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L
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CC B
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SA
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C
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Dep
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Po
litek
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Neg
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am
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1
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Pad
a
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B
u
lan
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Me
d
an
B
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Dis
tr
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Me
d
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2
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1
5
5
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d
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r
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tiria
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ac
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id
1.
I
NT
RO
D
UCT
I
O
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C
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p
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d
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-
m
ak
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lv
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m
u
ltip
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in
ter
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cr
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ia
th
at
m
u
s
t
b
e
ev
alu
ated
s
im
u
ltan
eo
u
s
ly
[
1
]
.
Mu
lti
-
cr
i
ter
ia
d
ec
is
io
n
an
aly
s
is
(
MC
DA)
p
r
o
v
id
es
a
s
tr
u
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ap
p
r
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ac
h
to
ass
ess
alter
n
ativ
es
an
d
g
en
e
r
ate
in
te
r
p
r
etab
le
r
an
k
i
n
g
s
[
2
]
.
I
n
p
r
a
ctice
,
d
ec
is
io
n
d
ata
u
s
u
ally
c
o
m
e
f
r
o
m
d
if
f
e
r
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t
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les,
m
ak
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o
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a
cr
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cial
s
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to
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s
u
r
e
f
air
co
m
p
ar
is
o
n
ac
r
o
s
s
cr
iter
ia
[
3
]
.
MCDA
h
as
b
ee
n
wid
ely
ap
p
lied
in
to
u
r
is
m
an
d
h
o
s
p
itality
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in
clu
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in
g
h
alal
to
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is
m
p
lan
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g
,
wh
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m
ak
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m
u
s
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b
alan
ce
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o
n
o
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ic,
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o
cial,
an
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en
v
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o
n
m
en
tal
co
n
s
id
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n
s
with
i
n
m
u
lti
-
s
tak
eh
o
ld
e
r
co
n
tex
ts
[
4
]
.
R
ec
en
t
h
alal
(
s
h
ar
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to
u
r
is
m
s
tu
d
ies
an
d
r
ev
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ews
also
em
p
h
asize
th
at
d
esti
n
atio
n
d
e
v
elo
p
m
e
n
t
m
u
s
t
r
ec
o
n
cile
I
s
lam
ic
co
m
p
lian
ce
r
eq
u
ir
e
m
en
ts
with
m
ar
k
et
co
m
p
etitiv
en
ess
an
d
s
u
s
tain
ab
ilit
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co
n
s
id
er
atio
n
s
,
wh
ich
n
atu
r
all
y
lead
s
to
m
u
lti
-
cr
iter
ia
ev
alu
atio
n
s
ettin
g
s
[
5
]
–
[
8
]
.
Dec
is
io
n
m
ak
in
g
u
s
in
g
th
e
MCDA
m
eth
o
d
g
o
es
th
r
o
u
g
h
s
ev
er
al
s
tag
es
s
u
c
h
as
d
ef
in
i
n
g
th
e
p
r
o
b
lem
th
at
n
ee
d
s
to
b
e
s
o
lv
ed
an
d
th
e
p
u
r
p
o
s
e
o
f
d
ec
is
io
n
m
ak
in
g
[
9
]
,
[
1
0
]
,
id
en
tify
in
g
r
elev
a
n
t
cr
iter
ia
f
o
r
ev
al
u
atin
g
alter
n
ativ
es
[
1
1
]
,
[
1
2
]
,
co
llectin
g
th
e
n
ec
ess
ar
y
d
ata
f
o
r
ea
c
h
alter
n
ativ
e
b
ased
o
n
th
e
p
r
e
d
eter
m
in
e
d
cr
iter
ia
[
1
3
]
,
n
o
r
m
alizin
g
th
e
d
ata
to
tr
an
s
f
o
r
m
d
if
f
e
r
en
t
m
ea
s
u
r
em
en
t
s
ca
les
s
o
th
at
th
e
d
ata
ca
n
b
e
co
m
p
ar
ed
f
air
l
y
[
1
4
]
,
d
eter
m
i
n
in
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
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2
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5
2
-
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9
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I
n
t J Ar
tif
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n
tell
,
Vo
l.
15
,
No
.
4
,
Au
g
u
s
t 2
0
2
6
:
3
8
2
7
-
3
8
3
5
3828
cr
iter
ia
weig
h
ts
[
1
5
]
,
e
v
alu
atin
g
alter
n
ativ
es
[
1
6
]
,
r
a
n
k
in
g
a
lter
n
ativ
es
[
1
7
]
,
co
n
d
u
ctin
g
s
e
n
s
itiv
ity
an
aly
s
is
to
u
n
d
er
s
tan
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w
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ts
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r
alter
n
ativ
e
v
alu
es
m
ay
af
f
ec
t
th
e
r
esu
lts
[
1
8
]
,
a
n
d
f
in
ally
s
u
p
p
o
r
tin
g
d
ec
is
io
n
m
a
k
in
g
[
1
9
]
.
Am
o
n
g
th
ese,
n
o
r
m
aliza
tio
n
p
lay
s
a
cr
itical
r
o
le
in
p
r
e
v
en
tin
g
s
ca
le
d
o
m
in
an
ce
a
n
d
r
e
d
u
cin
g
b
ias,
th
er
eb
y
en
s
u
r
in
g
p
r
o
p
o
r
tio
n
al
co
n
tr
ib
u
tio
n
o
f
ea
ch
cr
iter
io
n
[
2
0
]
.
I
n
a
d
d
itio
n
,
th
is
p
r
o
ce
s
s
r
ed
u
ce
s
b
ias
f
r
o
m
u
n
b
alan
ce
d
d
ata,
en
s
u
r
in
g
e
ac
h
cr
iter
io
n
co
n
tr
ib
u
tes
p
r
o
p
o
r
tio
n
ally
to
th
e
f
in
al
d
e
cisi
o
n
[
2
1
]
,
[
2
2
]
.
T
h
e
ch
o
ice
o
f
n
o
r
m
aliza
tio
n
m
eth
o
d
s
ig
n
if
ican
tly
af
f
ec
ts
alter
n
ativ
e
r
an
k
in
g
s
an
d
,
co
n
s
eq
u
en
tly
,
th
e
f
in
al
d
ec
is
io
n
[
1
9
]
.
Ho
we
v
er
,
th
e
c
h
o
ice
o
f
n
o
r
m
aliza
tio
n
tech
n
i
q
u
e
ca
n
s
ig
n
if
ican
tly
in
f
l
u
en
c
e
r
an
k
in
g
r
esu
lts
an
d
m
ay
ev
en
lead
to
i
n
co
n
s
is
ten
cies
s
u
ch
as
r
an
k
r
ev
er
s
al
[
2
3
]
,
[
2
4
]
.
T
h
u
s
,
n
o
r
m
aliza
tio
n
en
s
u
r
es
co
n
s
is
ten
t
an
d
s
tr
u
ctu
r
ed
ev
alu
atio
n
,
s
u
p
p
o
r
t
in
g
d
ec
is
io
n
v
alid
ity
[
2
5
]
.
I
t
i
s
a
cr
itical
co
m
p
o
n
en
t
af
f
ec
ti
n
g
r
eliab
ilit
y
,
with
s
tu
d
ies co
n
f
ir
m
in
g
its
s
ig
n
if
ican
t im
p
ac
t a
cr
o
s
s
co
n
tex
ts
,
in
c
lu
d
in
g
g
r
ee
n
s
u
p
p
lier
s
elec
tio
n
[
2
6
]
.
D
i
f
f
e
r
e
n
t
n
o
r
m
a
l
i
z
a
ti
o
n
t
e
c
h
n
iq
u
e
s
(
e
.
g
.
,
m
a
x
-
m
i
n
a
n
d
z
-
s
c
o
r
e
)
c
a
n
s
i
g
n
i
f
i
c
a
n
t
l
y
i
n
f
l
u
e
n
ce
r
a
n
k
i
n
g
r
e
s
u
l
ts
[
2
7
]
.
S
t
u
d
ie
s
s
h
o
w
t
h
a
t
m
e
t
h
o
d
s
e
l
ec
t
i
o
n
m
a
y
p
r
o
d
u
c
e
d
i
f
f
e
r
e
n
t
o
u
t
c
o
m
e
s
a
n
d
i
n
c
o
n
s
i
s
t
e
n
ci
e
s
[
2
8
]
,
[
2
9
]
,
wh
ile
r
ec
en
t
r
esear
ch
h
ig
h
lig
h
ts
its
r
o
le
in
r
an
k
r
ev
er
s
al
an
d
in
ter
ac
tio
n
with
d
is
tan
ce
m
ea
s
u
r
es
in
MCD
A
m
eth
o
d
s
s
u
ch
as
tech
n
iq
u
e
f
o
r
o
r
d
er
p
r
ef
er
en
ce
b
y
s
im
ilar
ity
to
id
ea
l
s
o
lu
tio
n
(
T
O
PS
I
S)
[
2
3
]
,
[
2
4
]
.
Ad
d
itio
n
ally
,
s
u
s
tain
ab
ilit
y
-
f
o
cu
s
ed
s
tu
d
ies
em
p
h
asize
th
e
n
ee
d
f
o
r
n
o
r
m
aliza
tio
n
ch
o
ices
th
at
ar
e
r
o
b
u
s
t,
in
ter
p
r
etab
le,
a
n
d
co
m
p
ar
a
b
le
ac
r
o
s
s
co
n
tex
ts
[
3
0
]
.
Desp
ite
ex
ten
s
iv
e
s
tu
d
ies
o
n
n
o
r
m
aliza
tio
n
in
MCDA,
co
n
s
is
ten
cy
an
d
s
y
s
tem
atic
ev
alu
atio
n
f
r
am
ewo
r
k
s
r
em
ain
lim
ited
[
3
1
]
–
[
3
3
]
.
T
h
is
h
ig
h
lig
h
ts
t
h
e
n
ee
d
f
o
r
i
n
teg
r
ated
ap
p
r
o
ac
h
es
th
at
ass
ess
tech
n
iq
u
es
b
ased
o
n
ac
cu
r
ac
y
,
co
n
s
is
ten
cy
,
a
n
d
s
en
s
itiv
ity
[
1
1
]
,
s
u
p
p
o
r
ted
b
y
r
ec
en
t
s
u
s
tain
ab
ilit
y
-
f
o
cu
s
ed
r
esear
ch
em
p
h
asizin
g
s
tr
u
ctu
r
ed
ev
alu
atio
n
an
d
r
ep
o
r
tin
g
[
3
0
]
.
I
n
s
h
ar
ia
to
u
r
is
m
s
ite
s
el
ec
tio
n
,
ch
o
o
s
in
g
th
e
r
ig
h
t
n
o
r
m
aliza
tio
n
tech
n
i
q
u
e
is
cr
u
cial
d
u
e
to
t
h
e
n
ee
d
t
o
b
alan
ce
s
o
cial,
ec
o
n
o
m
ic,
an
d
e
n
v
ir
o
n
m
en
tal
c
r
i
t
e
r
i
a
[
3
4
]
.
A
d
d
i
t
i
o
n
al
f
a
c
t
o
r
s
,
s
u
c
h
a
s
c
o
m
p
e
t
it
i
v
e
n
es
s
a
n
d
g
o
v
e
r
n
a
n
c
e
,
f
u
r
t
h
e
r
i
n
c
r
e
as
e
c
o
m
p
l
e
x
i
t
y
[
5
]
–
[
8
]
.
Prio
r
s
tu
d
ies
s
h
o
w
th
at
ap
p
r
o
p
r
iate
n
o
r
m
aliza
tio
n
im
p
r
o
v
es
d
ec
is
io
n
e
f
f
ec
tiv
en
ess
,
h
ig
h
lig
h
tin
g
th
e
im
p
o
r
tan
ce
o
f
s
y
s
tem
atic
f
r
a
m
ewo
r
k
s
in
s
u
s
tain
ab
le
to
u
r
i
s
m
p
lan
n
in
g
[
1
4
]
,
[
3
5
]
.
Su
s
tain
ab
ilit
y
-
o
r
ien
ted
to
u
r
is
m
d
e
v
el
o
p
m
e
n
t
r
e
q
u
i
r
es
in
t
eg
r
ate
d
e
v
al
u
a
ti
o
n
a
p
p
r
o
ac
h
es c
ap
a
b
le
o
f
b
ala
n
c
in
g
e
n
v
ir
o
n
m
e
n
tal
p
r
o
t
ec
t
io
n
,
v
is
i
to
r
e
n
g
a
g
e
m
e
n
t
,
an
d
d
esti
n
ati
o
n
c
o
m
p
eti
ti
v
e
n
ess
wi
th
in
i
n
c
r
e
asi
n
g
ly
c
o
m
p
le
x
t
o
u
r
is
m
e
co
s
y
s
t
em
s
[
3
6
]
.
Mu
lti
-
o
b
jectiv
e
p
r
ef
e
r
en
ce
a
n
aly
s
is
(
MO
PA
)
h
as
b
ee
n
p
r
o
p
o
s
ed
as
an
MCDA
m
eth
o
d
to
s
u
p
p
o
r
t
ev
alu
atio
n
an
d
r
an
k
i
n
g
in
co
m
p
lex
,
m
u
lti
-
o
b
jectiv
e
d
ec
is
io
n
s
[
1
6
]
.
Ho
wev
er
,
v
ar
iab
ilit
y
in
n
o
r
m
aliza
tio
n
ca
n
af
f
ec
t
th
e
cr
ed
ib
ilit
y
an
d
r
e
p
r
o
d
u
cib
ilit
y
o
f
r
esu
lts
,
esp
ec
ially
in
s
tak
eh
o
ld
er
-
d
r
iv
en
co
n
te
x
ts
s
u
ch
as
to
u
r
is
m
an
d
s
u
s
tain
ab
ilit
y
[
2
3
]
,
[
2
4
]
,
[
3
0
]
.
W
h
ile
n
o
r
m
aliza
tio
n
in
MCDA
h
as
b
ee
n
wid
ely
s
tu
d
ied
,
its
s
y
s
tem
atic
ev
alu
atio
n
with
in
th
e
MO
PA
f
r
am
ewo
r
k
r
em
ain
s
lim
ited
.
T
h
is
s
tu
d
y
ad
d
r
ess
es
th
is
g
ap
b
y
ex
am
in
i
n
g
h
o
w
n
o
r
m
aliza
tio
n
tech
n
iq
u
es
in
f
l
u
en
ce
r
a
n
k
in
g
b
e
h
av
io
r
in
M
OPA,
in
clu
d
in
g
an
ap
p
licatio
n
to
s
h
ar
ia
to
u
r
is
m
s
ite
s
elec
t
io
n
.
T
h
is
r
esear
ch
c
o
n
tr
ib
u
tes
b
y
p
r
o
v
id
in
g
a
co
m
p
ar
ativ
e
ev
alu
atio
n
o
f
n
o
r
m
aliza
tio
n
m
eth
o
d
s
in
MO
PA,
s
u
p
p
o
r
ted
b
y
s
tatis
tic
al
v
alid
atio
n
an
aly
s
is
o
f
v
ar
ia
n
ce
(
ANOV
A
)
an
d
n
o
n
-
p
ar
a
m
etr
ic
test
s
,
a
s
well
as
r
an
k
in
g
s
tab
ilit
y
an
aly
s
is
u
s
in
g
Ken
d
all’
s
t
au
.
I
t
also
d
e
m
o
n
s
tr
ates
p
r
ac
tical
ap
p
licab
ilit
y
in
a
r
ea
l
-
wo
r
ld
ca
s
e,
o
f
f
er
in
g
m
o
r
e
r
eliab
le
an
d
in
ter
p
r
eta
b
le
d
ec
is
io
n
-
m
a
k
in
g
in
s
ig
h
ts
.
2.
M
E
T
H
O
D
Sev
er
al
n
o
r
m
aliza
tio
n
m
eth
o
d
s
h
av
e
b
ee
n
in
tr
o
d
u
ce
d
an
d
p
r
o
p
o
s
ed
in
th
e
liter
atu
r
e
an
d
ca
n
b
e
u
s
ed
f
o
r
v
ar
i
o
u
s
p
r
o
b
lem
s
in
MCDA.
Vaf
ae
i
et
a
l.
[
2
8
]
p
r
o
p
o
s
ed
a
g
en
er
al
ev
alu
ati
o
n
f
r
a
m
ewo
r
k
th
at
in
clu
d
es
s
ev
er
al
m
etr
ics
to
ass
ess
d
if
f
er
en
t
n
o
r
m
aliza
tio
n
tech
n
iq
u
es
u
s
in
g
MCDA
m
eth
o
d
s
.
F
ig
u
r
e
1
s
h
o
ws
th
e
ev
alu
atio
n
f
r
am
ew
o
r
k
,
ad
a
p
te
d
f
r
o
m
[
2
8
]
.
T
h
is
r
esear
ch
m
e
th
o
d
will
f
o
cu
s
o
n
s
elec
tin
g
th
e
m
o
s
t
ap
p
r
o
p
r
iate
n
o
r
m
aliza
tio
n
tech
n
i
q
u
e
in
th
e
co
n
tex
t
o
f
MCDA
with
th
e
MO
PA
m
eth
o
d
,
u
s
in
g
a
ca
s
e
s
tu
d
y
o
f
to
u
r
is
m
d
ev
elo
p
m
e
n
t
lo
ca
tio
n
s
elec
tio
n
tak
e
n
f
r
o
m
th
e
liter
atu
r
e
[
2
7
]
.
T
h
e
d
ataset
co
m
p
r
i
s
es
1
8
alter
n
ativ
es
(
A1
–
A1
8
)
an
d
8
cr
iter
ia
(
C
1
–
C
8
)
,
wh
er
e
C
1
r
ep
r
esen
ts
a
co
s
t
cr
iter
io
n
,
an
d
th
e
r
em
ain
in
g
cr
iter
ia
ar
e
b
en
ef
it
cr
iter
ia.
T
h
e
s
elec
tio
n
o
f
t
h
is
d
ataset
is
b
ased
o
n
its
r
elev
an
ce
in
r
ep
r
esen
tin
g
a
r
ea
l
-
wo
r
ld
MCDA
p
r
o
b
lem
,
th
e
p
r
esen
ce
o
f
b
o
th
co
s
t
an
d
b
en
ef
it
cr
iter
ia,
a
n
d
its
u
s
e
in
p
r
ev
io
u
s
s
tu
d
ies,
wh
ich
s
u
p
p
o
r
ts
th
e
r
ep
r
o
d
u
cib
ilit
y
an
d
co
m
p
ar
ab
i
lity
o
f
th
e
r
esu
lts
.
Pre
v
io
u
s
s
tu
d
ies
m
ain
ly
r
elie
d
o
n
th
e
lin
ea
r
m
ax
n
o
r
m
aliza
tio
n
m
eth
o
d
.
I
n
th
is
r
esear
ch
,
ad
d
itio
n
al
n
o
r
m
aliza
tio
n
ap
p
r
o
ac
h
es
,
n
a
m
ely
lin
ea
r
m
ax
-
m
i
n
,
lin
ea
r
s
u
m
,
an
d
s
em
i
-
lin
ea
r
v
ec
to
r
,
ar
e
in
co
r
p
o
r
ated
to
f
ac
ilit
ate
co
m
p
ar
ativ
e
an
aly
s
i
s
an
d
ev
alu
ate
th
eir
in
f
lu
en
ce
o
n
th
e
r
esu
ltin
g
r
an
k
in
g
s
.
T
ab
le
1
s
u
m
m
ar
izes
th
e
f
o
r
m
u
las
o
f
th
e
s
elec
ted
n
o
r
m
aliza
tio
n
tech
n
iq
u
es
f
o
r
b
o
th
b
en
ef
it
a
n
d
c
o
s
t
cr
iter
ia
u
s
ed
in
th
is
co
m
p
ar
ativ
e
ass
ess
m
en
t
,
ad
ap
ted
f
r
o
m
[
2
8
]
.
T
h
e
n
o
r
m
aliza
tio
n
p
r
o
ce
s
s
is
illu
s
tr
ated
s
tep
-
by
-
s
tep
f
o
r
alter
n
ativ
e
A1
u
s
in
g
C
1
(
co
s
t)
an
d
C
2
(
b
en
ef
it),
with
r
ef
er
e
n
ce
b
o
u
n
d
s
m
in
=2
an
d
m
ax
=4
.
First,
u
n
d
er
lin
ea
r
m
a
x
–
m
in
n
o
r
m
al
izatio
n
,
th
e
b
en
ef
it
v
alu
e
f
o
r
C
2
is
co
m
p
u
ted
as
2
−
2
4
−
2
=
0
,
wh
ile
th
e
co
s
t
v
alu
e
f
o
r
C
1
is
o
b
tain
ed
as
4
−
3
4
−
2
=
0
.
5
.
Seco
n
d
,
u
s
in
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
n
a
lysi
s
o
f n
o
r
ma
liz
a
tio
n
tech
n
iq
u
e
o
n
mu
lti o
b
jective
p
r
efe
r
en
ce
a
n
a
lysi
s
meth
o
d
…
(
F
r
i
s
ti R
ia
n
d
a
r
i
)
3829
lin
ea
r
m
ax
n
o
r
m
aliza
tio
n
,
t
h
e
b
en
ef
it
v
alu
e
b
ec
o
m
es
2
/
4
=
0
.
5
,
an
d
c
o
s
t
v
alu
e
is
ca
lcu
lated
as
2
/
3
≈
0
.
667
.
T
h
ir
d
,
u
n
d
e
r
lin
ea
r
s
u
m
n
o
r
m
aliza
tio
n
,
ass
u
m
in
g
t
h
e
to
tal
v
alu
e
o
f
cr
iter
io
n
C
2
ac
r
o
s
s
all
alter
n
ativ
es
is
5
2
,
th
e
n
o
r
m
alize
d
s
co
r
e
is
2
/
52
=
0
.
0385
,
r
ef
le
ctin
g
a
c
o
m
p
r
ess
ed
s
ca
le.
Fin
ally
,
u
s
in
g
v
ec
to
r
n
o
r
m
aliza
tio
n
,
with
√
∑
2
2
=
14
.
42
,
th
e
n
o
r
m
alize
d
v
alu
e
is
2
/
14
.
42
≈
0
.
139
.
T
h
is
d
escen
d
in
g
,
s
tep
wis
e
co
m
p
u
tatio
n
d
em
o
n
s
tr
ates
h
o
w
ea
ch
n
o
r
m
aliza
tio
n
tech
n
iq
u
e
tr
an
s
f
o
r
m
s
th
e
s
am
e
r
aw
d
ata
in
t
o
d
if
f
er
e
n
t
s
ca
les,
h
ig
h
lig
h
tin
g
th
eir
v
a
r
y
in
g
s
en
s
itiv
ity
an
d
im
p
ac
t
o
n
s
u
b
s
eq
u
e
n
t r
an
k
in
g
r
esu
lts
.
Fig
u
r
e
1
.
T
h
r
ee
lev
els o
f
th
e
e
v
alu
atio
n
f
r
am
ewo
r
k
T
ab
le
1
.
Selecte
d
n
o
r
m
aliza
tio
n
tech
n
iq
u
es
N
o
r
mal
i
z
a
t
i
o
n
t
e
c
h
n
i
q
u
e
C
o
n
d
i
t
i
o
n
s
o
f
u
s
e
F
o
r
mu
l
a
t
i
o
n
Li
n
e
a
r
:
ma
x
B
e
n
e
f
i
t
s
+
=
C
o
s
t
−
=
1
−
Li
n
e
a
r
:
ma
x
-
mi
n
B
e
n
e
f
i
t
s
+
=
−
−
C
o
s
t
−
=
−
−
Li
n
e
a
r
:
su
m
B
e
n
e
f
i
t
s
+
=
∑
=
1
C
o
s
t
−
=
1
/
∑
1
/
=
1
S
e
mi
-
l
i
n
e
a
r
:
v
e
c
t
o
r
B
e
n
e
f
i
t
s
+
=
√
∑
2
=
1
C
o
s
t
−
=
1
−
√
∑
2
=
1
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
Selectin
g
an
ap
p
r
o
p
r
iate
n
o
r
m
aliza
tio
n
tech
n
iq
u
e
is
ess
e
n
tial
to
en
s
u
r
e
ac
cu
r
ate
an
d
r
elev
an
t
d
ec
is
io
n
an
aly
s
is
.
T
h
is
s
tu
d
y
ap
p
lies
a
b
en
c
h
m
ar
k
in
g
f
r
am
ewo
r
k
u
s
in
g
a
ca
s
e
s
tu
d
y
to
ev
alu
ate
f
o
u
r
n
o
r
m
aliza
tio
n
m
eth
o
d
s
an
d
i
d
en
tify
th
e
m
o
s
t
s
u
itab
le
ap
p
r
o
ac
h
f
o
r
s
h
ar
ia
to
u
r
is
m
d
ev
elo
p
m
en
t
u
s
in
g
th
e
MO
PA
m
eth
o
d
[
2
7
]
.
T
h
e
M
OPA
m
eth
o
d
was
f
ir
s
t
in
tr
o
d
u
ce
d
b
y
R
ian
d
ar
i
et
a
l
.
[
2
7
]
f
o
r
m
u
ltid
im
en
s
io
n
al
is
s
u
es.
T
h
e
r
elate
d
f
o
r
m
u
la
f
o
r
d
eter
m
in
in
g
th
e
r
a
n
k
in
g
o
f
alt
er
n
ativ
es is
d
eter
m
in
ed
b
y
(
1
)
.
=
∑
=
1
∗
1
,
2
,
3
…
(
1
)
I
n
th
e
p
r
o
p
o
s
ed
m
o
d
el,
r
ef
er
s
to
th
e
v
alu
e
o
f
an
alter
n
ativ
e
p
o
s
itio
n
ed
in
a
r
o
w
an
d
co
lu
m
n
,
wh
er
ea
s
∑
=
1
d
escr
ib
es
th
e
alter
n
a
tiv
e
s
co
r
e
ass
o
ciate
d
wi
th
ea
ch
cr
iter
io
n
.
Me
an
wh
ile,
r
ef
lects
th
e
p
r
io
r
ity
weig
h
t
o
f
e
v
er
y
cr
ite
r
io
n
ac
co
r
d
in
g
to
its
im
p
o
r
ta
n
ce
lev
el.
T
h
e
ca
s
e
s
tu
d
y
ev
a
lu
ates
eig
h
t
cr
iter
ia
(
C
1
–
C
8
)
,
wh
er
e
C
1
f
u
n
ctio
n
s
as
a
co
s
t
attr
ib
u
te
with
s
m
aller
v
alu
es
co
n
s
id
er
ed
m
o
r
e
f
a
v
o
r
ab
le,
wh
ile
th
e
o
th
er
cr
iter
ia
r
ep
r
esen
t
b
e
n
ef
it
attr
ib
u
tes
wh
er
e
lar
g
er
v
alu
es
ar
e
p
r
ef
er
r
e
d
.
A
t
o
t
a
l
o
f
1
8
a
lt
e
r
n
a
t
i
v
es
,
r
a
n
g
i
n
g
f
r
o
m
A
1
t
o
A
1
8
,
a
r
e
a
s
s
e
s
s
e
d
in
t
h
e
s
t
u
d
y
,
a
n
d
t
h
e
i
r
c
o
r
r
es
p
o
n
d
i
n
g
c
r
i
t
e
r
i
o
n
w
e
i
g
h
ts
a
r
e
s
u
m
m
a
r
i
z
e
d
i
n
T
a
b
l
e
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
15
,
No
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4
,
Au
g
u
s
t 2
0
2
6
:
3
8
2
7
-
3
8
3
5
3830
T
ab
le
2
.
Dec
is
io
n
m
atr
ix
in
p
u
t
d
ata
an
d
weig
h
ts
f
r
o
m
t
h
e
b
o
r
r
o
wed
ca
s
e
s
tu
d
y
[
2
7
]
A
l
t
.
C1
C2
C3
C4
C5
C6
C7
C8
A1
3
2
3
3
3
4
3
4
A2
4
4
2
3
4
2
3
3
A3
3
3
4
2
2
4
2
2
A4
4
2
4
3
4
2
3
4
A5
4
3
4
2
2
4
4
2
A6
2
3
2
3
2
3
2
4
A7
4
4
4
4
3
2
2
4
A8
3
3
4
4
2
4
4
4
A9
4
2
4
2
4
3
4
3
A
1
0
3
2
3
4
2
2
4
3
A
1
1
3
2
2
3
4
4
3
4
A
1
2
4
3
3
4
3
3
3
2
A
1
3
4
3
3
2
4
3
4
3
A
1
4
3
4
4
4
2
4
2
2
A
1
5
3
2
2
4
4
2
2
3
A
1
6
3
3
4
2
3
3
2
3
A
1
7
3
4
3
3
3
4
3
3
A
1
8
2
2
3
3
4
4
3
2
T
h
e
r
esu
lts
o
f
th
e
an
aly
s
is
with
f
o
u
r
s
elec
ted
n
o
r
m
aliza
tio
n
tech
n
iq
u
es,
n
am
ely
li
n
ea
r
-
m
ax
,
lin
ea
r
-
m
ax
-
m
i
n
,
lin
ea
r
-
s
u
m
,
an
d
s
em
i
-
lin
ea
r
-
v
ec
to
r
n
o
r
m
a
lizatio
n
f
o
r
th
e
r
elate
d
ca
s
e
s
tu
d
y
,
o
b
tain
ed
th
e
f
in
al
r
esu
lts
o
f
th
e
MO
PA
m
eth
o
d
,
with
alter
n
ativ
e
r
a
n
k
in
g
s
f
o
r
ea
ch
s
elec
ted
n
o
r
m
aliza
tio
n
tech
n
iq
u
e
p
r
esen
ted
in
T
ab
le
3
.
T
h
is
tab
le
s
h
o
ws
th
at
lin
ea
r
m
ax
-
m
in
an
d
s
em
i
-
lin
ea
r
v
ec
t
o
r
s
p
r
o
d
u
ce
m
o
r
e
d
is
cr
im
in
ativ
e
an
d
s
tab
le
r
an
k
in
g
p
atter
n
s
,
wh
ile
lin
ea
r
s
u
m
y
ield
s
co
m
p
r
ess
ed
s
co
r
e
d
is
tr
ib
u
tio
n
s
,
r
e
d
u
cin
g
d
if
f
er
en
tiatio
n
a
m
o
n
g
alter
n
at
iv
es.
T
h
e
s
lo
p
e
ch
ar
t
,
as
in
F
ig
u
r
e
2
,
s
h
o
ws
h
o
w
alter
n
ativ
e
r
an
k
in
g
s
ch
a
n
g
e
ac
r
o
s
s
n
o
r
m
aliza
tio
n
tech
n
iq
u
es.
Sev
er
al
alter
n
ativ
es,
s
u
c
h
as
A1
,
A8
,
a
n
d
A1
1
,
r
em
a
in
s
tab
le,
in
d
icatin
g
r
o
b
u
s
tn
ess
to
n
o
r
m
aliza
tio
n
c
h
o
ice.
I
n
co
n
t
r
ast,
s
o
m
e
alter
n
ativ
es
ex
h
ib
it
r
a
n
k
in
g
s
h
if
ts
,
p
ar
ticu
lar
ly
u
n
d
e
r
lin
ea
r
s
u
m
,
wh
ich
p
r
o
d
u
ce
s
m
o
r
e
co
m
p
r
ess
ed
an
d
less
d
is
cr
im
in
ativ
e
r
esu
lts
.
T
h
e
p
r
esen
ce
o
f
r
a
n
k
c
h
an
g
es
co
n
f
ir
m
s
th
at
n
o
r
m
aliza
tio
n
s
elec
tio
n
ca
n
in
f
l
u
en
ce
f
in
al
d
ec
is
io
n
o
u
tco
m
es,
wh
ile
li
n
ea
r
m
a
x
–
m
in
an
d
s
em
i
-
lin
ea
r
v
ec
to
r
d
em
o
n
s
tr
at
es
m
o
r
e
co
n
s
is
ten
t r
an
k
i
n
g
p
at
ter
n
s
.
T
ab
le
3
.
C
o
m
p
a
r
ativ
e
r
an
k
in
g
s
co
r
es u
n
d
er
d
if
f
er
e
n
t n
o
r
m
aliza
tio
n
tech
n
iq
u
es
A
l
t
.
Li
n
e
a
r
ma
x
Li
n
e
a
r
ma
x
-
mi
n
Li
n
e
a
r
s
u
m
S
e
mi
-
l
i
n
e
a
r
v
e
c
t
o
r
A1
0
.
8
4
6
0
.
6
9
1
0
.
0
6
2
0
.
8
4
3
A2
0
.
7
4
9
0
.
4
7
0
0
.
0
5
5
0
.
7
3
5
A3
0
.
6
1
1
0
.
2
2
1
0
.
0
4
4
0
.
6
0
8
A4
0
.
8
3
5
0
.
6
4
1
0
.
0
6
1
0
.
8
2
1
A5
0
.
7
5
7
0
.
4
8
6
0
.
0
5
5
0
.
7
4
3
A6
0
.
6
9
5
0
.
4
1
8
0
.
0
5
1
0
.
7
0
9
A7
0
.
7
6
2
0
.
4
9
5
0
.
0
5
5
0
.
7
4
8
A8
0
.
9
2
0
0
.
8
4
0
0
.
0
6
7
0
.
9
1
8
A9
0
.
8
5
3
0
.
6
7
7
0
.
0
6
2
0
.
8
3
8
A
1
0
0
.
7
6
2
0
.
5
2
4
0
.
0
5
6
0
.
7
6
0
A
1
1
0
.
8
6
3
0
.
7
2
6
0
.
0
6
3
0
.
8
6
1
A
1
2
0
.
7
0
3
0
.
3
7
7
0
.
0
5
1
0
.
6
8
8
A
1
3
0
.
8
4
5
0
.
6
6
1
0
.
0
6
2
0
.
8
3
0
A
1
4
0
.
6
4
8
0
.
2
9
6
0
.
0
4
7
0
.
6
4
6
A
1
5
0
.
6
7
3
0
.
3
4
6
0
.
0
4
9
0
.
6
7
1
A
1
6
0
.
6
8
0
0
.
3
6
1
0
.
0
4
9
0
.
6
7
8
A
1
7
0
.
7
9
0
0
.
5
8
0
0
.
0
5
8
0
.
7
8
8
A
1
8
0
.
7
3
2
0
.
4
9
2
0
.
0
5
3
0
.
7
4
6
Prio
r
to
co
n
d
u
ctin
g
ANOV
A,
ass
u
m
p
tio
n
test
in
g
was
p
er
f
o
r
m
e
d
to
en
s
u
r
e
th
e
v
alid
ity
o
f
th
e
p
ar
am
etr
ic
ap
p
r
o
ac
h
.
No
r
m
ality
was
ass
es
s
ed
u
s
in
g
th
e
Sh
a
p
ir
o
-
W
ilk
test
,
an
d
h
o
m
o
g
en
ei
ty
o
f
v
ar
ian
ce
s
was
ev
alu
ated
u
s
in
g
L
e
v
en
e’
s
test
.
T
h
e
r
esu
lts
in
d
icate
t
h
at
th
e
d
ata
ar
e
n
o
r
m
ally
d
is
tr
ib
u
ted
(
p
>
0
.
0
5
)
an
d
th
at
v
ar
ian
ce
s
ac
r
o
s
s
g
r
o
u
p
s
ar
e
h
o
m
o
g
en
eo
u
s
,
s
atis
f
y
in
g
t
h
e
ass
u
m
p
tio
n
s
r
e
q
u
ir
e
d
f
o
r
ANOV
A.
Ho
wev
er
,
co
n
s
id
er
in
g
th
e
r
elativ
ely
s
m
all
d
ataset
an
d
th
e
d
is
cr
ete
n
atu
r
e
o
f
t
h
e
d
ata,
a
n
o
n
-
p
a
r
am
e
tr
ic
Kr
u
s
k
al
-
W
allis
test
was
al
s
o
co
n
d
u
cted
as
a
r
o
b
u
s
tn
ess
ch
ec
k
.
T
h
e
r
esu
lts
ar
e
co
n
s
is
ten
t
with
ANOV
A,
in
d
icatin
g
s
ig
n
if
ican
t
d
if
f
er
en
ce
s
am
o
n
g
n
o
r
m
aliza
tio
n
tech
n
iq
u
es.
T
h
is
co
n
s
is
ten
cy
co
n
f
ir
m
s
th
at
th
e
f
in
d
in
g
s
ar
e
r
o
b
u
s
t
an
d
n
o
t
d
ep
en
d
e
n
t so
lely
o
n
p
ar
am
etr
i
c
ass
u
m
p
tio
n
s
.
Nex
t,
an
an
aly
s
is
is
co
n
d
u
cte
d
to
ass
ess
th
e
lev
el
o
f
p
r
ec
is
io
n
o
f
th
e
f
o
u
r
n
o
r
m
aliza
tio
n
tech
n
iq
u
es
b
y
ev
alu
atin
g
th
ei
r
r
o
b
u
s
tn
ess
in
p
r
o
d
u
cin
g
co
n
s
is
ten
t
r
an
k
in
g
r
esu
lts
.
T
o
f
in
d
o
u
t
th
e
co
n
ce
p
t
o
f
th
e
n
o
r
m
aliza
tio
n
tech
n
iq
u
e
weig
h
t
co
n
v
in
cin
g
l
y
,
ANOV
A
wa
s
co
n
d
u
cte
d
o
n
t
h
e
ac
tu
al
f
in
al
s
elec
tio
n
in
d
e
x
(
FS
I
)
o
f
t
h
e
alter
n
ativ
es
u
s
in
g
a
s
ig
n
if
ican
ce
lev
el
o
f
α
=
0
.
0
5
.
ANOV
A
is
a
s
tatis
tica
l
tech
n
iq
u
e
u
s
ed
t
o
d
eter
m
in
e
w
h
eth
er
th
er
e
is
a
s
ig
n
if
ican
t
d
if
f
er
e
n
ce
b
etwe
e
n
th
e
m
ea
n
s
o
f
two
o
r
m
o
r
e
g
r
o
u
p
s
.
I
n
th
is
c
o
n
tex
t,
we
co
m
p
ar
e
th
e
r
esu
lts
o
f
ANOV
A
ap
p
lied
to
d
ata
th
at
h
as
b
ee
n
n
o
r
m
alize
d
u
s
in
g
f
o
u
r
d
if
f
er
e
n
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
n
a
lysi
s
o
f n
o
r
ma
liz
a
tio
n
tech
n
iq
u
e
o
n
mu
lti o
b
jective
p
r
efe
r
en
ce
a
n
a
lysi
s
meth
o
d
…
(
F
r
i
s
ti R
ia
n
d
a
r
i
)
3831
n
o
r
m
aliza
tio
n
tech
n
iq
u
es:
lin
ea
r
m
ax
n
o
r
m
aliza
tio
n
,
lin
ea
r
m
ax
-
m
in
n
o
r
m
aliza
tio
n
,
lin
ea
r
s
u
m
n
o
r
m
aliza
tio
n
,
an
d
s
em
i
-
lin
ea
r
v
ec
to
r
n
o
r
m
a
lizatio
n
.
T
h
e
0
.
0
5
(
o
r
5
%)
lev
el
o
f
s
ig
n
if
ican
ce
is
a
co
m
m
o
n
ly
u
s
ed
lev
el
in
s
tatis
t
ical
an
aly
s
i
s
.
I
t
i
s
b
ased
o
n
a
well
-
estab
lis
h
ed
co
n
v
en
tio
n
an
d
is
wid
ely
u
s
ed
in
s
cien
tific
r
esear
ch
.
T
h
is
lev
el
o
f
s
ig
n
if
ican
ce
in
d
icate
s
th
at
th
er
e
is
a
5
%
ch
an
ce
th
a
t
th
e
o
b
s
er
v
ed
d
if
f
er
e
n
ce
is
d
u
e
to
ch
an
ce
al
o
n
e.
ANOV
A
wa
s
co
n
d
u
cted
u
s
in
g
p
y
th
o
n
s
o
f
twar
e,
ANOV
A
was
co
n
d
u
cted
f
o
r
th
e
f
o
u
r
s
e
lecte
d
n
o
r
m
aliza
tio
n
tech
n
iq
u
es.
ANOV
A
was
test
ed
o
n
th
e
f
i
n
al
r
esu
lts
co
n
d
u
c
ted
with
th
e
f
o
u
r
s
elec
ted
n
o
r
m
aliza
tio
n
s
ag
ain
s
t
th
e
b
o
r
r
o
wed
ca
s
e
s
tu
d
y
.
T
h
e
r
esu
lts
o
f
th
e
ANOV
A
test
co
n
d
u
cted
u
s
in
g
Py
th
o
n
So
f
twar
e
o
b
tain
ed
th
e
r
esu
lts
th
at
ca
n
b
e
s
ee
n
in
T
a
b
le
4
.
Fig
u
r
e
2
.
Slo
p
e
ch
ar
t
o
f
r
a
n
k
in
g
ch
an
g
es a
cr
o
s
s
n
o
r
m
aliza
tio
n
tech
n
iq
u
es
T
ab
le
4
.
C
o
m
p
a
r
is
o
n
o
f
ANOV
A
r
esu
lts
N
o
r
m
a
l
i
z
a
t
i
o
n
t
e
c
h
n
i
q
u
e
F
-
s
t
a
t
i
s
t
i
c
P
–
v
a
l
u
e
Li
n
e
a
r
m
a
x
3
.
8
0
4
0
.
0
4
6
Li
n
e
a
r
ma
x
-
m
i
n
4
.
0
5
9
0
.
0
3
9
Li
n
e
a
r
s
u
m
3
.
7
0
3
0
.
0
4
9
S
e
mi
-
l
i
n
e
a
r
v
e
c
t
o
r
3
.
9
9
5
0
.
0
4
1
No
r
m
aliza
tio
n
en
s
u
r
es
c
o
m
p
a
r
ab
ilit
y
an
d
p
r
e
v
en
ts
s
ca
le
d
o
m
in
an
ce
,
b
u
t
d
if
f
er
e
n
t
tech
n
iq
u
es
ca
n
lead
to
d
if
f
er
en
t
o
u
tco
m
es.
E
m
p
ir
ical
s
tu
d
ies
s
h
o
w
th
at
ch
o
o
s
in
g
an
in
ap
p
r
o
p
r
iate
m
eth
o
d
m
ay
ev
en
d
eg
r
ad
e
m
o
d
el
p
e
r
f
o
r
m
an
ce
c
o
m
p
ar
e
d
to
u
n
s
ca
led
d
ata
[
3
7
]
.
T
h
i
s
f
in
d
in
g
h
elp
s
ex
p
lain
wh
y
m
ax
n
o
r
m
aliza
tio
n
p
r
o
d
u
ce
d
r
elativ
ely
wea
k
er
r
e
s
u
lts
in
th
e
ANOV
A
test
in
th
i
s
s
tu
d
y
.
L
in
ea
r
m
ax
-
mi
n
n
o
r
m
aliza
tio
n
ac
h
iev
ed
th
e
b
est
r
esu
lts
(
F
=4
.
0
6
0
;
p
=0
.
0
3
9
)
,
ef
f
ec
tiv
ely
s
ca
lin
g
d
ata
f
o
r
co
m
p
ar
is
o
n
an
d
en
h
a
n
cin
g
t
h
e
d
etec
tio
n
o
f
d
if
f
er
e
n
ce
s
,
co
n
s
is
ten
t
with
p
r
io
r
f
in
d
in
g
s
[
3
8
]
.
L
in
ea
r
s
u
m
n
o
r
m
aliza
tio
n
also
s
h
o
wed
s
ig
n
if
ican
t r
esu
lts
with
a
P
-
v
alu
e
o
f
0
.
0
4
9
.
Alth
o
u
g
h
th
e
r
e
s
u
lts
ar
e
g
o
o
d
,
th
is
tech
n
iq
u
e
ten
d
s
to
b
e
s
im
p
ler
an
d
ca
n
ig
n
o
r
e
cr
itical
v
ar
iat
io
n
s
in
th
e
d
ata.
C
h
en
[
3
9
]
s
h
o
ws
th
at
alth
o
u
g
h
s
u
m
n
o
r
m
aliza
tio
n
is
ea
s
y
to
ap
p
ly
,
it
ca
n
ca
u
s
e
d
is
to
r
tio
n
s
in
th
e
an
aly
s
is
,
esp
ec
ially
w
h
en
th
e
d
ata
is
n
o
t
n
o
r
m
ally
d
is
tr
ib
u
ted
.
Sem
i
-
lin
ea
r
v
ec
to
r
n
o
r
m
aliza
t
io
n
g
i
v
es
q
u
ite
g
o
o
d
r
esu
lts
with
a
P
-
v
alu
e
o
f
0
.
0
4
1
.
T
h
is
tech
n
iq
u
e,
wh
ich
co
m
b
in
es
s
ev
er
al
a
p
p
r
o
ac
h
es,
s
h
o
ws
th
e
p
o
ten
tial
to
p
r
o
v
i
d
e
m
o
r
e
b
alan
ce
d
r
esu
lts
.
Vaf
ae
i
et
a
l.
[
4
0
]
s
h
o
wed
th
at
th
e
s
em
i
-
lin
ea
r
a
p
p
r
o
ac
h
ca
n
p
r
o
v
id
e
a
d
v
an
ta
g
e
s
in
p
r
eser
v
in
g
d
ata
in
teg
r
ity
wh
ile
s
till
d
etec
tin
g
s
ig
n
if
ican
t
d
if
f
er
e
n
ce
s
b
etwe
e
n
g
r
o
u
p
s
.
T
h
e
r
esu
lts
s
u
p
p
o
r
t
th
is
f
in
d
in
g
,
s
u
g
g
esti
n
g
th
at
th
is
tech
n
iq
u
e
h
as
v
alu
e
in
th
e
co
n
tex
t
o
f
ANOVA
.
T
o
f
u
r
th
er
ex
p
lai
n
th
e
o
b
s
er
v
ed
d
if
f
er
en
ce
s
a
m
o
n
g
n
o
r
m
aliza
tio
n
tech
n
iq
u
es,
a
th
e
o
r
etica
l a
n
aly
s
is
is
p
r
o
v
id
ed
to
clar
if
y
th
e
u
n
d
er
ly
in
g
m
ec
h
a
n
is
m
s
.
T
h
e
in
f
er
io
r
p
er
f
o
r
m
an
c
e
o
f
lin
ea
r
s
u
m
n
o
r
m
aliza
tio
n
c
an
b
e
ex
p
lain
e
d
b
y
its
m
at
h
em
atica
l
s
tr
u
ctu
r
e,
wh
ich
d
is
tr
ib
u
tes
v
a
lu
es
p
r
o
p
o
r
tio
n
ally
ac
r
o
s
s
all
alter
n
ativ
es.
T
h
is
ten
d
s
to
co
m
p
r
ess
th
e
r
an
g
e
o
f
n
o
r
m
alize
d
v
alu
es,
r
ed
u
cin
g
c
o
n
tr
ast
b
etwe
en
h
ig
h
-
a
n
d
lo
w
-
p
er
f
o
r
m
in
g
alter
n
ativ
es.
As
a
r
esu
lt,
th
e
m
eth
o
d
b
ec
o
m
es
less
s
en
s
itiv
e
to
v
ar
iatio
n
s
in
in
p
u
t
d
ata,
lead
i
n
g
t
o
wea
k
er
d
is
cr
im
in
atio
n
p
o
we
r
.
I
n
co
n
tr
ast
,
lin
ea
r
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
15
,
No
.
4
,
Au
g
u
s
t 2
0
2
6
:
3
8
2
7
-
3
8
3
5
3832
m
ax
-
mi
n
n
o
r
m
aliza
tio
n
p
r
es
er
v
es
r
elativ
e
d
is
tan
ce
s
b
y
s
ca
lin
g
v
alu
es
with
in
a
f
ix
e
d
r
an
g
e,
e
n
h
an
ci
n
g
d
if
f
er
en
tiatio
n
.
Similar
ly
,
s
e
m
i
-
lin
ea
r
v
ec
to
r
n
o
r
m
aliza
ti
o
n
m
ain
tain
s
p
r
o
p
o
r
tio
n
al
r
elatio
n
s
h
ip
s
wh
ile
in
co
r
p
o
r
atin
g
m
a
g
n
itu
d
e
s
en
s
itiv
ity
,
allo
win
g
it to
b
etter
ca
p
tu
r
e
p
er
f
o
r
m
a
n
ce
v
ar
iatio
n
s
ac
r
o
s
s
alter
n
ativ
es.
T
h
ese
r
esu
lts
s
u
p
p
o
r
t
m
an
y
p
r
ev
io
u
s
s
tu
d
ies
th
at
em
p
h
asize
th
e
im
p
o
r
tan
ce
o
f
s
elec
tin
g
th
e
r
ig
h
t
n
o
r
m
aliza
tio
n
tech
n
iq
u
e.
Fo
r
ex
am
p
le,
Z
o
r
o
v
et
a
l.
[
4
1
]
f
o
u
n
d
th
at
c
h
o
o
s
in
g
r
ig
h
t
n
o
r
m
aliza
tio
n
tech
n
iq
u
e
ca
n
s
ig
n
if
ican
tly
af
f
ec
t
th
e
r
esu
lts
o
f
an
aly
s
is
,
in
clu
d
in
g
AN
OVA.
T
h
is
s
h
o
ws
th
at
ch
o
o
s
in
g
a
n
o
r
m
aliza
tio
n
m
eth
o
d
is
n
o
t j
u
s
t a
tech
n
ical
i
s
s
u
e,
b
u
t c
an
also
af
f
ec
t
d
ata
-
b
ased
d
ec
is
io
n
s
an
d
co
n
clu
s
io
n
s
d
r
awn
.
Ov
er
all,
th
e
v
ar
ia
n
ce
an
aly
s
i
s
h
ig
h
lig
h
ts
th
at
n
o
r
m
aliza
tio
n
ch
o
ice
s
ig
n
if
ican
tly
af
f
ec
ts
d
ec
is
io
n
ac
cu
r
ac
y
an
d
r
elev
a
n
ce
.
L
in
ea
r
m
ax
–
m
i
n
is
th
e
m
o
s
t
r
ec
o
m
m
en
d
ed
tec
h
n
iq
u
e
,
s
h
o
win
g
t
h
e
b
est
p
er
f
o
r
m
an
c
e
in
d
is
tin
g
u
is
h
in
g
alter
n
ativ
es,
wh
ile
s
em
i
-
lin
ea
r
v
ec
to
r
also
p
er
f
o
r
m
s
well
b
y
b
alan
ci
n
g
r
o
b
u
s
tn
ess
an
d
s
en
s
itiv
ity
.
I
n
co
n
tr
ast,
lin
ea
r
s
u
m
is
less
d
is
cr
im
in
ativ
e,
a
n
d
lin
ea
r
m
ax
s
h
o
ws
wea
k
er
p
er
f
o
r
m
an
ce
u
n
d
er
u
n
ev
en
d
ata
co
n
d
itio
n
s
.
T
h
ese
f
in
d
in
g
s
em
p
h
asize
th
at
n
o
r
m
aliza
tio
n
s
elec
tio
n
s
h
o
u
ld
b
e
alig
n
ed
with
d
ata
ch
ar
ac
ter
is
tics
an
d
a
n
aly
s
is
o
b
jectiv
es,
an
d
s
u
p
p
o
r
ted
b
y
s
en
s
itiv
ity
an
aly
s
is
to
e
n
s
u
r
e
s
tab
le
an
d
r
eliab
le
d
ec
is
io
n
o
u
tco
m
es.
T
o
f
u
r
th
er
ev
alu
ate
th
e
r
o
b
u
s
tn
ess
o
f
r
a
n
k
in
g
r
esu
lts
,
s
en
s
itiv
ity
an
aly
s
is
was c
o
n
d
u
cted
.
R
an
k
in
g
s
tab
ilit
y
u
n
d
er
d
if
f
er
en
t
n
o
r
m
aliza
tio
n
tech
n
iq
u
es
was
ev
alu
ated
u
s
in
g
Ke
n
d
all’
s
t
au
co
r
r
elatio
n
co
ef
f
icien
t.
T
h
e
r
e
s
u
lts
s
h
o
w
th
at
lin
ea
r
m
ax
-
m
in
an
d
s
em
i
-
lin
ea
r
v
ec
to
r
s
ex
h
ib
it
h
ig
h
s
tab
ilit
y
,
wh
ile
lin
ea
r
s
u
m
p
r
o
d
u
ce
s
g
r
ea
ter
r
an
k
v
ar
iab
ilit
y
.
T
h
e
s
e
f
in
d
in
g
s
in
d
icate
th
at
n
o
r
m
aliza
tio
n
ch
o
ice
s
ig
n
if
ican
tly
af
f
ec
ts
r
an
k
in
g
r
o
b
u
s
tn
ess
,
with
lin
ea
r
m
a
x
-
m
i
n
an
d
s
em
i
-
lin
ea
r
v
ec
t
o
r
p
r
o
v
id
in
g
m
o
r
e
r
eliab
le
an
d
co
n
s
is
ten
t d
ec
is
io
n
o
u
tco
m
es with
in
th
e
MO
PA
-
b
ased
d
ec
is
io
n
-
m
ak
in
g
f
r
am
ew
o
r
k
.
T
h
e
d
ec
is
io
n
-
m
a
k
in
g
p
r
o
ce
d
u
r
e
m
ay
u
tili
ze
v
ar
io
u
s
n
o
r
m
aliza
tio
n
m
eth
o
d
s
d
e
p
en
d
i
n
g
o
n
th
e
o
b
jectiv
es
an
d
ch
a
r
ac
ter
is
tics
o
f
th
e
d
ataset.
T
o
im
p
r
o
v
e
th
e
co
n
s
is
ten
cy
an
d
d
ep
en
d
ab
ilit
y
o
f
r
a
n
k
in
g
r
esu
lts
,
d
ec
is
io
n
-
m
ak
er
s
ca
n
im
p
lem
e
n
t
th
e
p
r
o
p
o
s
ed
ev
al
u
atio
n
f
r
a
m
ewo
r
k
th
r
o
u
g
h
t
h
e
p
r
ac
tical
m
o
d
el
p
r
esen
ted
in
Fig
u
r
e
3
.
T
h
e
f
ig
u
r
e
p
r
esen
ts
a
s
tr
u
ctu
r
ed
f
r
am
ewo
r
k
f
o
r
ev
alu
atin
g
n
o
r
m
aliza
tio
n
tech
n
iq
u
es
with
in
MCDA,
co
n
s
is
tin
g
o
f
th
r
ee
p
h
ases
.
Ph
ase
1
estab
lis
h
es
th
e
d
ec
is
io
n
m
o
d
el
th
r
o
u
g
h
m
atr
ix
co
n
s
tr
u
ctio
n
,
b
e
n
ef
it
–
co
s
t
id
en
tific
atio
n
,
an
d
weig
h
t
ass
ig
n
m
en
t.
Ph
ase
2
ass
es
s
es
n
o
r
m
aliza
tio
n
tech
n
iq
u
e
s
b
ased
o
n
d
ata
ch
ar
ac
ter
is
tics
,
h
ig
h
lig
h
tin
g
t
h
eir
in
f
lu
en
ce
o
n
r
an
k
in
g
o
u
tco
m
es.
Ph
ase
3
v
alid
ates
th
e
r
esu
lts
th
r
o
u
g
h
s
en
s
itiv
ity
an
aly
s
is
,
f
o
llo
we
d
b
y
s
tab
ilit
y
an
d
r
o
b
u
s
tn
e
s
s
ev
alu
atio
n
to
en
s
u
r
e
co
n
s
is
ten
t
an
d
r
eliab
le
r
an
k
in
g
s
.
T
h
is
f
r
am
ewo
r
k
e
m
p
h
asizes
th
at
n
o
r
m
aliza
tio
n
is
a
cr
itical
d
eter
m
in
a
n
t
o
f
d
ec
is
io
n
q
u
ality
an
d
s
u
p
p
o
r
ts
r
ep
r
o
d
u
cib
le
an
d
r
o
b
u
s
t d
ec
is
io
n
-
m
ak
in
g
.
Fig
u
r
e
3
.
Pra
ctica
l d
esig
n
m
o
d
el
f
o
r
th
e
a
n
aly
s
is
o
f
n
o
r
m
aliz
atio
n
tech
n
iq
u
es o
n
th
e
MCD
A
m
o
d
el
u
s
in
g
t
h
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
4.
CO
NCLU
SI
O
N
Sele
cti
n
g
t
h
e
ap
p
r
o
p
r
i
ate
n
o
r
m
aliz
ati
o
n
t
ec
h
n
iq
u
e
is
c
r
i
tic
al
f
o
r
ac
cu
r
ate
d
a
ta
-
d
r
i
v
en
d
ec
is
io
n
-
m
a
k
i
n
g
.
Am
o
n
g
th
e
f
o
u
r
m
et
h
o
d
s
e
v
a
l
u
at
e
d
,
li
n
e
ar
m
ax
-
m
i
n
p
e
r
f
o
r
m
s
b
est
,
ef
f
e
cti
v
e
ly
d
is
ti
n
g
u
is
h
i
n
g
al
te
r
n
at
iv
es
a
n
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J Ar
tif
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tell
I
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8
9
3
8
A
n
a
lysi
s
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f n
o
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ma
liz
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tech
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mu
lti o
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jective
p
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efe
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meth
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r
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3833
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m
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li
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r
v
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t
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als
o
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it
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li
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m
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ar
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r
e
less
d
is
cr
im
i
n
ati
v
e
.
T
h
ese
f
i
n
d
i
n
g
s
h
i
g
h
li
g
h
t
t
h
a
t
n
o
r
m
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ch
o
ic
e
s
h
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l
d
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g
n
wi
th
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ata
ch
ar
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t
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tics
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n
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tiv
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s
u
p
p
o
r
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b
y
s
en
s
i
ti
v
it
y
a
n
al
y
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is
t
o
e
n
s
u
r
e
s
t
ab
le
r
es
u
l
ts
.
T
h
is
s
t
u
d
y
p
r
o
v
i
d
es
p
r
ac
t
ic
al
g
u
id
a
n
ce
f
o
r
s
e
le
cti
n
g
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ita
b
l
e
n
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r
m
al
iz
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ch
n
i
q
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d
co
n
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tes
to
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lit
e
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r
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r
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v
e
a
n
d
v
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at
e
d
e
v
al
u
ati
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ew
o
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h
im
p
l
ica
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o
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r
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s
s
v
a
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s
a
p
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ca
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o
n
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m
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s
.
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DG
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h
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th
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ld
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o
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p
r
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is
ap
p
r
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n
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h
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Me
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in
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titu
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p
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a
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ass
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tan
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p
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in
g
t
h
is
ar
ticle.
T
h
is
s
u
p
p
o
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was
in
s
tr
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in
f
ac
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h
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d
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esear
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f
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F
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h
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T
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f
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h
in
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AUTHO
R
CO
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B
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h
is
jo
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C
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u
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(
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tio
n
s
,
r
ed
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th
o
r
s
h
ip
d
is
p
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tes,
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d
f
ac
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ate
co
llab
o
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Na
m
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f
Aut
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r
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Vi
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G
a
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Fer
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C
:
C
o
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p
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RE
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NC
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1
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