8AFyoNylc/jJXKaKFhKlSA hSS+oqxu2ntO
4B+Hn3KyjqSZRZLgfWsryUPK3IHM809wADlkevQbqVJUkKSQpJGQQeRr9rlt LSmNDRxEpZDH
blchSBxMDkVDJGOfLl/wrZYSXHoyXJBcSGFrUW1EBZCkgc+XfoNhtxLnpNx9 Nz2nHI4P/Gsl
EJxkE5IAwCeZOK5zi1phuFK1JxHlKGFEYIe5H/ia930cGctlDju3iMemWSeb uD+DI9YUG3Su
fGWpUhAcfaQ6XVBaFPKKinJAARtx3MHOf7azYSQYznEdKnJDqVZWSNoKwBju esKDdpWpLWym
W2JDy22uEs8nCkE5RjJH4TX5GStxcXjOOgcBbhTuKSrC0hOf7CDQblYPOtMp 3OuIbSTjKlAD
NabKwt5lIccMpT5Dje44SjcR6XuYCeef+NetwXw1xV8ZDOHvTrGQntFe+P8A rQerclh3+KdQ
7zAPDO7mTgdyvWtZUrjNtpTcEyT1Q0Dw+WAVgeso93nXk0kgR3OI6VOPupVl ZI2grAGO56wo
NxtaXG0uJOUrSFJOMZBGRX6tQQgrVnAGTgZrmgOtQ45YW4pxUHdgqJGQlGMD uDGTX6tYEWYp
mU2QlgkBD6nFBQ9fJAxQdHI3KTntknCh64Pv1AvsivUL1p8Dv/5TU0xHakzk rcUk8cHHFVnb
hOPX9c5Gf7Khn2RfqF615Y/8Hkf5TQbD3Rr0UsqCXtAaLbURkBVnjA4+RXqO i3oxIyOjnR/i
SN9CpDPW208t1MoNPcMAIIB3YyRy7p7vrVutFSmkqWnaogEjvGgiPYs6MfBz o/xJG+hTsWdG
Pg50f4kjfQqYUoIf2LOjHwc6P8SRvoU7FnRj4OdH+JI30KmFKCH9izox8HOj /Ekb6FOxZ0Y+
DnR/iSN9CphSgh/Ys6MfBzo/xJG+hTsWdGPg50f4kjfQqYUoIf2LOjHwc6P8 SRvoU7FnRj4O
dH+JI30KmFKCH9izox8HOj/Ekb6FOxZ0Y+DnR/iSN9CphSgh/Ys6MfBzo/xJ G+hTsWdGPg50
f4kjfQqYUoIf2LOjHwc6P8SRvoU7FnRj4OdH+JI30KmFKCH9izox8HOj/Ekb 6FOxZ0Y+DnR/
iSN9CphSgh/Ys6MfBzo/xJG+hTsWdGPg50f4kjfQqYUoIf2LOjHwc6P8SRvo VtWno36O4N0i
TYWgtKxZUd9DrD7NojocaWlQKVJUEZBBAII5gipNWcf+Pb/GH/Wg/JRIfcIS VdueQ/DVf6W9
XTXPwPZv806tiV0f3Ncl1a+lXX5UpZJ2u29Izn1gIeB+AVx+jW1uWfpg1rBd vV3vK02m0KVK
ua2VOnK53L+CabTtA74J7vPGAAsOP9uJ39Hjf5n6261I/wBuJ39Hjf5n626C HwPVMR8Kn93V
ZdVpA9UxHwqf3dVl0FdWj1P5/wCGf887Vi1XVo9T+f8Ahn/PO1YtBqXe522z 25243e4RLfCZ
xxZEp5LTSMkJGVKIAySBz9cio/2S+jn3f6U8cR/p1Q2qb2jVPSTfbre0B9uz XJ+2WuK6re3F
S0S04tI5Al1QKiSCR2qckJTjNL9uUkKTaGCCMgiH3f1a9yw9iVWlnTXVXdfz 9G95tt7Rizqm
mI6Puvbsl9HPu/0p44j/AE6dkvo593+lPHEf6dUK/NtbIUV2hgBACl5iJG1J OAeY5+vyGScH
AODWXVVp9r4P5BPkq8fp+Z5+E0b0+NI+nTuXx2S+jn3f6U8cR/p1y9V6r6JN UWJ6yXrXGmX4
Ly21rS1qBthYU24lxCkrbcStJC0JIII7lU31Vafa+D+QT5KdVWn2vg/kE+Sn J6fr0bzjPu6d
yyg70Jm0yrY70gwZUeS4y8TL1w9IcacaVvbW045JUplQVzygpJwM5wK8Sx0B LhXGJI1VpyWi
5rirnLk6pLzkhUZwuMqWtTxUSlR7ueYAScgACu+qrT7XwfyCfJTqq0+18H8g nyU5PT9ejecZ
93TuWjEmdBkexXCy+ivS0iJcnQ9MMvUSZDz7idu1annHVOFSdqdp3ZTtG3GK /Ytw6FWLeqCr
XFkltLlxpijN1aqU5xY7qXWTxHX1KwlaUnbnBxgggkVVvVVp9r4P5BPkr1iq gy3wxFtEZ90g
kIbihSiAMk4A9YAn+yk/p+Yi+bTRvdj2nf8A8dO5aEO6dC0XUKL8jXFhdnNu uPM9UaqL7TDj
gUFraZceLbaiFKGUJBwojuE1I+yX0c+7/SnjiP8ATqhkzLOoApgQCD3CGEeS v3qq0+18H8gn
yU5PT9ejecZ93TuXx2S+jn3f6U8cR/p07JfRz7v9KeOI/wBOqH6qtPtfB/IJ 8leinLemMmSq
1RQwpZbS4YydpUBkpBxjOOeKcn5j/s0bzjPu6dy9OyX0c+7/AEp44j/Tp2S+ jn3f6U8cR/p1
RkVUGW+GItojPukEhDcUKUQBknAHrAE/2V4pmWdQBTAgEHuEMI8lOT833cJo 3nGfd07l89kv
o593+lPHEf6dceV0i9Hxvctwa60uUKjR0hQuzGCQp7Izu9bI+MVT/VVp9r4P 5BPkp1Vafa+D
+QT5Kcnp+vRvc4z7uncuDsi9H3u60v42Y+lWtaukLQKIISvXGmUq48g4N1YB wX3CD6b1wQf7
aqjqq0+18H8gnyU6qtPtfB/IJ8lOT0/Xo3nGfd07lxJ6SNBIGE690yke9d2B /wD71+9kjQWS
fR7prJ5E9d2Pp1TnVVp9r4P5BPkp1Vafa+D+QT5Kcnp+vRvOM+7p3Lj7JGgs Eej3TXPu/wDi
7HP9evwdJGgUjA17pkD3rux9OqhjLgSXQzGtMV5wgkIbjJUcAZJwB6wBNeaZ loUARAgkH1ww
nyU5Pz0cJo3u8Z93TuXEnpI0CnknXumRjvXdj6dFdJGgVem17pk+tzu7H06p 3qq0+18H8gny
Vmw7bpD6GGLVEddcUEoQiMlSlKPIAADmacn5+vRvOM+7p3LfHSRoFIwNe6ZA 967sfTp2SNA7
dvo90zt73XdjH+eqeXJtSHFtrtsJK0KKVJMdIKSORBGORr86qtPtfB/IJ8lO T8z/AM9G84z7
uncuIdI+gRjGvdM8u5/4uxy/XoekfQJJJ15pk57ubuxz/XqneqrT7XwfyCfJ X6ZFrCErNthB
KiQk9TpwSMZHc98fHTk/P16N5xn3dO5cHZG0BjHo80xjGPtux9Ov3sj6Bzn0 eaZyTnPXdju/
LqoVOW9MZMlVqihhSy2lwxk7SoDJSDjGcc8V59VWn2vg/kE+Skfp+Z/7NG84 z7uncuJPSRoF
IwNe6ZAPrC7sfTr87I2gOX/x5pjlyH/i7H06qQdRGOiQLNHLK3C0hwRRtUsD JSDjBOOeO9Xi
JdoIyLfB/IJ8lcj9P39Fpo3nGfc07lvp6Q+j5L4eGu9MhQQUDF3Y7hIP3X/l FZnpH0ASSdea
ZOe7/wCLsc/16qOG7a35CWkWiI8SCdqUNo5AZJ3EYAAGSTXqRanHENsW2GHn M8JottrDpAyU
oWO6vBztISSO5moWvsiiyqimu0uv+29SjDq64vpo07lrjpH0CElI15pkA90d d2Mf56/E9I2g
EnKdd6YB74u7H06p1ubZ1oC0wIJB/wD7CfJX71Vafa+D+QT5K0cn5+vRvT4z 7uncuM9JOgzj
OvtNHByM3hj6dY9kbQHL/wCPNMcuQ/8AF2Pp1T3VVp9r4P5BPkp1Vafa+D+Q T5Kcnp+vRvc4
z7uncuHsjaA93mmOYx9t2Pp1+npI0CU7Tr3TJA9brux9Oqd6qtPtfB/IJ8lO qrT7XwfyCfJT
k9P16N5xn3dO5cXZI0Dz/wDj3TPM5P8A4ux3fl1DumrV2kL/ANE+prJadY6Z lXCfb3I8ZoXi
One4sYSMqWABk90kAd0kCod1Vafa+D+QT5KdVWn2vg/kE+SnJ6fr0bzjPu6d y5OyBoH3d6U8
dRvp07IGgfd3pTx1G+nVN9VWn2vg/kE+SvC4XC2sxFLZtlvW7kJQFMpxkkAZ 5dznTk/P16N7
vGfd07l19kDQPu70p46jfTp2QNA+7vSnjqN9OqI6tX/9HZvzD/8AdWDtyLDS nl2+zuJQNyki
HtJA7uDk4OPer54i/c0bzjPu6dy+uyBoH3d6U8dRvp07IGgfd3pTx1G+nVEG 4pdccUxb7Q20
HFIQFxAsnBxkkEeuDTq1f/0dm/MP/wB1OIv3NG84z7unc+i7DfbDf+N1iv1o uvA28bqKc09w
92du7Yo4zg4z3cHvV0+Ervo+WPLXyneHm3rc7c4YZtV9tXEVCnQ0ltTZ2hRw ARkKT2pByMEj
mCRX0VpF2Hf9KWi+9TS43XKCzL4PXKQvh8RtK9u7eM4zjOBnvV52H4BVgk03 zfEtmDYTFvE8
10wkXCV30fLHlpwld9Hyx5a0etsPvS/z+R9OnW2H3pf5/I+nXntLe4Su+j5Y 8tOErvo+WPLW
j1th96X+fyPp062w+9L/AD+R9Og3uErvo+WPLThK76Pljy1o9bYfel/n8j6d OtsPvS/z+R9O
g3uErvo+WPLThK76Pljy1o9bYfel/n8j6dOtsPvS/wA/kfToN7hK76Pljy04 Su+j5Y8taPW2
H3pf5/I+nTrbD70v8/kfToN7hK76Pljy04Su+j5Y8taPW2H3pf5/I+nTrbD7 0v8AP5H06De4
Su+j5Y8tOErvo+WPLWj1th96X+fyPp062w+9L/P5H06De4Su+j5Y8tZstlLy FFSAAoE9uPLX
O62w+9L/AD+R9OnW2H3pf5/I+nQRCZ0pWYyXQnTWvXEBwlK0aTuACgDyIy0D g+//AG1z+jWc
5eulLWN/atF7gQZNttbDKrna34ZcW2qZvCQ6lJVgLQSRkdsKn/W2H3pf5/I+ nTrbD70v8/kf
ToEf7cTv6PG/zP1t14xYseMXFMoWFObQtS3nHCQnOBlajgdse5369qCHwPVM R8Kn93VZdVpA
9UxHwqf3dVl0FdWj1P5/4Z/zztWLVdWj1P5/4Z/zztWLQfDWqLg/E11q1tpG 5J1FcVd3H/8A
MueSpfphnVj+om5UZiKqGWTHaZkLKWlFKchXL/WHpj/5cjvVFtRRuNrjVqsd zUVxH/MuVu9d
bv1v6h6pVwg4HAQSFBW3bnI/8pKcdzB7lfqcMwbDLexwScFquin3Zqi+6+Lo ydOb5vzdrZYP
a2tpw8zzXzF2W/5/Zu6juriYjsyM1KUkpSpK5LKU7isFK9yTntsFOCMYx2oA CTUS68y/Yv1v
/auvMk3OYwWJUx55skHas5Ga0eoPer2PZ1OFWVnONVRVVM/LoiPlH8Mt0RF0 LH6SNYOW2FZ7
cOkG92hx3SsRzrTEtKXmnSpsjKnirKd2Np5csZrGdaIEWBcbYjT81uPb9LC8 N6kXJWW33uGF
8PaRs2Ekp5dtkVXciI/JlCVLkyZTqWUsIU+6V7G0+lQnPcSPWA5CkuNOlW9N sfulzVbQrcIP
Va+p85znh5293n3KxU+z7azp/wDXXdN/P8tXTml6E4XZ1T/5U8yyLb6GpGtt KaZOnHlJm6fb
u9xlGcsZKojq+GhGeWVpSoqz62MYznz087pq+x9JBOln4T2p7TcJBULmtYhK jBzapII7dSto
zntR6wqvkN3BE0TkXO4olIYEZt5MpYcaZCSkNoVnKU7SRtHLBIrCNHmxupOp rncmOomVsxOH
KWnqdC870owe1CsnOMZyc12rAsKnotZ85++7ycjCbH50aI+29ONJwmbh0cyJ 1ztsdievTsq8
RXm5L63lBrcUqUkI4KEEp24Ktx7oxUktt1fuHStoHg2HqeLK0fxitp1xXFKo zpDCSrI7THdH
bdvzzgVU7YvDVratMe/3qPbmkKbTEZnOIZCVZChsBxggnI9fJ79GE3SOIqY1 6u7CYaFoipam
uJSwFDCuGAe03DkcYz69fNpgOE2szNVeX5z84mNnM7ThVlREXU5NCwdIWO13 dOi1TrDK0sq5
OzWHLW9KdcW+lhrelSSsbwSe1VgfgANZQo1slX+2iFYWpENyI89eVPt3FiNb kMrGXGy6htxx
Skqxs59sMcs1Wz8SbKnNTp90uk+WwgIZflS1uuNAHICVKOU4PPlW6Jmp0yFy U6w1Ql9SNhdT
dngvb3du7dnHvVzE8MiIutNMu4xYX9XQmGketF9a1fqI2RUGHAkMIt1slmWs tsLJSXXAwhb2
TtzyBCSo5OBWxLtVlmv2W3xpdxbt0nWhgtuyC8yoMqiNuAcFwJAXlWwLKATy PMYqAoN5RcHL
mNQXs3JxISqd1e51QQBgDiZ3YwByz61eD8OZJQG5lzuUxAfMnbIlLcBeKdpc IJ9PgAbu7gAV
9zgeFTVE8Jzc3zv/ALlfMYRYxHVWToS5SHOk2KwvQEzTcJhVwZW67KdWt9KY zuEnenAXyzlP
Ln3K09EtaevCtATBp5yBBvMW6iVBE9x3Bit7m1Bw4Vk55+tkdzFQt1y+vSWp D2pL+66ylSGF
LuLqiylQ2qCCVdqCCQcd0HFeEaPNioiIi3O5R0wkOIipalLQGA4MObMHtdw5 KxjPr1ycCwm/
3orum67pn77f4dxmy6Jp5r7+iPsn+k2LJquVoGaLYuzMX5u5dUw0zFvb1RQk oCVkbsrCueB6
3IZrYQnSjaLhdH7XJkNw9PTJ8iEyibGbS+y80EBtyS2hZ3JcwoEK2nPLuVWq YDyWoTIlzOFA
3GG3x1bY5UQVKQM9qSQCSMZwO9XtLF4mKeVN1BfJZfZMdwv3B1wraJBLZyea CQMpPLkO9SrA
8Lv5rWbs8uRhFh86GWnrgu/XR3qqVbNORnSt5szJKuCwjOUt79pJIBxkjnip R0LXZaulNlhq
QH2WUzUJkxTvDmxh3C28gZBxkZx61Q8W5GwI2ApAxgiveC1Lt76ZFtlyoEhA IQ9FdU04jIwc
KTgjkSP7a9G2s667Oqzirmmm7+ct7NRaU01RVd0Temeqrvc710UQ5zLuopr7 F9ZbXJ1JFbiy
VpcQpKWmdmQpJVgqBVywDit/VdutNutNvmPWtbDrGp2rRcGLY7IkKU2ptSlp BdQkLdSU4y32
vOq5uTFzurrLl5vd5uymDlnq+c4/wz307icGtia9fpz6HZ2pb9KLbiXWw9cH VJQtIISoAnkR
k4PdGT368yywLCbKmKaK7ue/pn7f3oa68Jsa5mZi9ZUS2xndYWZ+3xYjVhnJ uDTb8KZMYkLU
3GWsJcQ7tWhQxz2kg5IPLkY1ouPDvHRnENqtrDuoOoJEh9FxXJYylG4pXHcS OCoADmF4yQRk
VF3zeZFxbuUrUN8kzWULbZkO3BxTjSVDCkpUTlII5EDuisOFdBZhZUXu8N2v ZsMJE1xLCk+u
C2DtI/sr6jA8Lu7Tn5vnOWenzcxiwv6vN/8AE+mWqI30ezpU2JFtt1t7Ntec dakvu7UyXEIJ
dKkBoZSveEoJKcczWwtb1r6W7fYIGhJsGHAv0NgXl+U6ovoLie3KSnZheeW3 FV1LTd5cBNuk
3+9O29DSWUw1TnCxw0kFKNmcbQQMDuch3qzUq8qTGbOoL5wYi0rjM9cHeGwp PpVITnCSPWIx
iu4rhc3+9XfHPzXz8/70OYxYR0U6IS64ybDLhKu7Ngnw0t64XaJaY8pcl2Wy oOLU4EkdqvKD
hKRjBxzruRrBbrrrKwpYYt7Onp7stDTkZ+Uh+Qtpvelpbb6d6FcuZRnOTjBx mr240xttLbdy
uKUJldWYElYBkc/4Y8+bnM9t3eZ51+zmbjcJ7M+53i7XGVH/AIh2XMcdWzzz 2hUcp5gdzvUj
A8KpiKabTo+85P7cThNjMzM0uz0hzoEF+zrtDDjbktp4TGExpjTLS21DaW1S m0LVlKhkc9pH
viptpS4Kj9NukLSiKl6EnTKHWBxFAcZ5lxxx0AemUo5Rz9b3wKrCYzcJ8pMq 53W53N5CdqFz
Za3ygd4FROBXvJcuT67arqgtOW1pTUaQ0pSH0oKt6U7we4lRUU4wRvPPGAKV 4LbWljTZ11Xz
z6Y5vJ804RZ02k1Uxd0JPoacUWfRd3mWhcZ5/XJimCuY/wAKMgtoKRtUr0yC fTK7Y7QFEjlX
WtkWLcL10iXi62pqOu13bhMQHTMWjYt1SVPqDKVvHdt3DA2gqPcSBVcvRZsh KUyrncpSRJVK
2vylrBfUMF05PNZHLd3ffr2Qbui5OXNF+vSbi4NqpgnuB/aBgJ4md2MAcs+t UsRwjnqprum7
LP2feNWXRNPMmWtbj1n03bHNOuzFMxNbq6jU+y4y6U9RtOAKQtKVcirHNIyB nuGot0gSxauk
fU1ptyAYkS4rS0ArkgHCige8kkj+yvCEq4xp0aY7Ol3BcaX1ahqdIW80qRgD iKSTzVgAZ7pA
APLlWtEhvNSXZklZlyn3lvSFu8+MtZJUVfhyfjq+CYPa2Np71U39N/3vu/v8 p21tRXTdEO/p
1cp1xt+2SYt1U4wpEqEw6Ey2gtJCtrbgHE25z2pIOMHANe2pzqe4Fh3rbLgs tyEOvXC5JMFt
tSVLKAkvOKPpnlnO77lKR2vPiO2S1TMFifFaPd4E5wMuNn3lqwhQ98EH3hX4 q36ftrqZdxlJ
v89vnGgMvqfb3esXHPShHfCSSferNhEe/axae9EzHymOfy/3zQrZVXUe7ddG W/meWo7rIY1T
cmmiw6Cpt1SmVHhlbjaVr25A5birHIVp9eZfsX63/tXsiHJedelzV8SXJcU6 8vGMqUcms+oP
er2MHmaLKmmrpiGG1qiquZhrdeZfsX63/tTrzL9i/W/9q2eoPep1B71X4R8N brzL9i/W/wDa
nXmX7F+t/wC1bPUHvU6g96nCDW68y/Yv1v8A2p15l+xfrf8AtWz1B71OoPep wg1uvMv2L9b/
ANqJu0l11pDiMJLqMnd/5hWz1B71eci3OKaIaIS4CFJJHLIIIz8VfM13xc7H S0L83YZuoHDf
VMhKIrXA4sgtjO9zdjBGf9X/AIV7WJcGLaruzbFJMIOq4OxwrTjgozgknPbb v+NbPCuXsLH5
ZX0awfi3J5lbJQwhKxtKg4VEA93ltH/WsUYPEVe8vNpF117ORMiuaYfNw4oY UFlwNE787zjb
jnnOMY9euRpiTdOvLR1GqSZAZPUGcbNn+tv28uNjGfWx3PXrqNxJ7G5tpLK2 95UkqWUkAnOC
MH1yaz4Vy9hY/LK+jXa7D3q4qv8A7/egptIim55XW4vJemsoTlCxgnPfQBX1 f0R+pTpD4Dhf
MIr5PdgvJiSnpG0uKClkJ7gATgDPr8hX1h0R+pTpD4DhfMIrw/1F1bKM/wDp 6Hsvpr/j/aUU
pSvzD1ilKUClKUClKUClKUClKUClKUClKUClKUClKUFbajvvod1rFuHUvVXF 1RBg7OJsx1Sy
1H35wfS8Xdj19uMjORclUD0q/b63f16sXzsSr+oK6tHqfz/wz/nnasWq6tHq fz/wz/nnasWg
+WuhX+czrP8AGvP7xar6Kr516Ff5zOs/xrz+8Wq+iq14Z2keGn8YRsOrOedc lKUrIsUpSgUp
SgUpSgUriat1ZprSUZiTqa9QrSw+sttOynNiVKAztBPLOOePePeqOdmfoo8I Onvz1NBPqVAe
zP0UeEHT356mnZn6KPCDp789TQT6lQHsz9FHhB09+epp2Z+ijwg6e/PU0E+p UB7M/RR4QdPf
nqadmfoo8IOnvz1NBPqVAezP0UeEHT356mnZn6KPCDp789TQT6lQHsz9FHhB 09+epp2Z+ijw
g6e/PU0E+pUB7M/RR4QdPfnqadmfoo8IOnvz1NBPqVAezP0UeEHT356mnZn6 KPCDp789TQT6
lQHsz9FHhB09+epp2Z+ijwg6e/PU0E+pUB7M/RR4QdPfnqadmfoo8IOnvz1N BPqVAezP0UeE
HT356mnZn6KPCDp789TQT6lQHsz9FHhB09+epp2Z+ijwg6e/PU0E+pUB7M/R R4QdPfnqadmf
oo8IOnvz1NBPqVAezP0UeEHT356mnZn6KPCDp789TQT6lQHsz9FHhB09+epp 2Z+ijwg6e/PU
0E+pUB7M/RR4QdPfnqadmfoo8IOnvz1NBPqVAezP0UeEHT356mnZn6KPCDp7 89TQT6lQHsz9
FHhB09+eprr6T6QdE6suirZpnU9su8xLSnlsxHw4pDYIBWrHcTkgZPLJA7pF BJ6Vnwld9Hyx
5acJXfR8seWgwpWpe7lAssVqVc5Tcdp6UxEbOdxU886lptICcnmtaR3hzJwA SNugpn7Lj+Qd
k+HEfs0mp90R+pTpD4DhfMIqA/ZcfyDsnw4j9mk1PuiP1KdIfAcL5hFa6/ha PFVqpRp7WrNG
uUopSlZFilKUClKUClKUClKUClKUClKUClKUClKUClKUFOdKv2+t39erF87E q/qoHpV+31u/
r1YvnYlX9QV1aPU/n/hn/PO1YtV1aPU/n/hn/PO1YtB8tdCv85nWf415/eLV fRVfOvQr/OZ1
n+Nef3i1X0VWvDO0jw0/jCNh1ZzzrlwNU6wsempsGFdFXBcqeh1cZiFbJMxx aW9m9W1htZAH
ERzOPTVt6Z1DZ9SQFzbNNTJabdLLqShTbjLgxlDiFgKQoZHaqAPMd+oh0gzD aOlHSF7kW68S
YDFuubDztvtcibw1uKiFAUlhCynOxeCRjtTUeuSbk4xrXVvoTlO269u26I1B mw3VOKaaOx2W
7GR/ClIDn8XgLUlkZAB5ZFlr3m6QLNBE25SBHjl9mOFlJV/COupabTgAnmta R3hnngVu182q
0y7L0/q2KrSfVFlRcLFcIkWNpd6FHcSiV/pS2IjhWrdwkKCgAFKB9LhQ3b2o dO3SRrW7SpLc
mG69NjP2CcxpOXMkRoyG2tjTL7bqURQFJWFtuITnKircFcg+hK0rPdIF3jOy LdID7TMl6K4o
JKcOsuKbcTzA7i0KGe4ccsiqf1NpBx+drXULdglOXpvV1sftcpLCy6llLduS 4tk/c4DyVqTy
ISoKyE8uexbI2htLXLU8DTDsCfpjVM+bIaZt5Y6tgPyXU7W17Ql0BhxtSQkn BaQnljFBftKo
G4WC02jUmiYetrBIvy3rFd5t0Yj29c0GY9JhOOLU0gKUtIWtSR2qtvaHltyJ 70aW9cGwaSY1
BaJ5vDMeX1G680p1UFhS8padc5hK+FwkYJySgj1jQbWtvVU6L/hib+7JdTOw 6y0zfU3tVpur
ck2KW7DuSQhaVR3ms70lJAJAwcKGQcHBOKhmtvVU6L/hib+7JdRdWl9Q2WwX zWlis81d3Rd7
0zcLaGSly6252dIWgoSfTOI38Voj0wUpI5LFBeFouEO7WmHdbe9xoc1hEiO5 tKd7a0hSTggE
ZBHIgGtqvnORFvdl0feYL2l9RSpF76MrdbYaIltdexKZZmJcZcwMNLHHQcLx nmBkjFe1y0CZ
0HU11k6ZmuXc6ssnUcgMOpeTE4VrakqaI5pRs6oS4pOBhCgo9pyC8b3fodou tjt0lt9bt6mr
hRi2kFKFpjuvkryRgbWVjlk5I5YyR1a+eNRaSi2vVPUC9FXGRo+JrZEsQINs cdYTHXY1JcWh
pAwprjqIUlIIUoqThRVtONy03f3NGQUw7NJj6RTqqRKTZ5lnel8K3FlSWg5B QtDimg/uWGe6
kKQSghJSA+iaVQsDRirnZ9E2eWxcblZUatlSXWV2WRb2Y0VUCUQ1wHlKWiPx FBACyAQvZjBA
q92Gm2GUMstpbabSEoQkYCQBgADvUGdKUoFKUoFKUoFKUoFKUoFKUoFKUoFK UoFKUoFKUoFK
UoFKUoFKUoFeEn1//tL/AO1e9eEn1/8A7S/+1BGdQ3+xachIm6hvVttEVbga Q/OlIYbUsgkJ
ClkAnCVHHdwD3q4PZT6MfCNo/wAdxvp13tT2K0ansEywX+AzcLZNb4ciO6OS h3QcjmCCAQoE
FJAIIIBqjuhn7GHT2hNfzdT3Od19TFkhVgZebH+jJwCHXfWW8kkpSQAkbd+A pSQ2Fi9OH2h0
9/Wyy/t7NT6oD04faHT39bLL+3s1PqCmfsuP5B2T4cR+zSan3RH6lOkPgOF8 wioD9lx/IOyf
DiP2aTU+6I/Up0h8BwvmEVrr+Fo8VWqlGntas0a5SilKVkWKUpQKUpQKUpQK UpQKUpQKUpQK
UpQKUpQKUpQU50q/b63f16sXzsSr+qgelX7fW7+vVi+diVf1BXVo9T+f+Gf8 87Vi1XVo9T+f
+Gf887Vi0Hy10K/zmdZ/jXn94tV9FV869Cv85nWf415/eLVfRVa8M7SPDT+M I2HVnPOuSlK8
GZkN6QuOzKYceRne2lwFScHByO6OdZFnvSubqOc5bre1IacZbUubFYJd2YId kNtkds42MkLI
HbE5Iwlw4bV4sR9Tjhce72deNnF2WtxO7HB34zIO3O2RjOccRrO7hK4odiuV fdPWm+SIL10Z
ekdQuh5lrql1DJWClQK20qCHMKSkjeFbSMjBrBiPqccLj3ezrxs4uy1uJ3Y4 O/GZB252yMZz
jiNZ3cJXFMR9Tjhce72deNnF2WtxO7HB34zIO3O2RjOccRrO7hK4obbtqgO3 2NfHI+bhFjOx
WXt6u1adU2paducHJabOSMjbyxk53a47EfU44XHu9nXjZxdlrcTuxwd+MyDt ztkYznHEazu4
SuKYj6nHC493s68bOLstbid2ODvxmQdudsjGc44jWd3CVxQ09RWObctcaMvE dTCY1kmyZUni
KIKkriPMJSkAHKtzoPPAwlXPOAZr1Ynvo+M+SosxH1OOFx7vZ142cXZa3E7s cHfjMg7c7ZGM
5xxGs7uErimI+pxwuPd7OvGzi7LW4ndjg78ZkHbnbIxnOOI1ndwlcUJT1Ynv o+M+SnVie+j4
z5KizEfU44XHu9nXjZxdlrcTuxwd+MyDtztkYznHEazu4SuKYj6nHC493s68 bOLstbid2ODv
xmQdudsjGc44jWd3CVxQlPVie+j4z5KdWJ76PjPkqLMR9Tjhce72deNnF2Wt xO7HB34zIO3O
2RjOccRrO7hK4piPqccLj3ezrxs4uy1uJ3Y4O/GZB252yMZzjiNZ3cJXFCU9 WJ76PjPkp1Yn
vo+M+SosxH1OOFx7vZ142cXZa3E7scHfjMg7c7ZGM5xxGs7uErimI+pxwuPd 7OvGzi7LW4nd
jg78ZkHbnbIxnOOI1ndwlcUJT1Ynvo+M+SnVie+j4z5KizEfU44XHu9nXjZx dlrcTuxwd+My
DtztkYznHEazu4SuKYj6nHC493s68bOLstbid2ODvxmQdudsjGc44jWd3CVx QlPVie+j4z5K
dWJ76PjPkqLMR9Tjhce72deNnF2WtxO7HB34zIO3O2RjOccRrO7hK4piPqcc Lj3ezrxs4uy1
uJ3Y4O/GZB252yMZzjiNZ3cJXFCU9WJ76PjPkp1Ynvo+M+SosxH1OOFx7vZ1 42cXZa3E7scH
fjMg7c7ZGM5xxGs7uErimI+pxwuPd7OvGzi7LW4ndjg78ZkHbnbIxnOOI1nd wlcUJT1Ynvo+
M+SnVie+j4z5KizEfU44XHu9nXjZxdlrcTuxwd+MyDtztkYznHEazu4SuKYj 6nHC493s68bO
Lstbid2ODvxmQdudsjGc44jWd3CVxQlPVie+j4z5KdWJ76PjPkqLMR9Tjhce 72deNnF2WtxO
7HB34zIO3O2RjOccRrO7hK4piPqccLj3ezrxs4uy1uJ3Y4O/GZB252yMZzji NZ3cJXFCU9WJ
76PjPkp1Ynvo+M+SosxH1OOFx7vZ142cXZa3E7scHfjMg7c7ZGM5xxGs7uEr imI+pxwuPd7O
vGzi7LW4ndjg78ZkHbnbIxnOOI1ndwlcUJT1Ynvo+M+SnVie+j4z5KizEfU4 4XHu9nXjZxdl
rcTuxwd+MyDtztkYznHEazu4SuKYj6nHC493s68bOLstbid2ODvxmQdudsjG c44jWd3CVxQl
PVie+j4z5KdWJ76PjPkqLMR9Tjhce72deNnF2WtxO7HB34zIO3O2RjOccRrO 7hK4piPqccLj
3ezrxs4uy1uJ3Y4O/GZB252yMZzjiNZ3cJXFCU9WJ76PjPkp1Ynvo+M+Sosx H1OOFx7vZ142
cXZa3E7scHfjMg7c7ZGM5xxGs7uErimI+pxwuPd7OvGzi7LW4ndjg78ZkHbn bIxnOOI1ndwl
cUJT1Ynvo+M+SnVie+j4z5KizEfU44XHu9nXjZxdlrcTuxwd+MyDtztkYznH Eazu4SuKYj6n
HC493s68bOLstbid2ODvxmQdudsjGc44jWd3CVxQlPVie+j4z5KdWJ76PjPk qLMR9Tjhce72
deNnF2WtxO7HB34zIO3O2RjOccRrO7hK4piPqccLj3ezrxs4uy1uJ3Y4O/GZ B252yMZzjiNZ
3cJXFCU9WJ76PjPkp1Ynvo+M+SosxH1OOFx7vZ142cXZa3E7scHfjMg7c7ZG M5xxGs7uErim
I+pxwuPd7OvGzi7LW4ndjg78ZkHbnbIxnOOI1ndwlcUJT1Ynvo+M+SnVie+j 4z5KizEfU44X
Hu9nXjZxdlrcTuxwd+MyDtztkYznHEazu4SuKYj6nHC493s68bOLstbid2OD vxmQdudsjGc4
4jWd3CVxQlPVie+j4z5K52o7rIhWeTMg2qRd5LbZDUKI42hx4qIHJTqkIAHd OVDkDjJ
Re: How to define the scale of an X Axis (Chart Data) [message #73676 is a reply to message #73472] Tue, 13 September 2005 22:09 Go to previous messageGo to next message
Eclipse User
Originally posted by: mpadhye.actuate.com

Hi Tobi,

Um...sorry about that...the information in the previous email was
incomplete. The ability to specify a scale is dependent on the type of
the axis (Linear as you have specified) AND an option called 'Is
Category Axis' in the X Axis attributes page.

This will work if you specify the chart type as a scatter chart...since
that should be what you are looking for if you need to specify a scale.
Unfortunately, the screenshot you have attached does not indicate the
type of chart, so I cannot give any other suggestions.

Thanks,
Milind

Tobi wrote:
> hi milind, hi all,
>
>> As for a Scale for X-Axis, this is possible, but only if the X-Axis is
>> a Numerical (linear) type. It is not possible to set a scale for a
>> Text axis.
>
>
> this is exactly what i need. how can i set the datatype to "numerical"?
> the original db-data is oracle "number", the dataset-data in birt shows
> "decimal".
>
> pls look at my screenshot, the chart-data is set to linear but i can't
> enter a scale, the fields are grey. where is my failure .. ?
>
> thanks in advance,
> tobi
>
>> Hi Tobi,
>>
>> Unfortunately, in the current version of BIRT, there is no way to
>> have some of the labels not appear in the X Axis. This is something we
>> are looking at in v 2.0.
>>
>> As for a Scale for X-Axis, this is possible, but only if the X-Axis
>> is a Numerical (linear) type. It is not possible to set a scale for a
>> Text axis.
>>
>> One thing you might look into is using a script in the chart to set
>> the labels as trasnsparent for all except say every 20th entry. This
>> way you can still have the chart show up correctly. Of course, this is
>> more of a workaround, but it should work. Check the Chart FAQ and the
>> BIRT help for more info on scripting for charts.
>>
>> Thanks,
>> Milind
>
>
>
>>> Tobi wrote:
>>>
>>>> hi sudha, hi all,
>>>>
>>>> thanks for the quick reply!
>>>>
>>>> i allready tryed this (maybe you can see it in my screenshot, the
>>>> lables are rotated -90). the problem is, that there are many values
>>>> on x-axis, for every value i get a label.
>>>>
>>>> how can i skip most of the labels, so that i get only few labels
>>>> (e.g. 30 labels on x-axis like on y-axis) for hundredreds of values?
>>>>
>>>> thanks in advance
>>>>
>>>>
>>>>> Hey Tobi,
>>>>>
>>>>> I had similar problem with data overlapping on xaxis. what i did
>>>>> was to just slide the angle so that data on the chart will be set
>>>>> in an angle.TO do that go to Chart Dialog-> Attirbutes ->Font
>>>>> editor[click on the little button]->Rotate it an angle which you like.
>>>>>
>>>>> Thanks,
>>>>> Sudha
>>>>>
>>>>> Tobi wrote:
>>>>>
>>>>>> additional information: the screenshot makes my problem clear (see
>>>>>> attachment)
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>>> hi all,
>>>>>>> i want to plot a graph with a lot of input-data and i don't want
>>>>>>> to group the results. problem: the labels on my x-axis overlaps
>>>>>>> each other.
>>>>>>> i need to define a scale for x-axis but it is not possible
>>>>>>> because the responsible option-field is not active/grey, you
>>>>>>> can't enter a value (under: chart dialog-> data -> x-axis -> scale).
>>>>>>> this field is only active for the y-axis .. (like the screenshot
>>>>>>> in the help-document: working with data on a chart axis ->
>>>>>>> defining the scale of an axis). how can i enter data in this grey
>>>>>>> fields? the help-document seems to be wrong in this section?
>>>>>>>
>>>>>>> how can i define the scale for x axis??
>>>>>>>
>>>>>>> please help, urgent!
>>>>>>>
>>>>>>> thanks,
>>>>>>> tobi
>>>>>>>
>>>>>
>>>>>
>>
>>
>> ------------------------------------------------------------ ------------
>>
>>
>> ------------------------------------------------------------ ------------
>>
>
>
> ------------------------------------------------------------ ------------
>
>
> ------------------------------------------------------------ ------------
>
Re: How to define the scale of an X Axis (Chart Data) [message #77261 is a reply to message #73319] Tue, 27 September 2005 03:16 Go to previous message
Alexander Staubo is currently offline Alexander Staubo
Messages: 2
Registered: July 2009
Junior Member
Same problem here. Do you have an ETA on the label-hiding fix, or at
least some hints as to where this ought to be plugged in, so I could
look at producing a patch?

While it's possible to manually omit labels (or as you suggest, setting
them as transparent), you obviously don't know which ones to omit, which
depends on the rendered size of the chart. So it's not a very
satisfactory workaround.

Alexander.

Milind Padhye wrote:
> Hi Tobi,
>
> Unfortunately, in the current version of BIRT, there is no way to
> have some of the labels not appear in the X Axis. This is something we
> are looking at in v 2.0.
>
> As for a Scale for X-Axis, this is possible, but only if the X-Axis
> is a Numerical (linear) type. It is not possible to set a scale for a
> Text axis.
>
> One thing you might look into is using a script in the chart to set
> the labels as trasnsparent for all except say every 20th entry. This way
> you can still have the chart show up correctly. Of course, this is more
> of a workaround, but it should work. Check the Chart FAQ and the BIRT
> help for more info on scripting for charts.
>
> Thanks,
> Milind
>
> Tobi wrote:
>> hi sudha, hi all,
>>
>> thanks for the quick reply!
>>
>> i allready tryed this (maybe you can see it in my screenshot, the
>> lables are rotated -90). the problem is, that there are many values on
>> x-axis, for every value i get a label.
>>
>> how can i skip most of the labels, so that i get only few labels (e.g.
>> 30 labels on x-axis like on y-axis) for hundredreds of values?
>>
>> thanks in advance,
>> felix
>>
>>
>>> Hey Tobi,
>>>
>>> I had similar problem with data overlapping on xaxis. what i did was
>>> to just slide the angle so that data on the chart will be set in an
>>> angle.TO do that go to Chart Dialog-> Attirbutes ->Font editor[click
>>> on the little button]->Rotate it an angle which you like.
>>>
>>> Thanks,
>>> Sudha
>>>
>>> Tobi wrote:
>>>
>>>> additional information: the screenshot makes my problem clear (see
>>>> attachment)
>>>
>>>
>>>
>>>>> hi all,
>>>>> i want to plot a graph with a lot of input-data and i don't want to
>>>>> group the results. problem: the labels on my x-axis overlaps each
>>>>> other.
>>>>> i need to define a scale for x-axis but it is not possible because
>>>>> the responsible option-field is not active/grey, you can't enter a
>>>>> value (under: chart dialog-> data -> x-axis -> scale).
>>>>> this field is only active for the y-axis .. (like the screenshot in
>>>>> the help-document: working with data on a chart axis -> defining
>>>>> the scale of an axis). how can i enter data in this grey fields?
>>>>> the help-document seems to be wrong in this section?
>>>>>
>>>>> how can i define the scale for x axis??
>>>>>
>>>>> please help, urgent!
>>>>>
>>>>> thanks,
>>>>> tobi
>>>>>
>>>
>>>
Previous Topic:Creating a custom report parameter UI
Next Topic:Text Field in HTML Format
Goto Forum:
  


Current Time: Fri Oct 24 21:36:18 GMT 2014

Powered by FUDForum. Page generated in 0.04083 seconds
.:: Contact :: Home ::.

Powered by: FUDforum 3.0.2.
Copyright ©2001-2010 FUDforum Bulletin Board Software