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3
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with
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K
ey
w
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r
d
s
:
Ar
tific
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eu
r
al
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etwo
r
k
B
am
b
o
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ib
er
s
B
io
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m
p
o
s
ites
Feed
f
o
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war
d
b
ac
k
p
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p
ag
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Nu
m
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aly
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rticle
u
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CC B
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C
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A
uth
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r
:
Z
in
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aa
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id
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Sig
n
als,
Dis
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Sy
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tem
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an
d
Ar
tific
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I
n
tellig
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,
E
NSET
Mo
h
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m
e
d
ia
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Hass
an
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I
Un
iv
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s
ity
o
f
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asab
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ca
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o
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m
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r
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m
ail:
laab
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1.
I
NT
RO
D
UCT
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O
N
B
io
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co
m
p
o
s
ites
,
also
k
n
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n
o
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ativ
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m
ater
ials
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ar
e
in
cr
ea
s
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g
ly
attr
ac
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th
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tio
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o
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o
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ar
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m
e
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s
ad
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an
tag
es
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ey
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er
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win
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lar
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ac
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tem
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ties
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a
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ed
to
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d
iv
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al
co
n
s
titu
en
ts
,
as
well
as
th
eir
en
v
ir
o
n
m
en
tally
f
r
ien
d
ly
n
a
tu
r
e,
in
lin
e
with
th
e
p
r
in
ci
p
les
o
f
s
u
s
tain
ab
le
d
ev
elo
p
m
e
n
t.
Desp
ite
th
eir
d
i
f
f
er
en
ce
s
,
in
d
u
s
tr
ies
s
u
ch
as
au
to
m
o
tiv
e,
ae
r
o
s
p
ac
e,
f
o
o
d
p
r
o
ce
s
s
in
g
,
tex
tiles
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an
d
o
th
er
s
ar
e
co
n
v
er
g
in
g
to
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ar
d
s
th
e
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s
e
o
f
b
io
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co
m
p
o
s
ites
in
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p
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d
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cts.
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a
r
esu
lt,
u
n
d
er
s
tan
d
i
n
g
th
e
m
ec
h
an
ical
p
r
o
p
er
ties
o
f
ec
o
-
co
m
p
o
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ites
h
as b
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o
m
e
an
im
p
o
r
tan
t a
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ea
o
f
s
tu
d
y
,
p
ar
ticu
la
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ly
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th
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co
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tex
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th
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esear
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p
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t
ies
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ef
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b
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s
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ar
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s
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o
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p
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ch
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m
an
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f
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tu
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in
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p
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th
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p
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atr
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e
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t
m
ix
tu
r
es,
am
o
n
g
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th
er
s
[
1
]
.
Selectin
g
th
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ap
p
r
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p
r
iate
b
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p
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mp
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tifi
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p
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ed
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o
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a
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6905
u
n
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tan
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o
f
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p
e
r
ties
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en
s
u
r
e
its
p
r
o
p
er
ap
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licatio
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.
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lass
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eth
o
d
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wid
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s
ed
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y
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u
ch
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p
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im
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tatio
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o
m
o
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izatio
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a
n
d
f
in
ite
elem
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t
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aly
s
is
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av
e
p
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ed
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s
e
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l
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o
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atio
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u
t
ar
e
in
h
er
e
n
tly
lim
ited
c
o
m
p
ar
ed
to
m
o
d
er
n
tech
n
o
lo
g
ies.
T
h
e
h
o
m
o
g
en
izatio
n
m
eth
o
d
p
r
o
v
id
es
m
ac
r
o
s
co
p
ic
esti
m
atio
n
s
o
f
b
io
-
c
o
m
p
o
s
ite
p
r
o
p
er
ties
,
wh
ile
th
e
f
in
ite
elem
en
t
m
eth
o
d
r
elies
o
n
s
im
p
le
r
ep
r
esen
tativ
e
v
o
lu
m
es
[
2
]
.
Ho
wev
er
,
th
e
m
ec
h
an
ical
p
r
o
p
er
ties
o
f
b
io
-
co
m
p
o
s
ites
ar
e
in
f
lu
en
ce
d
b
y
n
u
m
er
o
u
s
p
ar
a
m
eter
s
an
d
ex
h
ib
it h
ig
h
l
y
co
m
p
lex
m
i
cr
o
s
tr
u
ctu
r
es.
I
n
th
is
s
tu
d
y
,
we
f
o
c
u
s
o
n
th
e
u
s
e
o
f
a
r
tific
ial
in
tellig
en
ce
t
o
ad
d
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ess
th
e
c
h
allen
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es
a
n
d
co
n
s
tr
ain
ts
ass
o
ciate
d
with
d
eter
m
in
in
g
th
e
p
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p
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r
ties
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f
n
ew
m
ater
ials
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p
ar
ticu
lar
ly
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io
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co
m
p
o
s
ites
,
as
m
en
tio
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ed
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r
lier
.
Sp
ec
if
ically
,
we
u
s
e
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
to
p
r
e
d
ict
h
o
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p
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e
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eh
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elastically
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en
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o
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ce
d
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s
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ee
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o
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b
ac
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ith
m
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ain
o
u
r
n
etwo
r
k
u
s
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g
th
e
d
ataset
g
en
er
ated
b
y
th
e
M
o
r
i
-
T
an
ak
a
h
o
m
o
g
en
izatio
n
m
eth
o
d
to
test
an
d
v
alid
ate
o
u
r
r
esu
lts
.
T
h
e
an
al
y
s
is
o
f
th
ese
r
esu
lts
d
em
o
n
s
tr
ates
th
e
r
eliab
ilit
y
o
f
th
e
ch
o
s
en
ar
tific
ial
m
o
d
el
an
d
h
ig
h
lig
h
ts
th
e
ad
a
p
tab
ilit
y
an
d
e
f
f
icien
cy
o
f
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
in
s
o
lv
in
g
co
m
p
lex
p
r
o
b
lem
s
.
Ad
d
itio
n
ally
,
o
u
r
f
in
d
in
g
s
u
n
d
er
s
co
r
e
th
e
ef
f
ec
tiv
en
ess
o
f
b
am
b
o
o
as
a
r
ein
f
o
r
ce
m
en
t,
a
s
ev
id
en
ce
d
b
y
th
e
s
ig
n
if
ican
t
im
p
r
o
v
e
m
en
t
in
th
e
r
ig
id
ity
o
f
p
o
ly
p
r
o
p
y
len
e
w
h
en
lo
ad
ed
with
th
is
n
atu
r
al
f
ib
er
co
m
p
a
r
ed
to
p
u
r
e
p
o
ly
p
r
o
p
y
le
n
e.
2.
M
AT
E
R
I
AL
S AN
D
M
E
T
H
O
DS
2
.
1
.
M
a
t
er
ia
ls
2
.
1
.
1
.
P
o
ly
pro
py
lene
W
ith
an
an
n
u
al
wo
r
ld
p
r
o
d
u
ctio
n
ex
ce
ed
in
g
1
0
m
illi
o
n
t
o
n
s
,
p
o
ly
p
r
o
p
y
len
e
is
r
an
k
e
d
am
o
n
g
th
e
m
o
s
t
wid
ely
u
s
ed
p
o
ly
m
er
s
i
n
v
a
r
io
u
s
f
iel
d
s
.
I
t
is
ch
ar
ac
te
r
ized
b
y
l
o
w
d
e
n
s
ity
,
g
o
o
d
f
l
ex
u
r
al
s
tr
en
g
th
,
an
d
co
m
p
atib
ilit
y
with
b
io
-
f
iller
s
.
I
t
is
also
r
ec
y
clab
le,
in
ex
p
e
n
s
iv
e,
an
d
v
e
r
s
atile
[
3
]
.
T
h
is
is
wh
y
r
esear
ch
er
s
co
n
s
id
er
it
a
g
o
o
d
ca
n
d
id
ate
a
s
a
m
atr
ix
to
s
tu
d
y
an
d
m
o
d
el
th
e
p
r
o
p
er
ties
o
f
b
io
co
m
p
o
s
ites
.
T
h
e
u
s
e
o
f
8
5
%
o
f
th
is
is
o
tactic
p
last
ic
r
ein
f
o
r
ce
d
b
y
n
atu
r
al
f
ib
er
s
is
th
e
th
em
e
o
f
th
is
s
tu
d
y
.
T
a
b
le
1
an
d
Fig
u
r
e
1
r
esp
ec
tiv
ely
r
ep
r
esen
t th
e
m
ec
h
an
ical
ch
ar
ac
ter
is
tics
an
d
ch
e
m
ical
s
tr
u
ctu
r
e
o
f
p
o
ly
p
r
o
p
y
le
n
e.
T
ab
le
1
.
Me
ch
a
n
ical
an
d
p
h
y
s
ical
p
r
o
p
er
ties
o
f
p
o
ly
p
r
o
p
y
le
n
e
[
4
]
P
r
o
p
e
r
t
i
e
s
V
a
l
u
e
s
U
n
i
t
s
S
p
e
c
i
f
i
c
h
e
a
t
s,
C
p
3
1
0
0
J/
K
g
°
C
P
o
i
ss
o
n
’
s
r
a
t
i
o
,
0
.
4
2
El
a
s
t
i
c
m
o
d
u
l
u
s
, E
1
0
3
4
M
P
a
M
e
l
t
d
e
n
s
i
t
y
,
0
.
7
7
5
1
g
/
C
m
3
Th
e
r
m
a
l
c
o
n
d
u
c
t
i
v
i
t
y
,
K
0
.
1
7
W
/
m°
C
Fig
u
r
e
1
.
Po
ly
p
r
o
p
y
len
e
s
tr
u
ct
u
r
e
[
5
]
2
.
1
.
2
.
B
a
m
bo
o
f
ibers
B
am
b
o
o
f
ib
er
s
ar
e
g
ain
in
g
att
en
tio
n
f
r
o
m
r
esear
ch
er
s
d
u
e
t
o
th
e
b
en
ef
its
o
f
th
is
u
n
co
m
m
o
n
ly
u
s
ed
m
ater
ial.
B
am
b
o
o
is
a
r
e
n
ewa
b
le
an
d
f
u
lly
r
ec
y
clab
le
m
ater
ial
th
at
h
as
a
cy
lin
d
r
ical
s
h
ap
e,
is
h
o
llo
w,
an
d
is
s
ep
ar
ated
b
y
k
n
o
ts
[
6
]
.
I
t
is
ch
ar
ac
ter
ized
b
y
g
r
ea
t
r
ig
id
it
y
an
d
f
lex
ib
ilit
y
.
I
ts
g
r
o
wth
i
s
v
er
y
f
ast,
ar
o
u
n
d
2
0
cm
p
er
d
ay
an
d
2
0
m
in
6
m
o
n
th
s
.
I
t
m
atu
r
es
in
4
y
ea
r
s
b
u
t
ca
n
b
e
ex
p
lo
ited
af
ter
ju
s
t
o
n
e
y
ea
r
o
f
g
r
o
wth
[
7
]
.
B
io
-
co
m
p
o
s
ites
f
r
o
m
b
am
b
o
o
f
ib
e
r
s
ar
e
m
ain
ly
u
s
ed
in
th
e
co
n
s
tr
u
ctio
n
f
ield
,
s
u
ch
as
s
u
p
p
o
r
ts
,
an
d
in
th
e
au
to
m
o
tiv
e
s
ec
to
r
f
o
r
v
e
n
ee
r
s
o
r
p
a
r
ts
s
u
ch
as
s
teer
in
g
wh
ee
ls
.
Fig
u
r
e
2
s
h
o
ws
a
s
am
p
le
o
f
d
if
f
e
r
en
t
b
r
ee
d
s
o
f
b
am
b
o
o
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
14
,
No
.
6
,
Dec
em
b
e
r
20
24
:
6
9
0
4
-
6
9
1
2
6906
Fig
u
r
e
2
.
A
s
am
p
le
o
f
d
if
f
er
en
t b
am
b
o
o
[
8
]
Mo
r
e
th
an
1
,
2
0
0
b
a
m
b
o
o
s
p
ec
ies
h
av
e
b
ee
n
id
en
tifie
d
wo
r
ld
wid
e
an
d
ea
ch
s
p
ec
i
es
h
as
its
m
ec
h
an
ical
p
r
o
p
er
ties
.
T
h
is
d
if
f
er
en
ce
is
ca
u
s
ed
b
y
th
e
g
r
o
wth
m
ed
iu
m
o
f
th
e
b
a
m
b
o
o
,
i
ts
g
eo
m
etr
ic
s
h
ap
e
(
d
iam
eter
o
f
th
e
cy
li
n
d
er
,
th
e
h
o
llo
w
.
.
.
)
,
an
d
o
th
er
s
.
T
h
e
p
r
o
ce
s
s
o
f
ex
tr
ac
tin
g
f
i
b
er
s
b
eg
in
s
with
ch
o
o
s
in
g
clea
n
s
tem
s
th
at
d
o
n
o
t
co
n
t
ain
m
o
ld
o
r
d
r
y
p
o
r
tio
n
s
.
Fr
o
m
th
e
3
5
cm
lo
n
g
s
ec
tio
n
s
o
b
tain
ed
b
y
cu
ttin
g
b
etwe
en
th
e
b
am
b
o
o
k
n
o
ts
,
th
e
f
ib
er
s
ar
e
m
an
u
ally
ex
tr
ac
te
d
an
d
b
o
iled
in
wate
r
f
o
r
5
h
o
u
r
s
an
d
th
e
n
d
r
ied
at
1
0
0
°C
in
air
[
7
]
.
Fig
u
r
e
3
s
h
o
ws th
e
s
tep
s
f
o
llo
wed
to
o
b
tai
n
b
am
b
o
o
f
i
b
er
s
.
Fig
u
r
e
3
.
T
h
e
s
tep
s
f
o
llo
wed
t
o
h
av
e
t
h
e
f
ib
er
s
b
e
g
in
with
c
h
o
o
s
in
g
th
e
ag
r
ee
d
b
am
b
o
o
p
l
an
t,
cu
ttin
g
th
e
s
ec
tio
n
s
,
th
en
ex
tr
ac
tin
g
t
h
e
f
i
b
er
s
.
T
h
e
f
i
b
er
s
ar
e
th
en
b
o
ile
d
,
th
en
d
r
ied
[
7
]
2
.
1
.
3
.
Co
m
po
s
it
e
pro
ce
s
s
ing
T
o
d
ete
r
m
in
e
th
e
m
ec
h
an
ical
ch
ar
ac
ter
is
tics
o
f
o
u
r
n
ew
m
a
ter
ial
an
d
to
s
tu
d
y
th
e
e
f
f
ec
t
o
f
b
am
b
o
o
f
ib
er
s
o
n
p
o
ly
p
r
o
p
y
len
e,
t
h
ese
two
co
m
p
o
n
en
ts
wer
e
m
ix
ed
at
1
8
0
°C
in
an
in
ter
n
al
m
ix
er
(
Haa
k
eRh
eo
m
ix
)
,
an
d
f
o
r
1
0
m
in
u
tes
at
a
s
p
ee
d
o
f
5
0
r
p
m
.
T
h
e
r
ate
o
f
n
atu
r
al
f
ib
er
s
was
s
et
at
1
5
%,
to
th
e
n
g
r
in
d
an
d
d
r
y
th
e
f
ib
er
/PP
m
ix
tu
r
es.
t
h
e
b
io
co
m
p
o
s
ite
was
m
o
ld
ed
in
an
in
jec
tio
n
m
ac
h
i
n
e
at
2
0
0
°C
(
Kr
au
s
s
Ma
f
f
ei
KM
5
0
-
1
8
0
C
X)
[
7
]
.
2
.
2
.
M
et
ho
ds
Sev
er
al
f
ac
to
r
s
ca
n
in
f
l
u
en
ce
th
e
d
eter
m
in
atio
n
o
f
th
e
m
ec
h
an
ical
ch
ar
ac
ter
is
tics
o
f
b
io
c
o
m
p
o
s
ites
,
y
et
ev
en
if
we
wo
r
k
u
n
d
e
r
th
e
s
am
e
co
n
d
itio
n
s
,
th
e
m
eth
o
d
s
u
s
ed
to
ca
lcu
late
th
e
p
r
o
p
er
ties
o
f
th
ese
n
ew
m
ater
ials
,
d
o
n
o
t
n
ec
ess
ar
ily
g
iv
e
th
e
s
am
e
r
esu
lts
.
E
ac
h
m
eth
o
d
u
s
es
an
d
is
b
ased
o
n
th
eo
r
em
s
an
d
ass
u
m
p
tio
n
s
v
er
y
d
if
f
er
en
t
f
r
o
m
th
e
o
th
er
s
,
th
is
is
wh
at
ca
u
s
es
d
if
f
er
en
t
o
u
tp
u
ts
;
h
o
wev
er
,
r
esear
ch
h
as
b
ee
n
ab
le
to
s
h
o
w
th
at
th
is
d
if
f
e
r
e
n
ce
is
ap
p
r
o
x
im
ate.
B
etwe
en
ex
p
er
im
en
t,
h
o
m
o
g
en
izatio
n
,
an
d
f
in
ite
elem
e
n
t
m
eth
o
d
s
th
e
r
e
is
a
ce
r
tain
d
e
g
r
ee
o
f
e
r
r
o
r
,
an
d
th
is
is
wh
at
m
ak
es
th
e
r
esu
lt
ev
en
m
o
r
e
r
elativ
e.
A
n
d
t
h
e
d
if
f
er
en
ce
is
in
th
e
p
r
ec
is
io
n
a
n
d
also
in
th
e
ca
lc
u
latio
n
tim
e
.
Sm
ar
t
s
o
lu
tio
n
s
h
a
v
e
c
o
m
e
to
clo
s
e
t
h
is
g
ap
an
d
d
eliv
er
m
o
r
e
ac
cu
r
ate
r
esu
lts
in
m
in
im
u
m
tim
e.
I
n
t
h
is
wo
r
k
,
we
will
u
s
e
th
e
h
o
m
o
g
e
n
i
za
tio
n
m
eth
o
d
an
d
m
o
r
e
p
r
ec
is
ely
th
e
Mo
r
i
T
a
n
ak
a
m
o
d
el
to
d
eter
m
in
e
t
h
e
elastic
b
eh
av
io
r
o
f
p
o
ly
p
r
o
p
y
len
e
lo
a
d
ed
with
n
atu
r
al
f
ib
er
s
.
Usi
n
g
th
e
r
esu
lts
o
f
th
is
m
o
d
el,
we
will
b
u
il
d
a
d
ataset
f
r
o
m
wh
ich
we
wi
ll
f
ee
d
an
ar
tific
ial
n
eu
r
al
n
etwo
r
k
to
p
r
ed
ict
th
e
e
last
icity
o
f
th
e
b
io
co
m
p
o
s
ite
s
tu
d
ied
.
2
.
2
.
1
.
Da
t
a
s
et
/M
o
ri
T
a
na
k
a
’
s
m
o
del
Usi
n
g
th
e
Mo
r
i
T
an
ak
a
m
o
d
e
l
in
Fig
u
r
e
4
,
we
ca
lcu
late
th
e
m
o
d
u
lu
s
o
f
elasticity
o
f
th
e
d
if
f
er
en
t
b
io
co
m
p
o
s
ites
.
B
y
in
tr
o
d
u
cin
g
Yo
u
n
g
'
s
m
o
d
u
lu
s
o
f
t
h
e
p
o
l
y
p
r
o
p
y
len
e
a
n
d
th
at
o
f
th
e
d
if
f
er
en
t
b
io
f
iller
s
,
we
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
I
mp
leme
n
ta
tio
n
o
f a
r
tifi
cia
l in
tellig
en
ce
in
th
e
p
r
ed
ictio
n
o
f
th
e
ela
s
tic
ch
a
r
a
cteris
tics
o
f
…
(
Zin
eb
La
a
b
id
)
6907
h
av
e
as a
n
o
u
tp
u
t o
f
th
is
m
o
d
e
l th
e
b
io
co
m
p
o
s
ites
'
Yo
u
n
g
's
m
o
d
u
lu
s
.
As a
r
esu
lt,
we
wer
e
ab
le
to
co
n
s
tr
u
ct
a
d
ataset
in
th
e
f
o
r
m
o
f
a
n
u
m
er
ical
tab
le
co
n
tain
in
g
th
e
d
if
f
er
en
t
v
alu
es
o
f
t
h
e
in
p
u
ts
an
d
o
u
tp
u
ts
o
f
th
e
m
o
d
el
ch
o
s
en
.
Mo
r
i T
a
n
ak
a
m
o
d
el
is
o
n
e
f
o
r
m
o
f
th
e
E
s
h
elb
y
s
o
lu
t
io
n
[
9
]
.
Fig
u
r
e
4
.
Me
an
f
ield
h
o
m
o
g
en
izatio
n
m
eth
o
d
[5
]
2
.
2
.
3
.
Art
if
icia
l
neura
l net
wo
rk
Mo
d
elin
g
a
r
e
g
r
ess
io
n
n
e
u
r
al
n
etwo
r
k
r
eq
u
ir
es
s
ev
er
al
elem
en
ts
to
b
e
d
e
f
in
ed
,
s
u
ch
as
th
e
o
b
jectiv
es
o
f
th
is
p
u
r
p
o
s
e,
th
e
d
ataset
,
t
h
e
tar
g
et,
an
d
o
t
h
er
s
.
As
ex
p
lain
ed
in
th
e
p
r
ev
i
o
u
s
s
ec
tio
n
2
.
2
.
1
,
we
u
s
ed
th
e
Ma
r
itan
ak
a
m
eth
o
d
to
c
o
n
s
tr
u
ct
o
u
r
d
ataset
.
T
h
is
d
ataset
will
s
er
v
e
as
in
p
u
t
d
ata
i
n
o
u
r
ar
tific
ial
n
eu
r
al
n
etwo
r
k
.
I
n
th
is
ar
ticle,
we
ai
m
to
p
r
e
d
ict
th
e
elastic
b
eh
av
i
o
r
o
f
b
io
co
m
p
o
s
ites
,
s
p
ec
if
ically
th
e
elasticity
o
f
p
o
ly
p
r
o
p
y
le
n
e
wh
e
n
it
is
lo
a
d
ed
with
1
5
%
b
io
ch
a
r
g
e
[
1
0
]
.
T
h
er
e
f
o
r
e,
a
p
r
elim
in
a
r
y
an
aly
s
is
o
f
th
e
in
p
u
t
elem
en
ts
was
co
n
d
u
cted
in
th
is
r
eg
ar
d
to
d
ef
in
e
th
e
ch
ar
ac
ter
is
tics
o
f
th
e
m
o
d
el
to
b
e
s
t
u
d
ied
.
T
h
e
d
ataset
d
er
iv
ed
f
r
o
m
Mo
r
i
T
an
ak
a'
s
m
o
d
el
ex
is
ts
in
d
ig
ital
f
o
r
m
a
t,
in
d
icatin
g
th
at
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
(
C
NNs),
wh
ich
ar
e
d
esig
n
ed
f
o
r
im
ag
e
p
r
o
ce
s
s
in
g
[
1
1
]
,
ar
e
n
o
t
ap
p
licab
le.
I
n
s
tead
,
r
eg
r
ess
io
n
n
eu
r
al
n
etwo
r
k
s
(
R
NNs)
will
b
e
em
p
lo
y
ed
.
R
NNs
o
p
er
ate
b
y
p
r
e
s
er
v
in
g
a
lay
er
'
s
o
u
tp
u
t
an
d
f
ee
d
in
g
it
b
ac
k
to
th
e
in
p
u
t
lay
er
to
g
en
e
r
ate
th
e
lay
er
'
s
o
u
tp
u
t.
T
h
ey
co
n
s
is
t
o
f
lay
er
s
th
at
allo
w
in
f
o
r
m
atio
n
p
r
o
ce
s
s
in
g
in
b
o
th
f
o
r
war
d
a
n
d
b
ac
k
war
d
d
ir
ec
ti
o
n
s
,
as d
ep
icted
in
Fig
u
r
e
5
.
Fig
u
r
e
5
.
Op
e
r
atin
g
p
r
in
cip
le
o
f
f
ee
d
-
f
o
r
war
d
b
ac
k
p
r
o
p
ag
ati
o
n
[
1
2
]
Feed
-
f
o
r
war
d
b
ac
k
p
r
o
p
ag
atio
n
(
FF
B
P)
is
th
e
m
o
s
t
co
m
m
o
n
a
n
d
wid
el
y
u
s
ed
ar
ch
i
tectu
r
e
in
p
r
ed
ictio
n
b
ased
o
n
a
d
ig
ital
d
ataset.
I
t
h
as
p
r
o
v
en
to
b
e
e
f
f
icien
t
an
d
h
as
g
iv
en
b
etter
r
esu
lts
in
d
iab
etes
p
r
ed
ictio
n
,
co
m
p
a
r
ed
to
o
th
e
r
b
ac
k
p
r
o
p
ag
atio
n
ar
ch
itectu
r
es
[
1
3
]
,
esp
ec
ially
wh
en
it
i
s
tr
ain
ed
with
th
e
L
ev
er
b
eg
-
Ma
r
q
u
a
r
t
(
L
M)
alg
o
r
ith
m
.
T
h
e
L
M
alg
o
r
ith
m
r
e
p
r
esen
ts
th
e
m
o
s
t
ef
f
icien
t
m
eth
o
d
to
ac
cu
r
ately
tr
ain
th
e
FF
B
P
[
1
4
]
.
T
h
e
r
ef
o
r
e,
an
d
s
in
ce
we
a
r
e
d
ea
lin
g
w
ith
a
n
o
n
-
lin
ea
r
r
e
g
r
ess
io
n
p
r
o
b
lem
,
t
h
e
n
etwo
r
k
ch
o
s
en
is
th
e
FF
B
P tr
ain
ed
b
y
th
e
L
M.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
14
,
No
.
6
,
Dec
em
b
e
r
20
24
:
6
9
0
4
-
6
9
1
2
6908
T
h
e
ch
o
s
en
n
etwo
r
k
th
en
c
o
n
s
is
t
s
o
f
th
r
ee
lay
er
s
,
its
to
p
o
lo
g
y
is
in
th
is
f
o
r
m
2
-
h
-
1
.
T
h
e
n
u
m
b
er
2
r
ep
r
esen
ts
n
eu
r
o
n
s
in
th
e
in
p
u
t
lay
er
.
Nu
m
b
e
r
1
m
ea
n
s
th
at
we
h
av
e
a
s
in
g
le
n
eu
r
o
n
i
n
o
u
tp
u
t
.
As
f
o
r
th
e
h
id
d
en
lay
er
(
h
)
,
we
test
ed
it
u
s
in
g
s
ev
er
al
n
eu
r
o
n
s
r
an
g
in
g
f
r
o
m
3
to
2
0
.
As
a
r
esu
lt
o
f
t
h
e
test
,
we
d
ed
u
ce
d
th
at
th
e
m
o
s
t
o
p
tim
al
n
u
m
b
er
o
f
n
eu
r
o
n
s
th
at
h
as
th
e
m
ax
im
u
m
p
er
f
o
r
m
an
ce
is
1
5
,
h
en
ce
t
h
e
to
p
o
lo
g
y
o
f
o
u
r
n
etwo
r
k
2
-
h
(
1
5
)
-
1
as sh
o
wn
b
y
th
e
ar
c
h
itectu
r
e
in
Fig
u
r
e
6
.
Fig
u
r
e
6
.
T
h
e
2
-
h
(
1
5
)
-
1
n
eu
r
a
l n
etwo
r
k
s
tu
d
ied
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
Af
ter
h
av
in
g
in
tr
o
d
u
ce
d
o
u
r
d
ataset
in
th
e
A
NN
,
an
d
af
ter
h
av
in
g
tr
ain
e
d
it,
th
e
r
esu
lts
ap
p
ea
r
in
g
in
th
e
tr
ain
in
g
win
d
o
w
ar
e
p
er
f
o
r
m
an
ce
an
d
r
eg
r
ess
io
n
p
lo
ts
.
T
h
e
f
ir
s
t
illu
s
tr
ates
p
er
f
o
r
m
an
ce
ag
ai
n
s
t
th
e
n
u
m
b
er
o
f
iter
atio
n
s
.
E
ac
h
o
f
th
e
en
tire
tr
ain
in
g
s
ets
r
ep
r
esen
ts
an
e
r
a.
At
ea
ch
ep
o
c
h
,
t
h
e
m
o
d
el
ad
ju
s
ts
th
e
weig
h
ts
au
to
m
atica
lly
to
g
r
a
d
u
ally
co
n
v
er
g
e
to
war
d
s
an
o
p
tim
al
s
et
o
f
r
esu
lts
.
Sev
er
al
p
er
io
d
s
a
r
e
o
f
ten
co
m
p
u
ls
o
r
y
b
ef
o
r
e
th
e
t
o
p
o
f
t
h
e
tr
ain
in
g
.
Sin
ce
th
e
d
eter
m
in
atio
n
o
f
m
ec
h
an
ical
ch
ar
ac
ter
is
tics
is
a
r
eg
r
ess
io
n
p
r
o
b
lem
,
we
ch
o
o
s
e
th
e
m
ea
n
s
q
u
ar
ed
er
r
o
r
as
th
e
o
b
jecti
v
e
f
u
n
ctio
n
,
wh
ich
is
d
en
o
t
ed
m
ea
n
s
q
u
ar
e
d
er
r
o
r
(
MS
E
)
[
1
5
]
.
T
h
e
m
ea
n
d
if
f
er
en
ce
b
etwe
en
tar
g
ets
a
n
d
r
esu
lts
is
k
n
o
wn
as
th
e
m
ea
n
s
q
u
ar
ed
er
r
o
r
,
it
r
ep
r
esen
ts
o
n
e
o
f
t
h
e
two
elem
en
ts
th
at
in
d
icate
th
at
th
e
ch
o
s
en
s
m
ar
t m
o
d
el
is
p
er
f
o
r
m
in
g
.
T
h
e
s
ec
o
n
d
in
d
icato
r
is
r
eg
r
ess
io
n
,
wh
ich
is
m
ain
ly
u
s
ed
f
o
r
v
alid
atio
n
o
f
t
h
e
m
o
d
el
u
n
d
er
s
tu
d
y
an
d
ju
s
t
if
ies
th
e
lin
k
b
etwe
en
o
b
jectiv
es
an
d
r
esu
lts
[
1
6
]
.
Ou
r
n
etwo
r
k
is
m
ad
e
u
p
o
f
two
in
p
u
t
lay
e
r
s
wh
ich
r
esp
ec
tiv
ely
r
ep
r
esen
t
th
e
Y
o
u
n
g
m
o
d
u
lu
s
o
f
p
o
ly
p
r
o
p
y
le
n
e
an
d
b
io
ch
ar
g
e
,
1
5
n
eu
r
o
n
s
in
t
h
e
h
id
d
en
,
a
n
d
o
n
e
o
u
tp
u
t
lay
e
r
wh
ich
r
e
p
r
esen
ts
th
e
Yo
u
n
g
m
o
d
u
lu
s
o
f
th
e
b
i
o
co
m
p
o
s
ite
.
T
ab
le
2
r
e
p
r
esen
ts
th
e
p
ar
am
et
er
s
o
f
th
e
ch
o
s
en
n
etwo
r
k
.
T
ab
le
2
.
Par
am
eter
s
o
f
th
e
n
et
wo
r
k
ch
o
s
en
D
a
t
a
d
i
v
i
si
o
n
Tr
a
i
n
i
n
g
P
e
r
f
o
r
ma
n
c
e
C
a
l
c
u
l
a
t
i
o
n
s
R
a
n
d
o
m
(
)
Le
v
e
n
b
e
r
g
–
M
a
r
q
u
a
r
d
t
(
)
M
e
a
n
sq
u
a
r
e
d
e
r
r
o
r
(
M
S
E
)
R
e
g
r
e
ssi
o
n
c
o
e
f
f
i
c
i
e
n
t
(
R
)
M
EX
T
h
e
ac
tiv
atin
g
f
u
n
ctio
n
o
f
th
e
s
e
n
eu
r
o
n
s
is
.
T
h
is
ac
tiv
atio
n
f
u
n
ctio
n
ap
p
ea
r
s
to
b
e
th
e
b
est
co
m
p
r
o
m
is
e
b
etwe
en
th
e
d
if
f
er
en
t
ac
tiv
atio
n
f
u
n
ctio
n
s
,
s
ig
m
o
id
,
an
d
lin
ea
r
[
1
7
]
.
T
h
is
h
y
p
e
r
b
o
lic
ta
n
g
en
t
f
u
n
ctio
n
is
u
s
ed
t
o
ac
tiv
ate
th
e
n
etwo
r
k
o
p
er
atio
n
,
an
d
it is
ch
ar
ac
ter
ized
as
(
1
)
.
(
)
=
ℎ
(
)
=
{
2
1
+
−
2
−
1
}
(
1
)
T
h
e
r
esp
o
n
s
e
d
ata
f
r
o
m
th
e
Mo
r
i
-
T
an
ak
a
m
o
d
el
is
u
s
ed
a
s
in
p
u
t
to
th
e
m
o
d
el
c
h
o
s
en
t
o
tr
ain
it
to
p
r
ed
ict
th
e
elastic
b
eh
av
io
r
s
o
f
b
io
co
m
p
o
s
ites
.
T
h
e
d
ata
was
g
r
o
u
p
ed
in
to
d
if
f
er
en
t
s
ets;
7
0
%
f
o
r
tr
ain
in
g
,
1
5
%
f
o
r
test
in
g
,
an
d
1
5
%
f
o
r
v
alid
atio
n
.
T
h
e
ar
tific
ial
n
eu
r
al
n
etwo
r
k
(
ANN)
was
tr
ain
ed
th
r
o
u
g
h
iter
ativ
e
test
in
g
an
d
er
r
o
r
ch
ec
k
i
n
g
to
m
in
im
ize
s
q
u
ar
ed
er
r
o
r
wh
il
e
m
ax
im
izin
g
r
eg
r
ess
io
n
v
al
u
es
[
1
8
]
.
T
o
av
o
i
d
o
v
er
f
itti
n
g
,
th
er
e
ar
e
s
ev
er
al
m
eth
o
d
s
to
f
o
llo
w
to
en
s
u
r
e
th
at
th
e
m
o
d
el
c
o
n
v
e
r
g
es
well.
W
e
ca
n
n
o
te
th
e
cr
o
s
s
-
v
alid
atio
n
[
1
9
]
,
th
e
lim
i
tatio
n
o
f
th
e
n
u
m
b
er
o
f
n
e
u
r
o
n
s
o
r
th
e
lim
itatio
n
o
f
t
h
e
tr
ai
n
in
g
d
u
r
atio
n
[
2
0
]
.
I
n
o
u
r
ca
s
e,
we
u
s
ed
1
5
n
eu
r
o
n
s
,
th
u
s
a
lim
ited
n
u
m
b
e
r
,
an
d
in
ad
d
itio
n
to
t
h
at,
in
ea
ch
tr
a
in
in
g
,
we
k
ep
t
t
h
e
m
o
d
el
an
d
its
ch
ar
ac
ter
is
tics
,
co
n
s
eq
u
en
tly
,
t
h
is
allo
wed
u
s
to
av
o
id
o
v
e
r
f
itti
n
g
[
2
1
]
.
Fig
u
r
e
7
s
h
o
ws
u
s
th
e
tr
ain
in
g
s
tate
Data
b
y
th
e
f
ee
d
-
f
o
r
war
d
b
ac
k
p
r
o
p
ag
atio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
I
mp
leme
n
ta
tio
n
o
f a
r
tifi
cia
l in
tellig
en
ce
in
th
e
p
r
ed
ictio
n
o
f
th
e
ela
s
tic
ch
a
r
a
cteris
tics
o
f
…
(
Zin
eb
La
a
b
id
)
6909
Fig
u
r
e
7
.
T
r
ain
in
g
s
tate
d
ata
o
f
n
eu
r
al
n
etwo
r
k
Usi
n
g
1
,
0
0
0
r
ep
ea
ted
ep
o
c
h
s
o
f
tr
ain
in
g
,
th
e
m
ea
n
s
q
u
ar
e
d
er
r
o
r
s
(
MSE
)
p
r
o
d
u
ce
d
b
y
th
e
f
ee
d
-
f
o
r
war
d
b
ac
k
p
r
o
p
a
g
atio
n
a
n
d
th
e
r
eg
r
ess
io
n
ar
e
as
s
h
o
wn
r
esp
ec
tiv
ely
in
F
ig
u
r
es
8
a
n
d
9
.
T
h
e
th
r
ee
cu
r
v
es,
test
,
tr
ain
in
g
,
an
d
v
alid
atio
n
,
f
o
llo
w
ap
p
r
o
x
im
atel
y
th
e
s
a
m
e
p
ath
a
n
d
g
a
v
e
1
5
.
7
2
as
th
e
b
est
v
alid
atio
n
p
er
f
o
r
m
an
ce
at
7
e
p
o
ch
s
.
T
h
e
r
eg
r
ess
io
n
co
ef
f
icien
t
f
o
r
p
o
ly
p
r
o
p
y
len
e
lo
a
d
ed
with
1
5
%
b
am
b
o
o
f
i
b
er
is
0
.
9
8
,
a
p
p
r
o
ac
h
in
g
1
,
i
n
d
icatin
g
th
e
ef
f
icien
c
y
an
d
ef
f
ec
tiv
e
n
ess
o
f
o
u
r
m
o
d
el
[
2
2
]
.
Fig
u
r
e
8
.
Valid
atio
n
p
er
f
o
r
m
a
n
ce
Fig
u
r
e
9
.
R
eg
r
ess
io
n
an
al
y
s
is
W
e
f
o
llo
wed
th
e
r
eg
r
ess
io
n
o
f
th
e
th
r
ee
s
ets
(
tr
ain
in
g
,
v
alid
a
tio
n
,
an
d
test
)
u
s
ed
in
th
e
tr
ain
in
g
o
f
th
e
p
r
ed
ictiv
e
m
o
d
el.
Fig
u
r
e
1
0
s
h
o
ws th
at
th
eir
r
eg
r
ess
io
n
co
ef
f
icien
ts
co
n
v
er
g
e
to
war
d
s
1
,
w
h
ich
f
u
r
th
e
r
p
r
o
v
es
th
at
o
u
r
n
etwo
r
k
is
p
er
f
o
r
m
in
g
well
[
2
3
]
.
T
h
e
co
r
r
elatio
n
b
etwe
en
th
e
p
r
e
d
icted
v
al
u
es
f
r
o
m
o
u
r
in
tellig
e
n
t
m
o
d
el
an
d
th
e
tar
g
et
v
alu
es
ten
d
s
to
1
(
0
.
9
8
)
,
wh
ich
m
ea
n
s
th
at
th
e
r
elatio
n
s
h
ip
b
etwe
en
th
ese
two
v
ar
iab
les
is
alm
o
s
t
lin
ea
r
[
2
4
]
.
So
,
o
u
r
p
r
ed
ictiv
e
m
o
d
el
was
ab
le
to
f
o
llo
w
th
e
tr
ain
i
n
g
a
n
d
was
ab
l
e
to
g
iv
e
its
o
u
tp
u
t
alm
o
s
t
id
en
tical
v
alu
es
s
im
ilar
to
t
h
o
s
e
o
f
th
e
T
ar
g
et.
I
n
d
ee
d
,
F
ig
u
r
e
1
1
s
h
o
ws
u
s
th
at
th
e
two
cu
r
v
es
(
tar
g
et
an
d
m
o
d
el
o
u
t
p
u
t
r
esp
ec
tiv
ely
)
f
o
llo
w
th
e
s
am
e
tr
ajec
t
o
r
y
a
n
d
th
at
th
e
d
if
f
er
e
n
ce
b
etwe
e
n
th
e
two
is
alm
o
s
t
ze
r
o
.
Hen
ce
th
e
ac
cu
r
ac
y
o
f
t
h
e
r
esp
o
n
s
e
o
f
o
u
r
p
r
ed
ictiv
e
m
o
d
el
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
14
,
No
.
6
,
Dec
em
b
e
r
20
24
:
6
9
0
4
-
6
9
1
2
6910
Fig
u
r
e
1
0
.
Valid
atio
n
p
e
r
f
o
r
m
an
ce
o
f
tr
ai
n
,
v
alid
atio
n
,
an
d
t
est d
ata
Fig
u
r
e
1
1
.
C
o
r
r
elatio
n
b
etwe
en
d
ataset
v
alu
es a
n
d
t
h
e
p
r
e
d
ictiv
e
v
alu
es
T
h
e
d
if
f
er
en
t
Yo
u
n
g
'
s
m
o
d
u
lu
s
o
f
p
o
ly
p
r
o
p
y
len
e,
b
am
b
o
o
f
ib
er
s
,
a
n
d
o
u
r
b
io
c
o
m
p
o
s
ite
ar
e
r
ep
r
esen
ted
in
T
a
b
le
3
-
wh
ich
co
n
tain
s
8
5
%
o
f
PP
r
ein
f
o
r
ce
d
b
y
1
5
%
o
f
b
a
m
b
o
o
f
ib
er
s
,
o
b
tain
ed
f
r
o
m
Mo
r
i
T
an
ak
a
a
n
d
th
e
in
tellig
en
t
m
o
d
els.
Fro
m
t
h
is
s
tu
d
y
,
it
wa
s
p
r
o
v
e
n
th
at
t
h
e
p
r
e
d
ictio
n
o
f
Yo
u
n
g
'
s
m
o
d
u
lu
s
u
s
in
g
ar
tific
ial
in
tellig
en
ce
g
av
e
an
ef
f
icien
t
r
esu
lt
g
iv
e
n
th
at
th
e
er
r
o
r
is
m
in
im
al,
a
n
d
th
is
with
b
etter
v
alid
atio
n
o
f
p
er
f
o
r
m
a
n
ce
[
2
5
]
.
T
ab
le
3
h
ig
h
lig
h
ts
an
in
cr
ea
s
e
in
t
h
e
s
tiff
n
ess
o
f
1
5
%
b
am
b
o
o
b
io
-
f
illed
PP
,
m
ar
k
ed
b
y
a
n
in
cr
ea
s
e
in
Yo
u
n
g
'
s
m
o
d
u
lu
s
o
f
5
5
0
.
3
MPa
co
m
p
ar
ed
to
PP
with
o
u
t
b
io
-
f
illi
n
g
.
T
h
is
o
b
s
er
v
atio
n
was
ex
p
ec
ted
,
as
th
e
s
tiff
n
ess
o
f
th
e
b
io
-
f
iller
is
h
i
g
h
er
t
h
an
t
h
at
o
f
th
e
PP
m
atr
ix
.
T
h
e
d
if
f
e
r
en
ce
b
etwe
en
t
h
e
n
u
m
e
r
ical
v
alu
es
an
d
th
e
r
esu
lts
f
r
o
m
th
e
ar
tific
ial
in
tellig
en
ce
to
o
l
is
a
p
p
r
o
x
im
ately
3
.
6
8
e
-
0
6
,
in
d
icatin
g
s
atis
f
ac
to
r
y
ac
cu
r
ac
y
.
T
ab
le
3
.
D
if
f
e
r
en
t
Yo
u
n
g
m
o
d
u
lu
s
s
tu
d
ied
M
a
t
e
r
i
a
l
PP
B
a
m
b
o
o
B
i
o
c
o
m
p
o
si
t
e
e
x
p
e
r
i
e
n
c
e
B
i
o
c
o
m
p
o
si
t
e
M
o
r
i
Ta
n
a
k
a
B
i
o
c
o
m
p
o
si
t
e
ANN
Y
o
u
n
g
M
o
d
u
l
u
s
(
M
P
a
)
1
0
3
4
1
4
6
0
0
1
5
7
2
.
9
1
5
8
4
.
3
1
5
8
4
.
2
9
9
4.
CO
NCLU
SI
O
N
I
n
th
is
ar
ticle,
we
h
a
v
e
p
r
o
p
o
s
ed
a
f
ee
d
f
o
r
war
d
b
ac
k
p
r
o
p
ag
atio
n
wh
ic
h
r
e
p
r
esen
ts
a
f
o
r
m
o
f
th
e
Dee
p
n
eu
r
al
n
etwo
r
k
m
eth
o
d
to
p
r
e
d
ict
th
e
p
o
ly
p
r
o
p
y
len
e
lo
ad
ed
with
n
atu
r
al
f
ib
er
s
el
asti
city
.
Ou
r
Feed
-
f
o
r
war
d
b
ac
k
-
p
r
o
p
ag
atio
n
tr
ai
n
ed
b
y
th
e
L
M
alg
o
r
ith
m
ca
n
ef
f
icien
tly
an
d
ac
cu
r
ately
p
r
ed
ict
th
e
elastic
ity
o
f
th
e
in
n
o
v
ated
m
ater
ial
b
y
f
ee
d
in
g
o
u
r
n
etwo
r
k
b
y
d
ataset
co
m
p
r
is
in
g
d
i
f
f
er
en
t
Yo
u
n
g
'
s
m
o
d
u
lu
s
co
r
r
esp
o
n
d
in
g
t
o
v
ar
i
o
u
s
b
io
f
iller
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
I
mp
leme
n
ta
tio
n
o
f a
r
tifi
cia
l in
tellig
en
ce
in
th
e
p
r
ed
ictio
n
o
f
th
e
ela
s
tic
ch
a
r
a
cteris
tics
o
f
…
(
Zin
eb
La
a
b
id
)
6911
T
h
is
in
tellig
en
t
m
eth
o
d
is
ea
s
y
to
u
s
e,
an
d
it
h
as
b
r
o
a
d
a
p
p
licatio
n
p
er
s
p
ec
tiv
es
in
th
e
m
o
d
elin
g
o
f
b
io
-
co
m
p
o
s
ite
m
ater
ials
.
I
t
m
ig
h
t
b
e
n
o
ticed
th
at
o
n
ly
8
5
%
o
f
p
o
ly
p
r
o
p
y
len
e
h
as
b
ee
n
tr
ea
ted
as
an
illu
s
tr
atio
n
o
f
t
h
is
s
m
ar
t
m
eth
o
d
in
th
is
ar
ticle.
Ho
we
v
er
,
w
e
ca
n
e
x
ten
d
th
is
m
eth
o
d
to
d
eter
m
in
e
th
e
elastic
b
eh
av
io
r
o
f
b
io
co
m
p
o
s
ites
at
d
if
f
er
en
t
m
i
x
in
g
p
e
r
ce
n
tag
es.
Fu
r
th
er
m
o
r
e
,
we
ca
n
ap
p
ly
th
i
s
p
r
o
p
o
s
ed
m
et
h
o
d
to
ca
lcu
late
an
d
d
eter
m
in
e
o
th
er
th
er
m
o
m
ec
h
a
n
ical
ch
ar
ac
te
r
is
tics
o
f
b
io
co
m
p
o
s
ites
,
we
c
an
cite
th
e
Po
is
s
o
n
r
atio
,
th
e
th
er
m
al
c
o
n
d
u
ctio
n
co
ef
f
icien
t,
th
e
f
atig
u
e
r
esis
tan
ce
,
an
d
d
o
in
g
all
o
f
th
is
in
th
e
s
h
o
r
test
am
o
u
n
t
o
f
tim
e
an
d
with
th
e
least a
m
o
u
n
t o
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er
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.
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R
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NC
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S
[
1
]
A
.
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k
h
d
a
r
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M
.
J
a
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.
[
2
]
C
.
N
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s
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s,
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.
K
o
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o
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a
n
d
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.
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a
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s,
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5
.
[
3
]
H
.
Essa
b
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,
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.
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mm
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i
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me
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mat
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.
[
4
]
T.
Ja
mn
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k
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l
.
,
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o
w
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.
[
5
]
A
.
M
o
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m
e
n
,
M
.
Jamm
o
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k
h
,
L.
Za
h
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n
d
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.
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[
6
]
P
.
To
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ssa
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,
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p
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(
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)
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h
.
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.
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ssert
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0
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0
.
[
7
]
P
.
N
.
Tr
i
,
A
.
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t
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& Te
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t
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[
8
]
Le
a
,
“
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a
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n
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g
f
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s
:
B
a
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a
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s:
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c
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l
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2
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,
2
0
2
1
)
.
[
9
]
X
.
W
a
n
g
,
K
.
S
u
n
,
J.
S
h
a
o
,
a
n
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.
M
a
,
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[
1
0
]
A
.
Ja
v
a
d
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a
n
,
I
.
F
.
C
.
S
m
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,
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.
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.
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.
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b
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,
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M
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.
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ma
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.
[
1
1
]
L.
T
.
N
g
h
i
a
,
Q
.
H
.
A
n
h
,
P
.
T.
T
a
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a
n
d
N
.
T.
A
n
,
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A
h
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[
1
2
]
Z.
La
a
b
i
d
,
A
.
M
o
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m
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n
,
A
.
L
a
k
h
d
a
r
,
a
n
d
K
.
M
a
n
so
u
r
i
,
“
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o
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t
e
s,
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O
P
C
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f
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re
n
c
e
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r
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e
s:
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t
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.
[
1
3
]
R
.
C
e
r
u
t
t
i
e
t
a
l
.
,
“
N
A
D
+
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,
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y
p
u
rsu
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g
h
e
r
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h
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a
t
th
e
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i
v
e
rsity
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ss
a
n
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sa
b
lan
c
a
sin
c
e
2
0
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0
.
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r
re
se
a
rc
h
fo
c
u
se
s
o
n
a
d
v
a
n
c
in
g
t
h
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m
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h
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ra
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teriz
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ti
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f
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o
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b
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se
d
p
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ly
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si
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telli
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m
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h
e
h
a
s
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c
ti
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l
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tri
b
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ted
t
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p
e
rime
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tal
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n
d
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u
m
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stu
d
ies
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b
io
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b
a
s
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d
p
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m
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rs,
p
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rti
c
u
larly
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n
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n
d
e
rsta
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g
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a
v
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o
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it
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s.
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c
a
n
b
e
c
o
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tac
ted
a
t
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m
a
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:
laa
b
id
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z
in
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b
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m
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il
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c
o
m
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e
lg
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n
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k
h
d
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r
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b
o
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laa
o
f
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ra
g
h
n
a
s
in
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9
8
1
.
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is
a
P
h
D
in
m
e
c
h
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n
ica
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n
g
in
e
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n
d
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rti
ficia
l
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telli
g
e
n
c
e
a
t
t
h
e
Un
i
v
e
r
sity
Ha
ss
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n
II
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sa
b
lan
c
a
,
ENS
ET
I
n
stit
u
te
sin
c
e
2
0
2
1
.
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re
se
a
rc
h
is
fo
c
u
se
d
o
n
c
o
n
tri
b
u
t
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e
x
p
e
rime
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tal
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n
d
n
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m
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rica
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h
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ra
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teriz
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ti
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f
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se
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VC
su
b
jec
ted
to
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g
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n
d
re
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y
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li
n
g
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c
a
n
b
e
c
o
n
tac
ted
a
t
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m
a
il
:
lak
h
d
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ra
b
d
e
lg
h
a
n
i1
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@g
m
a
il
.
c
o
m
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h
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li
fa
M
a
n
so
u
r
i
b
o
rn
in
1
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6
8
i
n
Az
il
a
l,
M
o
r
o
c
c
o
,
h
e
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c
u
rre
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tl
y
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c
o
m
p
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ter
sc
ien
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h
e
r
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n
d
re
se
a
rc
h
e
r
a
t
th
e
ENS
ET
In
sti
tu
te
o
f
Ha
ss
a
n
II
Un
iv
e
rsity
Ca
sa
b
lan
c
a
.
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re
se
a
rc
h
in
tere
sts
e
n
c
o
m
p
a
ss
e
-
lea
rn
in
g
sy
ste
m
s
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n
d
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n
d
u
strial
sy
ste
m
s
,
with
a
fo
c
u
s
o
n
m
o
d
e
li
n
g
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o
p
ti
m
iza
ti
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n
,
a
n
d
n
u
m
e
rica
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c
o
m
p
u
ti
n
g
.
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e
a
rn
e
d
h
is
d
ip
l
o
m
a
fro
m
ENS
ET
M
o
h
a
m
m
e
d
ia
in
1
9
9
1
,
f
o
ll
o
we
d
b
y
a
CEA
d
i
p
lo
m
a
in
1
9
9
2
.
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c
o
m
p
lete
d
h
is
P
h
.
D
.
i
n
c
a
lcu
latio
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n
d
o
p
ti
m
iza
ti
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stru
c
tu
re
s
a
t
M
o
h
a
m
m
e
d
V
Un
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in
Ra
b
a
t
i
n
1
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4
,
o
b
tai
n
e
d
HD
R
(Ac
c
re
d
it
a
ti
o
n
t
o
S
u
p
e
r
v
ise
Re
se
a
rc
h
)
in
2
0
1
0
,
a
n
d
e
a
rn
e
d
a
P
h
.
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i
n
c
o
m
p
u
ter
sc
ien
c
e
fro
m
Ha
ss
a
n
II
Un
i
v
e
rsity
Ca
sa
b
lan
c
a
i
n
2
0
1
6
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
z
in
e
b
.
m
e
d
tah
a
@g
m
a
il
.
c
o
m
.
Ali
S
ia
d
a
t
c
o
m
p
lete
d
h
is
P
h
.
D.
in
r
o
b
o
ti
c
s
a
n
d
d
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g
it
a
l
si
g
n
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l
p
ro
c
e
ss
in
g
a
t
In
stit
u
te
Na
ti
o
n
a
l
P
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tec
h
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iq
u
e
d
e
Lo
rra
i
n
e
in
F
ra
n
c
e
.
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g
i
n
n
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n
g
h
is
c
a
re
e
r
a
s
a
n
a
ss
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iate
p
ro
fe
ss
o
r
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n
c
o
m
p
u
ter
a
n
d
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n
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u
strial
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n
g
in
e
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rin
g
a
t
Arts
e
t
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e
ti
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rs
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a
risTec
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h
e
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s
p
ro
m
o
te
d
t
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fu
ll
p
ro
fe
ss
o
r
i
n
2
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a
t
t
h
e
sa
m
e
in
stit
u
ti
o
n
.
S
in
c
e
2
0
1
1
,
h
e
h
a
s
se
rv
e
d
a
s
h
e
a
d
o
f
th
e
Ap
p
li
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d
M
a
th
e
m
a
ti
c
s
a
n
d
Co
m
p
u
ter
En
g
in
e
e
rin
g
De
p
a
rtme
n
t
.
He
h
a
s
a
lso
h
e
ld
p
o
si
ti
o
n
s
a
s
a
g
u
e
st
p
r
o
fe
ss
o
r
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t
v
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rio
u
s
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n
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rsiti
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s
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n
Ch
in
a
,
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n
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M
o
r
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c
c
o
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a
n
d
Bra
z
il
.
His
re
se
a
rc
h
fo
c
u
se
s
o
n
a
rti
ficia
l
in
telli
g
e
n
c
e
,
in
fo
rm
a
ti
o
n
sy
ste
m
s,
k
n
o
wl
e
d
g
e
fo
rm
a
li
z
a
ti
o
n
,
a
n
d
o
p
e
ra
ti
o
n
s res
e
a
rc
h
a
p
p
li
e
d
to
m
a
n
u
fa
c
tu
ri
n
g
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
:
h
a
il
.
m
o
u
a
d
@g
m
a
il
.
c
o
m
.
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