Nuclear	B-Task
theory	I-Task
devoted	O
major	O
efforts	O
since	O
4	O
decades	O
to	O
describe	O
thermalization	B-Task
in	O
nuclear	B-Process
reactions	I-Process
,	O
predominantly	O
using	O
semi-classical	B-Process
methods	I-Process
[	O
13,14,10	O
]	O
,	O
in	O
line	O
with	O
similar	O
problems	O
in	O
quantum	B-Material
liquids	I-Material
[	O
15,16	O
]	O
.	O
There	O
were	O
attempts	O
to	O
develop	O
improved	B-Process
molecular	I-Process
dynamics	I-Process
methods	I-Process
combining	B-Task
quantum	I-Task
features	I-Task
with	O
a	O
semi	B-Process
classical	I-Process
treatment	I-Process
of	I-Process
dynamical	I-Process
correlations	I-Process
[	O
17,18	O
]	O
.	O
Still	O
,	O
no	O
clear-cut	O
quantum	B-Process
approach	I-Process
is	O
readily	O
available	O
yet	O
,	O
in	O
spite	O
of	O
numerous	O
formal	O
attempts	O
[	O
19,20,10	O
]	O
.	O
The	O
field	B-Task
of	I-Task
clusters	I-Task
and	I-Task
nano	I-Task
structures	I-Task
is	O
far	O
younger	O
but	O
fast	O
developing	O
in	O
relation	O
to	O
the	O
ongoing	O
developments	O
of	O
lasers	B-Process
and	O
imaging	B-Process
techniques	I-Process
.	O
Semiclassical	O
approaches	O
were	O
also	O
considered	O
in	O
the	O
field	O
to	O
include	O
some	O
dynamical	O
corrections	O
[	O
21,22	O
]	O
and	O
could	O
qualitatively	B-Process
describe	I-Process
dynamical	I-Process
processes	I-Process
.	I-Process
But	O
such	O
approaches	O
are	O
bound	O
to	O
simple	B-Material
metals	I-Material
with	O
sufficiently	O
delocalized	O
wave	O
functions	O
,	O
and	O
thus	O
smooth	O
potentials	O
justifying	O
semiclassical	O
approximations	O
.	O
The	O
case	O
of	O
organic	B-Task
systems	I-Task
,	O
in	O
particular	O
the	O
much	O
celebrated	O
C60	B-Material
[	O
4,23	O
]	O
,	O
cannot	O
be	O
treated	O
this	O
way	O
.	O
Semi	O
classical	O
,	O
and	O
even	O
classical	O
approaches	O
,	O
can	O
be	O
used	O
at	O
very	O
high	O
excitations	O
such	O
as	O
delivered	O
by	O
very	B-Process
intense	I-Process
laser	I-Process
pulses	I-Process
[	O
2	O
]	O
.	O
In	O
such	O
cases	O
the	O
system	O
is	O
blown	O
up	O
and	O
details	O
of	O
its	O
quantum	B-Task
mechanical	I-Task
features	I-Task
do	O
not	O
matter	O
anymore	O
.	O
But	O
for	O
less	O
violent	O
scenarios	O
,	O
quantum	B-Process
shell	I-Process
effects	I-Process
cannot	O
be	O
ignored	O
.	O

The	O
next	O
important	O
step	O
might	O
be	O
the	O
derivation	O
of	O
the	O
Dirac	B-Process
equation	I-Process
.	O
The	B-Process
Creutz	I-Process
model	I-Process
[	O
32	O
]	O
suggests	O
that	O
we	O
should	O
consider	O
incorporating	O
into	O
the	O
logical	O
inference	O
treatment	O
,	O
the	O
additional	O
knowledge	O
that	O
one	O
has	O
objects	B-Process
hopping	I-Process
on	I-Process
a	I-Process
lattice	I-Process
instead	I-Process
of	I-Process
particles	I-Process
moving	O
in	O
a	O
space-time	O
continuum	O
.	O
Recall	O
that	O
up	O
to	O
Section	O
2.4	O
,	O
the	O
description	O
of	O
the	O
measurement	B-Task
scenario	I-Task
,	O
robustness	O
etc.	O
is	O
explicitly	O
discrete	O
.	O
In	O
Section	O
2.4	O
,	O
the	O
continuum	O
limit	O
was	O
taken	O
only	O
because	O
our	O
aim	O
was	O
to	O
derive	O
the	O
Pauli	B-Process
equation	I-Process
,	O
which	O
is	O
formulated	O
in	O
continuum	B-Material
space-time	I-Material
.	O
Of	O
course	O
,	O
the	O
description	O
of	O
the	O
motion	B-Task
of	I-Task
the	I-Task
particle	I-Task
in	O
Section	O
2.6	O
is	O
entirely	O
within	O
a	O
continuum	O
description	O
but	O
there	O
is	O
no	O
fundamental	O
obstacle	O
to	O
replace	O
this	O
treatment	O
by	O
a	O
proper	O
treatment	O
of	O
objects	B-Material
hopping	O
on	O
a	O
lattice	B-Material
.	O
Therefore	O
it	O
seems	O
plausible	O
that	O
the	O
logical	O
inference	O
approach	O
can	O
be	O
extended	O
to	O
describe	O
massless	B-Material
spin-1	I-Material
/	I-Material
2	I-Material
particles	I-Material
moving	O
in	O
continuum	O
space-time	O
by	O
considering	O
the	O
continuum	O
limit	O
of	O
the	O
corresponding	O
lattice	B-Process
model	I-Process
.	O
An	O
in-depth	O
,	O
general	O
treatment	O
of	O
this	O
problem	O
is	O
beyond	O
the	O
scope	O
of	O
the	O
present	O
paper	O
and	O
we	O
therefore	O
leave	O
this	O
interesting	O
problem	O
for	O
future	B-Task
research	I-Task
.	O

A	O
fluctuating	B-Material
vacuum	I-Material
is	O
a	O
general	O
feature	O
of	O
quantum	B-Process
fields	I-Process
,	O
of	O
which	O
the	O
free	B-Process
Maxwell	I-Process
field	I-Process
considered	O
in	O
[	O
1	O
–	O
12	O
]	O
is	O
but	O
one	O
example	O
.	O
Fermionic	B-Process
fields	I-Process
such	O
as	O
that	O
describing	O
the	O
electron	O
,	O
also	O
undergo	B-Process
vacuum	I-Process
fluctuations	I-Process
,	O
consequently	O
one	O
expects	O
to	O
find	O
Casimir	B-Process
effects	I-Process
associated	O
with	O
such	B-Process
fields	I-Process
whenever	O
they	O
are	O
confined	O
in	O
some	O
way	O
.	O
Such	O
effects	O
were	O
first	O
investigated	O
in	O
the	O
context	O
of	O
nuclear	B-Task
physics	I-Task
,	O
within	O
the	O
so-called	O
“	B-Task
MIT	I-Task
bag	I-Task
model	I-Task
”	I-Task
of	I-Task
the	I-Task
nucleon	I-Task
[	O
13	O
]	O
.	O
In	O
the	O
bag-model	O
one	O
envisages	O
the	O
nucleon	O
as	O
a	B-Task
collection	I-Task
of	I-Task
fermionic	I-Task
fields	I-Task
describing	I-Task
confined	I-Task
quarks	I-Task
.	O
These	O
quarks	O
are	O
subject	O
to	O
a	O
boundary	O
condition	O
at	O
the	O
surface	O
of	O
the	O
‘	O
bag’	O
that	O
represents	O
the	O
nucleon	O
’s	O
surface	O
.	O
Just	O
as	O
in	O
the	O
electromagnetic	O
case	O
,	O
the	B-Process
bag	I-Process
boundary	I-Process
condition	I-Process
modifies	I-Process
the	I-Process
vacuum	I-Process
fluctuations	I-Process
of	I-Process
the	I-Process
field	I-Process
,	O
which	O
results	O
in	O
the	O
appearance	O
of	O
a	O
Casimir	B-Task
force	I-Task
[	O
14	O
–	O
18	O
]	O
.	O
This	O
force	O
,	O
although	O
very	O
weak	O
at	O
a	O
macroscopic	O
scale	O
,	O
can	O
be	O
significant	O
on	O
the	O
small	O
length	O
scales	O
encountered	O
in	O
nuclear	B-Task
physics	I-Task
.	O
It	O
therefore	O
has	O
important	O
consequences	O
for	O
the	O
physics	O
of	O
the	O
bag-model	B-Material
nucleon	I-Material
[	O
19	O
]	O
.	O

We	O
have	O
presented	O
spectrally	B-Process
resolved	I-Process
femtosecond	I-Process
three-pulse	I-Process
photon	I-Process
echo	I-Process
measurements	I-Process
on	O
Zn	B-Material
(	I-Material
II	I-Material
)–	I-Material
OEP	I-Material
,	O
Ni	B-Material
(	I-Material
II	I-Material
)–	I-Material
OEP	I-Material
and	O
Co	B-Material
(	I-Material
II	I-Material
)–	I-Material
OEP	I-Material
.	O
Increased	B-Task
degree	I-Task
of	I-Task
freedom	I-Task
in	O
scans	O
of	O
time	O
delays	O
allows	O
one	O
to	O
separate	B-Process
and	I-Process
extract	I-Process
specific	I-Process
type	I-Process
of	I-Process
spectroscopic	I-Process
information	I-Process
in	O
complex	B-Material
molecules	I-Material
by	O
studying	O
spectral	B-Process
and	I-Process
temporal	I-Process
evolution	I-Process
of	I-Process
the	I-Process
photon	I-Process
echo	I-Process
signals	I-Process
.	O
By	O
varying	B-Process
the	I-Process
population	I-Process
times	I-Process
,	O
population	B-Task
relaxation	I-Task
dynamics	I-Task
and	O
inhomogeneous	B-Process
broadening	I-Process
is	O
revealed	O
in	O
the	O
photon	B-Material
echo	I-Material
spectra	I-Material
.	O
Time-integrated	B-Process
photon	I-Process
echo	I-Process
signals	I-Process
show	O
two	O
different	O
timescales	B-Material
.	O
The	O
electronic	B-Material
relaxation	I-Material
timescale	I-Material
is	O
found	O
to	O
be	O
sub	B-Material
50fs	I-Material
whereas	O
the	O
timescale	B-Material
for	O
intramolecular	B-Process
vibrational	I-Process
relaxation	I-Process
,	O
occurring	O
in	O
Q00	B-Material
band	I-Material
,	O
was	O
found	O
to	O
be	O
over	O
a	O
picosecond	O
for	O
Co	B-Material
(	I-Material
II	I-Material
)–	I-Material
OEP	I-Material
and	O
Ni	B-Material
(	I-Material
II	I-Material
)–	I-Material
OEP	I-Material
and	O
within	B-Material
a	I-Material
picosecond	I-Material
for	I-Material
Zn	I-Material
(	I-Material
II	I-Material
)–	I-Material
OEP	I-Material
.	O

Water	B-Material
is	O
the	O
most	O
important	O
liquid	B-Material
,	O
and	O
the	O
nature	O
of	O
its	O
structure	O
remains	O
a	O
topic	O
of	O
keen	O
debate	O
and	O
an	O
active	O
area	O
of	O
research	O
[	O
1	O
–	O
9	O
]	O
.	O
Much	O
of	O
this	O
debate	O
centers	O
around	O
whether	O
water	B-Material
has	O
a	O
mainly	O
tetrahedral	O
structure	O
with	O
a	O
continuum	O
of	O
distorted	B-Material
hydrogen	I-Material
bonds	I-Material
,	O
or	O
if	O
it	O
contains	O
a	O
mixture	O
of	O
two	O
distinct	O
components	O
.	O
One	O
major	O
development	O
in	O
recent	O
years	O
is	O
the	O
application	O
of	O
inner-shell	B-Process
spectroscopic	I-Process
techniques	I-Process
,	O
such	O
as	O
X-ray	B-Process
absorption	I-Process
spectroscopy	I-Process
(	O
XAS	B-Process
)	O
and	O
X-ray	B-Process
emission	I-Process
spectroscopy	I-Process
(	O
XES	B-Process
)	O
at	O
the	O
oxygen	B-Material
K-edge	I-Material
to	O
investigate	O
the	O
structure	O
of	O
water	B-Material
[	O
2,10	O
–	O
12	O
]	O
.	O
These	O
methods	O
can	O
provide	O
a	O
direct	B-Process
structural	I-Process
probe	I-Process
of	I-Process
water	I-Process
,	O
providing	O
insight	O
into	O
the	O
nature	O
of	O
its	O
hydrogen	B-Task
bonding	I-Task
network	I-Task
.	O
Theoretical	O
studies	O
play	O
a	O
critical	O
role	O
in	O
these	O
studies	O
,	O
since	O
the	O
analysis	O
of	O
the	O
experimental	B-Material
data	I-Material
requires	O
calculations	B-Material
to	O
provide	O
a	O
link	O
between	O
the	O
observed	B-Process
spectral	I-Process
features	I-Process
and	O
the	O
underlying	B-Task
structure	I-Task
.	O
However	O
,	O
the	O
simulation	O
of	O
the	O
XAS	B-Process
or	O
XES	B-Process
for	O
liquid	B-Material
water	I-Material
presents	O
a	O
difficult	O
challenge	O
because	O
it	O
requires	O
accurate	B-Process
molecular	I-Process
dynamics	I-Process
simulations	I-Process
to	O
provide	O
a	O
correct	O
description	O
of	O
the	O
molecular	B-Task
structure	I-Task
coupled	O
with	O
accurate	O
calculations	O
of	O
the	O
spectral	B-Task
properties	I-Task
,	O
i.e.	O
excitation	B-Task
energies	I-Task
and	O
line	B-Task
intensities	I-Task
.	O
Furthermore	O
,	O
adequate	B-Material
sampling	I-Material
over	O
molecular	B-Process
configurations	I-Process
also	O
needs	O
to	O
be	O
accounted	O
for	O
.	O

Under	O
these	O
experimental	O
conditions	O
,	O
the	O
observed	B-Process
dynamics	I-Process
has	O
to	O
occur	O
where	O
the	O
probe	B-Process
laser	I-Process
induces	O
the	O
reactions	B-Task
resulting	O
in	O
further	B-Process
ionization	I-Process
[	O
30	O
]	O
.	O
The	O
two-step	B-Process
decay	I-Process
model	I-Process
[	O
26	O
]	O
was	O
applied	O
to	O
explain	O
the	O
above-mentioned	O
fragmentation	B-Process
of	I-Process
DCPD	I-Process
to	I-Process
CPD	I-Process
,	O
shown	O
in	O
Figure	O
8a	O
.	O
The	O
fitting	O
of	O
the	O
rise	B-Material
and	I-Material
decay	I-Material
components	I-Material
of	O
the	O
transients	B-Material
were	O
done	O
by	O
Matlab	B-Process
®	I-Process
programming	I-Process
using	O
the	O
curve	B-Process
fitting	I-Process
Levenberg	I-Process
–	I-Process
Marquardt	I-Process
algorithm	I-Process
.	O
The	O
best	B-Process
fit	I-Process
decay	I-Process
constants	I-Process
for	O
the	O
biexponential	B-Material
decay	I-Material
components	I-Material
of	O
C10H12	B-Material
+	I-Material
ion	I-Material
signal	I-Material
is	O
τ1	B-Process
=	I-Process
35fs	I-Process
and	O
τ2	B-Process
=	I-Process
240fs	I-Process
,	O
while	O
that	O
for	O
C5H6	B-Material
+	I-Material
ion	I-Material
signal	I-Material
is	O
τ1	B-Process
=	I-Process
36fs	I-Process
and	O
τ2	B-Process
=	I-Process
280fs	I-Process
,	O
respectively	O
.	O
These	O
decay	B-Task
constants	I-Task
conform	O
to	O
the	O
previously	O
reported	O
time	O
constants	O
of	O
norbornene	B-Material
and	O
norbornadiene	B-Material
[	O
22,23	O
]	O
.	O
The	O
transients	O
of	O
the	O
reaction	O
fragment	O
C5H6	O
+	O
are	O
sufficiently	O
different	O
from	O
that	O
of	O
the	O
parent	B-Material
ion	I-Material
C10H12	B-Material
+	I-Material
indicating	O
that	O
we	O
are	O
studying	O
the	O
distinct	B-Task
dynamics	I-Task
of	I-Task
the	I-Task
neutrals	I-Task
and	O
not	O
that	O
of	O
the	O
parent	B-Task
ion	I-Task
fragmentation	I-Task
[	O
24	O
]	O
.	O
Applying	B-Process
laser	I-Process
control	I-Process
principles	I-Process
under	O
such	O
experimental	O
circumstances	O
also	O
confirms	O
that	O
we	O
are	O
controlling	B-Process
the	I-Process
product	I-Process
yield	I-Process
of	I-Process
C5H6	I-Process
+	I-Process
,	O
resulting	O
from	O
the	O
photochemical	B-Process
reaction	I-Process
of	I-Process
DCPD	I-Process
.	O

Experimental	O
studies	O
of	O
the	O
dynamics	O
of	O
individual	O
carbon	B-Material
atoms	I-Material
in	O
graphene	B-Material
have	O
been	O
empowered	O
by	O
the	O
recent	O
progress	O
in	O
aberration-corrected	B-Process
transmission	I-Process
electron	I-Process
microscopy	I-Process
(	O
AC-TEM	B-Process
)	O
capable	O
of	O
sub-Ångstrom	B-Process
resolution	I-Process
.	O
The	O
examples	O
include	O
AC-TEM	B-Process
observations	I-Process
of	I-Process
the	I-Process
formation	I-Process
and	I-Process
annealing	I-Process
of	I-Process
Stone	I-Process
–	I-Process
Wales	I-Process
defects	I-Process
[	O
1	O
]	O
,	O
edge	B-Process
reconstruction	I-Process
[	O
2,3	O
]	O
and	O
formation	B-Process
of	I-Process
a	I-Process
large	I-Process
hole	I-Process
in	I-Process
graphene	I-Process
sheet	I-Process
from	I-Process
a	I-Process
single	I-Process
vacancy	I-Process
defect	I-Process
[	O
3	O
]	O
.	O
The	O
AC-TEM	O
has	O
been	O
also	O
exploited	O
in	O
visualization	O
in	O
real	O
time	O
of	O
the	O
process	B-Process
of	I-Process
self-assembly	I-Process
of	I-Process
graphene	I-Process
nanoribbons	I-Process
from	O
molecular	B-Material
precursors	I-Material
[	O
4,5	O
]	O
and	O
formation	B-Process
of	I-Process
nanometre	I-Process
size	I-Process
hollow	I-Process
protrusion	I-Process
on	I-Process
the	I-Process
nanotube	I-Process
sidewall	I-Process
[	O
6	O
]	O
.	O
Based	O
on	O
AC-TEM	B-Process
observations	I-Process
of	O
transformation	O
of	O
small	B-Material
finite	I-Material
graphene	I-Material
flake	I-Material
into	O
fullerene	B-Material
,	O
a	O
new	O
‘	B-Task
top-down’	I-Task
mechanism	I-Task
for	I-Task
the	I-Task
formation	I-Task
of	I-Task
fullerene	I-Task
under	O
the	O
electron	O
beam	O
radiation	O
has	O
been	O
proposed	O
[	O
7	O
]	O
.	O
The	O
critical	O
step	O
in	O
the	O
proposed	B-Task
‘	I-Task
top-down’	I-Task
mechanism	I-Task
of	I-Task
the	I-Task
fullerene	I-Task
formation	I-Task
is	O
creation	B-Process
of	I-Process
vacancies	I-Process
in	I-Process
small	I-Process
graphene	I-Process
flake	I-Process
as	O
a	O
result	O
of	O
knock-on	B-Process
damage	I-Process
by	I-Process
electrons	I-Process
of	I-Process
the	I-Process
imaging	I-Process
electron	I-Process
beam	I-Process
(	I-Process
e-beam	I-Process
)	I-Process
.	O
The	O
subsequent	B-Task
formation	I-Task
of	I-Task
pentagons	I-Task
at	O
the	O
vacancy	O
sites	O
near	O
the	O
edge	O
reduces	O
the	O
number	O
of	O
dangling	O
bonds	O
and	O
triggers	O
the	O
curving	O
process	O
of	O
graphene	O
flake	O
into	O
a	O
closed	O
fullerene	O
structure	O
[	O
7	O
]	O
.	O
Thus	O
,	O
dynamic	O
behaviour	O
of	O
vacancies	O
near	O
graphene	O
edge	O
plays	O
a	O
crucial	O
role	O
in	O
explaining	O
mechanisms	O
of	O
the	O
e-beam	B-Task
assisted	I-Task
self-assembly	I-Task
and	O
structural	B-Task
transformations	I-Task
in	I-Task
graphene-like	I-Task
structures	I-Task
.	O

The	O
PESs	O
here	O
employed	O
have	O
already	O
been	O
tested	O
in	O
order	O
to	O
verify	B-Process
their	I-Process
validity	I-Process
for	I-Process
dynamical	I-Process
purposes	I-Process
.	O
Such	O
tests	O
include	O
studies	B-Task
of	I-Task
the	I-Task
nitrogen	I-Task
exchange	I-Task
reaction	I-Task
[	O
14	O
]	O
both	O
adiabatic	O
by	O
running	O
trajectories	O
on	O
the	O
lowest	O
surfaces	O
and	O
non-adiabatic	O
by	O
using	O
the	O
trajectory	B-Process
surface	I-Process
hoping	I-Process
(	O
TSH	B-Process
)	O
method	O
[	O
22,23	O
]	O
for	O
transitions	O
to	O
the	O
excited	O
state	O
of	O
same	O
symmetry	O
.	O
It	O
was	O
concluded	O
that	O
nonadiabatic	O
transitions	O
could	O
not	O
make	O
a	O
significant	O
impact	O
on	O
the	O
rate	B-Material
coefficients	I-Material
,	O
and	O
therefore	O
all	O
trajectories	O
here	O
reported	O
are	O
independently	B-Process
integrated	I-Process
for	I-Process
each	I-Process
symmetry	I-Process
on	O
the	O
corresponding	O
lowest	O
adiabatic	O
PES	O
.	O
In	O
fact	O
,	O
we	O
have	O
tested	O
the	O
impact	O
of	O
running	O
the	O
trajectories	O
starting	O
on	O
the	O
upper	O
sheets	O
,	O
and	O
found	O
no	O
vibrational	B-Process
transition	I-Process
to	O
take	O
place	O
,	O
only	O
small	O
amounts	O
of	O
rotational	B-Task
energy	I-Task
is	O
exchanged	O
in	O
this	O
case	O
.	O
Also	O
neglected	O
are	O
electronic	B-Process
transitions	I-Process
to	I-Process
the	I-Process
quartet	I-Process
state	I-Process
which	O
are	O
believed	O
to	O
be	O
far	O
less	O
probable	O
than	O
the	O
simple	O
vibrational	O
energy	O
transfer	O
here	O
studied	O
due	O
to	O
their	O
spin-forbidden	O
character	O
.	O
It	O
should	O
also	O
be	O
noted	O
that	O
the	O
use	B-Process
of	I-Process
quasiclassical	I-Process
trajectories	I-Process
is	O
justified	O
by	O
the	O
large	O
masses	O
of	O
the	O
atoms	B-Material
involved	O
[	O
24	O
]	O
.	O

The	O
optical	O
properties	O
of	O
charged	O
excitations	O
are	O
important	O
for	O
understanding	O
organic	B-Task
semiconductor	I-Task
photophysics	I-Task
.	O
The	O
injection	O
of	O
electric	B-Material
charge	I-Material
into	O
organic	B-Material
materials	I-Material
polarizes	O
the	O
surroundings	O
and	O
changes	O
the	O
bond	O
lengths	O
around	O
it	O
,	O
such	O
an	O
excitation	O
is	O
defined	O
as	O
a	O
charged	B-Material
polaron	I-Material
.	O
Absorption	O
of	O
light	O
and	O
fluorescence	O
quenching	O
by	O
polarons	O
are	O
important	O
issues	O
in	O
the	O
operation	B-Task
of	I-Task
organic	I-Task
optoelectronic	I-Task
devices	I-Task
.	O
It	O
is	O
particularly	O
relevant	O
to	O
the	O
development	B-Task
of	I-Task
electrically	I-Task
pumped	I-Task
lasers	I-Task
.	O
With	O
recent	O
advances	O
in	O
materials	O
properties	O
and	O
optical	O
design	O
the	O
lasing	B-Task
threshold	I-Task
of	I-Task
organic	I-Task
structures	I-Task
under	I-Task
optical	I-Task
pumping	I-Task
is	O
now	O
low	O
enough	O
to	O
enable	O
pumping	O
by	O
inorganic	B-Material
laser	I-Material
diodes	I-Material
[	O
1	O
–	O
3	O
]	O
and	O
LEDs	O
[	O
4	O
]	O
which	O
is	O
promising	O
for	O
fabrication	O
of	O
very	B-Material
sensitive	I-Material
low-cost	I-Material
devices	I-Material
for	O
biosensing	B-Process
and	O
chemosensing	B-Process
[	O
5,6	O
]	O
.	O
However	O
,	O
light	B-Process
absorption	I-Process
by	O
injected	O
charges	O
has	O
been	O
reported	O
to	O
be	O
the	O
major	O
obstacle	O
to	O
electrically	B-Process
pumped	I-Process
lasing	I-Process
[	O
7	O
]	O
.	O
Injected	O
charges	O
can	O
also	O
quench	O
luminescence	O
as	O
they	O
accept	B-Process
energy	I-Process
from	I-Process
excitons	I-Process
by	O
resonant	B-Task
dipole	I-Task
–	I-Task
dipole	I-Task
interactions	I-Task
and	O
this	O
is	O
an	O
important	O
loss	O
mechanism	O
in	O
organic	B-Material
LEDs	I-Material
as	O
well	O
as	O
in	O
lasers	B-Material
.	O
Absorption	O
cross-sections	O
of	O
polarons	O
are	O
not	O
known	O
to	O
the	O
desired	O
accuracy	O
because	O
of	O
the	O
difficulty	O
of	O
quantifying	O
the	O
charge	B-Process
density	I-Process
injected	O
into	O
the	O
film	O
.	O
Previous	O
studies	O
used	O
controlled	B-Process
electrical	I-Process
injection	I-Process
of	I-Process
charges	I-Process
in	O
unipolar	B-Material
devices	I-Material
through	O
contacting	B-Material
electrodes	I-Material
and	O
field-dependent	B-Material
charge	I-Material
mobility	I-Material
measurements	I-Material
to	O
estimate	O
the	O
charge	O
densities	O
which	O
were	O
compared	O
with	O
the	O
values	O
obtained	O
by	O
capacitance	B-Process
–	I-Process
voltage	I-Process
analysis	I-Process
and	O
the	O
two	O
results	O
differed	O
by	O
a	O
factor	O
of	O
three	O
[	O
8,9	O
]	O
.	O

In	O
this	O
Letter	O
we	O
revisit	O
the	O
Chesnavich	B-Process
model	I-Process
Hamiltonian	I-Process
[	O
37	O
]	O
in	O
the	O
light	O
of	O
recent	O
developments	O
in	O
TST	B-Material
.	O
For	O
barrierless	O
systems	O
such	O
as	O
ion	B-Process
–	I-Process
molecule	I-Process
reactions	I-Process
,	O
the	O
concepts	O
of	O
OTS	B-Material
and	O
TTS	B-Material
can	O
be	O
clearly	O
formulated	O
in	O
terms	O
of	O
well	O
defined	O
phase	O
space	B-Material
geometrical	I-Material
objects	I-Material
.	O
(	O
For	O
work	O
on	O
the	O
phase	O
space	O
description	O
of	O
OTS	B-Material
,	O
see	O
Refs.	O
[	O
38	O
–	O
40	O
].)	O
The	O
first	O
goal	O
of	O
the	O
present	O
article	O
is	O
the	O
identification	B-Process
of	O
these	O
notions	O
with	O
well	B-Material
defined	I-Material
phase	I-Material
space	I-Material
dividing	I-Material
surfaces	I-Material
attached	I-Material
to	I-Material
NHIMs	I-Material
.	O
The	O
second	O
and	O
main	O
goal	O
is	O
an	O
elucidation	O
of	O
the	O
roaming	O
phenomenon	O
in	O
the	O
context	O
of	O
the	O
Chesnavich	B-Process
model	I-Process
Hamiltonian	I-Process
.	O
The	O
associated	B-Process
potential	I-Process
function	I-Process
,	O
possessing	O
many	O
features	O
associated	O
with	O
a	O
realistic	O
molecular	O
PES	O
,	O
leads	O
to	O
dynamics	B-Task
which	I-Task
clearly	I-Task
reveal	I-Task
the	I-Task
origins	I-Task
of	I-Task
the	I-Task
roaming	I-Task
effect	I-Task
.	O
Based	O
on	O
our	O
trajectory	B-Material
simulations	I-Material
,	O
we	O
show	O
how	O
the	O
identification	O
of	O
the	O
TTS	O
and	O
OTS	O
DSs	O
with	O
periodic	B-Material
orbit	I-Material
dividing	I-Material
surfaces	I-Material
(	O
PODS	B-Material
)	O
provides	O
the	O
natural	O
framework	O
for	O
analysis	O
of	O
the	O
roaming	O
mechanism	O
.	O

Within	O
the	O
range	B-Material
of	I-Material
temperatures	I-Material
chosen	I-Material
,	O
alanine	B-Material
dipeptide	I-Material
exhibits	O
very	O
simple	O
behaviour	O
.	O
This	O
result	O
is	O
due	O
to	O
the	O
relatively	O
small	O
number	O
of	O
physically	B-Process
relevant	I-Process
minima	I-Process
(	O
seven	O
were	O
characterised	O
using	O
this	O
force	B-Process
field	I-Process
and	I-Process
solvent	I-Process
model	I-Process
)	O
and	O
the	O
larger	B-Material
potential	I-Material
energy	I-Material
spacing	I-Material
between	O
the	O
global	O
minimum	O
and	O
higher	O
energy	O
minima	O
.	O
Indeed	O
,	O
cross-overs	B-Process
in	I-Process
the	I-Process
approximate	I-Process
global	I-Process
free	I-Process
energy	I-Process
minimum	O
for	O
this	O
system	O
(	O
where	O
the	O
free	O
energy	O
of	O
the	O
second-lowest	O
potential	O
energy	O
minimum	O
becomes	O
lower	O
than	O
that	O
of	O
the	O
global	O
potential	O
energy	O
minimum	O
)	O
in	O
the	O
harmonic	O
approximation	O
would	O
occur	O
at	O
1170K	O
.	O
In	O
general	O
,	O
the	O
harmonic	O
prediction	O
for	O
the	O
crossover	O
temperature	O
between	O
two	O
minima	O
is	O
(	O
4	O
)	O
kBTxo	O
=	O
V1	O
−	O
V2ln	O
((	O
o2ν	O
¯	O
2κ	O
)/(	O
o1ν	O
¯	O
1κ	O
))	O
,	O
from	O
Eq	O
.	O
(	O
3	O
)	O
,	O
which	O
clearly	O
illustrates	O
the	O
balance	B-Task
between	I-Task
potential	I-Task
energy	I-Task
and	I-Task
well	I-Task
entropy	I-Task
.	O

Both	O
methods	O
of	O
structure	B-Process
solution	I-Process
reveal	O
a	O
bent	B-Process
conformation	I-Process
of	O
the	O
central	B-Material
terthiophene	I-Material
units	I-Material
of	O
the	O
DOTT	B-Material
molecule	I-Material
as	O
is	O
clearly	O
visible	O
in	O
all	O
three	O
cases	O
in	O
Figure	O
5	O
.	O
However	O
,	O
there	O
is	O
a	O
fundamental	O
difference	O
in	O
the	O
conformation	O
of	O
the	O
octyl	B-Material
side	I-Material
chains	I-Material
.	O
Whilst	O
for	O
the	O
single	O
crystal	O
phase	O
at	O
T	O
=	O
100K	O
linearly	B-Material
extended	I-Material
chains	I-Material
are	O
observed	O
(	O
Figure	O
5B	O
)	O
,	O
a	O
defined	B-Process
rotation	I-Process
of	I-Process
the	I-Process
octyl	I-Process
chains	I-Process
relative	O
to	O
the	O
terthiophene	B-Material
unit	I-Material
is	O
found	O
for	O
the	O
three	O
thin	B-Material
film	I-Material
phases	O
(	O
Figure	O
5A	O
)	O
.	O
The	O
rotation	O
angle	O
of	O
about	O
±	O
70	O
°	O
results	O
from	O
a	O
twist	B-Process
of	I-Process
the	I-Process
first	I-Process
CC	I-Process
single	I-Process
bond	I-Process
at	O
the	O
link	O
between	O
the	O
terthiophene	B-Material
unit	I-Material
and	O
the	O
octyl	B-Material
chain	I-Material
(	O
see	O
arrows	O
Figure	O
5A	O
)	O
.	O
Two	O
features	O
of	O
this	O
rotated	O
conformation	O
are	O
interesting	O
.	O
First	O
,	O
a	O
molecule	O
with	O
rotated	B-Material
side	I-Material
chains	I-Material
represents	O
the	O
equilibrium	O
state	O
of	O
an	O
isolated	O
single	O
DOTT	B-Material
molecule	I-Material
as	O
obtained	O
by	O
combined	B-Process
MD	I-Process
and	I-Process
VASP	I-Process
calculations	I-Process
[	O
33	O
]	O
.	O
Second	O
,	O
the	O
rotated	O
conformation	O
of	O
the	O
octyl	B-Material
chains	I-Material
allows	O
a	O
dense	B-Process
packing	I-Process
of	O
the	O
octyl	B-Material
side	I-Material
chains	I-Material
for	O
both	O
molecules	O
.	O
Interestingly	O
,	O
the	O
single	O
crystal	B-Material
structure	I-Material
at	O
room	O
temperature	O
shows	O
the	O
twisted	O
as	O
well	O
as	O
the	O
linear	O
conformation	O
of	O
the	O
octyl	B-Material
side	I-Material
chains	I-Material
within	O
one	O
molecule	O
(	O
Figure	O
5C	O
)	O
.	O

Previous	O
studies	O
have	O
shown	O
that	O
there	O
are	O
two	O
main	O
mechanisms	O
for	O
the	O
development	B-Process
of	I-Process
radiation-induced	I-Process
DSBs	I-Process
[	O
16,17	O
]	O
.	O
For	O
γ-ray	B-Process
radiation	I-Process
,	O
single	B-Process
step	I-Process
is	O
the	O
main	O
process	O
to	O
cause	O
DSBs	B-Process
(	O
see	O
Figure	O
3b	O
)	O
,	O
which	O
is	O
attributed	O
to	O
the	O
generation	B-Process
of	I-Process
number	I-Process
of	I-Process
ROS	I-Process
upon	O
the	O
incident	O
of	O
individual	O
photon	B-Material
of	I-Material
γ-ray	I-Material
.	O
Whereas	O
photo-radiation	B-Process
causes	O
DSBs	B-Process
through	O
two	B-Process
step	I-Process
mechanism	I-Process
(	O
Figure	O
3a	O
)	O
by	O
reflecting	O
that	O
each	O
single	O
photon	B-Material
causes	O
mostly	O
single	O
ROS	B-Material
and	O
thus	O
induces	O
only	O
single	B-Process
strand	I-Process
break	I-Process
.	O
Then	O
,	O
when	O
a	O
second	O
single	B-Process
strand	I-Process
break	I-Process
occurs	O
where	O
near	O
the	O
existing	O
single	B-Process
strand	I-Process
break	I-Process
,	O
DBS	B-Process
is	O
caused	O
,	O
i.e.	O
,	O
the	O
two	B-Process
step	I-Process
mechanism	I-Process
.	O
Summarizing	O
the	O
results	O
and	O
discussion	O
we	O
may	O
conclude	O
as	O
that	O
:	O
(	O
1	O
)	O
The	O
significant	O
protective	O
effect	O
of	O
AA	B-Material
against	O
photo-induced	B-Process
damage	I-Process
may	O
reflect	O
the	O
effective	O
diminish	O
of	O
ROS	O
by	O
AA	O
.	O
(	O
2	O
)	O
For	O
the	O
γ-ray	O
induced	O
DSB	O
,	O
the	O
protective	O
effect	O
by	O
AA	O
is	O
a	O
little	O
bit	O
weaker	O
than	O
the	O
case	O
of	O
photo	B-Material
irradiation	O
.	O
This	O
may	O
be	O
due	O
to	O
the	O
generation	O
of	O
numbers	O
of	O
ROS	B-Material
by	O
single	O
photon	O
of	O
γ-ray	O
.	O
Surviving	O
oxygen	O
species	O
against	O
the	O
diminishment	O
effect	O
by	O
AA	B-Material
may	O
cause	O
DSBs	O
.	O
(	O
3	O
)	O
As	O
for	O
the	O
DSBs	O
by	O
ultrasound	O
,	O
damage	O
is	O
caused	O
by	O
the	O
shockwave	O
through	O
the	O
generation	O
of	O
cavitations	O
[	O
18	O
]	O
.	O
Thus	O
,	O
the	O
chemical	O
effect	O
of	O
AA	O
to	O
diminish	O
ROS	O
is	O
considered	O
to	O
be	O
negligibly	O
small	O
for	O
the	O
protection	O
of	O
DSBs	O
.	O

In	B-Process
situ	I-Process
oxidation	I-Process
,	O
experiments	O
were	O
carried	O
out	O
using	O
3mm	B-Material
diameter	I-Material
discs	I-Material
with	O
one	O
surface	O
ground	B-Process
and	I-Process
polished	I-Process
to	O
a	O
1μm	O
diamond	O
finish	O
.	O
The	O
3mm	O
discs	O
were	O
then	O
oxidised	B-Process
in	O
a	O
Philips	B-Material
XL-30	I-Material
FEG	I-Material
ESEM	I-Material
with	O
a	O
hot	B-Material
stage	I-Material
attachment	I-Material
.	O
The	O
oxidising	O
atmosphere	O
used	O
was	O
laboratory	O
air	O
at	O
a	O
pressure	O
of	O
266Pa	O
.	O
During	O
the	O
experiment	O
,	O
the	O
sample	O
was	O
observed	O
and	O
imaged	O
using	O
a	O
primary	O
beam	O
energy	O
of	O
20kV	O
and	O
an	O
Everhart	O
–	O
Thornley	O
secondary	O
electron	O
detector	O
.	O
The	O
sample	O
was	O
heated	B-Process
at	O
a	O
rate	O
of	O
100	O
°	O
C	O
/	O
min	O
to	O
a	O
temperature	O
of	O
700	O
°	O
C	O
and	O
held	O
at	O
this	O
temperature	O
for	O
8min	O
to	O
stabilise	B-Process
the	I-Process
stage	I-Process
and	I-Process
the	I-Process
microscope	I-Process
.	O
The	O
sample	O
was	O
then	O
heated	B-Process
to	I-Process
a	I-Process
final	I-Process
temperature	I-Process
of	I-Process
900	I-Process
°	I-Process
C	I-Process
at	O
the	O
same	O
heating	O
rate	O
.	O
The	O
total	O
time	O
of	O
exposure	O
of	O
the	O
sample	O
was	O
120min	O
before	O
cooling	O
to	O
room	O
temperature	O
by	O
turning	O
off	O
the	O
heating	B-Material
coils	I-Material
.	O
The	O
samples	O
were	O
then	O
examined	O
in	O
the	O
LEO	B-Material
1530VP	I-Material
FEGSEM	I-Material
with	O
chemical	O
information	O
gathered	O
using	O
EDS	B-Material
.	O
Cross-sections	O
and	O
Transmission	B-Material
Electron	I-Material
Microscope	I-Material
(	O
TEM	B-Material
)	O
samples	O
were	O
produced	O
using	O
a	O
dual	B-Material
beam	I-Material
FEI	I-Material
Nova	I-Material
Nanolab	I-Material
600	I-Material
for	O
Focused	B-Material
Ion	I-Material
Beam	I-Material
(	O
FIB	B-Material
)	O
milling	O
perpendicular	O
to	O
the	O
phase	O
boundaries	O
to	O
determine	O
their	O
influence	O
on	O
the	O
oxide	B-Material
development	O
and	O
imaged	O
using	O
a	O
Jeol	B-Material
2000FX	I-Material
W-filament	I-Material
TEM	I-Material
.	O
EDS	B-Material
maps	O
of	O
the	O
TEM	B-Material
samples	I-Material
were	O
collected	O
using	O
the	O
Nanolab	B-Material
600	I-Material
with	O
a	O
Scanning	B-Material
TEM	I-Material
(	O
STEM	B-Material
)	O
detector	O
and	O
an	O
EDAX	B-Material
Genesis	I-Material
EDS	I-Material
system	I-Material
at	O
an	O
accelerating	B-Process
voltage	I-Process
of	I-Process
30kV	I-Process
.	O

The	O
study	O
outlines	O
a	O
trial	O
of	O
transient	B-Task
response	I-Task
analysis	I-Task
on	O
full-scale	O
motorway	B-Material
bridge	I-Material
structures	I-Material
to	O
obtain	O
information	O
concerning	O
the	O
steel	B-Task
–	I-Task
concrete	I-Task
interface	I-Task
and	O
is	O
part	O
of	O
a	O
larger	O
study	O
to	O
assess	O
the	O
long-term	B-Process
sustained	I-Process
benefits	I-Process
offered	O
by	O
Impressed	B-Process
Current	I-Process
Cathodic	I-Process
Protection	I-Process
(	O
ICCP	B-Process
)	O
after	O
the	O
interruption	B-Process
of	I-Process
the	I-Process
protective	I-Process
current	I-Process
[	O
1	O
]	O
.	O
These	O
structures	O
had	O
previously	O
been	O
protected	O
for	O
5	O
–	O
16years	O
by	O
an	O
ICCP	B-Process
system	I-Process
prior	O
to	O
the	O
start	O
of	O
the	O
study	O
.	O
The	O
protective	B-Process
current	I-Process
was	I-Process
interrupted	I-Process
,	O
in	O
order	O
to	O
assess	B-Process
the	I-Process
long-term	I-Process
benefits	I-Process
provided	O
by	O
ICCP	B-Process
after	O
it	O
has	O
been	O
turned	O
off	O
.	O
This	O
paper	O
develops	B-Task
and	I-Task
examines	I-Task
a	I-Task
simplified	I-Task
approach	I-Task
for	O
the	O
on-site	O
use	O
of	O
transient	B-Process
response	I-Process
analysis	I-Process
and	O
discusses	B-Process
the	I-Process
potential	I-Process
advantages	I-Process
of	O
the	O
technique	O
as	O
a	O
tool	O
for	O
the	O
assessment	B-Task
of	I-Task
the	I-Task
corrosion	I-Task
condition	I-Task
of	O
steel	B-Material
in	O
reinforced	B-Material
concrete	I-Material
structures	I-Material
.	O

The	O
results	O
from	O
two	O
types	O
of	O
oxidation	B-Process
test	I-Process
are	O
combined	O
in	O
this	O
study	O
.	O
Table	O
1	O
shows	O
the	O
test	B-Material
matrix	I-Material
with	O
the	O
two	O
approaches	O
included	O
.	O
All	O
the	O
100h	O
tests	O
and	O
the	O
test	O
conducted	O
at	O
650	O
°	O
C	O
were	O
performed	O
using	O
a	O
thermogravimetric	B-Process
balance	I-Process
(	O
TGA	B-Process
)	O
.	O
The	O
weight	B-Material
change	I-Material
during	I-Material
these	I-Material
tests	I-Material
was	I-Material
monitored	I-Material
continually	I-Material
and	O
adjusted	B-Process
to	I-Process
accommodate	I-Process
buoyancy	I-Process
effects	I-Process
.	O
All	O
other	O
tests	O
were	O
conducted	O
in	O
horizontal	B-Material
tube	I-Material
furnaces	I-Material
.	O
For	O
these	O
latter	O
tests	O
,	O
batches	O
of	O
specimens	O
were	O
placed	O
in	O
alumina	B-Material
boats	I-Material
and	O
inserted	O
into	O
the	O
furnaces	B-Material
at	O
temperature	O
.	O
Intermittent	O
weighing	O
at	O
room	O
temperature	O
was	O
used	O
to	O
determine	B-Task
the	I-Task
oxidation	I-Task
kinetics	I-Task
.	O
At	O
selected	O
time	O
intervals	O
,	O
a	O
specimen	O
was	O
removed	O
from	O
the	O
batch	O
for	O
examination	O
before	O
the	O
high	O
temperature	O
exposure	O
continued	O
for	O
the	O
remainder	O
of	O
the	O
batch	O
.	O
Table	B-Material
1	I-Material
shows	I-Material
the	I-Material
time	I-Material
intervals	I-Material
chosen	I-Material
for	I-Material
examination	I-Material
.	O
At	O
600	O
°	O
C	O
one	O
isothermal	O
test	O
,	O
having	O
an	O
exposure	O
time	O
of	O
1000h	O
,	O
has	O
been	O
performed	O
to	O
date	O
.	O

The	O
adhesion	B-Process
/	I-Process
cohesion	I-Process
of	O
the	O
coating	O
was	O
evaluated	O
by	O
the	O
scratch	B-Process
test	I-Process
method	I-Process
,	O
using	O
a	O
Revetest	B-Material
system	I-Material
(	I-Material
CSM	I-Material
Instruments	I-Material
SA	I-Material
,	I-Material
Switzerland	I-Material
)	I-Material
equipped	I-Material
with	I-Material
a	I-Material
H-270	I-Material
diamond	I-Material
indentor	I-Material
(	I-Material
200μm	I-Material
diameter	I-Material
)	I-Material
.	O
Six	O
scratch	B-Process
indentations	I-Process
were	I-Process
carried	I-Process
out	I-Process
under	O
previously	O
optimized	O
conditions	O
(	O
linear	O
progressive	O
load	O
mode	O
1	O
–	O
4N	O
,	O
4Nmin	O
−	O
1	O
)	O
.	O
In	O
order	O
to	O
aid	O
in	O
determination	B-Task
of	I-Task
location	I-Task
of	I-Task
spallation	I-Task
/	I-Task
delamination	I-Task
,	O
an	O
extended	O
scratch	O
length	O
of	O
6mm	O
was	O
employed	O
.	O
The	O
scratch	O
tracks	O
were	O
subsequently	O
observed	O
by	O
SEM	O
to	O
determine	O
the	O
locations	O
of	O
the	O
first	O
coating	O
failure	O
and	O
to	O
understand	B-Task
the	I-Task
nature	I-Task
of	I-Task
the	I-Task
coating	I-Task
failure	I-Task
.	O
During	O
the	O
scratch	O
tests	O
,	O
the	O
loading	B-Material
force	I-Material
and	I-Material
penetration	I-Material
depth	I-Material
were	I-Material
recorded	I-Material
and	O
their	O
respective	O
values	O
were	O
correlated	O
with	O
the	O
observed	O
failure	O
locations	O
.	O
The	O
surface	B-Task
roughness	I-Task
of	I-Task
the	I-Task
coating	I-Task
was	I-Task
evaluated	I-Task
using	O
a	O
surface	B-Material
roughness	I-Material
tester	I-Material
(	O
TR200	O
,	O
Timegroup	O
Inc.	O
)	O
according	O
to	O
ISO	O
standard	O
[	O
29	O
]	O
.	O
Due	O
to	O
the	O
presence	O
of	O
the	O
open	O
porosity	O
in	O
the	O
outer	B-Material
layer	I-Material
of	O
the	O
coating	B-Process
,	O
a	O
measurement	O
length	O
for	O
determination	O
of	O
the	O
roughness	B-Process
(	O
Ra	B-Process
)	O
of	O
0.8mm	O
was	O
used	O
.	O
In	O
total	O
,	O
eight	B-Material
measurements	I-Material
were	O
carried	O
out	O
in	O
different	O
directions	O
.	O

One	O
surface	O
was	O
then	O
polished	O
and	O
cleaned	O
using	O
a	O
protocol	O
designed	O
to	O
eliminate	O
as	O
much	O
preparation-related	O
contamination	O
as	O
possible	O
.	O
This	O
is	O
as	O
follows	O
:	O
The	O
lead	O
surface	O
was	O
polished	O
by	O
hand	O
using	O
a	O
damp	B-Material
abrasive	I-Material
disc	I-Material
(	O
BuehlerMet	O
II	O
®)	O
to	O
remove	O
visible	O
surface	B-Material
defects	O
and	O
to	O
expose	O
a	O
fresh	O
metal	B-Material
surface	I-Material
.	O
Coupons	O
were	O
then	O
polished	O
using	O
a	O
sequence	O
of	O
diamond	B-Material
polishes	I-Material
with	O
decreasing	O
particle	O
sizes	O
(	O
6μm	O
,	O
3μm	O
,	O
1μm	O
Buehler	O
MetaDi	O
®	O
polycrystalline	B-Material
diamond	I-Material
suspension	O
)	O
.	O
A	O
polishing	B-Material
cloth	I-Material
(	O
Buehler	O
MicroCloth	O
®)	O
was	O
saturated	O
with	O
the	O
appropriate	O
diamond	B-Material
suspension	O
.	O
A	O
custom-made	B-Material
jig	I-Material
fitted	O
to	O
an	O
automatic	O
polisher	B-Material
(	O
Buehler	O
Minimet	O
®	O
1000	O
)	O
was	O
used	O
to	O
hold	O
the	O
coupons	B-Material
in	O
place	O
during	O
automated	B-Process
polishing	I-Process
.	O
Coupons	B-Material
were	O
polished	B-Process
for	O
15min	O
using	O
each	O
diamond	B-Process
suspension	I-Process
followed	O
by	O
rinsing	O
with	O
2-propanol	B-Material
(	O
99.5	O
%	O
,	O
reagent	O
grade	O
)	O
and	O
cleaning	O
in	O
2-propanol	O
for	O
5min	O
in	O
an	O
ultrasonic	B-Material
bath	I-Material
.	O
After	O
polishing	B-Process
with	O
the	O
1μm	O
diamond	B-Material
suspension	I-Material
,	O
the	O
coupons	B-Material
were	O
ultrasonically	O
cleaned	O
in	O
2-propanol	B-Material
for	O
3	O
×	O
5min	O
,	O
with	O
fresh	O
propanol	B-Material
for	O
each	O
cleaning	B-Process
cycle	O
.	O
Polished	O
coupons	B-Material
were	O
stored	O
in	O
2-propanol	B-Material
until	O
required	O
.	O

Poor	O
oxidation	B-Process
behavior	O
is	O
the	O
major	O
barrier	O
to	O
the	O
increased	O
use	O
of	O
Ti-based	B-Material
alloys	I-Material
in	O
high-temperature	O
structural	O
applications	O
.	O
The	O
demand	O
to	O
increase	O
the	O
service	O
temperature	O
of	O
these	O
alloys	O
beyond	O
550	O
°	O
C	O
(	O
the	O
typical	O
temperature	O
limit	O
)	O
requires	O
careful	O
study	O
to	O
understand	B-Task
the	I-Task
role	I-Task
that	I-Task
composition	I-Task
has	I-Task
on	I-Task
the	I-Task
oxidation	I-Task
behavior	I-Task
of	I-Task
Ti-based	I-Task
alloys	I-Task
[	O
1	O
–	O
3	O
]	O
.	O
The	O
attempt	O
to	O
overcome	O
this	O
limitation	O
in	O
Ti-based	O
alloys	O
has	O
led	O
to	O
the	O
production	O
of	O
alloys	B-Material
with	I-Material
substantially	I-Material
improved	I-Material
oxidation	I-Material
resistance	I-Material
such	O
as	O
β-21S	B-Material
and	O
also	O
development	B-Task
of	I-Task
coatings	I-Task
and	I-Task
pre-oxidation	I-Task
techniques	I-Task
[	O
1,4	O
–	O
6	O
]	O
.	O
While	O
it	O
is	O
tempting	O
to	O
extrapolate	O
the	O
oxidation	B-Process
behavior	O
(	O
e.g.	O
oxidation	O
rate	O
law	O
,	O
depth	O
of	O
oxygen	B-Material
ingress	O
and	O
scale	O
thickness	O
)	O
observed	O
for	O
a	O
limited	O
number	O
of	O
compositions	O
under	O
a	O
certain	O
oxidation	B-Process
condition	O
to	O
a	O
broader	O
compositional	O
range	O
,	O
there	O
are	O
numerous	O
examples	O
in	O
the	O
literature	O
where	O
deviations	O
from	O
the	O
expected	O
relations	O
are	O
observed	O
[	O
7,8	O
]	O
.	O

The	O
oxide	B-Process
thickness	O
is	O
calculated	O
using	O
the	O
weight	O
gain	O
and	O
surface	B-Material
area	O
.	O
Artificially	O
changing	O
the	O
surface	O
profile	O
will	O
modify	O
the	O
surface	O
area	O
and	O
calculated	O
oxide	B-Material
thickness	O
.	O
SEM	B-Material
images	I-Material
of	O
samples	O
removed	O
after	O
111	O
days	O
oxidation	B-Process
were	O
used	O
to	O
define	O
the	O
change	O
in	O
surface	B-Material
profile	I-Material
length	O
with	O
variation	O
in	O
applied	O
roughness	O
.	O
The	O
profile	O
lengths	O
extracted	O
from	O
the	O
images	B-Material
were	O
then	O
used	O
to	O
modify	O
the	O
length	O
of	O
the	O
sample	B-Material
and	O
therefore	O
the	O
surface	B-Material
area	I-Material
.	O
Table	O
1	O
shows	O
the	O
original	O
oxide	B-Material
thicknesses	O
after	O
111	O
days	O
oxidation	O
,	O
the	O
modified	O
oxide	O
thicknesses	O
based	O
on	O
the	O
surface	B-Material
profile	O
length	O
and	O
the	O
percentage	O
difference	O
.	O
Results	O
show	O
a	O
maximum	O
decrease	O
in	O
the	O
oxide	B-Material
thickness	O
of	O
4	O
%	O
when	O
using	O
a	O
surface	B-Material
which	O
accounts	O
for	O
roughness	O
.	O
Comparing	O
the	O
change	O
in	O
oxide	B-Material
thickness	O
between	O
different	O
surface	O
finishes	O
indicates	O
a	O
variation	O
of	O
less	O
than	O
1	O
%	O
.	O
As	O
such	O
,	O
the	O
impact	O
of	O
the	O
variation	O
in	O
the	O
profile	O
length	O
on	O
the	O
calculated	O
oxide	O
thickness	O
is	O
considered	O
to	O
be	O
insignificant	O
.	O
In	O
addition	O
,	O
if	O
the	O
differences	O
in	O
weight	O
gain	O
were	O
only	O
due	O
to	O
differences	O
in	O
surface	B-Material
area	I-Material
,	O
rougher	O
samples	B-Material
would	O
be	O
expected	O
to	O
demonstrate	O
thicker	O
oxides	B-Material
at	O
the	O
earliest	O
stages	O
of	O
oxidation	B-Process
.	O

There	O
have	O
been	O
relatively	O
few	O
attempts	O
to	O
observe	O
and	O
in	O
some	O
cases	O
extract	O
the	O
average	O
current	O
density	O
from	O
video	B-Material
images	I-Material
taken	O
of	O
growing	O
2D	B-Material
pits	I-Material
.	O
Frankel	O
presented	O
a	O
method	O
to	O
directly	O
measure	O
the	O
average	O
anodic	B-Material
current	I-Material
density	O
from	O
the	O
growing	O
pit	B-Material
boundary	O
velocity	O
in	O
Al	B-Material
[	O
33	O
]	O
,	O
an	O
Al	B-Material
alloy	I-Material
[	O
34	O
]	O
and	O
Ni	B-Material
–	I-Material
Fe	I-Material
[	I-Material
35	I-Material
]	I-Material
thin	I-Material
films	I-Material
.	O
Subsequently	O
,	O
Ryan	O
et	O
al	O
.	O
[	O
27,36	O
]	O
determined	O
the	O
anodic	O
current	O
density	O
in	O
pits	B-Material
propagating	O
as	O
2D	B-Material
disks	I-Material
in	O
stainless	B-Material
steel	I-Material
thin	I-Material
films	I-Material
by	O
measuring	O
the	O
pit	B-Material
edge	I-Material
movement	O
velocity	O
.	O
Ernst	O
and	O
Newman	O
[	O
11,12,37	O
]	O
studied	O
stability	O
of	O
pit	B-Process
growth	I-Process
in	O
detail	O
and	O
measured	B-Process
the	I-Process
kinetics	I-Process
of	I-Process
2D	I-Process
pit	I-Process
propagation	I-Process
in	O
depth	O
and	O
width	O
and	O
compared	O
the	O
results	O
with	O
kinetics	O
in	O
1D	B-Material
pencil	I-Material
electrodes	I-Material
.	O
They	O
developed	O
a	O
semi-quantitative	B-Process
model	I-Process
for	O
pit	B-Task
propagation	I-Task
which	O
explained	O
the	O
lacy	B-Process
pit	I-Process
cover	I-Process
formation	I-Process
during	O
the	O
pit	B-Process
growth	I-Process
,	O
although	O
they	O
did	O
not	O
measure	O
current	O
density	O
within	O
the	O
pit	O
.	O
More	O
recently	O
,	O
Tang	O
and	O
Davenport	O
[	O
38	O
]	O
tracked	O
the	O
pit	O
boundary	O
movement	O
and	O
computed	O
the	O
instantaneous	O
but	O
average	O
current	O
density	O
in	O
Fe-Co	B-Material
thin	I-Material
films	I-Material
.	O
However	O
,	O
there	O
have	O
been	O
no	O
previous	O
attempts	O
to	O
quantify	O
the	O
local	O
current	O
density	O
during	O
inhomogeneous	O
growth	O
of	O
pits	B-Material
,	O
although	O
such	O
local	O
variation	O
in	O
current	B-Process
density	O
has	O
long	O
been	O
recognised	O
[	O
7	O
]	O
.	O

Anodizing	B-Process
processes	I-Process
are	O
widely	O
used	O
for	O
protecting	B-Task
aluminium	I-Task
alloys	I-Task
against	O
corrosion	B-Process
[	O
1	O
]	O
.	O
The	O
resultant	B-Material
films	I-Material
are	O
composed	O
of	O
amorphous	B-Material
alumina	I-Material
and	O
consist	O
of	O
a	O
relatively	O
thick	O
,	O
porous	O
,	O
outer	O
region	O
and	O
a	O
thinner	O
,	O
non-porous	O
,	O
inner	O
region	O
[	O
2,3	O
]	O
.	O
The	O
porous	O
region	O
contains	O
the	O
major	B-Material
pores	I-Material
of	O
the	O
film	B-Material
,	O
which	O
extend	B-Process
from	I-Process
the	I-Process
film	I-Process
surface	I-Process
to	O
the	O
barrier	B-Material
layer	I-Material
.	O
Near	O
the	O
film	B-Material
surface	I-Material
,	O
shorter	O
,	O
incipient	B-Material
pores	I-Material
are	O
also	O
present	O
,	O
whose	O
growth	B-Process
stopped	I-Process
in	I-Process
the	I-Process
early	I-Process
stages	I-Process
of	I-Process
anodizing	I-Process
.	O
The	O
diameter	O
of	O
the	O
major	B-Material
pores	I-Material
and	O
the	O
thickness	O
of	O
the	O
inner	O
,	O
barrier	O
region	O
are	O
dependent	O
on	O
the	O
potential	O
applied	O
during	O
anodizing	B-Process
,	O
with	O
typical	O
proportionalities	O
of	O
∼	O
1nmV	O
−	O
1	O
[	O
3,4	O
]	O
.	O
Studies	O
of	O
ionic	B-Task
migration	I-Task
in	O
barrier-type	O
and	O
porous	O
anodic	B-Material
alumina	I-Material
films	I-Material
have	O
usually	O
found	O
a	O
transport	O
number	O
of	O
O2	B-Material
−	I-Material
ions	I-Material
of	O
∼	O
0.6	O
[	O
5,6	O
]	O
.	O
During	O
the	O
formation	O
of	O
porous	B-Material
films	I-Material
,	O
the	O
outward	B-Material
migrating	I-Material
Al3	I-Material
+	I-Material
ions	I-Material
,	O
constituting	O
the	O
remainder	O
of	O
the	O
ionic	B-Material
current	I-Material
,	O
are	O
ejected	B-Process
to	O
the	O
electrolyte	B-Material
at	O
the	O
pore	B-Material
bases	I-Material
[	O
7	O
]	O
.	O
The	O
electronic	O
current	O
in	O
the	O
barrier	B-Material
region	I-Material
is	O
generally	O
considered	O
to	O
be	O
negligible	O
.	O
The	O
thickness	O
of	O
the	O
barrier	O
region	O
,	O
which	O
is	O
relatively	O
constant	O
during	O
the	O
growth	O
of	O
a	O
film	B-Material
under	O
either	O
a	O
constant	O
potential	O
or	O
constant	O
current	O
density	O
,	O
is	O
maintained	O
by	O
a	O
balance	O
between	O
growth	B-Process
of	I-Process
the	I-Process
barrier	I-Process
layer	I-Process
by	I-Process
continued	I-Process
oxidation	I-Process
of	O
the	O
aluminium	B-Material
substrate	I-Material
and	O
thinning	B-Process
of	I-Process
the	I-Process
barrier	I-Process
layer	I-Process
by	I-Process
either	I-Process
field-assisted	I-Process
dissolution	I-Process
of	O
the	O
alumina	B-Material
at	O
the	O
pore	B-Material
bases	I-Material
[	O
8	O
]	O
or	O
field-assisted	B-Process
flow	I-Process
of	I-Process
alumina	I-Process
from	O
the	O
barrier	B-Material
layer	I-Material
to	O
the	O
pore	B-Material
walls	I-Material
[	O
9	O
–	O
13	O
]	O
.	O
The	O
pores	B-Material
may	O
be	O
widened	O
toward	O
the	O
film	B-Material
surface	I-Material
by	O
chemical	B-Process
dissolution	I-Process
to	O
an	O
extent	O
dependent	O
on	O
the	O
anodizing	O
conditions	O
.	O

Failure	B-Process
of	I-Process
structural	I-Process
components	I-Process
is	O
a	O
major	O
concern	O
in	O
the	O
nuclear	O
power	O
industry	O
and	O
represents	O
not	O
only	O
a	O
safety	O
issue	O
,	O
but	O
also	O
a	O
hazard	O
to	O
economic	O
performance	O
.	O
Stress	B-Process
corrosion	I-Process
cracking	I-Process
(	O
SCC	B-Process
)	O
,	O
and	O
especially	O
intergranular	B-Process
stress	I-Process
corrosion	I-Process
cracking	I-Process
(	O
IGSCC	B-Process
)	O
,	O
have	O
proved	O
to	O
be	O
a	O
significant	O
potential	O
cause	O
of	O
failures	O
in	O
the	O
nuclear	O
industry	O
in	O
materials	O
such	O
as	O
Alloy	B-Material
600	I-Material
(	O
74	B-Material
%	I-Material
Ni	I-Material
,	I-Material
16	I-Material
%	I-Material
Cr	I-Material
and	I-Material
8	I-Material
%	I-Material
Fe	I-Material
)	O
and	O
stainless	B-Material
steels	I-Material
,	O
especially	O
in	O
Pressurised	B-Material
Water	I-Material
Reactors	I-Material
(	O
PWR	B-Material
)	O
[	O
1	O
–	O
5	O
]	O
.	O
Stress	B-Process
corrosion	I-Process
cracking	I-Process
in	O
pressurized	B-Material
water	I-Material
reactors	I-Material
(	O
PWSCC	B-Material
)	O
occurs	O
in	O
Alloy	B-Material
600	I-Material
in	O
safety	B-Material
critical	I-Material
components	I-Material
,	O
such	O
as	O
steam	B-Material
generator	I-Material
tubes	I-Material
,	O
heater	B-Material
sleeves	I-Material
,	O
pressurized	B-Material
instrument	I-Material
penetrations	I-Material
and	O
control	B-Material
rod	I-Material
drive	I-Material
mechanisms	I-Material
[	O
2,6,7	O
]	O
.	O
Understanding	O
the	O
mechanisms	O
that	O
control	O
SCC	B-Material
in	O
this	O
alloy	B-Material
will	O
allow	O
for	O
continued	O
extensions	O
of	O
life	O
in	O
current	O
plant	B-Material
as	O
well	O
as	O
safer	B-Task
designs	I-Task
of	I-Task
future	I-Task
nuclear	I-Task
reactors	I-Task
.	O

A	O
key	O
part	O
of	O
this	O
problem	O
is	O
that	O
an	O
inspector	O
only	O
has	O
access	O
to	O
data	O
from	O
a	O
small	O
inspected	O
area	O
.	O
In	O
this	O
area	O
,	O
there	O
is	O
only	O
one	O
minimum	O
thickness	O
,	O
which	O
does	O
not	O
provide	O
enough	O
information	O
to	O
build	B-Task
a	I-Task
model	I-Task
of	I-Task
the	I-Task
smallest	I-Task
thicknesses	I-Task
.	O
An	O
inspector	O
can	O
generate	O
a	O
sample	B-Material
of	O
the	O
smallest	O
thickness	O
measurements	O
by	O
partitioning	B-Process
the	I-Process
inspection	I-Process
data	I-Process
into	O
a	O
number	O
of	O
equally	B-Material
sized	I-Material
blocks	I-Material
.	O
In	O
each	O
block	B-Material
the	O
minimum	O
thickness	O
is	O
recorded	O
.	O
This	O
set	O
forms	O
a	O
sample	B-Material
of	O
the	O
smallest	O
thickness	O
measurements	O
.	O
From	O
this	O
sample	O
,	O
one	O
can	O
build	O
a	O
model	B-Process
which	I-Process
takes	I-Process
into	I-Process
account	I-Process
the	I-Process
variations	I-Process
of	I-Process
the	I-Process
smallest	I-Process
thickness	I-Process
measurements	I-Process
.	O
Extreme	B-Process
value	I-Process
analysis	I-Process
(	O
EVA	B-Process
)	O
provides	O
a	O
limiting	O
form	O
for	O
this	O
model	O
.	O
It	O
states	O
that	O
,	O
if	O
the	O
underlying	O
thickness	O
measurements	O
in	O
each	O
block	O
are	O
taken	O
from	O
independent	O
and	O
identical	O
distributions	O
,	O
then	O
the	O
sample	O
of	O
minimum	O
thickness	O
measurements	O
will	O
follow	O
a	O
generalized	B-Process
extreme	I-Process
value	I-Process
distribution	I-Process
(	O
GEVD	B-Process
)	O
.	O

The	O
thermodynamics	B-Task
of	I-Task
copper-zinc	I-Task
alloys	I-Task
(	I-Task
brass	I-Task
)	I-Task
was	O
subject	O
of	O
numerous	O
investigations	O
.	O
Brass	B-Material
is	O
characterised	O
by	O
an	O
excess	B-Process
enthalpy	I-Process
and	I-Process
excess	I-Process
entropy	I-Process
of	I-Process
mixing	I-Process
,	O
both	O
of	O
which	O
are	O
negative	O
.	O
The	O
enthalpic	B-Material
data	I-Material
were	O
measured	O
by	O
solution	B-Process
calorimetry	I-Process
e.g.	O
,	O
[	O
1	O
–	O
3	O
]	O
and	O
based	O
on	O
chemical	B-Material
potential	I-Material
data	I-Material
calculated	O
from	O
phase	B-Process
equilibrium	I-Process
experiments	I-Process
e.g.	O
,	O
[	O
4	O
–	O
6	O
]	O
,	O
the	O
excess	B-Task
entropy	I-Task
of	I-Task
mixing	I-Task
could	O
be	O
evaluated	O
e.g.	O
,	O
[	O
7	O
–	O
9	O
]	O
.	O
This	O
excess	O
entropy	O
contains	O
both	O
,	O
the	O
vibrational	B-Task
and	I-Task
the	I-Task
configurational	I-Task
parts	I-Task
.	O
The	O
excess	O
vibrational	O
entropy	O
,	O
defined	O
as	O
the	O
deviation	O
from	O
the	O
entropy	O
of	O
a	O
mechanical	O
mixture	O
of	O
the	O
end	O
members	O
A	O
and	O
B	O
(	O
i.e.	O
,	O
Smmechmix	B-Material
=	I-Material
XASmA	I-Material
+	I-Material
XBSmB	I-Material
)	O
,	O
can	O
be	O
determined	O
by	O
measuring	B-Process
the	I-Process
low	I-Process
temperature	I-Process
heat	I-Process
capacity	I-Process
(	I-Process
5	I-Process
to	I-Process
300K	I-Process
)	I-Process
versus	I-Process
composition	I-Process
behaviour	I-Process
.	O
The	O
determination	O
of	O
the	O
excess	B-Task
configurational	I-Task
entropy	I-Task
,	O
i.e.	O
,	O
the	O
excess	O
entropy	O
coming	O
from	O
non-random	O
atomic	O
distributions	O
and	O
defects	O
,	O
is	O
much	O
more	O
difficult	O
.	O
Here	O
,	O
neutron	B-Process
scattering	I-Process
investigations	I-Process
together	O
with	O
computer	B-Process
simulations	I-Process
are	O
normally	O
used	O
.	O
If	O
,	O
however	O
,	O
reliable	B-Material
data	I-Material
of	I-Material
the	I-Material
total	I-Material
excess	I-Material
entropy	I-Material
(	O
from	O
enthalpic	B-Material
and	I-Material
chemical	I-Material
potential	I-Material
data	I-Material
)	O
are	O
available	O
,	O
the	O
measurement	B-Process
of	I-Process
the	I-Process
excess	I-Process
vibrational	I-Process
entropy	I-Process
enables	O
the	O
determination	B-Process
of	I-Process
the	I-Process
excess	I-Process
configurational	I-Process
entropy	I-Process
simply	O
by	O
subtraction	B-Process
.	O
Since	O
configurational	O
and	O
vibrational	O
entropies	O
may	O
have	O
different	O
temperature	O
dependencies	O
,	O
it	O
is	O
worthwhile	O
to	O
separate	B-Task
the	I-Task
entropic	I-Task
effects	I-Task
.	O
This	O
is	O
one	O
aim	O
of	O
this	O
study	O
.	O
Another	O
aim	O
is	O
to	O
deliver	B-Task
experimental	I-Task
data	I-Task
so	O
that	O
first	O
principles	O
studies	O
can	O
test	O
their	O
models	O
on	O
a	O
disordered	B-Material
alloy	I-Material
,	O
whose	O
structural	O
details	O
(	O
short-range	O
order	O
)	O
depend	O
on	O
temperature	O
.	O

The	O
algorithm	O
allows	O
the	O
modelling	B-Task
of	I-Task
plasmas	I-Task
of	I-Task
arbitrary	I-Task
degeneracy	I-Task
under	O
the	O
binary	B-Process
collision	I-Process
approximation	I-Process
.	O
It	O
uses	O
a	O
numerical	B-Process
interpolation	I-Process
of	O
the	O
inverse	O
cumulative	O
density	O
function	O
of	O
the	O
Fermi	B-Process
–	I-Process
Dirac	I-Process
distribution	I-Process
to	O
initialise	O
simulation	B-Material
particles	I-Material
,	O
and	O
collisions	B-Process
are	O
subject	O
to	O
Pauli	B-Process
blocking	I-Process
.	O
It	O
is	O
not	O
appropriate	O
in	O
the	O
limit	O
of	O
very	O
strong	O
coupling	B-Process
because	O
the	O
plasma	B-Task
theory	I-Task
which	O
the	O
Monte	B-Process
Carlo	I-Process
code	I-Process
is	O
based	O
on	O
breaks	O
down	O
.	O
The	O
strong	B-Task
coupling	I-Task
limit	I-Task
corresponds	O
to	O
lnΛ	O
≲	O
3	O
,	O
with	O
lnΛ	O
the	O
Coulomb	B-Process
logarithm	I-Process
[	O
10	O
]	O
.	O
The	O
code	O
is	O
designed	O
for	O
lnΛ	O
>	O
3	O
in	O
collisional	B-Material
plasmas	I-Material
with	O
a	O
non-negligible	O
level	O
of	O
degeneracy	O
.	O
It	O
is	O
noted	O
that	O
Monte	B-Process
Carlo	I-Process
techniques	I-Process
with	O
degenerate	O
capabilities	O
have	O
been	O
developed	O
for	O
studying	O
transport	B-Task
in	I-Task
semi-conductors	I-Task
[	O
11	O
]	O
but	O
no	O
such	O
method	O
exists	O
for	O
fully-ionised	B-Material
plasmas	I-Material
.	O
Some	O
of	O
the	O
techniques	O
described	O
are	O
potentially	O
applicable	O
to	O
other	O
types	O
of	O
codes	O
,	O
for	O
example	O
,	O
Particle-In-Cell	O
(	O
PIC	O
)	O
codes	O
.	O

Similar	O
numerical	B-Process
oscillations	I-Process
to	O
those	O
described	O
above	O
also	O
emerge	O
in	O
the	O
ISPM	B-Process
when	O
utilising	O
classical	B-Process
IBM	I-Process
kernels	I-Process
due	O
to	O
their	O
lack	O
of	O
regularity	O
(	O
with	O
discontinuous	O
second	O
derivatives	O
)	O
.	O
Furthermore	O
,	O
it	O
is	O
important	O
to	O
remark	O
that	O
the	O
immersed	O
structure	O
stresses	O
are	O
captured	O
in	O
the	O
Lagrangian	B-Process
description	I-Process
and	O
hence	O
,	O
in	O
order	O
to	O
compute	O
them	O
accurately	O
,	O
it	O
is	O
important	O
to	O
ensure	O
that	O
these	O
spurious	O
oscillations	B-Material
are	O
not	O
introduced	O
via	O
the	O
kernel	B-Process
interpolation	I-Process
functions	I-Process
.	O
In	O
this	O
paper	O
,	O
the	O
authors	O
have	O
specifically	O
designed	O
a	O
new	B-Task
family	I-Task
of	I-Task
kernel	I-Task
functions	I-Task
which	O
do	B-Task
not	I-Task
introduce	I-Task
these	I-Task
spurious	I-Task
oscillations	I-Task
.	O
The	O
kernel	B-Process
functions	I-Process
are	O
obtained	O
by	O
taking	O
into	O
account	O
discrete	O
reproducibility	O
conditions	O
as	O
originally	O
introduced	O
by	O
Peskin	O
[	O
14	O
]	O
(	O
in	O
our	O
case	O
,	O
tailor-made	O
for	O
Cartesian	B-Process
staggered	I-Process
grids	I-Process
)	O
and	O
regularity	O
requirements	O
to	O
prevent	O
the	O
appearance	O
of	O
spurious	O
oscillations	B-Material
when	O
computing	O
derivatives	O
.	O
A	O
Maple	B-Material
computer	I-Material
program	I-Material
has	O
been	O
developed	O
to	O
obtain	B-Task
explicit	I-Task
expressions	I-Task
for	I-Task
the	I-Task
new	I-Task
kernels	I-Task
.	O

After	O
all	O
micro	O
elements	O
reach	O
a	O
relaxed	O
steady-state	O
,	O
measurements	O
are	O
obtained	O
using	O
a	O
cumulative	B-Process
averaging	I-Process
technique	I-Process
to	O
reduce	B-Task
noise	I-Task
.	O
Each	O
micro	B-Material
element	I-Material
is	O
divided	O
into	O
spatially-oriented	B-Material
bins	I-Material
in	O
the	O
y-direction	O
in	O
order	O
to	O
resolve	B-Task
the	I-Task
velocity	I-Task
and	I-Task
shear-stress	I-Task
profiles	I-Task
.	O
Velocity	O
in	O
each	O
bin	B-Material
is	O
measured	O
using	O
the	O
Cumulative	B-Process
Averaging	I-Process
Method	I-Process
(	O
CAM	B-Process
)	O
[	O
24	O
]	O
,	O
while	O
the	O
stress	B-Process
tensor	I-Process
field	I-Process
is	O
measured	O
using	O
the	O
Irving	B-Process
–	I-Process
Kirkwood	I-Process
relationship	I-Process
[	O
25	O
]	O
.	O
A	O
least-squares	B-Process
polynomial	I-Process
fit	I-Process
to	O
the	O
data	O
is	O
performed	O
,	O
which	O
helps	O
reduce	B-Process
noise	I-Process
further	O
.	O
The	O
fit	O
produces	O
a	O
continuous	B-Process
function	I-Process
that	O
avoids	B-Process
stability	I-Process
issues	I-Process
arising	O
from	O
supplying	O
highly	B-Material
fluctuating	I-Material
data	I-Material
to	O
the	O
macro	B-Material
solver	I-Material
.	O
A	O
least-squares	B-Process
fit	I-Process
is	O
applied	O
to	O
an	O
Nth	B-Material
order	I-Material
polynomial	I-Material
for	O
the	O
velocity	O
profile	O
in	O
the	O
core	B-Material
region	I-Material
,	O
and	O
an	O
Mth	B-Material
order	I-Material
polynomial	I-Material
for	O
the	O
velocity	O
profile	O
in	O
the	O
constrained	O
region	O
:(	O
16	O
)〈	O
ui,core	O
〉=∑	O
k	O
=	O
1Nbk,iyi′	O
(	O
N	O
−	O
k	O
)	O
,	O
for	O
0	O
⩽	O
yi′	O
⩽	O
hcore	O
,	O
and	O
(	O
17	O
)〈	O
ui,cs	O
〉=∑	O
k	O
=	O
1Mck,iyi	O
″(	O
M	O
−	O
k	O
)	O
,	O
for	O
0	O
⩽	O
yi	O
″⩽	O
hcs	O
,	O
where	O
bk,i	O
and	O
ck,i	O
are	O
the	O
coefficients	B-Material
of	O
the	O
polynomials	B-Material
used	O
in	O
the	O
core	B-Material
micro	I-Material
region	I-Material
and	O
constrained	B-Material
region	I-Material
respectively	O
.	O
An	O
estimate	O
of	O
the	O
new	O
slip	O
velocity	O
uB	O
for	O
input	O
to	O
the	O
macro	B-Material
solution	I-Material
(	O
6	O
)	O
is	O
taken	O
directly	O
from	O
the	O
compressed	B-Material
wall	I-Material
micro-element	I-Material
solution	I-Material
(	O
16	O
)	O
,	O
at	O
yi′	O
=	O
0	O
.	O

It	O
is	O
interesting	O
to	O
quantify	O
the	O
effects	O
of	O
the	O
Schmidt	O
number	O
and	O
the	O
chemical	B-Process
reaction	I-Process
rate	O
on	O
the	O
bulk-mean	B-Process
concentration	I-Process
of	O
B	B-Material
in	O
water	B-Material
.	O
The	O
data	O
could	O
present	O
important	O
information	O
on	O
evaluating	O
the	O
environmental	O
impacts	O
of	O
the	O
degradation	B-Process
product	O
of	O
B	B-Material
,	O
as	O
well	O
as	O
acidification	B-Process
of	O
water	B-Material
by	O
the	O
chemical	B-Process
reaction	I-Process
.	O
Here	O
,	O
the	O
bulk-mean	B-Material
concentration	I-Material
of	O
B	B-Material
is	O
defined	O
by	O
(	O
24	O
)	O
CB	O
⁎¯=∫	O
01	O
〈	O
CB	O
⁎〉(	O
z	O
⁎)	O
dz	O
⁎	O
Fig.	O
15	O
depicts	O
the	O
effect	O
of	O
the	O
Schmidt	O
and	O
the	O
chemical	B-Process
reaction	I-Process
rate	O
on	O
the	O
bulk-mean	B-Material
concentration	I-Material
CB	B-Material
⁎¯	I-Material
.	O
It	O
is	O
worth	O
to	O
mention	O
here	O
that	O
the	O
bulk-mean	B-Process
concentration	I-Process
of	O
B	B-Material
reaches	O
approximately	O
0.6	O
as	O
the	O
chemical	B-Process
reaction	I-Process
rate	O
and	O
the	O
Schmidt	O
number	O
increase	O
to	O
infinite	O
,	O
and	O
the	O
concentration	O
is	O
smaller	O
than	O
the	O
equilibrium	O
concentration	O
of	O
A	B-Material
at	O
the	O
interface	O
.	O
This	O
figure	O
indicates	O
that	O
progress	O
of	O
the	O
chemical	B-Process
reaction	I-Process
is	O
somewhat	O
interfered	O
by	O
turbulent	B-Process
mixing	I-Process
in	O
water	B-Material
,	O
and	O
the	O
efficiency	O
of	O
the	O
chemical	B-Process
reaction	I-Process
is	O
up	O
to	O
approximately	O
60	O
%	O
.	O
The	O
efficiency	O
of	O
the	O
chemical	O
reaction	O
in	O
water	B-Material
will	O
be	O
a	O
function	O
of	O
the	O
Reynolds	O
number	O
of	O
the	O
water	B-Process
flow	I-Process
,	O
and	O
the	O
efficiency	O
could	O
increase	O
as	O
the	O
Reynolds	O
number	O
increases	O
.	O
We	O
need	O
an	O
extensive	B-Task
investigation	I-Task
on	I-Task
the	I-Task
efficiency	I-Task
of	I-Task
the	I-Task
aquarium	I-Task
chemical	I-Task
reaction	I-Task
in	O
the	O
near	O
future	O
to	O
extend	O
the	O
results	O
of	O
this	O
study	O
further	O
to	O
establish	B-Task
practical	I-Task
modelling	I-Task
for	I-Task
the	I-Task
gas	I-Task
exchange	I-Task
between	I-Task
air	I-Task
and	I-Task
water	I-Task
.	O

Numerical	B-Task
simulation	I-Task
of	O
the	O
gas	O
flow	O
through	O
such	O
non-trivial	O
internal	O
geometries	O
is	O
,	O
however	O
,	O
extremely	O
challenging	O
.	O
This	O
is	O
because	O
conventional	B-Task
continuum	I-Task
fluid	I-Task
dynamics	I-Task
,	O
which	O
assumes	O
that	O
locally	O
a	O
gas	B-Material
is	O
close	O
to	O
a	O
state	O
of	O
thermodynamic	B-Task
equilibrium	I-Task
,	O
becomes	O
invalid	O
or	O
inaccurate	O
as	O
the	O
smallest	O
characteristic	O
scale	O
of	O
the	O
geometry	O
(	O
e.g.	O
the	O
channel	B-Material
height	I-Material
)	O
approaches	O
the	O
mean	O
distance	O
between	O
molecular	B-Process
collisions	I-Process
,	O
λ	O
[	O
1	O
]	O
.	O
An	O
accurate	O
and	O
flexible	O
modelling	O
alternative	O
for	O
these	O
cases	O
is	O
the	O
direct	B-Process
simulation	I-Process
Monte	I-Process
Carlo	I-Process
method	I-Process
(	O
DSMC	B-Process
)	O
[	O
2	O
]	O
.	O
However	O
,	O
DSMC	O
can	O
be	O
prohibitively	O
expensive	O
for	O
internal-flow	B-Task
applications	I-Task
,	O
which	O
typically	O
have	O
a	O
geometry	O
of	O
high-aspect	B-Process
ratio	I-Process
(	O
i.e.	O
are	O
extremely	O
long	O
,	O
relative	O
to	O
their	O
cross-section	O
)	O
.	O
The	O
high-aspect	O
ratio	O
creates	O
a	O
formidable	B-Task
multiscale	I-Task
problem	I-Task
:	O
processes	O
need	O
to	O
be	O
resolved	O
occurring	O
over	O
the	O
smallest	O
characteristic	O
scale	O
of	O
the	O
geometry	O
(	O
e.g.	O
a	O
channel	O
ʼs	O
height	O
)	O
,	O
as	O
well	O
as	O
over	O
the	O
largest	O
characteristic	O
scale	O
of	O
the	O
geometry	O
(	O
e.g.	O
the	O
length	O
of	O
a	O
long	O
channel	O
network	O
)	O
,	O
simultaneously	O
.	O

The	O
test	O
cases	O
confirm	O
that	O
the	O
high-order	B-Process
discretisation	I-Process
retains	O
exponential	O
convergence	O
properties	O
with	O
increasing	O
geometric	O
and	O
expansion	O
polynomial	O
order	O
if	O
both	O
the	O
solution	B-Material
and	O
true	B-Material
surface	I-Material
are	O
smooth	O
.	O
Errors	O
are	O
found	O
to	O
saturate	O
when	O
the	O
geometric	O
errors	O
,	O
due	O
to	O
the	O
parametrisation	B-Process
of	I-Process
the	I-Process
surface	I-Process
elements	I-Process
,	O
begin	O
to	O
dominate	O
the	O
temporal	O
and	O
spatial	O
discretisation	O
errors	O
.	O
For	O
the	O
smooth	O
solutions	O
considered	O
as	O
test	O
cases	O
,	O
the	O
results	O
show	O
that	O
this	O
dominance	O
of	O
geometric	O
errors	O
quickly	O
limits	O
the	O
effectiveness	O
of	O
further	O
increases	O
in	O
the	O
number	O
of	O
degrees	O
of	O
freedom	O
,	O
either	O
through	O
mesh	B-Process
refinement	I-Process
or	O
higher	B-Task
solution	I-Task
polynomial	I-Task
orders	I-Task
.	O
Increasing	O
the	O
order	O
of	O
the	O
geometry	O
parametrisation	O
reduces	B-Task
the	I-Task
geometric	I-Task
error	I-Task
.	O
The	O
analytic	O
test	O
cases	O
presented	O
here	O
use	O
a	O
coarse	B-Material
curvilinear	I-Material
mesh	I-Material
;	O
for	O
applications	O
,	O
meshes	B-Material
are	O
typically	O
more	O
refined	O
in	O
order	O
to	O
capture	O
features	O
in	O
the	O
solution	B-Material
and	O
so	O
will	O
better	O
capture	O
the	O
geometry	O
and	O
consequently	O
reduce	O
this	O
lower	O
bound	O
on	O
the	O
solution	O
error	O
.	O
If	O
the	O
solution	O
is	O
not	O
smooth	O
,	O
we	O
do	O
not	O
expect	O
to	O
see	O
rapid	O
convergence	O
.	O
In	O
the	O
case	O
that	O
the	O
solution	O
is	O
smooth	O
,	O
but	O
the	O
true	O
surface	O
is	O
not	O
,	O
then	O
exponential	O
convergence	O
with	O
P	B-Material
and	O
Pg	B-Material
can	O
only	O
be	O
achieved	O
if	O
,	O
and	O
only	O
if	O
,	O
the	O
discontinuities	O
are	O
aligned	O
with	O
element	O
boundaries	O
.	O
However	O
,	O
if	O
discontinuities	O
lie	O
within	O
an	O
element	O
,	O
convergence	O
will	O
be	O
limited	O
by	O
the	O
geometric	B-Process
approximation	I-Process
,	O
since	O
the	O
true	O
surface	B-Material
cannot	O
be	O
captured	O
.	O
In	O
the	O
cardiac	B-Task
problem	I-Task
,	O
we	O
consider	O
both	O
the	O
true	B-Material
surface	I-Material
and	O
solution	B-Material
to	O
be	O
smooth	O
.	O

Designers	O
of	O
microfluidic	B-Material
devices	I-Material
are	O
in	O
need	O
of	O
computational	O
tools	O
that	O
can	O
be	O
used	O
to	O
analyse	B-Task
problems	I-Task
that	I-Task
involve	I-Task
rarefied	I-Task
gas	I-Task
flows	I-Task
in	I-Task
complex	I-Task
micro	I-Task
geometries	I-Task
.	O
Numerical	B-Process
simulation	I-Process
of	O
the	O
gas	B-Material
flow	I-Material
through	O
such	O
geometries	O
is	O
,	O
however	O
,	O
extremely	O
challenging	O
.	O
Conventional	B-Process
continuum	I-Process
fluid	I-Process
dynamics	I-Process
(	O
CFD	B-Process
)	O
becomes	O
invalid	O
or	O
inaccurate	O
as	O
the	O
characteristic	O
scale	O
of	O
the	O
geometry	O
(	O
e.g.	O
the	O
channel	O
height	O
,	O
h	O
)	O
approaches	O
the	O
molecular	O
mean	O
free	O
path	O
,	O
λ	O
[	O
1,2	O
]	O
.	O
When	O
λ	O
/	O
h	O
≳	O
0.1	O
,	O
the	O
error	O
in	O
solutions	O
obtained	O
from	O
CFD	O
may	O
be	O
significant	O
,	O
and	O
we	O
must	O
consider	O
the	O
fluid	O
for	O
what	O
it	O
is	O
:	O
a	O
collection	B-Material
of	I-Material
interacting	I-Material
particles	I-Material
.	O
However	O
,	O
the	O
computational	O
expense	O
of	O
simulating	O
the	O
flow	B-Material
of	I-Material
a	I-Material
rarefied	I-Material
gas	I-Material
in	O
high-aspect-ratio	B-Material
micro	I-Material
geometries	I-Material
(	O
i.e.	O
ones	O
that	O
are	O
long	B-Material
,	I-Material
relative	I-Material
to	I-Material
their	I-Material
cross	I-Material
section	I-Material
)	O
using	O
a	O
particle	B-Process
method	I-Process
,	O
such	O
as	O
the	O
direct	B-Process
simulation	I-Process
Monte	I-Process
Carlo	I-Process
(	O
DSMC	B-Process
)	O
method	O
[	O
2	O
]	O
,	O
can	O
be	O
prohibitively	O
high	O
[	O
3,4	O
]	O
.	O
The	O
computational	O
intensity	O
of	O
the	O
particle	B-Process
method	I-Process
is	O
greater	O
still	O
when	O
simulating	O
low-speed	O
microfluidic	B-Material
devices	I-Material
where	O
there	O
are	O
only	O
small	B-Material
deviations	I-Material
from	I-Material
equilibrium	I-Material
,	O
characterised	O
by	O
extremely	B-Material
low	I-Material
Mach	I-Material
numbers	I-Material
and	I-Material
weak	I-Material
temperature	I-Material
gradients	I-Material
.	O

In	O
this	O
work	O
,	O
we	O
have	O
developed	O
a	O
simple	O
numerical	B-Process
scheme	I-Process
based	O
on	O
the	O
Galerkin	B-Process
finite	I-Process
element	I-Process
method	I-Process
for	O
a	O
multi-term	B-Task
time	I-Task
fractional	I-Task
diffusion	I-Task
equation	I-Task
which	O
involves	O
multiple	B-Process
Caputo	I-Process
fractional	I-Process
derivatives	I-Process
in	I-Process
time	I-Process
.	O
A	O
complete	O
error	O
analysis	O
of	O
the	O
space	O
semidiscrete	O
Galerkin	B-Process
scheme	I-Process
is	O
provided	O
.	O
The	O
theory	O
covers	O
the	O
practically	O
very	O
important	O
case	O
of	O
nonsmooth	O
initial	O
data	O
and	O
right	O
hand	O
side	O
.	O
The	O
analysis	O
relies	O
essentially	O
on	O
some	O
new	B-Material
regularity	I-Material
results	I-Material
of	O
the	O
multi-term	O
time	O
fractional	O
diffusion	O
equation	O
.	O
Further	O
,	O
we	O
have	O
developed	O
a	O
fully	B-Process
discrete	I-Process
scheme	I-Process
based	O
on	O
a	O
finite	B-Process
difference	I-Process
discretization	I-Process
of	I-Process
the	I-Process
Caputo	I-Process
fractional	I-Process
derivatives	I-Process
.	O
The	O
stability	O
and	O
error	O
estimate	O
of	O
the	O
fully	O
discrete	O
scheme	O
were	O
established	O
,	O
provided	O
that	O
the	O
solution	O
is	O
smooth	O
.	O
The	O
extensive	O
numerical	B-Process
experiments	I-Process
in	O
one	B-Process
-	I-Process
and	I-Process
two-dimension	I-Process
fully	O
confirmed	O
our	O
convergence	O
analysis	O
:	O
the	O
empirical	B-Material
convergence	I-Material
rates	I-Material
agree	O
well	O
with	O
the	O
theoretical	O
predictions	O
for	O
both	O
smooth	O
and	O
nonsmooth	O
data	O
.	O

In	O
this	O
work	O
,	O
light	B-Task
propagation	I-Task
in	I-Task
a	I-Task
scattering	I-Task
medium	I-Task
with	I-Task
piece-wise	I-Task
constant	I-Task
refractive	I-Task
index	I-Task
using	O
the	O
radiative	B-Process
transport	I-Process
equation	I-Process
was	O
studied	O
.	O
Light	O
propagation	O
in	O
each	O
sub-domain	O
with	O
a	O
constant	O
refractive	O
index	O
was	O
modeled	O
using	O
the	O
RTE	B-Process
and	O
the	O
equations	O
were	O
coupled	O
using	O
boundary	O
conditions	O
describing	O
Fresnel	B-Process
reflection	I-Process
and	I-Process
transmission	I-Process
phenomenas	I-Process
on	O
the	O
interfaces	O
between	O
the	O
sub-domains	O
.	O
The	O
resulting	O
coupled	B-Process
system	I-Process
of	I-Process
RTEs	I-Process
was	O
numerically	O
solved	O
using	O
the	O
FEM	B-Process
.	O
The	O
proposed	O
model	O
was	O
tested	O
using	O
simulations	B-Process
and	O
was	O
compared	O
with	O
the	O
solution	B-Material
of	I-Material
the	I-Material
Monte	I-Material
Carlo	I-Material
method	I-Material
.	O
The	O
results	O
show	O
that	O
the	O
coupled	O
RTE	B-Process
model	O
describes	O
light	O
propagation	O
accurately	O
in	O
comparison	O
with	O
the	O
Monte	B-Material
Carlo	I-Material
method	I-Material
.	O
In	O
addition	O
,	O
results	O
show	O
that	O
neglecting	O
internal	O
refractive	O
index	O
changes	O
can	O
lead	O
to	O
erroneous	O
boundary	O
measurements	O
of	O
scattered	O
light	O
.	O
This	O
indicates	O
that	O
the	O
quality	O
of	O
the	O
DOT	B-Process
reconstructions	I-Process
could	O
possible	O
be	O
increased	O
by	O
incorporating	O
a	O
model	O
for	O
internal	O
refractive	B-Process
index	I-Process
changes	I-Process
in	O
the	O
image	B-Process
reconstruction	I-Process
procedure	I-Process
.	O

The	O
validity	B-Task
of	I-Task
semi-classical	I-Task
boundary	I-Task
conditions	I-Task
for	O
the	O
WTE	O
as	O
introduced	O
in	O
[	O
8	O
]	O
is	O
a	O
topic	O
under	O
vivid	O
debate	O
,	O
especially	O
after	O
recent	O
works	O
which	O
address	O
the	O
non-uniqueness	B-Process
and	I-Process
the	I-Process
symmetry	I-Process
properties	I-Process
of	I-Process
the	I-Process
Wigner	I-Process
function	I-Process
[	O
27,28,46	O
]	O
.	O
The	O
numerical	B-Material
test	I-Material
cases	I-Material
presented	O
therein	O
are	O
for	O
symmetric	B-Material
potentials	I-Material
for	O
which	O
we	O
cannot	O
provide	O
reliable	O
,	O
i.e.	O
well-resolved	O
,	O
results	O
due	O
to	O
the	O
presence	O
of	O
singular	O
terms	O
in	O
the	O
steady	B-Process
state	I-Process
Wigner	I-Process
functions	I-Process
,	O
see	O
Section	O
4.3	O
.	O
Other	B-Material
recent	I-Material
studies	I-Material
demonstrate	O
the	O
convergence	B-Task
of	I-Task
the	I-Task
WTE	I-Task
calculations	I-Task
upon	O
increasing	B-Process
the	I-Process
size	I-Process
of	I-Process
the	I-Process
simulation	I-Process
domain	I-Process
[	O
44	O
]	O
as	O
well	O
as	O
possible	O
improvements	O
by	O
adapting	B-Process
the	I-Process
boundary	I-Process
distribution	I-Process
to	O
the	O
physical	O
state	O
of	O
the	O
active	O
device	O
region	O
[	O
47	O
]	O
.	O
Despite	O
their	O
approximate	O
nature	O
we	O
employ	B-Process
inflow	I-Process
/	I-Process
outflow	I-Process
boundary	I-Process
conditions	I-Process
here	O
as	O
well	O
and	O
demonstrate	O
that	O
accurate	B-Task
and	I-Task
physically	I-Task
valid	I-Task
results	I-Task
can	O
be	O
achieved	O
for	O
sufficiently	O
large	O
values	O
of	O
Lres	O
.	O
Due	O
to	O
the	O
problematics	O
with	O
singular	O
terms	O
we	O
present	B-Task
simulations	I-Task
only	I-Task
for	I-Task
non-zero	I-Task
bias	I-Task
voltages	I-Task
VDS	O
≠	O
0	O
V	O
.	O

A	O
multi-physics	B-Process
description	I-Process
of	I-Process
a	I-Process
multiscale	I-Process
system	I-Process
is	O
often	O
referred	O
to	O
as	O
a	O
‘	B-Process
hybrid’	I-Process
model	I-Process
.	O
In	O
fluid	B-Task
dynamics	I-Task
,	O
a	O
typical	O
hybrid	B-Process
combines	O
a	O
molecular	B-Process
treatment	I-Process
(	O
a	O
‘	B-Process
micro’	I-Process
model	I-Process
)	O
with	O
a	O
continuum-fluid	B-Process
one	I-Process
(	O
a	O
‘	O
macro’	B-Process
model	I-Process
)	O
,	O
with	O
the	O
aim	O
of	O
obtaining	B-Task
the	I-Task
accuracy	I-Task
of	I-Task
the	I-Task
former	I-Task
with	I-Task
the	I-Task
efficiency	I-Task
of	I-Task
the	I-Task
latter	I-Task
[	O
1	O
–	O
4	O
]	O
.	O
The	O
micro	B-Process
and	I-Process
macro	I-Process
models	I-Process
generally	O
have	O
characteristic	O
timescales	O
that	O
are	O
very	O
different	O
,	O
which	O
means	O
that	O
time-accurate	O
simulations	O
can	O
be	O
extremely	O
challenging	O
:	O
the	O
size	O
of	O
the	O
timestep	O
required	O
to	O
make	O
the	O
micro	O
model	O
stable	O
and	O
accurate	O
is	O
so	O
small	O
that	O
simulations	O
over	O
significant	O
macro-scale	O
time	O
periods	O
are	O
intractable	O
.	O
If	O
the	O
system	O
is	O
‘	O
scale-separated’	O
,	O
a	O
physical	O
(	O
as	O
distinct	O
from	O
numerical	O
)	O
approximation	O
can	O
be	O
made	O
that	O
enables	O
the	O
coupled	O
models	O
to	O
advance	O
at	O
different	O
rates	O
(	O
asynchronously	O
)	O
with	O
negligible	O
penalty	O
on	O
macro-scale	O
accuracy	O
.	O
E	O
et	O
al	O
.	O
[	O
5	O
]	O
were	O
the	O
first	O
to	O
introduce	O
and	O
implement	O
this	O
concept	O
in	O
a	O
time-stepping	O
method	O
for	O
coupled	O
systems	O
,	O
referred	O
to	O
in	O
the	O
classification	O
of	O
Lockerby	O
et	O
al	O
.	O
[	O
6	O
]	O
as	O
a	O
continuous	O
asynchronous	O
(	O
CA	O
)	O
scheme	O
(‘	O
continuous’	O
since	O
the	O
micro	O
and	O
macro	O
models	O
advance	O
without	O
interruption	O
[	O
5	O
])	O
.	O
In	O
this	O
paper	O
we	O
extend	O
this	O
idea	O
to	O
multiscale	O
systems	O
comprising	O
an	O
arbitrary	O
number	O
of	O
coupled	O
models	O
.	O

The	O
particular	O
phase	B-Task
field	I-Task
model	I-Task
we	O
employ	O
is	O
an	O
extension	O
of	O
[	O
6	O
]	O
,	O
and	O
is	O
based	O
on	O
the	O
three	B-Process
dimensional	I-Process
thermal	I-Process
phase	I-Process
field	I-Process
model	I-Process
of	O
[	O
7	O
]	O
and	O
two	B-Process
dimensional	I-Process
thermal-solutal	I-Process
phase	I-Process
field	I-Process
model	I-Process
of	O
[	O
8	O
]	O
.	O
One	O
feature	O
of	O
the	O
physical	O
problem	O
is	O
that	O
it	O
is	O
purely	O
dissipative	O
,	O
or	O
entropy	O
increasing	O
,	O
as	O
all	O
natural	O
relaxational	O
phenomena	O
are	O
.	O
The	O
resulting	O
PDEs	B-Material
are	O
of	O
Allen	O
–	O
Cahn	O
[	O
9	O
]	O
and	O
Carn	O
–	O
Hilliard	O
type	O
[	O
10	O
]	O
.	O
That	O
is	O
to	O
say	O
,	O
the	O
model	O
involves	O
time	B-Process
derivatives	I-Process
of	I-Process
the	I-Process
three	I-Process
fields	I-Process
coupled	I-Process
to	I-Process
forms	I-Process
involving	I-Process
variational	I-Process
derivatives	I-Process
of	O
some	O
functional	B-Process
–	O
typically	O
the	O
free	B-Process
energy	I-Process
functional	I-Process
.	O
As	O
the	O
dendrite	B-Material
grows	O
the	O
free	O
energy	O
reduces	O
monotonically	O
with	O
time	O
but	O
never	O
achieves	O
equilibrium	O
if	O
the	O
domain	O
boundary	O
is	O
far	O
from	O
the	O
dendrite	O
.	O
Although	O
we	O
have	O
listed	O
some	O
of	O
the	O
difficult	O
aspects	O
of	O
this	O
model	O
,	O
the	O
relaxational	O
aspect	O
is	O
typically	O
an	O
asset	O
and	O
results	O
in	O
stable	O
numerical	B-Process
schemes	I-Process
:	O
there	O
is	O
no	O
convection	O
,	O
for	O
example	O
(	O
at	O
least	O
in	O
the	O
absence	O
of	O
flow	O
in	O
the	O
melt	B-Material
)	O
.	O

Inspired	O
by	O
energy-fueled	O
phenomena	O
such	O
as	O
cortical	B-Process
cytoskeleton	I-Process
flows	I-Process
[	O
46,45,32	O
]	O
during	O
biological	B-Process
morphogenesis	I-Process
,	O
the	O
theory	B-Task
of	I-Task
active	I-Task
polar	I-Task
viscous	I-Task
gels	I-Task
has	O
been	O
developed	O
[	O
37,33	O
]	O
.	O
The	O
theory	O
models	B-Process
the	I-Process
continuum	I-Process
,	I-Process
macroscopic	I-Process
mechanics	I-Process
of	O
a	O
collection	O
of	O
uniaxial	B-Material
active	I-Material
agents	I-Material
,	O
embedded	O
in	O
a	O
viscous	B-Material
bulk	I-Material
medium	I-Material
,	O
in	O
which	O
internal	O
stresses	O
are	O
induced	O
due	O
to	O
dissipation	O
of	O
energy	O
[	O
41,58	O
]	O
.	O
The	O
energy-consuming	O
uniaxial	B-Material
polar	I-Material
agents	I-Material
constituting	O
the	O
gel	B-Material
are	O
modeled	O
as	O
unit	O
vectors	O
.	O
The	O
average	O
of	O
unit	O
vectors	O
in	O
a	O
small	O
local	O
volume	O
at	O
each	O
point	O
defines	O
the	O
macroscopic	O
directionality	O
of	O
the	O
agents	O
and	O
is	O
described	O
by	O
a	O
polarization	B-Process
field	I-Process
.	O
The	O
polarization	O
field	O
is	O
governed	O
by	O
an	O
equation	B-Process
of	I-Process
motion	I-Process
accounting	O
for	O
energy	O
consumption	O
and	O
for	O
the	O
strain	O
rate	O
in	O
the	O
fluid	B-Material
.	O
The	O
relationship	O
between	O
the	O
strain	O
rate	O
and	O
the	O
stress	O
in	O
the	O
fluid	O
is	O
provided	O
by	O
a	O
constitutive	B-Process
equation	I-Process
that	O
accounts	O
for	O
anisotropic	O
,	O
polar	O
agents	O
and	O
consumption	O
of	O
energy	O
.	O
These	B-Process
equations	I-Process
,	I-Process
along	I-Process
with	I-Process
conservation	I-Process
of	I-Process
momentum	I-Process
,	O
provide	O
a	O
continuum	B-Process
hydrodynamic	I-Process
description	I-Process
modeling	O
active	O
polar	B-Material
viscous	I-Material
gels	I-Material
as	O
an	O
energy	B-Material
consuming	I-Material
,	I-Material
anisotropic	I-Material
,	I-Material
non-Newtonian	I-Material
fluid	I-Material
[	O
37,33,32,41	O
]	O
.	O
The	O
resulting	O
partial	O
differential	O
equations	O
governing	O
the	O
hydrodynamics	O
of	O
active	B-Material
polar	I-Material
viscous	I-Material
gels	I-Material
are	O
,	O
however	O
,	O
in	O
general	O
analytically	O
intractable	O
.	O

The	O
boundary	B-Process
element	I-Process
method	I-Process
(	O
BEM	B-Process
)	O
has	O
clear	O
advantages	O
when	O
applied	O
to	O
shape	B-Task
optimisation	I-Task
of	I-Task
high-voltage	I-Task
devices	I-Task
,	O
see	O
[	O
4	O
–	O
8	O
]	O
for	O
an	O
introduction	O
to	O
BEM	B-Process
.	O
First	O
of	O
all	O
,	O
BEM	O
relies	O
only	O
on	O
a	O
surface	B-Process
discretisation	I-Process
so	O
that	O
there	O
is	O
no	O
need	O
to	O
maintain	O
an	O
analysis-suitable	O
volume	O
discretisation	O
during	O
the	O
shape	B-Process
optimisation	I-Process
process	O
.	O
Moreover	O
,	O
BEM	B-Process
is	O
ideal	O
for	O
solving	B-Task
problems	I-Task
in	I-Task
unbounded	I-Task
domains	I-Task
that	O
occur	O
in	O
electrostatic	B-Process
field	I-Process
analysis	I-Process
.	O
In	O
gradient-based	B-Task
shape	I-Task
optimisation	I-Task
the	O
shape	B-Process
derivative	I-Process
of	O
the	O
cost	O
functional	O
with	O
respect	O
to	O
geometry	O
perturbations	O
is	O
needed	O
[	O
9	O
–	O
11	O
]	O
.	O
To	O
this	O
purpose	O
,	O
we	O
use	O
the	O
adjoint	B-Process
approach	I-Process
and	O
solve	O
the	O
primary	B-Task
and	I-Task
the	I-Task
adjoint	I-Task
boundary	I-Task
value	I-Task
problems	I-Task
with	O
BEM	B-Process
.	O
The	O
associated	O
linear	O
systems	O
of	O
equations	O
are	O
dense	O
and	O
an	O
acceleration	O
technique	O
,	O
such	O
as	O
the	O
fast	B-Process
multipole	I-Process
method	I-Process
[	O
12,13	O
]	O
,	O
is	O
necessary	O
for	O
their	O
efficient	O
solution	O
.	O
For	O
some	O
recent	O
applications	O
of	O
fast	B-Task
BEM	I-Task
in	I-Task
shape	I-Task
optimisation	I-Task
and	O
Bernoulli-type	B-Task
free-boundary	I-Task
problems	I-Task
we	O
refer	O
to	O
[	O
14	O
–	O
16	O
]	O
.	O

The	O
extrapolation	B-Task
of	I-Task
the	I-Task
upwind	I-Task
value	I-Task
required	O
for	O
TVD	B-Process
differencing	I-Process
is	O
a	O
particular	O
hurdle	O
for	O
the	O
application	O
on	O
unstructured	B-Material
meshes	I-Material
.	O
As	O
discussed	O
in	O
Section	O
3.2	O
,	O
two	O
methods	O
to	O
extrapolate	O
the	O
value	O
at	O
the	O
virtual	O
upwind	O
node	O
,	O
using	O
data	B-Material
readily	I-Material
available	I-Material
on	I-Material
unstructured	I-Material
meshes	I-Material
,	O
are	O
considered	O
.	O
Given	O
how	O
the	O
virtual	B-Process
upwind	I-Process
node	I-Process
is	I-Process
incorporated	I-Process
in	I-Process
the	I-Process
gradient	I-Process
ratio	I-Process
rf	I-Process
,	O
the	O
extrapolation	O
method	O
of	O
Darwish	B-Process
and	I-Process
Moukalled	I-Process
[	O
13	O
]	O
is	O
referred	O
to	O
as	O
implicit	B-Process
extrapolation	I-Process
and	O
the	O
method	B-Process
introduced	I-Process
by	I-Process
Ubbink	I-Process
and	I-Process
Issa	I-Process
[	O
12	O
]	O
as	O
explicit	B-Process
extrapolation	I-Process
.	O
Both	O
methods	O
precisely	O
reconstruct	O
the	O
upwind	O
value	O
for	O
equidistant	B-Material
,	I-Material
rectilinear	I-Material
meshes	I-Material
but	O
fail	O
to	O
do	O
so	O
on	O
non-equidistant	B-Material
or	O
non-rectilinear	B-Material
meshes	I-Material
,	O
as	O
discussed	O
in	O
Section	O
3.2	O
.	O
Using	O
the	O
explicit	B-Process
extrapolation	I-Process
method	O
this	O
issue	O
can	O
be	O
rectified	O
by	O
imposing	O
appropriate	O
limits	O
on	O
the	O
extrapolated	O
upwind	O
value	O
.	O

A	O
popular	O
choice	O
is	O
to	O
couple	B-Process
a	I-Process
set	I-Process
of	I-Process
quadrature	I-Process
points	I-Process
with	I-Process
an	I-Process
equal	I-Process
number	I-Process
of	I-Process
nodal	I-Process
Lagrange	I-Process
polynomials	I-Process
defined	O
at	O
the	O
same	O
points	O
,	O
leading	O
to	O
a	O
collocation	B-Process
method	I-Process
.	O
There	O
are	O
many	O
examples	O
of	O
this	O
throughout	O
the	O
literature	O
,	O
both	O
in	O
terms	O
of	O
the	O
more	O
traditionally	O
utilised	O
continuous	O
Galerkin	B-Process
(	O
CG	B-Process
)	O
and	O
discontinuous	B-Process
Galerkin	I-Process
(	O
DG	B-Process
)	O
formulations	O
,	O
as	O
well	O
as	O
newer	B-Process
extensions	I-Process
such	O
as	O
the	O
flux	B-Process
reconstruction	I-Process
(	O
FR	B-Process
)	O
technique	O
as	O
presented	O
by	O
Huynh	O
[	O
23	O
]	O
.	O
In	O
collocation	B-Process
methods	I-Process
,	O
while	O
most	O
linear	B-Material
operators	I-Material
can	O
be	O
exactly	O
integrated	O
in	O
this	O
setting	O
depending	O
on	O
the	O
choice	O
of	O
quadrature	O
,	O
integrals	O
of	O
nonlinear	O
terms	O
typically	O
incur	O
numerical	O
error	O
.	O
However	O
,	O
the	O
computational	O
efficiencies	O
that	O
can	O
be	O
attained	O
through	O
the	O
use	O
of	O
a	O
collocation	B-Task
formulation	I-Task
,	I-Task
especially	I-Task
given	I-Task
the	I-Task
presence	I-Task
of	I-Task
a	I-Task
diagonal	I-Task
mass	I-Task
matrix	I-Task
,	O
often	O
outweigh	O
the	O
numerical	O
error	O
that	O
is	O
incurred	O
.	O

An	O
inherent	O
problem	O
of	O
the	O
phase-space	B-Task
discretisation	I-Task
is	O
the	O
spurious	B-Process
separation	I-Process
of	I-Process
energy	I-Process
into	I-Process
the	I-Process
discretised	I-Process
bins	I-Process
.	O
This	O
is	O
called	O
the	O
“	O
Garden	B-Task
Sprinkler	I-Task
Effect	I-Task
”	O
and	O
has	O
been	O
extensively	O
studied	O
in	O
[	O
48,49,20	O
]	O
.	O
(	O
In	O
the	O
Boltzmann	O
transport	O
community	O
this	O
is	O
known	O
as	O
the	O
ray	B-Task
effect	I-Task
.	O
)	O
To	O
showcase	O
this	O
effect	O
in	O
the	O
angular	O
dimension	O
,	O
a	O
large	O
spatial	B-Process
domain	I-Process
(	I-Process
4000km	I-Process
×	I-Process
4000km	I-Process
)	I-Process
is	I-Process
simulated	I-Process
,	O
with	O
a	O
monochromatic	B-Material
wave	I-Material
propagating	O
over	O
a	O
long	O
distance	O
in	O
deep	B-Material
water	I-Material
(	O
d	O
=	O
10000m	O
)	O
.	O
For	O
the	O
spatial	B-Process
discretisation	I-Process
a	O
structured	B-Process
triangle	I-Process
mesh	I-Process
is	O
used	O
,	O
with	O
an	O
element	O
edge	O
length	O
of	O
67km	O
(	O
Fig.	O
11	O
(	O
a	O
))	O
.	O
The	O
initial	O
wave	B-Material
field	I-Material
,	O
located	O
500km	O
from	O
the	O
lower	O
and	O
left	O
side	O
has	O
a	O
Gaussian	B-Material
distribution	I-Material
in	I-Material
space	I-Material
,	O
with	O
a	O
significant	O
wave	B-Material
height	O
of	O
Hs	O
=	O
2.5m	O
and	O
a	O
standard	O
deviation	O
of	O
150km	O
(	O
Fig.	O
11	O
(	O
b	O
))	O
.	O
Its	O
mean	O
direction	O
is	O
30	O
°	O
with	O
an	O
angular	O
distribution	O
of	O
cos2	O
⁡(	O
θ	O
)	O
and	O
a	O
frequency	O
of	O
0.1Hz	O
.	O
The	O
simulation	B-Process
is	O
time-dependent	O
and	O
runs	O
for	O
5	O
days	O
with	O
a	O
time-step	O
of	O
600s	O
.	O

Multi-phase	B-Task
flows	I-Task
are	O
common	O
,	O
in	O
fact	O
quite	O
general	O
,	O
in	O
environmental	B-Process
and	I-Process
industrial	I-Process
processes	I-Process
.	O
Broadly	O
these	O
may	O
be	O
modelled	O
as	O
continuous	O
problems	O
where	O
phases	B-Process
are	I-Process
mixed	I-Process
(	O
e.g.	O
oil	B-Process
–	I-Process
water	I-Process
homogenisation	I-Process
[	O
36	O
]	O
,	O
sediment	B-Process
transport	I-Process
[	O
18	O
])	O
or	O
interface	B-Process
problems	I-Process
where	O
phases	O
are	O
distinct	O
and	O
interact	O
at	O
the	O
interface	O
(	O
e.g.	O
gas-assisted	B-Process
injection	I-Process
moulding	I-Process
[	O
21	O
]	O
,	O
liquid	B-Process
jet	I-Process
breakup	I-Process
[	O
40	O
])	O
.	O
In	O
some	O
cases	O
flows	O
start	O
as	O
interface	O
problems	O
but	O
as	O
mixing	B-Process
occurs	O
at	O
the	O
interface	O
they	O
become	O
effectively	O
continuous	O
,	O
at	O
least	O
locally	O
.	O
Air	B-Task
entrainment	I-Task
,	O
perhaps	O
due	O
to	O
wave	B-Process
breaking	I-Process
,	O
is	O
an	O
obvious	O
example	O
.	O
We	O
consider	O
here	O
two-phase	B-Task
interface	I-Task
problems	I-Task
where	O
the	O
interface	O
remains	O
distinct	O
and	O
the	O
density	O
difference	O
is	O
high	O
,	O
e.g.	O
air	B-Material
and	O
water	B-Material
,	O
and	O
where	O
one	O
phase	O
may	O
be	O
considered	O
incompressible	O
.	O
The	O
interface	O
is	O
transient	O
and	O
may	O
become	O
highly	O
distorted	B-Process
and	I-Process
interconnected	I-Process
.	O
Such	O
problems	O
have	O
been	O
tackled	O
with	O
mesh-based	B-Process
methods	I-Process
using	O
periodic	O
(	O
or	O
adaptive	O
)	O
re-meshing	B-Process
or	O
additional	O
phase	B-Process
tracking	I-Process
functions	I-Process
[	O
40	O
]	O
.	O
However	O
,	O
these	O
approaches	O
can	O
be	O
time-consuming	O
to	O
implement	O
and	O
prone	O
to	O
errors	O
in	O
surface	O
representation	O
[	O
50	O
]	O
or	O
mass	O
conservation	O
[	O
34	O
]	O
.	O

This	O
section	O
is	O
devoted	O
to	O
the	O
discretization	B-Task
of	I-Task
the	I-Task
advection	I-Task
–	I-Task
diffusion	I-Task
equation	I-Task
and	O
to	O
the	O
analysis	O
of	O
dispersion	B-Task
and	I-Task
diffusion	I-Task
eigencurves	I-Task
for	O
different	O
polynomial	O
orders	O
.	O
The	O
spectral	O
/	O
hp	O
continuous	O
Galerkin	B-Process
method	I-Process
considered	O
closely	O
resembles	O
the	O
formulation	O
presented	O
in	O
[	O
7	O
]	O
.	O
Sec	O
.	O
2.1	O
describes	O
in	O
detail	O
the	O
derivation	B-Process
of	I-Process
the	I-Process
semi-discrete	I-Process
advection	I-Process
–	I-Process
diffusion	I-Process
problem	I-Process
as	O
applied	O
to	O
wave-like	B-Material
solutions	I-Material
,	O
from	O
which	O
the	O
relevant	O
eigencurves	O
can	O
be	O
obtained	O
.	O
The	O
inviscid	B-Material
case	I-Material
(	O
linear	B-Material
advection	I-Material
)	O
is	O
then	O
addressed	O
in	O
Sec	O
.	O
2.2	O
,	O
where	O
the	O
role	O
of	O
primary	O
and	O
secondary	O
eigencurves	O
is	O
discussed	O
from	O
the	O
perspective	O
introduced	O
in	O
[	O
9	O
]	O
.	O
The	O
viscous	B-Material
case	I-Material
is	O
subsequently	O
considered	O
in	O
Sec	O
.	O
2.3	O
,	O
where	O
eigencurves	O
are	O
shown	O
to	O
feature	O
irregular	O
oscillations	O
for	O
problems	O
strongly	O
dominated	O
by	O
either	O
convection	O
or	O
diffusion	O
.	O

Discovering	O
that	O
both	O
the	O
vacancy	O
and	O
interstitial	O
defect	B-Task
migration	I-Task
pathways	I-Task
are	O
confined	O
to	O
Ga-free	O
regions	O
suggests	O
changes	O
in	O
recombination	O
rates	O
of	O
isolated	O
vacancy-interstitial	O
pairs	O
in	O
comparison	O
to	O
pure	O
Pu	O
.	O
The	O
degree	O
to	O
which	O
the	O
rates	O
are	O
effected	O
depends	O
on	O
the	O
distribution	O
of	O
residual	O
defects	O
post	O
a	O
cascade	O
event	O
,	O
in	O
addition	O
to	O
the	O
concentration	O
and	O
ordering	O
of	O
the	O
Ga	B-Material
atoms	O
.	O
If	O
vacancies	O
and	O
interstitials	O
become	O
greatly	O
separated	O
after	O
the	O
collision	O
cascade	O
,	O
then	O
pathways	O
to	O
recombination	O
are	O
likely	O
to	O
become	O
restricted	O
and	O
recovery	O
times	O
will	O
be	O
extended	O
.	O
This	O
is	O
viable	O
for	O
cascades	O
that	O
created	O
a	O
vacancy	O
rich	O
core	O
surrounded	O
by	O
dispersed	O
interstitials	O
,	O
as	O
found	O
for	O
the	O
low	O
energy	O
cascades	O
in	O
Pu	O
and	O
PuGa	O
[	O
11,12	O
]	O
.	O
This	O
may	O
also	O
be	O
the	O
case	O
for	O
channelling	O
events	O
,	O
where	O
energetic	O
atoms	O
travel	O
deep	O
into	O
the	O
lattice	O
through	O
channels	O
of	O
low	O
atomic	O
density	O
.	O

Our	O
simulations	B-Material
confirm	O
experimental	O
observations	O
that	O
W	B-Task
net	I-Task
erosion	I-Task
represents	O
only	O
tiny	O
fraction	O
(	O
in	O
our	O
simulation	O
∼	O
1	O
%)	O
of	O
the	O
W	B-Task
gross	I-Task
erosion	I-Task
.	O
The	O
estimated	B-Process
upstream	I-Process
W	I-Process
fluxes	I-Process
,	O
FWupstrem	B-Process
,	O
are	O
in	O
good	O
agreement	O
with	O
the	O
experimentally	B-Material
observed	I-Material
values	I-Material
⩽	O
1019m-2s-1	O
[	O
16	O
]	O
.	O
Moreover	O
,	O
this	O
value	O
is	O
not	O
very	O
sensitive	O
to	O
the	O
divertor	B-Material
plasma	O
temperature	O
.	O
For	O
low	O
temperatures	O
the	O
energy	O
of	O
D	B-Material
and	I-Material
C	I-Material
ions	I-Material
hitting	B-Process
to	I-Process
the	I-Process
divertor	I-Process
plates	I-Process
is	O
too	O
low	O
to	O
sputter	O
sufficient	O
amount	O
of	O
W	B-Material
.	O
With	O
increasing	B-Process
energy	I-Process
the	O
W	B-Process
sputtering	I-Process
increases	O
,	O
but	O
the	O
potential	O
drop	O
in	O
the	O
divertor	B-Material
plasma	I-Material
increases	O
too	O
.	O
As	O
a	O
result	O
,	O
most	O
of	O
the	O
W	B-Material
atoms	I-Material
are	O
ionized	B-Process
in	O
the	O
vicinity	O
of	O
the	O
divertor	B-Material
and	O
return	B-Process
back	O
to	O
the	O
plates	B-Material
.	O
There	O
are	O
two	O
effects	O
leading	O
to	O
the	O
observed	B-Task
prompt	I-Task
redeposition	I-Task
of	O
W	B-Material
ions	I-Material
:	O
first	O
is	O
the	O
“	O
near-divertor	B-Process
”	I-Process
ionization	I-Process
of	O
W	B-Material
due	O
to	O
low	O
ionization	B-Process
potential	O
−	O
7.86eV	O
(	O
for	O
comparison	O
the	O
ionization	O
potentials	O
for	O
D	B-Material
and	O
C	B-Material
are	O
13,6	O
and	O
10.6eV	O
)	O
,	O
second	O
,	O
W	B-Material
+	I-Material
n	I-Material
ions	I-Material
have	O
large	B-Process
Larmor	I-Process
radius	I-Process
∼	O
2	O
/	O
nmm	O
,	O
so	O
that	O
they	O
are	O
redeposited	O
within	O
the	O
distance	O
of	O
a	O
Larmor	O
radius	O
.	O
Important	O
to	O
note	O
that	O
a	O
significant	O
fraction	O
of	O
W	B-Material
ions	I-Material
escaping	O
this	O
prompt	O
redeposition	O
are	O
returned	O
back	O
due	O
to	O
the	O
friction	B-Process
with	I-Process
the	I-Process
main	I-Process
ions	I-Process
.	O

The	O
displacement	B-Task
cascade	I-Task
is	O
a	O
rapid	O
process	O
(	O
of	O
order	O
picoseconds	O
)	O
.	O
Further	O
migration	B-Process
of	I-Process
vacancies	I-Process
and	I-Process
SIAs	I-Process
,	O
mainly	O
by	O
diffusion	B-Process
,	O
happens	O
over	O
a	O
timescale	O
of	O
order	O
nanoseconds	O
[	O
17	O
]	O
.	O
This	O
is	O
still	O
short	O
compared	O
to	O
operating	O
times	O
,	O
so	O
is	O
important	O
to	O
consider	O
the	O
equilibrium	O
result	O
of	O
such	O
processes	O
:	O
If	O
the	O
vacancies	O
and	O
SIAs	O
were	O
likely	O
to	O
find	O
their	O
Frenkel	O
partner	O
,	O
recombine	O
,	O
and	O
annihilate	O
,	O
then	O
the	O
metal	O
should	O
essentially	O
return	O
to	O
its	O
original	O
structure	B-Material
;	O
however	O
,	O
if	O
defects	B-Material
instead	O
formed	O
large	O
clusters	O
of	O
a	O
single	O
type	O
this	O
could	O
result	O
in	O
formation	O
of	O
voids	B-Material
,	O
dislocation	B-Material
loops	I-Material
or	O
swelling	B-Material
,	O
possibly	O
weakening	O
the	O
material	O
in	O
the	O
process	O
.	O
Defects	O
can	O
be	O
trapped	O
at	O
grain	B-Material
boundaries	O
or	O
surface	O
,	O
so	O
for	O
an	O
ODS	B-Material
particle	I-Material
to	O
effect	O
the	O
diffusion	O
,	O
there	O
concentration	O
must	O
be	O
such	O
that	O
there	O
are	O
many	O
such	O
particles	O
in	O
each	O
grain	B-Material
.	O

The	O
dashed	O
curve	O
represents	O
the	O
PuO2	B-Material
molar	O
fraction	O
on	O
the	O
sample	O
surface	O
.	O
It	O
shows	O
that	O
,	O
following	O
the	O
UO2	B-Task
–	I-Task
PuO2	I-Task
phase	I-Task
boundaries	I-Task
,	O
rather	O
well	O
established	O
in	O
this	O
compositional	O
range	O
(	O
see	O
Section	O
4.3	O
below	O
)	O
,	O
the	O
newly	O
formed	O
liquid	B-Material
surface	I-Material
is	O
initially	O
enriched	B-Process
in	I-Process
plutonium	I-Process
dioxide	I-Process
.	O
Subsequently	O
,	O
due	O
to	O
fast	O
diffusion	O
in	O
the	O
liquid	B-Material
phase	O
,	O
the	O
initial	O
sample	O
composition	O
(	O
x	O
(	O
PuO2	B-Material
)=	O
0.25	O
)	O
tends	O
to	O
be	O
rapidly	O
restored	O
.	O
It	O
is	O
however	O
clear	O
,	O
from	O
the	O
simulation	B-Process
,	O
that	O
the	O
fast	O
cooling	B-Process
occurring	O
after	O
the	O
end	O
of	O
the	O
laser	B-Process
pulse	I-Process
leads	O
to	O
onset	O
of	O
solidification	B-Process
before	O
the	O
initial	O
composition	O
is	O
fully	O
recovered	O
in	O
the	O
liquid	B-Material
.	O
A	O
surface	B-Material
solid	I-Material
crust	I-Material
forms	O
then	O
upon	O
freezing	B-Process
before	O
the	O
total	O
liquid	B-Material
mass	O
has	O
crystallised	O
(	O
see	O
insets	O
in	O
Fig.	O
4	O
)	O
.	O
The	O
double	O
inflection	O
during	O
cooling	B-Process
in	O
this	O
case	O
corresponds	O
to	O
the	O
solidification	B-Process
onset	O
on	O
the	O
sample	B-Material
surface	I-Material
(	O
first	O
inflection	O
)	O
and	O
to	O
the	O
disappearance	O
of	O
the	O
last	O
liquid	B-Material
inside	O
the	O
material	O
(	O
second	O
inflection	O
)	O
.	O
The	O
highest	O
recalescence	O
temperature	O
represents	O
the	O
solidification	B-Process
point	O
of	O
a	O
composition	O
very	O
close	O
to	O
the	O
initial	O
one	O
(	O
approximately	O
±	O
0.01	O
on	O
x	O
(	O
PuO2	O
)	O
in	O
the	O
current	O
example	O
)	O
,	O
except	O
for	O
small	B-Task
segregation	I-Task
effects	I-Task
.	O
These	O
latter	O
have	O
been	O
studied	O
also	O
experimentally	O
in	O
the	O
present	O
research	O
,	O
by	O
post-melting	B-Process
material	I-Process
characterisation	I-Process
.	O

The	O
vapour	B-Material
phase	I-Material
consists	O
of	O
a	O
number	B-Material
of	I-Material
different	I-Material
gases	I-Material
with	O
silicon	B-Material
exhibiting	O
a	O
far	O
higher	O
partial	O
pressure	O
than	O
all	O
carbon	B-Material
containing	O
species	O
over	O
the	O
full	O
temperature	O
range	O
.	O
As	O
an	O
immediate	O
result	O
the	O
vapour	B-Material
contains	O
a	O
higher	O
amount	O
of	O
silicon	B-Material
leaving	O
the	O
solid	B-Process
phase	I-Process
with	O
excess	O
carbon	B-Material
.	O
This	O
carbon	O
is	O
likely	O
to	O
precipitate	B-Process
on	I-Process
the	I-Process
surface	I-Process
of	I-Process
the	I-Process
SiC	I-Process
grains	I-Process
,	O
a	O
process	O
that	O
becomes	O
very	O
rapid	O
as	O
the	O
temperature	O
approaches	O
2100K	O
[	O
24	O
]	O
.	O
Within	O
the	O
TRISO	B-Material
particle	I-Material
the	O
SiC	B-Material
layer	O
is	O
sandwiched	O
between	O
two	O
coatings	O
of	O
dense	O
carbon	B-Material
.	O
The	O
partial	B-Material
pressure	I-Material
in	O
thermodynamic	O
equilibrium	O
of	O
gaseous	B-Material
carbon	I-Material
forming	O
above	O
graphite	B-Material
was	O
calculated	O
using	O
data	B-Material
taken	I-Material
from	I-Material
JANAF	I-Material
tables	I-Material
and	O
added	O
to	O
Fig.	O
1	O
[	O
25	O
]	O
,	O
which	O
showed	O
that	O
in	O
the	O
whole	O
temperature	O
range	O
relevant	O
for	O
this	O
study	O
the	O
vapour	B-Task
pressure	I-Task
of	I-Task
carbon	I-Task
is	O
several	O
magnitudes	O
smaller	O
than	O
that	O
of	O
the	O
dominant	O
gas	B-Material
phases	O
above	O
SiC	B-Material
.	O

The	O
second	B-Task
stress	I-Task
state	I-Task
is	O
a	O
tri-axial	B-Task
tensile	I-Task
stress	I-Task
designed	O
to	O
represent	O
the	O
zone	O
ahead	O
of	O
an	O
advancing	B-Material
crack	I-Material
tip	I-Material
.	O
Micro-scale	O
lateral	O
cracks	O
have	O
been	O
observed	O
in	O
the	O
oxide	B-Material
layer	I-Material
,	O
and	O
appear	O
to	O
form	O
very	O
close	O
to	O
or	O
at	O
the	O
metal	B-Material
–	I-Material
oxide	I-Material
interface	I-Material
(	O
Fig.	O
1	O
)	O
.	O
Finite	B-Process
element	I-Process
analysis	I-Process
by	O
Parise	O
et	O
al.	O
indicated	O
that	O
these	O
cracks	B-Material
form	O
as	O
a	O
result	O
of	O
localised	B-Material
tensile	I-Material
stresses	I-Material
above	O
peaks	O
in	O
the	O
metal	B-Material
–	I-Material
oxide	I-Material
interface	I-Material
roughness	O
[	O
31	O
]	O
.	O
These	O
cracks	O
are	O
considered	O
separate	O
to	O
any	O
nano-scale	O
cracks	O
that	O
might	O
result	O
from	O
the	O
tetragonal	B-Process
to	I-Process
monoclinic	I-Process
phase	I-Process
transformation	I-Process
.	O
An	O
assumption	O
is	O
made	O
here	O
that	O
whether	O
the	O
micro-scale	O
lateral	O
cracks	O
form	O
via	O
fracture	O
of	O
the	O
oxide	B-Material
or	O
by	O
de-bonding	B-Process
at	O
the	O
interface	O
a	O
triaxial	B-Process
tensile	I-Process
stress	I-Process
state	O
will	O
still	O
be	O
present	O
.	O
In	O
manufactured	B-Material
partially	I-Material
stabilised	I-Material
zirconia	I-Material
cracks	O
would	O
be	O
expected	O
to	O
destabilise	O
the	O
tetragonal	B-Process
phase	I-Process
.	O
This	O
is	O
simulated	B-Task
by	I-Task
applying	I-Task
tensile	I-Task
stress	I-Task
in	I-Task
direction	I-Task
1	I-Task
,	I-Task
2	I-Task
and	I-Task
3	I-Task
.	O
As	O
this	O
the	O
maximum	B-Process
stress	I-Process
at	O
the	O
crack	O
tip	O
is	O
not	O
known	O
,	O
the	O
applied	B-Process
tensile	I-Process
stresses	I-Process
cover	O
a	O
range	O
from	O
0.1GPa	O
up	O
to	O
a	O
maximum	O
stress	O
value	O
of	O
2.2GPa	O
as	O
it	O
is	O
approximately	O
equal	O
to	O
three	O
times	O
the	O
fracture	B-Process
strength	O
of	O
bulk	O
fracture	O
strength	O
for	O
manufactured	B-Material
stabilized	I-Material
zirconia	I-Material
[	O
34	O
]	O
.	O
For	O
the	O
biaxial	B-Task
compressive	I-Task
and	I-Task
triaxial	I-Task
tensile	I-Task
stress	I-Task
states	I-Task
it	O
is	O
the	O
trends	O
in	O
behaviour	O
rather	O
than	O
the	O
absolute	O
values	O
that	O
are	O
considered	O
of	O
greatest	O
importance	O
for	O
this	O
work	O
.	O

The	O
early	O
theoretical	O
work	O
of	O
Catlow	O
assessed	O
a	O
number	O
of	O
Willis	B-Material
type	I-Material
clusters	I-Material
and	O
found	O
them	O
all	O
to	O
be	O
stable	O
using	O
potential-based	B-Process
methods	I-Process
[	O
6	O
]	O
.	O
More	O
recently	O
“	O
split	B-Task
interstitial	I-Task
”	I-Task
type	I-Task
clusters	I-Task
(	O
Fig.	O
1	O
)	O
have	O
emerged	O
from	O
computational	O
studies	O
as	O
stable	O
species	O
following	O
the	O
potential	B-Process
based	I-Process
investigation	I-Process
of	O
Govers	O
et	O
al.	O
which	O
found	O
the	O
2:2:2	O
cluster	O
in	O
a	O
UO2	B-Material
supercell	I-Material
relaxed	O
to	O
a	O
split	O
di-interstitial	B-Material
[	O
13	O
]	O
(	O
Fig.	O
1	O
(	O
b	O
))	O
;	O
a	B-Material
single	I-Material
VO	I-Material
with	I-Material
three	I-Material
Oi	I-Material
displaced	O
approximately	O
1.6Å	O
in	O
〈	O
111	O
〉	O
directions	O
from	O
the	O
VO	O
.	O
This	O
result	O
was	O
later	O
confirmed	O
by	O
the	O
LSDA	B-Process
+	I-Process
U	I-Process
calculations	O
of	O
Geng	O
et	O
al	O
.	O
[	O
7	O
]	O
.	O
The	O
family	O
of	O
split	B-Material
interstitial	I-Material
clusters	I-Material
was	O
extended	O
to	O
include	O
tri-interstitials	B-Material
[	O
8	O
]	O
(	O
a	O
di-interstitial	B-Material
with	I-Material
the	I-Material
fourth	I-Material
Oi	I-Material
site	I-Material
occupied	I-Material
)	O
and	O
quad-interstitials	B-Material
[	O
9	O
]	O
(	O
two	B-Material
di-interstitials	I-Material
on	I-Material
adjacent	I-Material
sites	I-Material
,	O
giving	O
a	O
total	O
of	O
two	O
VO	B-Material
and	O
six	O
Oi	B-Material
)	O
(	O
Fig.	O
1	O
(	O
d	O
))	O
.	O
Following	O
this	O
Andersson	O
et	O
al.	O
postulated	O
a	O
model	B-Material
for	I-Material
U4O9	I-Material
based	O
on	O
a	O
UO2	B-Material
supercell	I-Material
containing	O
multiple	B-Material
split	I-Material
quad-interstitial	I-Material
clusters	I-Material
;	O
following	O
the	O
prediction	O
of	O
their	O
LSDA	B-Process
+	I-Process
U	I-Process
calculations	O
that	O
the	O
quad-interstitial	B-Material
is	O
more	O
stable	O
than	O
its	O
cuboctahedral	B-Material
counterpart	O
[	O
12	O
]	O
.	O

In	O
the	O
calculations	O
for	O
the	O
formation	B-Task
energy	I-Task
,	O
the	O
box	B-Material
size	O
is	O
set	O
to	O
30a0	O
×	O
30a0	O
×	O
30a0	O
,	O
where	O
a0	O
is	O
the	O
bcc	O
Fe	B-Material
lattice	I-Material
parameter	O
.	O
For	O
all	O
calculations	O
periodic	O
boundary	O
conditions	O
and	O
constant	O
volume	O
are	O
used	O
.	O
The	O
Monte	B-Process
Carlo	I-Process
algorithm	I-Process
used	O
to	O
determine	B-Process
the	I-Process
lowest	I-Process
energy	I-Process
configuration	I-Process
of	O
the	O
cluster	O
[	O
28	O
]	O
is	O
organised	O
as	O
follows	O
.	O
First	O
,	O
the	O
energetics	O
of	O
voids	B-Material
without	I-Material
helium	I-Material
are	O
investigated	O
.	O
A	O
vacancy	O
is	O
introduced	O
into	O
the	O
simulation	B-Material
cell	I-Material
and	O
the	O
system	O
is	O
minimised	O
using	O
a	O
conjugate	B-Process
gradient	I-Process
algorithm	I-Process
,	O
yielding	O
a	O
single	B-Material
vacancy	I-Material
formation	I-Material
energy	I-Material
Evac	B-Material
of	O
1.72eV	O
.	O
Next	O
,	O
the	O
atom	O
with	O
the	O
highest	O
potential	O
energy	O
is	O
removed	O
from	O
the	O
system	O
and	O
again	O
the	O
system	O
is	O
minimised	B-Process
.	O
This	O
scheme	O
is	O
iteratively	O
continued	O
to	O
create	O
voids	O
up	O
to	O
the	O
number	O
of	O
target	O
vacancies	O
and	O
the	O
formation	O
energy	O
of	O
each	O
is	O
calculated	O
.	O
Next	O
,	O
helium	B-Material
atoms	I-Material
are	O
introduced	O
to	O
the	O
vacancies	O
.	O
The	O
total	B-Material
system	I-Material
energy	I-Material
is	O
measured	O
and	O
recorded	O
.	O
At	O
this	O
point	O
,	O
a	O
Metropolis	B-Process
MC	I-Process
scheme	I-Process
[	O
29	O
]	O
is	O
used	O
to	O
find	O
the	O
low	B-Material
energy	I-Material
configurations	I-Material
.	O
Every	O
helium	B-Material
in	O
the	O
system	O
is	O
randomly	O
displaced	O
from	O
its	O
site	O
up	O
to	O
a	O
maximum	O
of	O
rmax	O
(	O
4.5Å	O
,	O
the	O
cut	O
off	O
distance	O
for	O
He	B-Process
–	I-Process
He	I-Process
interactions	I-Process
)	O
in	O
each	O
of	O
the	O
x	O
,	O
y	O
and	O
z	O
directions	O
and	O
then	O
minimised	B-Process
using	O
the	O
conjugate	B-Process
gradient	I-Process
algorithm	I-Process
.	O
Each	O
bubble	O
is	O
continued	O
for	O
a	O
minimum	O
of	O
10,000	O
steps	O
.	O
After	O
that	O
,	O
the	O
searches	O
will	O
be	O
terminated	O
if	O
the	O
system	O
energy	O
does	O
not	O
drop	O
within	O
a	O
further	O
10	O
steps	O
.	O
A	O
schematic	O
of	O
this	O
iterative	B-Process
process	I-Process
is	O
shown	O
in	O
Fig.	O
1	O
.	O

The	O
class	B-Material
of	I-Material
steels	I-Material
known	O
as	O
oxide	B-Material
dispersion	I-Material
strengthened	I-Material
(	O
ODS	B-Material
)	O
ferritic	B-Material
alloys	I-Material
(	O
also	O
known	O
as	O
nanostructured	B-Material
ferritic	B-Material
alloys	I-Material
)	O
consist	O
of	O
a	O
dispersion	B-Material
of	I-Material
ultra-fine	I-Material
oxide	I-Material
particles	I-Material
throughout	I-Material
the	I-Material
matrix	I-Material
.	O
These	O
oxide	O
particles	O
serve	O
to	O
improve	B-Task
the	I-Task
mechanical	I-Task
properties	I-Task
of	I-Task
the	I-Task
system	I-Task
,	O
particularly	O
at	O
high	O
temperatures	O
,	O
of	O
the	O
system	O
through	O
inhibiting	O
dislocation	O
motion	O
and	O
grain	O
boundary	O
sliding	O
.	O
In	O
nuclear	O
applications	O
the	O
oxide	B-Material
particles	I-Material
have	O
been	O
suggested	O
to	O
act	O
as	O
point	O
defect	O
sinks	O
[	O
10,11	O
]	O
to	O
improve	B-Task
radiation	I-Task
tolerance	I-Task
,	O
and	O
as	O
preferential	O
sites	O
for	O
the	O
formation	O
of	O
nano-scale	B-Material
He	I-Material
bubbles	I-Material
therefore	O
reducing	B-Task
swelling	I-Task
compared	O
to	O
non-ODS	B-Material
steels	I-Material
[	O
12	O
–	O
15	O
]	O
.	O
The	O
ability	O
of	O
the	O
oxide	B-Material
particles	I-Material
to	O
improve	O
these	O
properties	O
depends	O
on	O
the	O
structure	B-Material
and	I-Material
composition	I-Material
of	I-Material
the	I-Material
particles	I-Material
[	O
10,11,16,17	O
]	O
and	O
their	O
stability	O
under	O
irradiation	B-Process
.	O
Typical	O
compositions	O
of	O
ODS	B-Material
steels	I-Material
include	O
between	O
9	B-Material
and	I-Material
14at.	I-Material
%	I-Material
Cr	I-Material
for	O
oxidation	B-Process
resistance	O
(	O
most	O
commonly	O
14at.	O
%)	O
;	O
W	B-Material
for	O
solid	O
solution	O
hardening	O
;	O
Y2O3	B-Material
that	O
is	O
put	O
into	O
solid	B-Material
solution	I-Material
during	O
the	O
initial	O
,	O
mechanical	B-Process
alloying	I-Process
,	O
process	O
but	O
then	O
during	O
consolidation	B-Process
at	O
high	O
temperatures	O
forms	O
precipitates	B-Material
;	O
and	O
Ti	B-Material
to	O
inhibit	O
significant	O
growth	O
of	O
the	O
oxide	B-Material
particles	I-Material
;	O
the	O
balance	B-Material
being	O
made	O
up	O
of	O
Fe	B-Material
and	I-Material
impurities	I-Material
[	O
18	O
]	O
.	O
For	O
this	O
reason	O
these	O
steels	B-Material
are	O
often	O
referred	O
to	O
as	O
14YWT	B-Material
,	O
reflecting	O
the	O
constituent	B-Material
elements	I-Material
.	O

Zirconium	B-Material
alloys	I-Material
are	O
used	O
as	O
cladding	B-Material
to	O
encapsulate	O
fuel	B-Material
pellets	I-Material
in	O
pressurised	O
and	O
boiling	O
water	B-Material
nuclear	I-Material
reactors	I-Material
.	O
Research	O
into	O
oxidation	B-Task
of	I-Task
these	I-Task
alloys	I-Task
has	O
been	O
significant	O
since	O
the	O
introduction	O
of	O
the	O
material	O
.	O
However	O
,	O
the	O
microstructure	B-Process
and	I-Process
electro-chemical	I-Process
processes	I-Process
during	O
oxidation	B-Process
are	O
complex	O
and	O
many	O
questions	O
still	O
remain	O
unanswered	O
.	O
One	O
such	O
issue	O
is	O
the	O
formation	O
of	O
lateral	O
cracks	O
near	O
the	O
metal-oxide	B-Material
interface	I-Material
.	O
Small	O
cracks	O
have	O
been	O
seen	O
to	O
form	O
continuously	O
during	O
oxidation	B-Process
,	O
with	O
large	O
scale	O
networks	O
of	O
lateral	O
cracks	O
forming	O
cyclically	O
every	O
∼	O
2μm	O
of	O
oxide	O
growth	O
.	O
These	O
networks	O
of	O
cracks	O
can	O
be	O
correlated	O
with	O
acceleration	O
in	O
the	O
corrosion	B-Material
kinetics	I-Material
[	O
1	O
–	O
7	O
]	O
.	O
These	O
lateral	O
cracks	O
might	O
enable	O
the	O
link	B-Material
up	I-Material
of	I-Material
nano	I-Material
pores	I-Material
along	O
grain	B-Material
boundaries	O
perpendicular	O
to	O
the	O
metal	B-Material
/	I-Material
oxide	I-Material
interface	I-Material
as	O
reported	O
in	O
[	O
8,9	O
]	O
.	O
Experiments	O
using	O
Synchrotron	B-Process
X-Ray	I-Process
Diffraction	I-Process
(	O
S-XRD	B-Process
)	O
by	O
both	O
Polatidis	O
et	O
al.	O
and	O
Petigny	O
et	O
al.	O
,	O
have	O
separately	O
shown	O
that	O
oxides	B-Material
formed	O
on	O
Zircaloy-4	B-Material
are	O
composed	O
of	O
monoclinic	B-Material
and	I-Material
stabilised	I-Material
tetragonal	I-Material
phases	I-Material
,	O
with	O
an	O
∼	B-Material
7	I-Material
%	I-Material
reduction	I-Material
in	I-Material
the	I-Material
tetragonal	I-Material
phase	I-Material
fraction	O
from	O
1	O
to	O
3μm	O
oxide	O
growth	O
[	O
4,10	O
]	O
.	O
One	O
theory	O
is	O
that	O
the	O
lateral	O
cracks	O
may	O
destabilise	O
the	O
tetragonal	O
phase	O
close	O
to	O
the	O
metal-oxide	B-Material
interface	I-Material
.	O
The	O
phase	B-Process
transformation	I-Process
has	O
an	O
∼	O
6	O
%	O
expansion	O
associated	O
with	O
it	O
,	O
which	O
could	O
lead	O
to	O
fracture	B-Process
perpendicular	O
to	O
the	O
metal-oxide	B-Material
interface	I-Material
,	O
thereby	O
generating	O
fast	O
ingress	B-Process
routes	O
for	O
oxygen	B-Material
containing	I-Material
species	I-Material
[	O
11,12	O
]	O
.	O

Spark	B-Process
plasma	I-Process
sintering	I-Process
(	O
SPS	B-Process
)	O
is	O
a	O
relatively	O
new	O
sintering-based	B-Process
technique	I-Process
[	O
17	O
]	O
in	O
which	O
the	O
powder	B-Material
to	O
be	O
consolidated	O
is	O
loaded	O
into	O
an	O
electrically	B-Process
and	I-Process
thermally	I-Process
conductive	I-Process
graphite	I-Process
mould	I-Process
and	I-Process
a	I-Process
large	I-Process
DC	I-Process
pulsed	I-Process
current	I-Process
(	I-Process
1000	I-Process
–	I-Process
5000A	I-Process
)	I-Process
is	I-Process
applied	I-Process
under	I-Process
a	I-Process
uniaxial	I-Process
pressure	I-Process
.	O
When	O
current	O
passes	O
through	O
the	O
graphite	O
mould	O
(	O
and	O
the	O
powder	B-Material
if	O
it	O
is	O
electrically	O
conductive	O
)	O
,	O
the	O
powder	O
is	O
heated	O
both	O
from	O
the	O
outside	O
(	O
the	O
mould	B-Material
acts	O
as	O
a	O
heating	B-Material
element	I-Material
)	O
and	O
inside	O
(	O
due	O
to	O
Joule	O
heating	O
from	O
the	O
intrinsic	O
electrical	O
resistance	O
of	O
the	O
powder	B-Material
material	I-Material
)	O
.	O
SPS	B-Process
is	O
characterised	O
by	O
very	O
fast	O
heating	O
(	O
up	O
to	O
2000	O
°	O
C	O
/	O
min	O
)	O
and	O
cooling	O
rates	O
and	O
short	O
holding	O
times	O
(	O
minutes	O
)	O
to	O
achieve	O
near	O
theoretical	O
density	O
[	O
17	O
]	O
.	O
Thus	O
SPS	O
occupies	O
a	O
very	O
different	O
time	O
–	O
temperature	O
–	O
density	O
space	O
in	O
powder	B-Process
consolidation	I-Process
maps	I-Process
when	O
compared	O
with	O
conventional	B-Process
methods	I-Process
,	O
such	O
as	O
hot	B-Process
pressing	I-Process
sintering	I-Process
and	O
HIP	B-Process
with	O
ramp	O
rate	O
of	O
50	O
–	O
80	O
°	O
C	O
/	O
min	O
and	O
a	O
few	O
hours	O
holding	O
time	O
.	O
Although	O
SPS	B-Process
has	O
been	O
studied	O
for	O
a	O
rapidly	O
growing	O
number	O
of	O
materials	O
[	O
17	O
]	O
,	O
there	O
are	O
only	O
a	O
small	O
number	O
of	O
studies	O
on	O
the	O
fabrication	B-Task
and	I-Task
microstructural	I-Task
characterisation	I-Task
of	I-Task
ODS	I-Task
steels	I-Task
processed	I-Task
by	I-Task
SPS	I-Task
,	O
briefly	O
reviewed	O
below	O
.	O

To	O
conclude	O
,	O
the	O
electrochemical	B-Task
reduction	I-Task
of	I-Task
uranium	I-Task
dioxide	I-Task
to	O
uranium	B-Material
metal	I-Material
has	O
been	O
studied	O
in	O
a	O
lithium	B-Material
chloride	I-Material
–	I-Material
potassium	I-Material
chloride	I-Material
eutectic	I-Material
molten	I-Material
salt	I-Material
at	O
450	O
°	O
C	O
.	O
Both	O
electrochemical	B-Process
and	O
synchrotron	B-Process
X-ray	I-Process
techniques	O
have	O
been	O
utilised	O
to	O
deduce	B-Task
the	I-Task
electrochemical	I-Task
reduction	I-Task
potential	I-Task
,	I-Task
mechanism	I-Task
and	I-Task
reduction	I-Task
pathway	I-Task
.	O
The	O
electrochemical	O
reduction	O
potential	O
of	O
the	O
UO2	B-Material
|	I-Material
U	I-Material
couple	I-Material
is	O
dependent	O
on	O
the	O
activity	O
of	O
oxide	B-Material
ions	I-Material
existing	O
within	O
the	O
melt	B-Material
.	O
The	O
electrochemical	B-Process
reduction	I-Process
of	O
uranium	B-Material
dioxide	I-Material
to	O
uranium	B-Material
metal	I-Material
seems	O
to	O
occur	O
in	O
a	O
single	O
,	O
4-electron-step	B-Process
,	O
process	O
;	O
indicated	O
by	O
a	O
single	O
reduction	O
peak	O
(	O
C1	O
)	O
in	O
the	O
cyclic	O
voltammograms	O
and	O
also	O
by	O
the	O
exclusion	O
of	O
any	O
other	O
phases	O
in	O
the	O
EDXD	O
data	O
.	O
The	O
electrochemical	O
reduction	O
may	O
be	O
impeded	O
by	O
an	O
increase	O
in	O
oxo-acidity	O
of	O
the	O
molten	O
salt	O
.	O
That	O
is	O
,	O
O2	O
−	O
ions	O
that	O
are	O
liberated	O
by	O
the	O
electroreduction	O
may	O
not	O
react	O
at	O
the	O
counter	O
electrode	O
and	O
,	O
thus	O
,	O
not	O
be	O
removed	O
from	O
the	O
molten	O
salt	O
.	O
This	O
could	O
be	O
due	O
to	O
the	O
electrode	O
geometry	O
and	O
/	O
or	O
the	O
inherent	O
microstructure	O
of	O
the	O
working	O
electrode	O
:	O
a	O
high	O
tortuosity	O
,	O
for	O
example	O
,	O
would	O
impede	O
the	O
diffusion	O
of	O
O2	O
−	O
ions	O
out	O
of	O
the	O
working	O
electrode	O
.	O
This	O
could	O
then	O
cause	O
an	O
increase	O
in	O
the	O
activity	O
of	O
oxide	O
ions	O
existing	O
within	O
the	O
melt	O
and	O
hence	O
inhibit	O
the	O
electrochemical	O
reduction	O
–	O
exploration	O
of	O
the	O
microstructure	O
of	O
working	O
electrodes	O
will	O
be	O
the	O
focus	O
of	O
future	O
work	O
.	O

The	O
primary	O
benefit	O
of	O
using	O
a	O
3D	B-Process
model	I-Process
is	O
that	O
it	O
allows	O
the	O
application	O
of	O
anisotropic	B-Task
material	I-Task
properties	I-Task
.	O
As	O
a	O
hexagonal	O
close	O
packed	O
lattice	O
structure	O
,	O
a	O
single	O
zirconium	B-Material
grain	I-Material
is	O
plastically	O
anisotropic	O
due	O
to	O
the	O
difficulty	O
of	O
activating	O
slip	O
with	O
a	O
〈	O
c	O
〉	O
component	O
[	O
23	O
–	O
26	O
]	O
.	O
Abaqus	B-Process
allows	O
this	O
to	O
be	O
represented	O
by	O
setting	O
plasticity	B-Process
potential	I-Process
ratios	I-Process
.	O
The	O
anisotropic	B-Material
elastic	I-Material
and	I-Material
plastic	I-Material
constants	I-Material
are	O
shown	O
in	O
Table	O
1	O
.	O
Zirconium	B-Material
alloys	O
can	O
often	O
have	O
a	O
bimodal	B-Process
basal	I-Process
pole	I-Process
distribution	I-Process
,	O
with	O
a	O
tilt	O
on	O
the	O
basal	O
normal	O
or	O
c	O
direction	O
of	O
±	O
30	O
°	O
in	O
the	O
normal	O
direction	O
being	O
quoted	O
for	O
recrystallized	B-Material
Zircaloy-4	I-Material
[	O
27,28	O
]	O
.	O
However	O
,	O
for	O
simplicity	O
the	O
basal	O
normal	O
or	O
c	O
direction	O
has	O
been	O
taken	O
as	O
being	O
parallel	O
to	O
the	O
normal	O
direction	O
.	O
As	O
such	O
the	O
1	O
,	O
2	O
and	O
3	O
directions	O
in	O
Table	O
1	O
correlate	O
with	O
the	O
X	O
,	O
Y	O
and	O
Z	O
global	O
coordinate	O
system	O
for	O
the	O
3D	B-Process
simulations	I-Process
,	O
with	O
the	O
3	O
direction	O
correlating	O
to	O
the	O
c	O
direction	O
of	O
a	O
zirconium	B-Material
unit	I-Material
lattice	I-Material
.	O
Table	O
1	O
also	O
shows	O
the	O
elastic	O
properties	O
incorporated	O
into	O
the	O
simulations	B-Process
.	O
The	O
oxide	B-Material
layer	I-Material
has	O
been	O
simulated	O
as	O
a	O
purely	B-Material
elastic	I-Material
material	I-Material
.	O
Although	O
it	O
is	O
known	O
that	O
the	O
oxide	B-Material
is	O
strongly	O
textured	O
[	O
29	O
]	O
,	O
it	O
is	O
still	O
simulated	O
as	O
a	O
homogenous	O
solid	O
therefore	O
isotropic	B-Task
material	I-Task
properties	I-Task
have	O
been	O
used	O
for	O
the	O
oxide	B-Material
in	O
all	O
simulations	B-Process
.	O

Zirconium	B-Material
alloys	I-Material
are	O
commonly	O
used	O
as	O
the	O
fuel	B-Material
cladding	I-Material
for	O
water	B-Material
cooled	I-Material
nuclear	I-Material
fission	I-Material
reactors	I-Material
,	O
mainly	O
due	O
to	O
their	O
low	O
neutron	O
cross-section	O
,	O
good	O
corrosion	O
resistance	O
during	O
normal	O
operating	O
conditions	O
and	O
sufficient	O
mechanical	O
strength	O
[	O
1	O
]	O
.	O
Despite	O
high	O
corrosion	O
resistance	O
at	O
normal	O
operating	O
temperatures	O
(	O
around	O
300	O
°	O
C	O
)	O
[	O
2	O
]	O
,	O
Zr	B-Material
alloys	I-Material
oxidise	B-Process
very	O
rapidly	O
when	O
exposed	O
to	O
temperatures	O
a	O
few	O
hundred	O
degrees	O
higher	O
.	O
This	O
is	O
an	O
exothermic	B-Process
reaction	I-Process
,	O
which	O
can	O
further	O
accelerate	O
oxidation	B-Process
and	O
,	O
at	O
temperatures	O
beyond	O
1000	O
°	O
C	O
,	O
potentially	O
lead	O
to	O
disintegration	B-Process
of	I-Process
the	I-Process
fuel	I-Process
rods	I-Process
,	O
as	O
highlighted	O
during	O
the	O
Fukushima	O
Daiichi	O
nuclear	O
accident	O
.	O
For	O
this	O
reason	O
new	O
research	O
activities	O
have	O
been	O
initiated	O
worldwide	O
to	O
develop	B-Task
accident	I-Task
tolerant	I-Task
fuels	I-Task
(	O
ATF	B-Material
)	O
.	O
Additionally	O
,	O
ATFs	O
could	O
also	O
provide	O
further	O
enhancements	B-Task
in	I-Task
corrosion	I-Task
performance	I-Task
during	O
normal	O
operating	O
conditions	O
enabling	O
the	O
development	B-Task
of	I-Task
fuel	I-Task
assemblies	I-Task
for	O
very	O
high	O
burn-up	O
.	O

In	O
all	O
these	O
studies	O
,	O
the	O
association	O
between	O
the	O
transition	O
and	O
lateral	O
cracking	O
in	O
the	O
oxide	B-Material
layer	I-Material
depicts	O
some	O
interaction	O
between	O
the	O
mechanical	O
behaviour	O
of	O
the	O
system	O
,	O
and	O
its	O
corrosion	O
kinetics	O
,	O
but	O
does	O
not	O
provide	O
a	O
clear	O
understanding	B-Task
of	I-Task
the	I-Task
morphology	I-Task
of	I-Task
the	I-Task
metal	I-Task
:	I-Task
oxide	I-Task
interface	I-Task
during	O
the	O
corrosion	B-Process
process	I-Process
,	O
at	O
the	O
nanometre	O
level	O
.	O
Understanding	B-Task
why	I-Task
this	I-Task
transition	I-Task
behaviour	I-Task
happens	I-Task
is	O
critical	O
when	O
modelling	B-Task
the	I-Task
rate	I-Task
of	I-Task
growth	I-Task
of	I-Task
oxide	I-Task
,	O
and	O
therefore	O
to	O
the	O
lifetime	O
prediction	O
of	O
Zr	B-Material
clads	I-Material
,	O
and	O
ultimately	O
to	O
the	O
safety	O
of	O
nuclear	B-Material
power	I-Material
reactors	I-Material
.	O
No	O
model	O
will	O
be	O
complete	O
without	O
a	O
nanoscale	O
understanding	B-Task
of	I-Task
what	I-Task
is	I-Task
going	I-Task
on	I-Task
during	I-Task
oxidation	I-Task
.	O
Thus	O
,	O
it	O
is	O
essential	O
that	O
the	O
oxide	B-Material
scale	I-Material
and	O
the	O
top	B-Material
layers	I-Material
of	I-Material
the	I-Material
metal	I-Material
are	O
studied	O
at	O
nanometre	O
resolution	O
to	O
reveal	O
the	O
detailed	O
structural	B-Process
and	I-Process
chemical	I-Process
changes	I-Process
associated	O
with	O
diffusion	B-Process
of	I-Process
oxygen	I-Process
and	O
the	O
resulting	O
oxidation	B-Process
of	I-Process
the	I-Process
metal	I-Process
.	O
Whilst	O
a	O
number	O
of	O
techniques	O
have	O
been	O
employed	O
for	O
this	O
purpose	O
,	O
it	O
is	O
clear	O
that	O
various	O
techniques	O
within	O
transmission	B-Process
electron	I-Process
microscopy	I-Process
(	O
TEM	B-Process
)	O
will	O
be	O
among	O
the	O
most	O
versatile	O
and	O
informative	O
for	O
this	O
purpose	O
,	O
although	O
additional	O
information	O
can	O
be	O
added	O
by	O
techniques	O
such	O
as	O
atom	B-Process
probe	I-Process
tomography	I-Process
.	O

Solid	B-Material
pieces	I-Material
of	O
23	O
–	O
114	O
mg	O
were	O
further	O
used	O
to	O
measure	B-Process
the	I-Process
enthalpy	I-Process
increments	I-Process
using	O
a	O
Setaram	B-Process
Multi-detector	I-Process
High	I-Process
Temperature	I-Process
Calorimeter	I-Process
(	O
MDHTC-96	B-Process
)	O
using	O
a	O
drop	B-Process
detector	I-Process
.	O
For	O
more	O
details	O
about	O
the	O
technique	O
we	O
refer	O
to	O
our	O
previous	O
studies	O
[	O
9,10	O
]	O
.	O
The	O
measurements	O
were	O
carried	O
out	O
under	O
an	O
argon	B-Material
atmosphere	O
(	O
with	O
an	O
oxygen	B-Material
content	O
of	O
7	O
ppm	O
)	O
,	O
using	O
pure	O
platinum	B-Material
ingots	I-Material
(	O
64	O
–	O
144	O
mg	O
)	O
of	O
99.95	O
at	O
%	O
purity	O
as	O
a	O
reference	B-Material
material	I-Material
.	O
The	O
temperature	O
range	O
of	O
the	O
experiment	O
was	O
from	O
430.3	O
K	O
to	O
1088.8	O
K	O
using	O
steps	O
of	O
50	O
K	O
.	O
Each	O
isothermal	B-Process
run	I-Process
consisted	O
of	O
2	B-Material
–	I-Material
4	I-Material
drops	I-Material
of	I-Material
Bi2UO6	I-Material
samples	I-Material
,	O
each	O
surrounded	O
by	O
two	O
drops	O
of	O
platinum	B-Material
from	O
which	O
the	O
sensitivity	O
of	O
the	O
device	O
was	O
determined	O
.	O
The	O
drops	O
were	O
separated	O
by	O
time	O
intervals	O
of	O
20	O
min	O
,	O
long	O
enough	O
to	O
re-stabilize	O
the	O
monitored	O
heat	B-Process
flow	I-Process
signal	O
.	O
Background	B-Process
subtraction	I-Process
and	I-Process
peak	I-Process
integration	I-Process
were	O
performed	O
using	O
commercially	B-Material
available	I-Material
software	I-Material
for	I-Material
data	I-Material
processing	I-Material
.	O
The	O
reported	O
temperatures	O
were	O
corrected	B-Process
in	O
accordance	O
with	O
the	O
calibration	O
curve	O
obtained	O
prior	O
to	O
measurement	O
using	O
several	O
high	B-Material
purity	I-Material
standard	I-Material
metals	I-Material
(	O
Sn	B-Material
,	O
Pb	B-Material
,	O
Zn	B-Material
,	O
Al	B-Material
,	O
Ag	B-Material
,	O
Ni	B-Material
)	O
with	O
various	O
melting	O
temperatures	O
in	O
order	O
to	O
cover	O
the	O
whole	O
temperature	O
range	O
of	O
the	O
measurement	O
.	O
After	O
drop	O
calorimetric	B-Process
measurements	I-Process
at	O
the	O
maximum	O
considered	O
temperature	O
,	O
the	O
material	O
was	O
subjected	O
to	O
a	O
new	O
XRD	B-Process
measurement	O
,	O
confirming	O
the	O
stability	O
of	O
the	O
compound	B-Material
under	O
the	O
experimental	O
conditions	O
.	O

The	O
fluence	B-Task
of	I-Task
each	I-Task
capsule	I-Task
was	O
determined	O
by	O
using	O
activation	B-Material
monitor	I-Material
sets	I-Material
.	I-Material
These	O
monitor	O
sets	O
consist	O
of	O
different	O
metal	B-Material
wire	I-Material
pieces	I-Material
that	O
have	O
an	O
activation	O
reaction	O
at	O
a	O
specific	O
energy	O
range	O
.	O
The	O
different	O
activation	O
energies	O
are	O
chosen	O
in	O
such	O
a	O
way	O
that	O
the	O
spectrum	O
can	O
be	O
reconstructed	O
.	O
In	O
BODEX	O
,	O
each	O
capsule	B-Material
contained	O
a	O
flux	B-Material
monitor	I-Material
set	I-Material
on	O
the	O
‘	O
back	O
side’	O
(	O
as	O
seen	O
from	O
the	O
core	O
)	O
and	O
one	O
on	O
the	O
front	O
side	O
,	O
positioned	O
at	O
the	O
central	O
height	O
of	O
the	O
capsules	B-Material
.	O
Additionally	O
,	O
one	O
detector	B-Material
was	O
placed	O
at	O
the	O
top	O
and	O
one	O
at	O
the	O
bottom	O
,	O
resulting	O
in	O
a	O
total	O
of	O
6	O
monitor	B-Material
sets	O
per	O
leg	O
.	O
The	O
fluence	O
in	O
each	O
capsule	B-Material
was	O
determined	O
as	O
the	O
average	O
between	O
the	O
two	O
flux	B-Material
monitor	I-Material
located	O
in	O
each	O
capsule	B-Material
.	O
The	O
sets	O
have	O
been	O
analysed	O
by	O
determining	B-Task
the	I-Task
activation	I-Task
of	I-Task
each	I-Task
wire	I-Task
piece	I-Task
,	O
which	O
indicates	O
the	O
fluence	B-Process
of	O
a	O
specific	O
energy	O
range	O
.	O
Table	O
3	O
show	O
the	O
values	O
of	O
the	O
fluences	B-Process
for	O
the	O
two	O
capsules	B-Material
containing	O
molybdenum	B-Material
.	O

Following	O
fission	B-Process
,	O
noble	B-Material
gas	I-Material
atoms	I-Material
will	O
be	O
distributed	O
in	O
the	O
fuel	B-Process
matrix	I-Process
initially	O
accommodated	O
at	O
point	B-Process
defects	I-Process
trap	I-Process
sites	I-Process
,	O
generally	O
thought	O
to	O
be	O
Schottky	B-Process
trivacancy	I-Process
defects	I-Process
[	O
4,5,31	O
]	O
.	O
Diffusion	B-Process
to	O
either	O
bubbles	B-Material
or	O
grain	B-Material
boundaries	I-Material
is	O
then	O
facilitated	O
by	O
associating	O
a	O
further	O
uranium	B-Material
vacancy	O
defect	O
for	O
the	O
gas	B-Material
atom	I-Material
to	O
‘	B-Process
hop’	I-Process
into	I-Process
,	O
with	O
the	O
original	O
vacancy	O
then	O
able	O
to	O
loop	B-Process
around	I-Process
to	O
ensure	O
continued	O
diffusion	B-Process
.	O
The	O
rate	O
determining	O
step	O
in	O
the	O
process	O
is	O
not	O
the	O
migration	B-Process
of	O
the	O
Xe	B-Material
itself	O
but	O
rather	O
the	O
rearrangement	B-Process
of	O
the	O
VU	B-Material
defect	O
to	O
facilitate	O
net	O
Xe	B-Process
diffusion	I-Process
[	O
6	O
–	O
8	O
]	O
.	O
Activation	B-Process
energies	O
for	O
the	O
overall	O
process	O
depend	O
on	O
the	O
availability	O
of	O
the	O
defect	B-Process
trap	I-Process
sites	I-Process
,	O
which	O
in	O
turn	O
depends	O
on	O
the	O
crystal	B-Process
stoichiometry	I-Process
.	O
For	O
Xe	B-Material
diffusion	O
in	O
UO2	B-Material
−	I-Material
x	I-Material
,	O
UO2	O
and	O
UO2	B-Material
+	I-Material
x	I-Material
the	O
activation	O
energies	O
calculated	O
using	O
DFT	B-Process
are	O
7.04	O
–	O
12.92	O
eV	O
,	O
4.15	O
–	O
7.88	O
eV	O
and	O
1.38	O
–	O
4.07	O
eV	O
with	O
the	O
ranges	O
reflecting	O
the	O
way	O
the	O
calculations	O
were	O
performed	O
depending	O
on	O
the	O
charge	B-Process
states	O
of	O
the	O
defects	O
involved	O
and	O
the	O
presence	O
of	O
a	O
Jahn	B-Process
–	I-Process
Teller	I-Process
distortion	I-Process
[	O
7	O
]	O
.	O
Activation	O
energies	O
calculated	O
using	O
empirical	B-Process
pair	I-Process
potentials	I-Process
can	O
vary	O
strongly	O
depending	O
on	O
the	O
choice	O
of	O
potential	B-Process
.	O
Govers	O
et	O
al.	O
examined	O
three	O
different	O
potentials	B-Process
for	O
UO2	B-Material
(	O
those	O
of	O
Basak	O
[	O
9	O
]	O
,	O
Jackson	O
[	O
10	O
]	O
and	O
Morelon	O
[	O
11	O
])	O
coupled	O
with	O
different	O
parameterisations	O
for	O
the	O
U	B-Material
–	I-Material
Xe	I-Material
and	O
O	B-Material
–	I-Material
Xe	I-Material
interactions	O
from	O
Geng	O
[	O
12	O
]	O
and	O
Nicoll	O
[	O
13	O
]	O
and	O
recommend	O
values	O
of	O
6.5	O
eV	O
,	O
4.5	O
eV	O
and	O
2.4	O
eV	O
[	O
6	O
]	O
for	O
the	O
different	O
stoichiometric	O
regimes	O
in	O
very	O
good	O
agreement	O
with	O
the	O
experimental	O
values	O
of	O
6.0	O
eV	O
,	O
3.9	O
eV	O
and	O
1.7	O
eV	O
respectively	O
[	O
14	O
]	O
.	O

Ferritic	B-Material
and	I-Material
martensitic	I-Material
steels	I-Material
are	O
candidate	O
materials	O
for	O
use	O
in	O
nuclear	B-Material
reactors	I-Material
[	O
1,2	O
]	O
.	O
The	O
transmutation-created	B-Material
inert	I-Material
gas	I-Material
,	O
especially	O
He	B-Material
,	O
plays	O
an	O
important	O
role	O
in	O
the	O
microstructural	B-Task
evolution	I-Task
of	I-Task
these	I-Task
steels	I-Task
under	I-Task
neutron	I-Task
irradiation	I-Task
.	O
In	O
a	O
previous	O
paper	O
[	O
3	O
]	O
the	O
mechanisms	B-Task
by	I-Task
which	I-Task
He	I-Task
in	I-Task
a	I-Task
perfect	I-Task
body-centred-cubic	I-Task
(	I-Task
bcc	I-Task
)	I-Task
Fe	I-Task
lattice	I-Task
,	I-Task
can	I-Task
agglomerate	I-Task
into	I-Task
bubbles	I-Task
was	O
discussed	O
.	O
It	O
was	O
shown	O
that	O
small	O
He	B-Material
interstitial	I-Material
clusters	I-Material
are	O
highly	O
mobile	O
but	O
become	O
effectively	O
pinned	O
with	O
the	O
emission	O
of	O
Fe	B-Material
interstitials	I-Material
when	O
the	O
clusters	O
contain	O
5	O
or	O
more	O
He	B-Material
atoms	I-Material
.	O
Small	O
bubbles	B-Material
up	O
to	O
around	O
1.5	O
nm	O
in	O
diameter	O
can	O
easily	O
form	O
at	O
room	O
temperature	O
from	O
such	O
seed	O
points	O
but	O
larger	O
bubbles	O
are	O
more	O
difficult	O
to	O
form	O
by	O
diffusion	B-Process
alone	O
due	O
to	O
the	O
induced	B-Process
strain	I-Process
in	O
the	O
bcc	B-Material
lattice	I-Material
which	O
increases	O
the	O
energy	O
barriers	O
for	O
diffusion	B-Material
towards	O
the	O
bubbles	B-Material
whilst	O
reducing	O
them	O
in	O
a	O
direction	O
away	O
from	O
the	O
bubbles	O
.	O
Subsequent	O
bubble	B-Material
enlargement	O
can	O
then	O
only	O
occur	O
either	O
through	O
increased	O
temperature	O
or	O
by	O
radiation	B-Process
induced	I-Process
mechanisms	I-Process
which	O
increase	O
the	O
number	O
of	O
vacancies	O
in	O
the	O
bubble	B-Material
and	O
reduce	B-Task
the	I-Task
lattice	I-Task
strain	I-Task
.	O
Emission	B-Process
of	I-Process
interracial	I-Process
loops	I-Process
from	O
such	O
a	O
bubble	B-Material
was	O
not	O
observed	O
in	O
molecular	B-Process
dynamics	I-Process
simulations	I-Process
.	O

An	O
increase	O
of	O
neutron	B-Process
leakage	I-Process
from	O
the	O
core	B-Material
region	I-Material
can	O
be	O
achieved	O
through	O
modifications	B-Process
in	I-Process
the	I-Process
core	I-Process
geometry	I-Process
(	O
usually	O
by	O
adopting	O
a	O
pan-cake	B-Process
geometry	I-Process
of	I-Process
the	I-Process
active	I-Process
core	I-Process
region	I-Process
at	O
the	O
expense	O
of	O
the	O
general	O
neutron	B-Material
economy	O
)	O
.	O
Extensive	O
studies	O
determined	O
a	O
set	O
of	O
core	B-Task
design	I-Task
modifications	I-Task
that	I-Task
optimised	I-Task
the	I-Task
total	I-Task
sodium	I-Task
void	I-Task
reactivity	I-Task
(	O
becoming	O
less	O
positive	O
)	O
.	O
Among	O
the	O
most	O
efficient	O
design	B-Process
solutions	I-Process
identified	O
is	O
an	O
enlarged	B-Process
sodium	I-Process
plenum	I-Process
above	O
the	O
active	O
core	B-Material
region	O
in	O
combination	O
with	O
an	O
absorber	B-Process
layer	I-Process
above	O
the	O
sodium	B-Material
plenum	I-Material
(	O
to	O
reduce	B-Task
neutron	I-Task
backscattering	I-Task
from	I-Task
the	I-Task
reflector	I-Task
region	I-Task
above	I-Task
the	I-Task
plenum	I-Task
)	O
.	O
Fig.	O
19	O
shows	O
the	O
combined	O
effect	O
of	O
different	O
upper	B-Material
plenum	I-Material
thicknesses	O
of	O
the	O
absorber	B-Material
and	I-Material
boron	I-Material
layers	I-Material
.	O
It	O
can	O
be	O
observed	O
that	O
the	O
sequential	O
increase	B-Process
of	I-Process
the	I-Process
layer	I-Process
's	I-Process
thickness	I-Process
converge	O
to	O
an	O
asymptotic	O
value	O
of	O
reactivity	B-Process
reduction	I-Process
slightly	O
over	O
800pcm	O
.	O
The	O
pair	O
of	O
values	O
selected	O
was	O
60cm	O
for	O
the	O
sodium	B-Material
plenum	I-Material
and	O
30cm	O
for	O
the	O
boron	B-Material
layer	I-Material
.	O
These	O
modifications	O
implied	O
a	O
considerable	O
increase	B-Process
in	I-Process
the	I-Process
sub-assembly	I-Process
length	I-Process
that	O
was	O
compensated	O
by	O
reducing	B-Task
the	I-Task
upper	I-Task
axial	I-Task
reflector	I-Task
width	I-Task
(	O
Sun	O
et	O
al.	O
,	O
2013	O
)	O
.	O

The	O
design	O
,	O
and	O
the	O
temperature	O
reached	O
in	O
the	O
sample	B-Material
holders	I-Material
,	O
guarantees	O
that	O
the	O
Na	B-Material
remains	O
liquid	O
during	O
operation	O
to	O
improve	B-Task
the	I-Task
heating	I-Task
transfer	I-Task
and	O
avoiding	O
solid	B-Process
formation	I-Process
(	O
too	O
cold	O
working	O
temperature	O
)	O
or	O
sodium	B-Material
boiling	O
(	O
too	O
hot	O
working	O
temperature	O
)	O
.	O
The	O
temperature	O
above	O
and	O
just	O
below	O
the	O
Na	B-Material
surface	I-Material
will	O
be	O
monitored	B-Process
by	I-Process
six	I-Process
dedicated	I-Process
thermocouples	I-Process
.	O
In	O
order	O
to	O
prevent	O
oxidation	B-Process
of	O
the	O
Na	B-Material
,	O
the	O
plenum	O
of	O
the	O
1st	O
containment	O
is	O
filled	O
with	O
high-purity	B-Material
He	I-Material
at	O
0.1MPa	O
,	O
sealed	O
after	O
final	O
assembly	O
and	O
kept	O
closed	O
during	O
in-pile	O
operation	O
(	O
no	O
gas	B-Process
circulation	I-Process
in	O
the	O
1st	O
containment	O
)	O
.	O
The	O
heat	O
generated	O
by	O
fission	B-Process
and	I-Process
gamma	I-Process
absorption	I-Process
in	O
the	O
materials	O
will	O
be	O
radially	O
dissipated	O
through	O
the	O
Na	B-Material
bath	I-Material
,	O
the	O
structural	O
materials	O
and	O
the	O
gas	B-Material
gaps	O
by	O
conduction	O
and	O
radiation	O
to	O
the	O
downstream	O
primary	O
coolant	O
of	O
the	O
TRIO	B-Material
wet	I-Material
channel	I-Material
.	O

Geomagnetic	B-Process
jerks	I-Process
are	O
conspicuous	O
yet	O
poorly	O
understood	O
phenomena	O
of	O
Earth	B-Material
’s	I-Material
magnetic	I-Material
field	I-Material
,	O
motivating	O
investigations	O
of	O
their	O
morphology	O
and	O
the	O
theory	O
behind	O
their	O
origins	O
.	O
Jerks	B-Process
are	O
most	O
commonly	O
defined	O
by	O
their	O
observed	O
form	O
at	O
a	O
single	O
observatory	O
as	O
‘	O
V’	O
shapes	O
in	O
a	O
single	O
component	O
of	O
the	O
geomagnetic	B-Process
secular	I-Process
variation	I-Process
(	O
SV	B-Process
)	O
,	O
the	O
first	O
time	O
derivative	O
of	O
the	O
main	B-Material
magnetic	I-Material
field	I-Material
(	O
MF	B-Material
)	O
.	O
The	O
times	O
of	O
the	O
gradient	B-Process
changes	I-Process
,	O
which	O
separate	O
linear	O
trends	O
of	O
several	O
years	O
,	O
have	O
associated	O
step	O
changes	O
in	O
the	O
second	O
time	O
derivative	O
of	O
the	O
MF	B-Material
(	O
secular	B-Process
acceleration	I-Process
(	O
SA	B-Process
))	O
and	O
impulses	O
in	O
the	O
third	O
time	O
derivative	O
.	O
The	O
‘	O
V’	O
shape	O
SV	O
definition	O
of	O
jerks	B-Process
includes	O
an	O
implicit	O
expectation	O
of	O
a	O
‘	B-Process
large’	I-Process
magnitude	I-Process
step	I-Process
change	I-Process
in	O
the	O
gradient	O
without	O
definition	O
of	O
this	O
scale	O
or	O
its	O
threshold	O
value	O
other	O
than	O
the	O
basic	O
need	O
for	O
it	O
to	O
be	O
observable	O
in	O
the	O
data	O
above	O
the	O
highly	O
variable	O
background	O
noise	O
.	O
Jerks	B-Process
can	O
be	O
described	O
by	O
their	O
amplitude	O
,	O
that	O
is	O
,	O
the	O
difference	O
in	O
the	O
gradients	O
of	O
the	O
two	O
linear	O
SV	B-Process
segments	I-Process
about	O
a	O
jerk	B-Process
,	O
A	O
=	O
a2-a1	O
,	O
where	O
a2	O
is	O
the	O
gradient	O
after	O
the	O
jerk	O
and	O
a1	O
is	O
the	O
gradient	O
before	O
the	O
jerk	O
.	O
This	O
measure	O
is	O
essentially	O
the	O
best	O
fit	O
SA	B-Process
change	I-Process
across	O
a	O
jerk	B-Process
.	O
Jerk	B-Process
amplitude	I-Process
is	O
thus	O
positive	O
for	O
a	O
positive	O
step	O
in	O
SA	B-Process
and	O
negative	O
for	O
a	O
negative	O
step	O
.	O
Here	O
we	O
do	O
not	O
consider	O
spatial	O
extent	O
in	O
our	O
definition	O
and	O
refer	O
to	O
individual	O
features	O
in	O
one	O
field	O
component	O
of	O
a	O
given	O
observatory	O
time	O
series	O
as	O
a	O
single	O
jerk	B-Process
.	O

Seismic	B-Process
tomography	I-Process
is	O
a	O
powerful	O
tool	O
to	O
investigate	B-Task
the	I-Task
deep	I-Task
structure	I-Task
under	I-Task
the	I-Task
volcanoes	I-Task
.	O
With	O
the	O
recently	O
rapid	O
development	O
of	O
Chinese	O
provincial	B-Process
seismic	I-Process
networks	I-Process
(	O
Zheng	O
et	O
al.	O
,	O
2009	O
,	O
2010	O
)	O
and	O
some	O
portable	B-Process
seismic	I-Process
arrays	I-Process
(	O
Hetland	O
et	O
al.	O
,	O
2004	O
;	O
Duan	O
et	O
al.	O
,	O
2009	O
;	O
Lei	O
et	O
al.	O
,	O
2012b	O
)	O
around	O
the	O
volcanoes	O
,	O
it	O
has	O
become	O
possible	O
to	O
image	B-Process
the	I-Process
detailed	I-Process
3-D	I-Process
velocity	I-Process
structure	I-Process
under	O
some	O
of	O
these	O
volcanoes	O
,	O
where	O
seismic	B-Material
stations	I-Material
are	O
densely	O
spaced	O
.	O
In	O
this	O
overview	O
,	O
we	O
synthesize	B-Task
the	I-Task
results	I-Task
from	I-Task
the	I-Task
deep	I-Task
seismic	I-Task
images	I-Task
of	I-Task
the	I-Task
upper	I-Task
mantle	I-Task
under	O
the	O
Changbaishan	B-Material
,	I-Material
Tengchong	I-Material
,	I-Material
Hainan	I-Material
volcanoes	I-Material
as	I-Material
well	I-Material
as	I-Material
the	I-Material
Datong	I-Material
volcano	O
(	O
Fig.	O
1	O
)	O
.	O
We	O
also	O
evaluate	O
the	O
advantages	B-Task
of	I-Task
recently	I-Task
updated	I-Task
seismic	I-Task
tomographic	I-Task
techniques	I-Task
for	O
deriving	O
potential	O
information	O
.	O
This	O
work	O
updates	O
a	O
previous	O
review	O
of	O
Zhao	O
and	O
Liu	O
(	O
2010	O
)	O
on	O
this	O
topic	O
,	O
with	O
more	O
detailed	O
synthesis	O
of	O
all	O
the	O
available	O
information	O
.	O

Microhardness	B-Task
can	O
be	O
related	O
to	O
other	O
macroscopic	O
mechanical	O
properties	O
such	O
as	O
yield	O
stress	O
,	O
σ	O
,	O
and	O
elastic	O
modulus	O
,	O
E	O
,	O
both	O
derived	O
from	O
compression	B-Process
testing	I-Process
.	O
For	O
work-hardened	B-Material
metals	I-Material
,	O
Tabor	O
derived	B-Process
a	I-Process
direct	I-Process
proportionality	I-Process
between	O
hardness	O
and	O
compressive	O
yield	O
stress	O
:	O
H	O
≈	O
3σ	O
[	O
20	O
]	O
.	O
However	O
,	O
it	O
was	O
soon	O
realized	O
that	O
Tabor	O
's	O
relationship	O
only	O
applies	O
to	O
materials	B-Material
that	I-Material
exhibit	I-Material
full	I-Material
plasticity	I-Material
[	O
9,10	O
]	O
.	O
Deviations	B-Task
from	I-Task
this	I-Task
relationship	I-Task
have	O
been	O
reported	O
for	O
a	O
number	O
of	O
metals	O
,	O
glasses	B-Material
and	O
polymers	B-Material
where	I-Material
the	I-Material
elastic	I-Material
strains	I-Material
are	O
non-negligible	O
[	O
9	O
]	O
.	O
Hence	O
,	O
the	B-Task
different	I-Task
expressions	I-Task
describing	I-Task
the	I-Task
correlation	I-Task
of	I-Task
hardness	I-Task
with	I-Task
conventional	I-Task
macroscopic	I-Task
mechanical	I-Task
properties	I-Task
rely	O
on	O
the	O
validity	O
of	O
the	O
above-mentioned	O
elasto-plastic	O
models	O
.	O
In	O
this	O
way	O
,	O
hardness	O
and	O
yield	O
stress	O
no	O
longer	O
hold	B-Process
direct	I-Process
proportionality	I-Process
but	I-Process
their	I-Process
relationship	I-Process
depends	O
on	O
the	O
specific	B-Task
material	I-Task
properties	I-Task
,	I-Task
such	O
as	O
Poisson	O
's	O
ratio	O
and	O
elastic	O
modulus	O
[	O
9,11	O
–	O
13	O
]	O
.	O
It	O
has	O
been	O
shown	O
that	O
these	O
elasto-plastic	O
models	O
not	O
only	O
satisfactorily	O
explain	O
an	O
H	O
/	O
σ	O
ratio	O
of	O
≈	O
2	O
for	O
a	O
number	O
of	O
polyethylene	B-Material
materials	I-Material
of	O
different	O
nature	O
,	O
but	O
also	O
theoretically	O
account	O
for	O
the	O
range	O
of	O
H	O
/	O
E	O
ratios	O
experimentally	O
determined	O
[	O
21	O
]	O
.	O

Up	O
to	O
now	O
,	O
morphological	B-Task
studies	I-Task
of	O
the	O
multi-component	B-Material
polymeric	I-Material
materials	I-Material
have	O
been	O
carried	O
out	O
by	O
various	O
microscopic	B-Process
and	I-Process
scattering	I-Process
methods	I-Process
.	O
Optical	B-Material
microscopes	I-Material
,	O
transmission	B-Material
electron	I-Material
microscopes	I-Material
(	O
TEMs	B-Material
)	O
,	O
scanning	B-Material
electron	I-Material
microscopes	I-Material
(	O
SEMs	B-Material
)	O
and	O
atomic	B-Material
force	I-Material
microscopes	I-Material
(	O
AFMs	B-Material
)	O
are	O
commercially	O
available	O
and	O
widely	O
used	O
.	O
The	O
biggest	O
advantage	O
of	O
microscopy	B-Task
is	O
that	O
they	O
provide	O
intuitive	O
real-space	O
representations	O
of	O
the	O
various	O
morphologies	O
.	O
However	O
,	O
when	O
it	O
comes	O
to	O
“	O
measurements	O
”	O
,	O
especially	O
in	O
a	O
quantitative	O
way	O
,	O
microscopy	O
sometimes	O
lacks	O
a	O
statistical	O
accuracy	O
due	O
to	O
the	O
small	O
field	O
of	O
view	O
.	O
In	O
contrast	O
,	O
the	O
scattering	B-Process
methods	I-Process
provide	O
much	O
a	O
superior	O
statistical	O
accuracy	O
than	O
that	O
of	O
microscopy	B-Task
simply	O
because	O
the	O
observation	O
volume	O
is	O
larger	O
than	O
that	O
of	O
the	O
microscopes	B-Material
.	O
One	O
must	O
remember	O
,	O
however	O
,	O
that	O
the	O
scattering	B-Process
methods	I-Process
normally	O
require	O
“(	B-Process
hypothesized	I-Process
)	I-Process
models	I-Process
”	I-Process
for	O
data	B-Task
analysis	I-Task
in	O
advance	O
:	O
They	O
do	O
not	O
provide	O
an	O
intuitive	O
insight	O
into	O
the	O
morphologies	O
as	O
does	O
microscopy	B-Task
.	O
After	O
all	O
,	O
for	O
the	O
complete	O
characterization	O
of	O
a	O
specific	O
morphology	O
,	O
one	O
may	O
need	O
to	O
first	O
know	O
the	O
morphologies	O
from	O
the	O
microscopy	O
and	O
subsequently	O
to	O
evaluate	O
the	O
structural	O
parameters	O
by	O
scattering	O
on	O
the	O
basis	O
of	O
the	O
morphology	O
;	O
the	O
two	O
methods	O
are	O
complementary	O
.	O

We	O
deal	O
with	O
the	O
intensity	O
scattered	O
by	O
a	O
random	O
mixture	O
of	O
deuterated	B-Process
/	O
hydrogenated	B-Process
PE	B-Material
chains	I-Material
.	O
The	O
algorithm	O
used	O
by	O
us	O
to	O
evaluate	O
the	O
Kratky	B-Material
plots	I-Material
by	O
sets	O
of	O
parallel	B-Material
polymer	I-Material
stems	I-Material
is	O
very	O
simplified	O
.	O
We	O
checked	O
it	O
to	O
be	O
adequate	O
in	O
the	O
reciprocal	O
coordinate	O
range	O
under	O
investigation	O
[	O
0	O
<	O
q	O
(=	O
4πsinθ	O
/	O
λ	O
)≤	O
0.25Å	O
−	O
1	O
]	O
comparing	O
the	O
results	O
with	O
more	O
precise	O
calculations	B-Task
.	O
The	O
scattering	B-Material
centres	I-Material
are	O
identified	O
with	O
pseudo-atoms	B-Material
repeating	O
after	O
a	O
constant	O
distance	O
of	O
1.27Å	O
along	O
straight	O
lines	O
coinciding	O
with	O
the	O
stem	B-Material
axes	I-Material
,	O
100	O
scattering	B-Process
centres	O
being	O
placed	O
on	O
each	O
stem	O
;	O
the	O
scattering	O
by	O
atoms	B-Material
belonging	O
to	O
chain	B-Material
folds	I-Material
is	O
neglected	O
.	O
The	O
parallel	B-Material
stem	I-Material
axes	I-Material
are	O
disposed	O
according	O
to	O
a	O
hexagonal	O
setting	O
–	O
a	O
rough	O
approximation	O
to	O
the	O
monoclinic	O
,	O
pseudo-hexagonal	O
structure	O
of	O
PE	B-Material
–	O
and	O
the	O
scattering	O
centres	O
have	O
the	O
same	O
axial	O
coordinates	O
in	O
all	O
the	O
stems	B-Material
.	O
Defining	O
an	O
integer	O
i	O
going	O
from	O
1	O
to	O
the	O
total	O
number	O
ns	O
·	O
100	O
of	O
scattering	B-Material
centres	I-Material
,	O
we	O
have	O
(	O
q	O
<	O
1	O
)	O
[	O
9	O
](	O
1A	O
)	O
q2	O
·	O
I	O
(	O
q	O
)=	O
C	O
·(	O
bH	O
−	O
bD	O
)	O
2	O
∑	O
i	O
=	O
1ns	O
·	O
100	O
∑	O
j	O
=	O
1ns	O
·	O
1004πqsin	O
(	O
q	O
·	O
dij	O
)	O
dij	O
;	O
dij2	O
=	O
Δij2	O
+(	O
zj	O
−	O
zi	O
)	O
2	O
;	O
q	O
=	O
2πsinθλwhere	O
bH	O
,	O
bD	O
respectively	O
are	O
the	O
scattering	O
lengths	O
of	O
hydrogen	B-Material
and	O
deuterium	B-Material
,	O
dij	O
is	O
the	O
distance	O
between	O
C	B-Material
atoms	I-Material
,	O
2θ	O
is	O
the	O
diffraction	O
angle	O
and	O
λ	O
the	O
wavelength	O
.	O
The	O
i-th	O
C	O
atom	O
coordinate	O
along	O
the	O
stem	O
axis	O
is	O
zi	O
and	O
Δij	O
is	O
the	O
distance	O
between	O
the	O
stem	O
axes	O
where	O
the	O
atoms	O
i	O
and	O
j	O
belong	O
.	O
For	O
all	O
the	O
stems	B-Material
we	O
have	O
the	O
same	O
set	O
of	O
zi	O
coordinates	O
.	O
The	O
sum	O
in	O
Eq	O
.	O
(	O
1A	O
)	O
is	O
extended	O
to	O
all	O
the	O
stems	O
of	O
the	O
crystalline	B-Material
domain	O
,	O
see	O
Figs.	O
2	O
and	O
10	O
for	O
examples	O
.	O

With	O
ever	O
increasing	O
computer	O
performance	O
,	O
simulations	B-Process
in	O
much	O
larger	O
systems	O
have	O
become	O
feasible	O
.	O
However	O
,	O
full-atomistic	B-Process
approaches	I-Process
to	O
polymer	B-Task
crystallization	I-Task
need	O
extremely	O
large	O
computer	O
power	O
even	O
in	O
the	O
case	O
of	O
simple	B-Material
polymers	I-Material
,	O
and	O
appropriate	B-Process
modeling	I-Process
or	O
coarse-graining	B-Process
of	I-Process
the	I-Process
system	I-Process
is	O
imperative	O
.	O
From	O
a	O
series	O
of	O
work	O
on	O
the	O
development	O
of	O
coarse-grained	B-Process
models	I-Process
for	O
polymers	B-Material
,	O
Mayer	O
and	O
Muller-Plathe	O
have	O
build	O
up	O
a	O
model	O
of	O
poly	B-Material
(	I-Material
vinyl	I-Material
alcohol	I-Material
)	I-Material
(	O
PVA	B-Material
)	O
for	O
studying	O
early	O
stage	O
of	O
crystallization	B-Process
.	O
They	O
investigated	O
the	O
emergence	O
of	O
crystalline	B-Material
order	O
from	O
the	O
isotropic	B-Material
melt	I-Material
by	O
rapid	B-Process
quenching	I-Process
[	O
51,52	O
]	O
.	O
They	O
could	O
reproduce	O
many	O
elementary	O
processes	O
of	O
homogenous	B-Process
nucleation	I-Process
that	O
showed	O
good	O
correspondence	O
with	O
experiments	O
and	O
other	O
simulations	B-Process
,	O
in	O
temperature	O
dependence	O
of	O
lamella	B-Material
thickness	O
,	O
structure	O
of	O
fold	B-Material
surface	I-Material
,	O
etc	O
.	O
In	O
their	O
work	O
,	O
they	O
neglected	O
long-range	O
force	O
(	O
van	O
der	O
Waals	O
attraction	O
)	O
to	O
accelerate	O
computation	O
.	O
Their	O
model	O
has	O
the	O
energy	O
contribution	O
due	O
to	O
intrachain	B-Process
interactions	I-Process
only	O
and	O
the	O
dominant	O
driving	O
force	O
for	O
crystallization	B-Process
is	O
entropic	O
,	O
which	O
seems	O
to	O
ignore	O
dominant	O
driving	O
force	O
for	O
polymer	B-Process
crystallization	I-Process
in	O
conventional	O
sense	O
.	O
However	O
,	O
their	O
work	O
is	O
reminiscent	O
of	O
the	O
classical	B-Process
solid	I-Process
–	I-Process
liquid	I-Process
transition	I-Process
in	O
systems	O
of	O
repulsive	B-Material
spherical	I-Material
atoms	I-Material
[	O
53	O
]	O
and	O
poses	O
an	O
intriguing	O
problem	O
as	O
to	O
the	O
intrinsic	O
driving	O
force	O
for	O
polymer	B-Process
crystallization	I-Process
.	O

SPM	B-Process
,	O
and	O
AFM	B-Process
in	O
particular	O
,	O
has	O
been	O
widely	O
applied	O
to	O
questions	B-Task
in	I-Task
polymer	I-Task
crystallization	I-Task
.	O
The	O
technique	O
has	O
several	O
strengths	O
that	O
make	O
it	O
ideally	O
suited	O
for	O
such	O
studies	O
.	O
It	O
is	O
a	O
high	B-Process
resolution	I-Process
technology	I-Process
,	O
routinely	O
resolving	B-Process
sub	I-Process
10nm	I-Process
features	I-Process
[	O
13,14	O
]	O
,	O
and	O
hence	O
allowing	O
the	O
fundamental	O
length	O
scale	O
of	O
the	O
polymer	B-Material
lamellar	I-Material
crystal	I-Material
,	O
its	O
thickness	O
,	O
to	O
be	O
observed	O
.	O
AFM	B-Process
requires	O
no	O
staining	B-Process
or	O
metal	B-Process
coating	I-Process
of	O
the	O
sample	O
,	O
so	O
sample	B-Process
preparation	I-Process
is	O
relatively	O
straightforward	O
.	O
Also	O
,	O
it	O
is	O
non-destructive	O
under	O
many	O
circumstances	O
.	O
This	O
allows	O
images	O
to	O
be	O
obtained	O
while	O
a	O
process	O
such	O
as	O
crystal	B-Material
growth	O
or	O
melting	B-Process
is	O
occurring	O
,	O
giving	O
time-resolved	B-Material
data	I-Material
at	O
lamellar	O
or	O
sub-lamellar	O
resolution	O
[	O
15	O
–	O
18	O
]	O
.	O
It	O
is	O
this	O
final	O
feature	O
that	O
provides	O
many	O
of	O
the	O
most	O
exciting	O
possibilities	O
of	O
AFM	B-Process
for	O
studying	B-Task
polymer	I-Task
crystallization	I-Task
,	O
as	O
it	O
is	O
now	O
possible	O
to	O
watch	O
crystal	B-Process
growth	I-Process
,	O
crystal	B-Process
melting	I-Process
,	O
and	O
re-organisations	B-Process
within	I-Process
crystals	I-Process
at	I-Process
the	I-Process
lamellar	I-Process
scale	I-Process
,	O
seeing	O
how	O
structure	O
evolves	O
and	O
local	O
conditions	O
influence	O
kinetics	O
.	O
AFM	B-Material
has	O
a	O
wide	O
range	O
of	O
different	O
measuring	O
modes	O
,	O
and	O
,	O
with	O
the	O
ever	O
increasing	O
number	O
of	O
functional	O
semicrystalline	B-Material
polymers	I-Material
available	O
(	O
e.g.	O
[	O
19	O
])	O
,	O
the	O
breadth	O
of	O
experiments	O
that	O
can	O
be	O
carried	O
out	O
with	O
a	O
single	O
machine	O
is	O
also	O
one	O
of	O
the	O
techniques	O
attractions	O
.	O

Inverse	B-Process
miniemulsion	I-Process
polymerization	I-Process
is	O
a	O
water-in-oil	B-Process
(	O
W	B-Process
/	I-Process
O	I-Process
)	O
heterogeneous	B-Process
polymerization	I-Process
process	I-Process
that	O
forms	O
kinetically	B-Material
stable	I-Material
macroemulsions	I-Material
at	O
,	O
below	O
,	O
or	O
around	O
the	O
critical	B-Material
micellar	I-Material
concentration	I-Material
(	O
CMC	B-Material
)	O
.	O
This	O
process	O
contains	O
aqueous	B-Material
droplets	I-Material
(	O
including	O
water-soluble	B-Material
monomers	I-Material
)	O
stably	O
dispersed	O
,	O
with	O
the	O
aid	O
of	O
oil-soluble	B-Material
surfactants	I-Material
,	O
in	O
a	O
continuous	O
organic	B-Material
medium	I-Material
.	O
Stable	B-Material
inverse	I-Material
miniemulsions	I-Material
are	O
formed	O
under	O
high	O
shear	O
by	O
either	O
a	O
homogenizer	B-Material
or	O
a	O
high	B-Material
speed	I-Material
mechanical	I-Material
stirrer	I-Material
.	O
Oil-soluble	B-Material
nonionic	I-Material
surfactants	I-Material
with	O
hydrophilic-lipophilic	O
balance	O
(	O
HLB	O
)	O
value	O
around	O
4	O
are	O
used	O
to	O
implement	B-Task
colloidal	I-Task
stability	I-Task
of	I-Task
the	I-Task
resulting	I-Task
inverse	I-Task
emulsion	I-Task
.	O
Upon	O
addition	O
of	O
radical	B-Material
initiators	I-Material
,	O
polymerization	B-Process
occurs	O
within	O
the	O
aqueous	B-Material
droplets	I-Material
producing	O
colloidal	B-Material
particles	I-Material
(	O
Fig.	O
2	O
)	O
[	O
83	O
]	O
.	O
Several	O
reports	O
have	O
demonstrated	O
the	O
preparation	O
of	O
stable	B-Material
particles	I-Material
of	I-Material
hydrophilic	I-Material
and	I-Material
water-soluble	I-Material
polymers	I-Material
[	O
86	O
–	O
89	O
]	O
,	O
polyaniline	B-Material
nanoparticles	I-Material
[	O
90	O
]	O
,	O
and	O
organic	B-Material
–	I-Material
inorganic	I-Material
hybrid	I-Material
particles	I-Material
[	O
91	O
–	O
93	O
]	O
.	O
This	O
method	O
also	O
allows	O
for	O
the	O
preparation	B-Process
of	I-Process
crosslinked	I-Process
microgels	I-Process
in	I-Process
the	I-Process
presence	I-Process
of	I-Process
difunctional	I-Process
crosslinkers	I-Process
[	O
27,94	O
–	O
100	O
]	O
.	O
In	O
addition	O
,	O
CRP	B-Process
techniques	I-Process
including	O
ATRP	B-Process
[	O
78,79,82,101,102	O
]	O
and	O
RAFT	B-Process
[	O
103	O
]	O
in	O
inverse	B-Material
miniemulsion	I-Material
have	O
been	O
explored	O
to	O
prepare	O
well-defined	O
nanoparticles	B-Material
and	O
nanogels	B-Material
.	O

In	O
contrast	O
with	O
polymers	B-Material
,	O
which	O
are	O
typically	O
synthesized	B-Process
in	I-Process
the	I-Process
liquid	I-Process
phase	I-Process
,	O
SWNTs	B-Material
are	O
produced	B-Process
through	I-Process
a	I-Process
variety	I-Process
of	I-Process
synthesis	I-Process
techniques	I-Process
that	O
typically	O
involve	O
the	B-Process
reaction	I-Process
of	I-Process
a	I-Process
gaseous	I-Process
carbon	I-Process
feedstock	I-Process
to	I-Process
form	I-Process
the	I-Process
nanotubes	I-Process
on	I-Process
catalyst	I-Process
particles	I-Process
.	O
MWNTs	B-Task
were	O
first	O
observed	B-Process
in	I-Process
arc	I-Process
discharge	I-Process
fullerene	I-Process
reactors	I-Process
[	O
1,26	O
]	O
;	O
this	B-Task
technique	I-Task
was	O
later	O
adapted	O
to	O
produce	O
SWNTs	B-Material
[	O
3	O
]	O
.	O
Similarly	O
,	O
the	B-Process
fullerene	I-Process
production	I-Process
method	I-Process
of	I-Process
laser	I-Process
ablation	I-Process
[	O
27	O
]	O
was	O
adapted	O
to	O
produce	O
SWNTs	B-Material
(∼	O
1.4nm	O
diameter	O
)	O
in	O
larger	O
quantities	O
on	O
metal	B-Material
catalyst	I-Material
particles	I-Material
[	O
28	O
–	O
30	O
]	O
.	O
A	O
number	O
of	O
chemical	B-Process
vapor	I-Process
deposition	I-Process
(	O
CVD	B-Process
)	O
processes	O
have	O
been	O
developed	O
to	O
grow	O
SWNTs	B-Material
and	O
MWNTs	B-Material
,	O
all	O
involving	O
the	B-Process
reaction	I-Process
of	I-Process
a	I-Process
gaseous	I-Process
carbon	I-Process
compound	I-Process
as	I-Process
feedstock	I-Process
.	O
These	O
processes	O
include	O
fluidized	B-Process
bed	I-Process
[	O
31	O
]	O
,	O
“	B-Process
carpet	I-Process
”	I-Process
growth	I-Process
of	I-Process
carbon	I-Process
nanotubes	I-Process
(	I-Process
CNTs	I-Process
)	I-Process
from	I-Process
catalyst	I-Process
particles	I-Process
embedded	I-Process
in	I-Process
a	I-Process
substrate	I-Process
[	O
32	O
–	O
35	O
]	O
as	O
shown	O
in	O
Fig.	O
3	O
,	O
and	O
“	B-Process
catalytic	I-Process
gas	I-Process
flow	I-Process
CVD	I-Process
”	I-Process
[	O
36,37	O
]	O
.	O
One	O
of	O
the	B-Task
most	I-Task
effective	I-Task
,	I-Task
cheap	I-Task
,	I-Task
and	I-Task
scalable	I-Task
CVD	I-Task
techniques	I-Task
is	O
the	O
HiPco	B-Process
(	I-Process
high-pressure	I-Process
CO	I-Process
)	I-Process
process	I-Process
,	O
which	O
does	B-Process
not	I-Process
use	I-Process
pre-formed	I-Process
catalyst	I-Process
particles	I-Process
unlike	O
most	O
other	O
CVD	B-Process
techniques	I-Process
[	O
38	O
]	O
.	O

ELRs	B-Material
are	O
particularly	O
attractive	O
for	O
the	O
synthesis	B-Process
of	I-Process
block	I-Process
copolymers	I-Process
that	O
self-assemble	O
into	O
polymer	B-Material
nanostructures	I-Material
such	O
as	O
micelles	B-Material
.	O
The	O
first	O
work	O
in	O
this	O
area	O
involved	O
an	O
elastin-mimetic	B-Material
di-block	I-Material
copolymer	I-Material
containing	O
VPGEG	B-Material
–(	I-Material
IPGAG	I-Material
)	I-Material
4	I-Material
and	O
VPGFG	B-Material
–(	I-Material
IPGVG	I-Material
)	I-Material
4	I-Material
as	O
the	O
hydrophilic	B-Material
and	I-Material
hydrophobic	I-Material
blocks	I-Material
,	O
respectively	O
[	O
49	O
]	O
.	O
The	O
resulting	O
micelles	B-Material
were	O
studied	O
by	O
dynamic	B-Process
light	I-Process
scattering	I-Process
(	O
DLS	B-Process
)	O
and	O
DSC	B-Process
was	O
used	O
to	O
measure	O
the	O
enthalpy	O
of	O
self-assembly	B-Process
.	O
A	O
tri-block	B-Material
copolymer	I-Material
was	O
subsequently	O
synthesized	O
and	O
the	O
TEM	B-Material
images	I-Material
of	O
this	O
polymer	B-Material
showed	O
that	O
it	O
formed	O
spherical	B-Material
aggregates	I-Material
[	O
50	O
]	O
.	O
Other	O
multivalent	B-Material
spherical	I-Material
micelles	I-Material
have	O
been	O
obtained	O
from	O
linear	B-Material
elastin-like	I-Material
AB	I-Material
di-block	I-Material
copolymers	I-Material
in	O
the	O
temperature	O
range	O
37	O
–	O
42	O
°	O
C	O
with	O
the	O
aim	O
of	O
targeting	B-Task
cancer	I-Task
cells	I-Task
[	O
51	O
]	O
.	O
Bidwell	O
et	O
al.	O
have	O
also	O
exploited	O
the	O
ELRs	B-Material
for	O
its	O
ability	O
to	O
serve	O
as	O
macromolecular	B-Material
carriers	I-Material
for	O
thermally	B-Task
targeted	I-Task
delivery	I-Task
of	I-Task
drugs	I-Task
.	O
Attachment	O
of	O
doxorubicin	B-Material
to	O
ELR-based	B-Material
system	I-Material
showed	O
enhanced	O
cytotoxicity	B-Process
in	O
uterine	B-Material
sarcoma	I-Material
cells	I-Material
when	O
aggregation	B-Process
was	O
induced	O
with	O
hyperthermia	B-Process
[	O
52	O
]	O
.	O

In	O
general	O
,	O
the	O
ion	B-Process
exchange	I-Process
capacity	I-Process
(	O
IEC	B-Process
)	O
is	O
closely	O
related	O
to	O
the	O
proton	B-Process
conductivity	I-Process
of	O
PEMs	B-Material
because	O
the	O
acid	O
functionalities	O
,	O
such	O
as	O
sulfonic	B-Material
acid	I-Material
groups	I-Material
,	O
contribute	O
to	O
the	O
proton	B-Process
conduction	I-Process
in	O
a	O
membrane	B-Material
.	O
Beyond	O
a	O
certain	O
sulfonation	O
degree	O
,	O
PEMs	B-Material
tend	O
to	O
absorb	O
too	O
much	O
water	B-Material
or	O
are	O
even	O
soluble	O
in	O
water	O
,	O
which	O
negatively	O
affect	O
their	O
mechanical	O
resistance	O
and	O
water	O
resistance	O
[	O
17,18	O
]	O
.	O
Therefore	O
,	O
the	O
improvement	B-Task
of	I-Task
proton	I-Task
conductivity	I-Task
using	I-Task
aromatic	I-Task
polymers	I-Task
with	I-Task
moderately	I-Task
adjusted	I-Task
IEC	I-Task
values	I-Task
has	O
been	O
under	O
intense	O
investigation	O
[	O
19	O
–	O
23	O
]	O
.	O
To	O
achieve	O
high	B-Process
proton	I-Process
conductivity	I-Process
with	O
moderate	O
IEC	B-Process
values	O
,	O
the	O
formation	B-Process
of	I-Process
ion	I-Process
channel	I-Process
structures	I-Process
,	O
which	O
enable	O
effective	B-Process
proton	I-Process
conduction	I-Process
,	O
has	O
been	O
studied	O
.	O
In	O
the	O
course	O
of	O
these	O
studies	O
,	O
an	O
ideal	O
morphology	O
has	O
been	O
pursued	O
by	O
microphase	O
separation	O
of	O
segmented	B-Material
block	I-Material
copolymers	I-Material
in	O
which	O
hydrophilic	B-Material
sulfonated	I-Material
polymer	I-Material
segments	I-Material
form	O
an	O
interconnected	B-Material
three-dimensional	I-Material
network	I-Material
responsible	O
for	O
efficient	O
proton	B-Process
transport	I-Process
,	O
while	O
a	O
complementary	B-Process
network	I-Process
of	I-Process
hydrophobic	I-Process
non-sulfonated	I-Process
segments	I-Process
imparts	O
a	O
reinforcing	O
effect	O
,	O
preventing	B-Task
excessive	I-Task
swelling	I-Task
in	I-Task
water	I-Task
and	O
enhancing	B-Task
the	I-Task
mechanical	I-Task
properties	I-Task
.	O
An	O
image	O
of	O
the	O
ideal	O
morphology	O
for	O
PEMs	B-Material
is	O
shown	O
in	O
Fig.	O
2	O
.	O

From	O
a	O
general	O
point	O
of	O
view	O
,	O
polymerization	B-Process
techniques	I-Process
can	O
be	O
divided	O
into	O
two	O
types	O
of	O
chemical	B-Process
reactions	I-Process
:	O
step-growth	B-Process
polymerization	I-Process
and	O
free	B-Process
radical	I-Process
polymerization	I-Process
.	O
Step-growth	B-Process
polymerization	I-Process
is	O
widely	O
used	O
for	O
synthesis	B-Task
of	I-Task
polyesters	I-Task
,	O
polyamide	B-Material
and	O
epoxies	B-Material
while	O
the	O
synthesis	B-Process
of	I-Process
polyacrylics	I-Process
requires	O
the	O
use	O
of	O
free	B-Process
radical	I-Process
polymerization	I-Process
.	O
These	O
polymerization	B-Process
reactions	I-Process
can	O
be	O
performed	O
either	O
in	O
bulk	B-Material
or	O
in	O
solution	B-Material
or	O
in	O
dispersed	B-Material
media	I-Material
.	O
Heterophase	B-Process
polymerizations	I-Process
(	O
i.e.	O
emulsion	B-Process
,	O
dispersion	B-Process
and	O
miniemulsion	B-Process
polymerizations	I-Process
)	O
present	O
the	O
advantage	O
of	O
easier	O
removal	B-Task
of	I-Task
the	I-Task
resulting	I-Task
product	I-Task
from	O
the	O
reactor	B-Material
compared	O
to	O
bulk	B-Process
polymerization	I-Process
thanks	O
to	O
the	O
low	O
viscosity	O
of	O
the	O
reaction	B-Material
medium	I-Material
.	O
Polymerization	B-Process
in	O
solution	B-Material
also	O
induces	O
lower	O
viscosity	O
but	O
also	O
lower	O
reaction	B-Process
rates	O
due	O
to	O
dilution	B-Process
of	O
the	O
reactants	B-Material
and	O
higher	O
cost	O
and	O
environmental	O
impact	O
due	O
to	O
the	O
use	O
of	O
organic	B-Material
solvents	I-Material
.	O
These	O
problems	O
are	O
solved	O
in	O
the	O
case	O
of	O
heterophase	B-Process
polymerizations	I-Process
where	O
the	O
reactants	B-Material
are	O
confined	O
inside	O
droplets	B-Material
(	O
no	O
dilution	B-Process
effect	I-Process
)	O
and	O
water	B-Material
is	O
used	O
as	O
medium	O
.	O
The	O
use	O
of	O
surfactant	B-Material
molecules	I-Material
are	O
usually	O
needed	O
for	O
the	O
stabilization	B-Process
of	I-Process
the	I-Process
monomer	I-Process
droplet	I-Process
and	O
subsequent	O
polymer	B-Material
particles	I-Material
in	O
the	O
water	B-Material
phase	O
.	O

Note	O
that	O
the	O
quantitative	B-Task
introduction	I-Task
of	I-Task
a	I-Task
reactive	I-Task
functionality	I-Task
into	I-Task
the	I-Task
polymer	I-Task
chain	I-Task
end	I-Task
can	O
be	O
easily	O
achieved	O
by	O
adopting	O
the	O
living	B-Process
ROMP	I-Process
technique	I-Process
especially	O
using	O
the	O
Schrock	B-Material
type	I-Material
molybdenum	I-Material
alkylidene	I-Material
initiator	I-Material
[	O
7,12,21,61	O
–	O
65	O
]	O
.	O
The	O
exclusive	O
preparation	B-Process
of	I-Process
end-functionalized	I-Process
ring-opened	I-Process
polymers	I-Process
(	O
realized	O
by	O
a	O
living	B-Process
polymerization	I-Process
with	I-Process
quantitative	I-Process
initiation	I-Process
)	O
can	O
be	O
applied	O
not	O
only	O
to	O
prepare	O
block	B-Material
copolymers	I-Material
(	O
ABCs	B-Material
)	O
coupled	O
with	O
another	O
living	B-Process
polymerization	I-Process
techniques	I-Process
[	O
66	O
]	O
,	O
but	O
also	O
for	O
preparation	B-Process
of	I-Process
macromonomers	I-Process
,	O
as	O
described	O
below	O
.	O
In	O
contrast	O
,	O
the	O
initiation	O
efficiency	O
is	O
not	O
always	O
perfect	O
as	O
seen	O
in	O
the	O
molybdenum	B-Material
alkylidene	I-Material
initiators	I-Material
,	O
because	O
dissociation	B-Process
of	O
ligand	B-Material
(	O
PR3	B-Material
etc.	O
)	O
should	O
be	O
required	O
to	O
generate	B-Process
the	I-Process
catalytically	I-Process
active	I-Process
species	I-Process
in	I-Process
the	I-Process
ROMP	I-Process
with	O
the	O
ruthenium	B-Material
carbene	I-Material
catalysts	I-Material
(	O
Scheme	O
2	O
)	O
[	O
67	O
–	O
69	O
]	O
.	O
An	O
equilibrium	O
between	O
coordination	B-Process
and	O
dissociation	B-Process
of	O
PR3	B-Material
should	O
be	O
present	O
even	O
in	O
the	O
propagation	B-Process
process	I-Process
,	O
and	O
replacement	O
of	O
halogen	B-Material
with	O
the	O
other	O
anionic	B-Material
ligand	I-Material
(	O
and	O
/	O
or	O
replacement	O
of	O
PR3	B-Material
with	O
the	O
other	O
neutral	B-Material
donor	I-Material
ligands	I-Material
/	I-Material
substrates	I-Material
)	O
can	O
also	O
be	O
considered	O
as	O
the	O
probable	O
side	O
reactions	O
.	O
Importance	O
of	O
using	O
the	O
molybdenum	B-Material
catalysts	I-Material
should	O
be	O
thus	O
emphasized	O
for	O
their	O
precise	O
preparations	O
,	O
although	O
the	O
initiators	O
are	O
highly	O
sensitive	O
to	O
moisture	O
and	O
both	O
monomers	B-Material
and	O
solvent	B-Material
have	O
to	O
be	O
thus	O
strictly	O
purified	O
to	O
avoid	O
the	O
catalyst	B-Material
decomposition	B-Process
(	O
deactivation	B-Process
)	O
.	O

An	O
application	O
of	O
ROMP	B-Material
derived	I-Material
random	I-Material
copolymers	I-Material
is	O
the	O
covalent	B-Task
incorporation	I-Task
of	I-Task
optical	I-Task
sensor	I-Task
moieties	I-Task
into	I-Task
a	I-Task
polymer	I-Task
matrix	I-Task
.	O
ROM	B-Material
polymers	I-Material
have	O
been	O
tested	O
as	O
matrix	B-Material
materials	I-Material
for	O
the	O
oxygen	B-Material
sensing	O
phosphorescent	B-Material
complex	I-Material
platinum	I-Material
tetrakis	I-Material
(	I-Material
pentafluorophenyl	I-Material
)	I-Material
porphyrin	I-Material
.	O
A	O
correlation	O
between	O
the	O
nature	O
of	O
the	O
ROM	B-Material
polymer	I-Material
’s	I-Material
side	I-Material
chain	I-Material
and	O
the	O
optical	O
response	O
of	O
the	O
sensor	B-Material
molecules	I-Material
has	O
been	O
established	O
[	O
34	O
]	O
.	O
Several	O
works	O
are	O
dedicated	O
to	O
the	O
synthesis	B-Task
of	I-Task
ROMP-able	I-Task
optical	I-Task
sensor	I-Task
molecules	I-Task
such	O
as	O
phenantroimidazoles	B-Material
[	O
35,36	O
]	O
,	O
europium	B-Material
complexes	I-Material
[	O
37	O
]	O
or	O
xanthene	B-Material
dyes	I-Material
[	O
38	O
]	O
,	O
their	O
random	O
copolymerization	B-Process
and	O
the	O
evaluation	B-Task
of	I-Task
their	I-Task
sensing	I-Task
profiles	I-Task
in	I-Task
the	I-Task
copolymers	I-Task
.	O
Another	O
application	O
comprises	O
random	B-Task
copolymers	I-Task
with	I-Task
covalently	I-Task
bound	I-Task
eosin	I-Task
and	I-Task
/	I-Task
or	I-Task
ethyl	I-Task
dimethylamino	I-Task
benzoate	I-Task
units	I-Task
which	O
were	O
tested	O
as	O
macroinitiators	B-Material
for	O
the	O
photopolymerization	O
of	O
acrylates	B-Material
aiming	O
at	O
an	O
initiator	B-Material
/	I-Material
coinitiator	I-Material
system	I-Material
which	O
combines	O
good	O
polymerization	B-Process
activity	O
with	O
improved	O
migration	O
stability	O
[	O
39	O
]	O
.	O

Another	B-Process
choice	I-Process
was	O
to	O
graft	B-Process
the	I-Process
fluorinated	I-Process
groups	I-Process
on	I-Process
the	I-Process
copolymers	I-Process
with	I-Process
functional	I-Process
groups	I-Process
.	O
Casazza	O
et	O
al	O
.	O
[	O
52	O
]	O
synthesized	O
an	B-Task
acrylic	I-Task
terpolymer	I-Task
with	I-Task
pendent	I-Task
perfluoroether	I-Task
segments	I-Task
via	O
grafting	B-Process
fluorinated	I-Process
groups	I-Process
into	O
a	O
poly	B-Material
(	O
butyl	B-Material
methacrylate-co-hydroxyehtyl	I-Material
acrylate-co-ethyl	I-Material
acrylate	I-Material
)	O
random	O
copolymer	B-Material
through	O
hexamethylene	B-Process
diisocyanate	I-Process
functionality	I-Process
.	O
Malshe	O
et	O
al	O
.	O
[	O
53,54	O
]	O
studied	O
the	B-Task
coating	I-Task
properties	I-Task
of	I-Task
fluorinated	I-Task
acrylic	I-Task
copolymers	I-Task
based	O
on	O
MMA	B-Material
,	O
BA	B-Material
,	O
and	O
2-hydroxyethyl	B-Material
methacrylate	I-Material
(	O
HEMA	B-Material
)	O
.	O
They	O
partially	O
esterified	O
hydroxyl	B-Task
functionality	I-Task
of	I-Task
HEMA	I-Task
with	I-Task
tetrafluoro	I-Task
propanoic	I-Task
acid	I-Task
and	O
cured	O
the	B-Material
polymer	I-Material
with	I-Material
butylated	I-Material
melamine	I-Material
formaldehyde	I-Material
resin	I-Material
.	O
Such	O
methods	O
were	O
suited	O
for	O
the	O
synthesis	O
of	O
copolymers	B-Material
containing	I-Material
complicated	I-Material
fluorinated	I-Material
groups	I-Material
or	O
difficult	O
to	O
be	O
provided	O
directly	O
by	O
living	B-Process
polymerization	I-Process
.	O

Assuming	O
a	O
constant	O
cell	B-Process
electrical	I-Process
conversion	I-Process
efficiency	O
of	O
15	O
%	O
,	O
a	O
constant	O
fraction	O
of	O
the	O
incident	O
solar	B-Process
radiation	I-Process
would	O
be	O
dissipated	O
by	O
the	O
solar	O
cell	O
for	O
each	O
solar	O
radiation	O
intensity	O
level	O
.	O
From	O
Table	O
2	O
,	O
it	O
can	O
be	O
seen	O
that	O
for	O
the	O
worst	O
scenario	O
when	O
the	O
ambient	O
temperature	O
was	O
50	O
°	O
C	O
with	O
natural	B-Process
convection	I-Process
only	O
,	O
the	O
predicted	B-Task
cell	I-Task
electrical	I-Task
conversion	I-Task
efficiency	I-Task
would	O
have	O
reduced	O
to	O
approximately	O
8	O
%	O
rather	O
than	O
the	O
15	O
%	O
assumed	O
.	O
The	O
energy	O
dissipated	O
as	O
heat	O
from	O
the	O
cell	O
would	O
thus	O
be	O
7	O
%	O
higher	O
.	O
To	O
correct	O
for	O
this	O
effect	O
the	O
apparent	B-Process
insolation	I-Process
level	I-Process
should	O
be	O
modified	O
using	O
the	O
following	O
formula	O
:(	O
3	O
)	O
Iact	O
=	O
I	O
×	O
0.850.85	O
+	O
dηwhere	O
Iact	O
is	O
the	O
actual	O
incident	O
solar	O
radiation	O
intensity	O
,	O
dη	O
is	O
solar	O
cell	O
efficiency	O
difference	O
between	O
the	O
initially	O
assumed	O
15	O
%	O
and	O
final	O
calculated	O
cell	O
efficiency	O
based	O
on	O
measured	O
cell	O
temperature	O
.	O

Thermal	B-Task
performance	I-Task
of	I-Task
the	I-Task
smart	I-Task
window	I-Task
has	O
been	O
predicted	O
under	O
different	O
simulated	B-Process
parameters	I-Process
namely	O
,	O
direct	B-Process
solar	I-Process
radiation	I-Process
intensity	I-Process
,	O
ambient	B-Process
temperature	I-Process
,	O
water	B-Process
inlet	I-Process
temperature	I-Process
,	O
and	O
water	B-Process
flow	I-Process
rate	I-Process
.	O
Fig.	O
11	O
shows	O
a	O
sample	O
of	O
temperature	O
distribution	O
of	O
all	O
window	B-Material
components	I-Material
at	O
a	O
plane	O
passing	O
through	O
the	O
horizontal	O
window	O
segment	O
based	O
on	O
simulation	B-Material
physical	I-Material
conditions	I-Material
listed	O
on	O
Table	O
3	O
.	O
Simulation	B-Material
data	I-Material
has	O
been	O
collected	O
from	O
all	O
successive	O
simulations	B-Process
.	O
The	O
effect	O
of	O
increasing	B-Process
direct	I-Process
solar	I-Process
radiation	I-Process
on	I-Process
both	I-Process
solar	I-Process
cells	I-Process
and	I-Process
water	I-Process
temperatures	I-Process
is	O
shown	O
in	O
Fig.	O
12.	O
Three	O
different	O
simulations	B-Process
were	O
performed	O
assuming	O
direct	O
solar	B-Process
radiation	I-Process
intensities	O
of	O
400	O
,	O
600	O
,	O
and	O
800W	O
/	O
m2	O
incident	O
on	O
the	O
window	B-Material
’s	I-Material
front	I-Material
pane	I-Material
with	O
set	O
ambient	O
temperature	O
,	O
water	O
inlet	O
temperature	O
and	O
water	O
flow	O
rate	O
of	O
respectively	O
273K	O
,	O
283K	O
and	O
0.01kg	O
/	O
s	O
.	O
Water	O
temperature	O
was	O
found	O
to	O
increase	O
by	O
5	O
°	O
C	O
as	O
it	O
passed	O
through	O
the	O
tube	B-Material
,	O
carrying	O
the	O
solar	B-Material
cells	I-Material
,	O
from	O
left	O
to	O
right	O
for	O
the	O
bottom	O
most	O
units	O
at	O
800W	O
/	O
m2	O
of	O
direct	O
incident	O
solar	B-Process
radiation	I-Process
.	O

Shading	B-Process
can	O
be	O
the	O
most	O
detrimental	O
factor	O
on	O
performance	O
for	O
a	O
domestic	B-Material
system	I-Material
.	O
The	O
impact	O
of	O
shading	B-Process
on	O
performance	O
varies	O
depending	O
on	O
the	O
electrical	O
series	O
and	O
parallel	O
arrangement	O
of	O
cells	B-Material
within	O
a	O
module	B-Material
and	O
modules	B-Material
within	O
an	O
installed	B-Material
array	I-Material
.	O
Whilst	O
many	O
approaches	O
to	O
shading	B-Process
analysis	I-Process
have	O
been	O
proposed	O
,	O
computational	O
efficiency	O
is	O
not	O
reported	O
despite	O
being	O
of	O
high	O
importance	O
when	O
incorporating	O
shading	B-Process
algorithms	I-Process
into	O
an	O
overall	O
energy	B-Process
yield	I-Process
model	I-Process
.	O
The	O
lack	O
of	O
consideration	O
of	O
the	O
non-linear	O
impacts	O
of	O
shading	B-Process
on	O
smaller	O
systems	O
for	O
example	O
means	O
that	O
the	O
shading	O
loss	O
is	O
significantly	O
underestimated	O
,	O
especially	O
from	O
supposedly	O
small	O
obstacles	O
such	O
as	O
antennas	B-Material
or	O
chimneys	B-Material
.	O
As	O
an	O
example	O
,	O
the	O
system	B-Material
shown	I-Material
in	I-Material
Fig.	I-Material
1	I-Material
illustrates	O
the	O
case	O
where	O
the	O
installer	O
may	O
have	O
attested	O
a	O
shade	B-Process
loss	I-Process
factor	O
close	O
to	O
unity	O
under	O
UK	O
microgeneration	O
guidelines	O
(	O
Microgeneration	O
Certification	O
Scheme	O
,	O
2013	O
)	O
,	O
i.e.	O
negligible	O
,	O
but	O
the	O
performance	O
of	O
the	O
system	B-Material
is	O
severely	O
compromised	O
due	O
to	O
the	O
non-linear	B-Process
cell	I-Process
mismatch	I-Process
effects	I-Process
.	O
An	O
effective	B-Process
shading	I-Process
sub-model	I-Process
therefore	O
needs	O
to	O
give	B-Task
feedback	I-Task
to	I-Task
inform	I-Task
decisions	I-Task
of	I-Task
array	I-Task
layout	I-Task
in	O
the	O
proximity	O
of	O
obstructions	O
but	O
must	O
not	O
rely	O
on	O
high	B-Process
power	I-Process
computing	I-Process
.	O

Progressive	B-Process
photon	I-Process
mapping	I-Process
was	O
first	O
proposed	O
by	O
Hachisuka	O
et	O
al	O
.	O
(	O
2008	O
)	O
as	O
an	O
iterative	B-Process
extension	I-Process
of	I-Process
the	I-Process
standard	I-Process
static	I-Process
photon	I-Process
mapping	I-Process
approach	O
as	O
implemented	O
in	O
the	O
Radiance	B-Process
extension	I-Process
.	O
It	O
combines	O
multiple	O
smaller	O
photon	B-Material
maps	I-Material
to	O
approximate	O
a	O
much	O
larger	O
one	O
which	O
may	O
not	O
fit	O
into	O
memory	O
using	O
the	O
traditional	B-Process
approach	I-Process
.	O
Through	O
iteration	O
,	O
the	O
process	O
mitigates	B-Process
the	I-Process
noise	I-Process
inherent	O
in	O
Monte	B-Process
Carlo	I-Process
raytracing	I-Process
by	O
combining	B-Process
successive	I-Process
results	I-Process
and	I-Process
averaging	I-Process
them	O
.	O
At	O
the	O
same	O
time	O
,	O
the	O
density	B-Process
estimate	I-Process
bandwidth1Bandwidth	I-Process
describes	O
the	O
support	B-Process
,	I-Process
or	I-Process
area	I-Process
of	I-Process
influence	I-Process
,	I-Process
of	I-Process
a	I-Process
filter	I-Process
used	O
to	O
weight	B-Process
the	I-Process
photons	I-Process
retrieved	O
from	O
the	O
photon	B-Material
map	I-Material
during	O
a	O
nearest	B-Process
neighbour	I-Process
lookup	I-Process
on	O
a	O
surface	B-Material
(	O
Jensen	O
,	O
2001	O
)	O
.	O
The	O
resulting	O
irradiance	B-Material
is	O
proportional	O
to	O
the	O
photon	B-Material
density	O
,	O
and	O
the	O
bandwidth	O
is	O
defined	O
by	O
the	O
distance	O
(	O
radius	O
)	O
to	O
the	O
furthest	O
photon	O
found	O
.	O
In	O
this	O
paper	O
,	O
we	O
generalise	O
the	O
term	O
to	O
describe	B-Task
either	I-Task
the	I-Task
radius	I-Task
or	I-Task
the	I-Task
number	I-Task
of	I-Task
nearest	I-Task
neighbours	I-Task
for	I-Task
a	I-Task
density	I-Task
estimate	I-Task
,	O
depending	O
on	O
the	O
implementation.1	O
(	O
radius	O
or	O
number	O
of	O
nearest	O
photons	B-Material
)	O
is	O
gradually	O
reduced	O
to	O
mitigate	B-Process
bias	I-Process
.	O
As	O
Hachisuka	O
points	O
out	O
,	O
the	O
accumulated	O
density	O
estimates	O
converge	O
to	O
an	O
unbiased	O
solution	O
in	O
the	O
limit	O
.	O

For	O
the	O
reverse	B-Process
current	I-Process
analysis	I-Process
,	O
for	O
both	O
scenarios	O
(	O
shading	O
and	O
short	O
circuits	O
)	O
were	O
tested	O
on	O
two	O
systems	O
,	O
one	O
system	O
using	O
standard	B-Material
silicon	I-Material
modules	I-Material
and	O
another	O
system	O
using	O
high	B-Material
efficiency	I-Material
modules	I-Material
.	O
For	O
the	O
standard	B-Material
silicon	I-Material
system	I-Material
,	O
a	O
power	O
of	O
50kWp	O
was	O
considered	O
,	O
with	O
a	O
system	O
composed	O
by	O
10	B-Material
strings	I-Material
of	I-Material
24	I-Material
modules	I-Material
per	I-Material
string	I-Material
and	O
an	O
approximate	O
system	O
Voc	B-Material
of	I-Material
864	I-Material
[	O
VDC	B-Material
]	O
.	O
For	O
the	O
high	B-Material
efficiency	I-Material
system	I-Material
,	O
a	O
power	O
of	O
40kWp	O
was	O
considered	O
,	O
with	O
a	O
system	O
composed	O
by	O
10	B-Material
strings	I-Material
of	I-Material
18	I-Material
modules	I-Material
per	I-Material
string	I-Material
and	O
an	O
approximate	O
system	O
Voc	B-Material
of	I-Material
873	I-Material
[	O
VDC	B-Material
]	O
.	O
Fig.	O
5	O
(	O
a	O
)	O
shows	O
the	O
reverse	B-Task
current	I-Task
present	O
in	O
one	O
string	O
when	O
different	O
numbers	O
of	O
modules	O
in	O
the	O
string	O
are	O
shaded	O
by	O
90	O
%	O
.	O
Fig.	O
5	O
(	O
b	O
)	O
shows	O
the	O
reverse	O
current	O
present	O
in	O
one	O
string	O
when	O
different	B-Process
numbers	I-Process
of	I-Process
modules	I-Process
of	I-Process
the	I-Process
string	I-Process
are	I-Process
short-circuited	I-Process
.	O
For	O
both	O
figures	O
the	O
continuous	O
lines	O
are	O
for	O
the	O
standard	B-Material
silicon	I-Material
system	I-Material
and	O
the	O
dashed	O
lines	O
are	O
for	O
the	O
high	B-Material
efficiency	I-Material
system	I-Material
.	O

The	O
final	O
contribution	O
to	O
the	O
force	O
is	O
the	O
van	B-Process
der	I-Process
Waals	I-Process
interaction	I-Process
.	O
It	O
includes	O
the	O
following	O
contributions	O
:	O
(	O
i	O
)	O
between	O
the	O
macroscopic	B-Material
Si	I-Material
tip	I-Material
of	O
conical	O
shape	O
with	O
the	O
sphere	O
of	O
radius	O
R	O
at	O
the	O
end	O
[	O
27	O
]	O
and	O
semi-infinite	B-Material
substrate	I-Material
;	O
(	O
ii	O
)	O
the	O
dispersion	B-Process
forces	I-Process
between	O
the	O
atoms	B-Material
in	O
the	O
sample	O
treated	O
atomistically	O
;	O
and	O
(	O
iii	O
)	O
the	O
interaction	O
between	O
the	O
macroscopic	B-Material
part	I-Material
of	I-Material
the	I-Material
tip	I-Material
and	O
the	O
sample	B-Material
atoms	I-Material
.	O
The	O
first	O
contribution	O
is	O
calculated	O
analytically	O
[	O
27	O
]	O
.	O
In	O
fact	O
,	O
the	O
macroscopic	O
contribution	O
to	O
the	O
van	B-Process
der	I-Process
Waals	I-Process
force	I-Process
is	O
the	O
same	O
in	O
each	O
of	O
the	O
three	O
systems	O
described	O
below	O
,	O
as	O
it	O
depends	O
only	O
on	O
the	O
tip	B-Process
–	I-Process
surface	I-Process
separation	I-Process
,	O
macroscopic	O
sphere	O
radius	O
,	O
cone-angle	O
and	O
Hamaker	O
constant	O
of	O
the	O
system	O
[	O
27	O
]	O
.	O
All	O
these	O
quantities	O
are	O
identical	O
in	O
each	O
system	O
we	O
look	O
at	O
,	O
so	O
that	O
the	O
van	B-Process
der	I-Process
Waals	I-Process
force	I-Process
acts	O
as	O
a	O
background	O
attractive	O
force	O
independent	O
of	O
the	O
microscopic	O
properties	O
of	O
the	O
system	O
[	O
8	O
]	O
.	O
The	O
Hamaker	O
constant	O
needed	O
for	O
the	O
calculation	B-Process
of	O
the	O
macroscopic	B-Process
van	I-Process
der	I-Process
Waals	I-Process
force	I-Process
is	O
estimated	O
to	O
be	O
0.5eV	O
[	O
32	O
]	O
.	O

In	O
the	O
present	O
work	O
we	O
use	O
the	O
mortar	B-Process
finite	I-Process
element	I-Process
method	O
for	O
the	O
coupling	B-Task
of	I-Task
nonconforming	I-Task
discretized	I-Task
sub-domains	I-Task
in	I-Task
the	I-Task
framework	I-Task
of	I-Task
nonlinear	I-Task
elasticity	I-Task
.	O
The	O
mortar	B-Process
method	I-Process
has	O
been	O
shown	O
to	O
preserve	O
optimal	B-Task
convergence	I-Task
rates	I-Task
(	O
see	O
Laursen	O
(	O
2002	O
)	O
[	O
25	O
]	O
for	O
details	O
)	O
and	O
is	O
variationally	O
consistent	O
.	O
We	O
show	O
that	O
the	O
method	O
can	O
be	O
applied	O
to	O
isogeometric	B-Task
analysis	I-Task
with	O
little	O
effort	O
,	O
once	O
the	O
framework	B-Material
of	I-Material
NURBS	I-Material
based	I-Material
shape	I-Material
functions	I-Material
has	O
been	O
implemented	O
.	O
Furthermore	O
,	O
a	O
specific	O
coordinate	B-Process
augmentation	I-Process
technique	I-Process
allows	O
the	O
design	O
of	O
an	O
energy	B-Task
–	I-Task
momentum	I-Task
scheme	I-Task
for	O
the	O
constrained	O
mechanical	B-Process
system	I-Process
under	O
consideration	O
.	O
The	O
excellent	O
performance	O
of	O
the	O
redesigned	O
mortar	B-Process
method	I-Process
as	O
well	O
as	O
the	O
energy	B-Task
–	I-Task
momentum	I-Task
scheme	I-Task
is	O
illustrated	O
in	O
representative	O
numerical	O
examples.In	O
the	O
present	O
work	O
we	O
use	O
the	O
mortar	B-Process
finite	I-Process
element	I-Process
method	I-Process
for	O
the	O
coupling	B-Task
of	I-Task
nonconforming	I-Task
discretized	I-Task
sub-domains	I-Task
in	I-Task
the	I-Task
framework	I-Task
of	I-Task
nonlinear	I-Task
elasticity	I-Task
.	O
The	O
mortar	B-Process
method	I-Process
has	O
been	O
shown	O
to	O
preserve	O
optimal	O
convergence	B-Task
rates	I-Task
(	O
see	O
Laursen	O
(	O
2002	O
)	O
[	O
25	O
]	O
for	O
details	O
)	O
and	O
is	O
variationally	O
consistent	O
.	O
We	O
show	O
that	O
the	O
method	O
can	O
be	O
applied	O
to	O
isogeometric	B-Task
analysis	I-Task
with	O
little	O
effort	O
,	O
once	O
the	O
framework	B-Material
of	I-Material
NURBS	I-Material
based	I-Material
shape	I-Material
functions	I-Material
has	O
been	O
implemented	O
.	O
Furthermore	O
,	O
a	O
specific	O
coordinate	B-Process
augmentation	I-Process
technique	I-Process
allows	O
the	O
design	O
of	O
an	O
energy	B-Task
–	I-Task
momentum	I-Task
scheme	I-Task
for	O
the	O
constrained	B-Process
mechanical	I-Process
system	I-Process
under	O
consideration	O
.	O
The	O
excellent	O
performance	O
of	O
the	O
redesigned	O
mortar	B-Process
method	I-Process
as	O
well	O
as	O
the	O
energy	B-Task
–	I-Task
momentum	I-Task
scheme	I-Task
is	O
illustrated	O
in	O
representative	O
numerical	O
examples	O
.	O

In	O
this	O
work	O
,	O
a	O
numerical	B-Process
strategy	I-Process
for	O
designing	O
an	O
optimal	B-Task
maintenance	I-Task
scheduling	I-Task
for	I-Task
a	I-Task
structure	I-Task
,	O
accounting	B-Process
explicitly	I-Process
for	I-Process
the	I-Process
effects	I-Process
of	I-Process
uncertainty	I-Process
is	O
suggested	O
.	O
This	O
contribution	O
,	O
which	O
can	O
be	O
regarded	O
as	O
an	O
extension	O
of	O
the	O
methods	O
developed	O
in	O
[	O
23	O
]	O
,	O
presents	O
several	O
novel	O
aspects	O
over	O
similar	O
approaches	O
proposed	O
in	O
the	O
literature	O
.	O
Firstly	O
,	O
the	O
initiation	B-Process
and	I-Process
propagation	I-Process
of	I-Process
fatigue	I-Process
crack	I-Process
is	O
modeled	O
efficiently	O
by	O
means	O
of	O
cohesive	B-Material
zone	I-Material
elements	I-Material
[	O
24	O
–	O
26	O
]	O
.	O
The	O
application	O
of	O
this	O
class	B-Material
of	I-Material
elements	I-Material
allows	O
modeling	B-Task
the	I-Task
crack	I-Task
initiation	I-Task
and	I-Task
propagation	I-Task
within	I-Task
a	I-Task
unified	I-Task
framework	I-Task
.	O
It	O
should	O
be	O
noted	O
that	O
cohesive	B-Material
zone	I-Material
elements	I-Material
have	O
already	O
been	O
used	O
for	O
uncertainty	B-Task
quantification	I-Task
of	I-Task
the	I-Task
crack	I-Task
propagation	I-Task
phenomenon	I-Task
[	O
27,28	O
]	O
.	O
However	O
its	O
application	O
within	O
the	O
context	O
of	O
maintenance	B-Task
scheduling	I-Task
constitutes	O
a	O
novelty	O
.	O
The	O
second	O
innovative	O
aspect	O
of	O
this	O
contribution	O
refers	O
to	O
the	O
assessment	B-Process
of	I-Process
the	I-Process
reliability	I-Process
sensitivity	I-Process
with	I-Process
respect	I-Process
to	I-Process
the	I-Process
variables	I-Process
that	I-Process
define	I-Process
the	I-Process
maintenance	I-Process
scheduling	I-Process
.	O
The	O
estimation	B-Process
of	I-Process
this	I-Process
sensitivity	I-Process
,	O
which	O
is	O
required	O
in	O
order	O
to	O
determine	O
the	O
optimal	B-Task
maintenance	I-Task
schedule	I-Task
within	I-Task
the	I-Task
proposed	I-Task
framework	I-Task
,	O
can	O
be	O
quite	O
demanding	O
as	O
the	O
model	B-Material
characterizing	I-Material
repair	I-Material
of	I-Material
a	I-Material
cracked	I-Material
structure	I-Material
leads	O
to	O
a	O
discontinuous	B-Task
performance	I-Task
function	I-Task
associated	I-Task
with	I-Task
the	I-Task
failure	I-Task
probability	I-Task
.	O
A	O
new	O
approach	O
for	O
modeling	O
this	O
function	O
is	O
proposed	O
herein	O
.	O
The	O
continuous	B-Material
and	I-Material
discontinuous	I-Material
parts	I-Material
respectively	O
of	O
the	O
function	O
are	O
considered	O
separately	O
to	O
estimate	B-Process
accurately	I-Process
the	I-Process
gradients	I-Process
of	I-Process
the	I-Process
failure	I-Process
events	I-Process
.	O

Finite	B-Process
Element	I-Process
Tearing	I-Process
and	I-Process
Interconnecting	I-Process
(	O
FETI	B-Process
)	O
methods	O
are	O
a	O
powerful	O
approach	O
to	O
designing	O
solvers	B-Task
for	I-Task
large-scale	I-Task
problems	I-Task
in	I-Task
computational	I-Task
mechanics	I-Task
.	O
The	O
numerical	B-Material
simulation	I-Material
problem	I-Material
is	O
subdivided	O
into	O
a	O
number	B-Material
of	I-Material
independent	I-Material
sub-problems	I-Material
,	O
which	O
are	O
then	O
coupled	O
in	O
appropriate	O
ways	O
.	O
NURBS	B-Process
-	O
(	O
Non-Uniform	B-Process
Rational	I-Process
B-spline	I-Process
)	O
based	O
isogeometric	B-Process
analysis	I-Process
(	O
IGA	B-Process
)	O
applied	O
to	O
complex	B-Material
geometries	I-Material
requires	O
to	O
represent	O
the	O
computational	B-Task
domain	I-Task
as	I-Task
a	I-Task
collection	I-Task
of	I-Task
several	I-Task
NURBS	I-Task
geometries	I-Task
.	O
Since	O
there	O
is	O
a	O
natural	O
decomposition	O
of	O
the	O
computational	O
domain	O
into	O
several	B-Material
subdomains	I-Material
,	O
NURBS-based	B-Process
IGA	I-Process
is	O
particularly	O
well	O
suited	O
for	O
using	O
FETI	O
methods.This	O
paper	O
proposes	O
the	O
new	O
IsogEometric	O
Tearing	O
and	O
Interconnecting	O
(	O
IETI	O
)	O
method	O
,	O
which	O
combines	O
the	O
advanced	O
solver	O
design	O
of	O
FETI	O
with	O
the	O
exact	O
geometry	O
representation	O
of	O
IGA	O
.	O
We	O
describe	O
the	O
IETI	O
framework	O
for	O
two	O
classes	O
of	O
simple	O
model	O
problems	O
(	O
Poisson	O
and	O
linearized	O
elasticity	O
)	O
and	O
discuss	O
the	O
coupling	O
of	O
the	O
subdomains	O
along	O
interfaces	O
(	O
both	O
for	O
matching	O
interfaces	O
and	O
for	O
interfaces	O
with	O
T-joints	O
,	O
i.e.	O
hanging	O
nodes	O
)	O
.	O
Special	O
attention	O
is	O
paid	O
to	O
the	O
construction	O
of	O
a	O
suitable	O
preconditioner	O
for	O
the	O
iterative	O
linear	O
solver	O
used	O
for	O
the	O
interface	O
problem	O
.	O
We	O
report	O
several	O
computational	O
experiments	O
to	O
demonstrate	O
the	O
performance	O
of	O
the	O
proposed	O
IETI	O
method	O
.	O

To	O
the	O
best	O
of	O
authors’	O
knowledge	O
,	O
so	O
far	O
there	O
are	O
only	O
very	O
few	O
papers	O
[	O
12,13,16,29	O
]	O
which	O
address	O
the	O
performance	B-Process
of	I-Process
linear	I-Process
algebra	I-Process
solvers	I-Process
.	O
In	O
Ref	O
.	O
[	O
16	O
]	O
,	O
the	O
authors	O
study	O
the	O
performance	B-Process
of	I-Process
direct	I-Process
solvers	I-Process
which	O
are	O
clearly	O
not	O
suitable	O
for	O
large	B-Process
problems	I-Process
,	O
specially	O
in	O
three-dimensions	O
.	O
In	O
Ref	O
.	O
[	O
29	O
]	O
,	O
the	O
tearing	B-Process
and	I-Process
interconnecting	I-Process
approach	I-Process
of	I-Process
finite	I-Process
element	I-Process
methods	I-Process
is	O
used	O
in	O
the	O
context	O
of	O
isogeometric	B-Task
analysis	I-Task
,	O
and	O
the	O
numerical	B-Material
tests	I-Material
(	O
in	O
absence	O
of	O
any	O
theoretical	B-Task
study	I-Task
)	O
suggest	O
almost	O
optimal	O
(	O
with	O
a	O
logarithmic	O
factor	O
)	O
convergence	O
rates	O
of	O
the	O
proposed	O
isogeometric	B-Process
tearing	I-Process
and	I-Process
interconnecting	I-Process
method	I-Process
.	O
The	O
only	O
paper	O
which	O
provides	O
rigorous	B-Task
theoretical	I-Task
study	I-Task
,	O
supported	O
by	O
extensive	B-Material
numerical	I-Material
examples	I-Material
,	O
is	O
by	O
Beirao	O
et	O
al	O
.	O
[	O
12	O
]	O
where	O
the	O
authors	O
discuss	O
the	O
overlapping	O
Schwarz	B-Process
methods	I-Process
.	O
The	O
same	O
authors	O
have	O
also	O
proposed	O
BDDC	B-Material
preconditioners	I-Material
for	O
isogeometric	B-Task
analysis	I-Task
in	O
[	O
13	O
]	O
.	O

An	O
attempt	O
of	O
a	O
quite	O
comprehensive	O
answer	O
to	O
this	O
question	O
is	O
made	O
hereafter	O
,	O
within	O
the	O
following	O
structure	O
of	O
the	O
remaining	O
paper	O
:	O
first	O
,	O
we	O
introduce	O
the	O
mathematical	B-Material
systems	I-Material
biology	I-Material
of	I-Material
bone	I-Material
,	O
starting	O
from	O
the	O
work	O
of	O
Pivonka	O
et	O
al	O
.	O
[	O
25,26	O
]	O
,	O
and	O
extending	O
it	O
to	O
mechanoregulatory	B-Process
feedback	I-Process
control	I-Process
(	O
Section	O
2	O
)	O
.	O
Then	O
,	O
we	O
introduce	O
a	O
continuum	B-Material
micromechanics	I-Material
representation	I-Material
adopted	O
from	O
Hellmich	O
et	O
al	O
.	O
[	O
30	O
]	O
,	O
in	O
order	O
to	O
scale	O
elasticity	O
and	O
strains	O
from	O
the	O
level	O
of	O
the	O
extravascular	O
bone	O
matrix	O
to	O
that	O
of	O
cortical	O
bone1In	O
this	O
paper	O
,	O
we	O
restrict	O
ourselves	O
to	O
cortical	O
bone	O
,	O
due	O
to	O
its	O
major	O
importance	O
in	O
providing	O
sufficient	O
load-carrying	O
capacity	O
.	O
However	O
,	O
extension	O
of	O
the	O
coupled	O
approach	O
proposed	O
here	O
to	O
trabecular	O
bone	O
is	O
straightforward	O
;	O
it	O
merely	O
requires	O
recalibration	O
of	O
underlying	O
parameters.1	O
and	O
vice	O
versa	O
(	O
Section	O
3	O
)	O
.	O
The	O
micromechanics	O
formulation	O
is	O
fed	O
with	O
composition	O
quantities	O
derived	O
from	O
the	O
systems	O
biology	O
approach	O
,	O
which	O
,	O
in	O
turn	O
,	O
is	O
provided	O
with	O
mechanical	O
stimuli	O
gained	O
from	O
the	O
micromechanics	O
model	O
.	O
We	O
then	O
apply	O
the	O
coupled	O
approach	O
to	O
biochemical	O
and	O
mechanical	O
conditions	O
typical	O
for	O
postmenopausal	O
osteoporosis	O
(	O
Section	O
4	O
)	O
and	O
microgravity	O
exposure	O
(	O
Section	O
5	O
)	O
,	O
and	O
discuss	O
key	O
sensitivity	O
features	O
(	O
Section	O
6	O
)	O
.	O
After	O
emphasizing	O
the	O
potentials	O
and	O
limitations	O
of	O
the	O
presented	O
approach	O
(	O
Section	O
7	O
)	O
,	O
we	O
conclude	O
the	O
paper	O
in	O
(	O
Section	O
8	O
)	O
.	O

The	O
choice	O
of	O
the	O
interpolation	B-Process
functions	I-Process
and	O
support	B-Material
point	I-Material
coordinates	I-Material
for	I-Material
the	I-Material
gradient	I-Material
field	I-Material
is	O
crucial	O
to	O
ensure	O
stability	B-Task
and	I-Task
accuracy	I-Task
of	I-Task
the	I-Task
formulation	I-Task
.	O
For	O
example	O
,	O
nodal	B-Process
integration	I-Process
and	I-Process
NS-FEM	I-Process
are	O
unstable	O
involving	O
the	O
appearance	O
of	O
spurious	O
low-energy	O
modes	O
.	O
They	O
need	O
non-physical	B-Material
penalty	I-Material
energy	I-Material
functions	I-Material
that	O
stabilize	B-Task
them	I-Task
.	O
The	O
articles	O
[	O
2,28	O
]	O
numerically	B-Task
verify	I-Task
the	I-Task
stability	I-Task
,	I-Task
convergence	I-Task
and	I-Task
accuracy	I-Task
of	I-Task
several	I-Task
W2	I-Task
variants	I-Task
including	O
new	O
elements	O
which	O
can	O
be	O
constructed	O
based	O
on	O
the	O
idea	O
of	O
assumed	O
continuous	B-Process
deformation	I-Process
gradients	I-Process
.	O
For	O
first	B-Material
order	I-Material
hexahedral	I-Material
elements	I-Material
,	O
[	O
2,28	O
]	O
found	O
good	O
results	O
for	O
the	O
element	B-Material
types	I-Material
C3D	B-Material
_	I-Material
8N	I-Material
_	I-Material
27C	I-Material
and	O
C3D	B-Material
_	I-Material
8N	I-Material
_	I-Material
8I	I-Material
.	O
The	O
first	O
is	O
defined	O
by	O
27	O
support	O
points	O
and	O
a	O
second	B-Process
order	I-Process
tensor-product	I-Process
interpolation	I-Process
of	O
the	O
deformation	O
gradient	O
by	O
Lagrange	O
polynomials	O
.	O
The	O
latter	O
element	O
type	O
is	O
defined	O
by	O
16	O
support	O
points	O
with	O
8	O
points	O
being	O
coincident	O
with	O
the	O
nodes	O
and	O
8	O
additional	O
points	O
in	O
the	O
element	O
interior	O
.	O
Among	O
the	O
tested	O
first	B-Material
order	I-Material
tetrahedra	I-Material
,	O
the	O
nodally	B-Process
integrated	I-Process
tetrahedron	I-Process
with	O
an	O
additional	O
bubble	O
mode	O
in	O
the	O
gradients	O
was	O
found	O
to	O
be	O
most	O
accurate	O
.	O
It	O
turned	O
out	O
to	O
be	O
even	O
the	O
most	O
efficient	O
with	O
respect	O
to	O
computing	B-Task
time	I-Task
in	I-Task
explicit	I-Task
analysis	I-Task
[	O
28	O
]	O
because	O
the	O
enlarged	O
critical	O
time	O
step	O
compensates	O
the	O
slightly	O
increased	O
numerical	O
cost	O
per	O
restoring	O
force	O
assembly	O
.	O
Fig.	O
1	O
illustrates	O
the	O
positions	O
of	O
support	O
points	O
for	O
various	O
CAG	B-Process
and	I-Process
SFEM	I-Process
formulations	I-Process
.	O

Algorithms	B-Material
regarding	I-Material
distance	I-Material
fields	I-Material
go	O
back	O
to	O
the	O
level	B-Process
set	I-Process
equation	I-Process
.	O
The	O
level	B-Process
set	I-Process
method	I-Process
was	O
presented	O
by	O
Osher	O
and	O
Sethian	O
[	O
20	O
]	O
who	O
described	O
the	O
temporal	B-Process
propagation	I-Process
of	O
moving	O
interfaces	O
by	O
numerical	B-Process
methods	I-Process
solving	O
the	O
Hamilton	B-Process
–	I-Process
Jacobi	I-Process
equation	I-Process
.	O
This	O
is	O
performed	O
by	O
a	O
finite	B-Process
difference	I-Process
scheme	I-Process
working	O
on	O
a	O
rectangular	B-Material
grid	I-Material
in	I-Material
two	I-Material
or	I-Material
three	I-Material
dimensions	I-Material
.	O
Information	O
on	O
normal	O
vectors	O
and	O
curvature	O
can	O
be	O
obtained	O
.	O
The	O
fast	B-Process
marching	I-Process
method	I-Process
[	O
21	O
]	O
provides	O
an	O
efficient	O
numerical	B-Task
scheme	I-Task
of	I-Task
complexity	I-Task
nlogn	I-Task
to	I-Task
compute	I-Task
the	I-Task
support	I-Task
values	I-Task
on	I-Task
the	I-Task
grid	I-Task
.	O
It	O
is	O
a	O
reinterpretation	O
of	O
the	O
propagation	B-Process
process	I-Process
,	O
i.e.	O
the	B-Process
time	I-Process
where	I-Process
the	I-Process
interface	I-Process
passes	I-Process
a	I-Process
certain	I-Process
grid	I-Process
point	I-Process
is	I-Process
influenced	I-Process
only	I-Process
by	I-Process
those	I-Process
neighboring	I-Process
grid	I-Process
points	I-Process
which	I-Process
are	I-Process
previously	I-Process
passed	I-Process
by	I-Process
the	I-Process
interface	I-Process
.	O
An	O
overview	O
on	O
the	O
theory	B-Process
of	I-Process
level	I-Process
set	I-Process
and	O
fast	B-Process
marching	I-Process
methods	O
and	O
their	O
applications	O
to	O
problems	O
of	O
various	O
areas	B-Task
are	O
given	O
in	O
[	O
22,23	O
]	O
,	O
for	O
example	O
shape	B-Task
offsetting	I-Task
,	O
computing	B-Task
distances	I-Task
,	O
photolithography	B-Task
development	I-Task
,	O
seismic	B-Task
travel	I-Task
times	I-Task
,	O
etc	O
.	O
Distance	B-Task
fields	I-Task
are	O
a	O
special	O
case	O
of	O
the	O
level	B-Process
set	I-Process
equation	O
where	O
the	O
absolute	O
value	O
of	O
the	O
advection	O
velocity	O
is	O
1	O
.	O

In	O
this	O
article	O
we	O
consider	O
an	O
extension	B-Task
to	I-Task
the	I-Task
equations	I-Task
of	I-Task
poroelasticity	I-Task
by	O
modelling	B-Task
the	I-Task
flow	I-Task
of	I-Task
a	I-Task
slightly	I-Task
compressible	I-Task
single	I-Task
phase	I-Task
fluid	I-Task
in	I-Task
a	I-Task
viscoelastic	I-Task
porous	I-Task
medium	I-Task
.	O
The	O
constitutive	B-Process
equations	I-Process
therefore	O
allow	O
for	O
the	O
presence	O
of	O
viscoelastic	B-Process
relaxation	I-Process
effects	I-Process
in	O
the	O
porous	B-Material
media	I-Material
(	O
but	O
not	O
the	O
fluid	B-Material
)	O
.	O
Fully	B-Process
discrete	I-Process
numerical	I-Process
schemes	I-Process
are	O
derived	O
based	O
on	O
a	O
lagged	B-Process
and	I-Process
non-lagged	I-Process
backward	I-Process
Euler	I-Process
time	I-Process
stepping	I-Process
method	I-Process
applied	O
to	O
a	O
mixed	B-Material
and	I-Material
Galerkin	I-Material
finite	I-Material
element	I-Material
spatial	I-Material
discretization	I-Material
.	O
We	O
show	O
that	O
the	O
lagged	O
scheme	O
is	O
unconditionally	O
stable	O
and	O
give	O
an	O
optimal	O
a	O
priori	O
error	O
bound	O
for	O
it	O
.	O
Furthermore	O
,	O
this	O
scheme	O
is	O
practical	O
and	O
useful	O
in	O
the	O
sense	O
that	O
it	O
can	O
be	O
easily	O
implemented	O
in	O
existing	O
poroelasticity	O
software	O
because	O
the	O
coupling	B-Process
between	O
the	O
viscous	O
stresses	O
and	O
pressures	O
and	O
the	O
elasticity	O
and	O
flow	B-Process
equations	I-Process
is	O
‘	O
lagged’	O
by	O
one	O
time	O
step	O
.	O
The	O
required	O
additional	O
coding	O
therefore	O
takes	O
the	O
form	O
of	O
extra	O
‘	O
right	O
hand	O
side	O
loads’	O
together	O
with	O
some	O
updating	B-Process
subroutines	I-Process
for	O
the	O
viscoelastic	O
internal	O
variables	O
,	O
but	O
the	O
solver	O
and	O
assembly	B-Material
engines	I-Material
remain	O
intact	O
.	O
This	O
idea	O
of	O
lagging	O
has	O
been	O
used	O
before	O
for	O
nonlinearly	B-Task
viscoelastic	I-Task
diffusion	I-Task
problems	I-Task
in	O
[	O
3,24	O
]	O
but	O
,	O
of	O
course	O
,	O
is	O
not	O
new	O
.	O
Lagging	O
in	O
numerical	B-Process
schemes	I-Process
is	O
discussed	O
more	O
widely	O
by	O
Lowrie	O
in	O
[	O
14	O
]	O
.	O

Traditionally	O
,	O
the	O
simulation	B-Task
of	I-Task
incompressible	I-Task
fluid	I-Task
flow	I-Task
by	O
the	O
SPH	B-Process
method	I-Process
has	O
been	O
through	O
a	O
weakly	B-Process
compressible	I-Process
SPH	I-Process
formulation	I-Process
(	O
WCSPH	B-Process
)	O
.	O
In	O
this	O
approach	O
,	O
the	O
pressure	O
is	O
treated	O
as	O
a	O
thermodynamic	O
variable	O
and	O
is	O
calculated	O
using	O
an	O
artificial	B-Process
equation	I-Process
of	I-Process
state	I-Process
.	O
The	O
sound	O
speed	O
is	O
set	O
to	O
be	O
sufficiently	O
high	O
to	O
limit	O
density	O
variations	O
to	O
within	O
a	O
small	O
fraction	O
of	O
the	O
actual	O
fluid	B-Material
density	O
.	O
In	O
practice	O
,	O
this	O
high	O
sound	O
speed	O
places	O
a	O
limitation	O
on	O
the	O
maximum	O
permissible	O
time-step	O
size	O
through	O
the	O
Courant	O
–	O
Friedrichs	O
–	O
Lewy	O
(	O
CFL	O
)	O
constraint	O
.	O
A	O
particular	O
weakness	O
relates	O
to	O
noise	O
in	O
the	O
pressure	O
field	O
since	O
a	O
small	O
perturbation	O
in	O
the	O
local	O
density	O
will	O
yield	O
a	O
large	O
variation	O
in	O
the	O
local	O
pressure	O
.	O
This	O
can	O
make	O
WCSPH	B-Process
formulations	O
ineffective	O
for	O
accurate	O
force	O
and	O
pressure	O
prediction	O
,	O
although	O
recent	O
developments	O
which	O
create	B-Task
more	I-Task
uniform	I-Task
particle	I-Task
distributions	I-Task
have	O
improved	O
this	O
[	O
1,2	O
]	O
.	O
A	O
review	O
of	O
the	O
SPH	B-Process
method	I-Process
can	O
be	O
found	O
in	O
[	O
3	O
]	O
while	O
a	O
review	O
of	O
the	O
classical	O
WCSPH	B-Process
formulation	O
applied	O
to	O
free-surface	B-Process
flows	I-Process
can	O
be	O
found	O
in	O
[	O
4	O
]	O
.	O

In	O
this	O
paper	O
,	O
however	O
,	O
we	O
prefer	O
the	O
simpler	O
‘	O
framed’	B-Process
cell	I-Process
employed	O
by	O
Hadjiconstantinou	O
and	O
Patera	O
[	O
16	O
]	O
,	O
where	O
the	O
shear	B-Process
stress	I-Process
is	O
generated	O
by	O
constraining	B-Process
the	I-Process
velocity	I-Process
in	O
a	O
‘	O
frame’	O
rather	O
than	O
by	O
modifying	O
the	O
shape	O
of	O
the	O
box	O
.	O
The	O
framed	B-Process
cell	I-Process
is	O
periodic	O
,	O
but	O
we	O
cannot	O
simply	O
calculate	O
the	O
average	O
stress	O
in	O
the	O
whole	O
box	O
because	O
the	O
presence	O
of	O
an	O
external	O
buffer	O
would	O
produce	O
spurious	O
results	O
.	O
We	O
need	O
the	O
local	O
stress	O
in	O
the	O
core	O
region	O
,	O
but	O
this	O
complicates	O
the	O
Oij	O
term	O
in	O
Eq	O
.	O
(	O
3	O
)	O
.	O
There	O
are	O
other	O
methods	O
to	O
calculate	B-Task
the	I-Task
stress	I-Task
tensor	I-Task
such	O
as	O
the	O
method	B-Process
of	I-Process
planes	I-Process
[	O
32	O
]	O
,	O
the	O
volume-average	B-Process
approach	I-Process
[	O
26,14	O
]	O
,	O
or	O
the	O
method	O
derived	O
from	O
the	O
Schweitz	B-Process
virial	I-Process
relation	I-Process
[	O
25	O
]	O
,	O
but	O
,	O
in	O
general	O
,	O
we	O
must	O
choose	O
between	O
a	O
complicated	B-Process
computational	I-Process
cell	I-Process
(	O
i.e.	O
Lees	B-Process
–	I-Process
Edwards	I-Process
cell	I-Process
)	O
and	O
simplifying	O
the	O
calculation	O
of	O
the	O
momentum	O
flux	O
,	O
or	O
a	O
simple	B-Process
cell	I-Process
(	O
i.e.	O
framed	B-Process
cell	I-Process
)	O
and	O
complicating	O
the	O
calculation	O
of	O
the	O
momentum	O
flux	O
.	O
The	O
new	O
method	O
we	O
propose	O
here	O
does	O
not	O
need	O
the	O
direct	O
calculation	O
of	O
the	O
flux	O
,	O
so	O
it	O
avoids	O
this	O
issue	O
altogether	O
:	O
we	O
can	O
use	O
the	O
framed	O
cell	O
and	O
,	O
at	O
the	O
same	O
time	O
,	O
avoid	O
the	O
calculation	O
of	O
the	O
IK	B-Process
equation	I-Process
.	O

Powder	B-Process
metallurgy	I-Process
is	O
a	O
versatile	O
technology	B-Process
for	I-Process
the	I-Process
manufacturing	I-Process
of	I-Process
components	I-Process
to	I-Process
(	I-Process
near	I-Process
)	I-Process
net-shape	I-Process
with	O
high	O
product	O
quality	O
.	O
For	O
a	O
hardmetal	B-Material
(	O
such	O
as	O
WC-Co	B-Material
)	O
cold	O
compaction	O
of	O
the	O
powder	B-Material
to	O
a	O
“	O
green	B-Material
body	I-Material
”	O
is	O
followed	O
by	O
liquid-phase	B-Process
sintering	I-Process
from	O
the	O
subsequent	O
heating	B-Process
.	O
This	O
means	O
that	O
the	O
binder	O
metal	O
Co	B-Process
is	I-Process
heated	I-Process
to	O
melt	O
in	O
order	O
to	O
obtain	B-Task
sufficient	I-Task
mobility	I-Task
via	I-Task
capillary	I-Task
action	I-Task
,	O
i.e.	O
,	O
via	O
surface	B-Process
traction	I-Process
,	O
stemming	O
from	O
stored	O
surface	O
energy	O
.	O
The	O
resulting	O
flow	O
causes	O
gradual	B-Process
filling	I-Process
of	I-Process
the	I-Process
pore	I-Process
space	O
and	O
brings	O
about	O
a	O
macroscopic	B-Process
shrinkage	I-Process
of	O
the	O
particle	B-Material
compact	O
until	O
a	O
completely	O
dense	B-Material
state	I-Material
is	O
obtained	O
,	O
at	O
least	O
ideally	O
.	O
To	O
model	B-Task
and	I-Task
quantitatively	I-Task
simulate	I-Task
the	I-Task
sintering	I-Task
process	I-Task
is	O
a	O
challenging	O
task	O
.	O
The	O
goal	O
is	O
to	O
(	O
i	O
)	O
estimate	B-Task
the	I-Task
final	I-Task
resulting	I-Task
quality	I-Task
(	O
i.e.	O
,	O
in	O
terms	O
of	O
porosity	B-Task
)	O
and	O
(	O
ii	O
)	O
to	B-Task
predict	I-Task
the	I-Task
final	I-Task
net	I-Task
shape	I-Task
and	I-Task
size	I-Task
of	O
the	O
sintered	B-Material
component	I-Material
.	O

As	O
mentioned	O
previously	O
,	O
the	O
weakly	B-Process
penalized	I-Process
system	O
can	O
be	O
thought	O
of	O
as	O
a	O
generalized	B-Process
formulation	I-Process
which	O
can	O
result	O
in	O
the	O
PL	B-Process
,	I-Process
penalty	I-Process
or	I-Process
statically	I-Process
condensed	I-Process
PL	I-Process
formulations	I-Process
depending	O
on	O
the	O
choice	O
of	O
the	O
projection	O
operator	O
.	O
The	O
equivalence	O
of	O
these	O
methods	O
under	O
the	O
weakly	O
penalized	O
regime	O
,	O
allows	O
us	O
to	O
combine	O
and	O
take	O
advantage	O
of	O
the	O
good	O
characteristics	O
of	O
each	O
method	O
.	O
For	O
instance	O
,	O
the	O
weakly	B-Process
penalized	I-Process
formulation	I-Process
combines	O
the	O
simplified	O
structure	O
of	O
the	O
penalty	B-Process
method	I-Process
with	O
the	O
convergence	O
characteristics	O
of	O
the	O
PL	B-Process
formulation	I-Process
.	O
However	O
,	O
due	O
to	O
the	O
stiffness	O
of	O
the	O
linear	O
system	O
at	O
high	O
values	O
of	O
the	O
bulk	O
modulus	O
,	O
the	O
penalized	B-Process
formulations	I-Process
(	O
classic	B-Process
penalty	I-Process
/	I-Process
weakly	I-Process
penalized	I-Process
)	O
exhibit	O
deteriorated	O
nonlinear	B-Material
convergence	I-Material
.	O
This	O
stands	O
in	O
stark	O
contrast	O
to	O
the	O
PL	B-Process
method	O
which	O
(	O
for	O
inf	B-Process
–	I-Process
sup	I-Process
stable	I-Process
schemes	I-Process
)	O
exhibits	O
fast	O
convergence	B-Material
even	O
for	O
high	O
bulk	O
modulus	O
.	O
However	O
,	O
we	O
observe	O
that	O
,	O
when	O
the	O
choice	O
of	O
πh	O
provides	O
equivalence	O
with	O
the	O
discrete	O
PL	B-Task
method	O
,	O
poor	B-Material
nonlinear	I-Material
convergence	I-Material
is	O
observed	O
though	O
,	O
in	O
principle	O
,	O
the	O
convergence	O
should	O
be	O
similar	O
.	O
Examining	O
the	O
update	O
formulae	O
for	O
both	O
weakly	B-Process
penalized	I-Process
and	I-Process
PL	I-Process
approaches	I-Process
(	O
see	O
Appendix	O
C	O
)	O
,	O
we	O
observe	O
that	O
deteriorated	B-Task
convergence	I-Task
stems	O
from	O
:	O
(	O
1	O
)	O
initial	B-Task
residual	I-Task
amplification	I-Task
,	O
and	O
(	O
2	O
)	O
the	O
amplification	B-Task
of	I-Task
the	I-Task
residual	I-Task
.	O

Energy	B-Process
conservation	I-Process
is	O
critical	O
to	O
ensure	O
stability	O
of	O
a	O
numerical	O
method	O
,	O
especially	O
for	O
contact	B-Task
and	I-Task
collision	I-Task
problems	I-Task
[	O
28,43	O
]	O
.	O
A	O
number	O
of	O
conserving	O
schemes	O
have	O
been	O
developed	O
to	O
ensure	O
energy	B-Task
conservation	I-Task
.	O
These	O
schemes	O
make	O
use	O
of	O
the	O
penalty	B-Process
regulation	I-Process
of	O
normal	O
contact	O
constraint	O
and	O
inherit	O
the	O
conservation	O
property	O
from	O
continuum	B-Task
problems	I-Task
.	O
These	O
conservation	B-Process
schemes	I-Process
can	O
conveniently	O
be	O
combined	O
with	O
the	O
finite	B-Process
element	I-Process
method	I-Process
to	O
simulate	B-Task
frictionless	I-Task
[	I-Task
44	I-Task
]	I-Task
and	I-Task
frictional	I-Task
[	I-Task
43	I-Task
]	I-Task
contact	I-Task
and	I-Task
collision	I-Task
.	O
Hesch	O
and	O
Betsch	O
[	O
45	O
]	O
formulated	O
the	O
node-to-segment	B-Process
contact	I-Process
method	I-Process
and	O
solved	O
large	B-Task
deformation	I-Task
contact	I-Task
problems	I-Task
with	O
the	O
conserving	B-Process
scheme	I-Process
.	O
More	O
recently	O
,	O
an	O
energy	B-Process
and	I-Process
momentum-conserving	I-Process
temporal	I-Process
discretization	I-Process
scheme	I-Process
[	O
46	O
]	O
was	O
developed	O
for	O
adhesive	B-Task
contact	I-Task
problems	I-Task
without	O
considering	O
friction	B-Process
and	O
dissipation	B-Process
.	O
Even	O
though	O
the	O
conserving	B-Process
scheme	I-Process
improves	O
numerical	O
stability	O
,	O
it	O
also	O
inherits	O
from	O
the	O
penalty	O
method	O
the	O
difficulty	O
of	O
having	O
to	O
determine	O
penalty	O
parameters	O
.	O
In	O
order	O
to	O
remove	O
penalty	O
sensitivity	O
,	O
Chawla	O
and	O
Laursen	O
[	O
47	O
]	O
proposed	O
an	O
energy	B-Process
and	I-Process
momentum	I-Process
conserving	I-Process
algorithm	I-Process
,	O
which	O
makes	O
use	O
of	O
Lagrange	O
multipliers	O
instead	O
of	O
penalty	O
parameters	O
.	O

In	O
this	O
article	O
we	O
propose	O
a	O
method	O
which	O
adopts	O
a	O
different	O
approach	O
to	O
the	O
generation	B-Process
procedure	I-Process
outlined	O
above	O
and	O
that	O
helps	O
to	O
address	O
the	O
problem	O
of	O
generating	B-Task
high-order	I-Task
meshes	I-Task
for	I-Task
high	I-Task
Reynolds	I-Task
number	I-Task
flows	I-Task
.	O
The	O
method	O
is	O
conceptually	O
simple	O
,	O
cheap	O
to	O
implement	O
and	O
does	O
not	O
require	O
a	O
dense	B-Material
linear	I-Material
boundary-layer	I-Material
mesh	I-Material
.	O
It	O
is	O
based	O
on	O
the	O
use	O
of	O
an	O
isoparametric	B-Process
[	I-Process
17	I-Process
]	I-Process
or	I-Process
,	I-Process
in	I-Process
general	I-Process
,	I-Process
a	I-Process
transfinite	I-Process
interpolation	I-Process
[	O
18	O
]	O
where	O
a	O
high-order	B-Material
coarse	I-Material
boundary-layer	I-Material
prismatic	I-Material
mesh	I-Material
is	O
subdivided	B-Process
into	I-Process
either	I-Process
prisms	I-Process
or	I-Process
tetrahedra	I-Process
using	O
the	O
mapping	O
that	O
defines	O
the	O
coarse	B-Material
high-order	I-Material
prisms	I-Material
.	O
The	O
procedure	O
is	O
also	O
very	O
versatile	O
as	O
it	O
permits	O
meshes	B-Material
with	O
different	O
distributions	O
of	O
y	O
+	O
to	O
be	O
generated	O
with	O
ease	O
and	O
furthermore	O
,	O
the	O
validity	O
of	O
these	O
meshes	O
is	O
guaranteed	O
if	O
the	O
initial	B-Material
mesh	I-Material
is	O
valid	O
and	O
the	O
polynomial	B-Material
space	I-Material
is	O
chosen	O
appropriately	O
.	O

We	O
shall	O
establish	O
the	O
variational	B-Material
format	I-Material
in	I-Material
the	I-Material
space	I-Material
–	I-Material
time	I-Material
domain	I-Material
S	B-Material
=	I-Material
defΩ	I-Material
×	I-Material
I	I-Material
,	O
for	O
given	O
spatial	B-Material
domain	I-Material
Ω	I-Material
and	O
time	B-Material
domain	I-Material
I	I-Material
=(	I-Material
0,T	I-Material
)	I-Material
,	O
for	O
a	O
quite	O
broad	O
class	O
of	O
problems	O
involving	O
a	O
first	B-Task
order	I-Task
time-derivative	I-Task
.	O
In	O
particular	O
,	O
the	O
coupled	B-Task
problem	I-Task
of	I-Task
consolidation	I-Task
of	I-Task
geomaterials	I-Task
falls	O
within	O
this	O
class	O
.	O
Another	O
interesting	O
application	O
is	O
the	O
problem	B-Task
of	I-Task
dynamics	I-Task
,	I-Task
rewritten	I-Task
in	I-Task
first-order	I-Task
form	I-Task
,	O
i.e.	O
through	O
a	O
Hamiltonian	B-Task
description	I-Task
.	O
It	O
is	O
of	O
considerable	O
interest	O
to	O
note	O
from	O
the	O
outset	O
that	O
,	O
due	O
to	O
the	O
forward	O
transport	O
of	O
information	O
in	O
time	O
,	O
it	O
is	O
always	O
possible	O
to	O
consider	O
a	O
set	O
of	O
finite	O
time	O
intervals	O
,	O
whereby	O
the	O
solution	O
at	O
the	O
end	O
of	O
any	O
such	O
interval	O
will	O
act	O
as	O
the	O
initial	O
data	O
for	O
the	O
next	O
one	O
.	O
To	O
this	O
end	O
,	O
we	O
introduce	O
a	O
partition	O
0	O
=	O
t0	O
<	O
t1	O
<⋯<	O
tN	O
=	O
T	O
of	O
the	O
considered	O
time	O
domain	O
I	O
=(	O
0,T	O
)	O
into	O
time-intervals	O
In	O
=(	O
tn	O
−	O
1,tn	O
)	O
of	O
length	O
Δtn	O
=	O
tn	O
−	O
tn	O
−	O
1.11The	O
abbreviated	O
notation	O
Δt	O
=	O
Δtn	O
will	O
be	O
used	O
henceforth	O
for	O
the	O
current	O
time	O
step	O
associated	O
with	O
In	O
.	O
Hence	O
,	O
we	O
define	O
space	B-Material
–	I-Material
time	I-Material
slabs	I-Material
Sn	B-Material
=	I-Material
defΩ	I-Material
×	I-Material
In	I-Material
such	O
that	O
the	O
space	B-Material
–	I-Material
time	I-Material
domain	I-Material
can	O
be	O
given	O
as	O
S	B-Material
=	I-Material
defΩ	I-Material
×	I-Material
I	I-Material
=	I-Material
S1	I-Material
∪	I-Material
S2	I-Material
⋯∪	I-Material
Sn	I-Material
.	O

Isogeometric	B-Task
analysis	I-Task
.	O
The	O
central	O
idea	O
of	O
isogeometric	B-Task
analysis	I-Task
is	O
to	O
use	O
the	O
same	O
ansatz	B-Process
functions	I-Process
for	O
the	O
discretization	O
of	O
the	O
partial	O
differential	O
equation	O
at	O
hand	O
,	O
as	O
are	O
used	O
for	O
the	O
representation	O
of	O
the	O
problem	B-Task
geometry	I-Task
.	O
Usually	O
,	O
the	O
problem	O
geometry	O
Ω	O
is	O
represented	O
in	O
computer	B-Process
aided	I-Process
design	I-Process
(	O
CAD	B-Process
)	O
by	O
means	O
of	O
NURBS	B-Material
or	O
T-splines	B-Material
.	O
This	O
concept	O
,	O
originally	O
invented	O
in	O
[	O
1	O
]	O
for	O
finite	B-Process
element	I-Process
methods	I-Process
(	O
IGAFEM	B-Process
)	O
has	O
proved	O
very	O
fruitful	O
in	O
applications	O
[	O
1,2	O
]	O
;	O
see	O
also	O
the	O
monograph	O
[	O
3	O
]	O
.	O
Since	O
CAD	B-Process
directly	O
provides	O
a	O
parametrization	O
of	O
the	O
boundary	O
∂	O
Ω	O
,	O
this	O
makes	O
the	O
boundary	B-Process
element	I-Process
method	I-Process
(	O
BEM	B-Process
)	O
the	O
most	O
attractive	O
numerical	B-Process
scheme	I-Process
,	O
if	O
applicable	O
(	O
i.e.	O
,	O
provided	O
that	O
the	O
fundamental	O
solution	O
of	O
the	O
differential	O
operator	O
is	O
explicitly	O
known	O
)	O
.	O
Isogeometric	B-Process
BEM	I-Process
(	O
IGABEM	B-Process
)	O
has	O
first	O
been	O
considered	O
for	O
2D	B-Process
BEM	I-Process
in	O
[	O
4	O
]	O
and	O
for	O
3D	B-Process
BEM	I-Process
in	O
[	O
5	O
]	O
.	O
Unlike	O
standard	B-Process
BEM	I-Process
with	O
piecewise	O
polynomials	O
which	O
is	O
well-studied	O
in	O
the	O
literature	O
,	O
cf.	O
the	O
monographs	O
[	O
6,7	O
]	O
and	O
the	O
references	O
therein	O
,	O
the	O
numerical	B-Task
analysis	I-Task
of	I-Task
IGABEM	I-Task
is	O
essentially	O
open	O
.	O
We	O
only	O
refer	O
to	O
[	O
2,8	O
–	O
10	O
]	O
for	O
numerical	O
experiments	O
and	O
to	O
[	O
11	O
]	O
for	O
some	O
quadrature	B-Task
analysis	I-Task
.	O
In	O
particular	O
,	O
a	B-Process
posteriori	I-Process
error	I-Process
estimation	I-Process
has	O
been	O
well-studied	O
for	O
standard	B-Process
BEM	I-Process
,	O
e.g.	O
,	O
[	O
12	O
–	O
18	O
]	O
as	O
well	O
as	O
the	O
recent	O
overview	O
article	O
[	O
19	O
]	O
,	O
but	O
has	O
not	O
been	O
treated	O
for	O
IGABEM	B-Process
so	O
far	O
.	O
The	O
purpose	O
of	O
the	O
present	O
work	O
is	O
to	O
shed	B-Task
some	I-Task
first	I-Task
light	I-Task
on	I-Task
a	I-Task
posteriori	I-Task
error	I-Task
analysis	I-Task
for	I-Task
IGABEM	I-Task
which	O
provides	O
some	O
mathematical	B-Task
foundation	I-Task
of	I-Task
a	I-Task
corresponding	I-Task
adaptive	I-Task
algorithm	I-Task
.	O

In	O
recent	O
years	O
,	O
the	O
Discontinuous	B-Process
Galerkin	I-Process
(	O
DG	B-Process
)	O
method	O
has	O
emerged	O
as	O
a	O
more	O
thorough	O
alternative	O
for	O
locally	O
solving	B-Task
conservation	I-Task
laws	I-Task
of	I-Task
the	I-Task
shallow	I-Task
water	I-Task
equations	I-Task
with	O
higher	O
accuracy	O
[	O
21	O
–	O
27	O
]	O
.	O
The	O
DG	B-Process
method	I-Process
further	O
involves	O
finite	B-Process
element	I-Process
weak	I-Process
formulation	I-Process
to	O
–	O
inherently	O
from	O
conservation	B-Process
principles	I-Process
–	O
shape	B-Task
a	I-Task
piecewise-polynomial	I-Task
solution	I-Task
over	I-Task
each	I-Task
local	I-Task
discrete	I-Task
cell	I-Task
,	O
via	O
local	B-Process
basis	I-Process
functions	I-Process
.	O
On	O
this	O
basis	O
,	O
the	O
DG	B-Process
polynomial	I-Process
accuracy	I-Process
is	O
spanned	O
by	O
a	O
set	O
of	O
coefficients	O
,	O
describing	O
accuracy	O
information	O
,	O
which	O
are	O
all	O
locally	O
evolved	O
in	O
time	O
from	O
conservation	B-Process
principles	I-Process
at	O
the	O
discrete	O
level	O
,	O
with	O
an	O
arbitrary	O
order	O
of	O
accuracy	O
.	O
A	O
DG-based	B-Task
shallow	I-Task
water	I-Task
model	I-Task
appeals	O
in	O
providing	O
higher	O
quality	O
solutions	O
on	O
very	B-Material
coarse	I-Material
meshes	I-Material
than	O
a	O
traditional	O
finite	O
volume	O
counterpart	O
,	O
but	O
is	O
comparatively	O
expensive	O
to	O
run	O
and	O
imposes	O
a	O
more	O
restrictive	O
stability	O
condition	O
for	O
the	O
CFL	B-Process
number	I-Process
[	O
28,29	O
]	O
.	O

FR	B-Process
schemes	I-Process
are	O
similar	O
to	O
nodal	B-Process
DG	I-Process
schemes	I-Process
,	O
which	O
are	O
arguably	O
the	O
most	O
popular	O
type	O
of	O
unstructured	B-Process
high-order	I-Process
method	I-Process
(	O
at	O
least	O
in	O
the	O
field	O
of	O
computational	B-Task
aerodynamics	I-Task
)	O
.	O
Like	O
nodal	B-Process
DG	I-Process
schemes	I-Process
,	O
FR	B-Process
schemes	I-Process
utilise	O
a	O
high-order	B-Process
(	O
nodal	B-Process
)	O
polynomial	O
basis	O
to	O
approximate	B-Process
the	I-Process
solution	I-Process
within	O
each	O
element	O
of	O
the	O
computational	B-Task
domain	I-Task
,	O
and	O
like	O
nodal	B-Process
DG	I-Process
schemes	I-Process
,	O
FR	B-Process
schemes	I-Process
do	O
not	O
explicitly	O
enforce	B-Process
inter-element	I-Process
solution	I-Process
continuity	I-Process
.	O
However	O
,	O
unlike	O
nodal	B-Process
DG	I-Process
schemes	I-Process
,	O
FR	B-Process
methods	I-Process
are	O
based	O
solely	O
on	O
the	O
governing	O
system	O
in	O
a	O
differential	O
form	O
.	O
A	O
description	O
of	O
the	O
FR	B-Process
approach	I-Process
in	O
1D	O
is	O
presented	O
below	O
.	O
For	O
further	O
information	O
see	O
the	O
original	O
paper	O
of	O
Huynh	O
[	O
2	O
]	O
.	O

The	O
immersed	B-Process
boundary	I-Process
method	I-Process
(	O
IBM	B-Process
)	O
,	O
proposed	O
by	O
Peskin	O
for	O
studying	B-Task
flow	I-Task
patterns	I-Task
around	I-Task
heart	I-Task
valves	I-Task
[	O
3	O
]	O
,	O
has	O
been	O
applied	O
to	O
a	O
wide	O
range	O
of	O
problems	O
including	O
arterial	B-Task
blood	I-Task
flow	I-Task
[	O
4	O
]	O
,	O
modelling	B-Task
of	I-Task
the	I-Task
cochlea	I-Task
[	O
5	O
]	O
,	O
modelling	B-Task
of	I-Task
red	I-Task
blood	I-Task
cells	I-Task
in	I-Task
Poiseuille	I-Task
flow	I-Task
[	O
6	O
]	O
and	O
flows	B-Task
involving	I-Task
suspended	I-Task
particles	I-Task
[	O
7	O
]	O
.	O
A	O
comprehensive	O
list	O
of	O
applications	O
can	O
be	O
found	O
in	O
[	O
8	O
]	O
.	O
The	O
IBM	B-Process
is	O
both	O
a	O
mathematical	O
formulation	O
and	O
a	O
numerical	O
scheme	O
for	O
fluid	B-Task
–	I-Task
structure	I-Task
interaction	I-Task
problems	I-Task
.	O
As	O
mentioned	O
above	O
,	O
in	O
a	O
classical	O
fluid	B-Task
–	I-Task
structure	I-Task
interaction	I-Task
problem	I-Task
,	O
the	O
fluid	B-Material
and	O
the	O
structure	B-Material
are	O
considered	O
separately	O
and	O
then	O
coupled	B-Process
together	I-Process
via	O
some	O
suitable	O
jump	O
conditions	O
.	O
In	O
the	O
IBM	B-Process
however	O
,	O
the	O
structure	O
–	O
which	O
is	O
usually	O
immersed	O
in	O
a	O
Newtonian	B-Material
fluid	I-Material
–	O
is	O
viewed	O
as	O
being	O
part	O
of	O
the	O
surrounding	B-Material
fluid	I-Material
.	O
This	O
means	O
that	O
only	O
a	O
single	O
equation	O
of	O
motion	B-Process
needs	O
to	O
be	O
solved	O
(	O
i.e.	O
a	O
one-phase	O
formulation	O
)	O
.	O
Additionally	O
,	O
the	O
IBM	B-Process
allows	O
the	O
immersed	O
structure	O
to	O
move	O
freely	O
over	O
the	O
underlying	B-Material
fluid	I-Material
mesh	I-Material
,	O
alleviating	O
the	O
need	O
for	O
the	O
remeshing	B-Process
required	O
in	O
a	O
classical	O
formulation	O
.	O

We	O
consider	O
the	O
shape	B-Task
optimisation	I-Task
of	O
two	B-Material
-	I-Material
and	I-Material
three-dimensional	I-Material
solids	I-Material
by	O
combining	B-Process
multiresolution	I-Process
subdivision	I-Process
surfaces	I-Process
with	O
immersed	B-Material
finite	I-Material
elements	I-Material
.	O
As	O
widely	O
discussed	O
in	O
isogeometric	B-Material
analysis	I-Material
literature	I-Material
,	O
the	O
geometry	B-Process
representations	I-Process
used	O
in	O
today	O
’s	O
computer	B-Process
aided	I-Process
design	I-Process
(	O
CAD	B-Process
)	O
and	O
finite	B-Process
element	I-Process
analysis	I-Process
(	O
FEA	B-Process
)	O
software	O
are	O
inherently	O
incompatible	O
[	O
1	O
]	O
.	O
This	O
is	O
particularly	O
limiting	O
in	O
shape	B-Task
optimisation	I-Task
during	O
which	O
a	O
given	O
CAD	B-Process
geometry	I-Process
model	I-Process
is	O
to	O
be	O
iteratively	O
updated	O
based	O
on	O
the	O
results	O
of	O
a	O
finite	B-Process
element	I-Process
computation	I-Process
.	O
The	O
inherent	O
shortcomings	O
of	O
present	O
geometry	B-Task
and	I-Task
analysis	I-Task
representations	I-Task
have	O
motivated	O
the	O
proliferation	O
of	O
various	O
shape	B-Process
optimisation	I-Process
techniques	I-Process
.	O
In	O
the	O
most	O
prevalent	O
approaches	O
a	O
surrogate	B-Process
geometry	I-Process
model	I-Process
[	O
2	O
–	O
8	O
]	O
or	O
the	O
analysis	B-Process
mesh	I-Process
[	O
9,10	O
]	O
instead	O
of	O
the	O
true	O
CAD	B-Process
model	I-Process
is	O
optimised	O
,	O
see	O
also	O
[	O
11	O
]	O
and	O
references	O
therein	O
.	O
Generally	O
,	O
it	O
is	O
tedious	O
or	O
impossible	O
to	O
map	O
the	O
optimised	B-Process
surrogate	I-Process
geometry	I-Process
model	I-Process
or	O
analysis	B-Process
mesh	I-Process
back	O
to	O
the	O
original	O
CAD	B-Process
model	I-Process
,	O
which	O
is	O
essential	O
for	O
continuing	O
with	O
the	O
design	O
process	O
and	O
later	O
for	O
manufacturing	O
purposes	O
.	O
Moreover	O
,	O
geometric	O
design	O
features	O
are	O
usually	O
defined	O
with	O
respect	O
to	O
the	O
CAD	O
model	O
and	O
cannot	O
be	O
easily	O
enforced	O
on	O
the	O
surrogate	B-Process
model	I-Process
.	O
Recently	O
,	O
the	O
shape	B-Task
optimisation	I-Task
of	O
shells	B-Material
,	O
solids	B-Material
and	O
other	O
applications	O
using	O
isogeometric	B-Process
analysis	I-Process
has	O
been	O
explored	O
;	O
that	O
is	O
,	O
through	O
directly	B-Process
optimising	I-Process
the	O
CAD	B-Process
geometry	I-Process
model	I-Process
[	O
12	O
–	O
15	O
]	O
.	O

The	O
exquisite	O
manipulation	O
and	O
exact	B-Task
measurement	I-Task
of	I-Task
properties	I-Task
of	O
individual	O
nanomaterials	B-Material
,	O
compared	O
with	O
notable	O
progress	O
in	O
their	O
preparation	O
,	O
have	O
not	O
been	O
thoroughly	O
addressed	O
albeit	O
being	O
of	O
prime	O
importance	O
for	O
the	O
sustained	B-Task
development	I-Task
of	I-Task
new	I-Task
devices	I-Task
[	O
58	O
–	O
61	O
]	O
.	O
To	O
date	O
,	O
several	O
instruments	B-Material
have	O
been	O
designed	O
for	O
such	O
goals	O
,	O
namely	O
,	O
scanning	B-Material
electron	I-Material
microscopes	I-Material
(	O
SEM	B-Material
)	O
,	O
atomic	B-Material
force	I-Material
microscopes	I-Material
(	O
AFM	B-Material
)	O
and	O
transmission	B-Material
electron	I-Material
microscopes	I-Material
(	O
TEM	B-Material
)	O
[	O
62,63	O
]	O
.	O
Compared	O
with	O
the	O
first	O
two	O
setups	O
,	O
which	O
have	O
no	O
direct	O
access	O
to	O
the	O
material	B-Task
internal	I-Task
structure	I-Task
and	O
atomic	B-Task
bonding	I-Task
information	I-Task
[	O
64	O
–	O
67	O
]	O
,	O
the	O
state-of-the-art	O
in	O
situ	O
high-resolution	O
TEM	B-Process
technique	O
allows	O
one	O
to	O
not	O
only	O
manipulate	B-Process
with	I-Process
an	I-Process
individual	I-Process
object	I-Process
at	O
the	O
nano-scale	O
precision	O
but	O
to	O
also	O
get	O
deep	O
insights	O
into	O
its	O
physical	O
,	O
chemical	O
,	O
and	O
microstructural	O
statuses	O
[	O
68	O
–	O
71	O
]	O
.	O
Combining	O
the	O
capabilities	O
of	O
a	O
conventional	O
high-resolution	O
TEM	B-Process
and	O
AFM	B-Process
or	O
STM	B-Process
probes	I-Process
produces	O
advanced	O
and	O
dedicated	O
TEM	B-Process
holders	I-Process
,	O
which	O
are	O
becoming	O
the	O
powerful	O
tools	O
in	O
nanomaterials	B-Task
manipulation	I-Task
and	O
properties	B-Task
analysis	I-Task
.	O
Such	O
holders	B-Process
have	O
been	O
commercialized	O
,	O
for	O
instance	O
,	O
by	O
“	O
Nanofactory	O
Instruments	O
AB	O
’	O
’	O
,	O
Goteborg	O
,	O
Sweden	O
[	O
72	O
]	O
.	O
The	O
full	O
usefulness	O
of	O
these	O
advanced	O
in-situ	O
TEM	B-Process
techniques	I-Process
is	O
apparent	O
with	O
respect	O
to	O
mechanical	B-Task
and	I-Task
thermal	I-Task
property	I-Task
analysis	I-Task
of	O
individual	O
nanostructures	B-Material
,	O
e.g.	O
,	O
elasticity	O
,	O
plasticity	O
and	O
strength	O
data	O
while	O
employing	O
direct	B-Process
bent	I-Process
or	I-Process
tensile	I-Process
tests	I-Process
[	O
73	O
–	O
75	O
]	O
,	O
probing	B-Process
electrical	I-Process
characteristics	I-Process
,	O
e.g.	O
,	O
field	B-Task
emission	I-Task
[	O
27,76,77	O
]	O
,	O
electrical	B-Task
transport	I-Task
tracing	I-Task
[	O
78	O
–	O
80	O
]	O
,	O
soldering	B-Task
[	O
81,82	O
]	O
,	O
and	O
doping	B-Task
[	O
83	O
]	O
,	O
etc	O
.	O

MINERAL	B-Task
(	I-Task
MINeral	I-Task
ERror	I-Task
AnaLysis	I-Task
)	I-Task
is	O
a	B-Task
new	I-Task
MATLAB	I-Task
®	I-Task
based	I-Task
program	I-Task
that	O
provides	B-Process
mineral	I-Process
formula	I-Process
recalculations	I-Process
combined	I-Process
with	I-Process
the	I-Process
associated	I-Process
propagation	I-Process
of	I-Process
the	I-Process
analytical	I-Process
uncertainties	I-Process
.	O
Methods	B-Task
are	O
based	O
on	O
the	O
work	O
of	O
Giamarita	O
and	O
Day	O
(	O
1990	O
)	O
.	O
However	O
,	O
additional	O
features	O
have	O
been	O
added	O
to	B-Task
provide	I-Task
users	I-Task
with	I-Task
greater	I-Task
flexibility	I-Task
in	I-Task
data	I-Task
reporting	I-Task
.	O
Many	O
programs	O
exist	O
to	O
recalculate	O
wt	O
%	O
data	O
into	O
formula	O
unit	O
cations	O
.	O
Some	B-Task
generalized	I-Task
programs	I-Task
can	O
be	O
used	O
to	O
recalculate	B-Process
the	I-Process
formula	I-Process
of	I-Process
multiple	I-Process
minerals	I-Process
e.g.	O
CALCMIN	B-Material
(	O
Brandelik	O
,	O
2009	O
)	O
and	O
HYPER-FORM	B-Material
(	O
De	O
Bjerg	O
et	O
al.	O
,	O
1992	O
)	O
.	O
Other	O
programs	O
are	O
mineral	O
specific	O
e.g.	O
AMPH	B-Material
CLASS	I-Material
(	O
Esawi	O
,	O
2004	O
)	O
and	O
PROBE	B-Material
AMPH	I-Material
(	O
Tindle	O
and	O
Webb	O
,	O
1994	O
)	O
for	O
the	O
recalculation	B-Task
of	I-Task
amphibole	I-Task
analyses	I-Task
;	O
ILMAT	B-Material
(	O
Lepage	O
,	O
2003	O
)	O
for	O
the	O
recalculation	B-Task
of	I-Task
magnetite	I-Task
and	I-Task
ilmenite	I-Task
;	O
and	O
PX-NOM	B-Material
(	O
Sturm	O
,	O
2002	O
)	O
for	O
the	O
recalculation	B-Task
of	I-Task
pyroxene	I-Task
analyses	I-Task
.	O
MINERAL	B-Material
provides	O
a	O
rapid	O
method	O
for	O
the	O
recalculation	B-Task
of	I-Task
multiple	I-Task
common	I-Task
minerals	I-Task
.	O
However	O
,	O
its	O
strength	O
lies	O
in	O
the	O
fact	O
that	O
is	O
the	O
first	O
tool	O
to	O
incorporate	B-Process
the	I-Process
associated	I-Process
uncertainty	I-Process
propagation	I-Process
calculations	I-Process
.	I-Process
As	O
these	O
are	O
performed	O
concurrently	O
with	O
the	O
standard	O
recalculations	O
,	O
no	O
additional	O
time	O
is	O
needed	O
to	O
perform	O
uncertainty	O
propagation	O
.	O
While	O
an	O
understanding	O
of	O
the	O
underlying	O
calculations	O
is	O
strongly	O
recommended	O
,	O
MINERAL	B-Material
is	O
designed	O
to	O
allow	B-Task
users	I-Task
with	I-Task
little	I-Task
or	I-Task
no	I-Task
experience	I-Task
operating	I-Task
MATLAB	I-Task
®	I-Task
and	I-Task
/	I-Task
or	I-Task
performing	I-Task
mineral	I-Task
formula	I-Task
recalculations	I-Task
and	I-Task
uncertainty	I-Task
propagation	I-Task
to	I-Task
undertake	I-Task
both	I-Task
with	I-Task
ease	I-Task
.	O

A	O
number	O
of	O
model	O
parameters	O
can	O
change	O
regionally	O
or	O
seasonally	O
,	O
in	O
particular	O
the	O
inherent	O
optical	O
properties	O
of	O
water	B-Material
constituents	I-Material
[	O
ai	B-Material
⁎(	I-Material
λ	I-Material
)	O
,	O
aY	B-Material
⁎(	I-Material
λ	I-Material
)	I-Material
,	O
aD	B-Material
⁎(	I-Material
λ	I-Material
)	I-Material
,	O
bX	B-Material
(	I-Material
λ	I-Material
)	I-Material
,	O
bb,X	B-Material
⁎	I-Material
,	O
bb,Mie	O
⁎]	O
and	O
the	O
apparent	O
optical	O
properties	O
of	O
the	O
bottom	O
[	O
Rib	O
(	O
λ	O
)	O
,	O
Bi	O
]	O
and	O
the	O
atmosphere	O
.	O
The	O
database	O
provided	O
with	O
WASI	O
has	O
been	O
derived	O
from	O
in-situ	O
measurements	O
from	O
lakes	O
in	O
Southern	O
Germany	O
(	O
Gege	O
,	O
1998	O
;	O
Heege	O
,	O
2000	O
;	O
Pinnel	O
,	O
2007	O
)	O
.	O
If	O
no	O
site-specific	O
information	O
is	O
available	O
,	O
it	O
can	O
be	O
used	O
as	O
a	O
first	O
approximation	O
for	O
other	O
ecosystems	O
as	O
well	O
.	O
The	O
variability	O
within	O
an	O
ecosystem	O
can	O
be	O
as	O
large	O
as	O
between	O
different	O
ecosystem	O
,	O
i.e.	O
ecosystem-specific	O
sets	O
of	O
optical	O
properties	O
do	O
not	O
exist	O
.	O
However	O
,	O
region	O
or	O
season	O
specific	O
information	O
should	O
be	O
used	O
whenever	O
available	O
.	O
Ideally	O
,	O
the	O
optical	O
properties	O
should	O
be	O
measured	O
at	O
the	O
test	O
site	O
close	O
to	O
the	O
airplane	O
or	O
satellite	O
overpass	O
.	O
This	O
is	O
however	O
not	O
always	O
possible	O
.	O
A	O
valuable	O
source	O
of	O
information	O
is	O
the	O
IOCCG	O
webpage	O
(	O
IOCCG	O
,	O
2013b	O
)	O
.	O
It	O
maintains	O
a	O
list	O
of	O
links	O
to	O
publicly	O
available	O
data	O
sets	O
,	O
for	O
example	O
the	O
IOCCG	O
(	O
2006	O
)	O
data	O
bank	O
,	O
the	O
NASA	O
bio-Optical	O
Marine	O
Algorithm	O
Data	O
set	O
(	O
NOMAD	O
)	O
and	O
the	O
SeaWiFS	O
Bio-Optical	O
Archive	O
and	O
Storage	O
System	O
(	O
SeaBASS	O
)	O
.	O

Artificial	B-Task
Neural	I-Task
Networks	I-Task
(	O
ANN	B-Task
)	O
have	O
been	O
widely	O
used	O
in	O
science	B-Task
and	I-Task
engineering	I-Task
problems	I-Task
.	O
They	O
attempt	O
to	O
model	B-Process
the	I-Process
ability	I-Process
of	I-Process
biological	I-Process
nervous	I-Process
systems	I-Process
to	I-Process
recognize	I-Process
patterns	I-Process
and	I-Process
objects	I-Process
.	O
ANN	B-Task
basic	I-Task
architecture	I-Task
consists	O
of	O
networks	B-Material
of	I-Material
primitive	I-Material
functions	I-Material
capable	O
of	O
receiving	B-Process
multiple	I-Process
weighted	I-Process
inputs	I-Process
that	O
are	O
evaluated	O
in	O
terms	O
of	O
their	O
success	O
at	O
discriminating	O
the	O
classes	O
in	O
Τa	O
.	O
Different	B-Task
types	I-Task
of	I-Task
primitive	I-Task
functions	I-Task
and	I-Task
network	I-Task
configurations	I-Task
result	O
in	O
varying	O
models	O
(	O
Hastie	O
et	O
al.	O
,	O
2009	O
;	O
Rojas	O
,	O
1996	O
)	O
.	O
During	O
training	B-Task
network	I-Task
connection	I-Task
weights	B-Material
are	O
adjusted	B-Process
if	I-Process
the	I-Process
separation	I-Process
of	I-Process
inputs	I-Process
and	I-Process
predefined	I-Process
classes	I-Process
incurs	I-Process
an	I-Process
error	I-Process
.	O
Convergence	B-Process
proceeds	O
until	O
the	B-Task
reduction	I-Task
in	I-Task
error	I-Task
between	I-Task
iterations	I-Task
reaches	I-Task
a	I-Task
decay	I-Task
threshold	I-Task
(	O
Kotsiantis	O
,	O
2007	O
;	O
Rojas	O
,	O
1996	O
)	O
.	O
We	O
use	O
feed-forward	B-Process
networks	I-Process
with	I-Process
a	I-Process
single	I-Process
hidden	I-Process
layer	I-Process
of	I-Process
nodes	I-Process
,	O
a	O
so	O
called	O
Multi-Layer	B-Process
Perceptron	I-Process
(	O
MLP	B-Process
)	O
(	O
Venables	O
and	O
Ripley	O
,	O
2002	O
)	O
,	O
and	O
select	B-Process
one	I-Process
of	I-Process
two	I-Process
possible	I-Process
parameters	I-Process
:	O
size	O
,	O
the	O
number	O
nodes	O
in	O
the	O
hidden	O
layer	O
.	O

Hitherto	O
,	O
the	O
investigation	B-Task
of	I-Task
fossil-orientation	I-Task
was	O
only	O
used	O
for	O
the	O
topmost	O
surface	O
of	O
fossil	O
mass	O
occurrences	O
,	O
deposited	O
directly	O
on	O
the	O
sea	O
floor	O
.	O
Due	O
to	O
the	O
fast	O
development	O
of	O
virtual	O
methods	O
(	O
e.g.	O
,	O
macro-CT	O
,	O
µ-CT	O
,	O
nano-CT	O
,	O
etc.	O
)	O
it	O
became	O
possible	O
,	O
to	O
investigate	O
the	O
interior	O
orientation	O
of	O
such	O
fossil	O
mass	O
occurrences	O
in	O
three-dimensional	O
detail	O
.	O
Although	O
,	O
a	O
series	O
of	O
paleontological	O
studies	O
deal	O
with	O
3D-visualization	O
of	O
fossil-elements	O
,	O
no	O
mass	O
occurrence	O
has	O
previously	O
been	O
reconstructed	O
three	O
dimensionally	O
for	O
investigating	O
their	O
interior	O
orientation	O
.	O
This	O
study	O
illustrates	O
an	O
interdisciplinary	O
approach	O
of	O
virtual	O
reconstruction	O
,	O
analyses	O
and	O
interpretation	O
of	O
the	O
interior	O
orientation	O
of	O
an	O
ammonoid	O
mass	O
occurrence	O
.	O
The	O
method	O
established	O
herein	O
produces	O
clear	O
and	O
consistent	O
results	O
using	O
planispirally	O
coiled	O
ammonoid	O
shells	O
–	O
fossils	O
,	O
that	O
so	O
far	O
would	O
have	O
been	O
used	O
only	O
with	O
caution	O
for	O
depositional	O
interpretations	O
.	O
This	O
method	O
can	O
be	O
applied	O
to	O
any	O
kind	O
of	O
fossil	O
mass	O
occurrence	O
,	O
or	O
even	O
other	O
abundant	O
organic	O
elements	O
and	O
particles	O
,	O
to	O
examine	O
their	O
orientation	O
and	O
depositional	O
conditions	O
to	O
conclude	O
on	O
their	O
paleoenvironment	O
,	O
particularly	O
on	O
paleocurrents	O
.	O

The	O
above	O
discussion	O
summarizes	O
the	O
state	O
of	O
the	O
art	O
related	O
to	O
impacts	O
and	O
interpretations	O
of	O
communication	B-Material
latency	I-Material
between	O
RT	B-Process
simulators	I-Process
.	O
However	O
,	O
research	O
is	O
focused	O
primarily	O
on	O
the	O
effect	O
of	O
the	O
data	O
loss	O
during	O
the	O
communication	O
and	O
how	O
to	O
mitigate	O
it	O
[	O
34	O
]	O
.	O
In	O
the	O
thermo-electric	B-Process
co-simulation	I-Process
example	O
in	O
[	O
35	O
]	O
,	O
the	O
time	O
constant	O
is	O
larger	O
in	O
the	O
thermal	O
simulation	O
than	O
that	O
of	O
power	O
system	O
simulation	O
.	O
Thus	O
the	O
communication	B-Material
latency	I-Material
will	O
not	O
significantly	O
affect	O
the	O
accuracy	O
of	O
co-simulation	B-Process
.	O
In	O
[	O
36	O
]	O
,	O
the	O
co-simulation	O
is	O
performed	O
using	O
resources	O
at	O
the	O
same	O
location	O
without	O
synthetically	O
introduced	O
delays	O
,	O
which	O
means	O
the	O
communication	O
latency	O
between	O
RT	O
simulators	O
is	O
ignored	O
.	O
In	O
[	O
37	O
]	O
,	O
the	O
authors	O
have	O
mentioned	O
the	O
communication	O
latency	O
as	O
an	O
important	O
factor	O
in	O
the	O
distributed	B-Process
simulation	I-Process
and	O
that	O
its	O
effect	O
on	O
simulation	O
stability	O
will	O
be	O
studied	O
as	O
future	O
work	O
.	O
An	O
in-depth	O
research	O
about	O
the	O
role	B-Material
of	I-Material
communication	I-Material
latency	I-Material
and	O
mitigation	O
measure	O
for	O
geographically	O
distributed	O
RT	O
simulations	O
is	O
identified	O
as	O
a	O
technical	O
gap	O
and	O
addressed	O
in	O
this	O
paper	O
.	O

Despite	O
the	O
fact	O
that	O
SRC-HE	B-Process
reduces	O
the	O
number	O
of	O
FEs	O
,	O
audio	O
measurements	O
extraction	O
based	O
on	O
SRC	O
would	O
still	O
be	O
not	O
suitable	O
for	O
real-time	O
applications	O
[	O
39	O
]	O
.	O
The	O
previous	O
SRC-HE	O
module	O
is	O
then	O
replaced	O
by	O
the	O
generalised	B-Process
cross	I-Process
correlation	I-Process
phase	I-Process
transform	I-Process
(	O
GCC-PHAT	B-Process
)	O
introduced	O
in	O
Section	O
2.1	O
,	O
as	O
this	O
does	O
not	O
involve	O
cumbersome	O
point	O
function	O
estimations	O
.	O
The	O
drawback	O
is	O
that	O
the	O
basic	O
GCC	B-Process
algorithm	I-Process
can	O
only	O
detect	O
one	O
source	O
at	O
a	O
time	O
and	O
it	O
is	O
known	O
to	O
be	O
sensitive	O
to	O
room	O
reverberations	O
[	O
5	O
]	O
,	O
however	O
it	O
is	O
still	O
effective	O
under	O
moderate	O
reverberant	O
environments	O
(	O
T60	O
≈	O
0.3s	O
)	O
[	O
40	O
]	O
.	O
For	O
these	O
reasons	O
,	O
at	O
first	O
experiments	O
where	O
only	O
a	O
speaker	B-Process
is	O
active	O
at	O
any	O
given	O
time	O
are	O
carried	O
out	O
,	O
as	O
it	O
often	O
happens	O
in	O
a	O
polite	O
conversation	O
between	O
two	O
or	O
more	O
people	O
.	O
Speech	B-Material
segments	I-Material
using	O
a	O
voice	B-Process
activity	I-Process
detector	I-Process
(	O
VAD	B-Process
)	O
[	O
41	O
]	O
are	O
further	O
extracted	O
and	O
processed	O
using	O
a	O
GCC-PHAT	B-Process
step	I-Process
,	O
for	O
the	O
signal	O
to	O
be	O
more	O
robust	O
to	O
reverberations	O
.	O
Thus	O
,	O
the	O
measure	O
vector	O
obtained	O
za	O
(	O
see	O
Section	O
2.1	O
)	O
can	O
now	O
be	O
rewritten	O
as	O
za	O
={	O
τm	O
(	O
t	O
)}	O
,	O
where	O
each	O
component	O
τm	O
is	O
the	O
TDOA	O
collected	O
at	O
the	O
m-th	B-Process
microphone	I-Process
pair	I-Process
at	O
each	O
time	O
step	O
t	O
.	O
Since	O
TDOAs	O
are	O
not	O
linear	O
in	O
the	O
speaker	O
position	O
,	O
they	O
must	O
be	O
input	O
into	O
an	O
extended	O
Kalman	B-Process
filter	I-Process
(	O
EKF	B-Process
)	O
,	O
as	O
in	O
[	O
10	O
]	O
to	O
get	O
an	O
audio	B-Task
position	I-Task
estimation	I-Task
.	O

In	O
this	O
paper	O
we	O
construct	O
such	O
a	O
physical	B-Process
model	I-Process
with	O
a	O
continuous	B-Task
distribution	I-Task
of	I-Task
relaxations	I-Task
.	O
It	O
is	O
based	O
on	O
the	O
phenomenological	B-Task
theory	I-Task
of	I-Task
relaxation	I-Task
processes	I-Task
which	O
have	O
a	O
long	O
history	O
in	O
physics	O
literature	O
and	O
was	O
recently	O
summarized	O
in	O
a	O
monograph	O
in	O
which	O
references	O
to	O
other	O
relevant	O
publications	O
can	O
be	O
found	O
,	O
[	O
24	O
]	O
;	O
also	O
see	O
[	O
25	O
]	O
.	O
The	O
present	O
work	O
is	O
confined	O
to	O
relaxation	B-Process
mechanisms	I-Process
which	O
result	O
from	O
changes	O
in	O
normal	O
stresses	O
.	O
More	O
specifically	O
,	O
we	O
are	O
interested	O
in	O
the	O
local	B-Task
mechanisms	I-Task
of	I-Task
irreversible	I-Task
energy	I-Task
loss	I-Task
caused	O
by	O
uniform	O
compression	B-Process
or	I-Process
expansion	I-Process
of	I-Process
a	I-Process
medium	I-Process
for	O
which	O
all	O
components	O
remain	O
unchanged	O
,	O
rather	O
than	O
the	O
losses	O
caused	O
by	O
friction	B-Process
between	I-Process
different	I-Process
layers	I-Process
of	I-Process
a	I-Process
medium	I-Process
which	O
move	O
with	O
different	O
velocities	O
(	O
for	O
a	O
more	O
detailed	O
discussion	O
of	O
this	O
issue	O
see	O
[	O
26	O
])	O
.	O
No	O
attempt	O
is	O
made	O
to	O
model	O
effects	O
of	O
shear	B-Process
viscosity	I-Process
and	O
heat	B-Process
conduction	I-Process
beyond	O
the	O
conventional	O
Navier	B-Process
–	I-Process
Stokes	I-Process
approach	I-Process
,	O
since	O
this	O
topic	O
goes	O
far	O
beyond	O
the	O
scope	O
of	O
this	O
paper	O
.	O

The	O
propagation	B-Task
of	I-Task
unsteady	I-Task
disturbances	I-Task
in	O
ducts	B-Material
of	O
slowly-varying	O
geometry	O
,	O
such	O
as	O
those	O
typical	O
of	O
an	O
aeroengine	B-Material
,	O
can	O
be	O
successfully	O
modelled	O
using	O
a	O
multiple	B-Process
scales	I-Process
approach	I-Process
.	O
From	O
the	O
first	O
application	O
[	O
1	O
]	O
of	O
multiple-scales	B-Process
analysis	I-Process
to	O
sound	B-Process
propagation	I-Process
in	O
ducts	B-Material
of	O
rectangular	O
and	O
circular	O
cross	O
section	O
without	O
mean	B-Process
flow	I-Process
,	O
more	O
recent	O
developments	O
have	O
extended	O
the	O
method	O
to	O
cases	O
with	O
uniform	B-Process
mean	I-Process
flow	I-Process
[	O
2	O
]	O
,	O
mean	B-Process
swirling	I-Process
flow	I-Process
[	O
3	O
]	O
,	O
ducts	B-Material
of	O
arbitrary	O
cross	O
section	O
[	O
4	O
]	O
(	O
with	O
uniform	B-Process
mean	I-Process
flow	I-Process
)	O
and	O
strongly	B-Material
curved	I-Material
ducts	I-Material
[	O
5	O
]	O
.	O
The	O
multiple-scales	B-Process
approach	I-Process
has	O
a	O
number	O
of	O
distinct	O
advantages	O
over	O
full	O
numerical	B-Process
methods	I-Process
as	O
it	O
is	O
ideally	O
suited	O
to	O
handle	O
higher	B-Process
frequencies	I-Process
and	O
the	O
computational	O
complexity	O
is	O
only	O
marginally	O
more	O
than	O
calculating	B-Process
the	I-Process
eigenmodes	I-Process
inside	O
a	O
straight	B-Material
parallel	I-Material
duct	I-Material
.	O
The	O
accuracy	O
and	O
usefulness	O
of	O
the	O
multiple	B-Process
scales	I-Process
approach	I-Process
has	O
been	O
validated	O
against	O
finite-element	B-Process
methods	I-Process
[	O
6	O
]	O
for	O
realistic	O
aeroengine	B-Material
configurations	O
and	O
acoustic	B-Task
frequencies	I-Task
[	O
7,8	O
]	O
.	O

We	O
describe	O
three	O
ways	O
to	O
solve	O
the	O
reflection	B-Task
problem	I-Task
.	O
The	O
first	O
way	O
is	O
very	O
simple	O
(	O
Section	O
4	O
)	O
.	O
We	O
exploit	O
the	O
consequences	O
of	O
shifting	B-Process
the	I-Process
semi-infinite	I-Process
row	I-Process
by	O
one	O
period	O
(	O
to	O
the	O
right	O
or	O
left	O
)	O
.	O
In	O
effect	O
,	O
we	O
regard	O
the	O
semi-infinite	O
row	O
as	O
two	B-Process
scatterers	I-Process
,	O
one	O
of	O
which	O
is	O
another	O
semi-infinite	B-Process
row	I-Process
.	O
This	O
idea	O
goes	O
back	O
to	O
a	O
series	O
of	O
papers	O
by	O
Millar	O
in	O
the	O
1960s	O
,	O
starting	O
with	O
[	O
2	O
]	O
.	O
He	O
used	O
it	O
for	O
several	O
two-dimensional	B-Task
grating	I-Task
problems	I-Task
.	O
A	O
similar	O
approach	O
was	O
used	O
for	O
layered	B-Material
media	I-Material
by	O
Shenderov	O
[	O
3	O
]	O
.	O
In	O
our	O
one-dimensional	O
context	O
,	O
we	O
obtain	B-Process
a	I-Process
quadratic	I-Process
equation	I-Process
for	O
R	O
;	O
we	O
show	O
how	O
to	O
select	O
the	O
correct	O
solution	O
.	O
We	O
remark	O
that	O
there	O
has	O
been	O
much	O
recent	O
interest	O
in	O
related	O
two-dimensional	B-Task
waveguide	I-Task
problems	I-Task
;	O
see	O
,	O
for	O
example	O
,	O
[	O
4	O
–	O
6	O
]	O
,	O
where	O
the	O
shifting-by-one-period	B-Process
idea	I-Process
is	O
again	O
employed	O
,	O
leading	O
to	O
a	O
quadratic	B-Process
equation	I-Process
for	O
a	O
certain	O
operator	O
.	O

Max-linear	B-Process
programs	I-Process
have	O
been	O
used	O
to	O
describe	O
optimisation	B-Process
problems	I-Process
for	O
multiprocessor	B-Material
interactive	I-Material
systems	I-Material
.	O
In	O
some	O
instances	O
the	O
variables	B-Material
used	O
in	O
this	O
model	O
are	O
required	O
to	O
be	O
integer	O
;	O
however	O
,	O
no	O
method	O
seems	O
to	O
exist	O
for	O
finding	O
integer	B-Process
solutions	I-Process
to	O
max-linear	O
programs.For	O
a	O
generic	O
class	O
of	O
matrices	O
,	O
we	O
show	O
that	O
integer	O
solutions	O
to	O
two-sided	O
max-linear	O
systems	O
and	O
programs	O
can	O
be	O
found	O
in	O
polynomial	O
time	O
.	O
For	O
general	O
matrices	O
,	O
we	O
adapt	O
the	O
existing	O
methods	O
for	O
finding	O
real	O
solutions	O
to	O
obtain	O
algorithms	O
for	O
finding	O
integer	O
solutions	O
.	O

We	O
study	O
sequences	B-Task
of	I-Task
optimal	I-Task
walks	I-Task
of	I-Task
a	I-Task
growing	I-Task
length	I-Task
in	I-Task
weighted	I-Task
digraphs	I-Task
,	O
or	O
equivalently	O
,	O
sequences	B-Task
of	I-Task
entries	I-Task
of	I-Task
max-algebraic	I-Task
matrix	I-Task
powers	I-Task
with	I-Task
growing	I-Task
exponents	I-Task
.	O
It	O
is	O
known	O
that	O
these	O
sequences	O
are	O
eventually	O
periodic	O
when	O
the	O
digraphs	B-Process
are	I-Process
strongly	I-Process
connected	I-Process
.	O
The	O
transient	O
of	O
such	O
periodicity	O
depends	O
,	O
in	O
general	O
,	O
both	O
on	O
the	O
size	O
of	O
digraph	B-Material
and	O
on	O
the	O
magnitude	O
of	O
the	O
weights	O
.	O
In	O
this	O
paper	O
,	O
we	O
show	O
that	O
some	O
bounds	O
on	O
the	O
indices	O
of	O
periodicity	O
of	O
(	B-Material
unweighted	I-Material
)	I-Material
digraphs	I-Material
,	O
such	O
as	O
the	O
bounds	O
of	O
Wielandt	O
,	O
Dulmage	O
–	O
Mendelsohn	O
,	O
Schwarz	O
,	O
Kim	O
and	O
Gregory	O
–	O
Kirkland	O
–	O
Pullman	O
,	O
apply	O
to	O
the	O
weights	O
of	O
optimal	O
walks	O
when	O
one	O
of	O
their	O
ends	O
is	O
a	O
critical	O
node	O
.	O

Owing	O
to	O
widespread	O
availability	O
,	O
the	O
most	O
extensively	O
adopted	O
tomography	B-Process
technique	I-Process
utilizes	O
the	O
milling	O
power	O
of	O
a	O
focused	B-Material
ion	I-Material
beam	I-Material
(	O
FIB	B-Material
)	O
in	O
conjunction	O
with	O
the	O
imaging	O
capabilities	O
of	O
high	B-Process
resolution	I-Process
FE-SEM	I-Process
,	O
to	O
provide	O
a	O
sequence	O
of	O
2D	B-Material
images	I-Material
that	O
can	O
be	O
effectively	O
re-combined	O
in	O
3D	O
space	O
.	O
However	O
,	O
because	O
this	O
technique	O
is	O
destructive	O
,	O
studies	B-Task
of	I-Task
microstructural	I-Task
evolution	I-Task
are	O
influenced	O
by	O
inherent	O
sample	O
variability	O
.	O
Non-destructive	B-Process
X-ray	I-Process
nano-computed	I-Process
tomography	I-Process
(	O
CT	B-Process
)	O
[	O
9	O
–	O
11	O
]	O
provides	O
a	O
platform	O
for	O
exploring	B-Task
dynamic	I-Task
microstructural	I-Task
change	I-Task
in	O
the	O
absence	O
of	O
these	O
possible	O
complications	O
and	O
is	O
compatible	O
with	O
both	O
laboratory	B-Material
and	I-Material
synchrotron	I-Material
radiation	I-Material
.	O
The	O
authors	O
have	O
previously	O
demonstrated	O
a	O
technique	O
for	O
preparation	B-Task
of	I-Task
optimal	I-Task
sample	I-Task
geometries	I-Task
for	I-Task
X-ray	I-Task
nano-CT	I-Task
[	O
12	O
]	O
,	O
while	O
this	O
FIB	B-Process
sample	I-Process
preparation	I-Process
route	I-Process
will	O
involve	O
the	O
selective	O
removal	O
of	O
portions	O
of	O
the	O
fuel	B-Material
cell	I-Material
electrode	I-Material
microstructure	I-Material
(	O
and	O
therefore	O
may	O
be	O
destructive	O
to	O
the	O
working	B-Material
fuel	I-Material
cell	I-Material
)	O
,	O
the	O
non-destructive	B-Process
X-ray	I-Process
characterization	I-Process
technique	I-Process
allows	O
repeated	O
,	O
non-destructive	O
characterization	O
of	O
the	O
selected	B-Material
sample	I-Material
which	O
facilitates	O
the	O
study	O
of	O
microstructural	B-Process
evolution	I-Process
processes	I-Process
in	O
response	O
to	O
various	O
environmental	O
changes	O
.	O

In	O
conclusion	O
,	O
a	O
new	B-Task
approach	I-Task
to	I-Task
the	I-Task
“	I-Task
grind-free	I-Task
”	I-Task
nanoprecursor	I-Task
route	I-Task
to	I-Task
direct	I-Task
combinatorial	I-Task
solid	I-Task
state	I-Task
synthesis	I-Task
of	O
several	O
“	O
difficult	O
to	O
make	O
”	O
and	O
hitherto	O
unknown	O
phase-pure	B-Material
heterometallic	I-Material
Ruddlesden	I-Material
Popper	I-Material
type	I-Material
La4Ni3	I-Material
−	I-Material
xFexO10	I-Material
materials	I-Material
has	O
been	O
described	O
.	O
The	O
new	O
approach	O
used	O
a	O
high-throughput	B-Process
reactor	I-Process
and	O
robotic	B-Process
automation	I-Process
(	O
RAMSI	B-Process
)	O
to	O
rapidly	O
synthesise	B-Process
a	I-Process
range	I-Process
of	I-Process
nanoparticle	I-Process
co-precipitate	I-Process
precursors	I-Process
in	O
cloned	O
libraries	O
at	O
a	O
rate	B-Process
of	I-Process
7.5	I-Process
samples	I-Process
an	I-Process
hour	I-Process
.	O
Each	O
library	O
could	O
then	O
be	O
heat-treated	B-Process
at	O
a	O
different	O
temperature	O
and	O
an	O
initial	O
powder	B-Process
XRD	I-Process
screen	I-Process
was	O
used	O
to	O
locate	B-Process
and	I-Process
approximate	I-Process
phase	I-Process
boundary	I-Process
.	O
A	O
more	O
focussed	O
second	O
synthesis	B-Process
and	I-Process
XRD	I-Process
characterisation	I-Process
of	O
selected	O
larger	O
heat-treated	B-Material
powders	I-Material
was	O
then	O
performed	O
to	O
reconfirm	O
the	O
locations	O
of	O
the	O
phase	O
boundaries	O
with	O
the	O
highest	O
dopant	O
level	O
being	O
achieved	O
for	O
La4Ni2FeO10	B-Material
which	O
is	O
significantly	O
greater	O
Fe	B-Material
doping	O
than	O
has	O
been	O
achieved	O
by	O
anyone	O
previously	O
(	O
despite	O
several	O
notable	O
efforts	O
)	O
.	O
EXAFS	B-Material
data	I-Material
suggested	O
that	O
Fe3	B-Material
+	I-Material
was	O
located	O
onto	O
Ni	B-Material
sites	O
in	O
all	O
cases	O
and	O
did	O
not	O
exist	O
as	O
a	O
separate	O
iron	B-Material
oxide	I-Material
phase	O
.	O

At	O
200	O
–	O
300	O
°	O
C	O
:	O
nuclear	O
densities	O
are	O
localised	O
in	O
the	O
tetrahedral	O
volume	O
roughly	O
covering	O
the	O
8c	O
and	O
32f	O
positions	O
with	O
“	B-Material
bulges	I-Material
”	I-Material
of	O
nuclear	O
densities	O
pointing	O
toward	O
the	O
48i	O
position	O
,	O
while	O
at	O
400	O
and	O
500	O
°	O
C	O
continuous	O
nuclear	O
densities	O
forming	O
a	O
straight	O
line	O
along	O
the	O
<	O
100	O
>	O
direction	O
are	O
found	O
,	O
indicative	O
of	O
oxide-ion	B-Process
diffusion	I-Process
pathway	O
along	O
that	O
direction	O
.	O
In	O
the	O
literature	O
,	O
curved	O
pathways	O
along	O
the	O
<	O
100	O
>	O
direction	O
passing	O
through	O
the	O
48i	O
site	O
are	O
generally	O
observed	O
in	O
fluorite	O
materials	O
[	O
20	O
]	O
,	O
the	O
prevalence	O
of	O
curve	O
pathway	O
as	O
opposed	O
from	O
straight	O
pathway	O
is	O
explained	O
by	O
the	O
repulsion	O
between	O
cation	O
and	O
anions	O
,	O
the	O
curved	O
pathway	O
allowing	O
the	O
cation	O
–	O
anion	O
to	O
maintain	O
a	O
reasonable	O
distance	O
.	O
However	O
,	O
a	O
straight	O
pathway	O
is	O
observed	O
for	O
Y0.785Ta0.215O1.715	O
[	O
23	O
]	O
,	O
as	O
is	O
the	O
case	O
for	O
the	O
present	O
material	O
.	O
This	O
suggests	O
that	O
Ta	O
and	O
Re	O
cations	O
might	O
play	O
a	O
similar	O
role	O
in	O
these	O
systems	O
.	O

While	O
impedance	B-Process
spectroscopy	I-Process
is	O
a	O
quite	O
common	O
method	O
to	O
investigate	O
mixed	B-Material
conducting	I-Material
thin	I-Material
film	I-Material
electrodes	I-Material
,	O
[	O
6,10	O
–	O
12	O
]	O
oxygen	B-Process
tracer	I-Process
experiments	I-Process
are	O
often	O
performed	O
on	O
bulk	B-Material
samples	I-Material
[	O
13	O
–	O
16	O
]	O
.	O
Recently	O
,	O
several	O
IEDP	B-Process
measurements	I-Process
of	O
mixed	B-Material
conducting	I-Material
cathode	I-Material
materials	I-Material
were	O
published	O
with	O
the	O
oxide	B-Material
films	I-Material
being	O
deposited	O
on	O
insulating	B-Material
substrates	I-Material
[	O
17	O
–	O
19	O
]	O
.	O
However	O
,	O
to	O
the	O
best	O
of	O
the	O
authors'	O
knowledge	O
no	O
study	O
so	O
far	O
reported	O
experiments	O
with	O
both	O
techniques	O
being	O
applied	O
on	O
the	O
same	O
films	B-Material
at	O
the	O
same	O
temperature	O
.	O
This	O
contribution	O
reports	O
the	O
results	O
of	O
a	O
study	O
applying	B-Task
EIS	I-Task
and	I-Task
IEDP	I-Task
to	I-Task
one	I-Task
and	I-Task
the	I-Task
same	I-Task
La0.6Sr0.4CoO3	I-Task
−	I-Task
δ	I-Task
(	I-Task
LSC	I-Task
)	I-Task
thin	I-Task
film	I-Task
in	O
order	O
to	O
get	O
complementary	O
results	O
on	O
the	O
resistive	B-Task
contributions	I-Task
of	I-Task
the	I-Task
oxygen	I-Task
reduction	I-Task
kinetics	I-Task
on	O
such	O
films	B-Material
.	O
As	O
electrical	B-Process
measurements	I-Process
require	O
an	O
oxygen	B-Material
ion	I-Material
conductor	I-Material
,	O
yttria	B-Material
stabilized	I-Material
zirconia	I-Material
(	O
YSZ	B-Material
)	O
was	O
used	O
as	O
substrate	O
for	O
LSC	B-Material
films	I-Material
with	O
two	O
different	O
grain	B-Material
sizes	O
.	O
Quantitative	B-Task
material	I-Task
parameters	I-Task
are	O
deduced	O
from	O
both	O
types	O
of	O
experiments	O
and	O
comparison	B-Process
of	I-Process
the	I-Process
data	I-Process
allowed	O
testing	O
the	O
appropriateness	B-Task
of	I-Task
analysis	I-Task
models	I-Task
.	O

Thin	B-Material
MIEC	I-Material
layers	I-Material
of	O
GDC	B-Material
and	O
STFO	B-Material
on	O
single-crystalline	B-Material
YSZ	I-Material
substrates	I-Material
were	O
exposed	B-Process
to	I-Process
H2	I-Process
/	I-Process
H218O	I-Process
atmosphere	I-Process
for	O
thermally	O
and	O
electrochemically	O
driven	O
tracer	B-Task
exchange	I-Task
experiments	I-Task
.	O
Rectangular	B-Material
noble	I-Material
metal	I-Material
thin	I-Material
film	I-Material
current	I-Material
collectors	I-Material
were	O
deposited	B-Process
on	I-Process
top	I-Process
and	I-Process
beneath	I-Process
the	I-Process
MIEC	I-Process
layer	I-Process
and	O
used	O
for	O
polarization	B-Process
.	O
The	O
lateral	B-Process
distribution	I-Process
of	I-Process
the	I-Process
tracer	I-Process
revealed	O
several	O
interesting	O
features	O
:	O
(	O
i	O
)	O
In	O
case	O
of	O
thermal	B-Process
tracer	I-Process
exchange	I-Process
,	O
an	O
enhanced	O
tracer	B-Process
fraction	I-Process
is	O
found	O
on	O
top	O
of	O
the	O
metallic	B-Material
current	I-Material
collector	I-Material
due	O
to	O
its	O
ionically	O
blocking	O
nature	O
.	O
At	O
the	O
edges	O
of	O
the	O
current	O
collector	O
,	O
the	O
concentration	O
of	O
18O	B-Material
decreases	O
with	O
a	O
finite	O
step	O
width	O
that	O
is	O
correlated	O
with	O
in-plane	B-Process
diffusion	I-Process
of	I-Process
oxygen	I-Process
ions	I-Process
.	O
(	O
ii	O
)	O
Due	O
to	O
the	O
low	B-Process
electronic	I-Process
conductivity	I-Process
of	O
STFO	B-Material
and	O
GDC	B-Material
,	O
the	O
MIEC	O
area	O
that	O
is	O
influenced	O
by	O
an	O
applied	B-Process
bias	I-Process
is	O
restricted	O
to	O
a	O
region	O
close	O
to	O
the	O
current	B-Material
collector	I-Material
.	O
The	O
width	B-Task
of	I-Task
this	I-Task
active	I-Task
region	I-Task
depends	O
on	O
the	O
bias	O
.	O
It	O
amounts	O
to	O
only	O
10	O
–	O
15μm	O
for	O
STFO	B-Material
but	O
more	O
than	O
100μm	O
for	O
GDC	B-Material
at	O
a	O
cathodic	O
bias	O
of	O
−	O
500mV	O
.	O
(	O
iii	O
)	O
Not	O
only	O
enhanced	B-Process
tracer	I-Process
incorporation	I-Process
due	O
to	O
cathodic	O
bias	O
but	O
also	O
reduced	B-Process
incorporation	I-Process
due	O
to	O
anodic	O
bias	O
could	O
be	O
experimentally	O
resolved	O
in	O
the	O
active	O
region	O
.	O

Two	O
different	O
micro-contact	B-Material
set-ups	I-Material
were	O
used	O
in	O
the	O
experiments	O
.	O
The	O
asymmetrically	B-Material
heated	I-Material
measurement	I-Material
set-up	I-Material
(	O
Fig.	O
2a	O
)	O
allows	O
to	O
change	B-Process
the	I-Process
contacted	I-Process
electrode	I-Process
within	O
seconds	O
and	O
thereby	O
to	O
gain	B-Task
statistical	I-Task
information	I-Task
over	O
a	O
large	O
number	O
of	O
different	O
microelectrodes	B-Material
on	O
one	O
and	O
the	O
same	O
sample	O
in	O
a	O
relatively	O
short	O
time	O
.	O
It	O
also	O
enables	O
monitoring	B-Task
of	I-Task
optical	I-Task
changes	I-Task
during	O
the	O
measurement	O
in	O
real	O
time	O
.	O
However	O
,	O
the	O
asymmetrical	B-Process
heating	I-Process
from	O
the	O
bottom	O
side	O
and	O
local	B-Process
cooling	I-Process
(	O
e.g.	O
by	O
convection	B-Process
,	O
radiation	B-Process
,	O
and	O
the	O
contacting	B-Material
tip	I-Material
acting	O
as	O
a	O
heat	B-Material
sink	I-Material
)	O
is	O
known	O
to	O
cause	O
temperature	B-Process
gradients	I-Process
within	O
the	O
sample	O
[	O
11	O
]	O
.	O
Such	O
temperature	O
gradients	O
are	O
responsible	O
for	O
thermo-voltages	O
,	O
which	O
can	O
lead	O
to	O
measurement	O
artifacts	O
in	O
electrochemical	B-Process
experiments	I-Process
[	O
24	O
]	O
.	O
Moreover	O
,	O
in	O
this	O
set-up	O
temperature	B-Process
cycles	I-Process
can	O
hardly	O
be	O
performed	O
on	O
single	O
microelectrodes	B-Material
but	O
require	O
subsequent	O
contacting	B-Process
and	I-Process
de-contacting	I-Process
of	I-Process
different	I-Process
microelectrodes	I-Process
.	O

Room	O
temperature	O
powder	B-Process
X-ray	I-Process
diffraction	I-Process
(	O
XRD	B-Process
)	O
was	O
performed	O
on	O
a	O
PANalytical	B-Process
Empyrean	I-Process
diffractometer	I-Process
.	O
The	O
obtained	O
XRD	B-Material
patterns	I-Material
were	O
analysed	O
with	O
STOE	B-Process
Win	I-Process
XPOW	I-Process
software	I-Process
in	O
order	O
to	O
determine	O
phase	B-Task
purity	I-Task
,	O
the	O
crystal	B-Task
structure	I-Task
and	O
the	O
cell	B-Task
parameters	I-Task
of	O
the	O
samples	O
.	O
Thermogravimetric	B-Process
analysis	I-Process
(	O
TGA	B-Process
)	O
was	O
performed	O
using	O
a	O
Netzsch	B-Process
STA	I-Process
449C	I-Process
instrument	I-Process
equipped	O
with	O
Proteus	B-Process
thermal	I-Process
analysis	I-Process
software	I-Process
.	O
The	O
TGA	O
studies	O
were	O
carried	O
out	O
under	O
reducing	B-Process
conditions	I-Process
(	O
5	B-Material
%	I-Material
H2	I-Material
/	I-Material
Ar	I-Material
)	O
from	O
room	O
temperature	O
to	O
900	O
°	O
C	O
,	O
in	O
order	O
to	O
determine	O
the	O
weight	B-Task
change	I-Task
of	O
the	O
perovskite	B-Material
during	O
the	O
reduction	O
.	O
The	O
microstructure	O
of	O
the	O
samples'	O
surface	O
was	O
analysed	O
using	O
a	O
JEOL	O
JSM-6700	O
field	B-Process
emission	I-Process
74	I-Process
scanning	I-Process
electron	I-Process
microscope	I-Process
(	O
FEG-SEM	B-Process
)	O
.	O
The	O
total	B-Task
conductivity	I-Task
of	O
the	O
samples	O
was	O
measured	O
using	O
a	O
conventional	O
four-terminal	B-Process
method	I-Process
.	O
Bar	O
samples	O
were	O
prepared	O
by	O
calcination	B-Task
at	O
1300	O
°	O
C	O
for	O
1h	O
.	O
Gold	B-Material
wire	I-Material
contacts	I-Material
were	O
attached	O
to	O
the	O
bars	O
,	O
which	O
then	O
were	O
cured	O
at	O
850	O
°	O
C	O
for	O
1h	O
.	O
The	O
conductivity	B-Task
of	O
the	O
samples	O
was	O
measured	O
under	O
a	O
redox	B-Task
cycle	I-Task
at	O
900	O
°	O
C	O
.	O
Low	B-Process
oxygen	I-Process
partial	I-Process
pressure	I-Process
was	O
achieved	O
by	O
using	O
a	O
continuous	O
flow	B-Process
of	O
5	O
%	O
H2	B-Material
/	I-Material
Ar	I-Material
.	O

A	B-Task
nanocomposite	I-Task
system	I-Task
consisting	O
of	O
a	B-Material
semiconducting	I-Material
matrix	I-Material
and	I-Material
embedded	I-Material
ferromagnetic	I-Material
nanostructures	I-Material
has	O
been	O
fabricated	O
.	O
The	B-Task
ferromagnetic	I-Task
characteristics	I-Task
as	O
coercivity	B-Task
,	I-Task
remanence	I-Task
and	I-Task
magnetic	I-Task
anisotropy	I-Task
of	I-Task
the	I-Task
nanocomposite	I-Task
can	O
be	O
adjusted	O
by	O
the	B-Material
electrochemical	I-Material
parameters	I-Material
.	O
Furthermore	O
the	O
spatial	O
distribution	B-Process
of	O
the	O
metal	O
structures	O
within	O
the	O
pores	O
can	O
be	O
varied	O
which	O
means	O
that	O
the	O
magnetic	O
interactions	O
between	O
the	O
particles	O
can	O
be	O
influenced	O
.	O
In	O
the	O
case	O
of	O
densely	B-Task
packed	I-Task
particles	I-Task
within	O
the	O
pores	O
dipolar	O
coupling	O
between	O
them	O
occurs	O
and	O
results	O
in	O
quasi	O
magnetic	O
chains	O
which	O
offer	O
a	B-Material
much	I-Material
larger	I-Material
magnetic	I-Material
anisotropy	I-Material
than	O
non-interacting	B-Material
particles	I-Material
.	O
By	O
modifying	B-Process
the	I-Process
current	I-Process
density	I-Process
small	B-Material
Ni-particles	I-Material
(	I-Material
3	I-Material
–	I-Material
6nm	I-Material
)	I-Material
can	O
be	O
deposited	O
.	O
If	O
the	O
packing	O
density	O
of	O
these	B-Material
particles	I-Material
is	O
sufficiently	O
close	O
,	O
Ni-tubes	B-Material
of	O
a	O
few	O
nanometer	O
in	O
thickness	O
are	O
covering	O
the	O
pore	O
walls	O
.	O
The	B-Task
presented	I-Task
nanocomposite	I-Task
is	O
an	O
interesting	O
system	O
for	O
magnetic	O
applications	O
as	O
magnetic	O
sensor	O
technology	O
.	O
Silicon	B-Material
as	O
substrate	O
renders	O
this	O
composite	O
a	B-Task
good	I-Task
candidate	I-Task
for	I-Task
the	I-Task
integration	I-Task
in	O
existing	O
process	O
technology	O
.	O

As	O
the	O
progression	O
towards	O
smaller	B-Task
lithographic	I-Task
nodes	I-Task
continues	O
it	O
has	O
become	O
necessary	O
to	O
adopt	O
thinner	B-Material
resist	I-Material
films	I-Material
to	O
mitigate	O
problems	O
such	O
as	O
pattern	B-Process
collapse	I-Process
.	O
To	O
address	O
the	O
issue	O
of	O
reduced	B-Process
etch	I-Process
resistance	I-Process
of	I-Process
thin	I-Process
photoresist	I-Process
films	I-Process
the	O
semiconductor	O
industry	O
has	O
begun	O
to	O
develop	O
multilayer	B-Process
processes	I-Process
where	O
the	O
pattern	O
is	O
first	O
transferred	O
into	O
an	O
intermediate	B-Material
organic	I-Material
hardmask	I-Material
with	O
higher	O
etch	O
selectivity	O
before	O
final	O
silicon	B-Process
pattern	I-Process
transfer	I-Process
[	O
25	O
–	O
27	O
]	O
.	O
In	O
this	O
paper	O
we	O
demonstrate	O
how	O
the	O
introduction	O
of	O
such	O
a	O
multilayer	B-Process
process	I-Process
can	O
also	O
benefit	O
nanosphere	B-Task
lithography	I-Task
by	O
increasing	O
achievable	O
aspect	O
ratios	O
of	O
silicon	B-Material
nanopillars	I-Material
without	O
the	O
need	O
for	O
complex	B-Process
etch	I-Process
processes	I-Process
requiring	O
specialised	O
and	O
expensive	O
equipment	O
,	O
but	O
instead	O
needing	O
only	O
a	O
standard	O
SF6	O
/	O
C4F8	O
inductively	O
coupled	B-Material
plasma	I-Material
(	O
ICP	B-Material
)	O
mixed	O
mode	O
etch	O
process	O
at	O
room	O
temperature	O
[	O
28	O
]	O
.	O
As	O
intermediate	B-Material
layer	I-Material
material	I-Material
we	O
used	O
polyimide	B-Material
,	O
which	O
finds	O
widespread	O
use	O
as	O
encapsulation	B-Material
material	I-Material
for	O
IC	B-Task
production	I-Task
.	O
It	O
is	O
readily	O
patterned	O
in	O
oxygen	B-Material
plasma	I-Material
and	O
has	O
a	O
lower	O
etch	O
rate	O
than	O
silicon	B-Material
in	O
SF6	B-Material
gas	I-Material
.	O
Its	O
flexibility	O
can	O
also	O
be	O
used	O
for	O
the	O
fabrication	B-Task
of	I-Task
soft	I-Task
polymer	I-Task
pillars	I-Task
by	O
the	O
same	O
process	O
as	O
we	O
will	O
show	O
.	O
The	O
multilayer	B-Process
process	I-Process
slightly	O
increases	O
the	O
complexity	O
of	O
sample	B-Process
preparation	I-Process
but	O
allows	O
basic	O
ICP	B-Process
etching	I-Process
to	O
achieve	O
high	O
aspect	O
ratio	O
structures	O
at	O
smaller	O
feature	O
sizes	O
that	O
previously	O
reported	O
without	O
the	O
need	O
for	O
complex	O
etching	B-Material
equipment	I-Material
.	O

A	O
3D	B-Process
finite	I-Process
element	I-Process
based	I-Process
(	I-Process
FEM	I-Process
)	I-Process
COMSOL	I-Process
capacitance	I-Process
analysis	I-Process
is	O
combined	O
with	O
Monte	B-Process
Carlo	I-Process
single-electron	I-Process
circuit	I-Process
simulations	I-Process
to	O
model	B-Task
device	I-Task
operations	I-Task
during	I-Task
single	I-Task
electron	I-Task
detection	I-Task
.	O
The	O
3D	B-Material
structural	I-Material
data	I-Material
(	O
Fig.	O
1b	O
)	O
of	O
the	O
nanoscale	B-Material
DQD	I-Material
pair	I-Material
and	O
multiple	B-Material
gate	I-Material
electrodes	I-Material
are	O
precisely	O
input	O
into	O
COMSOL	B-Material
’s	I-Material
FEM-based	I-Material
electrostatics	I-Material
simulator	I-Material
.	O
Capacitances	O
between	O
different	O
device	B-Material
components	I-Material
are	O
then	O
extracted	O
and	O
fed	O
into	O
the	O
well-tested	B-Material
single	I-Material
electron	I-Material
circuit	I-Material
simulator	I-Material
SETSPICE	B-Material
[	O
11	O
]	O
,	O
based	O
on	O
the	O
orthodox	B-Task
theory	I-Task
of	O
single	B-Process
electron	I-Process
tunnelling	I-Process
[	O
12	O
]	O
.	O
For	O
our	O
target	B-Material
d1	I-Material
of	O
60nm	O
,	O
simulation	B-Process
results	O
(	O
Fig.	O
1c	O
)	O
showed	O
that	O
as	O
we	O
sweep	O
the	O
voltage	O
applied	O
on	O
gate	B-Material
G1	I-Material
,	O
VG1	B-Material
,	O
single	B-Process
electron	I-Process
tunnelling	I-Process
into	O
the	O
turnstile	B-Material
’s	I-Material
two	I-Material
QDs	I-Material
should	O
generate	O
shifts	B-Process
in	I-Process
the	I-Process
electrometer	I-Process
current	I-Process
,	O
IDS	B-Material
,	O
of	O
tens	O
of	O
pA	O
.	O
This	O
is	O
well	O
within	O
the	O
charge	O
sensitivity	O
of	O
DQD	B-Material
electrometer	I-Material
[	O
6	O
]	O
and	O
consistent	O
to	O
the	O
same	O
order	O
of	O
magnitude	O
with	O
previous	O
work	O
in	O
single	B-Task
electron	I-Task
detection	I-Task
[	O
13	O
]	O
.	O
In	O
addition	O
,	O
the	O
gate	B-Material
to	O
QD	B-Process
capacitive	I-Process
coupling	I-Process
appear	O
to	O
be	O
sufficient	O
for	O
the	O
control	B-Process
of	I-Process
QD	I-Process
occupations	I-Process
down	O
to	O
the	O
single	O
electron	O
limit	O
,	O
allowing	O
for	O
future	O
manipulation	B-Task
of	I-Task
single	I-Task
electron	I-Task
spins	I-Task
in	O
qubit	B-Task
research	I-Task
.	O

We	O
evaluated	B-Task
three	I-Task
spin-on	I-Task
carbon	I-Task
hardmasks	I-Task
from	I-Task
Irresistible	I-Task
Materials	I-Task
[	O
12	O
]	O
.	O
The	O
spin-on	B-Material
carbon	I-Material
compositions	I-Material
were	O
dissolved	O
in	O
a	O
suitable	O
solvent	B-Material
such	O
as	O
chloroform	B-Material
or	O
anisole	B-Material
with	O
a	O
concentration	O
in	O
the	O
range	O
5	O
–	O
50g	O
/	O
l	O
.	O
In	O
this	O
report	O
,	O
film	B-Process
thickness	I-Process
measurements	I-Process
were	O
made	O
for	O
IM-HM11-01	B-Material
and	O
IM-HM11-02	B-Material
films	I-Material
,	O
whilst	O
IM-HM11-03	B-Material
was	O
used	O
for	O
etching	B-Process
;	O
further	O
investigations	O
to	O
compare	B-Task
the	I-Task
performance	I-Task
of	I-Task
the	I-Task
different	I-Task
compositions	I-Task
across	I-Task
tasks	I-Task
are	O
underway	O
.	O
Films	B-Material
of	I-Material
the	I-Material
SoC	I-Material
were	O
prepared	O
by	O
spin	B-Process
coating	I-Process
on	O
hydrogen-terminated	B-Material
silicon	I-Material
substrates	I-Material
with	O
a	O
speed	O
varying	O
between	O
800	O
and	O
2000	O
RPM	O
for	O
60s	O
.	O
After	O
spin	O
coating	O
the	O
film	B-Material
was	O
baked	O
for	O
2min	O
at	O
temperatures	O
of	O
up	O
to	O
330	O
°	O
C	O
.	O
In	O
order	O
to	O
enable	O
further	O
processing	O
,	O
the	O
SoC	O
should	O
be	O
rendered	O
insoluble	O
in	O
typical	O
solvents	O
for	O
resist	O
and	O
spin-on-hardmask	O
to	O
enable	O
further	O
processing	O
.	O
The	O
elution	O
behavior	O
of	O
films	O
of	O
IM-HM11-01	O
and	O
IM-HM11-02	O
for	O
thicknesses	O
between	O
30	O
and	O
325nm	O
was	O
tested	O
as	O
a	O
function	O
of	O
the	O
baking	O
temperature	O
.	O
Fig.	O
1	O
shows	O
the	O
normalized	O
film	O
thickness	O
of	O
two	O
formulations	O
of	O
the	O
SoC	O
(	O
IM-HM11-01	O
and	O
IM-HM11-02	O
)	O
,	O
before	O
and	O
after	O
dipping	O
in	O
monochlorobenzene	O
(	O
MCB	O
)	O
:	O
IPA	O
1:1	O
solution	O
.	O
Prior	O
to	O
baking	O
the	O
thickness	O
of	O
IM-HM11-01	O
was	O
∼	O
320nm	O
,	O
and	O
the	O
thickness	O
of	O
IM-HM11-02	O
was	O
∼	O
250nm	O
.	O
For	O
temperatures	O
above	O
190	O
°	O
C	O
the	O
IM-HM11-02	O
film	O
was	O
rendered	O
insoluble	O
,	O
whilst	O
a	O
temperature	O
of	O
260	O
°	O
C	O
was	O
required	O
to	O
achieve	O
the	O
same	O
for	O
IM-HM11-01	O
.	O
Film	O
thickness	O
did	O
not	O
affect	O
the	O
elution	O
results	O
.	O

In	O
summary	O
,	O
we	O
have	O
developed	O
a	O
technique	O
for	O
site-specific	B-Task
nanowire	I-Task
size	I-Task
reduction	I-Task
by	O
FIB	B-Process
thinning	I-Process
.	O
Transmission	B-Material
electron	I-Material
microscope	I-Material
images	I-Material
of	O
a	O
thinned	B-Material
tungsten	I-Material
composite	I-Material
nanowire	I-Material
with	O
width	O
reduced	O
from	O
80	O
to	O
20nm	O
show	O
uniform	B-Process
shrinking	I-Process
along	O
the	O
length	O
of	O
the	O
wire	B-Material
and	O
high	B-Material
resolution	I-Material
images	I-Material
show	O
no	O
obvious	O
changes	O
of	O
the	O
morphology	O
after	O
thinning	B-Process
.	O
The	O
critical	O
current	O
density	O
of	O
the	O
as-deposited	B-Material
wire	I-Material
and	O
one	O
thinned	O
to	O
a	O
width	O
of	O
50nm	O
is	O
1.7	O
×	O
105	O
and	O
1.4	O
×	O
105A	O
/	O
cm2	O
at	O
4.26K	O
,	O
respectively	O
,	O
suggesting	O
insignificant	O
modulation	B-Process
of	I-Process
the	I-Process
electrical	I-Process
properties	I-Process
during	O
thinning	B-Process
.	O
These	O
results	O
suggest	O
that	O
FIB-milling	B-Process
is	O
a	O
potential	O
approach	O
for	O
controllable	B-Task
size	I-Task
reduction	I-Task
with	I-Task
high	I-Task
resolution	I-Task
towards	I-Task
the	I-Task
observation	I-Task
of	I-Task
size	I-Task
-	I-Task
and	I-Task
quantum	I-Task
effects	I-Task
,	O
as	O
well	O
as	O
for	O
construction	B-Task
of	I-Task
3D	I-Task
superconducting	I-Task
nanodevices	I-Task
.	O

There	O
have	O
been	O
suggestions	O
that	O
electrons	B-Material
can	O
be	O
trapped	B-Process
in	I-Process
the	I-Process
bulk	I-Process
and	I-Process
at	I-Process
surfaces	I-Process
of	I-Process
silica	I-Process
[	O
15	O
]	O
but	O
new	O
models	O
of	O
electron	B-Material
trapping	I-Material
centres	I-Material
started	O
to	O
appear	O
only	O
recently	O
.	O
It	O
has	O
been	O
suggested	O
by	O
Bersuker	O
et	O
al.	O
,	O
who	O
used	O
molecular	B-Process
models	I-Process
,	O
that	O
electrons	B-Material
can	O
be	O
trapped	B-Process
by	O
Si	B-Material
–	I-Material
O	I-Material
bonds	I-Material
in	O
a-SiO2	B-Material
leading	I-Material
to	O
their	O
weakening	B-Process
and	O
thus	O
facilitating	O
Si	B-Process
–	I-Process
O	I-Process
bond	I-Process
dissociation	I-Process
[	O
16	O
]	O
.	O
Further	O
calculations	O
by	O
Camellone	O
et	O
al.	O
have	O
shown	O
that	O
electrons	B-Material
can	O
spontaneously	B-Process
trap	I-Process
in	O
non-defective	B-Process
continuum	I-Process
random	I-Process
network	I-Process
model	I-Process
of	O
a-SiO2	B-Material
[	O
17	O
]	O
.	O
Recent	O
calculations	O
have	O
also	O
demonstrated	O
that	O
the	O
two	O
dominant	O
neutral	B-Process
paramagnetic	I-Process
defects	I-Process
at	O
surfaces	B-Material
of	I-Material
a-SiO2	I-Material
,	O
the	O
non-bridging	B-Material
oxygen	I-Material
centre	I-Material
and	O
the	O
silicon	B-Material
dangling	I-Material
bond	I-Material
,	O
are	O
deep	B-Material
electron	I-Material
traps	I-Material
and	O
can	O
form	O
the	O
corresponding	O
negatively	B-Material
charged	I-Material
defects	I-Material
[	O
18	O
]	O
.	O
However	O
,	O
these	O
theoretical	O
predictions	O
have	O
not	O
yet	O
been	O
confirmed	O
experimentally	O
,	O
emphasising	O
the	O
challenges	O
for	O
identifying	B-Task
defect	I-Task
centres	I-Task
.	O

Ever	O
since	O
the	O
identification	B-Process
of	I-Process
the	I-Process
paramagnetic	I-Process
E′	I-Process
centre	I-Process
in	I-Process
SiO2	I-Process
as	O
an	O
unpaired	B-Material
electron	I-Material
localised	O
in	O
an	O
sp3	B-Material
hybrid	I-Material
orbital	I-Material
of	O
an	O
Si	B-Material
atom	I-Material
backbonded	O
to	O
three	O
oxygen	B-Material
atoms	I-Material
,	O
a	O
number	O
of	O
attempts	O
has	O
been	O
made	O
at	O
explaining	B-Task
the	I-Task
optical	I-Task
and	I-Task
electronic	I-Task
properties	I-Task
of	I-Task
SiO2	I-Task
in	I-Task
the	I-Task
presence	I-Task
of	I-Task
E′	I-Task
centres	I-Task
.	O
The	O
irradiation	B-Process
or	O
hole	B-Process
injection	I-Process
induces	O
trapping	B-Process
of	I-Process
positive	I-Process
charge	I-Process
in	I-Process
thin	I-Process
layers	I-Process
of	I-Process
a-SiO2	I-Process
grown	I-Process
on	I-Process
silicon	I-Process
surfaces	I-Process
by	O
thermal	B-Process
oxidation	I-Process
.	O
This	O
effect	O
has	O
been	O
correlated	O
with	O
paramagnetic	B-Material
E′	I-Material
centre	I-Material
signals	I-Material
and	O
led	O
to	O
the	O
initial	O
assignment	O
of	O
the	O
neutral	B-Material
oxygen	I-Material
vacancy	I-Material
as	O
the	O
major	B-Material
hole	I-Material
trap	I-Material
in	O
a-SiO2	B-Material
[	O
1	O
–	O
3	O
]	O
.	O
In	O
this	O
model	O
,	O
originally	O
proposed	O
for	O
E′	B-Material
centres	I-Material
in	I-Material
α-quartz	I-Material
,	O
upon	O
trapping	O
a	O
hole	O
,	O
one	O
Si	B-Material
atom	I-Material
from	O
the	O
two	O
Si	B-Material
atoms	I-Material
constituting	O
the	O
vacancy	O
remains	O
neutral	O
and	O
hosts	O
the	O
localised	B-Material
unpaired	I-Material
electron	I-Material
while	O
its	O
counterpart	O
becomes	O
positively	O
charged	O
.	O
Although	O
this	O
model	O
has	O
initially	O
been	O
accepted	O
widely	O
for	O
its	O
simplicity	O
,	O
it	O
fails	O
to	O
account	O
for	O
a	O
number	O
of	O
observations	O
,	O
such	O
as	O
the	O
positive	B-Process
charge	I-Process
trapping	I-Process
without	O
generation	O
of	O
E′	B-Material
centres	I-Material
[	O
4	O
]	O
,	O
the	O
formation	O
of	O
high	B-Material
density	I-Material
of	I-Material
E′	I-Material
centres	I-Material
without	O
the	O
corresponding	O
density	O
of	O
positive	B-Process
charge	I-Process
[	O
5	O
]	O
,	O
and	O
the	O
absence	O
of	O
correlation	O
between	O
the	O
decrease	O
of	O
the	O
E′	B-Material
centre	I-Material
density	O
and	O
the	O
density	O
of	O
positive	O
charge	O
upon	O
post-irradiation	B-Process
electron	I-Process
injection	I-Process
in	O
SiO2	B-Material
[	O
6	O
]	O
.	O

Ge	B-Material
(	I-Material
100	I-Material
)	I-Material
wafers	I-Material
(	O
n	B-Material
-	I-Material
and	I-Material
p-type	I-Material
)	O
were	O
cleaned	B-Process
in	I-Process
ultra	I-Process
high	I-Process
vacuum	I-Process
(<	O
10	O
−	O
6mbar	O
)	O
at	O
500	O
°	O
C	O
and	O
600	O
°	O
C	O
for	O
10min	O
to	O
evaporate	B-Process
any	I-Process
native	I-Process
oxide	I-Process
and	O
so	O
achieve	O
an	O
oxide	B-Material
free	I-Material
surface	I-Material
.	O
Subsequently	O
,	O
wafers	B-Material
were	O
exposed	O
to	O
an	O
Al	B-Process
flux	I-Process
for	O
a	O
range	O
of	O
times	O
to	O
deposit	O
ultrathin	B-Material
Al	I-Material
layers	I-Material
.	O
The	O
samples	B-Material
were	O
then	O
oxidized	B-Process
at	O
ambient	O
temperatures	O
in	O
the	O
MBE	B-Material
load	I-Material
lock	I-Material
to	O
produce	B-Task
Al2O3	I-Task
layers	I-Task
.	O
The	O
samples	B-Material
were	O
transferred	O
within	O
1min	O
to	O
an	O
Oxford	B-Material
Instruments	I-Material
OpAL	I-Material
reactor	I-Material
and	O
thin	B-Material
films	I-Material
of	I-Material
HfO2	I-Material
were	O
deposited	O
on	O
the	O
Al2O3	B-Material
using	O
atomic	B-Process
layer	I-Process
deposition	I-Process
(	O
ALD	B-Process
)	O
.	O
The	O
HfO2	B-Process
depositions	I-Process
used	O
a	O
[(	B-Material
CpMe	I-Material
)	I-Material
2HfOMeMe	I-Material
]	I-Material
precursor	I-Material
coupled	O
with	O
an	O
O2	B-Material
plasma	I-Material
as	O
the	O
oxidizing	B-Material
species	I-Material
.	O
Between	O
30	O
and	O
130	O
ALD	O
cycles	O
were	O
used	O
to	O
grow	B-Process
HfO2	I-Process
thicknesses	I-Process
from	O
1.6	O
to	O
7nm	O
at	O
250	O
°	O
C	O
.	O
For	O
electrical	B-Task
measurements	I-Task
,	O
circular	B-Material
gold	I-Material
contacts	I-Material
of	O
area	O
1.96	O
×	O
10	O
−	O
3cm2	O
were	O
deposited	O
onto	O
the	O
films	B-Material
to	O
form	O
MOS	B-Material
gate	I-Material
electrodes	I-Material
and	O
Al	B-Material
was	O
deposited	O
on	O
the	O
back	O
of	O
the	O
Ge	B-Material
wafers	I-Material
to	O
provide	O
an	O
ohmic	B-Material
contact	I-Material
.	O
After	O
preliminary	O
measurements	O
,	O
the	O
samples	B-Material
were	O
annealed	B-Process
in	I-Process
forming	I-Process
gas	I-Process
(	O
FGA	B-Material
)	O
at	O
350	O
°	O
C	O
for	O
30min	O
.	O
The	O
oxide	B-Material
leakage	I-Material
current	I-Material
was	O
measured	B-Process
using	O
a	O
Keithley	B-Material
230B	I-Material
voltage	I-Material
source	I-Material
and	O
Keithley	B-Material
617B	I-Material
electrometer	I-Material
.	O
The	O
HP	B-Material
4192A	I-Material
low	I-Material
frequency	I-Material
(	I-Material
LF	I-Material
)	I-Material
impedance	I-Material
analyzer	I-Material
at	O
small	O
signal	O
frequencies	O
between	O
100Hz	O
to	O
1MHz	O
was	O
used	O
to	B-Task
perform	I-Task
high	I-Task
frequency	I-Task
capacitance	I-Task
–	I-Task
voltage	I-Task
(	I-Task
HF	I-Task
CV	I-Task
)	I-Task
measurements	I-Task
.	O

We	O
have	O
demonstrated	O
a	O
new	O
approach	O
to	O
the	O
manufacture	B-Task
of	I-Task
self-folding	I-Task
hydrogel	I-Task
scaffolds	I-Task
by	O
the	O
use	O
of	O
readily	B-Process
available	I-Process
and	I-Process
fast	I-Process
throughput	I-Process
methods	I-Process
.	O
The	O
process	O
shows	O
effective	B-Process
pattern	I-Process
transfer	I-Process
by	O
first	O
embossing	B-Process
a	I-Process
sacrificial	I-Process
layer	I-Process
and	O
using	B-Process
it	I-Process
as	I-Process
a	I-Process
soluble	I-Process
mould	I-Process
in	I-Process
the	I-Process
fabrication	I-Process
process	I-Process
.	O
The	O
use	O
of	O
a	O
sacrificial	B-Material
layer	I-Material
of	I-Material
PAA	I-Material
imparts	O
environmental	O
sensitivity	O
to	O
the	O
hydrogel	B-Material
film	I-Material
on	O
only	O
one	O
surface	B-Material
.	O
The	O
subsequent	O
swelling	B-Process
of	I-Process
the	I-Process
PAA	I-Process
inter-penetrating	I-Process
network	I-Process
(	O
IPN	B-Process
)	O
in	O
elevated	B-Material
pH	I-Material
causes	O
a	O
swelling	B-Process
differential	I-Process
across	O
the	O
film	B-Material
,	O
causing	O
it	O
to	O
roll	B-Process
to	O
accommodate	B-Task
the	I-Task
difference	I-Task
in	I-Task
surface	I-Task
area	I-Task
between	I-Task
the	I-Task
two	I-Task
surfaces	I-Task
.	O
The	O
surface	O
functionalization	O
and	O
patterning	O
stages	O
are	O
thus	O
combined	O
into	O
one	O
photolithographic	B-Process
operation	I-Process
.	O
The	O
net	O
result	O
is	O
a	O
method	O
of	O
producing	B-Process
environmentally	I-Process
triggered	I-Process
self-folding	I-Process
all	I-Process
hydrogel	I-Process
scaffolds	I-Process
by	O
a	O
,	O
to	O
the	O
authors’	O
knowledge	O
,	O
novel	B-Task
use	I-Task
of	I-Task
sacrificial	I-Task
layer	I-Task
embossing	I-Task
.	O
The	O
patterned	B-Material
hydrogel	I-Material
films	I-Material
can	O
be	O
triggered	B-Process
consecutively	I-Process
allowing	O
for	O
successive	O
rolling	B-Process
and	O
unrolling	B-Process
depending	O
on	O
the	O
aqueous	B-Material
pH	I-Material
.	O
The	O
choice	O
of	O
PEGDMA	B-Material
hydrogel	I-Material
provides	O
a	O
versatile	B-Material
platform	I-Material
for	I-Material
creating	I-Material
a	I-Material
variety	I-Material
of	I-Material
hydrogel	I-Material
scaffolds	I-Material
,	O
and	O
while	O
being	O
non-fouling	O
and	O
nontoxic	O
it	O
is	O
permeable	O
to	O
proteins	B-Material
.	O
Furthermore	O
PEGDMA	B-Material
can	O
be	O
modified	O
to	O
produce	B-Task
biodegradable	I-Task
and	I-Task
cell	I-Task
adhesive	I-Task
hydrogels	I-Task
for	O
a	O
variety	O
of	O
biomedical	B-Task
applications	I-Task
.	O

The	O
number	O
of	O
experiments	O
conducted	O
was	O
reduced	O
by	O
selecting	O
the	O
four	O
most	O
important	O
parameters	O
for	O
variation	O
,	O
Table	O
1	O
while	O
the	O
remaining	O
parameters	O
were	O
kept	O
constant	O
.	O
The	O
O2	O
flow	O
rate	O
(	O
QO2	O
)	O
was	O
keep	O
constant	O
at	O
99sccm	O
,	O
while	O
the	O
SF6	O
flow	O
rate	O
(	O
QSF6	O
)	O
was	O
varied	O
between	O
0	O
and	O
20sccm	O
.	O
The	O
pressure	B-Process
in	I-Process
the	I-Process
etch	I-Process
chamber	I-Process
was	O
controlled	O
to	O
keep	B-Task
the	I-Task
gas	I-Task
density	I-Task
stable	I-Task
.	O
Since	O
the	O
pressure	O
has	O
a	O
pronounced	O
effect	O
on	O
etch	O
characteristics	O
,	O
the	O
pressure	O
(	O
p	O
)	O
was	O
varied	O
between	O
20	O
and	O
40mTorr	O
.	O
It	O
should	O
be	O
noted	O
that	O
the	O
system	O
was	O
run	O
in	O
automatic	B-Process
pressure	I-Process
control	I-Process
mode	O
,	O
which	O
continuously	O
adjusts	B-Process
the	I-Process
throttle	I-Process
valve	I-Process
to	O
keep	O
a	O
constant	O
pressure	O
during	O
etch	O
.	O
The	O
coil	O
power	O
(	O
PC	O
)	O
was	O
fixed	O
at	O
1000W	O
,	O
while	O
the	O
bias	O
power	O
(	O
PB	O
)	O
was	O
varied	O
between	O
0	O
and	O
30W	O
.	O
Finally	O
,	O
the	O
substrate	O
chuck	O
temperature	O
(	O
T	O
)	O
was	O
controlled	O
between	O
10	O
and	O
50	O
°	O
C	O
.	O
This	O
design	O
resulted	O
in	O
a	O
full	O
factorial	B-Process
screening	I-Process
in	O
four	O
parameters	O
,	O
where	O
three	O
center	O
points	O
were	O
used	O
to	O
check	B-Process
for	I-Process
quadratic	I-Process
curvature	I-Process
,	O
where	O
the	O
quadratic	O
term	O
of	O
a	O
parameter	O
is	O
needed	O
to	O
generate	O
a	O
valid	O
model	O
.	O
The	O
total	O
number	O
of	O
experiments	O
in	O
this	O
setup	O
is	O
19	O
,	O
which	O
were	O
processed	O
for	O
20min	O
each	O
.	O
The	O
experiments	O
in	O
the	O
design	O
were	O
carried	O
out	O
in	O
random	O
order	O
.	O

We	O
used	O
2μm	O
of	O
ultra-nanocrystalline	B-Material
diamond	I-Material
(	O
UNCD	B-Material
)	O
grown	O
by	O
chemical	B-Process
vapour	I-Process
deposition	I-Process
(	O
CVD	B-Process
)	O
on	O
a	O
∼	O
520μm	O
silicon	O
carrier	O
wafer	O
from	O
Advanced	O
Diamond	O
Technologies	O
Ltd	O
.	O
Detailed	O
information	O
about	O
the	O
material	O
and	O
the	O
stamp	O
fabrication	O
can	O
be	O
found	O
in	O
our	O
earlier	O
paper	O
[	O
16	O
]	O
.	O
The	O
UNCD	B-Material
wafer	I-Material
was	O
scribed	O
into	O
1	O
×	O
1cm2	O
samples	O
and	O
subjected	O
to	O
RCA	B-Process
cleaning	I-Process
(	O
SC-1	O
)	O
,	O
followed	O
by	O
ultrasonic	B-Process
solvent	I-Process
cleaning	I-Process
.	O
Nanofeature	B-Task
stamps	I-Task
were	I-Task
then	I-Task
created	I-Task
from	I-Task
the	I-Task
samples	I-Task
using	O
conventional	B-Process
electron	I-Process
beam	I-Process
lithography	I-Process
(	O
EBL	B-Process
)	O
with	O
negative	B-Material
tone	I-Material
electron	I-Material
sensitive	I-Material
resist	I-Material
,	O
hydrogen	B-Material
silsesquioxane	I-Material
(	O
HSQ	B-Material
)	O
.	O
An	O
Al	B-Material
discharge	I-Material
layer	I-Material
was	O
required	O
above	O
the	O
resist	O
to	O
prevent	B-Task
e-beam	I-Task
deflection	I-Task
due	I-Task
to	I-Task
charge	I-Task
build-up	I-Task
on	I-Task
the	I-Task
surface	I-Task
[	O
17	O
]	O
.	O
Several	B-Task
stamps	I-Task
were	I-Task
produced	I-Task
with	O
this	O
process	O
and	O
the	O
pattern	O
written	O
varied	O
in	O
design	O
but	O
consisted	O
of	O
arrays	O
of	O
circular	B-Material
pillars	I-Material
.	O
After	O
EBL	O
and	O
HSQ	O
development	O
,	O
the	O
HSQ	O
was	O
used	O
as	O
an	O
etch	O
mask	O
for	O
RIE	O
with	O
a	O
mixture	O
of	O
oxygen	O
and	O
argon	O
gas	O
.	O
The	O
etched	O
diamond	O
nanopillars	O
were	O
typically	O
225nm	O
high	O
.	O
Fig.	O
1	O
displays	O
a	O
scanning	O
electron	O
micrograph	O
of	O
some	O
typical	O
stamp	O
features	O
.	O

Copper	B-Task
electro-chemical	I-Task
deposition	I-Task
(	I-Task
ECD	I-Task
)	I-Task
of	I-Task
through	I-Task
silicon	I-Task
via	I-Task
(	I-Task
TSV	I-Task
)	I-Task
is	O
a	O
key	O
challenge	O
of	O
3D	B-Task
integration	I-Task
.	O
This	O
paper	O
presents	O
a	O
numerical	B-Process
modeling	I-Process
of	I-Process
TSV	I-Process
filling	I-Process
concerning	O
the	O
influence	O
of	O
the	O
accelerator	B-Material
and	I-Material
the	I-Material
suppressor	I-Material
.	I-Material
The	O
diffusion	B-Process
–	I-Process
adsorption	I-Process
model	I-Process
was	O
used	O
in	O
the	O
simulation	B-Process
and	I-Process
effects	I-Process
of	I-Process
the	I-Process
additives	I-Process
were	O
incorporated	O
in	O
the	O
model	O
.	O
The	O
boundary	O
conditions	O
were	O
derived	O
from	O
a	O
set	B-Material
of	I-Material
experimental	I-Material
Tafel	I-Material
curves	I-Material
with	O
different	O
concentrations	O
of	O
additives	B-Material
,	O
which	O
provided	O
a	O
quick	O
and	O
accurate	O
way	O
for	O
copper	B-Process
ECD	I-Process
process	I-Process
prediction	I-Process
without	O
complicated	O
surface	O
kinetic	O
parameters	O
fitting	O
.	O
The	O
level	B-Process
set	I-Process
method	I-Process
(	O
LSM	B-Process
)	O
was	O
employed	O
to	O
track	B-Task
the	I-Task
copper	I-Task
and	I-Task
electrolyte	I-Task
interface	I-Task
.	O
The	O
simulation	B-Process
results	O
were	O
in	O
good	O
agreement	O
with	O
the	O
experiments	O
.	O
For	O
a	O
given	O
feature	O
size	O
,	O
the	O
current	O
density	O
for	O
superfilling	B-Material
could	O
be	O
predicted	O
,	O
which	O
provided	O
a	O
guideline	O
for	O
ECD	B-Process
process	I-Process
optimization	I-Process
.	O

To	O
restrict	B-Task
pollen	I-Task
tube	I-Task
growth	I-Task
to	I-Task
a	I-Task
single	I-Task
focal	I-Task
plane	I-Task
is	O
an	O
important	O
subject	O
to	O
enable	O
their	O
accurate	O
growth	B-Process
analysis	I-Process
under	I-Process
microscopic	I-Process
observation	I-Process
.	O
In	O
the	O
conventional	O
method	O
to	O
assay	B-Process
pollen	I-Process
tube	I-Process
growth	I-Process
,	I-Process
the	O
pollen	B-Process
tubes	I-Process
grow	I-Process
in	O
a	O
disorderly	O
manner	O
on	O
solid	O
medium	O
,	O
rendering	O
it	O
impossible	O
to	O
observe	B-Process
their	I-Process
growth	I-Process
in	I-Process
detail	I-Process
.	I-Process
Here	O
,	O
we	O
present	O
a	O
new	B-Task
method	I-Task
to	I-Task
assay	I-Task
pollen	I-Task
tube	I-Task
growth	I-Task
using	O
poly-dimethylsiloxane	B-Material
microchannel	I-Material
device	I-Material
to	O
isolate	B-Process
individual	I-Process
pollen	I-Process
tubes	I-Process
.	O
The	O
growth	O
of	O
the	O
pollen	B-Material
tube	I-Material
is	O
confined	O
to	O
the	O
microchannel	O
and	O
to	O
the	O
same	O
focal	O
plane	O
,	O
allowing	O
accurate	B-Task
microscopic	I-Task
observations	I-Task
.	O
This	O
methodology	O
has	O
the	O
potential	O
for	O
analyses	B-Task
of	I-Task
pollen	I-Task
tube	I-Task
growth	I-Task
in	O
microfluidic	O
environments	O
in	O
response	O
to	O
chemical	B-Material
products	I-Material
and	O
signaling	B-Material
molecules	I-Material
,	O
which	O
paves	O
the	O
way	O
for	O
various	O
experiments	B-Task
on	I-Task
plant	I-Task
reproduction	I-Task
.	O

In	O
order	O
to	O
study	B-Task
the	I-Task
mechanical	I-Task
behavior	I-Task
of	O
metal	B-Material
films	I-Material
on	O
compliant	O
polymer	B-Material
substrates	I-Material
,	O
fragmentation	B-Process
testing	I-Process
is	O
often	O
employed	O
[	O
8	O
–	O
12	O
]	O
.	O
During	O
fragmentation	O
testing	O
,	O
the	O
film-substrate	B-Material
couple	I-Material
is	O
strained	B-Process
under	I-Process
uni-axial	I-Process
tension	I-Process
and	O
observed	O
with	O
light	B-Process
microscopy	I-Process
(	O
LM	B-Process
)	O
or	O
scanning	B-Process
electron	I-Process
microscopy	I-Process
(	O
SEM	B-Process
)	O
.	O
Brittle	B-Material
metals	I-Material
or	O
ceramic	B-Material
films	I-Material
fracture	B-Process
,	O
forming	O
through	O
thickness	O
cracks	O
(	O
channel	O
cracks	O
)	O
at	O
low	O
strain	O
perpendicular	O
to	O
the	O
straining	O
direction	O
.	O
On	O
the	O
other	O
hand	O
,	O
ductile	O
metal	B-Material
films	I-Material
will	O
first	O
deform	B-Process
locally	I-Process
in	O
the	O
form	O
of	O
necks	O
at	O
low	O
strains	O
(	O
Fig.	O
1a	O
)	O
and	O
with	O
increased	O
strain	B-Process
through	O
thickness	O
cracks	O
(	O
TTC	O
)	O
can	O
evolve	O
(	O
Fig.	O
1b	O
)	O
.	O
Fragmentation	B-Process
testing	I-Process
is	O
best	O
performed	B-Task
in-situ	I-Task
with	I-Task
LM	I-Task
or	I-Task
SEM	I-Task
so	O
that	O
the	O
strain	O
when	O
the	O
first	O
crack	O
forms	O
can	O
be	O
observed	O
.	O
The	O
initial	O
fracture	B-Process
strain	I-Process
of	O
the	O
film	B-Material
,	O
also	O
known	O
as	O
the	O
crack	O
onset	O
strain	O
,	O
can	O
then	O
be	O
used	O
to	O
determine	O
the	O
interfacial	O
fracture	O
shear	B-Process
stress	I-Process
with	O
knowledge	O
of	O
the	O
crack	O
spacing	O
at	O
saturation	O
,	O
λ	O
,	O
film	B-Material
thickness	O
,	O
h	O
,	O
and	O
the	O
fracture	O
stress	O
,	O
σf	B-Process
=	I-Process
Efilmεf	I-Process
,	O
where	O
εf	O
is	O
the	O
fracture	B-Process
strain	I-Process
,	O
using	O
the	O
shear	B-Process
lag	I-Process
model	I-Process
[	O
8,13,14	O
]	O
.	O
In-situ	O
fragmentation	O
testing	O
with	O
LM	B-Process
or	O
SEM	B-Process
allows	O
for	O
the	O
crack	B-Process
spacing	I-Process
evolution	I-Process
to	O
be	O
observed	O
as	O
a	O
function	B-Process
of	I-Process
applied	I-Process
strain	I-Process
(	O
Fig.	O
1c	O
)	O
.	O
Under	O
tensile	B-Process
straining	I-Process
conditions	O
,	O
a	O
brittle	B-Material
film	I-Material
will	O
initially	O
fracture	B-Process
at	O
very	O
low	O
strains	O
(<	O
1	O
%)	O
and	O
then	O
with	O
further	O
strain	O
continue	O
to	O
form	B-Process
cracks	I-Process
until	O
the	O
saturation	O
crack	O
spacing	O
is	O
reached	O
.	O
After	O
the	O
saturation	O
spacing	O
has	O
been	O
reached	O
,	O
cracks	O
can	O
no	O
longer	O
form	O
between	O
existing	O
crack	O
fragments	O
and	O
the	O
film	O
could	O
delaminate	B-Process
via	O
buckling	B-Process
.	O

PDMS	B-Material
(	O
Polydimethylsiloxane	B-Material
)	O
has	O
become	O
by	O
far	O
the	O
most	O
popular	O
material	O
in	O
the	O
academic	O
microfluidics	B-Material
community	O
because	O
it	O
is	O
inexpensive	O
,	O
easy	O
to	O
fabricate	B-Task
by	O
replication	B-Process
of	I-Process
molds	I-Process
made	O
using	O
rapid	B-Process
prototyping	I-Process
or	O
other	O
techniques	O
,	O
flexible	O
,	O
optically	O
transparent	O
,	O
biocompatible	O
and	O
its	O
fabrication	O
does	O
not	O
require	O
high	O
capital	O
investment	O
and	O
cleanroom	O
conditions	O
.	O
Various	O
techniques	O
have	O
been	O
adapted	O
to	O
fabricate	B-Task
microfluidic	I-Task
structures	I-Task
in	O
PDMS	B-Material
,	O
including	O
wet	B-Process
and	I-Process
dry	I-Process
etching	I-Process
[	O
20	O
–	O
22	O
]	O
,	O
photolithographic	B-Process
patterning	I-Process
of	O
a	O
photosensitive	O
PDMS	B-Material
[	O
23	O
]	O
,	O
and	O
laser	B-Process
ablation	I-Process
[	O
24	O
]	O
.	O
But	O
,	O
it	O
was	O
the	O
“	B-Process
soft-lithography	I-Process
”	I-Process
techniques	I-Process
[	O
25	O
]	O
introduced	O
by	O
Whitesides	O
et	O
al.	O
that	O
enabled	O
the	O
widespread	O
use	O
of	O
PDMS	B-Material
and	O
opened	O
up	O
the	O
era	O
of	O
PDMS-based	B-Process
microfluidics	I-Process
in	O
the	O
late	O
1990s	O
.	O
Replica	O
molding	O
,	O
which	O
is	O
the	O
casting	O
of	O
prepolymer	O
against	O
a	O
master	O
and	O
generating	O
a	O
replica	O
of	O
the	O
master	O
in	O
PDMS	B-Material
,	O
has	O
become	O
a	O
standard	O
fabrication	O
technique	O
available	O
in	O
almost	O
every	O
research	O
laboratory	O
.	O
Detailed	O
overviews	O
of	O
soft-lithography	O
techniques	O
and	O
their	O
applications	O
can	O
be	O
found	O
from	O
the	O
reviews	O
by	O
McDonald	O
et	O
al	O
.	O
[	O
26	O
]	O
and	O
Sia	O
et	O
al	O
.	O
[	O
27	O
]	O
.	O
Nowadays	O
,	O
many	O
tools	O
dedicated	O
for	O
this	O
purpose	O
are	O
available	O
and	O
can	O
be	O
purchased	O
as	O
a	O
complete	O
set	O
(	O
e.g.	O
SoftLithoBox	O
®	O
provided	O
by	O
Elveflow	O
(	O
USA	O
)	O
[	O
28	O
])	O
.	O
Moreover	O
,	O
companies	O
,	O
such	O
as	O
FlowJEM	O
(	O
Canada	O
)	O
[	O
29	O
]	O
,	O
Microfluidic	O
Innovations	O
(	O
USA	O
)	O
[	O
30	O
]	O
,	O
and	O
Scientific	O
Device	O
Laboratory	O
(	O
USA	O
)	O
[	O
31	O
]	O
provide	O
rapid	O
prototyping	O
service	O
for	O
PDMS-based	O
LOC	O
devices	O
.	O

Unlike	O
conventional	O
materials	O
used	O
in	O
nerve	O
tissue	O
engineering	O
,	O
PAs	B-Material
can	O
be	O
directly	O
injected	B-Process
in	I-Process
vivo	I-Process
into	I-Process
models	I-Process
and	I-Process
spontaneously	I-Process
self-assemble	I-Process
into	I-Process
nanofibers	I-Process
in	I-Process
aqueous	I-Process
solutions	I-Process
.	O
Furthermore	O
,	O
PAs	B-Material
can	O
function	O
as	O
biomimetic	B-Material
materials	I-Material
exemplified	O
by	O
collagen-mimetic	B-Material
PAs	I-Material
[	O
92	O
]	O
.	O
Conventional	O
materials	O
often	O
rely	O
on	O
electrospinning	B-Process
as	O
a	B-Process
manufacturing	I-Process
method	I-Process
to	O
achieve	B-Task
fiber-like	I-Task
structures	I-Task
suitable	O
for	O
use	O
in	O
nerve	O
regeneration	O
.	O
The	O
self-assembly	O
nature	O
of	O
PAs	B-Material
allows	O
them	O
to	O
circumvent	O
costly	B-Process
manufacturing	I-Process
methods	I-Process
.	O
However	O
,	O
in	O
contrast	O
to	O
conventional	B-Process
manufacturing	I-Process
methods	I-Process
like	O
electrospinning	B-Process
where	O
quality	O
and	O
batch-to-batch	O
variability	O
can	O
be	O
tightly	O
controlled	O
,	O
merely	O
relying	O
on	O
self-assembly	B-Process
as	O
a	O
method	O
of	O
large-scale	B-Task
commercial	I-Task
production	I-Task
is	O
still	O
an	O
experimental	O
concept	O
.	O
Perhaps	O
the	O
next	O
step	O
would	O
be	O
to	O
carefully	O
compare	B-Process
and	I-Process
contrast	I-Process
the	O
robustness	B-Task
of	I-Task
self-assemled	I-Task
PAs	I-Task
to	I-Task
electrospun	I-Task
nanofibers	I-Task
.	O
Given	O
that	O
the	O
constituent	O
elements	O
in	O
PAs	O
and	O
external	O
factors	O
like	O
pH	O
can	O
affect	O
its	O
structural	O
assembly	O
,	O
parameters	O
must	O
be	O
finely	O
tuned	B-Process
and	I-Process
optimized	I-Process
in	O
order	O
for	O
PA	B-Material
nanofibers	I-Material
to	O
be	O
used	O
as	O
a	B-Task
full-fledged	I-Task
commercialized	I-Task
medical	I-Task
product	I-Task
[	O
93	O
]	O
.	O

Mice	B-Task
bearing	I-Task
the	I-Task
orthotopic	I-Task
model	I-Task
were	O
treated	O
starting	O
from	O
day	O
21	O
after	O
NB	O
cell	O
implant	O
;	O
mice	B-Task
with	I-Task
the	I-Task
pseudo-metastatic	I-Task
model	I-Task
received	O
the	O
first	O
treatment	O
4h	O
after	O
NB	O
cell	O
injection	O
.	O
These	B-Task
therapeutic	I-Task
schedules	I-Task
were	O
designed	O
to	O
test	B-Process
the	I-Process
effects	I-Process
of	O
our	B-Material
targeted	I-Material
formulations	I-Material
against	O
both	O
established	O
and	O
pseudo-metastatic	O
preclinical	B-Material
models	I-Material
of	O
human	O
NB	O
,	O
as	O
described	O
[	O
16,19	O
]	O
.	O
Animals	B-Task
were	O
treated	O
i.v.	O
once	O
a	O
week	O
for	O
3	O
weeks	O
with	O
untargeted	B-Material
(	O
SL	O
[	O
DXR	O
])	O
or	O
peptide-targeted	O
SL	B-Material
[	I-Material
DXR	I-Material
]	I-Material
(	O
5mg	O
/	O
kg	O
)	O
.	O
Scrambled	O
peptide-functionalized	O
liposomes	O
were	O
used	O
as	O
a	O
control	O
,	O
and	O
in	O
every	O
experiment	O
a	O
group	O
of	O
control	O
mice	O
received	O
HEPES-buffered	O
saline	O
.	O
Survival	O
times	O
were	O
used	O
as	O
the	O
main	O
criterion	O
for	O
determining	O
treatment	O
efficacy	O
.	O
In	O
the	O
orthotopic	O
model	O
,	O
time-dependent	O
anti-tumor	O
activity	O
was	O
also	O
evaluated	O
by	O
bioluminescence	O
imaging	O
(	O
BLI	O
)	O
and	O
X-ray	O
analyses	O
.	O
For	O
this	O
purpose	O
,	O
the	O
GI-LI-N	O
cell	O
line	O
was	O
infected	O
with	O
a	O
retrovirus	O
expressing	O
the	O
firefly	O
luciferase	O
gene	O
,	O
as	O
previously	O
reported	O
[	O
17	O
]	O
;	O
luciferase	O
activity	O
of	O
retrovirally-transduced	O
cells	O
was	O
visualized	O
in	O
vivo	O
by	O
BLI	O
(	O
IVIS	O
Caliper	O
Life	O
Sciences	O
,	O
Hopkinton	O
,	O
MA	O
)	O
after	O
a	O
10min	O
incubation	O
with	O
150μg	O
/	O
mL	O
of	O
d-luciferin	O
(	O
Caliper	O
Life	O
Sciences	O
)	O
,	O
as	O
described	O
[	O
17	O
]	O
.	O
X-ray	O
analysis	O
was	O
superimposed	O
to	O
the	O
luminescence	O
for	O
a	O
better	O
visualization	O
of	O
the	O
tumors	O
.	O

A	O
limitation	O
of	O
the	O
pharmacyte	B-Process
approach	I-Process
is	O
the	O
one-time	O
nature	O
of	O
the	O
intervention	O
:	O
ACT	B-Material
T-cells	I-Material
can	O
only	O
be	O
loaded	O
once	O
with	O
a	O
cargo	O
of	O
adjuvant	B-Material
drug	I-Material
prior	O
to	O
transfer	O
,	O
and	O
the	O
duration	O
of	O
stimulation	B-Process
is	O
inherently	O
limited	O
by	O
expansion	B-Process
of	I-Process
the	I-Process
cell	I-Process
population	I-Process
in	I-Process
vivo	I-Process
,	O
since	O
cell-bound	B-Material
particles	I-Material
are	O
diluted	O
with	O
each	O
cell	B-Process
division	I-Process
.	O
We	O
hypothesized	O
that	O
a	O
strategy	O
to	O
target	O
supporting	B-Material
drugs	I-Material
to	O
T-cells	B-Material
with	O
nanoparticle	B-Material
drug	I-Material
carriers	I-Material
directly	O
in	O
vivo	O
would	O
enable	O
transferred	B-Material
lymphocytes	I-Material
to	O
be	O
repeatedly	O
stimulated	O
with	O
supporting	B-Material
adjuvant	I-Material
drugs	I-Material
,	O
and	O
thereby	O
provide	O
continuous	O
supporting	O
signals	O
over	O
the	O
prolonged	O
durations	O
that	O
might	O
be	O
necessary	O
for	O
elimination	B-Task
of	I-Task
large	I-Task
tumor	I-Task
burdens	I-Task
.	O
Such	O
“	B-Process
re-arming	I-Process
”	I-Process
of	I-Process
T-cells	I-Process
with	I-Process
supporting	I-Process
drugs	I-Process
could	O
be	O
achieved	O
by	O
repeated	O
administration	B-Process
of	I-Process
targeted	I-Process
particles	I-Process
,	O
allowing	O
adoptively-transferred	B-Material
T-cells	I-Material
to	O
be	O
restimulated	O
multiple	O
times	O
directly	O
in	O
vivo	O
,	O
while	O
the	O
use	O
of	O
internalizing	B-Material
targeting	I-Material
ligands	I-Material
would	O
minimize	O
the	O
likelihood	O
of	O
immune	O
responses	O
against	O
the	O
nanoparticle	B-Material
carrier	I-Material
.	O
To	O
our	O
knowledge	O
,	O
only	O
two	O
prior	O
studies	O
have	O
attempted	O
to	O
target	O
nanoparticles	B-Material
to	O
T-cells	B-Material
in	O
vivo	O
[	O
17,18	O
]	O
.	O
In	O
both	O
of	O
these	O
studies	O
,	O
particles	O
were	O
targeted	O
to	O
T-cells	O
via	O
peptide-MHC	B-Material
ligands	I-Material
that	O
bind	O
to	O
specific	O
T-cell	B-Material
receptors	I-Material
.	O
However	O
,	O
peptide-MHC-functionalized	B-Material
nanoparticles	I-Material
have	O
recently	O
been	O
shown	O
to	O
deliver	O
an	O
anergizing	O
/	O
tolerizing	O
signal	O
to	O
T-cells	B-Material
[	O
18,19	O
]	O
—	O
which	O
is	O
ideal	O
for	O
treating	B-Task
graft	I-Task
rejection	I-Task
or	I-Task
autoimmunity	I-Task
,	O
but	O
runs	O
counter	O
to	O
the	O
goals	O
of	O
cancer	B-Task
immunotherapy	I-Task
.	O

The	O
α-ω-aminohexylcarbamate	B-Material
derivative	I-Material
of	I-Material
cyanocobalamin	I-Material
was	O
prepared	O
using	O
a	O
method	O
described	O
previously	O
[	O
18	O
]	O
.	O
Briefly	O
,	O
solid	B-Material
CDI	I-Material
(	O
260mg	O
,	O
0.32mmol	O
)	O
was	O
added	O
to	O
cyanocobalamin	B-Material
(	O
1.0g	O
,	O
0.148mmol	O
)	O
previously	O
dissolved	O
in	O
anhydrous	B-Material
dimethyl	I-Material
sulfoxide	I-Material
.	O
The	O
mixture	O
was	O
stirred	O
for	O
up	O
to	O
2h	O
at	O
30	O
°	O
C	O
,	O
followed	O
by	O
the	O
addition	O
of	O
dry	B-Material
1,6-hexanediamine	I-Material
(	O
314mg	O
,	O
0.54mmol	O
)	O
and	O
stirring	O
of	O
the	O
mixture	O
at	O
room	O
temperature	O
over	O
24h	O
.	O
The	O
mixture	O
was	O
poured	O
into	O
ethyl	B-Material
acetate	I-Material
(	O
30ml	O
)	O
and	O
left	O
to	O
stand	O
.	O
Following	O
centrifugation	B-Process
and	O
decanting	B-Process
of	O
the	O
supernatant	B-Material
,	O
the	O
residue	B-Material
was	O
sonicated	O
for	O
5min	O
in	O
acetone	B-Material
(	O
50ml	O
)	O
.	O
The	O
resulting	O
precipitate	B-Material
was	O
filtered	O
and	O
the	O
solid	B-Material
washed	O
in	O
acetone	B-Material
.	O
The	O
crude	B-Material
product	I-Material
was	O
purified	O
by	O
silica	B-Process
column	I-Process
chromatography	I-Process
(	O
45	O
%	O
v	O
/	O
v	O
2-propanol	O
,	O
30	O
%	O
v	O
/	O
v	O
n-butanol	O
,	O
2	O
%	O
v	O
/	O
v	O
ammonia	O
and	O
25	O
%	O
v	O
/	O
v	O
water	O
)	O
followed	O
by	O
lyophilisation	O
.	O

Immunopotentiators	B-Material
activate	B-Process
innate	I-Process
immunity	I-Process
directly	O
(	O
for	O
example	O
,	O
cytokines	O
)	O
or	O
through	O
pattern-recognition	B-Process
receptors	I-Process
(	O
PRRs	B-Process
,	O
such	O
as	O
those	O
for	O
bacterial	O
components	O
)	O
.	O
The	O
Toll-like	B-Process
receptors	I-Process
(	O
TLRs	B-Process
)	O
are	O
a	O
family	O
of	O
PRRs	B-Process
that	O
are	O
an	O
important	O
link	O
between	O
innate	O
and	O
adaptive	O
immunity	O
.	O
Some	O
studies	O
have	O
shown	O
that	O
TLR	B-Material
ligands	I-Material
have	O
adjuvant	O
activity	O
and	O
enhance	O
antigen-specific	O
antibody	O
and	O
cell-mediated	O
immune	O
responses	O
,	O
especially	O
when	O
they	O
are	O
combined	O
with	O
delivery	O
systems	O
that	O
promote	O
their	O
uptake	O
and	O
delivery	O
into	O
antigen-presenting	B-Material
cells	I-Material
[	O
22	O
–	O
24	O
]	O
.	O
For	O
clinical	B-Task
studies	I-Task
,	O
TLR9	B-Material
is	O
generally	O
stimulated	B-Process
with	O
synthetic	B-Material
oligodeoxynucleotides	I-Material
containing	O
one	O
or	O
more	O
unmethylated	B-Material
CpG	I-Material
dinucleotides	I-Material
.	O
In	O
humans	O
,	O
CpG	O
has	O
been	O
used	O
as	O
an	O
adjuvant	B-Material
for	O
infectious	B-Task
disease	I-Task
vaccination	I-Task
[	O
25,26	O
]	O
and	O
in	O
the	O
development	O
of	O
cancer	O
therapy	O
[	O
27	O
]	O
.	O
In	O
a	O
mouse	O
model	O
,	O
CpG	O
has	O
also	O
been	O
shown	O
to	O
induce	O
T	O
helper	O
1	O
(	O
Th1	O
)	O
immune	O
responses	O
,	O
which	O
are	O
characterized	O
by	O
the	O
production	O
of	O
IFN-γ	O
and	O
the	O
generation	O
of	O
IgG2a	O
[	O
28,29	O
]	O
.	O
Moreover	O
,	O
a	O
previous	O
study	O
had	O
demonstrated	O
that	O
different	O
liposomes	O
with	O
CpG	O
ODN	O
significantly	O
increased	O
Th1-biased	O
cytokines	O
and	O
augmented	O
cell	O
mediated	O
immune	O
response	O
[	O
30	O
]	O
.	O

Two	O
methods	O
of	O
formulating	B-Task
anionic	I-Task
nanocomplexes	I-Task
were	O
evaluated	O
.	O
In	O
both	O
,	O
nanocomplexes	O
were	O
prepared	B-Process
in	O
water	O
at	O
a	O
range	O
of	O
molar	O
charge	O
ratios	O
of	O
L	O
to	O
D	O
while	O
the	O
peptide	O
P	O
to	O
D	O
molar	O
charge	O
ratio	O
was	O
maintained	O
constant	O
at	O
3:1	O
.	O
Method	O
1	O
(	O
L	O
:	O
D	O
:	O
P	O
)	O
:	O
DNA	B-Material
was	O
first	O
added	B-Process
to	O
an	B-Material
anionic	I-Material
liposome	I-Material
(	O
LA	B-Material
,	I-Material
LAP1	I-Material
or	I-Material
LAP2	I-Material
)	O
and	O
incubated	B-Process
for	O
15min	O
at	O
room	O
temperature	O
and	O
then	O
the	O
peptide	O
was	O
added	B-Process
with	O
rapid	O
mixing	O
and	O
incubated	B-Process
at	O
room	O
temperature	O
for	O
a	O
further	O
20min	O
;	O
Method	O
2	O
(	O
P	O
:	O
D	O
:	O
L	O
)	O
:	O
the	O
peptide	B-Material
was	O
added	B-Process
to	O
the	O
DNA	O
and	O
incubated	B-Process
for	O
15min	O
at	O
room	O
temperature	O
and	O
then	O
liposome	B-Material
was	O
added	B-Process
with	O
rapid	O
mixing	O
and	O
incubated	B-Process
at	O
room	O
temperature	O
for	O
a	O
further	O
20min	O
.	O
Irrespective	O
of	O
the	O
method	O
of	O
order	O
of	O
mixing	O
,	O
all	O
molar	O
charge	O
ratios	O
in	O
this	O
study	O
refer	O
to	O
L	O
:	O
P	O
:	O
D	O
.	O
Cationic	O
formulations	O
LPD	B-Material
and	O
LCPRGPD	B-Material
were	O
prepared	O
in	O
the	O
order	O
L	O
:	O
P	O
:	O
D	O
as	O
described	O
previously	O
;	O
first	O
,	O
the	O
peptide	O
was	O
added	B-Process
to	O
the	O
liposome	O
DOTMA	B-Material
/	O
DOPE	B-Material
or	O
LCPRG	B-Material
followed	O
by	O
addition	O
of	O
the	O
DNA	B-Material
with	O
rapid	B-Process
mixing	I-Process
and	I-Process
incubated	I-Process
for	O
30min	O
at	O
room	O
temperature	O
to	O
allow	O
for	O
complex	O
formation	O
[	O
30	O
]	O
.	O
The	O
nanocomplexes	O
prepared	O
were	O
termed	O
LPD	B-Material
(	O
liposome	B-Material
DOTMA	I-Material
/	I-Material
DOPE	I-Material
)	O
,	O
LADP	B-Material
and	O
PDLA	B-Material
(	O
liposome	B-Material
LA	I-Material
)	O
,	O
PDLAP1	B-Material
(	O
liposome	B-Material
LAP1	I-Material
)	O
,	O
PDLAP2	B-Material
(	O
liposome	B-Material
LAP2	I-Material
)	O
,	O
PDLAPRG	B-Material
(	O
liposome	B-Material
LAPRG	I-Material
)	O
and	O
LCPRGPD	B-Material
(	O
liposome	B-Material
LCPRG	I-Material
)	O
.	O

The	O
crack	B-Process
band	I-Process
approach	I-Process
for	O
producing	O
mesh	O
independent	O
load	O
–	O
displacement	O
curves	O
for	O
fracture	O
in	O
plain	O
concrete	O
is	O
based	O
on	O
the	O
idea	O
that	O
the	O
crack	O
opening	O
is	O
transformed	O
into	O
inelastic	O
strain	O
by	O
distributing	O
it	O
over	O
an	O
element	O
length	O
dependent	O
zone	O
[	O
5	O
]	O
.	O
This	O
approach	O
will	O
only	O
produce	O
mesh	O
independent	O
load	O
–	O
displacement	O
curves	O
,	O
if	O
the	O
inelastic	O
strain	O
profiles	O
in	O
the	O
finite	O
element	O
analysis	O
are	O
mesh	O
size	O
dependent	O
.	O
This	O
requirement	O
is	O
an	O
important	O
difference	O
to	O
the	O
nonlocal	O
model	O
which	O
is	O
designed	O
to	O
produce	O
both	O
mesh	O
size	O
independent	O
load	O
–	O
displacement	O
curves	O
and	O
strain	O
profiles	O
.	O
In	O
CDPM2	O
,	O
the	O
crack	O
band	O
approach	O
is	O
applied	O
only	O
to	O
the	O
tensile	B-Material
part	I-Material
of	O
the	O
damage	O
algorithm	O
by	O
replacing	O
the	O
stress	B-Process
–	I-Process
inelastic	I-Process
strain	I-Process
law	I-Process
shown	O
in	O
Fig.	O
2	O
(	O
b	O
)	O
by	O
a	O
stress	B-Process
–	I-Process
inelastic	I-Process
displacement	I-Process
law	I-Process
of	O
the	O
form	O
(	B-Process
13	I-Process
)	I-Process
σ	I-Process
=	I-Process
ftexp	I-Process
(−	I-Process
ϵinhwft	I-Process
)	I-Process
if	I-Process
(	I-Process
ϵin	I-Process
>	I-Process
0	I-Process
)	I-Process
Here	O
,	O
wft	O
is	O
a	O
crack	O
opening	O
threshold	O
used	O
to	O
control	O
the	O
slope	O
of	O
the	O
softening	O
curve	O
and	O
h	O
is	O
the	O
width	O
of	O
the	O
crack-band	O
,	O
which	O
in	O
the	O
present	O
study	O
is	O
equal	O
to	O
the	O
maximum	O
dimension	O
of	O
the	O
element	O
along	O
the	O
principal	O
direction	O
of	O
the	O
strain	O
tensor	O
corresponding	O
to	O
the	O
maximum	O
tensile	O
principal	O
strain	O
at	O
the	O
onset	O
of	O
damage	O
.	O
For	O
the	O
compressive	O
part	O
,	O
a	O
stress	B-Process
–	I-Process
inelastic	I-Process
strain	I-Process
law	I-Process
was	O
used	O
to	O
determine	O
the	O
compressive	O
damage	O
parameter	O
,	O
since	O
it	O
was	O
reported	O
in	O
[	O
14	O
]	O
for	O
columns	O
subjected	O
to	O
eccentric	O
compression	O
that	O
inelastic	B-Material
strain	I-Material
profiles	I-Material
in	O
compression	O
do	O
not	O
exhibit	O
a	O
mesh	O
dependence	O
which	O
would	O
satisfy	O
the	O
assumptions	O
of	O
the	O
crack-band	B-Process
approach	I-Process
.	O
This	O
approach	O
of	O
applying	O
the	O
crack-band	O
approach	O
only	O
to	O
the	O
tensile	B-Material
part	I-Material
has	O
already	O
been	O
successfully	O
used	O
in	O
Grassl	O
et	O
al	O
.	O
[	O
16	O
]	O
.	O

HOMO	B-Material
–	I-Material
LUMO	I-Material
energy	O
band	O
gaps	O
between	O
ylides	B-Material
and	O
their	O
pyrene	B-Material
adducts	I-Material
propose	O
that	O
the	O
1,3-DC	B-Material
of	O
second	B-Material
pyridinium	I-Material
ylides	I-Material
to	O
ylidepyrene	B-Material
adducts	I-Material
are	O
HOMOylide	B-Material
–	I-Material
LUMOylide	I-Material
–	I-Material
pyrene	I-Material
controlled	O
since	O
the	O
energy	O
band	O
gap	O
is	O
smaller	O
than	O
HOMOylide	B-Material
–	I-Material
pyrene	I-Material
–	I-Material
LUMOylide	I-Material
.	O
Regioselectivity	B-Task
of	I-Task
second	I-Task
cycloaddition	I-Task
was	O
predicted	O
using	O
the	O
atomic	B-Process
orbital	I-Process
coefficients	I-Process
corresponding	O
to	O
HOMOylide	B-Material
–	I-Material
LUMOylide	I-Material
–	I-Material
pyrene	I-Material
.	O
According	O
to	O
Fukui	O
[	O
33	O
]	O
,	O
reactions	O
can	O
be	O
favorable	O
in	O
the	O
direction	O
of	O
maximal	B-Task
HOMO	I-Task
–	I-Task
LUMO	I-Task
overlapping	I-Task
of	O
larger	O
coefficients	O
at	O
the	O
reactive	O
sites	O
.	O
The	O
most	O
favorable	O
interactions	O
between	O
corresponding	O
ylides	B-Material
and	I-Material
ylidepyrene	I-Material
adducts	I-Material
to	O
form	O
the	O
most	B-Process
favorable	I-Process
regioisomer	I-Process
conformation	I-Process
are	O
given	O
in	O
Fig.	O
3.	O
Second	O
ylide	B-Material
addition	O
to	O
ylidepyrene	B-Material
structure	O
is	O
therefore	O
anticipated	O
to	O
proceed	O
via	O
ylideC2	B-Material
/	I-Material
C6	I-Material
–	I-Material
ylidepyrene-C3	I-Material
and	O
ylideC7	B-Material
–	I-Material
ylidepyrene	I-Material
/	I-Material
C2	I-Material
interactions	O
to	O
produce	O
the	O
same	O
regioisomer	B-Task
conformations	I-Task
.	O
Considering	O
the	O
theoretical	B-Process
calculations	I-Process
performed	O
for	O
pyrrolidine	B-Material
attached	I-Material
pyrene	I-Material
structure	O
,	O
it	O
is	O
also	O
expected	O
that	O
formation	B-Process
of	I-Process
the	I-Process
same	I-Process
type	I-Process
of	I-Process
regioisomers	I-Process
are	O
favorable	O
for	O
SWNTs	B-Process
after	O
the	O
1,3-DC	B-Material
of	I-Material
pyridinium	I-Material
ylides	I-Material
,	O
Fig.	O
3	O
.	O

Gamma	B-Material
titanium	I-Material
aluminides	I-Material
are	O
a	O
family	O
of	O
low	O
density	O
,	O
high	O
performance	O
alloys	B-Material
with	O
the	O
potential	O
to	O
replace	O
current	O
Ni-base	B-Material
superalloys	I-Material
used	O
in	O
the	O
production	B-Task
of	I-Task
aero-engine	I-Task
components	I-Task
.	O
Investment	B-Process
casting	I-Process
is	O
one	O
of	O
the	O
most	O
economical	O
methods	O
to	O
produce	B-Task
titanium	I-Task
and	I-Task
titanium	I-Task
aluminide	I-Task
alloy	I-Task
products	I-Task
,	O
increasing	O
the	O
components'	O
integrity	O
and	O
mechanical	O
properties	O
,	O
whilst	O
reducing	O
material	O
waste	O
and	O
machining	O
cost	O
[	O
1	O
]	O
.	O
Titanium	O
aluminides	O
are	O
difficult	O
to	O
process	O
mainly	O
due	O
to	O
the	O
low	O
fluidity	O
of	O
the	O
TiAl	O
alloy	O
around	O
its	O
melting	O
temperature	O
[	O
2	O
]	O
.	O
Due	O
to	O
the	O
high	O
affinity	O
of	O
elements	O
such	O
as	O
oxygen	O
,	O
nitrogen	O
etc.	O
,	O
titanium	O
and	O
its	O
alloys	O
can	O
easily	O
interact	O
with	O
mould	O
materials	O
during	O
the	O
investment	O
casting	O
process	O
,	O
resulting	O
in	O
an	O
interaction	O
hardened	O
layer	O
being	O
generated	O
at	O
the	O
metal	O
surface	O
[	O
3,4	O
]	O
.	O
This	O
hardened	O
layer	O
contains	O
a	O
large	O
amount	O
of	O
dissolved	O
oxygen	O
,	O
and	O
it	O
is	O
very	O
brittle	O
and	O
susceptible	O
to	O
crack	O
generation	O
and	O
propagation	O
[	O
5	O
]	O
.	O

From	O
this	O
study	O
where	O
a	O
commercial	O
Al	B-Material
–	I-Material
12Si	I-Material
alloy	I-Material
was	O
inoculated	B-Process
with	I-Process
different	I-Process
level	I-Process
of	I-Process
Nb	I-Process
+	I-Process
B	I-Process
addition	O
to	O
assess	O
the	O
grain	B-Task
refining	I-Task
potency	I-Task
of	I-Task
Nb	I-Task
+	I-Task
B	I-Task
inoculation	I-Task
it	O
can	O
be	O
concluded	O
that	O
in-situ	O
formed	O
Nb-based	B-Material
intermetallics	I-Material
compounds	I-Material
are	O
potent	O
heterogeneous	B-Material
nucleation	I-Material
substrates	I-Material
with	O
high	O
potency	O
for	O
the	O
refinement	B-Process
of	I-Process
Al	I-Process
–	I-Process
Si	I-Process
cast	I-Process
alloys	I-Process
.	O
The	O
primary	O
α-Al	B-Material
dendritic	I-Material
grain	I-Material
size	O
varies	O
with	O
the	O
addition	O
level	O
of	O
Nb	B-Material
and	O
B.	O
Moreover	O
,	O
significant	O
grain	O
refinement	O
over	O
a	O
wide	O
range	O
of	O
cooling	O
rates	O
is	O
obtained	O
via	O
enhanced	O
heterogeneous	O
nucleation	O
making	O
the	O
grain	O
size	O
of	O
the	O
material	O
less	O
sensitive	O
to	O
the	O
cooling	O
rate	O
.	O
Nb	O
+	O
B	B-Material
inoculants	O
are	O
characterised	O
by	O
some	O
fading	O
which	O
is	O
still	O
acceptable	O
after	O
4	O
h	O
of	O
contact	O
time	O
.	O
Moreover	O
,	O
alloys	O
refined	O
by	O
means	O
of	O
Nb	O
+	O
B	O
inoculants	O
can	O
be	O
recycled	O
obtaining	O
a	O
fine	O
grain	O
structure	O
with	O
small	O
addition	O
or	O
no	O
further	O
addition	O
of	O
inoculants	O
after	O
the	O
first	O
initial	O
addition	O
.	O
Concluding	O
,	O
Nb	O
+	O
B	O
inoculation	O
is	O
a	O
promising	O
candidate	O
for	O
the	O
refinement	O
of	O
cast	O
Al	O
alloy	O
which	O
could	O
lead	O
to	O
their	O
wider	O
employment	O
in	O
the	O
automotive	O
industry	O
with	O
the	O
resultant	O
intrinsic	O
advantages	O
of	O
lighter	O
structural	O
component	O
from	O
an	O
environmental	O
point	O
of	O
view	O
.	O

The	O
observed	B-Task
conductivity	I-Task
of	O
A2FeMoO6	B-Material
–	I-Material
δ	I-Material
(	O
A	O
=	O
Ca	B-Material
,	O
Sr	B-Material
,	O
Ba	B-Material
)	O
[	O
7	O
]	O
was	O
linked	O
to	O
a	O
potential	O
double	B-Process
exchange	I-Process
mechanism	I-Process
,	O
with	O
conduction	O
between	O
Fe3	B-Material
+-	I-Material
O-Mo-O-Fe2	I-Material
+	I-Material
.	O
Double-exchange	B-Process
mechanisms	I-Process
,	O
as	O
proposed	O
by	O
Zener	O
[	O
23	O
]	O
,	O
posit	O
that	O
electron	B-Process
transfer	I-Process
between	O
ions	B-Material
in	O
different	O
oxidation	B-Process
states	O
may	O
be	O
facilitated	O
if	O
the	O
electron	B-Material
does	O
not	O
have	O
to	O
alter	O
its	O
spin	O
state	O
.	O
Replacement	B-Process
of	O
Mo	B-Material
with	O
Fe	B-Material
in	O
this	O
mechanism	O
would	O
be	O
expected	O
to	O
result	O
in	O
a	O
reduction	B-Process
of	I-Process
the	I-Process
conductivity	I-Process
through	O
reduction	B-Process
of	I-Process
the	I-Process
available	I-Process
percolation	I-Process
pathways	I-Process
,	O
unless	O
delocalisation	B-Process
of	I-Process
Fe	I-Process
electrons	I-Process
through	O
Fe2	B-Process
+-	I-Process
O-Fe3	I-Process
+	I-Process
exchange	I-Process
could	O
also	O
occur	O
.	O
Double	B-Process
exchange	I-Process
mechanisms	I-Process
have	O
been	O
observed	O
previously	O
for	O
mixed	O
valent	O
iron	B-Material
in	I-Material
iron	I-Material
oxides	I-Material
[	O
24	O
]	O
,	O
and	O
,	O
as	O
iron	O
is	O
known	O
to	O
exist	O
in	O
a	O
mixed	O
valent	O
state	O
for	O
Ca2	B-Material
–	I-Material
xSrxFeMoO6	I-Material
–	I-Material
δ	I-Material
[	O
25	O
]	O
,	O
this	O
provides	O
a	O
plausible	O
explanation	O
for	O
the	O
observed	O
metallic	B-Task
conductivity	I-Task
.	O
Band	B-Process
structure	I-Process
calculations	I-Process
and	O
Mossbauer	B-Process
spectroscopy	I-Process
could	O
be	O
utilised	O
to	O
further	O
elucidate	O
the	O
conduction	B-Task
mechanism	I-Task
for	O
these	O
compounds	B-Material
,	O
however	O
this	O
is	O
outside	O
the	O
scope	O
of	O
this	O
enquiry	O
.	O

To	O
address	O
the	O
vertical	B-Task
displacement	I-Task
estimation	I-Task
of	I-Task
conventional	I-Task
pile	I-Task
groups	I-Task
subjected	I-Task
to	I-Task
mechanical	I-Task
loads	I-Task
,	O
various	O
numerical	B-Process
and	I-Process
analytical	I-Process
methods	I-Process
have	O
been	O
proposed	O
.	O
These	O
methods	O
include	O
the	O
finite	B-Process
element	I-Process
method	I-Process
[	O
e.g.	O
,	O
2,3	O
]	O
,	O
the	O
boundary	B-Process
element	I-Process
method	I-Process
[	O
e.g.	O
,	O
4,5	O
]	O
,	O
the	O
finite	B-Process
difference	I-Process
method	I-Process
[	O
e.g.	O
,	O
6	O
]	O
,	O
the	O
interaction	B-Process
factor	I-Process
method	I-Process
[	O
e.g.	O
,	O
7,8	O
–	O
11	O
]	O
,	O
the	O
equivalent	B-Process
pier	I-Process
and	I-Process
raft	I-Process
methods	I-Process
[	O
e.g.	O
,	O
12	O
–	O
14	O
]	O
,	O
and	O
the	O
settlement	B-Process
ratio	I-Process
method	I-Process
[	O
e.g.	O
,	O
15	O
]	O
.	O
The	O
finite	B-Process
element	I-Process
method	I-Process
,	O
while	O
providing	O
the	O
most	O
rigorous	O
and	O
exhaustive	O
representation	O
of	O
the	O
pile	B-Task
group-related	I-Task
problem	I-Task
,	O
is	O
generally	O
computationally	O
expensive	O
and	O
considered	O
mainly	O
a	O
research	B-Material
tool	I-Material
rather	O
than	O
a	O
design	B-Material
tool	I-Material
.	O
Conversely	O
,	O
the	O
versatility	B-Process
of	I-Process
simplified	I-Process
(	I-Process
approximate	I-Process
)	I-Process
methods	I-Process
,	O
such	O
as	O
the	O
interaction	B-Process
factor	I-Process
approach	I-Process
that	O
allows	O
capturing	B-Process
the	I-Process
(	I-Process
e.g.	I-Process
,	I-Process
vertical	I-Process
)	I-Process
displacements	I-Process
of	I-Process
any	I-Process
general	I-Process
pile	I-Process
group	I-Process
by	O
the	O
analysis	B-Process
of	I-Process
the	I-Process
displacement	I-Process
interaction	I-Process
between	I-Process
two	I-Process
identical	I-Process
piles	I-Process
and	O
by	O
the	O
use	O
of	O
the	O
elastic	B-Process
principle	I-Process
of	I-Process
superposition	I-Process
of	I-Process
effects	I-Process
,	O
makes	O
them	O
attractive	O
as	O
design	B-Material
tools	I-Material
because	O
they	O
allow	O
for	O
the	O
use	O
of	O
expedient	B-Task
parametric	I-Task
studies	I-Task
under	O
various	O
design	O
conditions	O
.	O

The	O
optimised	B-Task
structure	I-Task
at	O
the	O
B3LYP	B-Process
/	I-Process
aug-cc-pVTZ	I-Process
level	O
was	O
then	O
used	O
to	O
perform	O
calculations	O
of	O
the	O
lowest	B-Process
electronic	I-Process
singlet	I-Process
excited	O
states	O
with	O
the	O
coupled	O
cluster	O
linear	B-Process
response	I-Process
(	O
LR	B-Process
)	O
coupled	B-Process
cluster	I-Process
hierarchy	I-Process
CCS	B-Process
,	O
CC2	B-Process
,	O
CCSD	B-Process
and	O
CC3	B-Process
,	O
along	O
with	O
perturbative	B-Process
corrected	I-Process
methods	I-Process
CIS	B-Process
(	I-Process
D	I-Process
)	I-Process
and	O
CCSDR	B-Process
(	I-Process
3	I-Process
)	I-Process
.	O
The	O
correlated	B-Process
response	I-Process
methods	I-Process
were	O
performed	O
with	O
an	O
all-electron	O
atomic	B-Process
natural	I-Process
orbital	I-Process
(	O
ANO	B-Process
)	O
basis	O
set	O
contracted	O
to	O
6s5p4d3f1g	B-Process
on	O
manganese	B-Material
,	O
[	O
47	O
]	O
together	O
with	O
the	O
cc-pVTZ	B-Process
basis	O
set	O
on	O
the	O
oxygen	B-Material
atoms	O
.	O
The	O
all-electron	B-Process
correlated	I-Process
calculations	I-Process
invoked	O
a	O
13	O
orbital	O
frozen	O
core	O
(	O
O	B-Material
1s	O
,	O
Mn	B-Material
1s2s2p3s3p	O
)	O
.	O
Trial	O
calculations	O
correlating	O
these	O
orbitals	O
only	O
had	O
a	O
minor	O
effect	O
on	O
excitation	B-Task
energies	I-Task
.	O
For	O
comparison	O
the	O
EOM-CCSD	B-Process
method	I-Process
with	O
the	O
cc-pVTZ	B-Process
basis	O
on	O
all	O
atoms	O
was	O
tested	O
to	O
compare	O
with	O
LR-CCSD	B-Process
.	O
These	O
formally	O
give	O
exactly	O
the	O
same	O
excitation	B-Task
energies	I-Task
,	O
although	O
the	O
transition	B-Task
moments	I-Task
are	O
more	O
accurate	O
for	O
LR-CCSD	B-Process
.	O
Abelian	B-Process
symmetry	I-Process
(	O
D2	B-Process
)	O
was	O
used	O
in	O
all	O
correlated	O
excited	B-Process
state	I-Process
calculations	I-Process
.	O

Arrays	O
of	O
TFTs	B-Material
and	O
circuits	B-Material
were	O
fabricated	O
on	O
precleaned	O
,	O
5cm	O
×	O
5cm	O
,	O
125μm	O
thick	O
polyethylene	B-Material
naphthalate	I-Material
(	O
PEN	B-Material
)	O
substrates	O
(	O
Dupont-Teijin	O
)	O
.	O
Full	O
details	O
of	O
our	O
vacuum-fabrication	B-Process
procedures	I-Process
have	O
been	O
given	O
in	O
previous	O
publications	O
[	O
17	O
–	O
19,23	O
]	O
.	O
Briefly	O
,	O
aluminium	B-Material
gate	I-Material
electrodes	I-Material
and	O
associated	O
tracks	B-Material
were	O
vacuum	B-Process
evaporated	I-Process
onto	O
the	O
substrates	B-Material
through	O
shadow	B-Material
masks	I-Material
.	O
Subsequently	O
,	O
the	O
substrates	B-Material
were	O
attached	O
to	O
a	O
cooled	O
web-coater	B-Material
drum	I-Material
(	O
Aerre	O
Machines	O
)	O
.	O
With	O
the	O
drum	O
rotating	O
at	O
a	O
linear	O
speed	O
of	O
25m	O
/	O
min	O
under	O
vacuum	O
,	O
flash-evaporated	B-Material
TPGDA	I-Material
monomer	I-Material
vapour	I-Material
which	O
condensed	O
onto	O
the	O
substrates	B-Material
was	O
cross-linked	B-Process
by	O
exposure	B-Process
,	O
in	O
situ	O
,	O
to	O
a	O
plasma	B-Material
.	O
The	O
resulting	O
smooth	O
,	O
pinhole-free	O
films	B-Material
were	O
typically	O
500nm	O
to	O
1μm	O
thick	O
with	O
a	O
measured	O
dielectric	O
constant	O
varying	O
in	O
the	O
range	O
4	O
–	O
5	O
.	O
For	O
circuit	B-Task
fabrication	I-Task
,	O
the	O
insulator	B-Material
was	O
patterned	O
using	O
shadow	B-Material
masks	I-Material
to	O
define	O
rectangular	O
areas	O
separated	O
by	O
1mm	O
gaps	O
to	O
act	O
as	O
vias	O
for	O
inter-layer	B-Process
metallic	I-Process
connections	I-Process
.	O
The	O
substrates	B-Material
were	O
then	O
transferred	O
into	O
an	O
evaporator	B-Material
(	O
Minispectros	O
,	O
Kurt	O
Lesker	O
)	O
integrated	O
into	O
a	O
nitrogen	B-Material
glovebox	I-Material
for	O
the	O
vacuum-deposition	O
(	O
2.4nm	O
/	O
min	O
)	O
of	O
DNTT	O
onto	O
the	O
insulator	O
.	O
Without	O
exposing	O
the	O
substrates	O
to	O
ambient	O
air	O
,	O
the	O
gold	O
source	O
/	O
drain	O
metallisation	O
layer	O
was	O
deposited	O
through	O
a	O
shadow	O
mask	O
in	O
the	O
same	O
evaporator	O
.	O

For	O
decades	O
,	O
vibronic	B-Process
coupling	I-Process
models	I-Process
[	O
1	O
–	O
4	O
]	O
have	O
served	O
as	O
bridges	O
connecting	O
nuclear	B-Task
dynamics	I-Task
studies	I-Task
with	O
the	O
static	O
studies	O
of	O
electronic	B-Task
structure	I-Task
calculations	I-Task
[	O
5	O
]	O
.	O
The	O
vibronic	B-Process
coupling	I-Process
model	I-Process
is	O
a	O
simple	O
polynomial	B-Task
expansion	I-Task
of	O
diabatic	B-Task
potential	I-Task
energy	I-Task
surfaces	I-Task
and	I-Task
couplings	I-Task
.	O
The	O
expansion	B-Process
coefficients	I-Process
are	O
chosen	O
so	O
that	O
the	O
eigenvalues	O
of	O
the	O
potential	O
operator	O
map	O
on	O
to	O
the	O
adiabatic	O
potential	O
surfaces	O
.	O
This	O
diabatisation	B-Process
by	O
ansatz	O
circumvents	O
many	O
of	O
the	O
problems	O
of	O
describing	O
non-adiabatic	B-Task
systems	I-Task
.	O
It	O
is	O
also	O
the	O
inspiration	O
for	O
a	O
diabatisation	B-Process
scheme	I-Process
that	O
is	O
used	O
in	O
modern	O
,	O
direct-dynamic	B-Process
methods	I-Process
that	O
include	O
non-adiabatic	O
effects	O
[	O
6	O
]	O
.	O
For	O
a	O
model	B-Process
Hamiltonian	I-Process
to	O
correctly	O
approximate	O
the	O
eigenvectors	O
of	O
the	O
true	B-Process
Hamiltonian	I-Process
it	O
has	O
to	O
span	O
the	O
totally	O
symmetric	O
irreducible	B-Process
representation	I-Process
(	O
IrRep	B-Process
)	O
of	O
the	O
point	O
groups	O
the	O
molecule	O
belongs	O
to	O
,	O
at	O
the	O
appropriate	O
symmetric	B-Process
geometries	I-Process
[	O
7	O
]	O
.	O
In	O
recent	O
times	O
,	O
many	O
articles	O
have	O
demonstrated	O
the	O
advantages	O
of	O
using	O
symmetry	O
when	O
constructing	O
analytic	B-Process
model	I-Process
potentials	I-Process
[	O
8	O
–	O
12	O
]	O
,	O
most	O
often	O
in	O
the	O
context	O
of	O
permutation-inversion	B-Process
groups	I-Process
[	O
13	O
]	O
.	O

In	O
the	O
present	O
work	O
,	O
a	O
LIF	B-Process
technique	I-Process
is	O
applied	O
for	O
investigation	O
of	O
gas-sheared	B-Task
film	I-Task
flow	I-Task
in	O
horizontal	O
rectangular	O
duct	B-Material
.	O
The	O
technique	O
makes	O
it	O
possible	O
to	O
perform	O
field	B-Process
measurements	I-Process
of	I-Process
local	I-Process
film	I-Process
thickness	I-Process
,	O
resolved	O
in	O
both	O
space	O
and	O
time	O
,	O
similar	O
to	O
the	O
work	O
of	O
Alekseenko	O
et	O
al	O
.	O
(	O
2009	O
)	O
.	O
The	O
flat	O
shape	O
and	O
large	O
transverse	O
size	O
of	O
the	O
duct	B-Material
allow	O
us	O
to	O
resolve	B-Process
the	I-Process
film	I-Process
thickness	I-Process
in	O
transverse	O
coordinate	O
as	O
well	O
.	O
Alekseenko	O
et	O
al	O
.	O
(	O
2012	O
)	O
attempted	O
to	O
do	O
this	O
in	O
annular	B-Process
downward	I-Process
flow	I-Process
,	O
but	O
,	O
for	O
technical	O
reasons	O
,	O
the	O
sampling	B-Process
frequency	I-Process
was	O
not	O
high	O
enough	O
in	O
their	O
experiments	O
.	O
More	O
recently	O
Alekseenko	O
et	O
al	O
.	O
(	O
2014a	O
)	O
showed	O
that	O
the	O
LIF	B-Process
technique	I-Process
can	O
also	O
detect	B-Task
entrained	I-Task
droplets	I-Task
.	O
The	O
technique	O
allows	O
the	O
simultaneous	O
study	O
of	O
three-dimensional	B-Material
wavy	I-Material
structures	I-Material
and	O
liquid	B-Task
entrainment	I-Task
,	O
and	O
can	O
improve	B-Task
understanding	I-Task
of	I-Task
the	I-Task
entrainment	I-Task
phenomenon	I-Task
.	O

In	O
general	O
,	O
liquid	B-Process
film	I-Process
flows	I-Process
of	O
practical	O
relevance	O
are	O
turbulent	O
and	O
,	O
hence	O
,	O
are	O
associated	O
with	O
the	O
presence	O
of	O
broadband	B-Material
interfacial	I-Material
waves	I-Material
on	O
the	O
film	B-Material
surface	I-Material
.	O
A	O
thorough	O
understanding	B-Task
of	I-Task
the	I-Task
characteristic	I-Task
profiles	I-Task
,	I-Task
scales	I-Task
and	I-Task
dynamics	I-Task
of	I-Task
these	I-Task
interfacial	I-Task
waves	I-Task
is	O
of	O
essential	O
importance	O
in	O
making	O
accurate	O
and	O
reliable	O
predictions	O
of	O
heat	O
and	O
mass	O
transfer	O
rates	O
(	O
Mathie	O
and	O
Markides	O
,	O
2013a	O
;	O
Mathie	O
et	O
al.	O
,	O
2013	O
)	O
.	O
Previous	O
efforts	O
in	O
downwards	B-Process
annular	I-Process
flow	I-Process
have	O
focused	O
on	O
the	O
spatio	B-Process
/	I-Process
temporal	I-Process
measurement	I-Process
of	O
liquid	B-Process
film	I-Process
thickness	I-Process
,	O
followed	O
by	O
in-depth	O
statistical	B-Process
analyses	I-Process
of	O
this	O
film	B-Material
thickness	O
(	O
Webb	O
and	O
Hewitt	O
,	O
1975	O
;	O
Belt	O
et	O
al.	O
,	O
2010	O
;	O
Alekseenko	O
et	O
al.	O
,	O
2012	O
;	O
Zhao	O
et	O
al.	O
,	O
2013	O
)	O
.	O
These	O
efforts	O
have	O
contributed	O
to	O
a	O
much	O
improved	O
understanding	O
of	O
the	O
interfacial	B-Task
topology	I-Task
observed	O
in	O
downwards	B-Process
annular	I-Process
flows	I-Process
and	O
also	O
to	O
the	O
subsequent	O
proposal	O
of	O
a	O
series	O
of	O
correlations	O
for	O
the	O
quantification	B-Task
of	I-Task
the	I-Task
mean	I-Task
film	I-Task
thickness	I-Task
,	I-Task
wave	I-Task
amplitudes	I-Task
and	I-Task
liquid	I-Task
entrainment	I-Task
rates	I-Task
into	O
the	O
gas	B-Material
phase	O
(	O
Ambrosini	O
et	O
al.	O
,	O
1991	O
;	O
Karapantsios	O
and	O
Karabelas	O
,	O
1995	O
;	O
Azzopardi	O
,	O
1997	O
)	O
.	O
On	O
the	O
other	O
hand	O
,	O
less	O
has	O
been	O
published	O
on	O
the	O
velocity	B-Process
distribution	I-Process
and	O
the	O
flow	B-Process
structure	I-Process
within	I-Process
the	I-Process
liquid	I-Process
films	I-Process
,	O
underneath	O
the	O
film	B-Material
surface	I-Material
.	O
This	O
can	O
be	O
related	O
to	O
the	O
relative	O
difficulty	O
of	O
these	O
measurements	O
caused	O
by	O
:	O
(	O
i	O
)	O
the	O
extremely	O
restricted	O
measurement	O
space	O
,	O
due	O
to	O
the	O
small	O
thickness	B-Task
of	I-Task
the	I-Task
liquid	I-Task
films	I-Task
(	O
in	O
the	O
order	O
of	O
and	O
often	O
sub-mm	O
)	O
,	O
(	O
ii	O
)	O
the	O
highly	O
disturbed	O
and	O
intermittent	O
nature	O
of	O
the	O
gas	B-Process
–	I-Process
liquid	I-Process
interface	I-Process
,	O
(	O
iii	O
)	O
the	O
entrainment	B-Process
of	I-Process
gas	I-Process
inside	O
the	O
liquid	B-Material
film	I-Material
and	O
of	O
liquid	O
into	O
the	O
gas	B-Material
core	I-Material
,	O
and	O
(	O
iv	O
)	O
the	O
relatively	O
high	B-Task
velocities	I-Task
of	O
both	O
the	O
gas	B-Material
and	O
liquid	B-Material
phases	O
.	O

There	O
is	O
also	O
a	O
lack	O
of	O
agreement	O
as	O
to	O
what	O
constitutes	O
churn	B-Process
flow	I-Process
.	O
It	O
is	O
fairly	O
certainly	O
a	O
gas	B-Process
continuous	I-Process
flow	I-Process
.	O
There	O
is	O
growing	O
agreement	O
that	O
there	O
are	O
huge	B-Material
waves	I-Material
present	O
and	O
some	O
of	O
the	O
liquid	B-Material
is	O
carried	O
as	O
drops	O
.	O
Sekoguchi	O
and	O
Mori	O
(	O
1997	O
)	O
and	O
Sawai	O
et	O
al	O
.	O
(	O
2004	O
)	O
using	O
measurements	O
from	O
their	O
multiple	O
probes	B-Material
(	O
92	O
over	O
an	O
axial	O
length	O
of	O
2.325	O
m	O
)	O
obtained	O
time	B-Process
/	I-Process
axial	I-Process
position	I-Process
/	I-Process
void	I-Process
fraction	I-Process
information	I-Process
.	O
From	O
this	O
they	O
were	O
able	O
to	O
identify	B-Process
huge	I-Process
wave	I-Process
from	O
amongst	O
disturbance	B-Material
waves	I-Material
and	O
slugs	B-Material
.	O
They	O
classified	O
individual	O
structures	O
as	O
huge	B-Material
waves	I-Material
from	O
their	O
size	O
together	O
with	O
the	O
fact	O
that	O
their	O
velocities	O
depended	O
significantly	O
on	O
the	O
corresponding	O
axial	O
length	O
.	O
This	O
was	O
in	O
contrast	O
to	O
disturbance	B-Material
waves	I-Material
where	O
the	O
velocity	B-Process
of	I-Process
individual	I-Process
waves	I-Process
only	O
increased	O
slightly	O
with	O
the	O
axial	O
extent	O
of	O
these	O
waves	O
.	O
They	O
also	O
found	O
that	O
the	O
frequency	B-Process
of	I-Process
huge	I-Process
waves	I-Process
first	O
increased	O
and	O
then	O
decrease	O
with	O
increasing	O
gas	B-Material
superficial	O
velocity	O
.	O
Similarly	O
,	O
their	O
velocities	O
were	O
found	O
to	O
deviate	O
from	O
the	O
line	O
for	O
slug	B-Process
flow	I-Process
velocities	I-Process
and	O
pass	O
through	O
a	O
maximum	O
and	O
then	O
a	O
minimum	O
.	O

The	O
scheduling	B-Process
process	I-Process
we	O
adopt	O
matches	O
a	O
multiple	B-Process
stage	I-Process
stochastic	I-Process
programming	I-Process
approach	I-Process
.	O
Standard	O
two-stage	B-Process
stochastic	I-Process
programs	I-Process
with	O
linear	B-Process
or	I-Process
convex	I-Process
functions	I-Process
are	O
often	O
solved	O
using	O
the	O
L-shaped	B-Process
method	I-Process
or	O
Bender	B-Process
's	I-Process
decomposition	I-Process
[	O
44,6,7	O
]	O
.	O
However	O
,	O
our	O
recourse	B-Process
decision	I-Process
(	O
scheduled	B-Process
cancellations	I-Process
)	O
is	O
still	O
anticipative	O
to	O
further	O
uncertainty	O
,	O
namely	O
the	O
second	O
shift	O
surgery	O
durations	O
,	O
unavailability	O
and	O
cancellations	O
.	O
As	O
such	O
,	O
the	O
decision	B-Task
problem	I-Task
can	O
be	O
viewed	O
as	O
a	O
three-stage	B-Process
recourse	I-Process
model	I-Process
[	O
5,6	O
]	O
.	O
Solving	O
the	O
scheduling	B-Task
problem	I-Task
is	O
further	O
complicated	O
because	O
the	O
recourse	B-Process
function	I-Process
is	O
integer	O
.	O
Laporte	O
and	O
Louveaux	O
[	O
26	O
]	O
propose	O
modified	B-Process
L-shaped	I-Process
decomposition	I-Process
with	O
adjusted	O
optimal	O
cuts	O
for	O
two	B-Process
stage	I-Process
stochastic	I-Process
program	I-Process
with	O
integer	B-Process
recourse	I-Process
.	O
Angulo	O
et	O
al	O
.	O
[	O
1	O
]	O
alternately	O
generate	B-Process
optimal	I-Process
cuts	I-Process
of	I-Process
the	I-Process
linear	I-Process
sub-problem	I-Process
and	I-Process
the	I-Process
integer	I-Process
sub-problem	I-Process
,	O
which	O
improves	O
the	O
practical	O
convergence	O
(	O
see	O
also	O
[	O
15,8	O
])	O
.	O
We	O
follow	O
a	O
sample	B-Process
average	I-Process
approximation	I-Process
approach	I-Process
(	O
SAA	B-Process
)	O
which	O
uses	O
this	O
framework	O
.	O
Moreover	O
,	O
we	O
prove	O
and	O
exploit	O
a	O
specific	O
relationship	O
between	O
the	O
first-stage	B-Process
realization	I-Process
and	O
the	O
optimal	O
number	O
of	O
scheduled	O
cancellations	O
to	O
speed	O
up	O
the	O
computation	O
of	O
integer	B-Process
cuts	I-Process
.	O
We	O
use	O
Jensen	B-Process
's	I-Process
inequality	I-Process
[	O
17	O
]	O
to	O
upper	B-Task
bound	I-Task
the	I-Task
minus	I-Task
second	I-Task
(	I-Task
and	I-Task
third	I-Task
)	I-Task
stage	I-Task
cost	I-Task
,	O
a	O
technique	O
that	O
was	O
proposed	O
by	O
Batun	O
et	O
al	O
.	O
[	O
3	O
]	O
.	O

Modeling	B-Task
collaboration	I-Task
processes	I-Task
is	O
a	O
challenging	O
task	O
.	O
Existing	O
modeling	B-Process
approaches	I-Process
are	O
not	O
capable	O
of	O
expressing	O
the	O
unpredictable	O
,	O
non-routine	O
nature	O
of	O
human	B-Process
collaboration	I-Process
,	O
which	O
is	O
influenced	O
by	O
the	O
social	O
context	O
of	O
involved	O
collaborators	O
.	O
We	O
propose	O
a	O
modeling	B-Process
approach	I-Process
which	O
considers	O
collaboration	B-Process
processes	I-Process
as	O
the	O
evolution	B-Process
of	I-Process
a	I-Process
network	I-Process
of	I-Process
collaborative	I-Process
documents	I-Process
along	I-Process
with	I-Process
a	I-Process
social	I-Process
network	I-Process
of	I-Process
collaborators	I-Process
.	O
Our	O
modeling	B-Process
approach	I-Process
,	O
accompanied	O
by	O
a	O
graphical	B-Process
notation	I-Process
and	I-Process
formalization	I-Process
,	O
allows	O
to	O
capture	O
the	O
influence	B-Process
of	I-Process
complex	I-Process
social	I-Process
structures	I-Process
formed	O
by	O
collaborators	O
,	O
and	O
therefore	O
facilitates	O
such	O
activities	O
as	O
the	O
discovery	B-Task
of	I-Task
socially	I-Task
coherent	I-Task
teams	I-Task
,	O
social	O
hubs	O
,	O
or	O
unbiased	O
experts	O
.	O
We	O
demonstrate	O
the	O
applicability	O
and	O
expressiveness	O
of	O
our	O
approach	O
and	O
notation	O
,	O
and	O
discuss	O
their	O
strengths	O
and	O
weaknesses	O
.	O

We	O
start	O
by	O
outlining	O
the	O
motivation	O
,	O
structure	O
and	O
content	O
of	O
the	O
review	O
.	O
It	O
has	O
long	O
been	O
known	O
that	O
cardiovascular	B-Process
signals	I-Process
contain	O
a	O
number	O
of	O
oscillatory	B-Process
components	I-Process
that	O
are	O
not	O
exactly	O
periodic	O
.	O
To	O
put	O
it	O
differently	O
,	O
their	O
periods	B-Process
(	O
frequencies	B-Process
)	O
fluctuate	O
with	O
time	O
.	O
For	O
example	O
,	O
heart	B-Process
rate	I-Process
variability	I-Process
(	O
HRV	B-Process
)	O
has	O
in	O
itself	O
provided	O
a	O
major	O
topic	O
of	O
discussion	O
.	O
We	O
introduce	B-Task
one	I-Task
of	I-Task
the	I-Task
statistical	I-Task
approaches	I-Task
to	I-Task
HRV	I-Task
in	O
Section	O
3	O
.	O
However	O
,	O
in	O
order	O
to	O
understand	B-Task
the	I-Task
variability	I-Task
of	I-Task
the	I-Task
cardiovascular	I-Task
system	I-Task
,	O
discussion	O
of	O
a	O
single	O
source	O
is	O
insufficient	O
because	O
the	O
cardiovascular	O
system	O
is	O
composed	O
of	O
many	O
different	O
physiological	B-Material
components	I-Material
(	O
subsystems	B-Material
)	O
and	O
it	O
is	O
the	O
effects	O
of	O
their	O
mutual	O
interaction	O
that	O
combine	O
to	O
produce	O
HRV	B-Task
.	O
This	O
is	O
demonstrated	O
in	O
Section	O
4	O
,	O
revealed	O
by	O
results	O
obtained	O
using	O
the	O
wavelet	B-Process
transform	I-Process
.	O
In	O
Section	O
5	O
,	O
we	O
discuss	B-Task
the	I-Task
cardio-respiratory	I-Task
interaction	I-Task
in	O
terms	O
of	O
phase	B-Process
synchronization	I-Process
.	O
To	O
set	O
the	O
scene	O
for	O
these	O
later	O
discussions	O
,	O
we	O
summarize	B-Task
the	I-Task
basic	I-Task
principles	I-Task
of	I-Task
phase	I-Task
dynamics	I-Task
in	O
Section	O
2	O
.	O
For	O
readers	O
who	O
are	O
unfamiliar	O
with	O
the	O
physiological	O
aspects	O
of	O
the	O
research	O
,	O
we	O
provide	O
Appendices	O
A	O
on	O
the	O
cardiovascular	B-Material
system	I-Material
and	O
B	O
on	O
how	O
measurements	B-Process
of	I-Process
cardiovascular	I-Process
signals	I-Process
are	O
conducted	O
.	O
Appendix	O
C	O
provides	O
details	O
of	O
the	O
statistical	B-Process
methods	I-Process
used	O
in	O
the	O
group	B-Task
data	I-Task
analyses	I-Task
.	O

By	O
the	O
early	O
1970s	O
,	O
and	O
following	O
the	O
‘	O
golden	O
age’	O
of	O
general	B-Task
relativity	I-Task
that	O
took	O
place	O
in	O
the	O
1960s	O
,	O
there	O
was	O
a	O
wide	O
array	O
of	O
candidate	O
theories	B-Task
of	I-Task
gravity	I-Task
in	O
existence	O
that	O
could	O
rival	O
Einstein	O
’s	O
.	O
A	O
formalism	O
was	O
needed	O
to	O
deal	O
with	O
this	O
great	O
abundance	O
of	O
possibilities	O
,	O
and	O
this	O
was	O
provided	O
in	O
the	O
form	O
of	O
the	O
Parameterised	B-Process
Post-Newtonian	I-Process
(	O
PPN	B-Process
)	O
formalism	O
by	O
Kenneth	O
Nordtvedt	O
,	O
Kip	O
Thorne	O
and	O
Clifford	O
Will	O
.	O
The	O
PPN	B-Process
formalism	I-Process
was	O
built	O
on	O
the	O
earlier	O
work	O
of	O
Eddington	O
and	O
Dicke	O
,	O
and	O
allowed	O
for	O
the	O
numerous	O
theories	O
available	O
at	O
the	O
time	O
to	O
be	O
compared	O
to	O
cutting	B-Task
edge	I-Task
astrophysical	I-Task
observations	I-Task
such	O
as	O
lunar	B-Task
laser	I-Task
ranging	I-Task
,	O
radio	B-Task
echo	I-Task
,	O
and	O
,	O
in	O
1974	O
,	O
the	O
Hulse	B-Task
–	I-Task
Taylor	I-Task
binary	I-Task
pulsar	I-Task
.	O
The	O
PPN	B-Process
formalism	I-Process
provided	O
a	O
clear	O
structure	O
within	O
which	O
one	O
could	O
compare	B-Process
and	I-Process
assess	I-Process
various	O
theories	O
,	O
and	O
has	O
been	O
the	O
benchmark	B-Process
for	I-Process
how	I-Process
theories	I-Process
of	I-Process
gravity	I-Process
should	I-Process
be	I-Process
evaluated	I-Process
ever	O
since	O
.	O
We	O
will	O
give	O
an	O
outline	O
of	O
the	O
PPN	B-Process
formalism	I-Process
,	O
and	O
the	O
constraints	O
available	O
within	O
it	O
today	O
,	O
in	O
Section	O
2	O
.	O

Despite	O
the	O
ubiquity	O
of	O
time-dependent	B-Task
dynamical	I-Task
systems	I-Task
in	O
nature	O
,	O
there	O
has	O
been	O
relatively	O
little	O
work	O
done	O
on	O
the	O
analysis	B-Task
of	I-Task
time	I-Task
series	I-Task
from	O
such	O
systems	O
.	O
Mathematically	O
they	O
are	O
known	O
as	O
non-autonomous	B-Task
systems	I-Task
,	O
which	O
are	O
named	O
as	O
such	O
because	O
,	O
unlike	O
autonomous	B-Task
systems	I-Task
,	O
in	O
addition	O
to	O
the	O
points	O
in	O
space	O
over	O
which	O
they	O
are	O
observed	O
they	O
are	O
also	O
influenced	O
by	O
the	O
points	O
in	O
time	O
.	O
Recently	O
there	O
has	O
been	O
much	O
work	O
on	O
the	O
direct	O
‘	B-Process
bottom-up’	I-Process
approach	I-Process
to	O
these	O
systems	O
,	O
which	O
includes	O
the	O
introduction	O
of	O
a	O
subclass	O
known	O
as	O
chronotaxic	B-Task
systems	I-Task
that	O
are	O
able	O
to	O
model	B-Process
the	I-Process
stable	I-Process
but	I-Process
time-varying	I-Process
frequencies	I-Process
of	O
oscillations	O
in	O
living	B-Task
systems	I-Task
[	O
8,9	O
]	O
.	O
In	O
contrast	O
,	O
the	O
time	B-Process
series	I-Process
analysis	I-Process
of	O
these	O
systems	O
,	O
referred	O
to	O
as	O
the	O
inverse	B-Process
or	O
‘	B-Process
top-down’	I-Process
approach	O
,	O
has	O
not	O
been	O
studied	O
in	O
detail	O
before	O
.	O
This	O
is	O
partly	O
because	O
non-autonomous	B-Task
systems	I-Task
can	O
still	O
be	O
analysed	O
in	O
the	O
same	O
way	O
as	O
other	O
types	O
of	O
systems	O
in	O
both	O
the	O
deterministic	B-Process
[	O
10	O
]	O
and	O
the	O
stochastic	B-Process
[	O
11	O
]	O
regime	O
.	O
However	O
,	O
it	O
is	O
now	O
argued	O
that	O
this	O
type	O
of	O
analysis	O
is	O
insufficient	O
and	O
that	O
an	O
entirely	O
new	O
analytical	B-Task
framework	I-Task
is	O
required	O
to	O
provide	O
a	O
more	O
useful	O
picture	O
of	O
such	O
systems	O
.	O
In	O
the	O
case	O
of	O
chronotaxic	B-Task
systems	I-Task
some	O
methods	O
have	O
already	O
been	O
developed	O
for	O
the	O
inverse	B-Process
approach	I-Process
and	O
they	O
have	O
shown	O
to	O
be	O
useful	O
in	O
analysing	O
heart	B-Task
rate	I-Task
variability	I-Task
[	O
12	O
]	O
.	O
A	O
general	O
and	O
dedicated	O
procedure	O
for	O
analysing	O
non-autonomous	B-Task
systems	I-Task
has	O
still	O
not	O
been	O
tackled	O
though	O
.	O

The	O
purpose	O
of	O
this	O
Letter	O
is	O
to	O
answer	O
the	O
above	O
question	O
and	O
to	O
confront	B-Task
those	I-Task
six-zero	I-Task
textures	I-Task
of	I-Task
lepton	I-Task
mass	I-Task
matrices	I-Task
with	O
the	O
latest	O
experimental	B-Material
data	I-Material
.	O
First	O
,	O
we	O
shall	O
present	O
a	O
concise	O
analysis	O
of	O
the	O
lepton	B-Material
mass	I-Material
matrices	I-Material
in	O
Table	O
1	O
and	O
reveal	B-Task
their	I-Task
isomeric	I-Task
features	I-Task
,	O
namely	O
,	O
they	O
have	O
the	O
same	O
phenomenological	O
consequences	O
,	O
although	O
their	O
structures	O
are	O
apparently	O
different	O
.	O
Second	O
,	O
we	O
shall	O
examine	B-Task
the	I-Task
predictions	I-Task
of	I-Task
these	I-Task
lepton	I-Task
mass	I-Task
matrices	I-Task
by	O
comparing	O
them	O
with	O
the	O
2σ	O
and	O
3σ	O
intervals	O
of	O
two	O
neutrino	B-Process
mass-squared	I-Process
differences	I-Process
and	O
three	O
lepton	O
flavor	O
mixing	O
angles,22To	O
be	O
specific	O
,	O
we	O
make	O
use	O
of	O
the	O
2σ	O
and	O
3σ	O
intervals	O
of	O
two	O
neutrino	O
mass-squared	O
differences	O
and	O
three	O
lepton	B-Process
flavor	I-Process
mixing	I-Process
angles	I-Process
given	O
by	O
M.	O
Maltoni	O
et	O
al.	O
in	O
Ref	O
.	O
[	O
5	O
]	O
.	O
which	O
are	O
obtained	O
from	O
a	O
global	O
analysis	O
of	O
the	O
latest	O
solar	O
,	O
atmospheric	O
,	O
reactor	O
(	O
KamLAND	O
and	O
CHOOZ	O
[	O
10	O
])	O
and	O
accelerator	O
(	O
K2K	O
)	O
neutrino	O
data	O
.	O
We	O
find	O
no	O
parameter	O
space	O
allowed	O
for	O
six	O
isomeric	O
lepton	O
mass	O
matrices	O
at	O
the	O
2σ	O
level	O
.	O
At	O
the	O
3σ	O
level	O
,	O
however	O
,	O
their	O
results	O
for	O
neutrino	O
masses	O
and	O
lepton	O
flavor	O
mixing	O
angles	O
can	O
be	O
compatible	O
with	O
current	O
data	O
.	O
Third	O
,	O
we	O
incorporate	O
the	O
seesaw	O
mechanism	O
and	O
the	O
Fukugita	O
–	O
Tanimoto	O
–	O
Yanagida	O
hypothesis	O
[	O
9	O
]	O
in	O
the	O
charged	O
lepton	O
and	O
Dirac	O
neutrino	O
mass	O
matrices	O
with	O
six	O
texture	O
zeros	O
.	O
It	O
turns	O
out	O
that	O
their	O
predictions	O
,	O
including	O
θ23	O
≈	O
45	O
°	O
,	O
are	O
in	O
good	O
agreement	O
with	O
the	O
present	O
experimental	O
data	O
even	O
at	O
the	O
2σ	O
level	O
.	O

The	O
aim	O
of	O
this	O
note	O
is	O
nothing	O
more	O
than	O
to	O
bring	B-Task
both	I-Task
approaches	I-Task
on	I-Task
equal	I-Task
footing	I-Task
and	O
to	O
relax	B-Task
the	I-Task
assumptions	I-Task
under	O
which	O
the	O
results	O
of	O
[	O
11,13	O
]	O
have	O
been	O
derived	O
using	O
the	O
first	O
approach	O
.	O
More	O
concretely	O
,	O
we	O
generalize	B-Process
the	I-Process
one-loop	I-Process
partition	I-Process
functions	I-Process
,	I-Process
as	O
derived	O
in	O
[	O
11,13	O
]	O
for	O
levels	O
being	O
odd	O
,	O
to	O
the	O
case	O
of	O
even	O
levels	O
.	O
Moreover	O
,	O
on	O
the	O
level	O
of	O
partition	O
functions	O
we	O
implement	O
additional	B-Process
dressings	I-Process
of	I-Process
the	I-Process
world-sheet	I-Process
parity	I-Process
symmetry	I-Process
and	O
identify	O
them	O
with	O
the	O
dressings	O
introduced	O
in	O
[	O
12	O
]	O
in	O
the	O
crosscap	B-Process
state	I-Process
approach	I-Process
.	O
As	O
expected	O
,	O
all	O
the	O
physical	O
information	O
can	O
be	O
read	O
off	O
entirely	O
from	O
the	O
various	O
amplitudes	O
.	O
We	O
will	O
end	O
up	O
with	O
a	O
collection	O
of	O
very	O
explicit	O
and	O
general	O
one-loop	B-Process
partition	I-Process
functions	I-Process
and	O
tadpole	B-Process
cancellation	I-Process
conditions	I-Process
covering	O
simple	O
current	B-Process
extensions	I-Process
of	I-Process
all	I-Process
168	I-Process
Gepner	I-Process
models	I-Process
with	O
additional	O
dressings	B-Process
of	O
the	O
parity	B-Process
symmetry	I-Process
.	O
In	O
fact	O
providing	O
a	O
compact	O
collection	O
of	O
the	O
main	O
relevant	O
formulas	O
for	O
constructing	B-Task
supersymmetric	I-Task
Gepner	I-Task
model	I-Task
orientifolds	I-Task
was	O
one	O
of	O
the	O
motivations	O
for	O
writing	O
this	O
Letter	O
.	O
We	O
hope	O
that	O
these	O
expressions	O
turn	O
out	O
to	O
be	O
useful	O
for	O
a	O
systematic	B-Task
search	I-Task
for	I-Task
Standard-like	I-Task
models	I-Task
respectively	O
for	O
providing	B-Task
a	I-Task
statistical	I-Task
ensemble	I-Task
in	I-Task
the	I-Task
spirit	I-Task
of	I-Task
[	I-Task
29	I-Task
]	I-Task
.	O

Absorption	B-Task
events	I-Task
through	I-Task
the	I-Task
charged	I-Task
current	I-Task
reactions	I-Task
(	O
2	O
)	O
νe	O
+	O
40Ar	O
→	O
e	O
−+	O
40K	O
∗	O
andν	O
̄	O
e	O
+	O
40Ar	O
→	O
e	O
++	O
40Cl	O
∗	O
.	O
There	O
is	O
some	O
uncertainty	O
in	O
predicting	B-Task
e	I-Task
−(	I-Task
e	I-Task
+)	I-Task
event	I-Task
rates	I-Task
for	O
these	O
processes	O
which	O
arise	O
due	O
to	O
the	O
nuclear	B-Process
model	I-Process
dependencies	I-Process
of	O
the	O
absorption	O
cross	O
section	O
and	O
the	O
treatment	B-Process
of	I-Process
the	I-Process
Coulomb	I-Process
distortion	I-Process
of	O
electron	B-Material
(	O
positron	B-Material
)	O
in	O
the	O
field	O
of	O
the	O
residual	B-Task
nucleus	I-Task
.	O
The	O
nuclear	B-Task
absorption	I-Task
cross	I-Task
section	I-Task
for	I-Task
the	I-Task
charged	I-Task
current	I-Task
neutrino	I-Task
reactions	I-Task
in	O
40Ar	O
relevant	O
to	O
supernova	B-Material
neutrino	I-Material
energies	I-Material
was	O
first	O
calculated	O
by	O
Raghavan	O
[	O
10	O
]	O
and	O
Bahcall	O
et	O
al	O
.	O
[	O
11	O
]	O
for	O
Fermi	O
transitions	O
leading	O
to	O
isobaric	B-Process
analogue	I-Process
state	I-Process
(	O
IAS	B-Process
)	O
at	O
4.38	B-Process
MeV	I-Process
in	I-Process
40K	I-Process
∗	O
.	O
Later	O
Ormand	O
et	O
al	O
.	O
[	O
12	O
]	O
used	O
a	O
shell	B-Process
model	I-Process
to	O
calculate	O
the	B-Task
Fermi	I-Task
and	I-Task
Gamow	I-Task
–	I-Task
Teller	I-Task
transitions	I-Task
.	O
In	O
these	O
calculations	O
Fermi	O
function	O
F	O
(	O
Z,Ee	O
)	O
was	O
used	O
to	O
take	O
into	O
account	O
the	O
Coulomb	B-Process
effects	I-Process
.	O
In	O
a	O
recent	O
paper	O
Bueno	O
et	O
al	O
.	O
[	O
13	O
]	O
make	O
use	O
of	O
a	O
calculation	B-Process
by	I-Process
Martinez-Pinedo	I-Process
et	I-Process
al	I-Process
.	I-Process
[	O
14	O
]	O
who	O
use	O
a	O
shell	B-Process
model	I-Process
for	I-Process
Fermi	I-Process
and	I-Process
Gamow	I-Process
–	I-Process
Teller	I-Process
transitions	I-Process
and	O
a	O
continuum	B-Process
random	I-Process
phase	I-Process
approximation	I-Process
(	O
CRPA	B-Process
)	O
for	O
forbidden	O
transitions	O
to	O
calculate	O
the	B-Task
absorption	I-Task
cross	I-Task
sections	I-Task
.	O
In	O
this	O
calculation	O
the	O
Coulomb	O
distortion	O
of	O
the	O
produced	O
electron	O
is	O
treated	O
with	O
a	O
hybrid	O
model	O
where	O
a	O
Fermi	O
function	O
is	O
used	O
for	O
lower	O
electron	O
energies	O
and	O
modified	B-Process
effective	I-Process
momentum	I-Process
approximation	I-Process
(	O
MEMA	B-Process
)	O
for	O
higher	O
electron	O
energies	O
[	O
14	O
–	O
17	O
]	O
.	O
In	O
a	O
recent	O
work	O
Bhattacharya	O
et	O
al	O
.	O
[	O
18	O
]	O
have	O
measured	O
the	O
Fermi	B-Process
and	I-Process
Gamow	I-Process
–	I-Process
Teller	I-Process
transition	I-Process
strengths	I-Process
leading	O
to	O
excited	O
states	O
up	O
to	O
6	O
MeV	O
in	O
40K	O
∗	O
and	O
obtained	O
the	O
neutrino	B-Task
absorption	I-Task
cross	I-Task
section	I-Task
for	O
supernova	O
neutrinos	O
in	O
40Ar	O
.	O

Classical	O
,	O
two-dimensional	B-Task
sigma	I-Task
models	I-Task
on	I-Task
compact	I-Task
symmetric	I-Task
spaces	I-Task
G	I-Task
/	I-Task
H	I-Task
are	O
integrable	O
by	O
virtue	O
of	O
conserved	O
quantities	O
which	O
can	O
arise	O
as	O
integrals	B-Process
of	I-Process
local	I-Process
or	I-Process
non-local	I-Process
functions	I-Process
of	O
the	O
underlying	O
fields	O
(	O
the	O
accounts	O
in	O
[	O
1	O
–	O
5	O
]	O
contain	O
references	O
to	O
the	O
extensive	O
literature	O
)	O
.	O
Since	O
these	O
models	O
are	O
asymptotically	O
free	O
and	O
strongly	O
coupled	O
in	O
the	O
infrared	O
,	O
their	O
quantum	O
properties	O
are	O
not	O
straightforward	O
to	O
determine	O
.	O
Nevertheless	O
,	O
following	O
Lüscher	O
[	O
6	O
]	O
,	O
Abdalla	O
,	O
Forger	O
and	O
Gomes	O
showed	O
[	O
7	O
]	O
that	O
,	O
in	O
a	O
G	O
/	O
H	O
sigma	O
model	O
with	O
H	O
simple,11Here	O
,	O
and	O
throughout	O
this	O
Letter	O
,	O
we	O
shall	O
use	O
‘	O
simple’	O
to	O
mean	O
that	O
the	O
corresponding	O
Lie	O
algebra	O
has	O
no	O
non-trivial	O
ideals	O
.	O
Hence	O
U	O
(	O
1	O
)	O
is	O
simple	O
in	O
our	O
terminology	O
,	O
in	O
addition	O
to	O
the	O
usual	O
non-Abelian	O
simple	O
groups	O
of	O
the	O
Cartan	O
–	O
Killing	O
classification	O
[	O
13	O
]	O
.	O
the	O
first	O
conserved	O
non-local	O
charge	O
survives	O
quantization	O
(	O
after	O
an	O
appropriate	O
renormalization	O
[	O
6	O
–	O
8	O
])	O
,	O
which	O
suffices	O
to	O
ensure	O
quantum	O
integrability	O
of	O
the	O
theory	O
.	O
By	O
contrast	O
,	O
calculations	O
using	O
the	O
1	O
/	O
N	O
expansion	O
reveal	O
anomalies	O
that	O
spoil	O
the	O
conservation	O
of	O
the	O
quantum	O
non-local	O
charges	O
in	O
the	O
CPN	O
−	O
1	O
=	O
SU	O
(	O
N	O
)/	O
SU	O
(	O
N	O
−	O
1	O
)×	O
U	O
(	O
1	O
)	O
models	O
for	O
N	O
>	O
2	O
,	O
and	O
in	O
the	O
wider	O
class	O
of	O
theories	O
based	O
on	O
the	O
complex	O
Grassmannians	O
SU	O
(	O
N	O
)/	O
SU	O
(	O
n	O
)×	O
SU	O
(	O
N	O
−	O
n	O
)×	O
U	O
(	O
1	O
)	O
for	O
N	O
>	O
n	O
>	O
1	O
[	O
9	O
]	O
.	O

We	O
propose	O
a	O
method	O
for	O
the	O
lattice	B-Task
QCD	I-Task
computation	I-Task
of	I-Task
nucleon	I-Task
–	I-Task
nucleon	I-Task
low-energy	I-Task
interactions	I-Task
.	O
It	O
consists	O
in	O
simulating	B-Process
QCD	I-Process
in	O
the	O
background	O
of	O
a	O
“	O
electromagnetic	B-Material
”	I-Material
field	I-Material
whose	O
potential	O
is	O
non-vanishing	O
,	O
but	O
whose	O
field	B-Process
strength	I-Process
is	I-Process
zero	I-Process
.	O
By	O
tuning	B-Process
the	I-Process
background	I-Process
field	I-Process
,	O
phase-shifts	B-Task
at	I-Task
any	I-Task
(	I-Task
but	I-Task
small	I-Task
)	I-Task
momenta	I-Task
can	O
be	O
determined	O
by	O
measuring	B-Process
the	I-Process
shift	I-Process
of	I-Process
the	I-Process
ground	I-Process
state	I-Process
energy	I-Process
.	O
Lattice	B-Material
sizes	I-Material
as	O
small	O
as	O
5	O
Fermi	O
can	O
be	O
sufficient	O
for	O
the	O
calculation	B-Task
of	I-Task
phase	I-Task
shifts	I-Task
up	O
to	O
momenta	O
of	O
order	O
of	O
mπ	O
/	O
2	O
.	O

In	O
our	O
study	O
we	O
illustrate	O
the	O
properties	O
of	O
gauge	O
invariant	B-Task
extensions	I-Task
of	I-Task
local	I-Task
functionals	I-Task
.	O
We	O
aim	O
at	O
clarifying	O
,	O
via	O
specific	O
examples	O
,	O
the	O
relation	O
between	O
a	O
functional	B-Task
which	I-Task
is	I-Task
local	I-Task
in	I-Task
a	I-Task
particular	I-Task
gauge	I-Task
(	O
but	O
not	O
necessarily	O
gauge	O
invariant	O
)	O
,	O
and	O
its	B-Task
gauge	I-Task
invariant	I-Task
extension	I-Task
(	O
which	O
is	O
not	O
necessarily	O
local	O
)	O
.	O
We	O
show	O
that	O
the	O
non-localities	B-Process
found	I-Process
are	O
not	O
perturbatively	O
local	O
because	O
they	O
cannot	O
be	O
expressed	O
in	O
terms	O
of	O
an	O
infinite	O
derivative	O
expansion	O
.	O
We	O
believe	O
that	O
the	O
implications	O
of	O
this	O
observation	O
have	O
not	O
been	O
clearly	O
emphasised	O
in	O
the	O
literature	O
,	O
as	O
attested	O
by	O
the	O
absence	O
of	O
any	O
debate	O
about	O
it	O
in	O
recent	O
works	O
.	O
It	O
is	O
precisely	O
these	O
dangerous	O
infrared	O
modes	O
that	O
make	O
it	O
hard	O
to	O
define	O
a	O
gauge	O
independent	O
renormalisation	O
for	O
the	O
gauge	O
invariant	O
extensions	O
of	O
local	O
functionals	O
.	O
This	O
observation	O
supports	O
the	O
remark	O
in	O
[	O
2	O
]	O
that	O
the	O
expectation	O
value	O
receives	O
important	O
contributions	O
from	O
both	O
large	O
and	O
small	O
distances	O
.	O
Our	O
arguments	O
on	O
renormalisability	O
are	O
based	O
on	O
the	O
notion	O
of	O
renormalisation	O
in	O
the	O
modern	O
sense	O
[	O
8	O
]	O
which	O
relies	O
on	O
BRST	O
cohomology	O
theorems	O
.	O
The	O
BRST	O
terminology	O
will	O
therefore	O
be	O
frequently	O
used	O
here	O
,	O
even	O
though	O
it	O
is	O
not	O
always	O
necessary	O
.	O

Certainly	O
therefore	O
the	O
see-saw	B-Process
mechanism	I-Process
is	O
an	O
attractive	O
explanation	O
of	O
why	O
the	O
light	B-Task
neutrino	I-Task
masses	I-Task
are	O
so	O
small	O
.	O
However	O
,	O
it	O
is	O
not	O
without	O
its	O
faults	O
.	O
In	O
particular	O
,	O
there	O
is	O
a	O
tension	O
between	O
the	O
strongly	O
hierarchical	O
nature	O
of	O
the	O
observed	O
Yukawa	B-Task
couplings	I-Task
in	I-Task
the	I-Task
quark	I-Task
and	I-Task
charged	I-Task
lepton	I-Task
sectors	I-Task
,	O
and	O
the	O
essentially	O
hierarchy-free	B-Task
masses	I-Task
implied	I-Task
by	I-Task
the	I-Task
Δm2	I-Task
's	I-Task
.	O
Moreover	O
,	O
both	O
the	O
θ12	B-Task
and	I-Task
θ23	I-Task
mixing	I-Task
angles	I-Task
are	O
large	O
while	O
the	B-Task
angle	I-Task
θ13	I-Task
is	O
small	O
which	O
is	O
in	O
sharp	O
contrast	O
with	O
the	O
corresponding	O
mixings	O
in	O
the	O
quark	B-Process
sector	I-Process
which	O
are	O
all	O
small	O
.	O
These	O
problems	O
can	O
be	O
solved	O
in	O
specific	O
models	O
,	O
for	O
example	O
,	O
the	O
Δm2	B-Process
values	I-Process
can	I-Process
be	I-Process
fitted	I-Process
by	I-Process
taking	I-Process
the	I-Process
spectrum	I-Process
of	I-Process
rhd	I-Process
neutrino	I-Process
masses	I-Process
to	O
be	O
hierarchical	O
in	O
such	O
a	O
way	O
as	O
to	O
almost	O
compensate	O
for	O
the	O
hierarchical	B-Process
neutrino	I-Process
Yukawa	I-Process
couplings	I-Process
.	O
But	O
this	O
has	O
the	O
price	O
of	O
introducing	O
a	O
wide	O
range	O
of	O
rhd	B-Material
neutrino	I-Material
masses	I-Material
MR	B-Material
∼	I-Material
1010	I-Material
–	I-Material
1015	I-Material
which	O
then	O
require	O
explanation	O
.	O

We	O
analyze	O
the	O
diagonal	B-Task
and	I-Task
transition	I-Task
magnetic	I-Task
and	I-Task
electric	I-Task
dipole	I-Task
moments	I-Task
of	I-Task
charged	I-Task
leptons	I-Task
in	O
extended	B-Process
technicolor	I-Process
(	O
ETC	B-Process
)	O
models	O
,	O
taking	O
account	O
of	O
the	O
multiscale	O
nature	O
of	O
the	O
ETC	O
gauge	B-Process
symmetry	I-Process
breaking	I-Process
,	O
conformal	B-Process
(	I-Process
walking	I-Process
)	I-Process
behavior	I-Process
of	I-Process
the	I-Process
technicolor	I-Process
theory	I-Process
,	O
and	O
mixing	B-Process
in	I-Process
the	I-Process
charged-lepton	I-Process
mass	I-Process
matrix	I-Process
.	O
We	O
show	O
that	O
mixing	B-Task
effects	I-Task
dominate	O
the	O
ETC	B-Process
contributions	O
to	O
charged	B-Task
lepton	I-Task
electric	I-Task
dipole	I-Task
moments	I-Task
and	O
that	O
these	O
can	O
yield	O
a	O
value	B-Process
of	I-Process
|	I-Process
de	I-Process
|	I-Process
comparable	O
to	O
the	O
current	O
limit	O
.	O
The	O
rate	B-Process
for	I-Process
μ	I-Process
→	I-Process
eγ	I-Process
can	O
also	O
be	O
close	O
to	O
its	O
limit	O
.	O
From	O
these	O
and	O
other	O
processes	O
we	O
derive	O
constraints	O
on	O
the	O
charged	B-Task
lepton	I-Task
mixing	I-Task
angles	I-Task
.	O
The	O
constraints	O
are	O
such	O
that	O
the	O
ETC	B-Process
contribution	O
to	O
the	O
muon	B-Task
anomalous	I-Task
magnetic	I-Task
moment	I-Task
,	O
which	O
includes	O
a	O
significant	O
lepton	B-Process
mixing	I-Process
term	I-Process
,	O
can	O
approach	O
,	O
but	O
does	O
not	O
exceed	O
,	O
the	O
current	O
sensitivity	O
level	O
.	O

It	O
is	O
well	O
known	O
that	O
one	O
of	O
the	O
long	O
standing	O
problems	O
in	O
physics	O
is	O
understanding	B-Task
the	I-Task
confinement	I-Task
physics	I-Task
from	I-Task
first	I-Task
principles	I-Task
.	O
Hence	O
the	O
challenge	O
is	O
to	O
develop	B-Task
analytical	I-Task
approaches	I-Task
which	O
provide	O
valuable	O
insight	O
and	O
theoretical	O
guidance	O
.	O
According	O
to	O
this	O
viewpoint	O
,	O
an	O
effective	O
theory	O
in	O
which	O
confining	O
potentials	O
are	O
obtained	O
as	O
a	O
consequence	O
of	O
spontaneous	B-Process
symmetry	I-Process
breaking	I-Process
of	I-Process
scale	I-Process
invariance	I-Process
has	O
been	O
developed	O
[	O
1	O
]	O
.	O
In	O
particular	O
,	O
it	O
was	O
shown	O
that	O
a	O
such	O
theory	O
relies	O
on	O
a	O
scale-invariant	B-Process
Lagrangian	I-Process
of	O
the	O
type	O
[	O
2	O
]	O
(	O
1	O
)	O
L	O
=	O
14w2	O
−	O
12w	O
−	O
FμνaFaμν	O
,	O
where	O
Fμνa	O
=∂	O
μAνa	O
−∂	O
νAμa	O
+	O
gfabcAμbAνc	O
,	O
and	O
w	O
is	O
not	O
a	O
fundamental	O
field	O
but	O
rather	O
is	O
a	O
function	B-Process
of	I-Process
4-index	I-Process
field	I-Process
strength	I-Process
,	O
that	O
is	O
,	O
(	B-Process
2	I-Process
)	I-Process
w	I-Process
=	I-Process
εμναβ	I-Process
∂	I-Process
μAναβ	I-Process
.	O
The	O
Aναβ	B-Process
equation	I-Process
of	I-Process
motion	I-Process
leads	I-Process
to	O
(	O
3	O
)	O
εμναβ	B-Process
∂	I-Process
βw	I-Process
−−	I-Process
FγδaFaγδ	I-Process
=	I-Process
0	I-Process
,	O
which	O
is	O
then	O
integrated	B-Process
to	I-Process
(	I-Process
4	I-Process
)	I-Process
w	I-Process
=−	I-Process
FμνaFaμν	I-Process
+	I-Process
M	I-Process
.	O
It	O
is	O
easy	O
to	O
verify	O
that	O
the	O
Aaμ	B-Process
equation	I-Process
of	I-Process
motion	I-Process
leads	O
us	O
to	O
(	O
5	O
)∇	O
μFaμν	O
+	O
MFaμν	O
−	O
FαβbFbαβ	O
=	O
0	O
.	O
It	O
is	O
worth	O
stressing	O
at	O
this	O
stage	O
that	O
the	O
above	O
equation	O
can	O
be	O
obtained	O
from	O
the	B-Process
effective	I-Process
Lagrangian	I-Process
(	O
6	O
)	O
Leff	B-Process
=−	I-Process
14FμνaFaμν	I-Process
+	I-Process
M2	I-Process
−	I-Process
FμνaFaμν	I-Process
.	O
Spherically	B-Task
symmetric	I-Task
solutions	I-Task
of	O
Eq	O
.	O
(	O
5	O
)	O
display	O
,	O
even	O
in	O
the	O
Abelian	O
case	O
,	O
a	O
Coulomb	B-Material
piece	I-Material
and	O
a	O
confining	B-Material
part	I-Material
.	O
Also	O
,	O
the	O
quantum	B-Task
theory	I-Task
calculation	I-Task
of	I-Task
the	I-Task
static	I-Task
energy	I-Task
between	I-Task
two	I-Task
charges	I-Task
displays	O
the	O
same	O
behavior	O
[	O
1	O
]	O
.	O
It	O
is	O
well	O
known	O
that	O
the	O
square	O
root	O
part	O
describes	O
string	B-Process
like	I-Process
solutions	I-Process
[	O
3,4	O
]	O
.	O

In	O
the	O
supersymmetric	O
case	O
,	O
such	O
a	O
small	B-Task
coupling	I-Task
for	I-Task
quartic	I-Task
interaction	I-Task
cannot	O
be	O
realized	O
if	O
the	O
potential	O
is	O
lifted	O
by	O
the	O
gauge	B-Process
D-term	I-Process
interactions	I-Process
,	O
since	O
,	O
if	O
so	O
,	O
the	B-Material
coupling	I-Material
constant	I-Material
λ	I-Material
becomes	O
of	O
the	O
order	O
O	O
(	O
g2	O
)	O
where	O
g	O
is	O
the	O
gauge	B-Material
coupling	I-Material
constant	I-Material
in	O
the	O
standard	B-Process
model	I-Process
.	O
Therefore	O
,	O
we	O
focus	O
our	O
attention	O
on	O
the	O
D-flat	B-Task
directions	I-Task
.	O
For	O
D-flat	O
directions	O
,	O
we	O
have	O
to	O
be	O
more	O
careful	O
since	O
behaviors	B-Task
of	I-Task
the	I-Task
potential	I-Task
depend	O
on	O
which	O
flat	O
direction	O
we	O
consider	O
.	O
In	O
the	O
MSSM	O
,	O
Yukawa	B-Task
interactions	I-Task
exist	O
in	O
the	O
superpotential	O
to	O
generate	O
the	O
fermion	O
masses	O
.	O
Such	O
Yukawa	O
interactions	O
lift	O
some	O
of	O
the	O
D-flat	B-Task
directions	I-Task
.	O
In	O
addition	O
,	O
we	O
can	O
also	O
find	O
several	O
D-flat	O
directions	O
which	O
are	O
not	O
affected	O
by	O
the	O
Yukawa	B-Task
interactions	I-Task
associated	O
with	O
the	O
fermion	O
masses	O
;	O
without	O
R-parity	O
violation	O
,	O
such	O
D-flat	B-Task
directions	I-Task
are	O
only	O
lifted	O
by	O
the	O
effects	O
of	O
supersymmetry	O
breaking.33Here	O
,	O
we	O
assume	O
that	O
coefficients	B-Task
of	I-Task
non-renormalizable	I-Task
terms	I-Task
are	O
suppressed	O
enough	O
to	O
be	O
neglected	O
.	O
This	O
may	O
be	O
explained	O
by	O
the	O
R-symmetry	B-Process
,	O
assigning	O
R-charge	B-Process
23	I-Process
to	O
each	O
MSSM	B-Process
chiral	I-Process
superfields	I-Process
.	O
(	O
See	O
Ref.	O
[	O
6	O
]	O
for	O
the	O
details	O
.	O
)	O

Some	O
non-standard	B-Task
couplings	I-Task
,	O
which	O
should	O
be	O
determined	O
here	O
,	O
could	O
also	O
be	O
studied	O
in	O
the	O
standard	B-Process
e	I-Process
+	I-Process
e	I-Process
−	I-Process
option	I-Process
of	O
a	O
linear	B-Material
collider	I-Material
.	O
Therefore	O
,	O
it	O
is	O
worth	O
while	O
to	O
compare	O
the	O
potential	B-Task
power	I-Task
of	O
the	O
two	O
options	O
.	O
As	O
far	O
as	O
the	O
parameter	B-Task
αγ1	I-Task
is	O
concerned	O
,	O
the	O
γγ	B-Material
collider	I-Material
does	O
not	O
allow	O
for	O
its	O
determination	O
,	O
while	O
it	O
could	O
be	O
determined	O
at	O
e	B-Process
+	I-Process
e	I-Process
−	I-Process
.	O
The	O
second	O
tt	O
̄	O
γ	O
coupling	B-Task
αγ2	I-Task
,	O
which	O
is	O
proportional	O
to	O
the	O
real	O
part	O
of	O
the	O
top-quark	O
electric	O
dipole	O
moment,44See	O
[	O
23	O
]	O
taking	O
into	O
account	O
that	O
the	O
operators	O
OuB	O
,	O
OqB	O
and	O
OqW	O
are	O
redundant	O
.	O
can	O
be	O
measured	O
here	O
.	O
It	O
should	O
be	O
recalled	O
that	O
energy	O
and	O
polar-angle	B-Task
distributions	I-Task
of	I-Task
leptons	I-Task
and	I-Task
b-quarks	I-Task
in	O
e	B-Material
+	I-Material
e	I-Material
−	I-Material
colliders	I-Material
are	O
sensitive	O
only	O
to	O
the	O
imaginary	O
part	O
of	O
the	O
electric	O
dipole	O
moment,55However	O
,	O
it	O
should	O
be	O
emphasized	O
that	O
there	O
exist	O
observables	O
sensitive	O
also	O
to	O
the	O
real	O
part	O
of	O
the	O
top-quark	O
electric	O
dipole	O
moment	O
,	O
see	O
[	O
24	O
]	O
.	O
while	O
here	O
the	O
real	O
part	O
could	O
be	O
determined	O
.	O
For	O
the	O
measurement	O
of	O
γγH	O
couplings	O
,	O
e	O
+	O
e	O
−	O
colliders	O
are	O
,	O
of	O
course	O
,	O
useless	O
,	O
while	O
here	O
,	O
for	O
the	O
bX	O
final	O
state	O
both	O
αh1	O
and	O
αh2	O
could	O
be	O
measured	O
.	O
In	O
the	O
case	O
of	O
the	O
decay	O
form	O
factor	O
αd	O
measurement	O
,	O
the	O
e	O
+	O
e	O
−	O
option	O
seems	O
to	O
be	O
a	O
little	O
more	O
advantageous	O
,	O
especially	O
if	O
e	O
+	O
e	O
−	O
polarization	O
can	O
be	O
tuned	O
appropriately	O
[	O
25	O
]	O
.	O

In	O
contrast	O
to	O
the	O
H	O
particle	O
,	O
the	O
situation	O
for	O
the	O
Θ	B-Task
+	I-Task
baryon	I-Task
is	O
very	O
promising	O
.	O
Thus	O
,	O
in	O
this	O
Letter	O
we	O
explore	O
the	B-Task
formation	I-Task
of	I-Task
the	I-Task
Θ	I-Task
+-	I-Task
baryon	I-Task
within	O
a	O
new	O
approach	O
called	O
parton-based	B-Process
Gribov	I-Process
–	I-Process
Regge	I-Process
theory	I-Process
.	O
It	O
is	O
realized	O
in	O
the	O
Monte	B-Material
Carlo	I-Material
program	I-Material
NEXUS	B-Material
3.97	I-Material
[	O
22,23	O
]	O
.	O
In	O
this	O
model	O
high	B-Process
energy	I-Process
hadronic	I-Process
and	I-Process
nuclear	I-Process
collisions	I-Process
are	O
treated	O
within	O
a	O
self-consistent	B-Process
quantum	I-Process
mechanical	I-Process
multiple	I-Process
scattering	I-Process
formalism	I-Process
.	O
Elementary	B-Process
interactions	I-Process
,	O
happening	O
in	O
parallel	O
,	O
correspond	O
to	O
underlying	O
microscopic	B-Process
(	I-Process
predominantly	I-Process
soft	I-Process
)	I-Process
parton	I-Process
cascades	I-Process
and	O
are	O
described	O
effectively	O
as	O
phenomenological	B-Process
soft	I-Process
pomeron	I-Process
exchanges	I-Process
.	O
A	O
pomeron	O
can	O
be	O
seen	O
as	O
layers	O
of	O
a	O
(	B-Material
soft	I-Material
)	I-Material
parton	I-Material
ladder	I-Material
,	O
which	O
is	O
attached	O
to	O
projectile	B-Material
and	I-Material
target	I-Material
nucleons	I-Material
via	O
leg	B-Material
partons	I-Material
.	O
At	O
high	O
energies	O
one	O
accounts	O
also	O
for	O
the	O
contribution	O
of	O
perturbative	B-Material
(	I-Material
high	I-Material
pt	I-Material
)	I-Material
partons	I-Material
described	O
by	O
a	O
so-called	O
“	O
semihard	O
pomeron	O
”—	O
a	O
piece	O
of	O
the	O
QCD	O
parton	O
ladder	O
sandwiched	O
between	O
two	O
soft	O
pomerons	O
which	O
are	O
connected	O
to	O
the	O
projectile	O
and	O
to	O
the	O
target	O
in	O
the	O
usual	O
way	O
.	O
The	O
spectator	O
partons	O
of	O
both	O
projectile	O
and	O
target	O
nucleons	O
,	O
left	O
after	O
pomeron	O
emissions	O
,	O
form	O
nucleon	O
remnants	O
.	O
The	O
legs	O
of	O
the	O
pomerons	O
form	O
color	O
singlets	O
,	O
such	O
as	O
q	O
–	O
q	O
̄	O
,	O
q	O
–	O
qq	O
or	O
q	O
̄–	O
q	O
̄	O
q	O
̄	O
.	O
The	O
probability	O
of	O
q	O
–	O
qq	O
and	O
q	O
̄–	O
q	O
̄	O
q	O
̄	O
is	O
controlled	O
by	O
the	O
parameter	O
Pqq	O
and	O
is	O
fixed	O
by	O
the	O
experimental	O
yields	O
on	O
(	O
multi	O
-)	O
strange	O
baryons	O
[	O
23	O
]	O
.	O

I	O
also	O
could	O
not	O
resist	O
mentioning	O
another	O
wild	O
speculation	O
[	O
10	O
]	O
.	O
Many	O
years	O
ago	O
,	O
inspired	O
by	O
the	O
almost	O
exact	O
correspondence	O
between	O
Einstein	B-Process
's	I-Process
post-Newtonian	I-Process
equations	I-Process
of	I-Process
gravity	I-Process
and	O
Maxwell	B-Process
's	I-Process
equations	I-Process
of	I-Process
motion	I-Process
I	O
proposed	O
the	O
gravitipole	B-Process
in	O
analogy	O
with	O
Dirac	B-Process
's	I-Process
magnetic	I-Process
monopole	I-Process
.	O
After	O
Dirac	O
there	O
was	O
considerable	O
debate	O
on	O
how	O
a	O
field	B-Task
theory	I-Task
of	I-Task
magnetic	I-Task
monopoles	I-Task
may	O
be	O
formulated	O
.	O
Eventually	O
,	O
'	O
t	O
Hooft	O
and	O
Polyakov	O
showed	O
that	O
the	O
magnetic	B-Task
monopole	I-Task
exists	O
as	O
an	O
extended	O
solution	O
in	O
certain	O
non-abelian	B-Process
gauge	I-Process
theories	I-Process
.	O
Most	O
theorists	O
now	O
believe	O
that	O
electromagnetism	B-Process
is	O
merely	O
a	O
piece	O
of	O
a	O
grand	O
unified	O
theory	O
and	O
that	O
magnetic	B-Process
monopoles	I-Process
exist	O
.	O
Might	O
it	O
not	O
turn	O
out	O
that	O
Einstein	O
's	O
theory	O
is	O
but	O
a	O
piece	O
of	O
a	O
bigger	O
theory	O
and	O
that	O
gravitipoles	B-Process
exist	O
?	O
In	O
grand	O
unified	O
theory	O
the	O
electromagnetic	B-Process
field	I-Process
is	O
a	O
component	O
of	O
a	O
multiplet	O
.	O
Could	O
it	O
be	O
that	O
the	O
gravitational	B-Process
field	I-Process
also	O
somehow	O
carries	O
an	O
internal	O
index	O
and	O
that	O
the	O
field	O
we	O
observe	O
is	O
just	O
a	O
component	O
of	O
a	O
multiplet	O
?	O
Throwing	O
caution	O
to	O
the	O
wind	O
,	O
I	O
also	O
asked	O
in	O
[	O
10	O
]	O
if	O
the	O
gravitipole	B-Process
and	O
the	O
graviton	B-Process
might	O
not	O
form	O
a	O
representation	O
under	O
some	O
dual	O
group	O
just	O
as	O
the	O
magnetic	B-Process
monopole	I-Process
and	O
the	O
photon	B-Material
form	O
a	O
triplet	O
under	O
the	O
dual	O
group	O
of	O
Montonen	O
and	O
Olive	O
[	O
11	O
]	O
.	O

In	O
summary	O
,	O
we	O
have	O
shown	O
that	O
one	O
can	O
describe	B-Task
the	I-Task
experimental	I-Task
data	I-Task
of	I-Task
the	I-Task
HERMES	I-Task
Collaboration	I-Task
for	I-Task
hadron	I-Task
attenuation	I-Task
on	I-Task
nuclei	I-Task
without	O
invoking	O
any	O
changes	O
in	O
the	O
fragmentation	B-Process
function	I-Process
due	O
to	O
gluon	B-Process
radiation	I-Process
.	O
In	O
our	O
dynamical	B-Task
studies	I-Task
,	O
that	O
include	O
the	O
most	O
relevant	O
FSI	B-Process
,	O
we	O
employ	O
only	O
the	O
‘	B-Process
free’	I-Process
fragmentation	I-Process
function	I-Process
on	O
a	O
nucleon	B-Material
and	O
attribute	O
the	O
hadron	B-Material
attenuation	O
to	O
the	O
deceleration	O
of	O
the	O
produced	B-Material
(	I-Material
pre	I-Material
-)	I-Material
hadrons	I-Material
due	O
to	O
FSI	B-Process
in	O
the	O
surrounding	B-Material
medium	I-Material
.	O
We	O
find	O
that	O
in	O
particular	O
the	B-Process
z-dependence	I-Process
of	I-Process
RMh	I-Process
is	O
very	O
sensitive	O
to	O
the	O
interaction	O
cross	O
section	O
of	O
leading	B-Material
prehadrons	I-Material
and	O
can	O
be	O
used	O
to	O
determine	B-Task
σlead	I-Task
.	O
The	O
interaction	B-Process
of	I-Process
the	I-Process
leading	I-Process
prehadrons	I-Process
during	I-Process
the	I-Process
formation	I-Process
time	I-Process
could	O
be	O
interpreted	O
as	O
an	O
in-medium	B-Process
change	I-Process
of	I-Process
the	I-Process
fragmentation	I-Process
function	I-Process
,	O
which	O
however	O
could	O
not	O
be	O
given	O
in	O
a	O
closed	O
form	O
.	O
The	O
extracted	O
average	O
hadron	B-Material
formation	O
times	O
of	O
τf	O
≳	O
0.3	O
fm	O
/	O
c	O
are	O
compatible	O
with	O
the	O
analysis	B-Task
of	I-Task
antiproton	I-Task
attenuation	I-Task
in	O
p	B-Process
+	I-Process
A	I-Process
reactions	I-Process
at	O
AGS	O
energies	O
[	O
17	O
]	O
.	O
In	O
an	O
upcoming	O
work	O
we	O
will	O
investigate	O
in	O
detail	B-Task
the	I-Task
spectra	I-Task
for	I-Task
different	I-Task
particle	I-Task
species	I-Task
(	I-Task
π	I-Task
±	I-Task
,	I-Task
K	I-Task
±	I-Task
,	I-Task
p,p	I-Task
̄)	I-Task
to	O
examine	O
,	O
if	O
the	O
formation	O
times	O
of	O
mesons	O
and	O
antibaryons	O
are	O
about	O
equal	O
.	O
In	O
addition	O
we	O
will	O
improve	O
our	O
model	O
to	O
describe	O
the	O
primary	O
photon	O
–	O
nucleon	O
reaction	O
below	O
the	O
PYTHIA	O
threshold	O
of	O
W	O
⩾	O
4	O
GeV	O
.	O

Solitons	B-Material
present	O
the	O
possibility	O
of	O
extended	O
objects	O
as	O
stable	O
states	O
within	O
Quantum	O
Field	O
Theory	O
.	O
Although	O
these	O
solutions	O
are	O
obtained	O
from	O
semi-classical	B-Process
arguments	I-Process
in	I-Process
weak	I-Process
coupling	I-Process
limit	I-Process
,	O
their	O
validity	O
as	O
quantal	B-Task
states	I-Task
is	O
justified	O
based	O
on	O
the	O
associated	B-Process
topological	I-Process
conservation	I-Process
laws	I-Process
.	O
A	O
more	O
curious	O
occurrence	O
is	O
that	O
of	O
fermionic	B-Task
zero-energy	I-Task
modes	I-Task
trapped	I-Task
on	I-Task
such	I-Task
solutions	I-Task
.	O
Their	O
presence	O
requires	O
,	O
according	O
to	O
well-known	O
arguments	O
[	O
1,2	O
]	O
,	O
an	O
assignment	B-Process
of	I-Process
half-integer	I-Process
fermion	I-Process
number	I-Process
to	I-Process
the	I-Process
solitonic	I-Process
states	I-Process
.	O
In	O
the	O
usual	O
treatment	O
,	O
the	B-Task
back	I-Task
reaction	I-Task
of	I-Task
the	I-Task
fermion	I-Task
zero-modes	I-Task
on	I-Task
the	I-Task
soliton	I-Task
itself	O
is	O
ignored	O
.	O
However	O
,	O
the	O
fractional	B-Task
values	I-Task
of	I-Task
the	I-Task
fermionic	I-Task
charge	I-Task
have	O
interesting	O
consequence	O
for	O
the	B-Task
fate	I-Task
of	I-Task
the	I-Task
soliton	I-Task
if	O
the	O
latter	O
is	O
not	O
strictly	O
stable	O
.	O
The	O
reason	O
for	O
this	O
is	O
that	O
if	O
the	O
configuration	O
were	O
to	O
relax	O
to	O
trivial	O
vacuum	B-Material
in	O
isolation	O
,	O
there	O
is	O
no	O
particle-like	O
state	O
available	O
for	O
carrying	B-Task
the	I-Task
fractional	I-Task
value	I-Task
of	I-Task
the	I-Task
fermionic	I-Task
charge	I-Task
.	O
Dynamical	B-Task
stability	I-Task
of	I-Task
such	I-Task
objects	I-Task
was	O
pointed	O
out	O
in	O
[	O
3	O
]	O
,	O
in	O
cosmological	O
context	O
in	O
[	O
4,5	O
]	O
and	O
more	O
recently	O
in	O
[	O
6	O
–	O
8	O
]	O
.	O
Fractional	O
fermion	O
number	O
phenomenon	O
also	O
occurs	O
in	O
condensed	B-Material
matter	I-Material
systems	I-Material
and	O
its	O
wide	O
ranging	O
implications	O
call	O
for	O
a	O
systematic	B-Task
understanding	I-Task
of	I-Task
the	I-Task
phenomenon	I-Task
.	O

The	O
charmonium	B-Process
production	I-Process
has	O
long	O
been	O
considered	O
as	O
a	O
good	O
process	O
for	O
investigating	B-Task
both	I-Task
perturbative	I-Task
and	I-Task
nonperturbative	I-Task
properties	I-Task
of	I-Task
quantum	I-Task
chromodynamics	I-Task
(	I-Task
QCD	I-Task
)	O
,	O
because	O
of	O
the	O
relatively	O
large	O
difference	O
between	O
the	O
scale	O
at	O
which	O
the	O
charm	O
–	O
quark	O
pair	O
is	O
produced	O
at	O
the	O
parton	O
level	O
and	O
the	O
scale	O
at	O
which	O
it	O
evolves	O
into	O
a	O
quarkonium	O
.	O
In	O
particular	O
,	O
comparing	O
to	O
hadron	O
colliders	O
,	O
e	B-Material
+	I-Material
e	I-Material
−	I-Material
colliders	I-Material
,	O
provide	O
a	O
cleaner	O
environment	O
to	O
study	B-Task
the	I-Task
charmonium	I-Task
productions	I-Task
and	I-Task
decays	I-Task
.	O
However	O
,	O
some	O
puzzles	O
arise	O
from	O
the	O
recent	B-Process
measurements	I-Process
on	I-Process
the	I-Process
prompt	I-Process
J	I-Process
/	I-Process
ψ	I-Process
productions	O
at	O
BaBar	B-Process
and	O
Belle	B-Process
[	O
1	O
–	O
3	O
]	O
.	O
For	O
the	O
inclusive	B-Task
J	I-Task
/	I-Task
ψ	I-Task
productions	I-Task
,	O
the	O
cross	O
section	O
is	O
much	O
larger	O
than	O
the	O
predictions	O
of	O
nonrelativistic	B-Process
quantum	I-Process
chromodynamics	I-Process
(	O
NRQCD	B-Process
)	O
[	O
4	O
]	O
;	O
there	O
is	O
also	O
an	O
over-abundance	O
of	O
the	O
four-charm	B-Process
–	I-Process
quark	I-Process
processes	I-Process
including	O
the	O
exclusive	B-Process
J	I-Process
/	I-Process
ψ	I-Process
and	I-Process
charmonium	I-Process
productions	I-Process
;	O
there	O
is	O
no	O
apparent	O
signal	O
in	O
the	O
hard	O
J	O
/	O
ψ	O
spectrum	O
which	O
has	O
been	O
predicted	O
by	O
the	O
J	B-Process
/	I-Process
ψgg	I-Process
production	I-Process
mode	I-Process
as	O
well	O
as	O
the	O
color-octet	B-Process
mechanism	I-Process
in	I-Process
NRQCD	I-Process
.	O
To	O
provide	O
plausible	O
solutions	O
and	O
explanations	O
for	O
these	O
conflicts	O
,	O
theorists	O
have	O
studied	O
the	O
possibilities	B-Task
of	I-Task
the	I-Task
contribution	I-Task
from	I-Task
two-virtual-photon	I-Task
mediate	I-Task
processes	I-Task
[	O
5	O
]	O
,	O
large	B-Task
higher-order	I-Task
QCD	I-Task
corrections	I-Task
[	O
6,7	O
]	O
,	O
collinear	B-Task
suppression	I-Task
at	I-Task
the	I-Task
end-point	I-Task
region	I-Task
of	I-Task
the	I-Task
J	I-Task
/	I-Task
ψ	I-Task
momentum	I-Task
[	O
7,8	O
]	O
,	O
contribution	B-Task
from	I-Task
the	I-Task
J	I-Task
/	I-Task
ψ-glueball	I-Task
associated	I-Task
production	I-Task
[	O
9	O
]	O
and	O
contribution	B-Task
from	I-Task
a	I-Task
very	I-Task
light	I-Task
scalar	I-Task
boson	I-Task
[	O
10	O
]	O
.	O

States	O
outside	O
the	O
constituent	O
quark	O
model	O
have	O
been	O
hypothesized	O
to	O
exist	O
almost	O
since	O
the	O
introduction	O
of	O
color	O
[	O
1	O
–	O
4	O
]	O
.	O
Hybrid	B-Material
mesons	I-Material
,	O
qq	O
̄	O
states	O
with	O
an	O
admixture	O
of	O
gluons	B-Material
,	O
and	O
glueballs	B-Material
,	O
states	O
with	O
no	O
quark	B-Material
content	O
,	O
rely	O
on	O
the	O
self	O
interaction	O
property	O
of	O
gluons	B-Material
due	O
to	O
their	O
color	B-Process
charge	I-Process
.	O
Looking	B-Process
for	I-Process
glueballs	I-Process
would	O
be	O
the	O
most	O
obvious	O
way	O
to	O
find	B-Task
evidence	I-Task
for	I-Task
states	I-Task
with	I-Task
constituent	I-Task
gluons	I-Task
;	O
however	O
,	O
the	O
search	O
is	O
hindered	O
by	O
the	O
fact	O
that	O
these	O
states	O
may	O
significantly	O
mix	O
with	O
regular	O
qq	B-Material
̄-	I-Material
mesons	I-Material
in	O
the	O
region	O
where	O
the	O
lightest	O
are	O
predicted	O
to	O
occur	O
.	O
As	O
such	O
,	O
they	O
may	O
not	O
be	O
observable	O
as	O
pure	O
states	O
and	O
disentangling	B-Task
the	I-Task
observed	I-Task
spectra	I-Task
may	O
be	O
a	O
very	O
difficult	O
task	O
.	O
Instead	O
,	O
hybrid	B-Material
mesons	I-Material
(	O
qq	B-Material
̄	I-Material
gn	I-Material
)	O
may	O
be	O
a	O
better	O
place	O
to	O
search	O
for	O
evidence	O
of	O
resonances	B-Task
outside	I-Task
the	I-Task
constituent	I-Task
quark	I-Task
model	I-Task
,	O
especially	O
since	O
the	O
lightest	O
of	O
theses	O
states	O
are	O
predicted	O
to	O
have	O
exotic	O
quantum	O
numbers	O
of	O
spin	O
,	O
parity	O
,	O
and	O
charge	O
conjugation	O
,	O
JPC	B-Process
,	O
that	O
is	O
,	O
combinations	O
that	O
are	O
unattainable	O
by	O
regular	O
qq	B-Material
̄-	I-Material
mesons	I-Material
.	O

Correlation	B-Task
of	I-Task
charm-quark	I-Task
–	I-Task
charm-antiquark	I-Task
in	I-Task
γp	I-Task
scattering	I-Task
are	O
calculated	O
in	O
the	O
kt-factorization	O
approach	O
.	O
We	O
apply	O
different	O
unintegrated	O
gluon	O
distributions	O
(	O
uGDF	O
)	O
used	O
in	O
the	O
literature	O
.	O
The	O
results	O
of	O
our	O
calculations	O
are	O
compared	O
with	O
very	O
recent	O
experimental	O
results	O
from	O
the	O
FOCUS	O
Collaboration	O
.	O
The	O
CCFM	O
uGDF	O
developed	O
recently	O
by	O
Kwieciński	O
et	O
al.	O
gives	O
a	O
good	O
description	O
of	O
the	O
data	O
.	O
New	O
observables	O
are	O
suggested	O
for	O
future	O
studies	O
.	O
Predictions	O
and	O
perspectives	O
for	O
the	O
HERA	O
energies	O
are	O
presented	O
.	O

The	O
agreement	B-Process
between	I-Process
the	I-Process
new	I-Process
data	I-Process
and	I-Process
the	I-Process
calculations	I-Process
with	I-Process
the	I-Process
relativistic	I-Process
deuteron	I-Process
wave	I-Process
function	I-Process
should	O
not	O
be	O
considered	O
as	O
accidental	O
one	O
;	O
in	O
this	O
connection	O
other	O
results	O
should	O
be	O
mentioned	O
.	O
Previously	O
it	O
was	O
shown	O
[	O
15	O
]	O
that	O
calculations	B-Process
within	I-Process
the	I-Process
framework	I-Process
of	I-Process
light-front	I-Process
dynamics	I-Process
with	I-Process
Karmanov	I-Process
's	I-Process
deuteron	I-Process
wave	I-Process
function	I-Process
are	O
in	O
reasonably	O
good	O
agreement	O
with	O
the	O
experimental	O
data	O
on	O
the	O
T20	B-Process
parameter	I-Process
of	I-Process
deuteron	I-Process
breakup	I-Process
on	I-Process
H	I-Process
and	I-Process
C	I-Process
targets	I-Process
with	O
the	O
emission	B-Process
of	I-Process
protons	I-Process
at	I-Process
0	I-Process
°	I-Process
in	I-Process
the	I-Process
k	I-Process
region	I-Process
from	I-Process
0.4	I-Process
to	I-Process
0.8	I-Process
GeV	I-Process
/	I-Process
c	I-Process
.	O
Furthermore	O
,	O
within	O
the	O
same	O
approach	O
a	O
qualitative	B-Task
description	I-Task
of	I-Task
the	I-Task
momentum	I-Task
behaviour	I-Task
of	I-Task
the	I-Task
Ayy	I-Task
parameter	I-Task
of	I-Task
the	I-Task
9Be	I-Task
(	I-Task
d,p	I-Task
)	I-Task
X	I-Task
reaction	I-Task
at	O
a	O
deuteron	B-Process
momentum	I-Process
of	O
4.5	O
GeV	O
/	O
c	O
and	O
a	O
detected	O
proton	O
angle	O
of	O
80	O
mr	O
and	O
a	O
rather	O
good	O
description	O
of	O
the	O
Ayy	B-Material
data	I-Material
for	O
the	O
12C	B-Process
(	I-Process
d,p	I-Process
)	I-Process
X	I-Process
reaction	I-Process
at	O
9	O
GeV	O
/	O
c	O
and	O
85	O
mr	O
were	O
obtained	O
[	O
16	O
]	O
.	O

A	O
scenario	O
is	O
proposed	O
for	O
bi-large	B-Task
lepton	I-Task
mixing	I-Task
in	O
the	O
framework	O
of	O
nearly	O
threefold	O
degenerate	B-Material
Majorana	I-Material
neutrinos	I-Material
.	O
In	O
our	O
proposal	O
,	O
we	O
impose	O
Z3	O
symmetry	O
in	O
the	O
neutrino	B-Material
sector	I-Material
at	O
a	O
high	O
energy	O
scale	O
to	O
account	O
for	O
the	O
threefold	O
degenerate	B-Material
neutrinos	I-Material
and	O
the	O
maximal	O
mixing	O
between	O
νμ	O
and	O
ντ	O
.	O
In	O
order	O
to	O
obtain	O
the	O
atmospheric	B-Task
neutrino	I-Task
mass	I-Task
splitting	I-Task
while	I-Task
keeping	I-Task
the	I-Task
maximal	I-Task
mixing	I-Task
between	I-Task
νμ	I-Task
and	I-Task
ντ	I-Task
,	O
we	O
introduce	B-Process
a	I-Process
small	I-Process
perturbation	I-Process
to	I-Process
the	I-Process
neutrino	I-Process
mass	I-Process
matrix	I-Process
without	I-Process
breaking	I-Process
Z3	I-Process
symmetry	I-Process
.	O
On	O
the	O
other	O
hand	O
,	O
the	O
solar	B-Process
neutrino	I-Process
mixing	I-Process
arises	O
due	O
to	O
the	O
non-diagonal	B-Material
charged	I-Material
lepton	I-Material
mass	I-Material
matrix	I-Material
,	O
and	O
the	O
desirable	O
large	B-Task
mixing	I-Task
and	I-Task
mass	I-Task
splitting	I-Task
for	I-Task
the	I-Task
solar	I-Task
neutrino	I-Task
oscillation	I-Task
can	O
be	O
obtained	O
by	O
radiative	B-Process
corrections	I-Process
.	O

In	O
the	O
NJL	B-Task
model	I-Task
studied	I-Task
here	O
,	O
we	O
find	O
no	O
stable	B-Material
stars	I-Material
with	O
either	O
CFL	B-Material
or	I-Material
normal	I-Material
quark	I-Material
matter	I-Material
cores	I-Material
.	O
This	O
is	O
the	O
opposite	O
of	O
the	O
prediction	O
of	O
Ref	O
.	O
[	O
15	O
]	O
where	O
it	O
was	O
argued	O
that	O
there	O
is	O
no	O
2SC	O
phase	O
in	O
compact	B-Material
stars	I-Material
.	O
Let	O
us	O
be	O
more	O
precise	O
:	O
performing	O
a	O
Taylor	B-Process
expansion	I-Process
in	O
the	O
strange	B-Material
quark	I-Material
mass	I-Material
,	O
the	O
authors	O
of	O
Ref	O
.	O
[	O
15	O
]	O
found	O
that	O
in	O
beta-equilibrated	B-Material
electrically	I-Material
and	O
color	B-Material
neutral	I-Material
quark	I-Material
matter	I-Material
the	O
2SC	B-Process
phase	I-Process
is	O
always	O
less	O
favored	O
than	O
the	O
CFL	B-Process
phase	I-Process
or	O
normal	B-Material
quark	I-Material
matter	I-Material
.	O
From	O
this	O
observation	O
they	O
concluded	O
that	O
the	O
2SC	B-Process
phase	I-Process
is	O
absent	O
in	O
compact	B-Material
stars	I-Material
.	O
In	O
contrast	O
to	O
this	O
result	O
,	O
it	O
was	O
shown	O
in	O
Ref	O
.	O
[	O
16	O
]	O
in	O
the	O
framework	O
of	O
the	O
NJL	B-Process
model	I-Process
that	O
neutral	B-Material
2SC	I-Material
matter	I-Material
could	O
be	O
the	O
most	O
favored	O
quark	B-Process
phase	I-Process
in	O
a	O
certain	O
regime	O
.	O
However	O
,	O
the	O
authors	O
argued	O
that	O
this	O
interval	O
might	O
disappear	O
if	O
the	O
hadronic	B-Process
phase	I-Process
is	O
included	O
more	O
properly	O
.	O
This	O
is	O
indeed	O
what	O
we	O
found	O
for	O
parameter	O
set	O
RKH	O
,	O
while	O
for	O
parameter	O
set	O
HK	O
the	O
2SC	O
phase	O
survives	O
only	O
in	O
a	O
tiny	O
window	O
.	O
Nevertheless	O
,	O
if	O
Nature	O
chooses	O
to	O
be	O
similar	O
to	O
this	O
equation	O
of	O
state	O
,	O
it	O
will	O
be	O
this	O
tiny	O
window	O
which	O
gives	O
rise	O
to	O
hybrid	B-Material
stars	I-Material
,	O
whereas	O
the	O
CFL	B-Process
phase	I-Process
would	O
be	O
never	O
present	O
in	O
compact	B-Material
stars	I-Material
.	O

We	O
investigate	B-Task
the	I-Task
density	I-Task
behavior	I-Task
of	I-Task
the	I-Task
symmetry	I-Task
energy	I-Task
with	I-Task
respect	I-Task
to	I-Task
isospin	I-Task
equilibration	I-Task
in	O
the	O
combined	B-Process
systems	I-Process
Ru	B-Process
(	I-Process
Zr	I-Process
)+	I-Process
Zr	I-Process
(	I-Process
Ru	I-Process
)	I-Process
at	O
relativistic	B-Process
energies	I-Process
of	O
0.4	O
and	O
1.528A	O
GeV	O
.	O
The	O
study	B-Task
is	O
performed	O
within	O
a	O
relativistic	B-Process
framework	I-Process
and	O
the	O
contribution	B-Process
of	I-Process
the	I-Process
iso-vector	I-Process
,	O
scalar	B-Task
δ	I-Task
field	I-Task
to	I-Task
the	I-Task
symmetry	I-Task
energy	I-Task
and	I-Task
the	I-Task
isospin	I-Task
dynamics	I-Task
is	I-Task
particularly	I-Task
explored	I-Task
.	O
We	O
find	O
that	O
the	O
isospin	B-Task
mixing	I-Task
depends	O
on	O
the	O
symmetry	O
energy	O
and	O
a	O
stiffer	O
behavior	O
leads	O
to	O
more	O
transparency	O
.	O
The	O
results	O
are	O
also	O
nicely	O
sensitive	O
to	O
the	O
“	O
fine	O
structure	O
”	O
of	O
the	O
symmetry	O
energy	O
,	O
i.e.	O
,	O
to	O
the	O
covariant	O
properties	O
of	O
the	O
isovector	O
meson	O
fields.The	O
isospin	O
tracing	O
appears	O
much	O
less	O
dependent	O
on	O
the	O
in	O
medium	O
neutron	O
–	O
proton	O
cross	O
sections	O
(	O
σnp	O
)	O
and	O
this	O
makes	O
such	O
observable	O
very	O
peculiar	O
for	O
the	O
study	O
of	O
the	O
isovector	O
part	O
of	O
the	O
nuclear	O
equation	O
of	O
state.Within	O
such	O
a	O
framework	O
,	O
comparisons	O
with	O
experiments	O
support	O
the	O
introduction	O
of	O
the	O
δ	O
meson	O
in	O
the	O
description	O
of	O
the	O
iso-vector	O
equation	O
of	O
state	O
.	O

The	O
most	O
ambitious	O
goal	O
may	O
be	O
stated	O
as	O
the	O
one	O
of	O
detecting	B-Task
the	I-Task
location	I-Task
of	I-Task
,	I-Task
say	I-Task
,	I-Task
one	I-Task
missing	I-Task
level	I-Task
on	I-Task
an	I-Task
otherwise	I-Task
complete	I-Task
sequence	I-Task
.	O
Dyson	O
,	O
in	O
a	O
recent	O
review	O
[	O
7	O
]	O
,	O
uses	O
information	B-Process
theory	I-Process
concepts	I-Process
and	O
argues	O
that	O
correlations	B-Process
in	I-Process
a	I-Process
sequence	I-Process
may	O
provide	O
the	O
necessary	O
redundancy	O
from	O
which	O
error	B-Task
correcting	I-Task
codes	I-Task
can	I-Task
be	I-Task
constructed	I-Task
.	O
At	O
one	O
extreme	O
where	O
no	B-Process
correlations	I-Process
and	I-Process
therefore	I-Process
no	I-Process
redundancy	I-Process
are	I-Process
present	I-Process
(	O
Poissonian	B-Process
sequence	I-Process
)	O
,	O
there	O
is	O
no	O
possibility	O
of	O
detecting	B-Task
one	I-Task
missing	I-Task
level	I-Task
.	O
At	O
the	O
other	O
extreme	O
,	O
a	O
sequence	B-Task
of	I-Task
equally	I-Task
spaced	I-Task
levels	I-Task
(	O
picket	B-Task
fence	I-Task
)	O
,	O
there	O
is	O
a	O
maximum	O
redundancy	O
and	O
a	O
missed	B-Task
level	I-Task
can	I-Task
be	I-Task
obviously	I-Task
detected	I-Task
as	I-Task
a	I-Task
hole	I-Task
in	I-Task
the	I-Task
spectrum	I-Task
.	O
Eigenvalues	O
of	O
random	O
matrices	O
,	O
which	O
exhibit	O
characteristic	O
correlations	O
,	O
correspond	O
to	O
an	O
intermediate	O
situation	O
between	O
these	O
two	O
extremes	O
.	O
The	O
attempts	O
to	O
locate	B-Task
in	I-Task
the	I-Task
last	I-Task
case	I-Task
a	I-Task
single	I-Task
missed	I-Task
level	I-Task
have	O
remained	O
unsuccessful	O
so	O
far	O
.	O
However	O
,	O
it	O
should	O
be	O
mentioned	O
that	O
for	O
two-dimensional	B-Task
chaotic	I-Task
systems	I-Task
where	O
,	O
besides	O
correlations	O
of	O
the	O
order	O
of	O
one	O
mean	O
spacing	O
as	O
described	O
by	O
random	O
matrices	O
,	O
the	O
presence	O
and	O
the	O
role	O
of	O
long	O
range	O
correlations	O
governed	O
by	O
the	O
shortest	O
periodic	O
orbits	O
and	O
reflected	O
in	O
Weyl	B-Process
's	I-Process
law	I-Process
describing	I-Process
the	I-Process
average	I-Process
spectral	I-Process
density	I-Process
,	O
is	O
well	O
understood	O
.	O
It	O
is	O
then	O
possible	O
to	O
approximately	B-Task
locate	I-Task
,	O
from	O
the	O
study	B-Process
of	I-Process
spectral	I-Process
fluctuations	I-Process
,	O
a	B-Task
single	I-Task
missed	I-Task
level	I-Task
[	O
8	O
]	O
.	O

The	O
reason	O
to	O
investigate	B-Task
the	I-Task
BFKL	I-Task
and	I-Task
DGLAP	I-Task
equations	I-Task
in	I-Task
the	I-Task
case	I-Task
of	I-Task
supersymmetric	I-Task
theories	I-Task
is	O
based	O
on	O
a	O
common	O
belief	O
,	O
that	O
the	O
high	B-Task
symmetry	I-Task
may	I-Task
significantly	I-Task
simplify	I-Task
the	I-Task
structure	I-Task
of	I-Task
these	I-Task
equations	I-Task
.	O
Indeed	O
,	O
it	O
was	O
found	O
in	O
the	O
leading	B-Process
logarithmic	I-Process
approximation	I-Process
(	O
LLA	B-Process
)	O
[	O
10	O
]	O
,	O
that	O
the	O
so-called	O
quasi-partonic	O
operators	O
in	O
N	O
=	O
1	O
SYM	O
are	O
unified	O
in	O
supermultiplets	O
with	O
anomalous	B-Process
dimensions	I-Process
obtained	I-Process
from	I-Process
universal	I-Process
anomalous	I-Process
dimensions	I-Process
γuni	I-Process
(	I-Process
j	I-Process
)	I-Process
by	I-Process
shifting	I-Process
its	I-Process
arguments	I-Process
by	I-Process
an	I-Process
integer	I-Process
number	I-Process
.	O
Further	O
,	O
the	O
anomalous	B-Task
dimension	I-Task
matrices	I-Task
for	I-Task
twist-2	I-Task
operators	I-Task
are	O
fixed	B-Process
by	I-Process
the	I-Process
superconformal	I-Process
invariance	I-Process
[	O
10	O
]	O
.	O
Calculations	B-Process
in	I-Process
the	I-Process
maximally	I-Process
extended	I-Process
N	I-Process
=	I-Process
4	I-Process
SYM	I-Process
,	I-Process
where	I-Process
the	I-Process
coupling	I-Process
constant	I-Process
is	I-Process
not	I-Process
renormalized	I-Process
,	O
give	O
even	O
more	O
remarkable	O
results	O
.	O
Namely	O
,	O
it	O
turns	O
out	O
,	O
that	O
here	O
all	O
twist-2	B-Task
operators	I-Task
enter	O
in	O
the	O
same	O
multiplet	O
,	O
their	O
anomalous	B-Task
dimension	I-Task
matrix	I-Task
is	O
fixed	O
completely	O
by	O
the	O
super-conformal	B-Process
invariance	I-Process
and	O
its	O
universal	O
anomalous	O
dimension	O
in	O
LLA	O
is	O
proportional	O
to	O
Ψ	O
(	O
j	O
−	O
1	O
)−	O
Ψ	O
(	O
1	O
)	O
,	O
which	O
means	O
,	O
that	O
the	O
evolution	B-Task
equations	I-Task
for	I-Task
the	I-Task
matrix	I-Task
elements	I-Task
of	I-Task
quasi-partonic	I-Task
operators	I-Task
in	I-Task
the	I-Task
multicolor	I-Task
limit	I-Task
Nc	I-Task
→∞	I-Task
are	O
equivalent	O
to	O
the	O
Schrödinger	O
equation	O
for	O
an	O
integrable	O
Heisenberg	O
spin	O
model	O
[	O
11,12	O
]	O
.	O
In	O
QCD	O
the	O
integrability	O
remains	O
only	O
in	O
a	O
small	O
sector	O
of	O
the	O
quasi-partonic	O
operators	O
[	O
13	O
]	O
.	O
In	O
the	O
case	O
of	O
N	B-Task
=	I-Task
4	I-Task
SYM	I-Task
the	O
equations	O
for	O
other	O
sets	O
of	O
operators	O
are	O
also	O
integrable	O
[	O
14	O
–	O
16	O
]	O
.	O
Evolution	B-Task
equations	I-Task
for	I-Task
quasi-partonic	I-Task
operators	I-Task
are	O
written	O
in	O
an	O
explicitly	O
super-conformal	O
form	O
in	O
Ref	O
.	O
[	O
17	O
]	O
.	O

Thus	O
,	O
the	O
extension	B-Process
to	I-Process
the	I-Process
charmed	I-Process
analogue	I-Process
Θc	I-Process
(	I-Process
3099	I-Process
)	I-Process
provides	O
an	O
interesting	O
test	O
for	O
the	O
SDO	B-Task
sum	I-Task
rule	I-Task
and	I-Task
lattice	I-Task
calculations	I-Task
[	O
17	O
]	O
.	O
Here	O
,	O
the	O
charm	B-Material
quark	I-Material
is	O
quite	O
heavy	O
so	O
that	O
the	O
constituent-quark	B-Process
picture	I-Process
may	O
fit	O
well	O
and	O
the	O
JW	B-Process
prediction	I-Process
for	O
the	O
parity	O
is	O
expected	O
to	O
be	O
reproduced	O
from	O
QCD	B-Material
.	O
In	O
fact	O
,	O
quenched	B-Task
lattice	I-Task
calculation	I-Task
finds	O
the	O
parity	O
of	O
Θc	O
(	O
3099	O
)	O
to	O
be	O
positive	O
[	O
28	O
]	O
.	O
In	O
the	O
extension	O
to	O
the	O
Θc	O
(	O
3099	O
)	O
sum	O
rules	O
,	O
there	O
are	O
two	O
important	O
aspects	O
,	O
which	O
make	O
this	O
sum	B-Task
rule	I-Task
different	O
from	O
the	O
SDO	B-Process
sum	I-Process
rule	I-Process
.	O
First	O
of	O
all	O
,	O
since	O
the	O
charm	B-Material
quark	I-Material
is	O
too	O
heavy	O
to	O
form	O
quark	B-Material
condensate	I-Material
,	O
it	O
gives	O
non-perturbative	O
effects	O
only	O
by	O
radiating	B-Material
gluons	I-Material
.	O
The	O
quark	B-Material
–	I-Material
gluon	I-Material
mixed	I-Material
condensate	I-Material
〈	O
s	B-Material
̄	I-Material
gsσ	I-Material
·	I-Material
Gs	I-Material
〉	O
,	O
which	O
was	O
the	O
important	O
contribution	O
in	O
the	O
Θ	B-Process
+	I-Process
sum	I-Process
rule	I-Process
,	O
is	O
replaced	B-Process
by	I-Process
gluonic	I-Process
operators	I-Process
in	I-Process
the	I-Process
heavy	I-Process
quark	I-Process
expansion	I-Process
that	O
are	O
normally	O
suppressed	O
.	O
Secondly	O
,	O
the	O
charm	B-Process
quark	I-Process
mass	I-Process
has	I-Process
to	I-Process
be	I-Process
kept	I-Process
finite	I-Process
in	I-Process
the	I-Process
OPE	I-Process
,	O
which	O
can	O
be	O
done	O
by	O
using	O
the	O
momentum	B-Process
space	I-Process
expression	I-Process
for	O
the	O
charm-quark	B-Material
propagator	I-Material
.	O
This	O
is	O
different	O
from	O
the	B-Process
light-quark	I-Process
sum	I-Process
rule	I-Process
where	O
the	O
calculation	O
is	O
performed	O
in	O
the	O
coordinate	O
space	O
and	O
all	O
the	O
quark	B-Material
propagators	I-Material
are	O
obtained	O
based	O
on	O
the	O
expansion	O
with	O
the	O
small	B-Material
quark	I-Material
mass	O
.	O
Keeping	O
these	O
two	O
aspects	O
in	O
mind	O
,	O
we	O
construct	O
QCD	B-Process
sum	I-Process
rules	I-Process
for	O
Θc	O
(	O
3099	O
)	O
and	O
see	B-Task
how	I-Task
they	I-Task
are	I-Task
different	I-Task
from	I-Task
the	I-Task
Θ	I-Task
+(	I-Task
1540	I-Task
)	I-Task
sum	I-Task
rule	I-Task
.	O

Including	O
the	O
O	O
(	O
αs	O
)	O
corrections	O
,	O
all	O
the	O
operators	B-Task
listed	I-Task
in	I-Task
(	I-Task
9	I-Task
)	I-Task
and	I-Task
(	I-Task
10	I-Task
)	I-Task
have	I-Task
to	I-Task
be	I-Task
included	I-Task
.	O
A	O
convenient	O
framework	O
to	O
carry	O
out	O
these	O
calculations	O
is	O
the	O
QCD	B-Process
factorization	I-Process
framework	I-Process
[	O
14	O
]	O
which	O
allows	O
to	O
express	B-Process
the	I-Process
hadronic	I-Process
matrix	I-Process
elements	I-Process
in	I-Process
the	I-Process
schematic	I-Process
form	I-Process
:	I-Process
(	O
11	O
)〈	O
Vγ	O
|	O
Oi	O
|	O
B	O
〉=	O
FB	O
→	O
VTiI	O
+∫	O
dk	O
+	O
2π	O
∫	O
01duφB	O
,+(	O
k	O
+)	O
TiII	O
(	O
k	O
+	O
,	O
u	O
)	O
φV	O
⊥(	O
u	O
)	O
,	O
where	O
FB	O
→	O
V	O
are	O
the	O
transition	O
form	O
factors	O
defined	O
through	O
the	O
matrix	O
elements	O
of	O
the	O
operator	O
O7	O
,	O
φB	O
,+(	O
k	O
+)	O
is	O
the	O
leading-twist	O
B-meson	O
wave-function	O
with	O
k	O
+	O
being	O
a	O
light-cone	O
component	O
of	O
the	O
spectator	O
quark	O
momentum	O
,	O
φ	O
⊥	O
V	O
(	O
u	O
)	O
is	O
the	O
leading-twist	O
light-cone	O
distribution	O
amplitude	O
(	O
LCDA	O
)	O
of	O
the	O
transversely-polarized	O
vector	O
meson	O
V	O
,	O
and	O
u	O
is	O
the	O
fractional	O
momentum	O
of	O
the	O
vector	O
meson	O
carried	O
by	O
one	O
of	O
the	O
two	O
partons	O
.	O
The	O
quantities	O
TiI	O
and	O
TiII	O
are	O
the	O
hard-perturbative	O
kernels	O
calculated	O
to	O
order	O
αs	O
,	O
with	O
the	O
latter	O
containing	O
the	O
so-called	O
hard-spectator	O
contributions	O
.	O
The	O
factorization	O
formula	O
(	O
11	O
)	O
holds	O
in	O
the	O
heavy	O
quark	O
limit	O
,	O
i.e.	O
,	O
to	O
order	O
ΛQCD	O
/	O
MB	O
.	O
This	O
factorization	O
framework	O
has	O
been	O
used	O
to	O
calculate	O
the	O
branching	O
fractions	O
and	O
related	O
quantities	O
for	O
the	O
decays	O
B	O
→	O
K	O
∗	O
γ	O
[	O
15	O
–	O
17	O
]	O
and	O
B	O
→	O
ργ	O
[	O
15,17	O
]	O
.	O
The	O
isospin	O
violation	O
in	O
the	O
B	O
→	O
K	O
∗	O
γ	O
decays	O
in	O
this	O
framework	O
have	O
also	O
been	O
studied	O
[	O
18	O
]	O
.	O
(	O
For	O
applications	O
to	O
B	O
→	O
K	O
∗	O
γ	O
∗	O
,	O
see	O
Refs.	O
[	O
16,19,20	O
].)	O
Very	O
recently	O
,	O
the	O
hard-spectator	O
contribution	O
arising	O
from	O
the	O
chromomagnetic	O
operator	O
O8	O
have	O
also	O
been	O
calculated	O
in	O
next-to-next-to-leading	O
order	O
(	O
NNLO	O
)	O
in	O
αs	O
showing	O
that	O
the	O
spectator	O
interactions	O
factorize	O
in	O
the	O
heavy	O
quark	O
limit	O
[	O
21	O
]	O
.	O
However	O
,	O
the	O
numerical	O
effect	O
of	O
the	O
resummed	O
NNLO	O
contributions	O
is	O
marginal	O
and	O
we	O
shall	O
not	O
include	O
this	O
in	O
our	O
update	O
.	O

Several	O
methods	O
based	O
on	O
dynamical	O
assumptions	O
were	O
suggested	O
for	O
determination	B-Task
of	I-Task
the	I-Task
P-parity	I-Task
of	O
the	O
Θ	O
+	O
[	O
13	O
]	O
.	O
According	O
to	O
a	O
general	B-Process
theorem	I-Process
[	O
14	O
]	O
,	O
in	O
order	O
to	O
determine	B-Task
the	I-Task
parity	I-Task
of	O
one	O
particle	B-Material
in	O
a	O
binary	B-Process
reaction	I-Process
one	O
has	O
to	O
know	O
polarizations	O
at	O
least	O
of	O
two	O
fermions	B-Material
participating	O
in	O
this	B-Process
reaction	I-Process
.	O
Model	B-Process
independent	I-Process
methods	I-Process
for	O
determination	B-Task
of	I-Task
the	I-Task
P-parity	I-Task
of	O
the	O
Θ	O
+	O
were	O
suggested	O
recently	O
in	O
Refs	O
.	O
[	O
15,16	O
]	O
for	O
pp-collision	B-Process
,	O
and	O
in	O
Ref	O
.	O
[	O
17	O
]	O
for	O
photoproduction	B-Process
of	O
the	O
Θ	O
+	O
.	O
The	O
method	O
of	O
Refs	O
.	O
[	O
15,16	O
]	O
,	O
based	O
on	O
the	O
assumption	O
that	O
the	O
spin	O
of	O
the	O
Θ	O
+	O
equals	O
12	O
,	O
suggests	O
to	O
measure	B-Process
the	I-Process
spin	I-Process
–	I-Process
spin	I-Process
correlation	I-Process
parameter	I-Process
in	O
the	O
reaction	O
p	O
→	O
p	O
→→	O
Σ	O
+	O
Θ	O
+	O
near	O
the	O
threshold	O
.	O
We	O
generalize	O
here	O
this	O
method	O
for	O
an	O
arbitrary	O
spin	O
of	O
the	O
Θ	O
+	O
and	O
both	O
isospins	O
T	O
=	O
0	O
and	O
T	O
=	O
1	O
of	O
the	O
NN	O
channel	O
of	O
the	O
NN	O
→	O
YΘ	O
+	O
reaction	O
.	O
Furthermore	O
,	O
we	O
consider	O
a	O
polarization	B-Process
transfer	I-Process
from	O
a	O
nucleon	B-Material
to	O
the	B-Material
hyperon	I-Material
Y	I-Material
in	O
this	O
reaction	O
.	O
Our	O
consideration	O
is	O
model	O
independent	O
,	O
since	O
it	O
is	O
based	O
only	O
on	O
conservation	B-Process
of	I-Process
the	I-Process
P-parity	I-Process
,	O
total	B-Process
angular	I-Process
momentum	I-Process
and	O
isospin	B-Process
in	O
the	O
reaction	B-Process
and	O
the	O
generalized	O
Pauli	O
principle	O
for	O
nucleons	B-Material
.	O

A	O
central	O
question	O
from	O
the	O
point	O
of	O
view	O
of	O
nuclear	O
physics	O
involves	O
the	O
changes	B-Task
to	I-Task
the	I-Task
quark	I-Task
and	I-Task
antiquark	I-Task
distributions	I-Task
of	I-Task
a	I-Task
bound	I-Task
proton	I-Task
.	O
Since	O
one	O
must	O
develop	B-Task
a	I-Task
reliable	I-Task
model	I-Task
of	I-Task
both	I-Task
the	I-Task
free	I-Task
proton	I-Task
and	I-Task
the	I-Task
binding	I-Task
of	I-Task
nucleons	I-Task
starting	I-Task
from	I-Task
the	I-Task
quark	I-Task
level	I-Task
[	O
8	O
]	O
,	O
this	O
problem	O
is	O
rather	O
complicated	O
.	O
We	O
intend	O
to	O
report	O
on	O
our	O
investigation	O
of	O
that	O
problem	O
in	O
future	O
work	O
.	O
For	O
the	O
present	O
,	O
we	O
have	O
chosen	O
to	O
illustrate	B-Process
the	I-Process
formal	I-Process
ideas	I-Process
developed	I-Process
here	I-Process
by	I-Process
applying	I-Process
them	I-Process
to	I-Process
a	I-Process
toy	I-Process
model	I-Process
,	O
namely	O
the	O
quark	B-Material
distributions	I-Material
of	I-Material
isospin	I-Material
symmetric	I-Material
quark	I-Material
matter	I-Material
in	O
which	O
each	B-Process
quark	I-Process
feels	I-Process
a	I-Process
scalar	I-Process
potential	I-Process
,	I-Process
−	I-Process
Vsq	I-Process
,	I-Process
and	I-Process
a	I-Process
vector	I-Process
potential	I-Process
,	I-Process
Vvq	I-Process
.	O
This	O
is	O
the	O
premise	O
of	O
the	O
Quark	B-Process
–	I-Process
Meson	I-Process
Coupling	I-Process
(	O
QMC	B-Process
)	O
model	O
[	O
9	O
]	O
which	O
has	O
been	O
used	O
successfully	O
to	O
calculate	B-Task
the	I-Task
properties	I-Task
of	I-Task
nuclear	I-Task
matter	I-Task
as	I-Task
well	I-Task
as	I-Task
finite	I-Task
nuclei	I-Task
[	O
10,11	O
]	O
.	O
Most	O
recently	O
it	O
has	O
also	O
been	O
used	O
to	O
derive	O
an	O
effective	B-Task
nuclear	I-Task
force	I-Task
which	O
is	O
very	O
close	O
to	O
the	O
widely	O
used	O
Skyrme	O
III	O
force	O
[	O
12	O
]	O
.	O
(	O
Except	O
that	O
in	O
QMC	O
the	O
quarks	O
are	O
confined	O
by	O
the	O
MIT	O
bag	O
,	O
as	O
well	O
as	O
feeling	O
the	O
mean-field	O
scalar	O
and	O
vector	O
potentials	O
generated	O
by	O
the	O
surrounding	O
nucleons	O
.	O
)	O
In	O
the	O
mean	B-Process
field	I-Process
approximation	I-Process
,	O
the	O
Dirac	B-Task
equation	I-Task
for	I-Task
the	I-Task
quark	I-Task
in	I-Task
infinite	I-Task
quark	I-Task
matter	I-Task
is	O
written	O
as	O
:	O
(	B-Task
30	I-Task
)	I-Task
iγ	I-Task
·∂−	I-Task
m	I-Task
−	I-Task
Vqs	I-Task
−	I-Task
γ0VqvψQMq	I-Task
(	I-Task
x	I-Task
)=	I-Task
0	I-Task
.	O

Within	O
a	O
coalescence	B-Process
approach	I-Process
as	O
successfully	O
applied	O
earlier	O
in	O
the	O
light-quark	B-Task
sector	I-Task
,	O
we	O
have	O
evaluated	B-Process
transverse-momentum	I-Process
dependencies	I-Process
of	O
charmed	B-Material
hadrons	I-Material
in	O
central	B-Process
heavy-ion	I-Process
reactions	I-Process
at	O
RHIC	O
.	O
For	O
the	O
charm-quark	B-Task
distributions	I-Task
at	O
hadronization	O
we	O
have	O
considered	O
two	O
limiting	O
scenarios	O
,	O
i.e.	O
,	O
no	O
reinteractions	O
(	O
using	O
spectra	O
from	O
PYTHIA	O
)	O
and	O
complete	O
thermalization	O
with	O
transverse	O
flow	O
of	O
the	O
bulk	O
matter	O
.	O
The	O
resulting	O
J	O
/	O
ψ	O
(	O
mT	O
-)	O
spectra	O
differ	O
in	O
slope	O
by	O
up	O
to	O
a	O
factor	O
of	O
2	O
(	O
harder	O
for	O
pQCD	O
c-quarks	O
)	O
,	O
and	O
the	O
integrated	O
yield	O
is	O
about	O
a	O
factor	O
of	O
3	O
larger	O
in	O
the	O
thermal	O
case	O
.	O
For	O
D-mesons	O
,	O
we	O
found	O
that	O
the	O
difference	O
in	O
the	O
slope	O
parameters	O
of	O
the	O
pT-spectra	O
in	O
the	O
two	O
scenarios	O
is	O
less	O
pronounced	O
,	O
but	O
their	O
elliptic	O
flow	O
is	O
about	O
a	O
factor	O
of	O
2	O
larger	O
for	O
pT	O
⩾	O
1.5	O
GeV	O
in	O
the	O
thermalized	O
case	O
.	O
The	O
elliptic	O
flow	O
pattern	O
of	O
D-mesons	O
was	O
found	O
to	O
be	O
essentially	O
preserved	O
in	O
the	O
single-electron	O
decay	O
spectra	O
,	O
rendering	O
the	O
latter	O
a	O
very	O
promising	O
observable	O
to	O
address	O
the	O
strength	O
of	O
charm	O
reinteractions	O
in	O
the	O
QGP	O
.	O
The	O
present	O
study	O
can	O
be	O
straightforwardly	O
generalized	O
to	O
charmed	O
baryons	O
(	O
Λc	O
)	O
,	O
which	O
may	O
serve	O
as	O
a	O
complimentary	O
probe	O
for	O
charm-quark	O
reinteractions	O
in	O
the	O
QGP	O
.	O

One	O
of	O
the	O
challenges	O
in	O
quantum	B-Task
chromodynamics	I-Task
(	O
QCD	B-Task
)	O
is	O
the	O
relativistic	B-Task
bound	I-Task
state	I-Task
problem	I-Task
.	O
In	O
the	O
light-cone	B-Process
Hamiltonian	I-Process
approach	I-Process
[	O
1	O
]	O
light-cone	B-Task
wave	I-Task
functions	I-Task
can	O
be	O
constructed	O
in	O
a	O
boost	O
invariant	O
way	O
.	O
It	O
is	O
necessary	O
to	O
have	O
reliable	B-Material
light-cone	I-Material
wave	I-Material
functions	I-Material
if	O
one	O
wants	O
to	O
calculate	B-Task
high	I-Task
energy	I-Task
scattering	I-Task
,	O
especially	O
exclusive	O
reactions	B-Process
.	O
Many	O
parametrizations	O
assume	O
separability	O
of	O
the	O
dependence	O
on	O
the	O
longitudinal	O
momentum	O
fraction	O
and	O
transverse	O
momentum	O
which	O
is	O
very	O
unlikely	O
since	O
the	O
two	O
momenta	O
are	O
coupled	O
in	O
the	O
kinetic	O
energy	O
operator	O
.	O
Various	O
approaches	O
have	O
been	O
tried	O
to	O
compute	O
such	O
wave	O
functions	O
.	O
One	O
can	O
use	O
the	O
usual	O
equal	B-Process
time	I-Process
Hamiltonian	I-Process
[	O
2	O
]	O
and	O
transform	B-Process
the	I-Process
resulting	I-Process
wave	I-Process
functions	I-Process
into	I-Process
light-cone	I-Process
form	I-Process
with	O
the	O
help	O
of	O
kinematical	B-Process
on-shell	I-Process
equations	I-Process
.	O
The	O
light-cone	O
Hamiltonian	O
in	O
a	O
string	O
picture	O
is	O
formulated	O
in	O
Ref	O
.	O
[	O
3	O
]	O
.	O
More	O
ambitious	O
is	O
the	O
construction	B-Task
of	I-Task
an	I-Task
effective	I-Task
Hamiltonian	I-Task
including	I-Task
the	I-Task
gauge	I-Task
degrees	I-Task
of	I-Task
freedom	I-Task
explicitly	I-Task
and	O
then	O
solving	B-Process
the	I-Process
bound	I-Process
state	I-Process
problem	I-Process
.	O
For	O
mesons	B-Material
this	O
approach	O
[	O
4,5	O
]	O
still	O
needs	O
many	O
parameters	O
to	O
be	O
fixed	O
.	O
Attempts	O
have	O
been	O
made	O
to	O
solve	B-Task
the	I-Task
valence	I-Task
quark	I-Task
wave	I-Task
function	I-Task
for	I-Task
mesons	I-Task
in	O
a	O
simple	B-Process
Hamiltonian	I-Process
with	I-Process
a	I-Process
two-body	I-Process
potential	I-Process
[	O
6	O
]	O
.	O

The	O
microwave	B-Process
background	I-Process
is	O
not	O
the	O
only	O
universal	B-Material
photon	I-Material
field	I-Material
that	O
has	O
to	O
be	O
taken	B-Process
in	I-Process
consideration	I-Process
.	O
Especially	O
interesting	O
is	O
the	O
isotropic	B-Process
infrared	I-Process
and	I-Process
optical	I-Process
background	I-Process
(	O
IRB	B-Process
)	O
.	O
The	O
number	O
density	O
of	O
IRB	O
is	O
smaller	O
than	O
that	O
of	O
MBR	B-Process
by	O
more	O
that	O
two	O
orders	O
of	O
magnitude	O
.	O
On	O
the	O
other	O
hand	O
,	O
protons	B-Material
of	O
lower	O
energy	O
can	O
interact	O
on	O
the	O
IRB	B-Process
,	O
and	O
the	B-Process
smaller	I-Process
number	I-Process
density	I-Process
has	I-Process
to	I-Process
be	I-Process
weighted	I-Process
with	I-Process
the	I-Process
higher	I-Process
flux	I-Process
of	I-Process
interacting	I-Process
protons	I-Process
.	O
The	O
present	B-Task
Universe	I-Task
is	O
optically	O
thin	O
to	O
1019	O
eV	O
and	O
lower	B-Material
energy	I-Material
protons	I-Material
,	O
but	O
even	O
at	O
low	O
redshifts	O
the	O
proton	B-Material
interaction	O
rate	O
quickly	O
increases	O
.	O
This	O
is	O
different	O
from	O
the	O
interactions	B-Task
on	I-Task
MBR	I-Task
,	O
where	O
the	O
interacting	B-Material
protons	I-Material
quickly	B-Process
lose	I-Process
their	I-Process
energy	I-Process
even	I-Process
at	I-Process
z	I-Process
=	I-Process
0	I-Process
.	O
The	O
cosmological	B-Process
evolution	I-Process
of	I-Process
UHECR	I-Process
injection	I-Process
is	O
thus	O
of	O
major	O
importance	O
for	O
the	O
contribution	O
of	O
such	O
interactions	O
to	O
the	O
flux	B-Task
of	I-Task
cosmogenic	I-Task
neutrinos	I-Task
.	O

In	O
this	O
Letter	O
,	O
we	O
extend	B-Task
the	I-Task
McVittie	I-Task
's	I-Task
solution	I-Task
into	I-Task
charged	I-Task
black	I-Task
holes	I-Task
.	O
We	O
first	O
deduce	B-Process
the	I-Process
metric	I-Process
for	I-Process
a	I-Process
Reissner	I-Process
–	I-Process
Nordström	I-Process
black	I-Process
hole	I-Process
in	I-Process
the	I-Process
expanding	I-Process
universe	I-Process
;	O
several	O
special	O
cases	O
of	O
our	O
solution	O
are	O
exactly	O
the	O
same	O
as	O
some	O
solutions	O
discovered	O
previously	O
.	O
In	O
the	O
previous	O
work	O
[	O
6	O
]	O
we	O
have	O
applied	B-Task
the	I-Task
asymptotic	I-Task
conditions	I-Task
to	I-Task
derive	I-Task
the	I-Task
Schwarzschild	I-Task
metric	I-Task
in	I-Task
the	I-Task
expanding	I-Task
universe	I-Task
,	O
which	O
is	O
exactly	O
the	O
same	O
as	O
that	O
derived	O
by	O
McVittie	O
by	O
solving	B-Process
the	I-Process
full	I-Process
Einstein	I-Process
equations	I-Process
.	O
That	O
demonstrates	O
the	O
power	O
of	O
this	O
simple	O
and	O
straight-forward	O
approach	O
.	O
In	O
this	O
Letter	O
we	O
follow	B-Process
the	I-Process
same	I-Process
procedure	I-Process
to	I-Process
derive	I-Process
the	I-Process
metric	I-Process
for	O
the	O
Reissner	B-Task
–	I-Task
Nordström	I-Task
black	I-Task
holes	I-Task
in	I-Task
Friedman	I-Task
–	I-Task
Robertson	I-Task
–	I-Task
Walker	I-Task
universe	I-Task
.	O
We	O
then	O
study	B-Task
the	I-Task
influences	I-Task
of	I-Task
the	I-Task
evolution	I-Task
of	I-Task
the	I-Task
universe	I-Task
on	I-Task
the	I-Task
size	I-Task
of	I-Task
the	I-Task
black	I-Task
hole	I-Task
.	O
Finally	O
,	O
in	O
order	O
to	O
study	B-Task
the	I-Task
motion	I-Task
of	I-Task
the	I-Task
planet	I-Task
,	O
we	O
rewrite	B-Process
the	I-Process
metric	I-Process
from	I-Process
the	I-Process
cosmic	I-Process
coordinates	I-Process
system	I-Process
to	I-Process
the	I-Process
Schwarzschild	I-Process
coordinates	I-Process
system	I-Process
.	O

The	O
ART	B-Process
model	I-Process
is	O
a	O
hadronic	B-Process
transport	I-Process
model	I-Process
that	O
includes	O
baryons	B-Material
such	O
as	O
N	B-Material
,	I-Material
Δ	I-Material
(	I-Material
1232	I-Material
)	I-Material
,	O
N	B-Material
∗(	I-Material
1440	I-Material
)	I-Material
,	O
N	B-Material
∗(	I-Material
1535	I-Material
)	I-Material
,	O
Λ	B-Material
,	O
Σ	B-Material
,	O
and	O
mesons	B-Material
such	O
as	O
π	B-Material
,	O
ρ	B-Material
,	O
ω	B-Material
,	O
η	B-Material
,	O
K	B-Material
,	O
K	B-Material
∗	I-Material
.	O
Both	O
elastic	B-Task
and	I-Task
inelastic	I-Task
collisions	I-Task
among	O
most	O
of	O
these	B-Material
particles	I-Material
are	O
included	O
by	O
using	O
the	O
experimental	B-Material
data	I-Material
from	I-Material
hadron	I-Material
–	I-Material
hadron	I-Material
collisions	I-Material
.	O
The	O
ART	B-Process
model	I-Process
has	O
been	O
quite	O
successful	O
in	O
explaining	B-Task
many	I-Task
experimental	I-Task
observations	I-Task
,	O
including	O
the	O
surprisingly	O
large	O
kaon	B-Process
antiflow	I-Process
[	O
11,12	O
]	O
in	O
heavy	B-Material
ion	I-Material
collisions	O
at	O
AGS	B-Process
energies	I-Process
.	O
The	O
ART	B-Process
model	I-Process
also	O
allows	O
us	O
to	O
understand	B-Task
whether	I-Task
or	I-Task
not	I-Task
the	I-Task
strongly	I-Task
interacting	I-Task
matter	I-Task
formed	I-Task
in	I-Task
these	I-Task
collisions	I-Task
reaches	I-Task
chemical	I-Task
and	I-Task
/	I-Task
or	I-Task
thermal	I-Task
equilibrium	I-Task
.	O
In	O
the	O
present	O
study	O
,	O
we	O
extend	B-Task
the	I-Task
ART	I-Task
model	I-Task
to	I-Task
include	I-Task
perturbatively	I-Task
the	I-Task
Ξ	I-Task
particle	I-Task
as	O
in	O
the	O
studies	O
for	O
other	O
rare	B-Material
particles	I-Material
using	O
the	O
transport	B-Material
model	I-Material
[	O
6,13,14	O
]	O
.	O

Though	O
,	O
in	O
this	O
Letter	O
we	O
have	O
constructed	B-Task
the	I-Task
Born	I-Task
–	I-Task
Infeld	I-Task
black	I-Task
holes	I-Task
in	I-Task
the	I-Task
presence	I-Task
of	I-Task
a	I-Task
cosmological	I-Task
constant	I-Task
and	O
discussed	B-Task
their	I-Task
thermodynamical	I-Task
properties	I-Task
,	O
many	O
issues	O
however	O
still	O
remain	O
to	O
be	O
investigated	O
.	O
We	O
know	O
that	O
Reissner	B-Material
–	I-Material
Nordström	I-Material
AdS	I-Material
black	I-Material
holes	I-Material
undergo	O
Hawking	B-Process
–	I-Process
Page	I-Process
phase	I-Process
transition	I-Process
.	O
This	O
transition	O
gets	O
modified	O
as	O
we	O
include	O
Born	B-Process
–	I-Process
Infeld	I-Process
corrections	I-Process
into	O
account	O
.	O
We	O
hope	O
to	O
carry	O
out	O
a	O
detail	O
study	O
on	O
this	O
issue	O
in	O
the	O
future	O
.	O
Furthermore	O
,	O
in	O
the	O
context	O
of	O
brane	B-Task
world	I-Task
cosmology	I-Task
,	O
it	O
was	O
found	O
that	O
a	O
brane	O
moving	O
in	O
a	O
Reissner	B-Process
–	I-Process
Nordström	I-Process
AdS	I-Process
background	I-Process
generates	O
non-singular	B-Task
cosmology	I-Task
[	O
14	O
]	O
.	O
However	O
,	O
as	O
shown	O
in	O
[	O
15	O
]	O
,	O
the	O
brane	B-Material
always	O
crosses	B-Process
the	I-Process
inner	I-Process
horizon	I-Process
of	I-Process
the	I-Process
bulk	I-Process
geometry	I-Process
,	O
creating	O
an	O
instability	B-Process
.	O
It	O
would	O
be	O
interesting	O
to	O
study	B-Task
cosmology	I-Task
on	I-Task
the	I-Task
brane	I-Task
when	I-Task
it	I-Task
is	I-Task
moving	I-Task
in	I-Task
the	I-Task
charged	I-Task
black	I-Task
hole	I-Task
backgrounds	I-Task
that	O
we	O
have	O
constructed	O
.	O
Note	O
that	O
since	O
these	O
charged	B-Material
holes	I-Material
does	O
not	O
have	O
inner	O
horizon	O
for	O
certain	O
range	O
of	O
parameters	O
,	O
we	O
may	O
generate	B-Task
non-singular	I-Task
cosmology	I-Task
without	I-Task
creating	I-Task
the	I-Task
instabilities	I-Task
that	O
we	O
have	O
just	O
mentioned	O
.	O

We	O
prove	B-Process
the	I-Process
uniqueness	I-Process
of	O
the	O
supersymmetric	O
Salam	O
–	O
Sezgin	O
(	O
Minkowski	O
)	O
4	O
×	O
S2	O
ground	O
state	O
among	O
all	O
non-singular	B-Task
solutions	I-Task
with	I-Task
a	I-Task
four-dimensional	I-Task
Poincaré	I-Task
,	O
de	B-Task
Sitter	I-Task
or	I-Task
anti-de	I-Task
Sitter	I-Task
symmetry	I-Task
.	O
We	O
construct	B-Process
the	I-Process
most	I-Process
general	I-Process
solutions	I-Process
with	O
an	O
axial	B-Task
symmetry	I-Task
in	I-Task
the	I-Task
two-dimensional	I-Task
internal	I-Task
space	I-Task
,	I-Task
and	O
show	O
that	O
included	O
amongst	O
these	O
is	O
a	O
family	O
that	O
is	O
non-singular	B-Process
away	I-Process
from	I-Process
a	I-Process
conical	I-Process
defect	I-Process
at	O
one	O
pole	O
of	O
a	O
distorted	O
2-sphere	O
.	O
These	O
solutions	O
admit	O
the	O
interpretation	B-Process
of	I-Process
3-branes	I-Process
with	I-Process
negative	I-Process
tension	I-Process
.	O

Longitudinal	B-Task
beam	I-Task
and	I-Task
target	I-Task
single-spin	I-Task
asymmetries	I-Task
have	O
been	O
at	O
the	O
center	O
of	O
the	O
attention	O
lately	O
,	O
since	O
they	O
have	O
been	O
measured	O
by	O
the	O
HERMES	B-Process
and	I-Process
CLAS	I-Process
experimental	I-Process
Collaborations	I-Process
[	O
1	O
–	O
4	O
]	O
and	O
more	O
measurements	O
are	O
planned	O
.	O
They	O
were	O
originally	O
believed	O
to	O
be	O
signals	O
of	O
the	O
so-called	O
T-odd	B-Process
fragmentation	I-Process
functions	I-Process
[	O
5	O
]	O
,	O
in	O
particular	O
,	O
of	O
the	O
Collins	B-Process
function	I-Process
[	O
6	O
–	O
12	O
]	O
.	O
However	O
,	O
both	O
types	O
of	O
asymmetry	O
can	O
receive	B-Process
contributions	I-Process
also	I-Process
from	I-Process
T-odd	I-Process
distribution	I-Process
functions	I-Process
[	O
13	O
–	O
16	O
]	O
,	O
a	O
fact	O
that	O
has	O
often	O
been	O
neglected	O
in	O
analyses	O
.	O
An	O
exhaustive	B-Task
treatment	I-Task
of	I-Task
the	I-Task
contributions	I-Task
of	I-Task
T-odd	I-Task
distribution	I-Task
functions	I-Task
has	O
not	O
been	O
carried	O
out	O
completely	O
so	O
far	O
,	O
especially	O
up	O
to	O
subleading	B-Process
order	I-Process
in	I-Process
an	I-Process
expansion	I-Process
in	I-Process
1	I-Process
/	I-Process
Q	I-Process
,	O
Q2	O
being	O
the	O
virtuality	O
of	O
the	O
incident	B-Material
photon	I-Material
and	O
the	O
only	O
hard	O
scale	O
of	O
the	O
process	O
,	O
and	O
including	O
quark	B-Material
mass	O
corrections	O
.	O
It	O
is	O
the	O
purpose	O
of	O
the	O
present	O
work	O
to	O
describe	B-Task
the	I-Task
longitudinal	I-Task
beam	I-Task
and	I-Task
target	I-Task
spin	I-Task
asymmetries	I-Task
in	O
a	O
complete	O
way	O
in	O
terms	O
of	O
leading	O
and	O
subleading	O
twist	B-Process
distribution	I-Process
and	O
fragmentation	B-Process
functions	I-Process
.	O
We	O
consider	O
both	O
single-particle	B-Process
inclusive	I-Process
DIS	I-Process
,	O
e	B-Process
+	I-Process
p	I-Process
→	I-Process
e′	I-Process
+	I-Process
h	I-Process
+	I-Process
X	I-Process
,	O
and	O
single-jet	B-Process
inclusive	I-Process
DIS	I-Process
,	O
e	B-Process
+	I-Process
p	I-Process
→	I-Process
e′	I-Process
+	I-Process
jet	I-Process
+	I-Process
X	I-Process
.	O
We	O
assume	O
factorization	B-Process
holds	O
for	O
these	O
processes	O
,	O
even	O
though	O
at	O
present	O
there	O
is	O
no	O
factorization	B-Task
proof	I-Task
for	I-Task
observables	I-Task
containing	I-Task
subleading-twist	I-Task
transverse-momentum	I-Task
dependent	I-Task
functions	I-Task
(	O
only	O
recently	O
proofs	B-Task
for	I-Task
the	I-Task
leading-twist	I-Task
case	I-Task
have	O
been	O
presented	O
in	O
Refs.	O
[	O
17,18	O
])	O
.	O

There	O
are	O
many	O
possible	O
applications	B-Task
for	I-Task
this	I-Task
mechanism	I-Task
.	O
In	O
this	O
Letter	O
,	O
we	O
have	O
concentrated	O
on	O
its	B-Task
contribution	I-Task
to	I-Task
leptogenesis	I-Task
and	I-Task
baryogenesis	I-Task
.	O
Our	O
calculation	O
is	O
applicable	O
in	B-Process
the	I-Process
phase	I-Process
when	I-Process
the	I-Process
fields	I-Process
are	I-Process
rolling	I-Process
.	O
This	O
rolling	B-Process
phase	I-Process
will	O
start	O
when	O
the	O
Hubble	B-Process
constant	I-Process
drops	I-Process
to	I-Process
a	I-Process
value	I-Process
comparable	I-Process
to	I-Process
the	I-Process
mass	I-Process
of	I-Process
the	I-Process
scalar	I-Process
fields	I-Process
.	O
It	O
is	O
at	O
this	O
time	O
in	O
the	O
cosmological	B-Process
evolution	I-Process
that	O
CP	B-Process
violation	I-Process
is	O
most	O
efficient	O
.	O
After	O
the	O
fields	B-Process
have	I-Process
relaxed	I-Process
to	I-Process
their	I-Process
vacuum	I-Process
values	I-Process
,	O
our	O
CP	B-Process
violation	I-Process
mechanism	I-Process
turns	O
off	O
.	O
We	O
plan	O
to	O
discuss	O
more	O
details	O
,	O
in	O
particular	O
applications	B-Task
to	I-Task
concrete	I-Task
baryogenesis	I-Task
models	I-Task
,	O
in	O
a	O
future	O
publication	O
[	O
20	O
]	O
.	O
Note	O
that	O
string	B-Task
cosmology	I-Task
and	O
brane	B-Task
world	I-Task
scenarios	I-Task
may	O
provide	O
natural	O
settings	O
for	O
the	O
origin	O
of	O
the	O
scalar	B-Material
fields	I-Material
required	O
for	O
our	B-Process
mechanism	I-Process
(	O
e.g.	O
see	O
Ref	O
.	O
[	O
30	O
]	O
for	O
a	O
recent	O
paper	O
on	O
how	B-Task
scalar	I-Task
fields	I-Task
from	I-Task
brane	I-Task
world	I-Task
scenarios	I-Task
can	I-Task
play	I-Task
a	I-Task
new	I-Task
role	I-Task
in	I-Task
spontaneous	I-Task
baryogenesis	I-Task
)	O
.	O

In	O
the	O
brane	B-Material
system	I-Material
appearing	O
in	O
string	B-Process
/	I-Process
D-brane	I-Process
theory	I-Process
,	O
the	O
stableness	B-Task
is	O
the	O
most	O
important	O
requirement	O
.	O
We	O
find	O
some	O
stable	B-Process
brane	I-Process
configurations	I-Process
in	O
the	O
SUSY	B-Process
bulk-boundary	I-Process
theory	I-Process
.	O
We	O
systematically	O
solve	B-Task
the	I-Task
singular	I-Task
field	I-Task
equation	I-Task
using	O
a	O
general	B-Process
mathematical	I-Process
result	I-Process
about	I-Process
the	I-Process
free-wave	I-Process
solution	I-Process
in	O
S1	O
/	O
Z2-space	O
.	O
The	O
two	O
scalars	O
,	O
the	O
extra-component	B-Process
of	I-Process
the	I-Process
bulk-vector	I-Process
(	O
A5	B-Process
)	O
and	O
the	O
bulk-scalar	B-Process
(	O
Φ	B-Process
)	O
,	O
constitute	O
the	O
solutions	O
.	O
Their	O
different	B-Task
roles	I-Task
are	I-Task
clarified	I-Task
.	O
The	O
importance	O
of	O
the	O
“	O
parallel	O
”	O
configuration	O
is	O
disclosed	O
.	O
The	O
boundary	B-Process
condition	I-Process
(	O
of	O
A5	O
)	O
and	O
the	O
boundary	O
matter	O
fields	O
are	O
two	O
important	O
elements	O
for	O
making	O
the	O
localized	O
configuration	O
.	O
Among	O
all	O
solutions	O
,	O
the	O
solution	B-Process
(	O
c1	B-Process
=−	I-Process
1	I-Process
,	I-Process
c2	I-Process
=−	I-Process
1	I-Process
)	O
is	O
expected	O
to	O
be	O
the	O
thin-wall	B-Process
limit	I-Process
of	O
a	O
kink	B-Process
solution	I-Process
.	O
We	O
present	O
a	O
bulk	B-Process
Higgs	I-Process
model	I-Process
corresponding	O
to	O
the	O
non-singular	B-Process
solution	I-Process
.	O
The	O
model	O
is	O
expected	O
to	O
give	O
a	O
non-singular	B-Process
and	I-Process
stable	I-Process
brane	I-Process
solution	I-Process
in	O
the	O
SUSY	B-Process
bulk-boundary	I-Process
theory	I-Process
.	O

The	O
spins	B-Task
and	I-Task
parities	I-Task
of	I-Task
Θ	I-Task
+	I-Task
and	I-Task
Ξ	I-Task
−−	I-Task
are	O
not	O
yet	O
known	O
experimentally	O
.	O
In	O
this	O
new	B-Task
wave	I-Task
of	I-Task
pentaquark	I-Task
research	I-Task
,	O
most	O
theoretical	O
papers	O
take	B-Process
the	I-Process
spin	I-Process
equal	I-Process
to	I-Process
1	I-Process
/	I-Process
2	I-Process
.	O
The	O
parity	O
is	O
more	O
controversial	O
.	O
In	O
chiral	B-Process
soliton	I-Process
or	O
Skyrme	B-Process
models	I-Process
the	O
parity	O
is	O
positive	O
[	O
4	O
]	O
.	O
In	O
constituent	B-Process
quark	I-Process
models	I-Process
it	O
is	O
usually	O
positive	O
.	O
In	O
the	O
present	O
approach	O
,	O
the	O
parity	B-Task
of	I-Task
the	I-Task
pentaquark	I-Task
is	O
given	O
by	O
P	B-Task
=(−)	I-Task
ℓ	I-Task
+	I-Task
1	I-Task
,	O
where	O
ℓ	O
is	O
the	O
angular	B-Task
momentum	I-Task
associated	I-Task
with	I-Task
the	I-Task
relative	I-Task
coordinates	I-Task
of	I-Task
the	I-Task
q4	I-Task
subsystem	I-Task
.	O
We	O
analyze	O
the	O
case	O
where	O
the	O
subsystem	B-Task
of	I-Task
four	I-Task
light	I-Task
quarks	I-Task
is	I-Task
in	I-Task
a	I-Task
state	I-Task
of	I-Task
orbital	I-Task
symmetry	I-Task
[	O
31	O
]	O
O	O
and	O
carries	O
an	O
angular	O
momentum	O
ℓ	O
=	O
1	O
.	O
Although	O
the	O
kinetic	B-Process
energy	I-Process
of	O
such	O
a	O
state	O
is	O
higher	O
than	O
that	O
of	O
the	O
totally	B-Process
symmetric	I-Process
[	I-Process
4	I-Process
]	I-Process
O	I-Process
state	I-Process
,	O
the	O
[	O
31	O
]	O
O	B-Process
symmetry	I-Process
is	O
the	O
most	O
favourable	O
both	O
for	O
the	O
flavour	B-Process
–	I-Process
spin	I-Process
interaction	I-Process
[	O
12	O
]	O
and	O
the	O
colour	B-Process
–	I-Process
spin	I-Process
interaction	I-Process
[	O
13	O
]	O
.	O
In	O
the	O
first	O
case	O
the	O
statement	O
is	O
confirmed	O
by	O
the	O
comparison	B-Task
between	I-Task
the	I-Task
realistic	I-Task
calculations	I-Task
for	I-Task
positive	I-Task
parity	I-Task
[	I-Task
12	I-Task
]	I-Task
and	I-Task
negative	I-Task
parity	I-Task
[	I-Task
14	I-Task
]	I-Task
,	I-Task
based	I-Task
on	I-Task
the	I-Task
same	I-Task
quark	I-Task
model	I-Task
[	I-Task
15	I-Task
]	I-Task
.	O
In	O
Ref	O
.	O
[	O
12	O
]	O
the	O
antiquark	B-Material
was	O
heavy	O
,	O
c	O
or	O
b	O
,	O
and	O
accordingly	O
the	O
interaction	B-Process
between	I-Process
light	I-Process
quarks	I-Process
and	I-Process
the	I-Process
heavy	I-Process
antiquark	I-Process
was	I-Process
neglected	I-Process
,	I-Process
consistent	I-Process
with	I-Process
the	I-Process
heavy	I-Process
quark	I-Process
limit	I-Process
.	O
In	O
Ref	O
.	O
[	O
16	O
]	O
an	O
attractive	O
spin	B-Process
–	I-Process
spin	I-Process
interaction	I-Process
between	O
s	B-Material
̄	I-Material
and	O
the	O
light	B-Material
quarks	I-Material
was	O
incorporated	O
and	O
shown	O
that	O
a	O
stable	O
or	O
narrow	O
positive	O
parity	O
uudds	B-Material
̄	I-Material
pentaquark	I-Material
can	O
be	O
accommodated	O
within	O
such	O
a	O
model	O
.	O
This	O
interaction	O
has	O
a	O
form	O
that	O
corresponds	O
to	O
η	B-Process
meson	I-Process
exchange	I-Process
[	O
17	O
]	O
and	O
its	O
role	O
is	O
to	O
lower	B-Process
the	I-Process
energy	I-Process
of	I-Process
the	I-Process
whole	I-Process
system	I-Process
.	O

There	O
exist	O
some	O
interesting	O
cases	O
where	O
the	O
deformation	B-Process
structure	I-Process
becomes	O
simple	O
.	O
One	O
is	O
the	O
limit	O
to	O
the	O
N	B-Process
=	I-Process
1	I-Process
/	I-Process
2	I-Process
superspace	I-Process
[	O
5	O
]	O
,	O
where	O
the	O
action	O
should	O
reduce	O
to	O
N	B-Process
=	I-Process
1	I-Process
/	I-Process
2	I-Process
super-Yang	I-Process
–	I-Process
Mills	I-Process
theory	I-Process
with	I-Process
adjoint	I-Process
matter	I-Process
.	O
Another	O
interesting	O
case	O
is	O
the	O
singlet	B-Task
deformation	I-Task
[	O
10,11	O
]	O
,	O
where	O
the	O
deformation	B-Process
parameters	I-Process
belongs	O
to	O
the	O
singlet	B-Process
representation	I-Process
of	O
the	O
R-symmetry	B-Process
group	I-Process
SU	B-Process
(	I-Process
2	I-Process
)	I-Process
R	I-Process
.	O
In	O
this	O
Letter	O
,	O
we	O
will	O
study	O
N	B-Task
=	I-Task
2	I-Task
supersymmetric	I-Task
U	I-Task
(	I-Task
1	I-Task
)	I-Task
gauge	I-Task
theory	I-Task
in	O
the	O
harmonic	B-Process
superspace	I-Process
with	O
singlet	B-Process
deformation	I-Process
.	O
In	O
this	O
case	O
,	O
the	B-Task
gauge	I-Task
and	I-Task
supersymmetry	I-Task
transformations	I-Task
get	O
correction	B-Process
linear	I-Process
in	O
the	O
deformation	B-Process
parameter	I-Process
.	O
Therefore	O
we	O
can	O
easily	O
perform	O
the	O
field	B-Process
redefinition	I-Process
such	O
that	O
the	O
component	O
fields	O
transform	O
canonically	O
under	O
the	O
gauge	B-Process
transformation	I-Process
.	O
In	O
the	O
case	O
of	O
N	B-Task
=	I-Task
1	I-Task
/	I-Task
2	I-Task
super-Yang	I-Task
–	I-Task
Mills	I-Task
theory	I-Task
,	O
such	O
field	B-Process
redefinition	I-Process
is	O
also	O
possible	O
[	O
5	O
]	O
.	O
But	O
in	O
this	O
case	O
the	O
component	B-Task
fields	I-Task
do	O
not	O
transform	O
canonically	O
under	O
the	O
deformed	B-Process
supersymmtery	I-Process
transformation	I-Process
.	O
In	O
the	O
singlet	B-Task
case	I-Task
,	O
we	O
will	O
show	O
that	O
there	O
is	O
a	O
field	B-Process
redefinition	I-Process
such	O
that	O
the	O
redefined	O
fields	O
also	O
transform	B-Process
canonically	I-Process
under	I-Process
the	I-Process
deformed	I-Process
supersymmetry	I-Process
.	O
We	O
will	O
construct	O
a	O
deformed	B-Process
Lagrangian	I-Process
which	O
is	O
invariant	O
under	O
both	O
the	O
gauge	B-Process
and	I-Process
supersymmetry	I-Process
transformations	I-Process
.	O
We	O
find	O
that	O
the	O
deformed	B-Process
Lagrangian	I-Process
is	O
characterized	O
by	O
a	O
single	B-Process
function	I-Process
of	O
an	O
antiholomorphic	B-Process
scalar	I-Process
field	I-Process
.	O

We	O
consider	O
finite-time	B-Process
,	I-Process
future	I-Process
(	I-Process
sudden	I-Process
or	I-Process
Big	I-Process
Rip	I-Process
type	I-Process
)	I-Process
singularities	I-Process
which	O
may	O
occur	O
even	O
when	O
strong	B-Process
energy	I-Process
condition	I-Process
is	O
not	O
violated	O
but	O
equation	O
of	O
state	O
parameter	O
is	O
time-dependent	O
.	O
Recently	O
,	O
example	B-Task
of	I-Task
such	I-Task
singularity	I-Task
has	O
been	O
presented	O
by	O
Barrow	O
,	O
we	B-Task
found	I-Task
another	I-Task
example	I-Task
of	I-Task
it	I-Task
.	O
Taking	O
into	O
account	O
back	B-Task
reaction	I-Task
of	I-Task
conformal	I-Task
quantum	I-Task
fields	I-Task
near	I-Task
singularity	I-Task
,	O
it	O
is	O
shown	O
explicitly	O
that	O
quantum	B-Process
effects	I-Process
may	O
delay	O
(	O
or	O
make	O
milder	O
)	O
the	O
singularity	B-Process
.	O
It	O
is	O
argued	O
that	O
if	O
the	O
evolution	B-Task
to	I-Task
singularity	I-Task
is	O
realistic	O
,	O
due	O
to	O
quantum	B-Process
effects	I-Process
the	B-Process
universe	I-Process
may	I-Process
end	I-Process
up	I-Process
in	I-Process
de	I-Process
Sitter	I-Process
phase	I-Process
before	O
scale	B-Process
factor	I-Process
blows	I-Process
up	I-Process
.	O
This	O
picture	O
is	O
generalized	O
for	O
braneworld	B-Material
where	O
sudden	B-Process
singularity	I-Process
may	O
occur	O
on	O
the	O
brane	B-Material
with	O
qualitatively	B-Process
similar	I-Process
conclusions	I-Process
.	O

One	O
of	O
the	O
great	O
successes	O
of	O
the	O
experimental	O
program	O
carried	O
out	O
at	O
LEP	O
has	O
been	O
to	O
put	B-Process
a	I-Process
firm	I-Process
lower	I-Process
bound	I-Process
on	I-Process
the	I-Process
Higgs	I-Process
mass	I-Process
,	O
mH	B-Process
>	I-Process
114	I-Process
GeV	I-Process
[	O
1	O
]	O
,	O
and	O
at	O
the	O
same	O
time	O
,	O
together	O
with	O
the	O
information	O
coming	O
from	O
SLD	B-Material
,	O
to	O
give	O
a	O
strong	O
indirect	O
evidence	O
that	O
the	O
Higgs	B-Material
boson	I-Material
,	O
the	O
still	O
missing	O
particle	B-Material
of	O
the	O
Standard	B-Process
Model	I-Process
(	O
SM	B-Process
)	O
,	O
should	O
be	O
relatively	O
light	O
with	O
a	O
high	O
probability	O
for	O
its	O
mass	O
to	O
be	O
below	O
200	O
GeV	O
.	O
The	B-Task
search	I-Task
for	I-Task
the	I-Task
Higgs	I-Task
boson	I-Task
is	O
one	O
of	O
the	O
main	O
objective	O
of	O
the	O
Tevatron	B-Material
and	O
the	O
future	O
Large	B-Material
Hadron	I-Material
Collider	I-Material
(	B-Material
LHC	I-Material
)	I-Material
,	O
that	O
are	O
supposed	O
to	O
span	B-Process
all	I-Process
the	I-Process
Higgs	I-Process
mass	I-Process
regions	I-Process
up	I-Process
to	I-Process
1	I-Process
TeV	I-Process
.	O
At	O
hadron	B-Material
colliders	I-Material
the	O
main	B-Task
Higgs	I-Task
production	I-Task
mechanism	I-Task
is	O
the	O
gluon	B-Process
fusion	I-Process
[	O
2	O
]	O
,	O
a	O
process	O
whose	O
knowledge	O
is	O
fundamental	O
in	O
order	O
to	O
put	B-Process
limits	I-Process
on	I-Process
the	I-Process
Higgs	I-Process
mass	I-Process
or	O
,	O
in	O
case	O
the	O
Higgs	O
is	O
discovered	O
,	O
to	O
compare	B-Process
the	I-Process
measured	I-Process
cross	I-Process
section	I-Process
with	I-Process
the	I-Process
SM	I-Process
result	I-Process
.	O
Concerning	O
the	O
Higgs	B-Process
decay	I-Process
channels	O
,	O
it	O
is	O
quite	O
difficult	O
for	O
an	O
hadron	B-Material
collider	I-Material
to	O
access	B-Task
part	I-Task
of	I-Task
the	I-Task
mass	I-Task
range	I-Task
favored	I-Task
by	I-Task
the	I-Task
LEP	I-Task
results	I-Task
,	O
the	O
so-called	O
intermediate	B-Process
Higgs	I-Process
mass	I-Process
region	I-Process
114	B-Process
≲	I-Process
mH	I-Process
≲	I-Process
160	I-Process
GeV	I-Process
,	O
because	O
of	O
the	O
large	O
QCD	B-Material
background	I-Material
to	O
the	O
dominant	O
modes	O
.	O
In	O
this	O
region	O
the	O
rare	B-Process
decay	I-Process
H	B-Process
→	I-Process
γγ	I-Process
is	O
the	O
most	O
interesting	O
alternative	O
to	O
the	O
usual	O
decay	B-Process
channels	I-Process
.	O

Recent	O
publications	O
[	O
31	O
]	O
employ	O
a	O
variety	O
of	O
methods	O
for	O
calculating	B-Task
upper	I-Task
limits	I-Task
and	O
there	O
is	O
no	O
universally	O
accepted	O
procedure	O
[	O
27,32,33	O
]	O
.	O
We	O
choose	O
an	O
approach	O
similar	O
to	O
that	O
first	O
advocated	O
by	O
Feldman	O
and	O
Cousins	O
[	O
27	O
]	O
.	O
This	O
method	O
has	O
been	O
since	O
extended	O
by	O
Conrad	O
et	O
al	O
.	O
[	O
34	O
]	O
to	O
incorporate	B-Process
uncertainties	I-Process
in	O
detector	B-Task
sensitivity	I-Task
and	O
the	O
background	B-Process
estimate	I-Process
based	O
on	O
an	O
approach	O
described	O
by	O
Cousins	O
and	O
Highland	O
[	O
35	O
]	O
.	O
A	O
further	B-Process
refinement	I-Process
of	I-Process
the	I-Process
Conrad	I-Process
et	I-Process
al.	I-Process
method	I-Process
by	O
Hill	O
[	O
36	O
]	O
results	O
in	O
more	B-Process
appropriate	I-Process
behavior	I-Process
of	O
the	O
upper	O
limit	O
when	O
the	O
observed	O
number	O
of	O
events	O
is	O
less	O
than	O
the	O
estimated	O
background	O
,	O
as	O
is	O
the	O
case	O
for	O
the	O
present	O
measurement	B-Process
.	O
We	O
have	O
adopted	O
this	O
method	O
but	O
note	O
that	O
Table	O
2	O
contains	O
all	O
of	O
the	O
numbers	O
needed	O
to	O
calculate	B-Task
an	I-Task
upper	I-Task
limit	I-Task
using	O
any	O
of	O
the	O
methods	O
in	O
the	O
papers	O
cited	O
above	O
.	O
We	O
assume	O
that	O
the	O
probability	B-Process
density	I-Process
functions	I-Process
of	O
Fsens	O
and	O
background	B-Process
estimates	I-Process
are	O
Gaussian-distributed	B-Process
.	O

In	O
these	O
frameworks	O
,	O
however	O
,	O
the	O
physical	B-Task
spacetime	I-Task
dimension	I-Task
is	O
an	O
input	O
rather	O
than	O
a	O
prediction	B-Process
of	I-Process
the	I-Process
theory	I-Process
.	O
In	O
fact	O
,	O
in	O
standard	B-Process
theories	I-Process
whose	O
gravitational	O
sector	O
is	O
described	O
by	O
the	O
Einstein	B-Process
–	I-Process
Hilbert	I-Process
action	I-Process
,	O
there	O
is	O
no	O
obstruction	O
to	O
perform	O
dimensional	B-Process
reductions	I-Process
to	O
spacetimes	O
of	O
dimensions	O
d	O
≠	O
4	O
.	O
Then	O
the	O
question	O
arises	O
,	O
since	O
eleven-dimensional	O
Minkowski	B-Material
space	I-Material
is	O
a	O
maximally	O
(	B-Process
super	I-Process
)	I-Process
symmetric	I-Process
state	I-Process
,	O
and	O
the	O
theory	O
is	O
well-behaved	O
around	O
it	O
,	O
why	O
the	O
theory	O
does	O
not	O
select	B-Process
this	I-Process
configuration	I-Process
as	O
the	O
vacuum	O
,	O
but	O
instead	O
,	O
it	O
chooses	O
a	O
particular	O
compactified	B-Process
space	I-Process
with	O
less	O
symmetry	O
.	O
An	O
ideal	O
situation	O
,	O
instead	O
,	O
would	O
be	O
that	O
the	O
eleven-dimensional	B-Process
theory	I-Process
dynamically	O
predicted	O
a	O
low	B-Task
energy	I-Task
regime	I-Task
which	O
could	O
only	O
be	O
a	O
four-dimensional	B-Process
effective	I-Process
theory	I-Process
.	O
In	O
such	O
a	O
scenario	O
,	O
a	O
background	B-Process
solution	I-Process
with	O
an	O
effective	B-Process
spacetime	I-Process
dimension	I-Process
d	B-Process
>	I-Process
4	I-Process
should	O
be	O
expected	O
to	O
be	O
a	O
false	B-Process
vacuum	I-Process
where	O
the	O
propagators	B-Process
for	I-Process
the	I-Process
dynamical	I-Process
fields	I-Process
are	O
ill-defined	O
,	O
lest	O
a	O
low	B-Process
energy	I-Process
effective	I-Process
theory	I-Process
could	O
exist	O
in	O
dimensions	O
higher	O
than	O
four	O
.	O

The	O
presence	O
of	O
chaotic	B-Task
motion	I-Task
in	I-Task
nuclear	I-Task
systems	I-Task
has	O
been	O
firmly	O
related	O
with	O
the	O
statistics	B-Process
of	I-Process
high-lying	I-Process
energy	I-Process
levels	I-Process
[	O
8,9	O
]	O
.	O
Poisson	B-Process
distributions	I-Process
of	O
normalized	O
spacings	O
of	O
successive	O
nuclear	O
or	O
atomic	O
excited	B-Process
levels	I-Process
with	O
the	O
same	O
spin	B-Process
and	O
parity	B-Process
correspond	O
to	O
integrable	B-Process
classical	I-Process
dynamics	I-Process
,	O
while	O
Wigner	B-Material
's	I-Material
statistics	I-Material
signal	O
chaotic	B-Process
motion	I-Process
in	O
the	O
corresponding	O
classical	B-Task
regime	I-Task
[	O
10	O
]	O
.	O
Intermediate	O
situations	O
are	O
more	O
difficult	O
to	O
assess	O
.	O
Very	O
recently	O
a	O
proposal	O
has	O
been	O
made	O
to	O
treat	O
the	O
spectral	B-Process
fluctuations	I-Process
δn	B-Process
as	O
discrete	B-Process
time	I-Process
series	I-Process
[	O
11	O
]	O
.	O
Defining	O
(	O
1	O
)	O
δn	O
=∫−∞	O
En	O
+	O
1ρ	O
˜(	O
E	O
)	O
dE	O
−	O
n	O
,	O
with	O
ρ	O
˜(	O
E	O
)	O
the	O
mean	O
level	O
density	O
which	O
allows	O
the	O
mapping	B-Process
to	I-Process
dimensionless	I-Process
levels	I-Process
with	O
unitary	B-Process
average	I-Process
level	I-Process
density	I-Process
,	O
and	O
analyzing	B-Process
the	I-Process
energy	I-Process
fluctuations	I-Process
as	O
a	O
discrete	B-Process
time	I-Process
series	I-Process
,	O
they	O
found	O
that	O
nuclear	B-Task
power	I-Task
spectra	I-Task
behave	O
like	O
1f	O
noise	O
,	O
postulating	O
that	O
this	O
might	O
be	O
a	O
characteristic	O
signature	O
of	O
generic	O
quantum	B-Task
chaotic	I-Task
systems	I-Task
.	O
In	O
the	O
present	O
work	O
we	O
implement	O
this	O
idea	O
,	O
using	O
the	O
1f	B-Process
spectral	I-Process
behavior	I-Process
as	O
a	O
test	O
for	O
the	O
presence	O
of	O
chaos	B-Process
in	I-Process
nuclear	I-Process
mass	I-Process
errors	I-Process
.	O

Table	O
1	O
lists	O
8	O
pairs	O
of	O
B	B-Process
decays	I-Process
.	O
In	O
fact	O
,	O
there	O
are	O
more	O
decay	B-Process
pairs	I-Process
,	O
since	O
many	O
of	O
the	O
particles	O
in	O
the	O
final	O
states	O
can	O
be	O
observed	O
as	O
either	O
pseudoscalar	O
(	O
P	O
)	O
or	O
vector	O
(	O
V	O
)	O
mesons	B-Material
.	O
Note	O
that	O
certain	O
decays	B-Process
are	O
written	O
in	O
terms	O
of	O
VV	O
final	O
states	O
,	O
while	O
others	O
are	O
have	O
PP	O
states	O
.	O
There	O
are	O
three	O
reasons	O
for	O
this	O
.	O
First	O
,	O
some	O
decays	O
involve	O
a	O
final-state	O
π0	O
.	O
However	O
,	O
experimentally	O
it	O
will	O
be	O
necessary	O
to	O
find	B-Task
the	I-Task
decay	I-Task
vertices	I-Task
of	O
the	O
final	B-Material
particles	I-Material
.	O
This	O
is	O
virtually	O
impossible	O
for	O
a	O
π0	O
,	O
and	O
so	O
we	O
always	O
use	O
a	O
ρ0	O
.	O
Second	O
,	O
some	O
pairs	B-Material
of	I-Material
decays	I-Material
are	O
related	O
by	O
SU	O
(	O
3	O
)	O
in	O
the	O
SM	B-Material
only	O
if	O
an	O
(	O
ss	O
¯)	O
quark	B-Material
pair	I-Material
is	O
used	O
.	O
However	O
,	O
there	O
are	O
no	O
P	O
's	O
which	O
are	O
pure	O
(	O
ss	O
¯)	O
.	O
The	O
mesons	B-Material
η	B-Material
and	O
η′	B-Material
have	O
an	O
(	O
ss	O
¯)	O
component	O
,	O
but	O
they	O
also	O
have	O
significant	O
(	O
uu	O
¯)	O
and	O
(	O
dd	O
¯)	O
pieces	O
.	O
As	O
a	O
result	O
the	O
b	O
¯→	O
s	O
¯	O
and	O
b	O
¯→	O
d	O
¯	O
decays	B-Process
are	O
not	O
really	O
related	O
by	O
SU	O
(	O
3	O
)	O
in	O
the	O
SM	B-Material
if	O
the	O
final	O
state	O
involves	O
an	O
η	B-Material
or	O
η′	B-Material
.	O
We	O
therefore	O
consider	O
instead	O
the	O
vector	B-Material
meson	I-Material
ϕ	B-Material
which	O
is	O
essentially	O
a	O
pure	O
(	O
ss	O
¯)	O
quark	B-Material
state	O
.	O
Finally	O
,	O
we	O
require	O
that	O
both	O
B0	B-Material
and	O
B	B-Material
¯	I-Material
0	I-Material
be	O
able	O
to	O
decay	B-Process
to	I-Process
the	I-Process
final	I-Process
state	I-Process
.	O
This	O
cannot	O
happen	O
if	O
the	O
final	O
state	O
contains	O
a	O
single	O
K0	O
(	O
or	O
K	O
¯	O
0	O
)	O
meson	B-Material
.	O
However	O
,	O
it	O
can	O
occur	O
if	O
this	O
final-state	B-Material
particle	I-Material
is	O
an	O
excited	B-Material
neutral	I-Material
kaon	I-Material
.	O
In	O
this	O
case	O
one	O
decay	O
involves	O
K	B-Material
*	I-Material
0	I-Material
,	O
while	O
the	O
other	O
has	O
K	B-Material
¯*	I-Material
0	I-Material
.	O
Assuming	O
that	O
the	O
vector	B-Material
meson	I-Material
is	O
detected	O
via	O
its	O
decay	B-Process
to	I-Process
ψKsπ0	I-Process
(	O
as	O
in	O
the	O
measurement	B-Process
of	I-Process
sin2β	I-Process
via	O
Bd0	O
(	O
t	O
)→	O
J	O
/	O
ψK	O
*)	O
,	O
then	O
both	O
B0	B-Material
and	O
B	B-Material
¯	I-Material
0	I-Material
can	O
decay	B-Process
to	O
the	O
same	O
final	O
state	O
.	O

If	O
signals	O
suggesting	O
supersymmetry	B-Process
(	O
SUSY	B-Process
)	O
are	O
discovered	O
at	O
the	O
LHC	O
then	O
it	O
will	O
be	O
vital	O
to	O
measure	B-Task
the	I-Task
spins	I-Task
of	I-Task
the	I-Task
new	I-Task
particles	I-Task
to	O
demonstrate	B-Task
that	I-Task
they	I-Task
are	I-Task
indeed	I-Task
the	I-Task
predicted	I-Task
super-partners	I-Task
.	O
A	O
method	O
is	O
discussed	O
by	O
which	O
the	O
spins	O
of	O
some	O
of	O
the	O
SUSY	B-Material
particles	I-Material
can	O
be	O
determined	O
.	O
Angular	B-Process
distributions	I-Process
in	O
sparticle	B-Process
decays	I-Process
lead	O
to	O
charge	B-Process
asymmetry	I-Process
in	O
lepton-jet	B-Process
invariant	I-Process
mass	I-Process
distributions	I-Process
.	O
The	O
size	O
of	O
the	O
asymmetry	O
is	O
proportional	O
to	O
the	O
primary	B-Process
production	I-Process
asymmetry	I-Process
between	O
squarks	B-Material
and	O
anti-squarks	B-Material
.	O
Monte	B-Process
Carlo	I-Process
simulations	I-Process
are	O
performed	O
for	O
a	O
particular	O
mSUGRA	B-Process
model	I-Process
point	I-Process
at	O
the	O
LHC	O
.	O
The	O
resultant	O
asymmetry	B-Process
distributions	I-Process
are	O
consistent	O
with	O
a	O
spin-0	B-Material
slepton	I-Material
and	O
a	O
spin-12χ	O
˜	O
20	O
,	O
but	O
are	O
not	O
consistent	O
with	O
both	O
particles	B-Material
being	O
scalars	O
.	O

It	O
should	O
be	O
noted	O
that	O
BEBC	B-Material
[	O
21	O
]	O
and	O
NOMAD	B-Material
[	O
20	O
]	O
observed	B-Task
a	I-Task
discrepancy	I-Task
between	O
experimental	O
ρ	O
rates	O
and	O
those	O
estimated	O
with	O
JETSET	B-Material
[	O
16	O
]	O
.	O
NOMAD	B-Material
[	O
20	O
]	O
proposed	O
to	O
retune	B-Process
some	I-Process
of	I-Process
the	I-Process
parameters	I-Process
used	O
within	O
JETSET	B-Material
to	O
obtain	B-Task
better	I-Task
agreement	I-Task
.	O
Therefore	O
,	O
for	O
the	O
purpose	O
of	O
this	O
analysis	B-Task
events	B-Process
were	I-Process
simulated	I-Process
both	O
with	O
the	O
default	B-Process
setting	I-Process
and	O
with	O
the	O
setting	B-Process
proposed	I-Process
by	I-Process
NOMAD	I-Process
of	O
key	O
JETSET	B-Material
parameters	O
,	O
taking	B-Process
an	I-Process
average	I-Process
between	I-Process
them	I-Process
as	O
a	O
result	O
and	O
half	B-Process
a	I-Process
difference	I-Process
as	I-Process
a	I-Process
systematic	I-Process
error	I-Process
.	O
We	O
used	O
experimental	O
rates	O
of	O
light	B-Material
neutral	I-Material
mesons	I-Material
and	O
resonances	B-Process
where	O
available	O
(	O
Table	O
1	O
)	O
for	O
normalization	B-Process
purposes	O
.	O
The	O
uncertainty	O
introduced	O
by	O
the	O
JETSET	B-Material
parameter	B-Process
settings	I-Process
(	O
which	O
amounts	O
to	O
20	O
%	O
at	O
most	O
)	O
affects	O
only	O
the	O
production	O
of	O
the	O
η′	O
and	O
ϕ	O
for	O
which	O
no	O
experimental	B-Material
data	I-Material
are	O
available	O
.	O
This	O
uncertainty	O
is	O
reflected	O
in	O
the	O
error	O
quoted	O
in	O
the	O
table	O
.	O
However	O
,	O
since	O
the	O
contribution	O
from	O
η′	O
and	O
ϕ	O
is	O
small	O
,	O
the	O
overall	O
effect	O
is	O
less	O
important	O
.	O

In	O
this	O
Letter	O
,	O
we	O
present	O
results	O
of	O
a	O
relativistic	B-Task
calculation	I-Task
of	I-Task
decay	I-Task
constants	I-Task
in	O
the	O
framework	B-Process
of	I-Process
full	I-Process
Salpeter	I-Process
equation	I-Process
.	O
The	O
full	O
Salpeter	O
equation	O
is	O
a	O
relativistic	B-Process
equation	I-Process
describing	O
a	O
bound	O
state	O
.	O
Since	O
this	O
method	O
has	O
a	O
very	O
solid	O
basis	O
in	O
quantum	B-Process
field	I-Process
theory	I-Process
,	O
it	O
is	O
very	O
good	O
in	O
describing	B-Task
a	I-Task
bound	I-Task
state	I-Task
which	O
is	O
a	O
relativistic	B-Process
system	I-Process
.	O
In	O
a	O
previous	O
paper	O
[	O
16	O
]	O
,	O
we	O
solved	B-Task
the	I-Task
instantaneous	I-Task
Bethe	I-Task
–	I-Task
Salpeter	I-Task
equation	I-Task
[	O
17	O
]	O
,	O
which	O
is	O
also	O
called	O
full	B-Process
Salpeter	I-Process
equation	I-Process
[	O
18	O
]	O
.	O
After	O
we	O
solved	B-Task
the	I-Task
full	I-Task
Salpeter	I-Task
equation	I-Task
,	O
we	O
obtained	O
the	O
relativistic	B-Process
wave	I-Process
function	I-Process
of	I-Process
the	I-Process
bound	I-Process
state	I-Process
.	O
We	O
used	O
this	O
wave	O
function	O
to	O
calculate	B-Task
the	I-Task
average	I-Task
kinetic	I-Task
energy	I-Task
of	O
the	O
heavy	B-Material
quark	I-Material
inside	O
a	O
heavy	B-Material
meson	I-Material
in	O
0	O
−	O
state	O
,	O
and	O
obtained	B-Process
values	I-Process
which	O
agree	O
very	O
well	O
with	O
recent	O
experiments	O
.	O
We	O
also	O
found	O
there	O
that	O
the	O
relativistic	B-Process
corrections	I-Process
are	I-Process
quite	I-Process
large	I-Process
and	O
cannot	O
be	O
ignored	O
[	O
16	O
]	O
.	O
In	O
this	O
Letter	O
we	O
use	B-Process
this	I-Process
method	I-Process
to	O
predict	B-Task
the	I-Task
values	I-Task
of	I-Task
decay	I-Task
constants	I-Task
of	O
heavy	B-Material
mesons	I-Material
in	O
0	O
−	O
state	O
.	O

Our	O
aim	O
is	O
to	O
introduce	B-Task
vector	I-Task
mesons	I-Task
in	O
terms	O
of	O
a	O
Lagrangian	B-Process
which	O
satisfies	O
the	O
low	B-Process
energy	I-Process
current	I-Process
algebra	I-Process
.	O
One	O
consistent	O
method	O
is	O
in	O
terms	O
of	O
a	O
non-linear	B-Process
chiral	I-Process
Lagrangian	I-Process
with	O
a	O
hidden	B-Process
local	I-Process
symmetry	I-Process
[	O
6	O
]	O
.	O
In	O
this	O
theory	O
the	O
vector	B-Material
mesons	I-Material
emerge	O
as	O
dynamical	B-Material
vector	I-Material
mesons	I-Material
.	O
The	O
three	O
point	O
vector-pseudo	B-Process
scalar	I-Process
interaction	I-Process
is	O
given	O
by	O
(	O
11	O
)	O
ih4	B-Process
〈	I-Process
Vμ	I-Process
(	I-Process
P	I-Process
∂	I-Process
μP	I-Process
−∂	I-Process
μPP	I-Process
)〉	I-Process
,	O
where	O
h	B-Process
stands	O
for	O
the	O
vector-pseudoscalar	B-Process
coupling	I-Process
.	O
Some	O
typical	O
vertices	B-Process
of	I-Process
ρ	I-Process
's	I-Process
to	I-Process
pseudoscalar	I-Process
mesons	I-Process
are	O
(	O
12	O
)	O
π	O
+(	O
p1	O
)	O
π	O
−(	O
p2	O
)	O
ρ0	O
:	O
h	O
(	O
p1	O
−	O
p2	O
)	O
μεμ,π	O
+(	O
p1	O
)	O
π0	O
(	O
p2	O
)	O
ρ	O
−	O
:	O
h	O
(	O
p1	O
−	O
p2	O
)	O
μεμ,K	O
+(	O
p1	O
)	O
K	O
¯	O
0	O
(	O
p2	O
)	O
ρ	O
−	O
:	O
h2	O
(	O
p1	O
−	O
p2	O
)	O
μεμ,etc.	O
,	O
which	O
is	O
directly	O
related	O
to	O
the	O
ρ	O
decay	O
width	O
:	O
Γ	O
(	O
ρ	O
)=	O
h2	O
(|	O
pπ	O
|)	O
3	O
/(	O
6πmρ2	O
)	O
,	O
where	O
pπ	O
is	O
the	O
momentum	O
of	O
final	O
state	O
pions	O
in	O
the	O
ρ	O
rest	O
frame	O
.	O
With	O
Γ	O
(	O
ρ	O
)=	O
149.2MeV	O
,	O
we	O
find	O
h	O
=	O
5.95	O
.	O
We	O
note	O
in	O
passing	O
that	O
the	O
Kawarabayashi	O
–	O
Suzuki	O
–	O
Riazuddin	O
–	O
Fayyazuddin	O
relation	O
gives	O
the	O
value	O
h	O
=	O
mρ	O
/(	O
2fπ	O
)[	O
12	O
]	O
.	O
Thus	O
the	O
value	O
of	O
h	O
in	O
Eq	O
.	O
(	O
4	O
)	O
and	O
the	O
two	O
values	O
in	O
this	O
paragraph	O
differ	O
by	O
small	O
amounts	O
(∼	O
19	O
%)	O
.	O
The	O
strong	O
four-point	O
vertices	O
involving	O
pions	O
are	O
obtained	O
from	O
the	O
first	O
two	O
terms	O
of	O
Eq	O
.	O
(	O
5	O
)	O
.	O
The	O
weak	O
vertices	O
are	O
obtained	O
from	O
the	O
definitions	O
of	O
Q6	O
and	O
Q8	O
.	O
In	O
the	O
numerical	O
work	O
we	O
shall	O
use	O
the	O
value	O
of	O
h	O
from	O
Eq	O
.	O
(	O
4	O
)	O
and	O
also	O
h	O
=	O
5.95	O
obtained	O
from	O
the	O
decay	O
width	O
.	O

On	O
the	O
other	O
hand	O
,	O
the	O
other	O
local	O
fields	O
except	O
the	O
gravitational	O
field	O
are	O
not	O
always	O
localized	B-Task
on	I-Task
the	I-Task
brane	I-Task
even	O
in	O
the	O
warped	B-Process
geometry	I-Process
.	O
Indeed	O
,	O
in	O
the	O
Randall	B-Material
–	I-Material
Sundrum	I-Material
model	I-Material
in	O
five	B-Task
dimensions	I-Task
[	O
2	O
]	O
,	O
the	O
following	O
facts	O
are	O
well	O
known	O
:	O
spin	B-Process
0	I-Process
field	I-Process
is	I-Process
localized	I-Process
on	I-Process
a	I-Process
brane	I-Process
with	O
positive	B-Process
tension	I-Process
which	O
also	O
localizes	B-Process
the	I-Process
graviton	I-Process
while	O
the	O
spin	O
1	O
/	O
2	O
and	O
3	O
/	O
2	O
fields	O
are	O
localized	O
not	O
on	O
a	O
brane	O
with	O
positive	B-Process
tension	I-Process
but	O
on	O
a	O
brane	O
with	O
negative	B-Process
tension	I-Process
[	O
6	O
]	O
.	O
Spin	O
1	O
field	O
is	O
not	O
localized	O
neither	O
on	O
a	O
brane	O
with	O
positive	B-Process
tension	I-Process
nor	O
on	O
a	O
brane	O
with	O
negative	B-Process
tension	I-Process
[	O
7	O
]	O
.	O
In	O
six	B-Process
space	I-Process
–	I-Process
time	I-Process
dimensions	I-Process
,	O
the	O
spin	O
1	O
gauge	O
field	O
is	O
also	O
localized	B-Process
on	I-Process
the	I-Process
brane	I-Process
[	O
8	O
]	O
.	O
Thus	O
,	O
in	O
order	O
to	O
fulfill	O
the	O
localization	B-Task
of	O
Standard	B-Material
Model	I-Material
particles	O
on	O
a	O
brane	O
with	O
positive	B-Process
tension	I-Process
,	O
it	O
seems	O
that	O
some	O
additional	O
interactions	O
except	O
the	O
gravitational	O
interaction	O
must	O
be	O
also	O
introduced	B-Process
in	I-Process
the	I-Process
bulk	I-Process
.	O
There	O
is	O
a	O
lot	O
of	O
papers	O
devoted	O
to	O
the	O
different	O
localization	B-Process
mechanisms	I-Process
of	O
the	O
bulk	O
fields	O
in	O
various	O
brane	B-Process
world	I-Process
models	I-Process
.	O

First-principles	B-Process
calculations	I-Process
have	O
clarified	O
the	O
electronic	B-Task
structure	I-Task
and	I-Task
stability	I-Task
of	O
the	O
W	B-Material
@	I-Material
Si12	I-Material
cluster	I-Material
under	O
O2	B-Material
molecule	O
adsorption	B-Process
and	O
reaction	B-Process
.	O
Our	O
results	O
show	O
that	O
the	O
W-encapsulated	B-Process
Si12	I-Process
hexagonal	I-Process
prism	I-Process
cage	I-Process
is	O
very	O
inert	O
to	O
oxidation	B-Task
.	O
The	O
O2	B-Material
molecule	O
only	O
weakly	O
adsorbs	O
onto	O
the	O
cluster	B-Material
at	O
relatively	O
low	B-Process
temperatures	I-Process
,	O
in	O
the	O
range	O
of	O
several	O
tens	O
meV	O
.	O
However	O
,	O
significant	B-Task
reaction	I-Task
barriers	I-Task
(	O
0.593	B-Task
–	I-Task
1.118	I-Task
eV	I-Task
)	O
for	O
the	O
O2	B-Material
molecule	O
on	O
the	O
cluster	B-Material
are	O
identified	O
on	O
different	O
adsorption	B-Process
sites	O
,	O
nevertheless	O
,	O
these	O
reaction	B-Process
paths	O
are	O
spin	B-Task
forbidden	I-Task
reactions	I-Task
according	O
to	O
Winger	B-Process
ʼs	I-Process
spin	I-Process
selection	I-Process
rule	I-Process
.	O
These	O
results	O
imply	O
that	O
O2	B-Material
readily	O
desorb	O
from	O
the	O
cluster	B-Material
surface	O
rather	O
than	O
dissociate	O
and	O
oxide	O
the	O
W	B-Material
@	I-Material
Si12	I-Material
cluster	I-Material
upon	O
excitations	O
.	O
In	O
high	B-Process
temperature	I-Process
and	I-Process
high	I-Process
pressure	I-Process
conditions	I-Process
,	O
the	O
O2	B-Material
molecules	O
may	O
dissociate	O
on	O
the	O
preferential	O
edge	O
site	O
by	O
overcoming	B-Process
a	I-Process
significantly	I-Process
large	I-Process
energy	I-Process
barrier	I-Process
.	O

The	O
length	B-Task
effect	I-Task
is	O
always	O
important	O
in	O
nanodevices	B-Material
.	O
So	O
we	O
investigate	O
the	O
length	B-Task
dependence	I-Task
of	I-Task
electronic	I-Task
transport	I-Task
properties	I-Task
in	O
M3	O
by	O
increasing	B-Process
the	I-Process
number	I-Process
of	I-Process
carbon	I-Process
unit	I-Process
cells	I-Process
in	O
the	O
scattering	O
region	O
.	O
Here	O
we	O
present	O
the	O
transport	O
results	O
when	O
the	O
numbers	O
of	O
carbon	O
unit	O
cells	O
in	O
the	O
scattering	O
region	O
are	O
10	O
and	O
12	O
,	O
which	O
are	O
called	O
M4	O
and	O
M5	O
,	O
respectively	O
.	O
The	O
current	B-Process
–	I-Process
voltage	I-Process
characteristics	I-Process
shown	O
in	O
Fig.	O
8.	O
We	O
can	O
see	O
that	O
the	O
large	B-Process
rectifying	I-Process
ratio	I-Process
still	O
can	O
be	O
observed	O
irrespective	O
of	O
the	O
length	O
of	O
heterojunctions	O
.	O
This	O
is	O
due	O
to	O
the	O
fact	O
that	O
the	O
electronic	B-Task
transport	I-Task
properties	I-Task
for	O
M3	O
are	O
mainly	O
determined	O
by	O
the	O
parity	O
of	O
the	O
π	B-Process
and	I-Process
π	I-Process
⁎	I-Process
subbands	I-Process
of	O
left	O
and	O
right	O
electrodes	O
.	O
Thees	O
results	O
indicate	O
that	O
the	O
lengths	O
of	O
the	O
two	O
parts	O
in	O
the	O
scattering	O
regions	O
have	O
no	O
affects	O
on	O
the	O
qualitative	B-Task
charge	I-Task
transport	I-Task
in	O
M3	O
.	O

Topological	B-Process
insulators	I-Process
(	O
TIs	B-Process
)	O
are	O
promising	O
candidates	O
of	O
spintronics	B-Material
materials	I-Material
because	O
of	O
their	O
robust	O
helical	O
surface	O
states	O
and	O
the	O
extremely	O
strong	O
spin	B-Process
–	I-Process
orbit	I-Process
interaction	I-Process
[	O
1	O
–	O
3	O
]	O
.	O
Initially	O
,	O
binary	B-Material
chalcogenides	I-Material
Bi2Te3	B-Material
,	O
Sb2Te3	B-Material
and	O
Bi2Se3	B-Material
have	O
been	O
identified	O
as	O
three-dimensional	O
TIs	B-Process
by	O
surface	B-Process
sensitive	I-Process
probes	I-Process
such	O
as	O
angle	B-Process
resolved	I-Process
photoemission	I-Process
spectroscopy	I-Process
and	O
scanning	B-Process
tunneling	I-Process
microscopy	I-Process
/	I-Process
spectroscopy	I-Process
.	O
Later	O
,	O
ternary	B-Material
chalcogenide	I-Material
(	B-Material
BixSb1	I-Material
−	I-Material
x	I-Material
)	I-Material
2Te3	I-Material
[	O
4,5	O
]	O
,	O
which	O
has	O
similar	O
tetradymite	O
structure	O
to	O
the	O
parent	O
compounds	O
Bi2Te3	B-Material
and	O
Sb2Te3	B-Material
,	O
was	O
predicted	O
by	O
ab	B-Process
initio	I-Process
calculations	I-Process
and	O
confirmed	O
by	O
ARPES	B-Process
measurements	I-Process
as	O
a	O
tunable	O
topological	B-Process
insulator	I-Process
whose	O
Fermi	O
energy	O
and	O
carrier	O
density	O
can	O
be	O
adjusted	O
via	O
changing	B-Process
the	I-Process
Bi	I-Process
/	I-Process
Sb	I-Process
composition	I-Process
ratio	I-Process
with	O
stable	O
topological	O
surface	O
state	O
for	O
the	O
entire	O
composition	O
range	O
.	O
Combined	O
with	O
magnetism	B-Process
or	O
superconductivity	B-Process
,	O
TIs	B-Process
have	O
attracted	O
great	O
attention	O
due	O
to	O
the	O
rich	O
variety	O
of	O
new	O
physics	O
and	O
applications	O
.	O
The	O
ferromagnetism	B-Process
in	O
several	O
transition	B-Material
metal	I-Material
(	O
TM	B-Material
)	O
doped	O
TIs	B-Process
,	O
which	O
breaks	O
the	O
time-reversal	O
symmetry	O
,	O
has	O
been	O
reported	O
[	O
6	O
–	O
13	O
]	O
.	O
Ferromagnetism	B-Process
in	O
TIs	B-Process
is	O
important	O
because	O
the	O
combination	O
of	O
magnetism	B-Process
with	O
TIs	B-Process
makes	O
a	O
good	O
platform	O
to	O
study	O
fundamental	B-Task
physical	I-Task
phenomena	I-Task
,	O
such	O
as	O
the	O
quantum	O
anomalous	O
Hall	B-Task
effect	I-Task
[	O
14	O
–	O
17	O
]	O
,	O
Majorana	B-Task
fermions	I-Task
[	O
18	O
]	O
,	O
image	B-Task
magnetic	I-Task
monopole	I-Task
effect	I-Task
[	O
19	O
]	O
,	O
and	O
topological	O
contributions	O
to	O
the	O
Faraday	B-Task
and	I-Task
Kerr	I-Task
magneto-optical	I-Task
effect	I-Task
[	O
20	O
]	O
.	O

Observations	B-Process
show	O
that	O
in	O
the	O
same	O
area	O
with	O
dimensions	O
of	O
a	O
few	O
tenths	O
of	O
a	O
parsec	O
could	O
be	O
many	O
sources	O
,	O
some	O
of	O
which	O
only	O
emits	O
OH	B-Material
lines	I-Material
,	O
and	O
some	O
–	O
only	O
lines	O
H2O	B-Material
.	O
The	O
only	O
known	O
in	O
physics	O
the	O
emission	B-Process
mechanism	I-Process
that	O
can	O
give	O
tremendous	O
power	O
within	O
a	O
narrow	O
range	O
of	O
the	O
spectrum	O
,	O
is	O
coherent	O
(	O
i.e.	O
the	O
same	O
phase	O
and	O
direction	O
)	O
light	B-Process
lasers	I-Process
,	O
which	O
are	O
called	O
optical	B-Process
lasers	I-Process
,	O
and	O
radio-masers	B-Process
.	O
Cosmic	B-Material
maser	I-Material
radio	I-Material
sources	I-Material
emitting	O
in	O
the	O
lines	O
of	O
the	O
molecules	O
have	O
an	O
extremely	O
high	O
brightness	O
temperature	O
radiation	O
Tb	O
.	O
In	O
the	O
molecules	O
of	O
methanol	B-Material
masers	I-Material
(	O
CH3OH	B-Material
)	O
Tb	O
value	O
can	O
reach	O
109	O
K	O
,	O
with	O
masers	B-Material
hydroxyl	I-Material
molecules	I-Material
(	O
OH	B-Material
)	O
6	O
×	O
1012	O
K	O
.	O
The	O
typical	O
size	O
of	O
the	O
maser	B-Material
clusters	I-Material
is	O
about	O
1014	O
–	O
1015	O
m	O
and	O
the	O
neutron	B-Material
star	I-Material
radius	O
is	O
of	O
the	O
order	O
of	O
10	O
km	O
.	O
Thus	O
,	O
the	O
radiation	B-Process
dilution	I-Process
coefficient	I-Process
is	O
equaled	O
approximately	O
(	O
2.5	O
×	O
10	O
−	O
23	O
)–(	O
2.5	O
×	O
10	O
−	O
21	O
)	O
and	O
,	O
therefore	O
,	O
μB2B2	O
/	O
4	O
(	O
hν	O
)	O
2	O
∼(	O
2.4	O
×	O
10	O
−	O
5	O
)–(	O
2.4	O
×	O
10	O
−	O
7	O
)	O
for	O
the	O
hydrogen	B-Material
line	I-Material
21	O
cm	O
and	O
of	O
the	O
order	O
10	O
−	O
5	O
–	O
10	O
−	O
7	O
for	O
the	O
OH	O
18	O
cm	O
line	O
or	O
the	O
same	O
order	O
as	O
Eq	O
.	O
(	O
1	O
)	O
.	O

In	O
exploring	O
the	O
WKB	B-Task
limit	I-Task
of	I-Task
quantum	I-Task
theory	I-Task
,	O
Bohm	O
[	O
2	O
]	O
was	O
the	O
first	O
to	O
notice	O
that	O
although	O
one	O
starts	O
with	O
all	O
the	O
ambiguities	O
about	O
the	O
nature	O
of	O
a	O
quantum	O
system	O
,	O
the	O
first	O
order	O
approximation	O
fits	O
the	O
ordinary	O
classical	O
ontology	O
.	O
By	O
that	O
we	O
mean	O
that	O
the	O
real	O
part	O
of	O
the	O
Schrödinger	B-Process
equation	I-Process
under	I-Process
polar	I-Process
decomposition	I-Process
of	I-Process
the	I-Process
wave	I-Process
function	I-Process
becomes	O
the	O
classical	O
Hamilton	B-Process
–	I-Process
Jacobi	I-Process
equation	I-Process
in	O
the	O
limit	O
where	O
terms	O
involving	O
ℏ	O
are	O
neglected	O
.	O
In	O
contrast	O
to	O
this	O
approach	O
,	O
in	O
this	O
Letter	O
we	O
show	O
that	O
the	O
classical	O
trajectories	O
arise	O
from	O
a	O
short-time	B-Process
quantum	I-Process
propagator	I-Process
when	O
terms	O
of	O
O	O
(	O
Δt2	O
)	O
can	O
be	O
neglected	O
.	O
This	O
fact	O
was	O
actually	O
already	O
observed	O
by	O
Holland	O
some	O
twenty	O
years	O
ago	O
:	O
In	O
page	O
269	O
of	O
his	O
book	O
[	O
6	O
]	O
infinitesimal	B-Material
time	I-Material
intervals	I-Material
are	O
considered	O
whose	O
sequence	O
constructs	O
a	O
finite	O
path	O
.	O
It	O
is	O
shown	O
that	O
along	O
each	O
segment	O
the	O
motion	O
is	O
classical	O
(	O
negligible	O
quantum	O
potential	O
)	O
,	O
and	O
that	O
it	O
follows	O
that	O
the	O
quantum	O
path	O
may	O
be	O
decomposed	O
into	O
a	O
sequence	O
of	O
segments	O
along	O
each	O
of	O
which	O
the	O
classical	O
action	O
is	O
a	O
minimum	O
.	O
The	O
novel	O
contribution	O
of	O
the	O
present	O
Letter	O
is	O
an	O
improved	O
proof	O
of	O
Holland	O
ʼs	O
result	O
using	O
an	O
improved	O
version	O
of	O
the	O
propagator	B-Process
due	O
to	O
Makri	O
and	O
Miller	O
[	O
9,10	O
]	O
.	O
(	O
See	O
also	O
de	O
Gosson	O
[	O
3	O
]	O
for	O
a	O
further	O
discussion	O
.	O
)	O

The	O
goal	O
of	O
the	O
glued	B-Process
trees	I-Process
(	O
GT	B-Process
)	O
algorithm	O
for	O
quantum	B-Task
search	I-Task
is	O
the	O
following	O
:	O
beginning	O
from	O
the	O
left-most	O
vertex	O
of	O
a	O
given	O
GT	B-Material
graph	I-Material
,	O
traverse	O
the	O
graph	O
and	O
reach	O
the	O
right-most	O
vertex	O
,	O
referred	O
to	O
as	O
the	O
target	O
vertex	O
.	O
Childs	O
et	O
al	O
.	O
[	O
1	O
]	O
use	O
this	O
algorithm	O
to	O
show	O
quantum	B-Task
walk	I-Task
search	I-Task
to	O
be	O
fundamentally	O
more	O
effective	O
than	O
classical	B-Task
random	I-Task
walk	I-Task
search	I-Task
by	O
presenting	O
a	O
class	B-Material
of	I-Material
graphs	I-Material
(	O
the	O
GT	B-Material
graphs	I-Material
)	O
that	O
force	O
classical	B-Task
random	I-Task
walks	I-Task
to	O
make	O
exponentially	O
many	O
queries	O
to	O
an	O
oracle	O
encoding	O
the	O
structure	O
of	O
the	O
graph	O
,	O
but	O
that	O
are	O
traversable	O
by	O
quantum	O
walks	O
with	O
a	O
polynomial	O
number	O
of	O
queries	O
to	O
such	O
an	O
oracle	O
.	O
In	O
order	O
to	O
study	O
the	O
robustness	O
of	O
the	O
algorithm	O
to	O
the	O
detrimental	O
effects	O
of	O
decoherence	O
,	O
we	O
shall	O
determine	O
how	O
effectively	O
it	O
achieves	O
its	O
goal	O
when	O
subjected	O
to	O
an	O
increasing	B-Process
degree	I-Process
of	I-Process
phase	I-Process
damping	I-Process
noise	I-Process
.	O
For	O
this	O
reason	O
,	O
we	O
will	O
focus	O
on	O
the	O
probability	O
that	O
the	O
walker	O
is	O
on	O
the	O
target	O
vertex	O
at	O
the	O
end	O
of	O
the	O
walk	O
.	O
We	O
thus	O
consider	O
GT	B-Material
graphs	I-Material
such	O
as	O
the	O
one	O
illustrated	O
in	O
Fig.	O
1	O
(	O
b	O
)	O
,	O
i.e.	O
consisting	O
of	O
n	O
layers	O
before	O
the	O
gluing	B-Process
stage	I-Process
,	O
and	O
thus	O
labelled	O
as	O
G′n	O
.	O

Another	O
remarkable	O
feature	O
of	O
the	O
quantum	B-Task
field	I-Task
treatment	I-Task
can	O
be	O
revealed	O
from	O
the	O
investigation	O
of	O
the	O
vacuum	B-Material
state	I-Material
.	O
For	O
a	O
classical	O
field	O
,	O
vacuum	O
is	O
realized	O
by	O
simply	O
setting	O
the	O
potential	O
to	O
zero	O
resulting	O
in	O
an	O
unaltered	O
,	O
free	O
evolution	O
of	O
the	O
particle	B-Material
's	I-Material
plane	I-Material
wave	I-Material
(|	O
ψI	O
〉=|	O
ψIII	O
〉=|	O
k0	O
〉)	O
.	O
In	O
the	O
quantized	B-Task
treatment	I-Task
,	O
vacuum	B-Material
is	O
represented	O
by	O
an	O
initial	O
Fock	O
state	O
|	O
n0	O
=	O
0	O
〉	O
which	O
still	O
interacts	O
with	O
the	O
particle	O
and	O
yields	O
as	O
final	O
state	O
|	O
ΨIII	O
〉	O
behind	O
the	O
field	O
region	O
(	O
19	O
)|	O
ΨI	O
〉=|	O
k0	O
〉⊗|	O
0	O
〉⇒|	O
ΨIII	O
〉=∑	O
n	O
=	O
0	O
∞	O
t0n	O
|	O
k	O
−	O
n	O
〉⊗|	O
n	O
〉	O
with	O
a	O
photon	O
exchange	O
probability	O
(	O
20	O
)	O
P0,n	O
=|	O
t0n	O
|	O
2	O
=	O
1n	O
!	O
e	O
−	O
Λ2Λ2n	O
The	O
particle	O
thus	O
transfers	O
energy	O
to	O
the	O
vacuum	O
field	O
leading	O
to	O
a	O
Poissonian	O
distributed	O
final	O
photon	O
number	O
.	O
Let	O
's	O
consider	O
,	O
for	O
example	O
,	O
a	O
superconducting	B-Material
resonant	I-Material
circuit	I-Material
as	O
source	O
of	O
the	O
field	O
.	O
The	O
magnetic	B-Material
field	I-Material
along	O
the	O
axis	O
of	O
a	O
properly	B-Material
shaped	I-Material
coil	I-Material
is	O
well	O
approximated	O
by	O
the	O
rectangular	O
form	O
.	O
A	O
particle	B-Material
with	O
a	O
magnetic	O
dipole	O
moment	O
passing	O
through	O
the	O
coil	O
then	O
interacts	O
with	O
the	O
circuit	O
and	O
excites	O
it	O
with	O
a	O
measurable	O
loss	O
of	O
kinetic	O
energy	O
even	O
if	O
the	O
circuit	O
is	O
initially	O
uncharged	O
and	O
there	O
is	O
classically	O
no	O
field	O
it	O
can	O
couple	O
to	O
.	O
The	O
phenomenon	O
that	O
vacuum	B-Material
in	O
quantum	O
field	O
theory	O
does	O
not	O
mean	O
to	O
“	O
no	O
influence	O
”	O
as	O
known	O
from	O
Casimir	B-Process
forces	I-Process
or	I-Process
Lamb	I-Process
shift	I-Process
is	O
clearly	O
visible	O
here	O
as	O
well	O
.	O

The	O
systems	O
in	O
which	O
the	O
Stern	B-Process
–	I-Process
Gerlach	I-Process
force	I-Process
is	O
most	O
prominent	O
are	O
those	O
with	O
a	O
high	O
electromagnetic	B-Process
field	I-Process
gradient	I-Process
.	O
Section	O
2	O
considers	O
the	O
implications	O
of	O
the	O
coupling	O
between	O
the	O
spin	O
of	O
a	O
classical	O
electron	B-Material
and	O
the	O
rapidly	O
varying	O
electromagnetic	B-Material
field	I-Material
produced	O
by	O
a	O
laser-driven	B-Process
plasma	I-Process
wave	I-Process
.	O
Sufficiently	O
short	O
,	O
high-intensity	B-Process
laser	I-Process
pulses	I-Process
can	O
form	O
longitudinal	B-Material
waves	I-Material
within	O
the	O
electron	O
density	O
of	O
a	O
plasma	B-Material
.	O
These	O
density	B-Material
waves	I-Material
propagate	O
with	O
speed	O
comparable	O
to	O
the	O
group	O
speed	O
of	O
the	O
laser	B-Process
pulse	I-Process
.	O
Not	O
all	O
plasma	B-Material
electrons	I-Material
form	O
this	O
wave	O
,	O
however	O
;	O
some	O
of	O
the	O
electrons	O
are	O
caught	O
up	O
in	O
the	O
wave	O
and	O
accelerated	O
by	O
its	O
high	O
fields	O
.	O
The	O
wave	O
eventually	O
collapses	O
as	O
these	O
electrons	B-Process
damp	I-Process
the	I-Process
wave	I-Process
(	O
the	O
wave	B-Process
‘	I-Process
breaks’	I-Process
)	O
.	O
The	O
extremely	O
high	B-Material
electric	I-Material
field	I-Material
gradient	I-Material
of	I-Material
a	I-Material
plasma	I-Material
wave	I-Material
near	O
wavebreaking	O
provides	O
an	O
excellent	O
theoretical	O
testing	O
ground	O
for	O
the	O
effects	O
of	O
Stern	B-Task
–	I-Task
Gerlach-type	I-Task
contributions	I-Task
to	O
the	O
trajectory	O
of	O
a	O
test	O
electron	B-Material
.	O

Flow-induced	O
deformations	O
can	O
lead	O
to	O
irreversible	O
changes	O
in	O
the	O
structure	O
of	O
a	O
polymeric	B-Material
fluid	I-Material
;	O
if	O
the	O
rate	O
of	O
extension	O
far	O
exceeds	O
the	O
rate	O
of	O
relaxation	O
,	O
then	O
the	O
polymer	O
chain	O
can	O
be	O
broken	O
.	O
Mechanical	O
degradation	O
of	O
polymers	B-Material
in	O
extensional	O
flow	O
has	O
long	O
been	O
recognised	O
[	O
30	O
]	O
and	O
leads	O
to	O
a	O
reduction	B-Process
in	I-Process
the	I-Process
average	I-Process
molecular	I-Process
weight	I-Process
.	O
A-Alamry	O
et	O
al	O
.	O
[	O
1	O
]	O
have	O
recently	O
reported	O
evidence	O
of	O
flow-induced	O
polymer	O
degradation	O
in	O
DoD	B-Process
jetting	I-Process
.	O
Central	B-Task
scission	I-Task
is	O
observed	O
for	O
polystyrene	B-Material
in	O
a	O
number	O
of	O
good	O
solvents	O
under	O
certain	O
jetting	O
conditions	O
for	O
a	O
bounded	O
range	O
of	O
molecular	O
weights	O
.	O
Since	O
only	O
those	O
molecules	O
that	O
are	O
fully	O
extended	O
can	O
be	O
fractured	O
at	O
the	O
centre	O
of	O
the	O
polymer	O
chain	O
[	O
29	O
]	O
,	O
in	O
this	O
paper	O
we	O
investigate	O
whether	O
flow-induced	B-Task
central	I-Task
scission	I-Task
is	O
possible	O
under	O
the	O
conditions	O
of	O
DoD	B-Process
jetting	I-Process
.	O

This	O
conclusion	O
is	O
a	O
consequence	O
of	O
the	O
high	O
jet	O
speeds	O
and	O
small	O
nozzle	O
diameters	O
in	O
combination	O
with	O
the	O
relatively	O
high	O
viscosity	O
solvent	O
and	O
modest	O
molecular	O
weights	O
of	O
the	O
polystyrene	B-Material
,	O
which	O
results	O
in	O
high	O
Weissenberg	O
numbers	O
and	O
moderate	O
values	O
of	O
the	O
extensibility	O
,	O
L	O
studied	O
here	O
.	O
As	O
discussed	O
in	O
earlier	O
papers	O
[	O
3,6	O
]	O
,	O
other	O
jetting	B-Process
fluid	I-Process
combinations	I-Process
,	O
such	O
as	O
those	O
of	O
de	O
Gans	O
et	O
al	O
.	O
[	O
4	O
]	O
,	O
lie	O
in	O
a	O
different	O
jetting	O
regime	O
where	O
full	O
extension	O
does	O
not	O
occur	O
and	O
relaxation	O
time	O
controls	O
the	O
viscoelastic	O
behaviour	O
.	O
Consequently	O
inkjet	B-Process
fluid	I-Process
assessment	I-Process
methods	I-Process
need	O
to	O
provide	B-Task
a	I-Task
full	I-Task
characterisation	I-Task
including	I-Task
both	I-Task
linear	I-Task
and	I-Task
nonlinear	I-Task
viscoelastic	I-Task
properties	I-Task
.	O
This	O
complexity	O
suggests	O
assessments	O
of	O
inkjet	B-Material
fluids	I-Material
might	O
have	O
to	O
include	O
jetting	O
from	O
sets	O
of	O
DoD	B-Process
print	I-Process
head	I-Process
devices	I-Process
with	O
different	O
sensitivities	O
to	O
all	O
the	O
various	O
VE	O
parameters	O
[	O
37	O
]	O
,	O
rather	O
than	O
reliance	O
on	O
testing	O
without	O
jetting	B-Process
.	O
This	O
was	O
not	O
the	O
expected	O
outcome	O
from	O
the	O
present	O
work	O
but	O
does	O
echo	O
the	O
very	O
pragmatic	O
viewpoint	O
expressed	O
as	O
a	O
“	B-Process
map	I-Process
of	I-Process
misery	I-Process
”	I-Process
by	O
Clasen	O
et	O
al	O
.	O
[	O
38	O
]	O
and	O
may	O
provide	O
a	O
way	O
forward	O
for	O
future	O
R	O
&	O
D	O
strategies	O
towards	O
ink	B-Task
testing	I-Task
.	O

Denier	O
and	O
Hewitt	O
[	O
12	O
]	O
have	O
shown	O
that	O
bounded	O
solutions	O
to	O
9a	O
,	O
9b	O
and	O
9c	O
subject	O
to	O
(	O
10a	O
)	O
and	O
(	O
10b	O
)	O
exist	O
only	O
in	O
the	O
shear-thinning	O
case	O
for	O
n	O
>	O
12	O
.	O
In	O
the	O
shear-thickening	B-Process
case	O
they	O
have	O
shown	O
that	O
solutions	B-Material
become	O
non-differentiable	O
at	O
some	O
critical	O
location	O
ηc	O
,	O
and	O
although	O
it	O
transpires	O
that	O
this	O
singularity	B-Process
can	O
be	O
regularised	O
entirely	O
within	O
the	O
context	O
of	O
the	O
power-law	B-Process
model	I-Process
,	O
we	O
will	O
not	O
consider	O
such	O
flows	B-Material
here	O
.	O
Thus	O
in	O
this	O
study	O
we	O
will	O
consider	O
flows	O
with	O
power-law	O
index	O
in	O
the	O
range	O
12	O
<	O
n	O
⩽	O
1	O
.	O
They	O
have	O
also	O
shown	O
that	O
for	O
12	O
<	O
n	O
<	O
1	O
to	O
ensure	O
the	O
correct	B-Process
algebraic	I-Process
decay	I-Process
in	O
the	O
numerical	O
solutions	O
one	O
must	O
apply	O
the	O
Robin	O
condition	O
(	O
11	O
)(	O
u	O
¯	O
′	O
,	O
v	O
¯	O
′	O
)=	O
nη	O
(	O
n-1	O
)(	O
u	O
¯	O
,	O
v	O
¯)	O
asη	O
→∞	O
,	O
at	O
some	O
suitably	O
large	O
value	O
of	O
η	O
=	O
η	O
∞≫	O
1	O
.	O
In	O
the	O
Newtonian	O
case	O
this	O
relationship	O
becomes	O
singular	O
,	O
this	O
is	O
due	O
to	O
the	O
fact	O
that	O
when	O
n	O
=	O
1	O
the	O
functions	O
u	O
¯	O
and	O
v	O
¯	O
decay	O
exponentially	O
.	O
Cochran	O
[	O
13	O
]	O
showed	O
that	O
in	O
this	O
case	O
(	O
12	O
)(	O
u	O
¯	O
′	O
,	O
v	O
¯	O
′	O
)=	O
w	O
¯∞(	O
u	O
¯	O
,	O
v	O
¯)	O
asη	O
→∞	O
,	O
where	O
w	O
∞=-	O
2	O
∫	O
0	O
∞	O
u	O
¯	O
dη	O
.	O

In	O
order	O
for	O
DLS	B-Task
based	I-Task
micro-rheology	I-Task
to	O
be	O
successful	O
,	O
there	O
must	O
be	O
sufficient	O
scattering	O
contrast	O
between	O
the	O
sample	B-Material
and	I-Material
the	I-Material
tracer	I-Material
particles	I-Material
.	O
In	O
order	O
to	O
achieve	O
this	O
,	O
the	O
maximum	O
possible	O
concentration	O
of	O
tracer	O
particles	O
was	O
added	O
such	O
that	O
single	O
scattering	O
events	O
still	O
dominated	O
(	O
as	O
determined	O
by	O
measurements	O
of	O
diffusion	O
coefficients	O
in	O
water	B-Material
at	O
different	O
concentrations	O
)	O
.	O
In	O
order	O
to	O
determine	O
whether	O
or	O
not	O
the	O
background	O
scattering	O
from	O
the	O
sample	O
was	O
sufficiently	O
low	O
compared	O
to	O
that	O
of	O
the	O
tracer	B-Material
particles	I-Material
,	O
we	O
also	O
compared	O
the	O
scattering	B-Material
intensities	I-Material
obtained	O
from	O
samples	O
with	O
and	O
without	O
tracer	B-Material
particles	I-Material
as	O
a	O
function	O
of	O
time	O
.	O
The	O
results	O
of	O
this	O
exercise	O
are	O
shown	O
in	O
Fig.	O
6.	O
From	O
this	O
figure	O
it	O
can	O
be	O
seen	O
that	O
although	O
initially	O
the	O
scattering	B-Process
from	O
the	O
sample	O
without	O
tracer	B-Material
particles	I-Material
is	O
low	O
compared	O
to	O
those	O
containing	O
tracer	O
particles	O
,	O
as	O
gelation	B-Material
proceeds	O
this	O
eventually	O
ceases	O
to	O
be	O
the	O
case	O
.	O
This	O
is	O
presumably	O
because	O
of	O
the	O
development	O
of	O
supra-molecular	B-Material
structures	I-Material
,	O
such	O
as	O
those	O
seen	O
previously	O
(	O
Fig.	O
2B	O
)	O
.	O
Based	O
on	O
the	O
results	O
in	O
Fig.	O
6	O
it	O
was	O
decided	O
to	O
only	O
use	O
data	O
collected	O
in	O
the	O
first	O
240min	O
of	O
the	O
experiment	O
,	O
after	O
which	O
point	O
the	O
scattering	B-Material
from	I-Material
the	I-Material
gel	I-Material
network	I-Material
became	O
rather	O
too	O
large	O
to	O
ignore	O
.	O

Tack	B-Material
is	O
an	O
important	O
property	O
of	O
a	O
PSA	B-Material
as	O
it	O
quantifies	O
its	O
ability	O
to	O
form	O
instantly	O
a	O
bond	O
when	O
brought	O
into	O
contact	O
with	O
a	O
surface	O
.	O
The	O
final	O
adhesion	O
and	O
cohesive	O
strength	O
of	O
the	O
bond	O
are	O
influenced	O
by	O
numerous	O
factors	O
including	O
the	O
surface	O
energies	O
of	O
the	O
adhesive	B-Material
and	O
substrate	B-Material
,	O
dwell	O
time	O
,	O
contact	O
pressure	O
,	O
mechanical	O
properties	O
of	O
the	O
adhesive	B-Material
,	O
as	O
well	O
as	O
environmental	O
conditions	O
such	O
as	O
temperature	O
and	O
humidity	O
[	O
8	O
]	O
.	O
Therefore	O
,	O
tack	O
is	O
important	O
in	O
many	O
applications	O
where	O
an	O
instant	O
bond	O
is	O
required	O
,	O
however	O
it	O
is	O
equally	O
important	O
when	O
a	O
‘	B-Task
clean’	I-Task
separation	I-Task
of	O
the	O
initially	O
bonded	B-Material
surfaces	I-Material
is	O
desirable	O
.	O
Many	O
different	O
methods	O
for	O
measuring	O
the	O
tack	B-Material
have	O
been	O
devised	O
with	O
the	O
four	O
main	O
ones	O
being	O
the	O
rolling	B-Process
ball	I-Process
,	O
loop	B-Process
tack	I-Process
,	O
quick	B-Process
stick	I-Process
and	O
probe	B-Process
tack	I-Process
tests	B-Process
[	O
9	O
]	O
.	O
Each	O
has	O
its	O
own	O
advantages	O
and	O
disadvantages	O
and	O
the	O
specific	O
testing	O
method	O
should	O
be	O
selected	O
based	O
on	O
the	O
particular	O
application	O
.	O

The	O
first	O
of	O
these	O
systems	O
,	O
a	O
biopolymer	B-Process
gel	I-Process
,	O
involves	O
the	O
thermoreversible	B-Task
gelation	I-Task
of	O
aqueous	O
gelatin	O
solutions	O
to	O
form	O
a	O
physical	O
gel	O
,	O
whereas	O
the	O
other	O
systems	O
considered	O
herein	O
involve	O
the	O
formation	O
of	O
chemical	O
gels	O
featuring	O
permanent	O
cross-linked	B-Process
branching	I-Process
networks	I-Process
.	O
The	O
second	O
system	O
is	O
a	O
commercial	B-Process
silicone	I-Process
dielectric	I-Process
gel	I-Process
(	O
SDG	B-Process
)	O
which	O
is	O
used	O
in	O
the	O
production	B-Task
of	I-Task
electronic	I-Task
products	I-Task
created	O
by	O
industrial	O
printing	O
processes	O
.	O
The	O
third	O
experimental	O
system	O
is	O
a	O
fibrin	B-Process
gel	I-Process
formed	O
by	O
the	O
thrombin-induced	B-Process
polymerisation	I-Process
of	O
fibrinogen	B-Process
molecules	I-Process
.	O
The	O
gel	B-Process
network	I-Process
product	I-Process
in	O
the	O
latter	O
case	O
forms	O
the	O
principal	O
microstructural	B-Material
component	I-Material
of	O
a	O
blood	B-Material
clot	I-Material
[	O
8	O
]	O
.	O
The	O
latter	O
case	O
is	O
particularly	O
interesting	O
as	O
the	O
critical-gel	B-Material
which	O
is	O
established	O
at	O
the	O
GP	B-Material
serves	O
as	O
a	O
‘	B-Material
template’	I-Material
for	I-Material
the	I-Material
ensuing	I-Material
development	I-Material
of	I-Material
microstructure	I-Material
and	I-Material
associated	I-Material
rheological	I-Material
properties	I-Material
in	O
the	O
post-GP	O
phase	O
of	O
fibrin	O
clot	O
evolution	O
[	O
9	O
]	O
.	O

A	O
convenient	O
and	O
widely	O
reported	O
technique	O
for	O
detection	B-Task
of	I-Task
the	I-Task
GP	I-Task
involves	O
measurements	B-Process
of	I-Process
the	I-Process
complex	I-Process
shear	I-Process
modulus	I-Process
,	O
G	O
∗	O
,	O
over	O
a	O
range	O
of	O
frequencies	O
,	O
ω	O
,	O
in	O
oscillatory	O
shear	O
.	O
At	O
the	O
GP	O
the	O
elastic	O
and	O
viscous	O
components	O
of	O
the	O
complex	O
modulus	O
,	O
G′	O
and	O
G	O
″	O
,	O
respectively	O
scale	O
in	O
oscillatory	O
frequency	O
,	O
ω	O
,	O
as	O
G′	O
(	O
ω	O
)∼	O
G	O
″(	O
ω	O
)∼	O
ωα	O
where	O
α	O
is	O
termed	O
the	O
stress	O
relaxation	O
exponent	O
[	O
15	O
]	O
.	O
Thus	O
,	O
the	O
GP	O
may	O
be	O
identified	O
as	O
the	O
instant	O
where	O
the	O
G′	O
and	O
G	O
″	O
scale	O
in	O
frequency	O
according	O
to	O
identical	O
power	O
laws	O
[	O
15	O
]	O
,	O
behaviour	O
corresponding	O
to	O
attainment	O
of	O
a	O
frequency	O
independent	O
phase	O
angle	O
,	O
δ	O
(=	O
atan	O
(	O
G	O
″/	O
G′	O
))	O
.	O
GP	B-Process
measurements	I-Process
may	O
involve	O
‘	O
frequency	O
sweeps’	O
with	O
repeated	O
consecutive	O
application	O
of	O
a	O
set	O
of	O
small	O
amplitude	O
oscillatory	O
shear	O
,	O
SAOS	O
,	O
waveforms	O
[	O
15,16	O
]	O
,	O
or	O
by	O
Fourier	O
Transform	O
Mechanical	O
Spectroscopy	O
,	O
FTMS	O
,	O
in	O
which	O
G	O
∗(	O
ω	O
)	O
is	O
found	O
by	O
simultaneous	O
application	O
of	O
several	O
harmonic	O
frequencies	O
in	O
a	O
composite	O
waveform	O
and	O
its	O
subsequent	O
Fourier	O
analysis	O
[	O
17,18	O
]	O
.	O
Frequency	O
sweeps	O
are	O
limited	O
to	O
relatively	O
slow	O
gelation	O
processes	O
due	O
to	O
sample	O
mutation	O
and	O
interpolation	O
errors	O
[	O
9,19,20	O
]	O
.	O
FTMS	O
may	O
overcome	O
these	O
limitations	O
,	O
but	O
is	O
unsuitable	O
for	O
markedly	O
strain	O
sensitive	O
materials	O
,	O
such	O
as	O
fibrin	O
gels	O
,	O
due	O
to	O
the	O
strain	O
amplitude	O
of	O
the	O
composite	O
waveform	O
exceeding	O
the	O
linear	O
viscoelastic	O
range	O
(	O
LVR	O
)	O
[	O
9	O
]	O
.	O

Equilibrium	B-Material
surface	I-Material
tension	I-Material
was	O
measured	O
at	O
21	O
°	O
C	O
with	O
a	O
SITA	B-Process
pro	I-Process
line	I-Process
t-15	I-Process
bubble	I-Process
tensiometer	I-Process
.	O
Rheological	B-Process
measurements	I-Process
were	O
performed	O
with	O
an	O
ARES	B-Process
rheometer	I-Process
at	O
shear	O
rates	O
up	O
to	O
15s	O
−	O
1	O
and	O
with	O
a	O
piezo	B-Process
axial	I-Process
vibrator	I-Process
[	O
21	O
]	O
(	O
PAV	B-Process
)	O
at	O
frequencies	O
up	O
to	O
6kHz	O
.	O
Table	O
1	O
shows	O
the	O
measured	O
values	B-Material
of	I-Material
viscosity	I-Material
(	O
the	O
real	O
component	O
η′	O
of	O
complex	O
viscosity	O
)	O
at	O
1s	O
−	O
1	O
and	O
4000s	O
−	O
1	O
and	O
of	O
surface	B-Material
tension	I-Material
for	O
the	O
solutions	O
with	O
and	O
without	O
the	O
surfactant	O
mixture	O
.	O
For	O
the	O
most	B-Material
concentrated	I-Material
(	I-Material
1.1wt	I-Material
%)	I-Material
solution	I-Material
,	O
viscosity	O
fell	O
from	O
>	O
60mPas	O
at	O
low	O
shear	O
rate	O
to	O
about	O
4mPas	O
at	O
the	O
highest	O
shear	O
rates	O
.	O
The	O
PEDOT	B-Material
:	I-Material
PSS	I-Material
fluids	I-Material
also	O
exhibited	O
elasticity	O
that	O
steadily	O
reduced	O
with	O
increasing	O
frequency	O
[	O
4	O
]	O
.	O
All	O
the	O
aqueous	O
PEDOT	B-Material
:	I-Material
PSS	I-Material
solutions	I-Material
shear-thinned	O
significantly	O
,	O
but	O
the	O
presence	O
of	O
surfactants	O
did	O
not	O
affect	O
the	O
trends	O
in	O
the	O
rheological	O
behaviour	O
,	O
particularly	O
at	O
the	O
higher	O
frequencies	O
(	O
10	O
–	O
4000s	O
−	O
1	O
)	O
.	O

In	O
this	O
paper	O
,	O
we	O
propose	O
a	O
general	O
agent-based	O
distributed	O
framework	B-Task
where	O
each	B-Process
agent	I-Process
is	I-Process
implementing	I-Process
a	I-Process
different	I-Process
metaheuristic	I-Process
/	I-Process
local	I-Process
search	I-Process
combination	I-Process
.	O
Moreover	O
,	O
an	O
agent	O
continuously	O
adapts	B-Process
itself	O
during	O
the	O
search	O
process	O
using	O
a	B-Material
direct	I-Material
cooperation	I-Material
protocol	I-Material
based	O
on	O
reinforcement	B-Task
learning	I-Task
and	O
pattern	B-Task
matching	I-Task
.	O
Good	B-Task
patterns	I-Task
that	O
make	O
up	O
improving	O
solutions	O
are	O
identified	O
and	O
shared	O
by	O
the	O
agents	B-Material
.	O
This	O
agent-based	B-Task
system	I-Task
aims	O
to	O
provide	B-Process
a	I-Process
modular	I-Process
flexible	I-Process
framework	I-Process
to	O
deal	B-Process
with	I-Process
a	I-Process
variety	I-Process
of	I-Process
different	I-Process
problem	I-Process
domains	I-Process
.	O
We	O
have	O
evaluated	O
the	O
performance	O
of	O
this	B-Task
approach	I-Task
using	O
the	O
proposed	O
framework	O
which	O
embodies	B-Process
a	I-Process
set	I-Process
of	I-Process
well	I-Process
known	I-Process
metaheuristics	I-Process
with	I-Process
different	I-Process
configurations	I-Process
as	O
agents	O
on	O
two	B-Material
problem	I-Material
domains	I-Material
,	O
Permutation	B-Material
Flow-shop	I-Material
Scheduling	I-Material
and	I-Material
Capacitated	I-Material
Vehicle	I-Material
Routing	I-Material
.	O
The	O
results	O
show	O
the	O
success	O
of	O
the	O
approach	B-Task
yielding	O
three	O
new	O
best	O
known	O
results	O
of	O
the	O
Capacitated	B-Material
Vehicle	I-Material
Routing	I-Material
benchmarks	I-Material
tested	O
,	O
whilst	O
the	O
results	O
for	O
Permutation	B-Material
Flow-shop	I-Material
Scheduling	I-Material
are	O
commensurate	O
with	O
the	O
best	O
known	O
values	O
for	O
all	O
the	O
benchmarks	O
tested	O
.	O

As	O
mentioned	O
earlier	O
,	O
this	O
paper	O
represents	O
ongoing	O
efforts	O
to	O
efficiently	O
address	O
the	O
stochastic	B-Task
MPSP	I-Task
.	O
Future	O
work	O
may	O
consider	O
investigating	O
whether	O
the	O
algorithm	O
would	O
be	O
as	O
successful	O
or	O
not	O
in	O
solving	O
variants	O
of	O
the	O
MPSP	O
that	O
include	O
more	O
operational	B-Task
constraints	I-Task
,	O
such	O
as	O
variable	B-Task
cut-off	I-Task
grade	I-Task
,	O
grade	B-Task
blending	I-Task
,	O
and	O
stockpiling	B-Task
,	O
as	O
it	O
is	O
in	O
solving	O
the	O
“	O
classical	O
”	O
variant	O
considered	O
in	O
this	O
paper	O
.	O
Indeed	O
,	O
it	O
is	O
a	O
general-purpose	B-Process
algorithm	I-Process
and	O
should	O
be	O
applicable	O
to	O
any	O
of	O
these	O
variants	O
.	O
Other	O
research	O
avenues	O
include	O
considering	O
other	O
strategies	O
for	O
updating	O
the	O
penalties	O
within	O
PH	B-Process
and	O
other	O
methods	O
for	O
solving	O
the	O
sub-problems	O
.	O
Finally	O
,	O
another	O
important	O
research	O
direction	O
is	O
the	O
development	O
of	O
other	O
efficient	O
solution	O
approaches	O
.	O
Since	O
it	O
has	O
been	O
observed	O
empirically	O
that	O
the	O
problem	O
formulation	O
often	O
achieves	O
small	O
integrality	O
gaps	O
,	O
one	O
approach	O
could	O
be	O
to	O
solve	B-Process
the	I-Process
linear	I-Process
relaxation	I-Process
of	O
the	O
problem	O
using	O
an	O
efficient	O
algorithm	O
and	O
then	O
to	O
use	O
an	O
LP-rounding	B-Process
procedure	I-Process
to	O
get	O
an	O
integer	O
solution	O
.	O

We	O
propose	O
an	O
equilibrium	B-Process
model	I-Process
that	O
allows	O
to	O
analyze	B-Task
the	I-Task
long-run	I-Task
impact	I-Task
of	I-Task
the	I-Task
electricity	I-Task
market	I-Task
design	I-Task
on	I-Task
transmission	I-Task
line	I-Task
expansion	I-Task
by	O
the	O
regulator	O
and	O
investment	O
in	O
generation	O
capacity	O
by	O
private	O
firms	O
in	O
liberalized	O
electricity	O
markets	O
.	O
The	O
model	O
incorporates	O
investment	O
decisions	O
of	O
the	O
transmission	O
system	O
operator	O
and	O
private	O
firms	O
in	O
expectation	O
of	O
an	O
energy-only	O
market	O
and	O
cost-based	O
redispatch	O
.	O
In	O
different	O
specifications	O
we	O
consider	O
the	O
cases	O
of	O
one	O
vs.	O
multiple	O
price	O
zones	O
(	O
market	B-Process
splitting	I-Process
)	O
and	O
analyze	B-Process
different	I-Process
approaches	I-Process
to	I-Process
recover	I-Process
network	I-Process
cost	I-Process
—	O
in	O
particular	O
lump	B-Task
sum	I-Task
,	O
generation	B-Task
capacity	I-Task
based	I-Task
,	O
and	O
energy	B-Task
based	I-Task
fees	I-Task
.	O
In	O
order	O
to	O
compare	O
the	O
outcomes	O
of	O
our	O
multilevel	B-Process
market	I-Process
model	I-Process
with	O
a	O
first	B-Process
best	I-Process
benchmark	I-Process
,	O
we	O
also	O
solve	B-Process
the	I-Process
corresponding	I-Process
integrated	I-Process
planner	I-Process
problem	I-Process
.	O
Using	O
two	O
test	O
networks	O
we	O
illustrate	O
that	O
energy-only	O
markets	O
can	O
lead	O
to	O
suboptimal	O
locational	O
decisions	O
for	O
generation	O
capacity	O
and	O
thus	O
imply	O
excessive	B-Process
network	I-Process
expansion	I-Process
.	O
Market	B-Process
splitting	I-Process
heals	O
these	O
problems	O
only	O
partially	O
.	O
These	O
results	O
are	O
valid	O
for	O
all	O
considered	O
types	O
of	O
network	O
tariffs	O
,	O
although	O
investment	O
slightly	O
differs	O
across	O
those	O
regimes	O
.	O

Regarding	O
the	O
implications	B-Task
of	I-Task
the	I-Task
results	I-Task
of	O
this	O
paper	O
,	O
we	O
note	O
two	O
points	O
.	O
From	O
a	O
practical	O
point	O
of	O
view	O
,	O
we	O
have	O
endowed	O
the	B-Task
weighted	I-Task
additive	I-Task
model	I-Task
with	I-Task
a	I-Task
distance	I-Task
function	I-Task
structure	I-Task
,	O
which	O
takes	B-Process
negative	I-Process
values	I-Process
for	I-Process
points	I-Process
located	I-Process
outside	I-Process
the	I-Process
technology	I-Process
and	I-Process
non-negative	I-Process
values	I-Process
for	I-Process
points	I-Process
into	I-Process
the	I-Process
production	I-Process
possibility	I-Process
set	I-Process
.	O
In	O
this	O
respect	O
,	O
the	B-Task
weighted	I-Task
additive	I-Task
distance	I-Task
function	I-Task
methodologically	O
supports	O
the	O
branch	O
of	O
the	O
literature	O
that	O
resorts	O
to	O
the	O
weighted	B-Material
additive	I-Material
model	I-Material
or	O
some	B-Material
related	I-Material
approach	I-Material
to	O
measure	B-Process
productivity	I-Process
over	I-Process
time	I-Process
(	O
see	O
,	O
for	O
example	O
,	O
Mahlberg	O
&	O
Sahoo	O
,	O
2011	O
or	O
Chang	O
et	O
al.	O
,	O
2012	O
)	O
.	O
From	O
a	O
theoretical	O
point	O
of	O
view	O
,	O
we	O
have	O
provided	O
a	B-Material
new	I-Material
distance	I-Material
function	I-Material
with	O
some	O
interesting	O
properties	O
in	O
contrast	O
to	O
the	O
usual	O
ones	O
,	O
mainly	O
(	O
1	O
)	O
when	O
technical	O
inefficiency	O
has	O
to	O
be	O
estimated	O
,	O
the	B-Material
weighted	I-Material
additive	I-Material
distance	I-Material
function	I-Material
coincides	O
with	O
the	O
weighted	O
additive	O
model	O
,	O
which	O
means	B-Process
that	I-Process
technical	I-Process
inefficiency	I-Process
is	I-Process
measured	I-Process
following	I-Process
the	I-Process
Pareto-Koopmans	I-Process
notion	I-Process
of	I-Process
efficiency	I-Process
;	O
and	O
(	O
2	O
)	O
when	O
productivity	O
has	O
to	O
be	O
determined	B-Process
and	I-Process
decomposed	I-Process
over	I-Process
time	I-Process
the	O
weighted	B-Material
additive	I-Material
distance	I-Material
function	I-Material
emerges	O
as	O
an	O
attractive	O
tool	O
to	O
be	O
used	O
for	O
cross-period	O
evaluation	O
of	O
returns	O
to	O
scale	O
changes	O
,	O
since	O
this	O
distance	O
function	O
is	O
always	O
feasible	O
,	O
even	O
under	O
Variable	O
Returns	O
to	O
Scale	O
.	O

The	O
iron	B-Material
ore	I-Material
may	O
be	O
extracted	O
from	O
blocks	O
of	O
25	O
×	O
25	O
×	O
12meter3	O
located	O
at	O
three	O
consecutive	O
mining	O
benches	O
of	O
12meter	O
height	O
.	O
For	O
this	O
case	O
study	O
,	O
ten	O
equally	O
probable	B-Process
scenarios	I-Process
of	O
iron	B-Material
content	I-Material
,	O
phosphorous	B-Material
,	O
silica	B-Material
,	O
aluminum	B-Material
and	O
LOI	B-Material
are	O
used	O
to	O
quantify	B-Task
the	I-Task
joint	I-Task
uncertainty	I-Task
in	I-Task
the	I-Task
characteristics	I-Task
of	I-Task
the	I-Task
iron	I-Task
ore	I-Task
deposit	I-Task
considered	O
and	O
are	O
the	O
input	O
to	O
the	O
SSTPS	B-Process
formulation	I-Process
proposed	O
in	O
the	O
previous	O
section	O
.	O
The	O
simulated	B-Task
scenarios	I-Task
available	O
were	O
provided	O
and	O
generated	O
using	O
the	O
stochastic	B-Process
simulated	I-Process
technique	I-Process
detailed	O
in	O
Boucher	O
and	O
Dimitrakopoulos	O
(	O
2012	O
)	O
.	O
The	O
area	O
considered	O
is	O
bounded	O
by	O
the	O
limits	O
of	O
the	O
given	O
volume	O
of	O
production	O
in	O
the	O
long-term	O
first	O
year	O
production	O
schedule	O
provided	O
.	O
Fig.	O
4	O
shows	O
3	O
scenarios	B-Process
of	I-Process
iron	I-Process
ore	I-Process
content	I-Process
as	O
well	O
as	O
the	O
corresponding	O
conventional	O
and	O
single	O
estimated	O
(	O
average	O
)	O
representation	O
of	O
iron	O
content	O
(	O
Fe2O3	O
%)	O
for	O
the	O
upper	O
bench	O
.	O
In	O
total	O
,	O
734	B-Material
blocks	I-Material
from	O
3525	O
to	O
21,150	O
tonnes	O
,	O
with	O
Fe2O3	B-Material
from	O
54.59	O
%	O
to	O
60.63	O
%	O
,	O
P	B-Material
from	O
0.02	O
%	O
to	O
0.04	O
%	O
,	O
SiO2	B-Material
from	O
3.10	O
%	O
to	O
8.58	O
%	O
,	O
Al2O3	B-Material
from	O
0.53	O
%	O
to	O
1.88	O
%	O
and	O
LOI	B-Material
from	O
8.75	O
%	O
to	O
11.75	O
%	O
are	O
available	O
.	O

Two-state	B-Process
models	I-Process
are	O
often	O
insufficient	O
to	O
fit	O
complex	O
traces	O
,	O
therefore	O
we	O
also	O
study	B-Task
the	I-Task
approximate	I-Task
fitting	I-Task
of	I-Task
large	I-Task
M3PPs	I-Task
.	O
In	O
the	O
single	O
class	O
setting	O
,	O
a	O
known	O
limitation	O
of	O
MMPPs	B-Material
is	O
the	O
inability	O
to	O
simultaneously	O
fit	O
many	O
statistical	B-Process
descriptors	I-Process
due	O
to	O
the	O
non-linearity	O
of	O
their	O
underlying	B-Process
equations	I-Process
(	O
Bodrog	O
,	O
Heindl	O
,	O
Horváth	O
,	O
&	O
Telek	O
,	O
2008	O
;	O
Heindl	O
,	O
Horváth	O
,	O
&	O
Gross	O
,	O
2006	O
;	O
Horváth	O
&	O
Telek	O
,	O
2009	O
)	O
.	O
This	O
has	O
led	O
to	O
the	O
definition	O
of	O
several	O
approaches	O
to	O
fit	O
complex	O
traces	O
by	O
composing	B-Process
multiple	I-Process
small-sized	I-Process
MMPPs	I-Process
or	I-Process
MAPs	I-Process
using	O
Kronecker	B-Process
operators	I-Process
(	O
Andersen	O
&	O
Nielsen	O
,	O
1998	O
;	O
Casale	O
,	O
Zhang	O
,	O
&	O
Smirni	O
,	O
2010	O
;	O
Horváth	O
&	O
Telek	O
,	O
2002	O
)	O
.	O
These	O
methods	O
employ	O
composition	B-Process
operators	I-Process
for	O
moment	O
fitting	O
,	O
offering	O
a	O
different	O
trade-off	O
between	O
computational	O
cost	O
and	O
fitting	O
accuracy	O
compared	O
to	O
fitting	B-Task
methods	I-Task
based	O
on	O
the	O
EM	B-Process
algorithm	I-Process
(	O
Breuer	O
,	O
2002	O
;	O
Horváth	O
&	O
Okamura	O
,	O
2013	O
;	O
Klemm	O
,	O
Lindemann	O
,	O
&	O
Lohmann	O
,	O
2003	O
)	O
.	O
In	O
particular	O
,	O
the	O
superposition	B-Process
operator	I-Process
allows	O
one	O
to	O
describe	O
a	O
trace	O
by	O
the	O
statistical	B-Process
multiplexing	I-Process
of	I-Process
several	I-Process
MMPPs	I-Process
,	O
at	O
the	O
expense	O
of	O
an	O
exponential	O
growth	O
of	O
the	O
number	O
of	O
states	O
in	O
the	O
resulting	O
process	O
(	O
Sriram	O
&	O
Whitt	O
,	O
1986	O
)	O
.	O
This	O
state	B-Process
space	I-Process
explosion	I-Process
is	O
an	O
obstacle	O
for	O
the	O
application	O
of	O
MMPPs	B-Material
and	I-Material
MAPs	I-Material
to	O
modeling	B-Task
real	I-Task
systems	I-Task
;	O
for	O
example	O
it	O
considerably	O
slows	O
down	O
,	O
or	O
even	O
renders	O
infeasible	O
,	O
the	O
numerical	B-Task
evaluation	I-Task
of	I-Task
queueing	I-Task
models	I-Task
by	O
matrix	B-Task
geometric	I-Task
methods	I-Task
(	O
Bini	O
,	O
Meini	O
,	O
Steffé	O
,	O
Pérez	O
,	O
&	O
Houdt	O
,	O
2012	O
;	O
Pérez	O
,	O
Velthoven	O
,	O
&	O
Houdt	O
,	O
2008	O
)	O
.	O

For	O
mixtures	B-Material
described	O
by	O
an	O
equation	O
of	O
state	O
,	O
this	O
calculation	O
amounts	O
to	O
simultaneously	O
solving	O
the	O
condition	O
of	O
thermal	O
,	O
mechanical	O
and	O
diffusive	O
equilibria	O
(	O
equality	O
of	O
chemical	O
potential	O
)	O
amongst	O
two	O
fluid	O
phases	O
for	O
each	O
component	O
of	O
the	O
mixture	O
.	O
The	O
analytical	O
nature	O
of	O
this	O
calculation	O
lends	O
itself	O
to	O
a	O
reasonably	O
rapid	O
solution	O
by	O
numerical	B-Process
methods	I-Process
.	O
In	O
its	O
most	O
common	O
form	O
,	O
the	O
composition	O
and	O
temperature	O
are	O
fixed	O
and	O
the	O
pressures	B-Material
at	I-Material
either	I-Material
the	I-Material
bubble	I-Material
or	I-Material
the	I-Material
dew	I-Material
point	I-Material
are	O
recursively	O
calculated	O
.	O
The	O
reader	O
is	O
referred	O
to	O
the	O
excellent	O
textbooks	O
that	O
describe	O
the	O
common	B-Process
algorithms	I-Process
employed	I-Process
[	O
80	O
–	O
82	O
]	O
.	O
The	O
quality	O
of	O
the	O
result	O
is	O
obviously	O
limited	O
by	O
the	O
accuracy	O
of	O
the	O
EoS	B-Process
to	O
faithfully	O
represent	O
fluid	B-Material
mixtures	I-Material
.	O
Furthermore	O
,	O
the	O
fact	O
that	O
some	O
of	O
the	O
more	O
interesting	O
features	O
of	O
the	O
phase	O
diagram	O
are	O
close	O
to	O
the	O
critical	O
points	O
of	O
the	O
mixture	O
,	O
make	O
these	O
calculations	O
particularly	O
challenging	O
for	O
all	O
but	O
the	O
most	O
optimized	O
and	O
force-fitted	O
of	O
models	O
.	O

The	O
next	O
phase	O
of	O
our	O
current	O
study	O
is	O
to	O
use	O
the	O
parameters	O
obtained	O
from	O
pure-component	B-Process
systems	I-Process
in	O
a	O
transferable	O
manner	O
to	O
represent	O
the	O
corresponding	O
mixtures	O
.	O
Mixtures	B-Material
of	I-Material
n-alkanes	I-Material
and	I-Material
H2O	I-Material
have	O
been	O
studied	O
previously	O
with	O
SAFT-γ	B-Process
SW	I-Process
[	O
82	O
]	O
.	O
In	O
general	O
it	O
is	O
well	O
known	O
that	O
the	O
extreme	O
nature	O
of	O
the	O
phase	O
separation	O
[	O
150	O
]	O
makes	O
it	O
challenging	O
to	O
model	O
mixtures	B-Material
of	I-Material
H2O	I-Material
with	O
non-polar	B-Process
compounds	I-Process
.	O
Because	O
of	O
the	O
large	O
differences	O
in	O
the	O
dielectric	O
constant	O
of	O
the	O
two	O
phases	O
as	O
well	O
as	O
in	O
the	O
dipole	O
moment	O
of	O
H2O	B-Material
and	O
the	O
hydrophobic	B-Material
molecules	I-Material
,	O
it	O
especially	O
difficult	O
to	O
obtain	O
phase-independent	O
unlike	O
interaction	O
parameters	O
[	O
112	O
]	O
and	O
thus	O
to	O
model	O
simultaneously	O
the	O
equilibrium	O
phases	O
.	O
In	O
previous	O
work	O
[	O
82	O
]	O
,	O
emphasis	O
was	O
placed	O
on	O
obtaining	O
an	O
accurate	O
description	B-Task
of	I-Task
the	I-Task
alkane-rich	I-Task
phases	I-Task
(	O
both	O
liquid	B-Material
and	O
vapour	B-Material
)	O
,	O
while	O
small	O
absolute	O
(	O
but	O
not	O
relative	O
)	O
deviations	O
for	O
the	O
aqueous	O
phase	O
composition	O
were	O
achieved	O
.	O
The	O
systems	O
of	O
interest	O
in	O
our	O
current	O
work	O
are	O
typically	O
aqueous	B-Material
mixtures	I-Material
containing	O
a	O
high	O
proportion	O
of	O
H2O	B-Material
,	O
alkylamine	B-Material
,	O
and	O
CO2	B-Material
.	O
Consequently	O
,	O
in	O
order	O
to	O
provide	O
an	O
improved	O
overall	O
description	O
of	O
the	O
fluid-phase	O
equilibria	O
at	O
the	O
conditions	O
of	O
interest	O
,	O
refinements	O
have	O
been	O
made	O
to	O
the	O
unlike	O
parameters	O
presented	O
in	O
the	O
previous	O
study	O
[	O
129	O
]	O
relating	O
to	O
the	O
interactions	O
between	O
H2O	O
and	O
the	O
alkyl	O
groups	O
,	O
CH3	O
and	O
CH2	O
,	O
namely	O
ϵCH3,H2O	O
,	O
ϵCH2,H2O	O
and	O
λCH3,H2O	O
,	O
λCH2,H2O	O
.	O

Recently	O
,	O
fundamental	O
(	O
thermophysical	O
property	O
)	O
research	O
on	O
ionic	B-Material
clathrate	I-Material
hydrates	I-Material
has	O
experienced	O
remarkable	O
growth	O
,	O
particularly	O
over	O
the	O
last	O
ten	O
years	O
[	O
21	O
–	O
30	O
]	O
.	O
Previously	O
,	O
beginning	O
with	O
the	O
first	O
paper	O
on	O
unusual	B-Material
hydrates	I-Material
of	I-Material
tetrabutylammonium	I-Material
salts	I-Material
in	O
1940	O
[	O
31	O
]	O
,	O
a	O
number	O
of	O
studies	O
could	O
be	O
found	O
on	O
ionic	B-Material
clathrate	I-Material
hydrates	I-Material
(	O
hereafter	O
,	O
semiclathrate	B-Material
hydrates	I-Material
)	O
[	O
32	O
–	O
35	O
]	O
before	O
the	O
unified	O
terminology	O
semiclathrate	B-Material
hydrate	I-Material
was	O
generally	O
accepted	O
.	O
Semiclathrate	B-Material
hydrates	I-Material
have	O
been	O
attracting	O
increased	O
attention	O
because	O
of	O
their	O
promising	O
applications	O
as	O
phase	B-Process
change	I-Process
materials	I-Process
for	I-Process
refrigeration	I-Process
systems	I-Process
and	O
in	O
gas	B-Process
capture	I-Process
and	I-Process
storage	I-Process
[	O
36	O
–	O
41	O
]	O
.	O
In	O
addition	O
,	O
there	O
is	O
interesting	O
speculation	O
that	O
semiclathrate	B-Material
hydrate	I-Material
may	O
be	O
regarded	O
as	O
a	O
representative	O
substance	O
for	O
the	O
study	B-Task
of	I-Task
thermal	I-Task
conductivity	I-Task
in	O
clathrate	O
hydrate	O
in	O
general	O
.	O
This	O
is	O
because	O
:	O
(	O
1	O
)	O
it	O
can	O
reduce	O
characterization	O
problems	O
as	O
a	O
solid	O
sample	O
,	O
since	O
semiclathrate	B-Material
hydrate	I-Material
is	O
formed	O
around	O
ambient	O
temperature	O
under	O
atmospheric	O
pressure	O
and	O
is	O
easy	O
to	O
handle	O
;	O
(	O
2	O
)	O
accurately	O
measuring	B-Task
the	I-Task
thermal	I-Task
conductivity	I-Task
of	O
semiclathrate	B-Material
hydrates	I-Material
,	O
which	O
have	O
many	O
similarities	O
to	O
clathrate	B-Material
hydrates	I-Material
,	O
may	O
make	O
possible	O
a	O
deeper	O
understanding	O
of	O
the	O
unique	O
(	O
anomalous	O
)	O
behavior	O
of	O
the	O
thermal	B-Process
conductivity	I-Process
of	O
clathrate	B-Material
hydrates	I-Material
;	O
and	O
(	O
3	O
)	O
currently	O
,	O
there	O
are	O
no	O
experimental	O
studies	O
on	O
the	O
thermal	O
conductivity	O
of	O
semiclathrate	B-Material
hydrates	I-Material
.	O

With	O
development	O
of	O
performance-based	B-Task
design	I-Task
,	O
some	O
studies	O
have	O
been	O
conducted	O
on	O
fire	O
risk	O
analysis	O
in	O
buildings	O
from	O
different	O
perspectives	O
and	O
levels	O
.	O
Models	B-Process
such	O
as	O
FiRECAM	B-Process
[	O
11,12	O
]	O
and	O
FiERAsystem	B-Process
[	O
13	O
]	O
were	O
used	O
to	O
calculate	O
the	O
expected	O
life	O
risk	O
.	O
In	O
other	O
studies	O
probabilistic	B-Process
methods	I-Process
have	O
been	O
used	O
to	O
assess	O
levels	O
of	O
people	O
safety	O
in	O
buildings	O
[	O
14	O
]	O
.	O
Quantitative	B-Process
risk	I-Process
analysis	I-Process
approaches	I-Process
have	O
also	O
been	O
used	O
to	O
quantify	O
the	O
risk	O
to	O
occupants	O
using	O
stochastic	B-Process
factors	I-Process
[	O
15	O
]	O
.	O
However	O
,	O
studies	O
to	O
date	O
have	O
largely	O
been	O
concerned	O
with	O
various	O
aspects	B-Material
of	I-Material
fire	I-Material
risk	I-Material
analysis	I-Material
and	O
there	O
has	O
been	O
little	O
in	O
the	O
way	O
of	O
development	O
of	O
systematic	B-Process
theoretical	I-Process
methods	I-Process
for	O
analyzing	O
fire	O
risk	O
in	O
buildings	O
in	O
terms	O
of	O
fire	O
risk	O
management	O
.	O
Existing	O
fire	O
risk	O
management	O
involves	O
the	O
identification	B-Process
of	I-Process
alternative	I-Process
fire	I-Process
safety	I-Process
design	I-Process
options	I-Process
[	O
16,17	O
]	O
,	O
the	O
ongoing	B-Process
inspection	I-Process
,	O
maintenance	B-Process
of	I-Process
fire	I-Process
protection	I-Process
systems	I-Process
[	O
18	O
]	O
and	O
evacuation	B-Process
training	I-Process
and	I-Process
drills	I-Process
[	O
19	O
]	O
.	O
In	O
this	O
study	O
,	O
basic	B-Material
process	I-Material
of	I-Material
fire	I-Material
risk	I-Material
analysis	I-Material
in	O
building	O
is	O
described	O
,	O
and	O
a	O
fire	O
risk	O
analysis	O
model	O
based	O
on	O
scenario	O
clusters	O
is	O
established	O
with	O
consideration	O
of	O
the	O
characteristics	O
of	O
fire	O
dynamics	O
and	O
occupants'	O
behavior	O
.	O
The	O
number	B-Material
of	I-Material
deaths	I-Material
and	I-Material
directive	I-Material
property	I-Material
loss	I-Material
are	O
selected	O
as	O
fire	O
risk	O
indices	O
and	O
the	O
average	B-Material
fire	I-Material
risk	I-Material
of	O
residential	O
buildings	O
is	O
quantitatively	O
analyzed	O
,	O
so	O
that	O
appropriate	O
fire	O
risk	O
management	O
measures	O
can	O
be	O
adopted	O
.	O

The	O
mentioned	O
difficulties	O
associated	O
with	O
the	O
calibration	B-Process
process	I-Process
inspired	O
the	O
concept	O
of	O
inverse	B-Task
modelling	I-Task
.	O
In	O
this	O
case	O
,	O
the	O
experimental	B-Material
data	I-Material
become	O
entirely	O
integrated	O
in	O
the	O
calibration	B-Process
process	I-Process
and	O
an	O
optimization	B-Process
routine	I-Process
is	O
used	O
to	O
quantify	B-Task
the	I-Task
best	I-Task
set	I-Task
of	I-Task
parameters	I-Task
which	O
explain	O
the	O
observed	O
pyrolysis	B-Task
behaviour	I-Task
(	O
i.e.	O
multivariable	B-Task
curve	I-Task
fitting	I-Task
)	O
.	O
The	O
most	O
used	O
experimental	B-Material
data	I-Material
for	O
model	B-Process
calibration	I-Process
have	O
been	O
the	O
mass	B-Process
loss	I-Process
rate	I-Process
and	O
the	O
surface	B-Process
temperature	I-Process
[	I-Process
10	O
–	O
12	O
]	O
.	O
The	O
optimization	B-Process
technique	I-Process
used	O
is	O
function	O
of	O
the	O
number	O
of	O
variables	O
and	O
their	O
interactions	O
.	O
In	O
the	O
past	O
,	O
only	O
the	O
few	O
most	O
uncertain	O
parameters	B-Process
(	O
i.e.	O
the	O
kinetics	B-Process
parameters	I-Process
)	O
were	O
generally	O
used	O
as	O
potentiometers	B-Process
[	O
13	O
]	O
.	O
However	O
,	O
sophisticated	O
mathematical	B-Process
procedures	I-Process
have	O
been	O
developed	O
to	O
increase	O
the	O
number	O
of	O
parameters	B-Process
optimized	O
simultaneously	O
(	O
e.g.	O
Genetic	B-Process
Algorithm	I-Process
(	O
GA	B-Process
)	O
[	O
10,14	O
]	O
or	O
Shuffled	B-Process
Complex	I-Process
Evolution	I-Process
(	O
SCE	B-Process
)	O
[	O
11	O
])	O
.	O
Lautenberger	O
and	O
Fernandez-Pello	O
[	O
12	O
]	O
have	O
recently	O
investigated	O
the	O
influence	O
that	O
the	O
choice	B-Process
of	I-Process
algorithm	I-Process
can	O
have	O
on	O
the	O
optimized	O
parameters	O
.	O
They	O
generated	O
using	O
their	O
code	O
GPYRO	B-Material
a	O
set	O
of	O
synthetic	B-Material
data	I-Material
(	O
mass	O
loss	O
rate	O
and	O
surfaces	O
temperature	O
)	O
and	O
tried	O
with	O
different	O
algorithms	B-Process
to	O
find	O
back	O
the	O
set	O
of	O
input	O
parameters	O
.	O
The	O
four	O
optimization	B-Process
algorithms	I-Process
provided	O
results	O
with	O
an	O
absolute	O
average	O
error	O
between	O
1	O
%	O
and	O
25	O
%	O
.	O
SCE	B-Process
was	O
the	O
most	O
suitable	O
algorithm	B-Process
.	O
The	O
use	O
of	O
synthetic	B-Material
data	I-Material
conveniently	O
avoids	O
the	O
problem	O
of	O
agreement	O
between	O
the	O
actual	O
physical	O
phenomena	O
and	O
any	O
modelling	O
assumption	O
.	O

RemarkThe	O
purely	O
radiative	B-Task
spacetimes	I-Task
used	O
as	O
reference	B-Material
solutions	I-Material
in	O
our	O
analysis	O
are	O
not	O
perturbations	O
of	O
the	O
Minkowski	B-Task
spacetime	I-Task
.	O
A	O
way	O
of	O
seeing	O
this	O
is	O
to	O
consider	O
the	O
Newman	B-Material
–	I-Material
Penrose	I-Material
constants	I-Material
of	O
the	O
spacetime	O
.	O
The	O
Newman	O
–	O
Penrose	O
constants	O
are	O
a	O
set	O
of	O
absolutely	O
conserved	O
quantities	O
defined	O
as	O
integrals	O
of	O
certain	O
components	O
of	O
the	O
Weyl	B-Material
tensor	I-Material
and	O
the	O
Maxwell	B-Material
fields	I-Material
over	O
cuts	O
of	O
null	O
infinity	O
—	O
see	O
[	O
19	O
–	O
21	O
]	O
for	O
the	O
Einstein	O
–	O
Maxwell	O
case	O
.	O
In	O
[	O
22	O
]	O
it	O
has	O
been	O
shown	O
that	O
the	O
value	O
of	O
the	O
Newman	B-Material
–	I-Material
Penrose	I-Material
constants	I-Material
for	O
a	O
vacuum	B-Material
radiative	I-Material
spacetime	I-Material
coincides	O
with	O
the	O
value	O
of	O
the	O
rescaled	O
Weyl	O
spinor	O
at	O
i	O
+—	O
this	O
result	O
can	O
be	O
extended	O
to	O
the	O
electrovacuum	O
case	O
using	O
the	O
methods	O
of	O
this	O
article	O
.	O
For	O
the	O
radiative	B-Task
spacetimes	I-Task
arising	O
from	O
the	O
construction	O
of	O
[	O
17	O
]	O
it	O
can	O
be	O
seen	O
that	O
the	O
value	O
of	O
the	O
Weyl	O
spinor	O
at	O
i	O
+	O
is	O
essentially	O
the	O
mass	O
quadrupole	O
of	O
the	O
seed	O
static	O
spacetime	O
.	O
It	O
follows	O
,	O
that	O
the	O
Newman	B-Material
–	I-Material
Penrose	I-Material
constants	I-Material
of	O
the	O
radiative	B-Material
spacetime	I-Material
can	O
take	O
arbitrary	O
values	O
.	O
On	O
the	O
other	O
hand	O
,	O
for	O
the	O
Minkowski	B-Task
spacetime	I-Task
,	O
the	O
Newman	B-Material
–	I-Material
Penrose	I-Material
constants	I-Material
are	O
exactly	O
zero	O
,	O
and	O
those	O
of	O
perturbations	O
thereof	O
will	O
be	O
small	O
.	O
Thus	O
,	O
in	O
this	O
precise	O
sense	O
,	O
our	O
radiative	B-Task
spacetimes	I-Task
are	O
,	O
generically	O
,	O
not	O
perturbations	O
of	O
the	O
Minkowski	B-Task
spacetime	I-Task
,	O
unless	O
all	O
the	O
Newman	B-Material
–	I-Material
Penrose	I-Material
constants	I-Material
vanish	O
.	O

To	O
calculate	B-Task
hedonic	I-Task
price	I-Task
indices	I-Task
in	O
the	O
linear	B-Process
model	I-Process
,	O
the	O
initial	O
or	O
reference	O
price	O
has	O
to	O
be	O
calculated	O
(	O
Triplett	O
,	O
2006	O
)	O
.	O
The	O
present	O
study	O
adopts	O
the	O
approach	O
of	O
de	O
Haan	O
and	O
Diewert	O
(	O
2013	O
)	O
:	O
a	O
price	O
index	O
is	O
constructed	O
using	O
the	O
price	O
generated	O
by	O
the	O
estimated	O
coefficients	O
of	O
a	O
base	B-Process
period	I-Process
regression	I-Process
model	I-Process
,	O
and	O
it	O
is	O
calculated	O
based	O
on	O
the	O
based	O
period	O
average	O
values	O
of	O
a	O
given	O
cell	O
phone	O
plan	O
characteristic	O
z	O
¯	O
for	O
each	O
operator	O
(	O
Supplementary	O
Table	O
S5	O
)	O
.	O
For	O
continuous	O
characteristics	O
,	O
direct	O
averages	O
are	O
used	O
;	O
for	O
binary	O
characteristics	O
,	O
the	O
proportions	O
of	O
cell	O
phone	O
plans	O
containing	O
the	O
feature	O
are	O
used	O
.	O
The	O
resulting	O
prices	O
for	O
this	O
average	O
cell	O
phone	O
plan	O
are	O
converted	O
to	O
an	O
index	O
by	O
applying	B-Process
previously	I-Process
calculated	I-Process
pure	I-Process
price	I-Process
changes	I-Process
(	O
δs	O
)	O
.	O
Finally	O
,	O
the	O
overall	O
hedonic	O
price	O
index	O
is	O
calculated	O
as	O
the	O
weighted	O
average	O
of	O
firm-level	O
indices	O
.	O
Weights	O
correspond	O
to	O
the	O
relative	O
proportion	O
of	O
cell	O
phone	O
plans	O
by	O
operator	O
in	O
the	O
sample	O
(	O
0.3534	O
for	O
HT	O
,	O
0.3212	O
for	O
Vip	O
,	O
and	O
0.3254	O
for	O
Tele2	O
)	O
.	O

Social	B-Process
network	I-Process
gaming	I-Process
,	O
which	O
refers	O
to	O
playing	O
games	O
that	O
are	O
connected	O
to	O
social	B-Material
networking	I-Material
services	I-Material
(	O
SNS	B-Material
)	O
directly	O
,	O
or	O
through	O
mobile	B-Material
applications	I-Material
(	O
apps	B-Material
)	O
,	O
is	O
a	O
popular	O
online	O
activity	O
.	O
Social	B-Process
network	I-Process
games	I-Process
(	O
SNG	B-Process
)	O
are	O
generally	O
free-to-play	O
and	O
do	O
not	O
award	O
monetary	O
prizes	O
,	O
but	O
users	O
can	O
make	O
in-game	O
purchases	O
to	O
advance	O
within	O
the	O
game	O
,	O
customise	O
the	O
game	O
,	O
give	O
gifts	O
to	O
friends	O
,	O
and	O
access	O
other	O
exclusive	O
benefits	O
and	O
features	O
,	O
leading	O
to	O
these	O
games	O
being	O
referred	O
to	O
as	O
‘	O
freemium’	O
.	O
Although	O
SNG	O
are	O
connected	O
to	O
a	O
SNS	B-Process
and	O
encourage	O
users	O
to	O
interact	O
with	O
their	O
connections	O
,	O
most	O
SNG	B-Material
can	O
be	O
played	O
without	O
any	O
social	O
interaction	O
.	O
SNG	O
have	O
grown	O
rapidly	O
in	O
popularity	O
and	O
the	O
global	O
SNG	B-Material
market	I-Material
is	O
predicted	O
to	O
grow	O
annually	O
at	O
16	O
%	O
from	O
2013	O
to	O
2019	O
to	O
reach	O
a	O
total	O
market	O
value	O
of	O
US	O
$	O
17.4	O
billion	O
(	O
Transparency	O
Market	O
Research	O
,	O
2015	O
)	O
.	O
A	O
survey	B-Process
of	O
Facebook	O
users	O
in	O
Australia	O
in	O
November	O
2012	O
reported	O
that	O
there	O
are	O
over	O
3.5	O
million	O
social	O
gamers	O
across	O
Australia	O
and	O
almost	O
70	O
%	O
play	O
SNG	B-Material
daily	O
(	O
Spiral	O
Media	O
,	O
2013	O
)	O
,	O
and	O
it	O
is	O
highly	O
likely	O
that	O
the	O
use	O
of	O
SNG	O
has	O
increased	O
since	O
this	O
time	O
.	O

However	O
,	O
the	O
measured	O
reflectivity	O
is	O
less	O
than	O
the	O
predicted	O
value	O
(∼	O
96	O
%)	O
,	O
which	O
is	O
likely	O
to	O
relate	O
to	O
,	O
amongst	O
other	O
factors	O
,	O
the	O
roughness	O
of	O
the	O
GaN	B-Material
/	I-Material
AlN	I-Material
interfaces	I-Material
particularly	O
for	O
the	O
first	O
layer	O
in	O
the	O
DBR	B-Material
stack	I-Material
and	O
the	O
non-uniformity	O
of	O
the	O
DBR	B-Material
layer	I-Material
thicknesses	O
.	O
Using	O
STEM	B-Process
measurements	I-Process
of	O
the	O
thickness	O
of	O
each	O
layer	O
(	O
on	O
the	O
a-plane	O
)	O
through	O
the	O
thickness	O
of	O
the	O
stack	O
,	O
we	O
calculate	B-Process
a	I-Process
new	I-Process
model	I-Process
(	O
green	O
curve	O
)	O
in	O
which	O
the	O
overall	O
reflectivity	O
is	O
reduced	O
to	O
85	O
%	O
.	O
This	O
implies	O
that	O
variations	O
in	O
layer	O
thickness	O
through	O
the	O
stack	O
are	O
the	O
main	O
source	O
of	O
the	O
reduced	O
reflectivity	O
in	O
comparison	O
to	O
the	O
model	O
.	O
In	O
fact	O
,	O
a	O
closer	O
look	O
at	O
the	O
cross-sectional	B-Material
STEM	I-Material
data	I-Material
and	O
a	O
careful	O
extraction	B-Process
of	I-Process
layer	I-Process
thickness	I-Process
have	O
revealed	O
that	O
whilst	O
the	O
layer	O
thicknesses	O
are	O
fairly	O
consistent	O
through	O
the	O
DBR	B-Material
stack	I-Material
in	I-Material
the	I-Material
wing	I-Material
regions	I-Material
,	O
there	O
is	O
a	O
monotonic	O
variation	O
in	O
the	O
measured	O
layer	O
thicknesses	O
in	O
the	B-Material
window	I-Material
regions	I-Material
.	O
(	O
The	O
GaN	B-Material
layer	I-Material
width	O
smoothly	O
increases	O
,	O
while	O
the	O
AlN	B-Material
layer	I-Material
thickness	O
decreases	O
through	O
the	O
DBR	O
stack.	O
)	O
.	O
This	O
observation	O
could	O
potentially	O
be	O
of	O
practical	O
importance	O
,	O
for	O
samples	O
grown	O
on	O
templates	O
with	O
a	O
uniform	O
defect	O
density	O
,	O
as	O
one	O
could	O
achieve	O
much	O
better	O
reflectivities	O
simply	O
by	O
altering	O
the	O
growth	O
time	O
to	O
counteract	O
the	O
change	O
in	O
growth	O
rate	O
.	O
This	O
possibility	O
is	O
the	O
subject	O
of	O
ongoing	O
investigations	O
.	O
In	O
addition	O
,	O
the	O
presence	O
of	O
cracks	O
and	O
trenches	O
in	O
the	O
top	O
surface	O
may	O
also	O
reduce	O
the	O
measured	O
reflectivity	O
further	O
.	O

Note	O
that	O
the	O
presented	O
architecture	O
works	O
at	O
the	O
frame	O
level	O
,	O
meaning	O
that	O
each	O
single	O
frame	O
(	O
plus	O
its	O
corresponding	O
context	O
)	O
is	O
fed-forward	O
through	O
the	O
network	O
,	O
obtaining	O
a	O
class	O
posterior	O
probability	O
for	O
all	O
of	O
the	O
target	B-Material
languages	I-Material
.	O
This	O
fact	O
makes	O
the	O
DNNs	B-Process
particularly	O
suitable	O
for	O
real-time	O
applications	O
because	O
,	O
unlike	O
other	B-Process
approaches	I-Process
(	O
i.e.	O
i-vectors	B-Process
)	O
,	O
we	O
can	O
potentially	O
make	O
a	O
decision	O
about	O
the	O
language	O
at	O
each	O
new	O
frame	O
.	O
Indeed	O
,	O
at	O
each	O
frame	O
,	O
we	O
can	O
combine	B-Process
the	I-Process
evidence	I-Process
from	I-Process
past	I-Process
frames	I-Process
to	O
get	O
a	O
single	O
similarity	O
score	O
between	O
the	O
test	O
utterance	O
and	O
the	O
targetlanguages	O
.	O
A	O
simple	O
way	O
of	O
doing	O
this	O
combination	O
is	O
to	O
assume	B-Process
that	I-Process
frames	I-Process
are	I-Process
independent	I-Process
and	I-Process
multiply	I-Process
the	I-Process
posterior	I-Process
estimates	I-Process
of	I-Process
the	I-Process
last	I-Process
layer	I-Process
.	O
The	O
score	O
sl	O
for	O
language	O
l	O
of	O
a	O
given	O
test	B-Material
utterance	I-Material
is	O
computed	O
by	O
multiplying	B-Process
the	I-Process
output	I-Process
probabilities	I-Process
pl	I-Process
obtained	I-Process
for	I-Process
all	I-Process
of	I-Process
its	I-Process
frames	I-Process
;	O
or	O
equivalently	O
,	O
accumulating	B-Process
the	I-Process
logs	I-Process
as	I-Process
:(	I-Process
6	I-Process
)	I-Process
sl	I-Process
=	I-Process
1N	I-Process
∑	I-Process
t	I-Process
=	I-Process
1Nlogp	I-Process
(	I-Process
Ll	I-Process
|	I-Process
xt	I-Process
​	I-Process
,	I-Process
θ	I-Process
)	I-Process
where	O
p	O
(	O
Ll	O
|	O
xt	O
​	O
,	O
θ	O
)	O
represents	O
the	O
class	O
probability	O
output	O
for	O
the	O
language	O
l	O
corresponding	O
to	O
the	O
input	O
example	O
at	O
time	O
t	O
,	O
xt	O
by	O
using	O
the	O
DNN	B-Process
defined	O
by	O
parameters	O
θ	O
.	O

The	O
final	O
set	O
of	O
experiments	O
involved	O
an	O
adaptive	B-Process
retraining	I-Process
of	I-Process
the	I-Process
GMM	I-Process
–	I-Process
HMM	I-Process
parameters	I-Process
following	O
the	O
aNAT	B-Process
procedure	I-Process
.	O
This	O
new	O
model	O
only	O
provided	O
an	O
improvement	O
of	O
0.3	O
%	O
,	O
similar	O
to	O
using	O
the	O
aCMLLR	B-Process
transforms	I-Process
on	O
the	O
baseline	O
GMM	B-Process
–	I-Process
HMM	I-Process
model	I-Process
.	O
However	O
,	O
training	B-Process
show-based	I-Process
aCMLLR	I-Process
transforms	I-Process
on	O
top	O
of	O
the	O
adaptively	O
trained	O
model	O
boosted	O
the	O
improvement	O
to	O
0.8	O
%	O
absolute	O
.	O
This	O
showed	O
how	O
adaptive	B-Process
training	I-Process
provided	O
a	O
better	O
flexibility	O
of	O
the	O
model	O
to	O
adapt	O
to	O
specific	O
background	O
conditions	O
existing	O
in	O
each	O
show	O
.	O
Finally	O
,	O
the	O
factorisation	B-Process
approach	I-Process
using	I-Process
MLLR	I-Process
speaker	I-Process
transforms	I-Process
on	O
top	O
of	O
the	O
aNAT	B-Process
model	I-Process
and	O
show-based	O
aCMLLR	B-Process
transforms	I-Process
was	O
tested	O
.	O
This	O
only	O
increased	O
the	O
improvement	O
to	O
0.9	O
%	O
absolute	O
(	O
2.9	O
%	O
relative	O
)	O
,	O
which	O
reflects	O
the	O
difficulty	O
of	O
performing	O
accurate	O
speaker	B-Task
clustering	I-Task
in	O
this	O
task	O
and	O
how	O
this	O
actually	O
hampers	O
speaker	B-Task
adaptation	I-Task
.	O

The	O
research	O
work	O
in	O
this	O
paper	O
elaborates	B-Task
on	I-Task
the	I-Task
theoretical	I-Task
effectiveness	I-Task
of	I-Task
the	I-Task
proposed	I-Task
method	I-Task
based	I-Task
on	I-Task
the	I-Task
multivariate	I-Task
EMD	I-Task
.	O
It	O
also	O
clearly	O
indicates	O
through	O
numerical	B-Process
simulations	I-Process
and	O
applications	B-Task
to	I-Task
bearing	I-Task
monitoring	I-Task
that	O
the	O
expansion	B-Process
from	I-Process
standard	I-Process
EMD	I-Process
to	I-Process
multivariate	I-Process
EMD	I-Process
is	O
a	O
successful	O
exploration	O
.	O
Using	O
multiple	O
sensors	B-Material
to	O
collect	O
signal	O
from	O
different	O
locations	O
of	O
the	O
machine	O
and	O
using	O
the	O
multivariate	B-Process
EMD	I-Process
to	O
analyze	O
multivariate	B-Material
signal	I-Material
can	O
contribute	O
to	O
comprehensively	O
collect	O
all	O
the	O
frequency	B-Material
components	I-Material
related	O
to	O
any	O
bearing	O
fault	O
,	O
and	O
is	O
beneficial	O
to	O
extract	B-Task
fault	I-Task
information	I-Task
,	O
especially	O
for	O
early	O
weak	O
fault	O
characteristics	O
.	O
Both	O
the	O
characteristic	O
frequencies	O
of	O
simulated	O
signal	O
and	O
the	O
fault	O
frequencies	O
of	O
practical	B-Material
rolling	I-Material
bearing	I-Material
signal	I-Material
can	O
be	O
extracted	O
from	O
the	O
same	O
order	O
of	O
IMF	O
groups	O
,	O
thus	O
showing	O
that	O
multivariate	B-Process
EMD	I-Process
is	O
an	O
effective	O
signal	B-Process
decomposition	I-Process
algorithm	I-Process
and	O
can	O
be	O
competently	O
applied	O
to	O
fault	B-Process
diagnosis	I-Process
of	O
rolling	B-Material
bearings	I-Material
when	O
combined	O
with	O
a	O
multiscale	B-Process
reduction	I-Process
method	I-Process
and	O
fault	B-Process
correlation	I-Process
factor	I-Process
analysis	I-Process
.	O
In	O
signal	B-Task
acquisition	I-Task
and	I-Task
processing	I-Task
,	O
given	O
the	O
circumstance	O
that	O
there	O
is	O
a	O
trend	O
toward	O
the	O
use	O
of	O
multiple	O
sensors	B-Material
,	O
multivariate	B-Process
EMD	I-Process
appears	O
to	O
be	O
very	O
useful	O
and	O
meaningful	O
as	O
a	O
kind	O
of	O
multivariate	B-Process
data	I-Process
processing	I-Process
algorithm	I-Process
.	O
By	O
analyzing	O
the	O
simulated	B-Material
signal	I-Material
and	O
two	O
different	O
practical	B-Material
multivariate	I-Material
signals	I-Material
,	O
the	O
results	O
demonstrate	O
the	O
significance	O
of	O
the	O
proposed	O
method	O
in	O
the	O
field	O
of	O
fault	B-Task
diagnosis	I-Task
of	I-Task
rolling	I-Task
bearing	I-Task
.	O

The	B-Task
GFRFs	I-Task
of	I-Task
nonlinear	I-Task
systems	I-Task
can	O
be	O
determined	O
by	O
either	O
a	B-Material
parametric-model-based	I-Material
method	I-Material
or	O
a	B-Material
nonparametric-model-based	I-Material
method	I-Material
[	O
8	O
]	O
.	O
In	O
the	O
parametric	O
approach	O
,	O
a	B-Task
nonlinear	I-Task
parametric	I-Task
model	I-Task
is	O
first	O
identified	O
from	O
the	O
input	O
–	O
output	O
data	O
.	O
The	O
GFRFs	B-Task
are	O
then	O
obtained	O
by	O
mapping	B-Process
the	I-Process
resultant	I-Process
model	I-Process
into	I-Process
the	I-Process
frequency	I-Process
domain	I-Process
using	O
the	O
probing	B-Material
method	I-Material
[	O
9	O
]	O
.	O
The	O
nonparametric	B-Task
approach	I-Task
is	O
often	O
referred	O
to	O
as	O
frequency-domain	B-Task
Volterra	I-Task
system	I-Task
identification	I-Task
and	O
is	O
based	O
on	O
the	O
observation	O
that	O
the	O
Volterra	B-Task
model	I-Task
of	I-Task
nonlinear	I-Task
systems	I-Task
is	O
linear	O
in	O
terms	O
of	O
the	O
unknown	O
Volterra	O
kernels	O
,	O
which	O
,	O
in	O
the	O
frequency	O
domain	O
,	O
corresponds	B-Material
to	I-Material
a	I-Material
linear	I-Material
relation	I-Material
between	O
the	O
output	O
frequency	O
response	O
and	O
linear	O
,	O
quadratic	O
,	O
and	O
higher	O
order	O
GFRFs	O
.	O
This	O
linear	B-Task
relationship	I-Task
allows	O
the	O
use	O
of	O
a	B-Material
least	I-Material
squares	I-Material
(	I-Material
LS	I-Material
)	I-Material
approach	I-Material
to	O
solve	O
for	O
the	O
GFRFs	B-Task
.	O
Several	O
researchers	O
[	O
10	O
–	O
12	O
]	O
have	O
used	O
this	O
method	O
to	O
estimate	O
the	O
GFRFs	O
.	O
But	O
they	O
usually	O
made	B-Process
the	I-Process
assumption	I-Process
that	O
it	O
is	O
known	O
a	O
priori	O
that	O
the	B-Task
system	I-Task
under	O
study	O
can	O
be	O
represented	O
by	O
just	O
two	B-Material
or	I-Material
three	I-Material
terms	I-Material
.	O
However	O
,	O
such	O
information	O
is	O
rarely	O
available	O
a	O
priori	O
.	O

However	O
this	O
is	O
not	O
just	O
a	O
useful	O
depiction	O
of	O
an	B-Task
apposite	I-Task
well-supported	I-Task
statistical	I-Task
model	I-Task
.	O
If	O
we	O
are	O
prepared	O
to	O
allow	O
that	O
the	O
process	O
is	O
driven	O
by	O
a	O
CRG	B-Material
and	O
that	O
the	O
MAP	B-Material
model	I-Material
that	O
we	O
have	O
discovered	O
is	O
indeed	O
generating	O
the	O
idle	B-Process
process	I-Process
,	O
then	O
identifying	B-Process
the	I-Process
disconnected	I-Process
components	I-Process
of	I-Process
the	I-Process
system	I-Process
allows	O
us	O
to	O
immediately	O
make	O
assertions	O
about	O
the	B-Task
impact	I-Task
of	I-Task
various	I-Task
controls	I-Task
we	O
might	O
apply	O
to	O
this	O
regulatory	O
process	O
–	O
just	O
as	O
we	O
can	O
were	O
we	O
to	O
believe	O
the	O
model	O
was	O
a	O
causal	O
extension	O
of	O
a	O
BN	B-Material
.	O
In	O
the	O
context	O
of	O
microarrays	O
,	O
the	O
objective	O
of	O
clustering	B-Task
is	O
to	O
identify	B-Process
patterns	I-Process
among	I-Process
the	I-Process
data	I-Process
and	O
decide	O
which	O
genes	O
to	O
focus	O
on	O
in	O
further	O
,	O
more	O
gene-specific	O
,	O
experiments	O
.	O
It	O
is	O
therefore	O
necessary	O
for	O
the	O
scientist	O
to	O
make	O
such	O
causal	O
conjectures	O
about	O
the	B-Task
effect	I-Task
of	I-Task
controls	I-Task
available	O
to	O
her	O
on	O
the	O
expressions	O
reflecting	O
the	O
underlying	O
regulatory	O
process	O
she	O
studies	O
.	O
These	O
conjectures	B-Task
can	O
be	O
universal	O
or	O
nuanced	O
by	O
evoking	B-Material
ideas	I-Material
of	I-Material
parsimony	I-Material
.	O

The	O
first	O
step	O
of	O
PB	B-Material
,	O
the	O
enumeration	B-Material
of	I-Material
the	I-Material
conditional	I-Material
sample	I-Material
space	I-Material
through	O
abductive	O
logic	O
programming	O
,	O
could	O
be	O
compared	O
to	O
“	O
logical	B-Material
inference	I-Material
”	O
in	O
ProbLog	B-Material
[	O
9	O
]	O
.	O
While	O
both	O
languages	O
aim	O
to	O
generate	O
a	O
propositional	O
formula	O
and	O
compile	O
it	O
into	O
a	O
decision	O
diagram	O
,	O
“	B-Material
logical	I-Material
inference	I-Material
”	I-Material
in	I-Material
PB	I-Material
is	O
based	O
on	O
abductive	O
logic	O
programming	O
,	O
while	O
ProbLog	B-Material
grounds	O
the	O
relevant	O
parts	O
of	O
the	O
probabilistic	O
program	O
.	O
Moreover	O
,	O
in	O
PB	B-Material
compilation	I-Material
of	O
the	O
boolean	O
formulas	O
is	O
performed	O
using	O
(	B-Process
RO	I-Process
)	I-Process
BDDs	I-Process
,	O
while	O
ProbLog	B-Material
can	O
use	O
a	O
wider	O
range	O
of	O
decision	B-Process
diagrams	I-Process
,	O
e.g.	O
sentential	B-Process
decision	I-Process
diagrams	I-Process
(	O
SDD	B-Process
)	O
,	O
deterministic	B-Process
,	I-Process
decomposable	I-Process
negation	I-Process
normal	I-Process
form	I-Process
(	O
d-DNNF	B-Process
)	O
.	O
These	O
differences	O
reflect	O
the	O
different	O
aims	O
of	O
the	O
two	O
PPLs	B-Material
:	O
ProbLog	B-Material
focuses	O
on	O
models	O
where	O
“	O
logical	B-Task
inference	I-Task
”	O
needs	O
to	O
be	O
efficient	O
,	O
and	O
the	O
resulting	O
representation	O
,	O
the	O
decision	O
diagrams	O
,	O
need	O
to	O
be	O
compact	O
,	O
while	O
PB	B-Material
focuses	O
on	O
models	O
where	O
“	B-Task
logical	I-Task
inference	I-Task
”	I-Task
is	O
typically	O
easy	O
,	O
however	O
it	O
must	O
be	O
applied	O
repeatedly	O
,	O
according	O
to	O
the	O
nature	O
and	O
the	O
number	O
of	O
the	O
observations	O
.	O
However	O
,	O
in	O
future	O
work	O
,	O
PB	B-Material
could	O
benefit	O
from	O
the	O
use	B-Process
of	I-Process
more	I-Process
compact	I-Process
decision	I-Process
diagrams	I-Process
.	O

In	O
the	O
case	O
of	O
PSR	B-Task
applied	I-Task
to	I-Task
vessels	I-Task
,	O
preservation	O
of	O
high	O
curvature	O
and	O
branches	O
(	O
concavities	O
)	O
demands	O
a	O
high	O
value	O
of	O
the	O
d	O
parameter	O
,	O
resulting	O
in	O
models	O
with	O
high	O
number	O
of	O
polygons	O
.	O
To	O
cope	O
with	O
this	O
problem	O
,	O
Wu	O
et	O
al	O
.	O
(	O
2013	O
)	O
evaluates	O
a	O
variant	O
of	O
PSR	O
(	O
in	O
that	O
work	O
referred	O
to	O
as	O
scale-adaptive	B-Process
[	O
SA	B-Process
])	O
,	O
which	O
includes	O
curvature-dependent	B-Process
polygonization	I-Process
(	O
e.g.	O
increasing	B-Process
/	I-Process
decreasing	I-Process
the	I-Process
size	I-Process
of	I-Process
triangles	I-Process
according	I-Process
to	I-Process
the	I-Process
local	I-Process
curvature	I-Process
)	O
(	O
Wu	O
et	O
al.	O
,	O
2010	O
)	O
.	O
In	O
Wu	O
et	O
al	O
.	O
(	O
2013	O
)	O
,	O
other	O
methods	O
including	O
MC	B-Process
(	O
without	O
smoothing	O
and	O
decimation	O
)	O
are	O
evaluated	O
with	O
application	O
to	O
vessel	B-Task
modeling	I-Task
.	O
The	O
authors	O
,	O
point	O
at	O
SA	B-Material
as	O
a	O
suitable	O
method	O
for	O
reconstruction	B-Task
of	I-Task
vessels	I-Task
with	O
applications	O
to	O
surgery	B-Task
planning	I-Task
.	O
The	O
methods	O
evaluated	O
by	O
Wu	O
et	O
al	O
.	O
(	O
2013	O
)	O
could	O
be	O
also	O
compared	O
with	O
another	O
set	O
of	O
techniques	O
(	O
known	O
as	O
model-based	B-Material
methods	I-Material
)	O
(	O
Preim	O
and	O
Oeltze	O
,	O
2008	O
)	O
,	O
widely	O
used	O
in	O
the	O
context	O
of	O
vessel	B-Task
modeling	I-Task
for	I-Task
surgery	I-Task
planning	I-Task
.	O

The	O
need	O
for	O
power	B-Task
generation	I-Task
industry	O
to	O
improve	B-Task
the	I-Task
thermal	I-Task
efficiency	I-Task
of	I-Task
power	I-Task
plant	I-Task
has	O
led	O
to	O
the	O
development	O
of	O
9	B-Material
–	I-Material
12	I-Material
%	I-Material
Cr	I-Material
martensitic	I-Material
steels	I-Material
.	O
The	O
development	B-Task
of	I-Task
and	I-Task
research	I-Task
on	I-Task
P91	I-Task
steels	I-Task
started	O
since	O
late	O
1970s	O
and	O
early	O
1990s	O
,	O
respectively	O
[	O
1	O
]	O
.	O
The	O
work	O
has	O
focussed	O
on	O
their	O
creep	O
strengths	O
due	O
to	O
its	O
intended	O
application	O
at	O
high	O
temperature	O
.	O
Recently	O
,	O
the	O
introduction	O
of	O
more	O
cyclic	O
operation	O
of	O
power	B-Material
plant	I-Material
has	O
introduced	O
the	O
possibility	O
of	O
fatigue	B-Process
problems	I-Process
.	O
Bore	O
cracking	O
due	O
to	O
the	O
effects	O
of	O
varying	O
steam	B-Process
warming	I-Process
has	O
been	O
reported	O
[	O
2	O
]	O
.	O
The	O
temperature	B-Process
cycling	I-Process
causes	O
thermal	O
gradients	O
between	O
the	O
inside	O
and	O
outside	O
of	O
components	O
and	O
this	O
can	O
cause	O
cyclic	O
stress	O
levels	O
to	O
be	O
of	O
concerns	O
.	O
Recently	O
,	O
research	O
on	O
thermal	B-Task
–	I-Task
mechanical	I-Task
analysis	I-Task
of	I-Task
P91	I-Task
has	O
been	O
carried	O
out	O
including	O
the	O
characterisation	B-Task
of	I-Task
the	I-Task
cyclic	I-Task
behaviour	I-Task
of	I-Task
the	I-Task
material	I-Task
using	O
the	O
two-layer	B-Process
and	I-Process
unified	I-Process
visco-plasticity	I-Process
models	I-Process
[	O
3,4	O
]	O
.	O

In	O
previous	O
publications	O
the	O
present	O
authors	O
proposed	O
a	O
method	O
to	O
incorporate	B-Task
the	I-Task
thermodynamics	I-Task
of	I-Task
ternary	I-Task
alloys	I-Task
and	I-Task
liquid	I-Task
diffusion-governed	I-Task
solidification	I-Task
kinetics	I-Task
into	I-Task
a	I-Task
multiphase	I-Task
volume	I-Task
average	I-Task
solidification	I-Task
model	I-Task
[	O
23,24	O
]	O
.	O
Back	B-Process
diffusion	I-Process
was	O
disregarded	O
.	O
A	O
way	O
to	O
access	O
the	O
thermodynamic	B-Material
data	I-Material
(	O
e.g.	O
Thermo-Calc	O
[	O
1	O
])	O
through	O
a	O
tabulation	B-Material
and	I-Material
interpolation	I-Material
program	I-Material
ISAT	B-Material
(	O
In	B-Material
Situ	I-Material
Adaptive	I-Material
Tabulation	I-Material
)	O
was	O
suggested	O
.	O
With	O
the	O
ISAT	B-Process
approach	I-Process
it	O
is	O
possible	O
to	O
perform	O
an	O
online	O
call	O
of	O
the	O
thermodynamic	B-Material
data	I-Material
and	O
trace	O
the	O
formation	O
of	O
each	O
individual	O
solid	O
phase	O
(	O
primary	O
,	O
peritectic	O
,	O
eutectic	O
,	O
etc.	O
)	O
.	O
As	O
the	O
number	O
of	O
calls	O
of	O
the	O
thermodynamic	O
data	O
is	O
equal	O
to	O
the	O
product	O
of	O
the	O
number	O
of	O
the	O
discretized	B-Material
volume	I-Material
elements	I-Material
,	O
the	O
time	O
steps	O
and	O
the	O
calculation	O
iterations	O
per	O
time	O
step	O
,	O
the	O
calculation	O
becomes	O
exhausting	O
.	O
Therefore	O
,	O
the	O
current	B-Process
model	I-Process
is	O
a	O
modification	B-Process
of	I-Process
the	I-Process
previous	I-Process
model	I-Process
using	I-Process
a	I-Process
linearized	I-Process
phase	I-Process
diagram	I-Process
,	O
and	O
no	O
online	B-Process
call	I-Process
of	I-Process
thermodynamic	I-Process
data	I-Process
is	O
necessary	O
.	O
In	O
addition	O
,	O
the	O
model	O
presented	O
in	O
this	O
paper	O
is	O
extended	B-Task
to	I-Task
consider	I-Task
the	I-Task
back	I-Task
diffusion	I-Task
into	I-Task
the	I-Task
solid	I-Task
.	O
With	O
these	O
modifications	B-Process
,	O
the	O
model	O
can	O
be	O
used	O
to	O
perform	B-Task
casting	I-Task
process	I-Task
simulations	I-Task
with	I-Task
incorporated	I-Task
full	I-Task
diffusion-governed	I-Task
solidification	I-Task
kinetics	I-Task
for	O
ternary	B-Material
alloys	I-Material
at	O
a	O
reasonable	O
computation	O
cost	O
.	O

Due	O
to	O
the	O
complex	O
nature	O
of	O
the	O
thermal	B-Process
spray	I-Process
process	I-Process
,	O
modelling	B-Task
has	O
been	O
playing	O
a	O
key	O
role	O
in	O
providing	B-Task
some	I-Task
key	I-Task
insights	I-Task
for	I-Task
process	I-Task
design	I-Task
and	I-Task
operations	I-Task
.	O
The	O
relationships	O
among	O
processing	B-Process
conditions	O
,	O
particle	O
characteristics	O
,	O
and	O
the	O
resulting	O
coating	B-Process
properties	O
are	O
nonlinear	O
and	O
might	O
be	O
difficult	O
to	O
be	O
unravelled	O
by	O
the	O
experimental	B-Task
studies	I-Task
alone	O
(	O
e.g.	O
[	O
5	O
–	O
7	O
])	O
Detailed	O
information	O
on	O
the	O
atomic	B-Process
level	I-Process
changes	I-Process
leading	O
to	O
changes	O
observed	O
at	O
macroscale	O
can	O
appropriately	O
be	O
obtained	O
by	O
MD	B-Process
simulation	I-Process
and	O
the	O
effect	O
of	O
temperature	O
and	O
velocity	O
can	O
be	O
determined	O
more	O
precisely	O
.	O
In	O
this	O
work	O
,	O
relatively	O
simpler	O
spray	B-Process
system	I-Process
of	O
copper	B-Material
–	I-Material
copper	I-Material
particle	I-Material
was	O
simulated	O
to	O
obtain	O
a	O
better	O
understanding	O
of	O
particle	B-Process
recrystallization	I-Process
and	I-Process
solidification	I-Process
,	O
and	O
deformation	B-Process
mechanics	O
and	O
topography	O
of	O
the	O
impacting	O
particles	B-Material
.	O
Using	O
state-of-the-art	O
methods	O
to	O
examine	O
the	O
physical	O
mechanisms	O
involved	O
in	O
the	O
impacting	O
behavior	O
and	O
structure	O
–	O
property	O
relationship	O
,	O
it	O
can	O
be	O
suggested	O
that	O
the	O
consecutive	B-Process
layer	I-Process
deposition	I-Process
of	O
particles	B-Material
can	O
better	O
be	O
understood	O
by	O
understanding	B-Task
individual	I-Task
particle	I-Task
impacts	I-Task
.	O
The	O
particle	B-Process
–	I-Process
surface	I-Process
interaction	I-Process
mechanism	I-Process
and	O
its	O
relation	O
to	O
Reynolds	B-Process
number	I-Process
can	O
offer	O
information	O
on	O
the	O
quality	O
of	O
the	O
coating	B-Process
through	O
its	O
response	O
to	O
shock	B-Process
heating	I-Process
.	O
As	O
a	O
general	O
practice	O
,	O
engineering	B-Material
components	I-Material
are	O
thermally	B-Process
sprayed	I-Process
in	O
a	O
continuous	O
multilayer	O
mode	O
with	O
cooling	B-Process
;	O
therefore	O
there	O
is	O
an	O
opportunity	O
for	O
developing	B-Task
richer	I-Task
theoretical	I-Task
models	I-Task
for	I-Task
single	I-Task
or	I-Task
multiple	I-Task
particle	I-Task
impact	I-Task
in	O
conjunction	O
with	O
actual	O
spraying	B-Task
tests	I-Task
,	O
so	O
as	O
to	O
identify	O
cohesive	O
and	O
adhesive	O
strength	O
,	O
hardness	O
and	O
residual	O
stresses	O
.	O

Structural	B-Material
adhesives	I-Material
are	O
increasingly	O
used	O
for	O
bonding	B-Material
components	I-Material
within	O
critical	B-Material
load	I-Material
bearing	I-Material
engineering	I-Material
structures	I-Material
such	O
as	O
aerospace	B-Material
and	O
automotives	B-Material
.	O
Typically	O
these	O
adhesives	B-Material
are	O
based	O
on	O
epoxy	B-Material
polymers	I-Material
.	O
Epoxies	B-Material
are	O
inherently	O
brittle	O
due	O
to	O
their	O
homogeneous	O
microstructure	B-Material
and	O
highly	O
cross	O
linked	O
nature	O
.	O
Thus	O
,	O
there	O
has	O
been	O
much	O
research	O
focused	O
on	O
improving	B-Task
the	I-Task
fracture	I-Task
toughness	I-Task
of	I-Task
epoxy	I-Task
polymers	I-Task
by	O
incorporating	B-Process
a	I-Process
second	I-Process
minority	I-Process
phase	I-Process
at	I-Process
the	I-Process
nano-scale	I-Process
.	O
These	O
modifiers	O
fall	O
into	O
one	O
of	O
two	O
main	O
categories	O
:	O
inorganic	B-Material
additives	I-Material
,	O
e.g.	O
silica	B-Material
[	O
1,2	O
]	O
,	O
glass	B-Material
[	O
3	O
]	O
,	O
alumina	B-Material
[	O
4	O
]	O
,	O
nano-clays	B-Material
[	O
5	O
]	O
and	O
carbon	B-Material
nanotubes	I-Material
[	O
6,7	O
]	O
or	O
organic	B-Material
,	O
usually	O
rubber	B-Material
particles	I-Material
.	O
Rubbery	B-Material
additives	I-Material
can	O
be	O
either	O
core	B-Material
–	I-Material
shell	I-Material
rubber	I-Material
particles	I-Material
[	O
8	O
–	O
10	O
]	O
or	O
can	O
form	O
during	O
curing	B-Process
via	O
reaction	B-Process
induced	I-Process
phase	I-Process
separation	I-Process
mechanisms	I-Process
[	O
11,12	O
]	O
.	O
The	O
primary	O
energy	B-Process
dissipation	I-Process
mechanisms	I-Process
for	O
rubber	B-Material
toughened	I-Material
epoxies	I-Material
are	O
known	O
to	O
be	O
both	O
plastic	B-Task
void	I-Task
growth	I-Task
and	O
shear	B-Process
band	I-Process
development	I-Process
[	O
13	O
]	O
.	O
It	O
has	O
also	O
been	O
shown	O
that	O
a	O
combination	O
of	O
the	O
above	O
additives	B-Material
to	O
create	O
a	O
hybrid	B-Material
material	I-Material
can	O
provide	O
synergistic	B-Process
toughening	I-Process
effects	I-Process
,	O
e.g.	O
carbon	B-Material
nanotubes	I-Material
and	O
silica	B-Material
nanoparticles	I-Material
[	O
14	O
]	O
or	O
rubber	B-Material
with	I-Material
silica	I-Material
nanoparticles	I-Material
[	O
15	O
–	O
17	O
]	O
.	O

These	O
results	O
demonstrate	O
that	O
SW-SVR	B-Process
predicts	O
complicated	O
micrometeorological	B-Material
data	I-Material
with	O
the	O
best	O
prediction	O
performance	O
and	O
the	O
lowest	O
computational	O
complexity	O
compared	O
with	O
standard	B-Process
algorithms	I-Process
.	O
In	O
particular	O
,	O
we	O
found	O
that	O
dynamic	B-Process
aggregation	I-Process
of	I-Process
models	I-Process
built	O
from	O
very	O
little	O
extracted	O
data	O
by	O
D-SDC	B-Process
is	O
effective	O
for	O
compatibility	O
of	O
high	O
prediction	O
performance	O
and	O
low	O
computational	O
complexity	O
.	O
However	O
,	O
there	O
are	O
problems	O
to	O
be	O
solved	O
in	O
SW-SVR	B-Process
.	O
Firstly	O
,	O
the	O
prediction	O
performance	O
of	O
SW-SVR	O
sometimes	O
deteriorates	O
despite	O
an	O
increase	O
of	O
training	B-Material
data	I-Material
.	O
In	O
particular	O
,	O
this	O
problem	O
occurred	O
under	O
the	O
conditions	O
that	O
prediction	O
horizons	O
are	O
6	O
h	O
as	O
shown	O
in	O
Fig.	O
3.	O
This	O
is	O
because	O
data	O
extracted	O
by	O
D-SDC	B-Process
involves	O
unnecessary	O
training	B-Material
data	I-Material
for	O
highly	O
accurate	O
prediction	O
.	O
If	O
D-SDC	B-Process
extracts	O
the	O
same	O
data	O
as	O
the	O
extracted	O
data	O
when	O
training	O
periods	O
are	O
shorter	O
,	O
the	O
prediction	O
performance	O
of	O
SW-SVR	B-Process
never	O
deteriorates	O
due	O
to	O
an	O
increase	O
of	O
training	B-Material
data	I-Material
.	O
Therefore	O
,	O
we	O
must	O
review	O
both	O
feature	O
mapping	O
and	O
algorithms	O
of	O
D-SDC	B-Process
so	O
as	O
to	O
avoid	O
extracting	O
unnecessary	O
training	O
data	O
.	O
Meanwhile	O
,	O
SW-SVR	B-Process
is	O
based	O
on	O
a	O
combination	B-Process
of	I-Process
several	I-Process
algorithms	I-Process
:	O
kernel	B-Process
approximation	I-Process
,	O
PLS	B-Process
regression	I-Process
,	O
k-means	B-Process
,	O
D-SDC	B-Process
,	O
and	O
linear	B-Process
SVR	I-Process
.	O
Moreover	O
,	O
each	O
algorithm	O
has	O
several	O
parameters	O
.	O
Therefore	O
,	O
SW-SVR	B-Process
has	O
more	O
varied	O
parameters	O
,	O
and	O
it	O
takes	O
more	O
time	O
to	O
tune	B-Process
the	I-Process
parameters	I-Process
.	O
In	O
this	O
experiment	O
,	O
we	O
used	O
a	O
grid	B-Process
search	I-Process
roughly	O
so	O
as	O
to	O
decide	O
the	O
parameters	O
in	O
a	O
certain	O
time	O
.	O
However	O
,	O
there	O
is	O
still	O
room	O
for	O
improvement	O
in	O
the	O
prediction	O
performance	O
by	O
using	O
other	O
approaches	O
such	O
as	O
a	O
genetic	B-Process
algorithm	I-Process
instead	O
of	O
a	O
grid	B-Process
search	I-Process
(	O
Huang	O
&	O
Wang	O
,	O
2006	O
)	O
.	O

•	O
More	O
efforts	O
should	O
be	O
directed	O
towards	O
advancing	O
the	O
methods	O
of	O
feature	B-Task
extraction	I-Task
to	O
overcome	O
the	O
influence	O
of	O
dynamic	O
factors	O
that	O
limit	O
the	O
performance	O
.	O
The	O
use	O
of	O
advanced	B-Process
machine	I-Process
learning	I-Process
methods	I-Process
such	O
as	O
deep	B-Process
neural	I-Process
networks	I-Process
and	O
muscles	B-Process
synergies	I-Process
extraction	I-Process
should	O
also	O
be	O
investigated	O
on	O
problems	O
under	O
the	O
influence	O
of	O
multiple	O
dynamic	O
factors	O
as	O
such	O
methods	O
may	O
provide	O
substantial	O
improvements	O
upon	O
the	O
utilized	B-Process
time-and-frequency	I-Process
EMG	I-Process
feature	I-Process
extraction	I-Process
methods	I-Process
(	O
Diener	O
,	O
Janke	O
,	O
&	O
Schultz	O
,	O
2015	O
;	O
Ison	O
,	O
Vujaklija	O
,	O
Whitsell	O
,	O
Farina	O
,	O
&	O
Artemiadis	O
,	O
2016	O
;	O
Park	O
&	O
Lee	O
,	O
2016	O
)	O
.	O
Meanwhile	O
,	O
we	O
showed	O
that	O
the	O
performance	B-Material
of	O
the	O
learning	O
algorithms	O
can	O
be	O
improved	O
by	O
using	O
feature	B-Process
extraction	I-Process
methods	I-Process
that	O
rely	O
on	O
the	O
angular	O
information	O
of	O
muscle	O
activation	O
patterns	O
.	O
Features	B-Process
such	O
as	O
the	O
TD-PSD	B-Process
and	O
the	O
DFT	B-Process
proved	O
more	O
successful	O
than	O
others	O
in	O
reducing	B-Task
the	I-Task
impact	I-Task
of	I-Task
the	I-Task
two	I-Task
dynamic	I-Task
factors	I-Task
that	O
we	O
considered	O
in	O
this	O
paper	O
.	O
Such	O
features	O
can	O
be	O
readily	O
implemented	O
into	O
a	O
prosthesis	B-Process
controller	I-Process
for	O
real-time	B-Task
control	I-Task
,	O
especially	O
that	O
the	O
EMG	B-Process
pattern	I-Process
recognition	I-Process
systems	I-Process
are	O
nowadays	O
becoming	O
available	O
for	O
clinical	B-Task
testing	I-Task
,	O
e.g.	O
the	O
COAPT	B-Process
complete	I-Process
control	I-Process
system	I-Process
(	O
Kuiken	O
et	O
al.	O
,	O
2014	O
)	O
11https	O
://	O
www.coaptengineering.com	O
/	O
.	O

In	O
the	O
recent	O
years	O
and	O
mainly	O
motivated	O
by	O
the	O
impulse	O
of	O
data	B-Task
mining	I-Task
many	O
methods	O
for	O
dimensionality	B-Task
reduction	I-Task
have	O
arisen	O
.	O
Within	O
these	O
,	O
it	O
is	O
worth	O
highlighting	O
the	O
Principal	B-Material
Component	I-Material
Analysis	I-Material
method	I-Material
(	O
PCA	B-Material
)	O
(	O
Jolliffe	O
,	O
2002	O
)	O
.	O
In	O
an	O
N-dimensional	O
vector	O
space	O
,	O
the	B-Material
simplest	I-Material
version	I-Material
of	I-Material
PCA	I-Material
(	O
linear	B-Material
PCA	I-Material
)	O
is	O
a	O
technique	O
that	O
finds	B-Process
the	I-Process
mutually-uncorrelated	I-Process
vectors	I-Process
onto	O
which	O
the	B-Process
projection	I-Process
of	I-Process
the	I-Process
samples	I-Process
generates	I-Process
the	I-Process
highest	I-Process
variances	I-Process
.	O
The	O
result	O
is	O
a	B-Material
set	I-Material
of	I-Material
orthogonal	I-Material
vectors	I-Material
sorted	O
in	O
descending	O
order	O
of	O
achieved	O
variance	O
.	O
The	O
first	O
of	O
these	O
vectors	O
is	O
that	O
onto	O
which	O
the	B-Process
variance	I-Process
of	I-Process
the	I-Process
projection	I-Process
of	I-Process
the	I-Process
samples	I-Process
is	I-Process
maximum	I-Process
.	O
In	O
this	O
sense	O
,	O
the	O
original	O
KPIs	O
constitute	O
the	O
N-dimensional	B-Material
vector	I-Material
space	I-Material
basis	I-Material
,	O
whereas	O
the	O
N	B-Material
^	I-Material
synthetic	I-Material
KPIs	I-Material
represent	O
the	O
orthogonal	O
vectors	O
with	O
the	O
highest	O
variance	O
.	O
To	O
be	O
rigorous	O
,	O
up	B-Task
to	I-Task
N	I-Task
synthetic	I-Task
orthogonal	I-Task
KPIs	I-Task
may	O
be	O
computed	O
.	O
However	O
,	O
only	O
a	O
small	O
set	O
of	O
them	O
,	O
the	B-Material
first	I-Material
N	I-Material
^	I-Material
,	O
is	O
enough	O
to	O
account	O
for	O
most	O
of	O
the	O
variance	O
of	O
the	O
data	O
.	O

EM	B-Material
sensors	I-Material
exploit	O
the	O
difference	B-Task
in	I-Task
magnetic	I-Task
properties	I-Task
,	O
such	O
as	O
relative	B-Task
permeability	I-Task
,	O
and	O
electrical	B-Task
conductivity	I-Task
between	O
samples	O
with	O
different	O
microstructural	B-Task
phase	I-Task
balances	I-Task
.	O
In	O
ferromagnetic	B-Material
steels	I-Material
,	O
the	O
change	O
in	O
relative	B-Task
permeability	I-Task
has	O
a	O
significant	O
effect	O
.	O
Previously	O
,	O
multi-frequency	B-Material
EM	I-Material
sensors	I-Material
have	O
been	O
shown	O
to	O
be	O
able	O
to	O
measure	O
austenite	O
/	O
ferrite	O
fraction	O
from	O
0	O
%	O
to	O
100	O
%	O
in	O
model	O
(	O
HIPped	O
austenitic	O
/	O
ferritc	O
stainless	B-Material
steel	I-Material
powder	I-Material
)	O
alloys	O
[	O
7,8	O
]	O
.	O
The	O
large	O
difference	B-Process
in	I-Process
magnetic	I-Process
properties	I-Process
of	O
ferrite	B-Process
(	O
ferromagnetic	B-Process
)	O
and	O
austenite	B-Process
(	O
paramagnetic	B-Process
)	O
phases	O
makes	O
the	O
change	O
in	O
signal	O
large	O
and	O
hence	O
relatively	O
easy	O
to	O
measure	O
.	O
EM	B-Material
sensors	I-Material
have	O
also	O
measured	O
the	O
levels	O
of	O
decarburisation	B-Task
(	O
variation	B-Task
in	I-Task
ferrite	I-Task
content	I-Task
with	I-Task
depth	I-Task
)	O
in	O
steel	B-Material
rod	I-Material
[	O
9,10	O
]	O
.	O
The	O
approach	O
adopted	O
to	O
relate	O
the	O
overall	O
steel	O
EM	B-Process
sensor	I-Process
signal	I-Process
to	O
its	O
microstructure	O
has	O
been	O
to	O
construct	O
a	O
finite	B-Process
element	I-Process
(	O
FE	B-Process
)	O
model	O
for	O
the	O
microstructure	O
(	O
phase	O
,	O
region	O
size	O
and	O
distribution	O
)	O
.	O
The	O
EM	B-Process
properties	I-Process
of	I-Process
the	I-Process
individual	I-Process
phases	I-Process
are	O
assigned	O
to	O
those	O
regions	O
to	O
give	O
the	O
overall	B-Process
EM	I-Process
properties	I-Process
of	O
the	O
steel	B-Material
.	O
Within	O
the	O
model	O
the	O
particular	B-Process
sensor	I-Process
geometry	I-Process
is	O
included	O
(	O
e.g.	O
two-dimensional	O
axisymmetric	O
for	O
a	O
cylindrical	B-Material
sample	I-Material
and	O
tubular	B-Material
sensor	I-Material
[	O
10	O
])	O
and	O
the	O
interaction	O
with	O
the	O
steel	B-Material
and	O
any	O
external	B-Material
circuits	I-Material
predicted	O
.	O
In	O
this	O
way	O
different	O
microstructures	B-Material
and	O
sensor	B-Material
designs	O
can	O
be	O
compared	O
.	O

Many	O
applications	O
in	O
fluid	B-Task
mechanics	I-Task
have	O
shown	O
that	O
surface	B-Process
suction	I-Process
can	O
be	O
used	O
as	O
an	O
effective	O
flow-control	B-Process
mechanism	I-Process
.	O
For	O
example	O
,	O
Gregory	O
and	O
Walker	O
[	O
1	O
]	O
discuss	O
how	O
the	O
introduction	O
of	O
suction	O
extends	O
the	O
laminar-flow	O
region	O
over	O
a	O
swept	B-Material
wing	I-Material
by	O
reducing	B-Task
the	I-Task
thickness	I-Task
of	I-Task
the	I-Task
boundary	I-Task
layer	I-Task
and	I-Task
the	I-Task
magnitude	I-Task
of	I-Task
crossflow	I-Task
velocity	I-Task
.	O
Conclusions	O
for	O
the	O
swept-wing	B-Material
flow	O
arose	O
from	O
equivalent	O
studies	O
of	O
the	O
von	O
Kármán	O
(	O
rotating	B-Material
disk	I-Material
)	O
flow	O
(	O
see	O
Gregory	O
and	O
Walker	O
[	O
2	O
]	O
,	O
Stuart	O
[	O
3	O
])	O
and	O
work	O
has	O
since	O
continued	O
into	O
this	O
and	O
related	O
flows	O
using	O
numerical	B-Process
and	I-Process
asymptotic	I-Process
approaches	I-Process
(	O
see	O
Ockendon	O
[	O
4	O
]	O
,	O
Dhanak	O
[	O
5	O
]	O
,	O
Bassom	O
and	O
Seddougui	O
[	O
6	O
]	O
,	O
Lingwood	O
[	O
7	O
]	O
,	O
Turkyilmazoglu	O
[	O
8	O
]	O
,	O
Lingwood	O
and	O
Garrett	O
[	O
9	O
]	O
,	O
for	O
example	O
)	O
.	O
The	O
literature	O
shows	O
that	O
increasing	B-Process
suction	I-Process
has	O
a	O
stabilising	O
effect	O
on	O
the	O
general	O
class	O
of	O
“	B-Process
Bödewadt	I-Process
,	I-Process
Ekman	I-Process
and	I-Process
von	I-Process
Kármán	I-Process
”	I-Process
(	I-Process
BEK	I-Process
)	I-Process
flows	I-Process
which	O
results	O
in	O
an	O
increase	B-Process
in	I-Process
critical	I-Process
Reynolds	I-Process
numbers	I-Process
for	O
the	O
onset	O
of	O
convective	O
and	O
absolute	O
instabilities	O
,	O
a	O
narrowing	B-Process
in	I-Process
the	I-Process
range	I-Process
of	I-Process
unstable	I-Process
parameters	I-Process
and	O
a	O
decrease	B-Process
in	I-Process
amplification	I-Process
rates	I-Process
of	O
the	O
unstable	O
convective	O
modes	O
.	O
The	O
convective	B-Process
instability	I-Process
results	O
are	O
interpreted	O
in	O
terms	O
of	O
a	O
delay	O
in	O
the	O
onset	O
of	O
spiral	B-Material
vortices	I-Material
,	O
and	O
the	O
absolute	B-Process
instability	I-Process
results	O
in	O
terms	O
of	O
the	O
onset	O
of	O
laminar-turbulent	B-Process
transition	I-Process
(	O
Lingwood	O
[	O
7,10,11	O
])	O
.	O

We	O
have	O
developed	O
a	O
systematic	O
,	O
quantified	O
understanding	O
of	O
a	O
specific	O
problem	O
:	O
the	O
design	B-Task
of	I-Task
mobile-friendly	I-Task
unique	I-Task
identifiers	I-Task
.	O
But	O
our	O
results	O
also	O
apply	O
to	O
the	O
design	B-Task
of	I-Task
other	I-Task
text-based	I-Task
services	I-Task
.	O
There	O
has	O
been	O
a	O
trend	O
toward	O
bespoke	B-Material
and	I-Material
adaptive	I-Material
keyboards	I-Material
(	O
e.g.	O
,	O
Dunlop	O
and	O
Levine	O
,	O
2012	O
;	O
Karrenbauer	O
and	O
Oulasvirta	O
,	O
2014	O
;	O
Leiva	O
et	O
al.	O
,	O
2015	O
;	O
Wiseman	O
et	O
al.	O
,	O
2013	O
)	O
.	O
More	O
often	O
than	O
not	O
,	O
though	O
,	O
input	B-Material
devices	I-Material
are	O
a	O
fixed	O
constraint	O
in	O
the	O
design	B-Task
of	I-Task
a	I-Task
service	I-Task
.	O
Most	O
users	O
are	O
typing	O
on	O
the	O
keyboard	O
that	O
came	O
with	O
their	O
phone	O
.	O
Those	O
keyboards	O
have	O
advantages	O
,	O
limitations	O
and	O
quirks	O
.	O
The	O
mode-switching	B-Process
that	O
most	O
touchscreen	O
keyboards	O
require	O
to	O
reach	O
numbers	O
and	O
capital	O
letters	O
is	O
at	O
the	O
root	O
of	O
design	O
improvements	O
we	O
propose	O
in	O
this	O
paper	O
.	O
When	O
designing	B-Task
services	I-Task
,	O
it	O
is	O
vital	O
to	O
be	O
aware	O
of	O
the	O
fixed	O
constraints	O
of	O
a	O
system	O
and	O
to	O
then	O
focus	O
on	O
the	O
aspects	O
of	O
a	O
service	O
's	O
design	O
that	O
can	O
be	O
controlled	O
.	O
Making	O
changes	O
to	O
input	O
data	O
in	O
this	O
way	O
is	O
a	O
cheap	O
,	O
quick	O
and	O
easy	O
way	O
to	O
improve	O
user	O
experience	O
.	O

Probabilistic	B-Process
and	I-Process
stochastic	I-Process
approaches	I-Process
can	O
facilitate	O
the	O
search	B-Task
for	I-Task
local	I-Task
and	I-Task
global	I-Task
optima	I-Task
.	O
Evolutionary	B-Process
algorithms	I-Process
,	O
such	O
as	O
genetic	B-Process
population	I-Process
(	O
Jomier	O
et	O
al.	O
,	O
2006	O
;	O
Rivest-Henault	O
et	O
al.	O
,	O
2012	O
;	O
Ruijters	O
et	O
al.	O
,	O
2009	O
)	O
,	O
are	O
considered	O
as	O
a	O
strategy	O
that	O
is	O
“	O
less	O
likely	O
to	O
get	O
stuck	O
in	O
a	O
local	O
optimum	O
”	O
(	O
Ruijters	O
et	O
al.	O
,	O
2009	O
)	O
.	O
A	O
cost	B-Material
function	I-Material
consisting	O
of	O
the	O
“	O
sum	O
of	O
the	O
Gaussian-blurred	O
intensity	O
values	O
in	O
the	O
[	O
DSA	O
]	O
at	O
the	O
projected	O
model	O
points	O
”	O
(	O
Jomier	O
et	O
al.	O
,	O
2006	O
)	O
is	O
optimized	O
using	O
a	O
genetic	B-Process
algorithm	I-Process
optimizer	I-Process
.	O
Other	O
authors	O
“	O
use	O
the	O
Condensation	B-Process
form	I-Process
of	I-Process
sequential	I-Process
Monte	I-Process
Carlo	I-Process
sampling	I-Process
to	O
estimate	O
a	O
cost	O
function	O
gradient	O
”	O
(	O
Florin	O
et	O
al.	O
,	O
2005	O
)	O
for	O
finding	O
the	O
global	O
minimum	O
.	O
Besides	O
,	O
the	O
Kalman	O
filter	O
is	O
successfully	O
adopted	O
(	O
Curwen	O
et	O
al.	O
,	O
1994	O
;	O
Feldmar	O
et	O
al.	O
,	O
1997	O
;	O
Toledo	O
et	O
al.	O
,	O
1998	O
)	O
.	O

For	O
all	O
volunteers	O
the	O
AAMM	B-Task
technique	I-Task
significantly	O
(	O
p	O
<	O
0.01	O
)	O
outperformed	O
the	O
other	O
two	O
methods	O
in	O
all	O
of	O
the	O
intervals	O
as	O
can	O
be	O
seen	O
by	O
comparing	O
to	O
the	O
error	B-Material
curves	I-Material
shown	O
in	O
Fig.	O
8	O
and	O
the	O
figures	O
in	O
Table	O
1	O
in	O
the	O
supplementary	O
materials	O
.	O
Significance	O
was	O
assessed	O
using	O
a	O
1-tailed	B-Process
Wilcoxon	I-Process
signed	I-Process
rank	I-Process
test	I-Process
since	O
the	O
error	O
distributions	O
were	O
generally	O
not	O
symmetric	O
.	O
The	O
estimation	O
errors	O
for	O
AAMM	B-Task
and	O
its	O
non-adaptive	O
counterpart	O
,	O
AAMM	O
(	O
no	O
adapt.	O
)	O
,	O
were	O
similar	O
in	O
the	O
beginning	O
of	O
the	O
application	O
phase	O
,	O
but	O
as	O
anticipated	O
,	O
as	O
the	O
application	O
phase	O
went	O
on	O
,	O
the	O
AAMM	O
technique	O
continually	O
improved	O
its	O
accuracy	O
by	O
incorporating	O
more	O
and	O
more	O
data	O
into	O
the	O
model	O
.	O
On	O
average	O
the	O
motion	O
estimation	O
of	O
AAMM	O
improved	O
by	O
22.94	O
%	O
in	O
T5	O
with	O
respect	O
to	O
its	O
non-adaptive	O
counterpart	O
.	O
However	O
,	O
the	O
method	O
has	O
already	O
significantly	O
adapted	O
to	O
the	O
breathing	O
pattern	O
in	O
T2	O
,	O
i.e.	O
after	O
between	O
3	O
and	O
7	O
min	O
of	O
imaging	O
,	O
where	O
motion	O
estimations	O
where	O
on	O
average	O
16.87	O
%	O
more	O
accurate	O
than	O
at	O
the	O
beginning	O
of	O
the	O
adaptation	O
phase	O
.	O
By	O
visually	O
inspecting	O
the	O
curves	O
for	O
AAMM	O
in	O
Fig.	O
8	O
it	O
can	O
be	O
seen	O
that	O
for	O
many	O
volunteers	O
(	O
in	O
particular	O
volunteers	O
A	O
,	O
D	O
,	O
E	O
,	O
and	O
F	O
)	O
the	O
error	O
curves	O
start	O
to	O
flatten	O
approximately	O
around	O
the	O
7	O
min	O
mark	O
.	O
From	O
this	O
it	O
can	O
be	O
concluded	O
that	O
a	O
longer	O
calibration	O
scan	O
of	O
around	O
12	O
min	O
would	O
be	O
optimal	O
,	O
that	O
is	O
the	O
5	O
min	O
that	O
were	O
used	O
for	O
calibration	O
in	O
this	O
experiment	O
plus	O
7	O
min	O
worth	O
of	O
data	O
added	O
during	O
the	O
application	O
phase	O
.	O
Note	O
that	O
this	O
time	O
could	O
be	O
significantly	O
reduced	O
if	O
a	O
non-cardiac-gated	O
sequence	O
was	O
used	O
.	O

As	O
a	O
particular	O
case	O
of	O
survey	O
data	O
,	O
we	O
used	O
the	O
iUTAH	B-Process
“	I-Process
Utah	I-Process
Water	I-Process
Survey	I-Process
,”	I-Process
which	O
was	O
implemented	O
by	O
participating	O
researchers	O
from	O
several	O
Utah	O
institutions	O
of	O
higher	O
education	O
.	O
The	O
objectives	O
of	O
the	O
survey	O
were	O
to	O
document	O
how	O
a	O
representative	O
cross-section	O
of	O
Utah	B-Task
's	I-Task
adult	I-Task
population	I-Task
thinks	I-Task
about	I-Task
water	I-Task
issues	I-Task
.	O
The	O
survey	B-Process
included	O
three	O
core	O
blocks	O
of	O
questions	O
:	O
perceptions	B-Material
of	I-Material
the	I-Material
adequacy	I-Material
of	I-Material
local	I-Material
water	I-Material
supplies	I-Material
,	O
perceptions	B-Material
of	I-Material
the	I-Material
quality	I-Material
of	I-Material
local	I-Material
water	I-Material
resources	I-Material
,	O
and	O
concern	B-Material
about	I-Material
a	I-Material
range	I-Material
of	I-Material
water	I-Material
and	I-Material
non-water	I-Material
issues	I-Material
.	O
A	O
number	O
of	O
additional	O
questions	O
captured	O
information	O
about	O
respondents'	O
familiarity	B-Material
with	I-Material
water	I-Material
cost	I-Material
,	O
lawn-watering	O
behaviors	O
,	O
participation	O
in	O
water	O
based	O
recreation	O
,	O
and	O
demographic	O
attributes	O
.	O
Supplementary	O
material	O
to	O
this	O
paper	O
includes	O
a	O
document	O
with	O
a	O
description	O
of	O
the	O
dataset	O
as	O
a	O
whole	O
,	O
a	O
document	O
containing	O
the	O
complete	O
survey	O
instrument	O
,	O
and	O
two	O
data	O
files	O
containing	O
the	O
results	O
and	O
an	O
associated	O
codebook	O
(	O
see	O
Section	O
4.3	O
)	O
.	O

Isogeometric	B-Process
analysis	I-Process
(	O
IGA	B-Process
)	O
is	O
a	O
numerical	B-Process
simulation	I-Process
method	I-Process
which	O
is	O
directly	O
based	O
on	O
the	O
NURBS-based	B-Process
representation	I-Process
of	O
CAD	O
models	O
.	O
It	O
exploits	O
the	O
tensor-product	O
structure	O
of	O
2	O
-	O
or	O
3-dimensional	O
NURBS	B-Material
objects	O
to	O
parameterize	O
the	O
physical	O
domain	O
.	O
Hence	O
the	O
physical	O
domain	O
is	O
parameterized	O
with	O
respect	O
to	O
a	O
rectangle	O
or	O
to	O
a	O
cube	O
.	O
Consequently	O
,	O
singularly	O
parameterized	O
NURBS	O
surfaces	O
and	O
NURBS	O
volumes	O
are	O
needed	O
in	O
order	O
to	O
represent	O
non-quadrangular	O
or	O
non-hexahedral	O
domains	O
without	O
splitting	O
,	O
thereby	O
producing	O
a	O
very	O
compact	O
and	O
convenient	O
representation.The	O
Galerkin	O
projection	O
introduces	O
finite-dimensional	O
spaces	O
of	O
test	O
functions	O
in	O
the	O
weak	O
formulation	O
of	O
partial	O
differential	O
equations	O
.	O
In	O
particular	O
,	O
the	O
test	O
functions	O
used	O
in	O
isogeometric	O
analysis	O
are	O
obtained	O
by	O
composing	O
the	O
inverse	O
of	O
the	O
domain	O
parameterization	O
with	O
the	O
NURBS	O
basis	O
functions	O
.	O
In	O
the	O
case	O
of	O
singular	O
parameterizations	O
,	O
however	O
,	O
some	O
of	O
the	O
resulting	O
test	O
functions	O
do	O
not	O
necessarily	O
fulfill	O
the	O
required	O
regularity	O
properties	O
.	O
Consequently	O
,	O
numerical	O
methods	O
for	O
the	O
solution	O
of	O
partial	O
differential	O
equations	O
cannot	O
be	O
applied	O
properly.We	O
discuss	O
the	O
regularity	O
properties	O
of	O
the	O
test	O
functions	O
.	O
For	O
one	O
-	O
and	O
two-dimensional	O
domains	O
we	O
consider	O
several	O
important	O
classes	O
of	O
singularities	O
of	O
NURBS	O
parameterizations	O
.	O
For	O
specific	O
cases	O
we	O
derive	O
additional	O
conditions	O
which	O
guarantee	O
the	O
regularity	O
of	O
the	O
test	O
functions	O
.	O
In	O
addition	O
we	O
present	O
a	O
modification	O
scheme	O
for	O
the	O
discretized	O
function	O
space	O
in	O
case	O
of	O
insufficient	O
regularity	O
.	O
It	O
is	O
also	O
shown	O
how	O
these	O
results	O
can	O
be	O
applied	O
for	O
computational	O
domains	O
in	O
higher	O
dimensions	O
that	O
can	O
be	O
parameterized	O
via	O
sweeping	O
.	O

The	O
above	O
discussion	O
also	O
lays	O
bare	O
the	O
difference	O
of	O
perspectives	O
between	O
the	O
fusion	O
of	O
hard	B-Process
constraints	I-Process
and	O
knowledge-base	B-Process
merging	I-Process
:	O
the	O
idea	O
of	O
Konieczny	O
and	O
Pino-Perez	O
is	O
to	O
explain	O
the	O
fusion	B-Process
of	I-Process
plain	I-Process
epistemic	I-Process
states	I-Process
,	O
understood	O
as	O
a	O
set	O
of	O
plausible	O
worlds	O
,	O
by	O
the	O
existence	O
of	O
underlying	O
partial	B-Process
orderings	I-Process
or	O
numerical	B-Process
plausibility	I-Process
degrees	I-Process
(	O
obtained	O
by	O
distances	O
)	O
,	O
based	O
on	O
axioms	O
that	O
only	O
use	O
plausible	O
sets	O
attached	O
to	O
these	O
orderings	O
.	O
In	O
[	O
67	O
]	O
the	O
same	O
authors	O
use	O
both	O
hard	B-Process
(	I-Process
integrity	I-Process
)	I-Process
constraints	I-Process
and	O
belief	B-Process
sets	I-Process
referring	O
to	O
plausible	O
worlds	O
,	O
and	O
try	O
to	O
extend	O
both	O
the	O
AGM	B-Process
revision	I-Process
and	O
knowledge-based	B-Process
merging	I-Process
.	O
However	O
,	O
they	O
do	O
not	O
envisage	O
the	O
merging	B-Process
of	I-Process
integrity	I-Process
constraints	I-Process
discussed	O
in	O
the	O
previous	O
section	O
.	O
The	O
belief	B-Task
revision	I-Task
and	I-Task
merging	I-Task
literature	O
takes	O
an	O
external	O
point	O
of	O
view	O
on	O
cognitive	O
processes	O
under	O
study	O
.	O
The	O
underlying	O
ordered	B-Material
structures	I-Material
are	O
here	O
a	O
consequence	O
of	O
the	O
merging	O
postulates	O
,	O
but	O
they	O
do	O
not	O
appear	O
explicitly	O
in	O
the	O
axioms	O
and	O
they	O
are	O
not	O
observable	O
from	O
the	O
outside	O
.	O
On	O
the	O
contrary	O
,	O
our	O
approach	O
is	O
to	O
construct	B-Process
fusion	I-Process
rules	I-Process
that	O
only	O
rely	O
on	O
what	O
is	O
explicitly	O
supplied	O
by	O
sources	O
.	O
In	O
the	O
sequel	O
we	O
consider	O
the	O
counterpart	O
of	O
our	O
fusion	O
postulates	O
for	O
ranked	B-Process
models	I-Process
,	O
that	O
can	O
be	O
expressed	O
by	O
means	O
of	O
total	O
orders	O
of	O
possible	O
worlds	O
or	O
by	O
their	O
encodings	O
on	O
a	O
plausibility	O
scale	O
.	O

Methods	O
for	O
anomaly	B-Task
detection	I-Task
in	O
a	O
local	O
context	O
are	O
the	O
conceptual	O
opposite	O
to	O
the	O
afore-described	B-Material
centralized	I-Material
methods	I-Material
,	O
which	O
rely	O
on	O
globally	B-Material
shared	I-Material
models	I-Material
.	O
In	O
data	B-Task
mining	I-Task
,	O
the	O
notion	B-Task
of	I-Task
locality	I-Task
is	O
often	O
given	O
as	O
distance	B-Material
between	I-Material
data	I-Material
values	I-Material
(	O
given	O
a	B-Material
specific	I-Material
distance	I-Material
metric	I-Material
such	I-Material
as	I-Material
Euclidean	I-Material
distance	I-Material
)	O
.	O
A	O
data	O
point	O
is	O
compared	B-Process
to	I-Process
the	I-Process
value	I-Process
of	I-Process
its	I-Process
nearest	I-Process
neighbors	I-Process
in	O
terms	O
of	O
data	O
distance	O
[	O
42	O
]	O
.	O
However	O
,	O
the	B-Task
notion	I-Task
of	I-Task
locality	I-Task
can	O
also	O
be	O
given	O
in	O
a	B-Material
geographical	I-Material
distance	I-Material
between	O
the	O
sources	O
of	O
the	O
data	O
.	O
Many	B-Material
similar	I-Material
values	I-Material
(	O
i.e.	O
,	O
data	O
with	O
small	O
distance	O
among	O
each	O
other	O
)	O
result	O
in	O
a	O
higher	O
density	O
,	O
called	O
clusters	B-Material
,	O
while	O
values	B-Process
that	I-Process
are	I-Process
less	I-Process
similar	I-Process
result	I-Process
in	I-Process
a	I-Process
lower	I-Process
density	I-Process
.	O
Anomalies	B-Task
can	O
fall	O
outside	O
of	O
any	O
cluster	B-Material
but	O
,	O
when	O
frequently	O
occurring	O
,	O
can	B-Process
form	I-Process
a	I-Process
cluster	I-Process
too	O
.	O
Determining	B-Task
if	I-Task
a	I-Task
datum	I-Task
is	I-Task
normal	I-Task
or	I-Task
anomalous	I-Task
compared	O
to	O
local	O
neighborhood	O
data	O
is	O
a	O
challenge	O
.	O

MWSN	B-Process
routing	I-Process
protocols	I-Process
generally	O
take	O
influence	O
from	O
both	O
WSN	B-Process
and	O
mobile	B-Process
ad	I-Process
hoc	I-Process
network	I-Process
(	O
MANET	B-Process
)	O
routing	B-Process
protocols	I-Process
,	O
which	O
all	O
share	O
common	O
limitations	O
,	O
such	O
as	O
bandwidth	O
,	O
power	O
and	O
cost	O
.	O
WSNs	B-Process
often	O
share	O
the	O
same	O
aim	O
as	O
MWSNs	B-Process
,	O
in	O
that	O
they	O
wish	O
to	O
route	B-Process
data	I-Process
from	O
many	O
sensors	B-Material
to	O
a	O
single	O
sink	O
.	O
However	O
,	O
WSNs	B-Process
are	O
normally	O
considered	O
to	O
be	O
static	O
and	O
so	O
the	O
associated	O
routing	O
protocols	O
are	O
often	O
unable	O
to	O
cope	O
in	O
a	O
mobile	O
scenario	O
[	O
10	O
]	O
.	O
Alternatively	O
,	O
MANET	B-Process
protocols	I-Process
are	O
designed	O
to	O
be	O
able	O
to	O
cope	O
with	O
the	O
mobility	O
of	O
nodes	O
,	O
however	O
they	O
aim	O
to	O
allow	O
end-to-end	O
communication	O
to	O
occur	O
between	O
any	O
two	O
nodes	O
[	O
2	O
]	O
.	O
This	O
extra	O
functionality	O
is	O
often	O
not	O
required	O
by	O
MWSNs	B-Process
and	O
so	O
the	O
additional	O
overhead	O
is	O
unnecessary	O
.	O
Combined	O
with	O
the	O
high	O
packet	O
delivery	O
ratios	O
and	O
low	O
delays	O
that	O
are	O
demanded	O
by	O
emerging	O
applications	O
,	O
the	O
ideal	O
routing	O
solution	O
for	O
a	O
MWSN	B-Process
is	O
one	O
that	O
can	O
handle	O
the	O
mobility	O
of	O
nodes	O
and	O
allows	O
data	O
to	O
be	O
forwarded	O
from	O
the	O
sensors	O
to	O
the	O
sink	O
in	O
a	O
reliable	O
and	O
timely	O
manner	O
.	O
This	O
set	O
of	O
requirements	O
make	O
the	O
problem	O
of	O
routing	B-Task
in	I-Task
a	I-Task
MWSN	I-Task
a	O
unique	O
challenge	O
,	O
which	O
will	O
require	O
new	O
specifically	O
designed	O
solutions	O
.	O
For	O
this	O
reason	O
there	O
have	O
been	O
many	O
routing	O
protocols	O
designed	O
for	O
MWSNs	B-Process
.	O
As	O
such	O
,	O
this	O
section	O
will	O
give	O
an	O
overview	O
of	O
the	O
current	O
literature	O
,	O
which	O
highlights	O
the	O
different	O
techniques	O
and	O
commonly	O
used	O
protocols	O
in	O
MWSN	B-Process
routing	I-Process
.	O

In	O
order	O
to	O
test	B-Task
whether	I-Task
haptic	I-Task
patterns	I-Task
can	I-Task
convey	I-Task
or	I-Task
enhance	I-Task
the	I-Task
mood	I-Task
music	I-Task
of	O
a	O
movie	O
,	O
an	O
affective	B-Material
movie	I-Material
clip	I-Material
corpus	I-Material
was	O
required	O
consisting	O
of	O
clips	O
labeled	O
according	O
to	O
the	O
emotion	O
conveyed	O
in	O
the	O
mood	O
music	O
.	O
The	O
following	O
database	B-Material
collections	I-Material
were	O
examined	O
as	O
possible	O
sources	O
for	O
the	O
corpus	O
:	O
the	O
Emotional	B-Material
Movie	I-Material
Database	I-Material
(	O
EMDB	B-Material
)	O
[	O
19	O
]	O
,	O
and	O
Film	B-Material
Stim	I-Material
[	O
20	O
]	O
.	O
However	O
,	O
these	O
were	O
discarded	O
after	O
review	B-Process
as	O
unsuitable	O
.	O
The	O
aim	O
of	O
this	O
study	O
is	O
to	O
enhance	B-Task
the	I-Task
mood	I-Task
in	O
the	O
film	O
score	O
,	O
and	O
in	O
the	O
case	O
of	O
the	O
clips	O
in	O
the	O
EMDB	B-Material
,	O
no	O
audio	O
is	O
provided	O
which	O
deemed	O
the	O
clips	O
unsuitable	O
.	O
In	O
the	O
case	O
of	O
the	O
Film	B-Material
Stim	I-Material
database	I-Material
,	O
the	O
clips	O
are	O
in	O
French	O
rather	O
than	O
English	O
,	O
and	O
with	O
no	O
subtitles	O
which	O
where	O
also	O
deemed	O
unsuitable	O
since	O
the	O
studies	O
are	O
carried	O
out	O
with	O
English	B-Material
speaking	I-Material
participants	I-Material
.	O
Furthermore	O
,	O
the	O
Film	B-Material
Stim	I-Material
selection	I-Material
is	O
based	O
on	O
the	O
affective	O
content	O
of	O
the	O
narrative	O
as	O
in	O
most	O
of	O
them	O
there	O
is	O
no	O
music	O
which	O
is	O
also	O
unsuitable	O
as	O
discussed	O
.	O
From	O
our	O
review	B-Process
of	I-Process
available	I-Process
database	I-Process
collections	I-Process
,	O
it	O
was	O
found	O
that	O
at	O
present	O
there	O
is	O
no	O
standard	O
corpus	O
of	O
affective	O
movie	O
clips	O
where	O
the	O
affective	O
indexing	O
referred	O
to	O
the	O
musical	O
score	O
of	O
the	O
clip	O
.	O

Aspect-oriented	B-Process
Programming	I-Process
(	O
AOP	B-Process
)	O
can	O
well	O
solve	B-Task
the	I-Task
cross-cutting	I-Task
concerns	I-Task
.	O
Because	O
of	O
the	O
different	O
features	O
of	O
aspect	O
,	O
AOP	B-Process
requires	O
new	O
techniques	O
for	O
testing	O
.	O
First	O
,	O
this	O
paper	O
proposes	O
a	O
model	B-Task
to	I-Task
test	I-Task
aspect-oriented	I-Task
software	I-Task
.	O
In	O
order	O
to	O
support	O
the	O
testing	B-Process
model	I-Process
of	O
the	O
first	O
three	O
steps	O
,	O
we	O
propose	O
the	O
algorithm	B-Process
of	I-Process
selecting	I-Process
aspect	I-Process
relevant	I-Process
test	I-Process
cases	I-Process
.	O
Then	O
,	O
we	O
develop	B-Task
a	I-Task
new	I-Task
tool	I-Task
to	I-Task
implement	I-Task
the	I-Task
theoretical	I-Task
of	I-Task
automating	I-Task
select	I-Task
test	I-Task
case	I-Task
.	O
Finally	O
,	O
a	O
case	O
of	O
the	O
Bank	B-Process
Account	I-Process
System	I-Process
is	O
studied	O
to	O
illustrate	O
our	O
testing	O
approach	O
.	O

In	O
this	O
paper	O
,	O
the	O
design	B-Task
of	I-Task
a	I-Task
varnish	I-Task
plant	I-Task
at	O
Crocodile	O
Matchet	O
Limited	O
,	O
Tema	O
,	O
Ghana	O
was	O
considered	O
and	O
modification	B-Task
made	I-Task
to	I-Task
eliminate	I-Task
blooming	I-Task
and	I-Task
rusting	I-Task
of	O
its	O
product	O
at	O
the	O
final	O
processing	B-Material
plant	I-Material
when	O
there	O
is	O
high	O
moisture	O
content	O
in	O
the	O
atmosphere	O
.	O
The	O
proposed	O
design	O
included	O
pipelines	B-Material
or	O
ductsand	B-Material
hot	I-Material
air	I-Material
receiving	I-Material
chambers	I-Material
for	O
the	O
Varnish	O
Plant.Heat	O
from	O
the	O
exhaust	O
gas	O
which	O
would	O
have	O
otherwise	O
,	O
gone	O
wasted	O
,	O
was	O
utilised	O
by	O
redesigning	O
the	O
varnish	O
plant	O
and	O
this	O
yielded	O
6.74kW	O
of	O
heat	O
energy	O
which	O
was	O
transferred	O
into	O
the	O
air	O
chambers	O
to	O
aid	O
the	O
drying	O
ofmatchets	O
at	O
the	O
hardening	O
plant	O
.	O
Consequently	O
,	O
the	O
absorption	O
of	O
the	O
moisture	O
on	O
the	O
steel	O
and	O
the	O
dryness	O
of	O
the	O
product	O
were	O
improved	O
.	O
Further	O
studies	O
were	O
done	O
to	O
ensure	O
constant	O
supply	O
of	O
hot	O
air	O
into	O
the	O
air	O
chambers	O
.	O

Digital	B-Material
libraries	I-Material
promise	O
new	O
societal	O
benefits	O
,	O
especially	O
for	O
e-learning	B-Process
in	O
digital	O
or	O
mobile	O
times	O
,	O
starting	O
with	O
the	O
elimination	B-Process
of	I-Process
the	I-Process
time	I-Process
and	I-Process
space	I-Process
constraints	I-Process
of	O
traditional	O
bricks-and-mortar	B-Material
libraries	I-Material
.	O
The	O
library	O
and	O
information	O
professionals	O
are	O
required	O
to	O
acquire	B-Task
such	I-Task
knowledge	I-Task
and	I-Task
skills	I-Task
as	O
the	O
library	O
is	O
one	O
of	O
the	O
highly	O
IT	O
influenced	O
service	O
profession	O
.	O
This	O
paper	O
gives	O
an	O
overview	B-Task
of	I-Task
current	I-Task
trends	I-Task
in	I-Task
digital	I-Task
library	I-Task
research	I-Task
consists	O
of	O
digital	O
library	O
characteristic	O
,	O
advantage	O
,	O
disadvantages	O
and	O
function	O
.	O
This	O
paper	O
also	O
highlights	B-Task
on	I-Task
the	I-Task
impact	I-Task
of	I-Task
information	I-Task
technology	I-Task
on	O
the	O
traditional	O
library	B-Material
.	O

According	O
to	O
the	O
situation	O
that	O
the	O
IT	O
students	O
can	O
not	O
meet	O
the	O
software	O
industry	O
demand	O
for	O
qualified	O
personnel	O
,	O
a	O
“	B-Process
triple-driven	I-Process
”	I-Process
three-dimensional	I-Process
software	I-Process
development	I-Process
practical	I-Process
teaching	I-Process
system	I-Process
was	O
proposed	O
,	O
aiming	O
to	O
improve	B-Task
the	I-Task
software	I-Task
development	I-Task
capabilities	I-Task
and	I-Task
innovation	I-Task
sense	I-Task
of	I-Task
students	I-Task
.	O
This	O
system	O
can	O
effectively	O
improve	O
students	O
the	O
interest	O
of	O
software	O
development	O
and	O
the	O
practical	B-Process
skills	I-Process
and	O
sense	O
of	O
innovation	O
,	O
laying	O
a	O
solid	O
foundation	O
for	O
student	O
after	O
graduation	O
to	O
rapidly	O
integrate	O
into	O
the	O
software	B-Process
development	I-Process
process	I-Process
,	O
meeting	O
the	O
needs	O
of	O
software	B-Task
industry	I-Task
.	O

According	O
to	O
the	O
shortcomings	O
of	O
long	O
time	O
and	O
big	O
errors	O
about	O
the	O
moving	B-Process
plate	I-Process
recognition	I-Process
system	I-Process
,	O
we	O
present	O
the	O
moving	B-Process
plate	I-Process
recognition	I-Process
algorithm	I-Process
based	O
on	O
principal	B-Process
component	I-Process
analysis	I-Process
(	O
PCA	B-Process
)	O
color	B-Process
extraction	I-Process
.	I-Process
On	O
the	O
basis	O
of	O
the	O
analysis	B-Task
of	I-Task
moving	I-Task
plate	I-Task
recognition	I-Task
system	I-Task
's	I-Task
basic	I-Task
principles	I-Task
,	O
it	O
introduces	O
the	O
basic	O
principles	O
and	O
calculation	O
steps	O
about	O
PCA	B-Process
extraction	I-Process
algorithm	I-Process
,	O
and	O
discusses	O
the	O
feasibility	O
of	O
applying	B-Task
the	I-Task
algorithm	I-Task
to	I-Task
PRS	I-Task
in	O
the	O
paper	O
.	O
The	O
experimental	O
results	O
show	O
that	O
the	O
algorithm	O
has	O
the	O
advantages	O
of	O
faster	O
speed	O
and	O
higher	O
accuracy	O
of	O
recognition	B-Process
.	O
The	O
algorithm	O
provides	O
a	O
new	O
thought	O
for	O
the	O
research	B-Task
on	I-Task
the	I-Task
moving	I-Task
plate	I-Task
recognition	I-Task
algorithm	I-Task
.	O

With	O
the	O
development	O
of	O
sport	O
normal	O
students	O
in	O
china	O
,	O
Some	O
ideas	O
to	O
teaching	O
and	O
learning	O
that	O
view	O
learning	O
as	O
a	O
simple	O
process	O
of	O
knowledge	B-Process
have	O
become	O
outdated	O
and	O
ineffective	O
,	O
therefore	O
,	O
In	O
order	O
to	O
improving	B-Task
the	I-Task
quality	I-Task
of	I-Task
teaching	I-Task
and	I-Task
learning	I-Task
on	I-Task
sport	I-Task
normal	I-Task
students	I-Task
in	O
china	O
,	O
this	O
author	O
discussed	O
some	O
factors	B-Process
on	I-Process
promoting	I-Process
the	I-Process
level	I-Process
of	I-Process
teaching	I-Process
and	I-Process
learning	I-Process
for	O
sport	O
normal	O
students	O
,	O
such	O
as	O
implementation	B-Process
principle	I-Process
,	O
curriculum	B-Process
design	I-Process
,	O
education	B-Process
policy	I-Process
,	O
and	O
so	O
on	O
.	O
The	O
meaning	O
of	O
results	O
and	O
their	O
implication	O
of	O
future	B-Task
research	I-Task
are	O
discussed	O
.	O

A	O
process-driven	B-Process
model	I-Process
is	O
presented	O
to	O
build	O
an	O
instinctive	O
and	O
efficient	O
higher	O
educational	O
administrative	B-Process
management	I-Process
system	I-Process
to	O
overcome	B-Process
problems	I-Process
most	O
universities	O
facing	O
.	O
With	O
this	O
model	O
,	O
processes	B-Task
are	I-Task
identified	I-Task
explicitly	O
and	O
the	O
routine	O
of	O
educational	B-Process
administration	I-Process
is	O
broken	O
into	O
small	O
tasks	O
.	O
Each	O
task	O
has	O
designated	O
role	O
of	O
executors	O
.	O
A	O
process	O
describes	O
the	O
activities	O
and	O
relationships	O
among	O
them	O
.	O
A	O
prototype	O
of	O
higher	O
educational	O
administrative	O
system	O
is	O
built	O
with	O
Bonita	B-Material
open	I-Material
solution	I-Material
.	O
The	O
demo	O
shows	O
that	O
the	O
process-driven	O
higher	O
educational	O
administrative	B-Process
system	I-Process
helps	O
end	O
users	O
understand	B-Process
processes	I-Process
they	I-Process
are	I-Process
involved	I-Process
and	O
focus	O
on	O
what	O
to	O
do	O
.	O

With	O
employment	O
of	O
utilizing	O
the	O
investigation	B-Process
,	O
expert	B-Process
interviews	I-Process
and	O
comparison	O
,	O
this	O
article	O
investigates	B-Task
the	I-Task
curricula	I-Task
construction	I-Task
,	I-Task
curricula	I-Task
design	I-Task
and	I-Task
curricula	I-Task
content	I-Task
for	O
sports	O
free	O
normal	O
students	O
.	O
On	O
the	O
basis	O
of	O
the	O
investigation	B-Task
,	O
this	O
article	O
analyzed	O
the	O
theoretical	B-Process
framework	I-Process
of	O
curricular	O
construction	O
and	O
proposed	O
some	O
suggestions	O
.	O
We	O
hope	O
that	O
it	O
can	O
provide	O
some	O
evidences	O
for	O
curricula	B-Process
design	I-Process
for	O
sports	O
free	O
normal	O
students	O
.	O

It	O
has	O
been	O
more	O
than	O
a	O
century	O
since	O
the	O
emergence	O
of	O
the	O
lettered	B-Material
words	I-Material
.	O
After	O
that	O
,	O
with	O
the	O
development	B-Process
of	I-Process
economy	I-Process
and	I-Process
culture	I-Process
,	O
the	O
increase	O
of	O
international	O
contacts	O
and	O
communication	B-Process
between	O
China	O
and	O
foreign	O
countries	O
,	O
lettered	B-Material
words	I-Material
have	O
been	O
appearing	O
more	O
frequently	O
.	O
Lettered	B-Material
words	I-Material
have	O
become	O
an	O
indispensable	O
part	O
of	O
Chinese	B-Material
vocabulary	I-Material
,	O
such	O
as	O
WTO	B-Material
,	O
Ka	B-Material
la	I-Material
OK	I-Material
and	O
MP3	B-Material
.	O
As	O
a	O
new	O
phenomenon	O
in	O
the	O
vocabulary	B-Process
system	I-Process
of	O
the	O
modern	O
Chinese	O
,	O
the	O
lettered	B-Material
words	I-Material
draws	O
a	O
lot	O
of	O
academic	O
attention	O
.	O
Ecolinguistics	B-Process
is	O
a	O
new	O
branch	B-Process
of	I-Process
linguistic	I-Process
,	O
which	O
combine	B-Process
the	I-Process
linguistic	I-Process
with	I-Process
the	I-Process
ecology	I-Process
.	O
This	O
paper	O
is	O
trying	O
to	O
analyze	B-Task
the	I-Task
lettered	I-Task
words	I-Task
from	I-Task
the	I-Task
perspective	I-Task
of	I-Task
Ecolinguistics	I-Task
.	O
This	O
paper	O
will	O
discuss	B-Task
the	I-Task
reasons	I-Task
of	I-Task
appearing	I-Task
the	I-Task
lettered	I-Task
words	I-Task
and	O
the	O
influence	O
may	O
give	O
to	O
modern	O
Chinese	O
form	O
the	O
ecolinguistic	O
view	O
.	O

The	O
21st	O
century	O
in	O
the	O
face	O
of	O
an	O
aging	B-Process
population	I-Process
trend	I-Process
,	O
the	O
health	O
status	O
of	O
the	O
elderly	O
is	O
a	O
hot	O
issue	O
of	O
social	O
concern	O
,	O
therefore	O
,	O
to	O
explore	B-Task
the	I-Task
health	I-Task
status	I-Task
of	O
the	O
Chinese	O
population	O
aging	O
and	O
the	O
elderly	O
,	O
elderly	O
fitness	O
exercise	O
Misunderstanding	O
study	O
and	O
formulate	B-Task
measures	I-Task
and	I-Task
methods	I-Task
of	I-Task
fitness	I-Task
of	O
the	O
elderly	O
,	O
promoting	O
elderly	B-Process
fitness	I-Process
training	I-Process
towards	O
a	O
healthy	O
,	O
scientific	O
direction	O
,	O
to	O
promote	B-Task
a	I-Task
nationwide	I-Task
fitness	I-Task
activities	I-Task
carried	O
out	O
in	O
order	O
to	O
achieve	O
the	O
exercise	B-Process
of	I-Process
scientific	I-Process
fitness	I-Process
of	O
older	O
persons	O
.	O

Faced	O
with	O
deficient	O
ability	O
of	O
autonomic	B-Task
learning	I-Task
among	O
learners	O
and	O
low	B-Process
emotional	I-Process
involvement	I-Process
in	O
current	O
web-based	B-Material
instructional	I-Material
environment	I-Material
,	O
here	O
we	O
propose	B-Task
a	I-Task
construct	I-Task
model	I-Task
that	O
is	O
based	O
on	O
inter-subjectivity	O
fusing	B-Process
cognition	I-Process
with	I-Process
emotion	I-Process
to	O
make	O
up	O
for	O
these	O
shortages	O
.	O
Further	O
more	O
,	O
we	O
’ve	O
put	O
the	O
construct	B-Process
model	I-Process
into	O
practice	O
through	O
the	O
online	B-Process
teaching	I-Process
reformation	I-Process
of	O
the	O
quality	O
course	O
apparel	O
production	O
and	O
management	O
.	O

In	O
this	O
paper	O
,	O
we	O
present	O
algorithms	B-Task
for	I-Task
automatic	I-Task
generation	I-Task
of	I-Task
logic	I-Task
reasoning	I-Task
questions	I-Task
.	I-Task
The	O
algorithms	O
are	O
able	O
to	O
construct	B-Process
questions	I-Process
that	I-Process
are	I-Process
solvable	I-Process
with	I-Process
unique	I-Process
solutions	I-Process
.	O
The	O
algorithms	O
employ	O
AI	B-Process
techniques	I-Process
such	O
as	O
semantic	B-Process
networks	I-Process
to	O
produce	O
verbal	B-Material
questions	I-Material
.	O
These	O
algorithms	O
are	O
small	O
in	O
size	O
and	O
are	O
able	O
to	O
replace	O
traditional	B-Material
question	I-Material
databases	I-Material
.	O
They	O
are	O
particularly	O
suitable	O
for	O
implementation	O
on	O
the	O
memory	B-Process
constrained	I-Process
mobile	I-Process
platforms	I-Process
.	O
The	O
algorithms	O
can	O
be	O
applied	O
to	O
question	B-Process
generation	I-Process
for	I-Process
job	I-Process
interview	I-Process
,	O
civil	O
service	O
exam	O
,	O
etc	O
.	O

By	O
referring	O
to	O
many	O
relevant	O
data	B-Material
and	I-Material
essays	I-Material
,	O
this	O
paper	O
aims	O
at	O
discussing	O
and	O
analyzing	O
the	O
importance	O
of	O
hip-push	O
applied	O
in	O
walking	O
race,based	O
on	O
the	O
point	O
of	O
the	O
view	O
on	O
sports	O
biomechanics	O
.	O
With	O
redard	O
to	O
the	O
existing	O
problems,the	O
authors	O
have	O
made	O
an	O
objective	O
analysis	O
on	O
the	O
sports-biomechanics	O
factors	O
that	O
can	O
influence	O
the	O
race,hoping	O
to	O
provide	O
a	O
theoretical	O
basis	O
for	O
the	O
deep	O
development	O
and	O
training	O
of	O
walking	O
race	O
.	O

Amodel	O
are	O
proposed	O
for	O
modeling	B-Task
data-centric	I-Task
Web	I-Task
services	I-Task
which	O
are	O
powered	O
by	O
relational	B-Material
databases	I-Material
and	O
interact	B-Process
with	I-Process
users	I-Process
according	O
to	O
logical	B-Process
formulas	I-Process
specifying	O
input	O
constraints	O
,	O
control-flow	B-Process
constraints	I-Process
and	O
state	B-Process
/	I-Process
output	I-Process
/	I-Process
action	I-Process
rules	I-Process
.	O
The	O
Linear	B-Process
Temporal	I-Process
First-Order	I-Process
Logic	I-Process
(	O
LTL-FO	B-Process
)	O
formulas	O
over	O
inputs	O
,	O
states	O
,	O
outputs	O
and	O
actions	O
are	O
used	O
to	O
express	O
the	O
properties	O
to	O
be	O
verified.We	O
have	O
proven	O
that	O
automatic	O
verification	O
of	O
LTL-FO	O
properties	O
of	O
data-centric	B-Material
Web	I-Material
services	I-Material
under	O
input-bounded	O
constraints	O
is	O
decidable	O
by	O
reducing	B-Process
Web	I-Process
services	I-Process
to	I-Process
data-centric	I-Process
Web	I-Process
applications	I-Process
.	O
Thus	O
,	O
we	O
can	O
verify	B-Task
Web	I-Task
service	I-Task
specifications	I-Task
using	O
existing	O
verifier	O
designed	O
for	O
Web	B-Process
applications	I-Process
.	O

The	O
most	O
important	O
goal	O
of	O
the	O
software	B-Process
industry	I-Process
is	O
to	O
produce	B-Task
successful	I-Task
product	I-Task
.	O
During	O
the	O
process	O
of	O
production	B-Process
several	O
times	O
the	O
product	B-Process
fails	I-Process
due	O
to	O
lack	O
of	O
proper	O
management	B-Process
.	O
This	O
paper	O
is	O
exploring	O
the	O
role	B-Task
of	I-Task
software	I-Task
engineering	I-Task
courses	I-Task
in	O
computer	B-Task
engineering	I-Task
related	O
branches	O
and	O
then	O
reasons	O
why	O
software	O
developers	O
lack	O
project	B-Process
management	I-Process
in	O
proper	B-Process
software	I-Process
management	I-Process
trainings	I-Process
.	O
Our	O
findings	O
reflect	O
that	O
in	O
majority	O
of	O
computer	O
related	O
branches	O
like	O
computer	O
science	O
,	O
computer	O
engineering	O
,	O
information	O
system	O
engineering	O
there	O
is	O
no	O
place	O
for	O
software	B-Process
project	I-Process
management	I-Process
course	I-Process
.	O
Our	O
findings	O
are	O
based	O
on	O
a	O
survey	B-Task
of	I-Task
course	I-Task
curriculums	I-Task
of	O
computer	B-Task
engineering	I-Task
,	O
computer	B-Task
science	I-Task
and	I-Task
information	I-Task
system	I-Task
engineering	I-Task
courses	O
taught	O
in	O
Turkish	O
universities	O
.	O

Analyzing	O
the	O
significance	O
of	O
macroscopical	O
dynamic	O
monitoring	O
of	O
new	O
add	O
construction	B-Material
land	I-Material
,	O
considering	O
the	O
influence	O
of	O
various	O
factors	O
,	O
this	O
paper	O
selected	O
Yinchuan	B-Material
Plain	I-Material
for	O
a	O
typical	O
experimental	O
zone	O
,	O
built	O
knowledge	B-Material
base	I-Material
of	O
remote	B-Process
sensing	I-Process
images	I-Process
interpretation	I-Process
,	O
used	O
multi-temporal	B-Material
remote	I-Material
sensing	I-Material
images	I-Material
,	O
carried	O
through	O
interactive	B-Process
interpretation	I-Process
of	I-Process
change	I-Process
patterns	I-Process
of	O
new	O
add	O
construction	B-Material
land	I-Material
and	O
field	B-Process
validation	I-Process
.	O
Interpretation	B-Task
results	O
of	O
20m	O
scale	O
remote	B-Material
sensing	I-Material
image	I-Material
show	O
that	O
the	O
minimum	O
spot	O
average	O
area	O
of	O
new	O
construction	B-Material
land	I-Material
change	O
monitored	O
by	O
20m	O
scale	O
remote	B-Material
sensing	I-Material
data	I-Material
is	O
about	O
6	O
acres	O
.	O
The	O
ability	O
20m	O
scale	O
remote	O
sensing	O
data	O
identifies	B-Process
new	I-Process
increased	I-Process
construction	I-Process
land	I-Process
change	O
further	O
strengthens	O
,	O
shows	O
in	O
the	O
recognition	B-Process
of	I-Process
the	I-Process
smallest	I-Process
spot	I-Process
area	I-Process
reduces	O
and	O
the	O
recognition	O
accuracy	O
increases	O
.	O

Evolutionary	B-Process
Algorithms	I-Process
are	O
the	O
stochastic	B-Process
optimization	I-Process
methods	I-Process
,	O
simulating	O
the	O
behavior	O
of	O
natural	B-Process
evolution	I-Process
.	O
These	O
algorithms	O
are	O
basically	O
population	B-Process
based	I-Process
search	I-Process
procedures	I-Process
efficiently	O
dealing	O
with	O
complex	O
search	O
spaces	O
having	O
robust	O
and	O
powerful	O
search	B-Process
mechanism	I-Process
.	O
EAs	B-Process
are	O
highly	O
applicable	O
in	O
multiobjective	O
optimization	B-Process
problem	I-Process
which	O
are	O
having	O
conflicting	O
objectives	O
.	O
This	O
paper	O
reviews	B-Task
the	I-Task
work	I-Task
carried	I-Task
out	I-Task
for	I-Task
diversity	I-Task
and	I-Task
convergence	I-Task
issues	I-Task
in	I-Task
EMO	I-Task
.	O

In	O
this	O
paper	O
,	O
coordination	B-Task
problem	I-Task
of	I-Task
agricultural	I-Task
products	I-Task
supply	I-Task
chain	I-Task
with	O
stochastic	B-Process
yield	I-Process
is	O
studied	O
based	O
on	O
prices	B-Process
compensation	I-Process
strategy	I-Process
.	O
The	O
agricultural	B-Process
producing	I-Process
is	O
influenced	O
by	O
the	O
natural	O
conditions	O
,	O
and	O
the	O
yield	B-Process
is	O
uncertain	O
.	O
While	O
agricultural	B-Material
products	I-Material
is	O
rigid	O
demand	O
goods	O
,	O
the	O
fluctuations	B-Process
of	I-Process
yield	I-Process
cause	O
greater	O
volatility	O
of	O
prices	O
.	O
The	O
two	B-Process
-	I-Process
echelon	I-Process
supply	I-Process
chain	I-Process
with	O
one	O
supplier	O
and	O
one	O
retailor	O
is	O
studied	O
,	O
and	O
the	O
mathematical	B-Task
model	I-Task
is	I-Task
constructed	I-Task
.	O
The	O
model	O
showed	O
that	O
prices	B-Process
compensation	I-Process
strategy	I-Process
is	O
Pareto	O
improvement	O
for	O
agricultural	B-Process
products	I-Process
supply	I-Process
chain	I-Process
with	O
stochastic	B-Process
yield	I-Process
,	O
and	O
it	O
also	O
incentive	O
agricultural	B-Material
products	I-Material
supplier	O
to	O
rise	O
the	O
production	B-Process
plan	I-Process
and	O
balance	O
the	O
profit	B-Process
allocation	I-Process
of	O
supply	B-Process
chain	I-Process
.	O

It	O
is	O
difficult	O
in	O
directly	O
predicting	O
permeability	B-Process
from	O
porosity	B-Process
in	O
tight	O
sandstones	B-Material
due	O
to	O
the	O
poor	O
relationship	O
between	O
core	O
derived	O
porosity	B-Process
and	O
permeability	B-Process
that	O
caused	O
by	O
the	O
extreme	O
heterogeneity	O
.	O
The	O
classical	O
SDR	B-Process
(	O
Schlumberger	B-Process
Doll	I-Process
Research	I-Process
)	O
and	O
Timur-Coates	B-Process
models	I-Process
are	O
all	O
unusable	O
because	O
not	O
enough	O
core	O
samples	O
were	O
drilled	O
for	O
lab	O
NMR	B-Process
experimental	B-Process
measurements	I-Process
to	O
calibrate	B-Task
the	I-Task
involved	I-Task
model	I-Task
parameters	I-Task
.	O
Based	O
on	O
the	O
classification	B-Process
scale	I-Process
method	I-Process
(	O
CSM	B-Process
)	O
,	O
after	O
the	O
target	O
tight	O
sandstones	B-Material
are	O
classified	O
into	O
two	O
types	O
,	O
the	O
relationship	O
between	O
core	O
porosity	B-Process
and	O
permeability	B-Process
is	O
established	O
for	O
every	O
type	O
of	O
formations	O
,	O
and	O
the	O
corresponding	O
permeability	B-Task
estimation	I-Task
models	I-Task
are	O
established	O
.	O
Field	B-Material
examples	I-Material
show	O
that	O
the	O
classification	B-Process
scale	I-Process
method	I-Process
is	O
effective	O
in	O
estimating	O
tight	O
sandstone	B-Material
permeability	B-Process
.	O

The	O
opportunity	O
offered	O
by	O
digital	O
technologies	O
to	O
make	O
deep	O
rationalization	B-Process
in	O
purchase	O
of	O
supplies	B-Material
is	O
becoming	O
indispensable	O
in	O
competition	O
between	O
enterprises	O
,	O
considering	O
positive	O
effects	O
in	O
reducing	B-Process
the	I-Process
costs	I-Process
of	O
the	O
companies	O
that	O
have	O
adopted	O
the	O
E-Procurement	B-Process
.	O
As	O
it	O
has	O
been	O
confirmed	O
by	O
numerous	O
case	B-Task
studies	I-Task
,	O
automation	B-Process
of	I-Process
procedures	I-Process
for	O
the	O
purchase	O
through	O
e-procurement	B-Process
technology	I-Process
enables	O
companies	O
to	O
achieve	O
a	O
reduction	O
in	O
costs	O
(	O
average	O
8-12	O
%)	O
of	O
total	O
purchases	O
.	O
So	O
web-based	B-Process
models	I-Process
are	O
playing	O
a	O
critical	O
role	O
within	O
companies	O
,	O
especially	O
in	O
the	O
generation	B-Process
of	I-Process
value	I-Process
of	I-Process
supply	I-Process
chain	I-Process
.	O
This	O
article	O
focuses	O
on	O
the	O
role	B-Task
of	I-Task
e-procurement	I-Task
within	O
a	O
supply	B-Process
chain	I-Process
showing	O
,	O
through	O
simulations	B-Process
,	O
the	O
advantages	O
and	O
difficulties	O
of	O
implementing	O
a	O
systematic	O
use	O
of	O
the	O
Internet	O
and	O
defining	O
the	O
basic	O
structure	O
of	O
an	O
e-supply	B-Process
chain	I-Process
.	O

In	O
the	O
present	O
paper	O
,	O
a	O
hypergraph	B-Task
model	I-Task
for	O
the	O
structural	B-Process
system	I-Process
modeling	I-Process
and	O
reconfigurability	B-Process
analysis	I-Process
has	O
been	O
presented	O
.	O
At	O
first	O
,	O
we	O
represent	O
each	O
system	O
equation	O
by	O
a	O
hyperedge	O
,	O
and	O
then	O
we	O
extend	O
the	O
modeling	B-Process
hypergraph	I-Process
with	O
others	O
colored	O
hyperedges	O
(	O
red	O
and	O
blue	O
)	O
which	O
allows	O
us	O
to	O
perform	O
the	O
analysis	B-Task
task	I-Task
.	O
Based	O
on	O
the	O
bottom	B-Process
up	I-Process
analysis	I-Process
hypergraph	I-Process
model	I-Process
,	O
it	O
's	O
very	O
easy	O
to	O
check	O
the	O
system	B-Process
reconfigurability	I-Process
in	O
the	O
presence	O
of	O
fault	O
by	O
verifying	B-Process
the	I-Process
existence	I-Process
of	I-Process
paths	I-Process
from	I-Process
the	I-Process
affected	I-Process
hyperedge	I-Process
to	O
specifics	O
blue	O
hyperedges	O
passing	O
through	O
specifics	O
red	O
hyperedges	O
.	O
The	O
method	O
is	O
illustrated	O
through	O
a	O
pedagogical	B-Task
example	I-Task
.	O

This	O
paper	O
suggests	O
a	O
design	B-Task
of	I-Task
high	I-Task
quality	I-Task
real-time	I-Task
rotation	I-Task
face	I-Task
detection	I-Task
architecture	I-Task
for	I-Task
gesture	I-Task
recognition	I-Task
of	I-Task
smart	I-Task
TV	I-Task
.	O
For	O
high	O
performance	O
rotated	O
face	B-Process
detection	I-Process
,	O
the	O
multiple-MCT	O
(	O
Modified	O
Census	O
Transform	O
)	O
architecture	O
,	O
which	O
is	O
robust	O
against	O
lighting	O
change	O
,	O
was	O
used	O
.	O
The	O
Adaboost	O
learning	O
algorithm	O
was	O
used	O
for	O
creating	O
optimized	O
learning	O
data	O
.	O
The	O
proposed	O
hardware	O
structure	O
was	O
composed	O
of	O
Color	O
Space	O
Converter	O
,	O
Image	O
Resizer	O
,	O
Noise	O
Filter	O
,	O
Memory	O
Controller	O
Interface	O
,	O
Image	O
Rotator	O
,	O
Image	O
Scaler	O
,	O
MCT	B-Process
Generator	O
,	O
Candidate	O
Detector	O
,	O
Confidence	O
Switch	O
,	O
Confidence	O
Mapper	O
,	O
Position	O
Resizer	O
,	O
Data	O
Grouper	O
,	O
Overlay	O
Processor	O
and	O
Color	O
Overlay	O
Processer	O
.	O
As	O
a	O
result	O
,	O
suggested	O
face	O
detection	O
device	O
can	O
conduct	O
real-time	O
processing	O
at	O
speed	O
of	O
at	O
least	O
30	O
frames	O
per	O
second	O
.	O

Based	O
on	O
expectation-maximization	B-Process
algorithm	I-Process
,	O
parameter	B-Task
estimation	I-Task
was	O
proposed	O
for	O
data-driven	B-Process
nonlinear	I-Process
models	I-Process
in	O
this	O
work	O
.	O
On	O
this	O
basis	O
,	O
particle	B-Material
filters	I-Material
were	O
used	O
to	O
approximately	O
calculate	O
integrals	O
,	O
deriving	O
EM	B-Process
algorithm	I-Process
based	O
on	O
particle	B-Material
filter	I-Material
.	O
And	O
the	O
effectiveness	O
of	O
using	O
the	O
proposed	O
algorithm	O
for	O
the	O
soft	B-Material
sensor	I-Material
of	O
COx	B-Material
content	O
in	O
tail	B-Material
gas	I-Material
of	O
PX	B-Process
oxidation	I-Process
side	O
reactions	O
was	O
verified	O
through	O
simulation	B-Process
results	O
.	O

In	O
this	O
paper	O
,	O
we	O
present	O
a	O
project	O
aiming	O
at	O
integrating	B-Task
immersive	I-Task
virtual	I-Task
reality	I-Task
technologies	I-Task
into	I-Task
a	I-Task
three-dimensional	I-Task
virtual	I-Task
world	I-Task
.	O
We	O
use	O
an	O
educational	B-Material
platform	I-Material
vAcademia	B-Material
as	O
a	O
test	O
bed	O
for	O
the	O
project	O
,	O
and	O
focus	O
on	O
improving	O
the	O
learning	B-Process
process	I-Process
and	O
,	O
subsequently	O
–	O
the	O
outcomes	O
.	O
We	O
aim	O
at	O
increasing	B-Task
the	I-Task
immersiveness	I-Task
of	I-Task
3D	I-Task
virtual	I-Task
world	I-Task
experience	I-Task
by	O
applying	O
motion	B-Process
tracking	I-Process
for	O
controlling	B-Process
the	I-Process
avatar	I-Process
and	O
two	O
technologies	O
for	O
natural	B-Process
navigation	I-Process
:	O
immersive	B-Process
projection	I-Process
and	O
head-mounted	B-Material
display	I-Material
.	O
In	O
addition	O
,	O
we	O
propose	O
the	O
major	B-Task
types	I-Task
of	I-Task
learning	I-Task
scenarios	I-Task
for	O
the	O
use	O
of	O
the	O
designed	O
systems	O
.	O

In	O
order	O
to	O
solve	O
the	O
problem	B-Task
that	I-Task
the	I-Task
diesel	I-Task
engine	I-Task
PT	I-Task
fuel	I-Task
system	I-Task
is	I-Task
unable	I-Task
to	I-Task
field	I-Task
maintain	I-Task
,	O
developed	O
a	O
portable	B-Material
signal	I-Material
acquisition	I-Material
and	I-Material
analysis	I-Material
system	I-Material
for	O
diesel	B-Material
engine	I-Material
PT	I-Material
fuel	I-Material
system	I-Material
.	O
Firstly	O
,	O
the	O
PT	B-Task
pump	I-Task
work	I-Task
Principle	I-Task
was	I-Task
analyzed	I-Task
,	O
and	O
the	O
PT	O
pump	O
failure	O
mapping	O
relation	O
between	O
reason	O
and	O
failure	O
phenomenon	O
was	O
analyzed	O
;	O
Secondly	O
,	O
the	O
diesel	B-Material
engine	I-Material
PT	I-Material
pump	I-Material
failure	B-Task
fuel	I-Task
pressure	I-Task
characteristics	I-Task
were	I-Task
analyzed	I-Task
;	O
Lastly	O
,	O
using	O
the	O
portable	O
signal	B-Process
acquisition	I-Process
and	O
analysis	B-Process
system	I-Process
to	O
diagnose	B-Task
the	I-Task
diesel	I-Task
engine	I-Task
PT	I-Task
fuel	I-Task
system	I-Task
,	O
experiment	O
results	O
show	O
that	O
the	O
system	O
can	O
correctly	O
detect	B-Process
the	I-Process
diesel	I-Process
engine	I-Process
PT	I-Process
fuel	I-Process
system	I-Process
state	I-Process
.	O

The	O
behavior	O
of	O
cellular	B-Material
beam	I-Material
is	O
described	O
using	O
design	B-Task
methods	I-Task
according	O
to	O
BS	O
:	O
5950	O
,	O
considering	O
particularly	O
the	O
strength	O
of	O
tee	O
sections	O
and	O
web	O
post	O
element	O
.	O
Such	O
behavior	O
is	O
derived	O
from	O
parametric	O
study	O
involving	O
finite	B-Process
element	I-Process
analysis	I-Process
using	O
software	O
ANSYS	B-Material
.	O
The	O
design	B-Process
method	I-Process
is	O
based	O
on	O
plastic	B-Process
analysis	I-Process
of	I-Process
beam	I-Process
section	I-Process
at	O
ultimate	O
loads	O
and	O
elastic	B-Process
analysis	I-Process
at	O
serviceability	O
loads	O
.	O
The	O
procedure	B-Task
of	I-Task
design	I-Task
of	I-Task
cellular	I-Task
beam	I-Task
is	I-Task
illustrated	I-Task
and	O
an	O
example	O
based	O
on	O
design	B-Process
method	I-Process
is	O
worked	O
out	O
and	O
its	O
verification	O
is	O
done	O
for	O
checking	O
the	O
suitability	O
.	O

The	O
low-carbon	B-Process
economic	I-Process
development	I-Process
has	O
become	O
the	O
trend	O
and	O
orientation	O
of	O
regional	O
economic	O
development	O
.	O
As	O
the	O
residents	O
of	O
Heilongjiang	O
province	O
,	O
their	O
consumption	O
is	O
the	O
most	O
direct	O
way	O
to	O
achieve	O
the	O
low-carbon	O
lifestyle	O
.	O
Based	O
on	O
the	O
research	O
and	O
discussion	O
of	O
the	O
connotation	O
of	O
low-carbon	O
consumption	O
and	O
its	O
culture	O
,	O
behaviour	O
,	O
preferences	O
and	O
habits	O
,	O
it	O
is	O
concluded	O
that	O
the	O
low-carbon	O
consumption	O
requires	O
us	O
to	O
abide	O
by	O
the	O
life-style	O
of	O
knowledge	O
and	O
culture	O
.	O
Therefore	O
,	O
it	O
is	O
obvious	O
that	O
the	O
development	O
of	O
low-carbon	O
economy	O
is	O
a	O
complex	O
and	O
systematic	O
project	O
,	O
involved	O
the	O
economic	O
development	O
mode	O
,	O
technological	O
innovation	O
mode	O
,	O
consumption	O
values	O
and	O
changes	O
of	O
lifestyle	O
.	O

The	O
paper	O
presents	B-Task
the	I-Task
results	I-Task
of	I-Task
studies	I-Task
of	I-Task
the	I-Task
effect	I-Task
of	I-Task
multiwalled	I-Task
carbon	I-Task
nanotubes	I-Task
18-20nm	O
in	O
concentrations	O
of	O
1	O
and	O
10mg	O
/	O
ml	O
for	O
diatoms	O
Pseudo-nitzschia	B-Material
pungens	I-Material
(	O
clone	B-Material
PP-07	I-Material
)	O
and	O
golden	B-Material
alga	I-Material
Isochrysis	I-Material
galbana	I-Material
(	O
clone	B-Material
TISO	I-Material
)	O
.	O
The	O
toxic	B-Process
effects	I-Process
of	O
multiwalled	O
nanotubes	B-Material
on	O
both	O
types	O
of	O
algae	B-Material
is	O
revealed	O
,	O
which	O
results	O
in	O
a	O
decrease	O
of	O
the	O
linear	O
dimensions	O
of	O
cells	B-Material
,	O
chloroplasts	B-Material
,	O
and	O
a	O
reduced	O
number	O
of	O
cells	B-Material
when	O
incubated	B-Process
over	O
24h	O
(	O
Pseudo-nitzschia	B-Material
pungens	I-Material
)	O
and	O
36hours	O
(	O
Isochrysis	B-Material
galbana	I-Material
)	O
.	O

The	O
retrospective	B-Task
assessment	I-Task
of	I-Task
environmental	I-Task
carrying	I-Task
capacity	I-Task
aims	O
to	O
obtain	B-Task
the	I-Task
historical	I-Task
development	I-Task
situation	I-Task
of	I-Task
reclamation	I-Task
domain	I-Task
,	O
it	O
's	O
an	O
essential	O
tool	O
for	O
improving	B-Task
the	I-Task
managed	I-Task
level	I-Task
and	O
guiding	B-Task
the	I-Task
environmental	I-Task
management	I-Task
of	I-Task
reclamation	I-Task
.	O
In	O
this	O
paper	O
,	O
a	O
synthetic	B-Task
assessment	I-Task
method	I-Task
based	I-Task
on	I-Task
cloud	I-Task
theory	I-Task
is	I-Task
applied	I-Task
to	O
evaluate	O
the	O
single	O
factor	O
and	O
multiple	O
factors	O
environmental	B-Process
carrying	I-Process
capacity	I-Process
in	O
Caofeidian	O
marine	O
district	O
,	O
Tangshan	O
Bay	O
,	O
China	O
.	O
With	O
the	O
field	B-Material
data	I-Material
of	O
five	O
assessment	O
indexes	O
in	O
recent	O
six	O
years	O
,	O
the	O
assessment	O
results	O
are	O
obtained	O
which	O
show	O
that	O
the	O
marine	B-Process
reclamation	I-Process
has	O
a	O
certain	O
impact	O
for	O
the	O
marine	O
environment	O
.	O

This	O
study	O
is	O
focused	O
on	O
the	O
water-gas	B-Process
shift	I-Process
reaction	I-Process
(	O
WGSR	O
)	O
,	O
occurring	O
in	O
the	O
chemical	O
kinetics	O
equipment	O
,	O
which	O
is	O
used	O
to	O
increase	O
hydrogen	O
recovery	O
from	O
industrial	O
processes	O
.	O
The	O
research	O
deals	O
with	O
comparing	O
hydrogen	O
recovery	O
with	O
the	O
use	O
of	O
three	O
different	O
catalysts	O
.	O
The	O
amount	O
of	O
the	O
produced	O
hydrogen	O
depends	O
considerably	O
on	O
the	O
reaction	O
state	O
and	O
the	O
catalyst	O
composition	O
.	O
To	O
improve	O
the	O
course	O
of	O
the	O
reaction	O
,	O
natural	O
catalysts	O
–	O
calcite	O
,	O
coal	O
char	O
(	O
unburned	O
residues	O
from	O
coal	O
)	O
and	O
modified	O
olivine	O
–	O
are	O
added	O
to	O
the	O
gasification	O
process	O
and	O
heated	O
to	O
the	O
process	O
temperature	O
of	O
800	O
,	O
850	O
and	O
900oC	O
.	O

Several	O
inorganic	B-Material
flocculating	I-Material
agents	I-Material
,	O
including	O
FeSO4	B-Material
,	O
Al2	B-Material
(	I-Material
SO4	I-Material
)	I-Material
3	I-Material
,	I-Material
FeCl3	B-Material
and	O
an	O
organic	B-Material
coagulant	I-Material
aid	I-Material
PAM	B-Material
,	O
were	O
used	O
to	O
treat	B-Process
the	I-Process
wastewater	I-Process
from	O
domestic	O
anima	O
and	O
poultry	O
breeding	O
in	O
this	O
paper	O
.	O
The	O
ideal	O
operating	O
conditions	O
were	O
attained	O
by	O
single	B-Task
factor	I-Task
experiment	I-Task
and	O
orthogonal	B-Task
design	I-Task
experiment	I-Task
.	O
And	O
the	O
ideal	O
operating	O
conditions	O
are	O
follows	O
:	O
the	O
dose	O
of	O
FeSO4	B-Material
and	O
PAM	B-Material
is	O
135.2mg	O
/	O
L	O
and	O
0.384mg	O
/	O
L	O
respectively	O
when	O
keeping	O
the	O
pH	O
10	O
;	O
and	O
the	O
corresponding	O
removal	B-Process
rate	I-Process
is	O
55	O
%	O
and	O
60	O
%	O
for	O
COD	B-Material
and	O
turbidity	O
.	O
Based	O
on	O
the	O
experimental	O
results	O
,	O
this	O
paper	O
analyzes	B-Task
the	I-Task
main	I-Task
factors	I-Task
that	O
affect	O
wastewater	B-Process
flocculation	I-Process
treatment	I-Process
.	O

Many	O
models	B-Process
have	O
been	O
propounded	O
for	O
forecasting	B-Process
lightning	I-Process
.	O
Though	O
majority	O
of	O
the	O
model	B-Process
had	O
shown	O
accuracy	O
,	O
the	O
response	O
time	O
in	O
detecting	O
natural	B-Process
phenomenon	I-Process
is	O
quite	O
low	O
.	O
In	O
this	O
model	O
,	O
we	O
used	O
the	O
mathematical	B-Task
experimentation	I-Task
of	O
the	O
micro	B-Material
scale	I-Material
plasmas	I-Material
to	O
develop	O
the	O
macro	B-Material
scale	I-Material
atmospheric	I-Material
plasma	I-Material
which	O
we	O
believe	O
is	O
a	O
major	O
influence	O
of	O
lightning	B-Process
.	O
The	O
Schrödinger-electrostatic	B-Process
algorithm	I-Process
was	O
propounded	O
to	O
further	O
increase	O
both	O
the	O
accuracy	O
and	O
alacrity	O
of	O
detecting	B-Process
natural	I-Process
phenomena	I-Process
.	I-Process
According	O
to	O
our	O
theoretical	B-Task
experimentation	I-Task
,	O
the	O
air	B-Material
density	O
plays	O
a	O
major	O
role	O
in	O
lightning	B-Process
forecast	O
.	O
Our	O
guess	O
was	O
verified	O
using	O
the	O
Davis	B-Process
Weather	I-Process
Station	I-Process
to	O
track	B-Task
the	I-Task
air	I-Task
density	I-Task
both	O
in	O
the	O
upper	O
and	O
lower	O
atmosphere	O
.	O
The	O
air	O
density	O
in	O
the	O
upper	B-Material
atmosphere	I-Material
showed	O
prospect	O
as	O
a	O
vital	O
factor	O
for	O
lightning	B-Process
forecast	O
.	O

Design	B-Task
semantics	I-Task
is	O
an	O
integration	B-Task
of	I-Task
human	I-Task
mode	I-Task
of	I-Task
existence	I-Task
and	I-Task
view	I-Task
on	I-Task
culture	I-Task
and	I-Task
art	I-Task
,	O
which	O
means	O
it	O
is	O
a	O
unity	B-Task
of	I-Task
art	I-Task
and	I-Task
science	I-Task
.	O
Design	B-Task
semantics	I-Task
is	O
the	O
annotation	B-Task
of	I-Task
form	I-Task
and	I-Task
the	I-Task
reflection	I-Task
of	I-Task
its	I-Task
symbolic	I-Task
meaning	I-Task
,	O
which	O
means	O
it	O
is	O
an	O
explanation	B-Task
of	I-Task
the	I-Task
deposited	I-Task
human	I-Task
cultural	I-Task
spirit	I-Task
.	O
Chinese	O
art	O
stresses	O
Expression	O
,	O
Force	O
and	O
Qi	O
.	O
In	O
China	O
,	O
people	O
advocate	O
“	O
to	B-Process
learn	I-Process
from	I-Process
nature	I-Process
”	O
,	O
“	O
to	O
look	O
up	O
to	O
observe	O
the	O
sun	O
,	O
the	O
moon	O
and	O
stars	O
,	O
and	O
look	O
down	O
to	O
observe	B-Process
the	I-Process
surroundings	I-Process
”	O
,	O
and	O
take	O
“	O
Nature	O
and	O
Man	O
in	O
One	O
”	O
as	O
the	O
highest	O
state	O
of	O
spirit	O
.	O
Design	B-Task
semantics	I-Task
is	O
expressed	O
in	O
space	B-Task
environment	I-Task
design	I-Task
through	O
a	O
symbiotic	O
philosophical	O
view	O
that	O
natural	B-Material
and	I-Material
artificial	I-Material
forms	I-Material
are	O
complementary	O
and	O
interactive	O
.	O
This	O
form	O
of	O
design	O
leads	O
humans	O
back	O
to	O
a	O
better	O
state	O
of	O
living	O
,	O
i.e.	O
Nature	O
and	O
Man	O
in	O
One	O
.	O

It	O
has	O
been	O
acknowledged	O
that	O
megalopolises	O
are	O
playing	O
a	O
leading	O
role	O
in	O
the	O
processes	O
of	O
both	O
economic	B-Process
development	I-Process
and	O
culture	B-Process
change	I-Process
.	O
Thereupon	O
,	O
the	O
new	O
emphases	O
on	O
sustainability	B-Process
of	I-Process
transportation	I-Process
system	I-Process
in	O
megalopolis	O
are	O
creating	O
new	O
demands	O
for	O
adequate	O
approach	B-Task
to	I-Task
measure	I-Task
its	I-Task
performance	I-Task
and	I-Task
diagnosis	I-Task
potential	I-Task
drawbacks	I-Task
.	O
By	O
examining	O
the	O
descriptions	O
of	O
sustainable	O
transport	B-Process
system	I-Process
as	O
well	O
as	O
its	O
evaluating	O
approach	O
,	O
a	O
framework	B-Material
with	O
the	O
general	O
applicability	O
and	O
easily	O
accessible	O
data	O
resource	O
for	O
evaluating	O
sustainability	O
of	O
transport	B-Process
system	I-Process
in	O
megalopolis	O
is	O
developed	O
based	O
on	O
nature	O
of	O
regional	O
structure	O
and	O
the	O
feature	O
transport	O
demand	O
in	O
megalopolis	O
.	O
The	O
proposed	B-Task
framework	I-Task
is	I-Task
applied	I-Task
in	O
the	O
analysis	O
and	O
comparison	O
of	O
Jing-Jin-Ji	O
and	O
Yangtze	O
River	O
Delta.	O
.	O

In	O
this	O
paper	O
we	O
consider	O
problems	B-Task
of	I-Task
creating	I-Task
and	I-Task
introducing	I-Task
intelligent	I-Task
management	I-Task
systems	I-Task
as	O
one	O
of	O
the	O
most	O
important	O
mechanism	O
of	O
increasing	B-Task
energy	I-Task
efficiency	I-Task
in	O
industry	O
.	O
Operating	O
principles	O
of	O
intelligent	B-Process
electric	I-Process
power	I-Process
distribution	I-Process
systems	I-Process
developed	O
in	O
MSTU	B-Material
«	I-Material
STANKIN	I-Material
»	I-Material
for	O
AC	B-Material
and	I-Material
DC	I-Material
grids	I-Material
on	O
industrial	O
plants	O
are	O
described	O
.	O
Essential	O
devices	B-Process
composing	I-Process
the	I-Process
systems	I-Process
are	O
considered	O
,	O
their	O
technical	B-Task
characteristics	I-Task
are	I-Task
described	I-Task
.	O
Experimental	O
results	O
are	O
presented.In	O
this	O
paper	O
we	O
consider	O
problems	O
of	O
creating	B-Task
and	I-Task
introducing	I-Task
intelligent	I-Task
management	I-Task
systems	I-Task
as	O
one	O
of	O
the	O
most	O
important	O
mechanism	O
of	O
increasing	O
energy	O
efficiency	O
in	O
industry	O
.	O
Operating	O
principles	O
of	O
intelligent	O
electric	O
power	O
distribution	O
systems	O
developed	O
in	O
MSTU	O
«	O
STANKIN	O
»	O
for	O
AC	O
and	O
DC	O
grids	O
on	O
industrial	O
plants	O
are	O
described	O
.	O
Essential	O
devices	O
composing	O
the	O
systems	O
are	O
considered	O
,	O
their	O
technical	O
characteristics	O
are	O
described	O
.	O
Experimental	O
results	O
are	O
presented	O
.	O

Along	O
with	O
the	O
expansion	B-Task
of	I-Task
computer-based	I-Task
climate	I-Task
simulations	I-Task
,	O
efficient	O
visualization	B-Task
and	I-Task
analysis	I-Task
of	I-Task
massive	I-Task
climate	I-Task
data	I-Task
are	O
becoming	O
more	O
important	O
than	O
ever	O
.	O
In	O
this	O
paper	O
,	O
we	O
try	O
to	O
explore	B-Task
the	I-Task
factors	I-Task
behide	I-Task
climate	I-Task
changes	I-Task
by	O
combining	B-Process
window	I-Process
query	I-Process
and	I-Process
time-varying	I-Process
data	I-Process
mining	I-Process
techniques	I-Process
.	O
With	O
constant	O
query	O
time	O
and	O
acceptable	O
storage	O
cost	O
,	O
the	O
algorithms	B-Process
presented	O
support	O
various	O
queries	B-Process
on	O
3d	B-Process
time-varying	I-Process
datasets	I-Process
,	O
such	O
as	O
average	O
,	O
min	O
,	O
and	O
max	O
value	O
.	O
A	O
new	O
time-varying	B-Process
data	I-Process
analysis	I-Process
algorithm	I-Process
is	O
given	O
,	O
which	O
is	O
especially	O
suitable	O
for	O
analyzing	B-Task
big	I-Task
data	I-Task
.	O
All	O
these	O
algorithms	B-Process
have	O
been	O
implemented	B-Task
on	I-Task
and	I-Task
integrated	I-Task
into	I-Task
a	I-Task
visual	I-Task
analysis	I-Task
system	I-Task
,	O
with	O
tiled-LCD	B-Material
ultra-resolution	I-Material
display	I-Material
.	O
Experimental	O
results	O
on	O
several	B-Material
datasets	I-Material
from	I-Material
practical	I-Material
applications	I-Material
are	O
presented	O
.	O

Improving	B-Task
as	I-Task
well	I-Task
as	I-Task
evaluating	I-Task
the	I-Task
performance	I-Task
of	O
High	B-Task
Performance	I-Task
Computing	I-Task
(	O
HPC	B-Task
)	O
applications	O
by	O
migrating	B-Process
them	I-Process
to	I-Process
Cloud	I-Process
environments	I-Process
are	O
widely	O
considered	O
as	O
critical	O
issues	O
in	O
the	O
field	O
of	O
high	B-Task
performance	I-Task
and	I-Task
Cloud	I-Task
computing	I-Task
.	O
However	O
,	O
poor	B-Process
network	I-Process
performance	I-Process
,	O
heterogeneous	B-Process
and	I-Process
dynamic	I-Process
environments	I-Process
are	O
some	O
series	O
of	O
pitfalls	O
for	O
execution	B-Process
of	I-Process
HPC	I-Process
applications	I-Process
in	I-Process
Cloud	I-Process
.	O
This	O
paper	O
proposes	B-Task
a	I-Task
new	I-Task
approach	I-Task
to	I-Task
improve	I-Task
the	I-Task
performance	I-Task
and	I-Task
scalability	I-Task
of	I-Task
HPC	I-Task
applications	I-Task
on	O
Amazon	B-Material
's	I-Material
HPC	I-Material
Cloud	I-Material
.	O
The	O
evidence	O
from	O
our	O
approach	O
points	O
a	O
significant	O
improvement	O
in	O
speed	B-Process
up	I-Process
and	O
scale	B-Process
up	I-Process
with	O
the	O
response	B-Process
rate	I-Process
of	O
more	O
than	O
20	O
percent	O
parallel	B-Process
efficiency	I-Process
on	O
the	O
Cloud	B-Material
in	O
comparison	O
to	O
dedicated	O
HPC	B-Material
cluster	I-Material
.	O
We	O
state	O
that	O
the	O
EC2	B-Material
Cloud	I-Material
system	I-Material
is	O
a	O
feasible	O
platform	O
for	O
deploying	B-Process
on-demand	I-Process
,	I-Process
small	I-Process
sized	I-Process
HPC	I-Process
applications	I-Process
.	O

In	O
the	O
paper	O
we	O
present	O
an	O
extended	B-Task
version	I-Task
of	I-Task
the	I-Task
graph-based	I-Task
unsupervised	I-Task
Word	I-Task
Sense	I-Task
Disambiguation	I-Task
algorithm	I-Task
.	O
The	O
algorithm	O
is	O
based	O
on	O
the	O
spreading	B-Process
activation	I-Process
scheme	I-Process
applied	O
to	O
the	O
graphs	O
dynamically	O
built	O
on	O
the	O
basis	O
of	O
the	O
text	O
words	O
and	O
a	O
large	O
wordnet	O
.	O
The	O
algorithm	O
,	O
originally	O
proposed	O
for	O
English	O
and	O
Princeton	O
WordNet	O
,	O
was	O
adapted	O
to	O
Polish	O
and	O
plWordNet	O
.	O
An	O
extension	O
based	O
on	O
the	O
knowledge	O
acquired	O
from	O
the	O
corpus-derived	O
Measure	O
of	O
Semantic	O
Relatedness	O
was	O
proposed	O
.	O
The	O
extended	O
algorithm	O
was	O
evaluated	O
against	O
the	O
manually	O
disambiguated	O
corpus	O
.	O
We	O
observed	O
improvement	O
in	O
the	O
case	O
of	O
the	O
disambiguation	O
performed	O
for	O
shorter	O
text	O
contexts	O
.	O
In	O
addition	O
the	O
algorithm	O
application	O
expressed	O
improvement	O
in	O
document	O
clustering	O
task	O
.	O

Sentence	B-Task
reduction	I-Task
is	O
one	O
of	O
approaches	O
for	O
text	B-Task
summarization	I-Task
that	O
has	O
been	O
attracted	O
many	O
researchers	O
and	O
scholars	O
of	O
natural	B-Task
language	I-Task
processing	I-Task
field	O
.	O
In	O
this	O
paper	O
,	O
we	O
present	O
a	O
method	O
that	O
generates	B-Task
sentence	I-Task
reduction	I-Task
and	O
applying	O
in	O
Vietnamese	B-Process
text	I-Process
summarization	I-Process
using	O
Bayesian	B-Process
Network	I-Process
model	I-Process
.	O
Bayesian	B-Process
network	I-Process
model	I-Process
is	O
used	O
to	O
find	O
the	O
best	B-Task
likelihood	I-Task
short	I-Task
sentence	I-Task
through	O
compare	B-Process
difference	I-Process
of	I-Process
probability	I-Process
.	O
Experimental	O
results	O
with	O
980	B-Material
sentences	I-Material
,	O
show	O
that	O
our	O
method	O
really	O
effectively	O
in	O
generating	B-Process
sentence	I-Process
reduction	I-Process
that	O
understandable	O
,	O
readable	O
and	O
exactly	O
grammar	O
.	O

Knowledge	B-Task
Management	I-Task
(	O
KM	B-Task
)	O
is	O
one	O
of	O
the	O
hotspots	O
for	O
research	O
in	O
the	O
past	O
decade	O
.	O
In	O
most	O
cases	O
,	O
the	O
number	O
of	O
users	O
in	O
a	O
Knowledge	B-Process
Management	I-Process
System	I-Process
(	O
KMS	B-Process
)	O
is	O
very	O
large	O
,	O
and	O
they	O
are	O
from	O
varied	O
departments	O
,	O
even	O
other	O
companies	O
.	O
In	O
this	O
paper	O
,	O
some	O
defects	B-Task
when	I-Task
existing	I-Task
methods	I-Task
about	I-Task
access	I-Task
control	I-Task
and	I-Task
recommendation	I-Task
are	I-Task
deployed	I-Task
in	I-Task
KMS	I-Task
are	I-Task
analyzed	I-Task
to	O
show	O
that	O
these	O
widely-used	O
approaches	O
need	O
to	O
be	O
extended	B-Process
.	O
To	O
overcome	O
the	O
deficiencies	O
of	O
previous	O
work	O
,	O
this	O
paper	O
proposes	O
an	O
extended	O
Role-Based	O
Access	O
Control	O
(	O
RBAC	O
)	O
method	O
and	O
a	O
hybrid	O
recommendation	O
approach	O
for	O
Knowledge	O
Management	O
System	O
.	O
Also	O
,	O
a	O
real-life	O
system	O
is	O
presented	O
to	O
verify	O
the	O
proposed	O
methodology	O
.	O

Some	O
nonlinear	B-Process
wave	I-Process
equations	I-Process
are	O
more	O
difficult	O
to	O
investigate	B-Task
mathematically	I-Task
,	O
as	O
no	O
general	O
analytical	B-Process
method	I-Process
for	O
their	O
solutions	O
exists	O
.	O
The	O
Exponential	B-Process
Time	I-Process
Differencing	I-Process
(	O
ETD	B-Process
)	O
technique	O
requires	O
minimum	B-Material
stages	I-Material
to	O
obtain	O
the	O
requiredaccurateness	O
,	O
which	O
suggests	O
an	O
efficient	O
technique	O
relatingto	O
computational	B-Process
duration	I-Process
thatensures	O
remarkable	O
stability	B-Process
characteristicsupon	I-Process
resolving	B-Task
nonlinear	I-Task
wave	I-Task
equations	I-Task
.	O
This	O
article	O
solves	B-Task
the	I-Task
diagonal	I-Task
example	I-Task
of	I-Task
Kawahara	I-Task
equation	I-Task
via	O
the	O
ETD	B-Process
Runge-Kutta	I-Process
4	I-Process
technique	I-Process
.	O
Implementation	O
of	O
this	O
technique	O
is	O
proposed	O
by	O
short	O
Matlab	B-Material
programs	I-Material
.	O

Contractor	B-Task
selection	I-Task
for	O
a	O
project	O
is	O
an	O
important	O
decision	O
,	O
one	O
for	O
the	O
project	B-Material
time	I-Material
and	I-Material
cost	I-Material
,	O
next	O
for	O
the	O
quality	B-Material
obtained	O
by	O
the	O
project	O
.	O
Although	O
the	O
project	B-Material
managers	I-Material
can	O
easily	O
determine	B-Task
the	I-Task
project	I-Task
time	I-Task
and	I-Task
cost	I-Task
,	O
the	O
quality	O
is	O
usually	O
undefined	O
especially	O
for	O
un-experienced	B-Material
managers	I-Material
.	O
With	O
a	O
learnable	B-Process
property	I-Process
,	O
an	O
approach	O
is	O
first	O
introduced	O
in	O
this	O
paper	O
to	O
quantify	B-Task
the	I-Task
quality	I-Task
obtained	I-Task
for	I-Task
a	I-Task
gas	I-Task
well	I-Task
drilling	I-Task
project	I-Task
.	O
Then	O
,	O
based	O
on	O
these	O
three	O
objectives	B-Material
(	B-Material
time	I-Material
,	O
cost	B-Material
,	O
and	O
quality	B-Material
)	O
,	O
a	O
contractor	B-Task
selection	I-Task
problem	I-Task
is	O
converted	O
to	O
an	O
optimization	B-Task
problem	I-Task
.	O
Next	O
,	O
the	O
NSGA-II	B-Process
algorithm	I-Process
is	O
utilized	O
for	O
solution	O
.	O
At	O
the	O
end	O
,	O
a	O
sensitivity	B-Process
analysis	I-Process
is	O
performed	O
to	O
select	O
the	O
parameters	O
of	O
the	O
algorithm	O
.	O

In	O
this	O
paper	O
,	O
a	O
regression	B-Task
analysis	I-Task
based	O
method	O
is	O
proposed	O
to	O
calculate	B-Task
the	I-Task
Journal	I-Task
Influence	I-Task
Score	I-Task
.	O
This	O
Influence	O
Score	O
is	O
used	O
to	O
measure	B-Task
the	I-Task
scientific	I-Task
influence	I-Task
of	I-Task
scholarly	I-Task
journals	I-Task
.	O
Journal	B-Process
Influence	I-Process
Score	I-Process
is	O
calculated	O
by	O
using	B-Process
various	I-Process
factors	I-Process
in	I-Process
a	I-Process
weighted	I-Process
manner	I-Process
.	O
The	O
Score	O
is	O
then	O
compared	B-Task
with	I-Task
the	I-Task
SCImago	I-Task
Journal	I-Task
Score	I-Task
.	O
The	O
results	O
show	O
that	O
the	O
error	O
is	O
small	O
between	O
the	O
existing	O
and	O
proposed	O
methods	O
,	O
proving	O
that	O
the	O
model	O
is	O
a	O
feasible	O
and	O
effective	O
way	O
of	O
calculating	B-Task
scientific	I-Task
impact	I-Task
of	I-Task
journals	I-Task
.	O

In	O
this	O
paper	O
,	O
we	O
present	O
a	O
tele-operated	B-Process
mobile	I-Process
robot	I-Process
system	I-Process
for	O
old	B-Task
age	I-Task
surveillance	I-Task
.	O
The	O
robot	O
operates	O
in	O
autonomous	B-Process
mode	I-Process
in	O
which	O
the	O
robots	O
navigates	B-Process
in	I-Process
the	I-Process
environment	I-Process
and	O
search	B-Process
for	I-Process
unusual	I-Process
situation	I-Process
of	O
elderly	B-Material
people	I-Material
.	O
If	O
a	O
patient	B-Material
is	O
lying	B-Process
on	I-Process
the	I-Process
floor	I-Process
,	O
the	O
robot	B-Material
informs	B-Process
the	I-Process
user	I-Process
.	O
The	O
user	O
switches	B-Process
the	I-Process
control	I-Process
mode	I-Process
from	I-Process
autonomous	I-Process
to	I-Process
haptic	I-Process
based	I-Process
user	O
control	O
.	O
In	O
the	O
autonomous	B-Process
mode	I-Process
,	O
the	O
robot	B-Material
utilizes	O
the	O
visual	B-Task
sensor	I-Task
and	I-Task
landmarks	I-Task
to	I-Task
monitor	I-Task
the	O
entire	O
environment	O
.	O
The	O
robot	B-Material
is	O
equipped	O
microphone	B-Material
,	O
speaker	B-Material
and	O
monitor	B-Material
making	O
it	O
possible	O
to	O
communicate	B-Process
with	O
the	O
user	O
in	O
remote	B-Material
place	I-Material
.	O
In	O
addition	O
,	O
the	O
robot	B-Material
utilizes	O
the	O
vital	B-Process
sensors	I-Process
to	O
check	O
the	O
patient	B-Material
's	I-Material
condition	I-Material
.	O
The	O
preliminary	O
surveillance	O
experiments	O
show	O
a	O
good	O
performance	O
.	O

Recently	O
,	O
a	O
network	B-Task
virtualization	I-Task
technology	I-Task
has	O
attracted	O
considerable	O
attention	O
as	O
one	O
of	O
new	B-Task
generation	I-Task
network	I-Task
technologies	I-Task
.	O
In	O
this	O
paper	O
,	O
in	O
order	O
to	O
permit	O
the	O
rapid	B-Task
changing	I-Task
for	I-Task
a	I-Task
topology	I-Task
of	I-Task
a	I-Task
virtual	I-Task
network	I-Task
,	O
we	O
propose	O
a	O
new	O
virtual	B-Process
network	I-Process
construction	I-Process
method	I-Process
based	O
on	O
the	O
shortest	B-Process
path	I-Process
betweenness	I-Process
.	O
In	O
our	O
proposed	B-Process
method	I-Process
,	O
at	O
first	O
,	O
a	O
service	B-Material
provider	I-Material
receives	O
a	O
user	O
's	O
request	O
for	O
the	O
reconfiguration	B-Process
of	I-Process
the	I-Process
constructed	I-Process
virtual	I-Process
network	I-Process
.	O
In	O
this	O
case	O
,	O
the	O
service	B-Material
provider	I-Material
reconfigures	B-Process
the	I-Process
topology	I-Process
of	I-Process
the	I-Process
constructed	I-Process
virtual	I-Process
network	I-Process
rapidly	O
based	O
on	O
shortest	B-Process
path	I-Process
betweenness	I-Process
.	O
We	O
evaluate	O
the	O
performance	O
of	O
our	O
proposed	O
method	O
with	O
simulation	B-Process
,	O
and	O
we	O
show	O
the	O
effectiveness	O
of	O
our	O
proposed	O
method	O
.	O

Security	O
issues	O
of	O
data	B-Material
hosted	O
in	O
a	O
Cloud	B-Material
Computing	I-Material
provider	I-Material
remain	O
hidden	O
seen	O
excessive	B-Process
marketing	I-Process
that	O
led	O
to	O
a	O
totally	O
unrealistic	O
view	O
of	O
cloud	B-Task
computing	I-Task
security	I-Task
.	O
Although	O
Cloud	B-Task
Computing	I-Task
has	O
not	O
yet	O
reached	O
the	O
level	O
of	O
maturity	O
expected	O
by	O
its	O
customers	O
,	O
and	O
that	O
the	O
problems	O
of	O
confidentiality	B-Process
,	I-Process
integrity	I-Process
,	I-Process
reliability	I-Process
and	I-Process
consistency	I-Process
(	O
CIRC	B-Process
)	O
are	O
still	O
open	O
,	O
the	O
researchers	O
in	O
this	O
field	O
have	O
already	O
considered	O
a	O
future	B-Process
cloud	I-Process
strategy	I-Process
which	O
aims	O
:	O
a	O
better	O
QoS	O
,	O
reliability	O
and	O
high	O
availability	O
,	O
it	O
is	O
the	O
Multi-Clouds	B-Process
,	O
Cloud	B-Process
of	I-Process
Clouds	I-Process
or	O
Interclouds.This	O
paper	O
will	O
present	O
the	O
security	O
limitations	O
in	O
the	O
single	O
Cloud	O
and	O
the	O
usefulness	O
of	O
adopting	O
rather	O
Multi-Clouds	O
strategy	O
to	O
reduce	O
security	O
risks	O
,	O
through	O
the	O
use	O
of	O
DepSky	O
which	O
is	O
a	O
virtual	O
storage	O
system	O
that	O
ensures	O
better	O
availability	O
and	O
high	O
confidentiality	O
of	O
data	O
.	O

This	O
paper	O
presents	O
general	O
results	O
on	O
the	O
Java	B-Task
source	I-Task
code	I-Task
snippet	I-Task
detection	I-Task
problem	O
.	O
We	O
propose	O
the	O
tool	O
which	O
uses	O
graph	B-Process
and	I-Process
subgraph	I-Process
isomorphism	I-Process
detection	I-Process
.	O
A	O
number	O
of	O
solutions	O
for	O
all	O
of	O
these	O
tasks	O
have	O
been	O
proposed	O
in	O
the	O
literature	O
.	O
However	O
,	O
although	O
that	O
all	O
these	O
solutions	O
are	O
really	O
fast	O
,	O
they	O
compare	B-Process
just	I-Process
the	I-Process
constant	I-Process
static	I-Process
trees	I-Process
.	O
Our	O
solution	O
offers	O
to	O
enter	B-Process
an	I-Process
input	I-Process
sample	I-Process
dynamically	I-Process
with	O
the	O
Scripthon	B-Material
language	I-Material
while	O
preserving	B-Process
an	I-Process
acceptable	I-Process
speed	I-Process
.	O
We	O
used	O
several	B-Process
optimizations	I-Process
to	O
achieve	O
very	O
low	O
number	O
of	O
comparisons	B-Process
during	O
the	O
matching	B-Process
algorithm	I-Process
.	O

In	O
this	O
paper	O
,	O
adaptive	B-Task
beamforming	I-Task
techniques	I-Task
for	O
smart	B-Material
antennas	I-Material
based	O
upon	O
Least	B-Process
Mean	I-Process
Squares	I-Process
(	O
LMS	B-Process
)	O
,	O
Sample	B-Process
Matrix	I-Process
Inversion	I-Process
(	O
SMI	B-Process
)	O
,	O
Recursive	B-Process
Least	I-Process
Squares	I-Process
(	O
RLS	B-Process
)	O
and	O
Conjugate	B-Process
Gradient	I-Process
Method	I-Process
(	O
CGM	B-Process
)	O
are	O
discussed	B-Task
and	I-Task
analyzed	I-Task
.	O
The	O
beamforming	B-Process
performance	I-Process
is	O
studied	O
by	O
varying	B-Process
the	I-Process
element	I-Process
spacing	I-Process
and	I-Process
the	I-Process
number	I-Process
of	I-Process
antenna	I-Process
array	I-Process
elements	I-Process
for	O
each	O
algorithm	B-Material
.	O
These	O
four	O
algorithms	B-Material
are	O
compared	O
for	O
their	O
rate	B-Process
of	I-Process
convergence	I-Process
,	O
beamforming	B-Process
and	O
null	B-Process
steering	I-Process
performance	I-Process
(	O
beamwidth	B-Process
,	O
null	B-Process
depths	I-Process
and	O
maximum	B-Process
side	I-Process
lobe	I-Process
level	I-Process
)	O
.	O

In	O
this	O
paper	O
,	O
three	B-Task
different	I-Task
approaches	I-Task
for	O
implementing	B-Task
a	I-Task
quantum	I-Task
search	I-Task
algorithm	I-Task
by	I-Task
adiabatic	I-Task
evolution	I-Task
are	O
shown	O
.	O
As	O
expected	O
,	O
either	O
one	O
of	O
them	O
can	O
provide	O
a	O
quadratic	B-Process
speed	I-Process
up	I-Process
as	O
opposed	O
to	O
the	O
classical	B-Process
search	I-Process
algorithm	I-Process
.	O
This	O
implies	O
that	O
adiabatic	B-Process
evolution	I-Process
based	O
quantum	B-Task
computation	I-Task
gives	O
more	O
feasibilities	B-Material
than	O
the	O
quantum	B-Process
circuit	I-Process
model	I-Process
,	O
although	O
the	O
equivalence	O
between	O
them	O
has	O
already	O
been	O
proven	O
in	O
the	O
corresponding	O
literature	O
.	O

Video-oculography	B-Process
(	O
VOG	B-Process
)	O
is	O
one	O
of	O
eye	B-Process
movement	I-Process
measurement	I-Process
methods	I-Process
.	O
A	O
key	O
problem	O
of	O
VOG	B-Process
is	O
to	O
accurately	B-Task
estimate	I-Task
the	I-Task
pupil	I-Task
center	I-Task
.	O
Then	O
a	O
pupil	B-Process
location	I-Process
method	I-Process
based	O
on	O
morphology	B-Process
and	O
Canny	B-Process
algorithm	I-Process
was	O
proposed	O
for	O
a	O
WIFI-based	B-Process
VOG	I-Process
system	I-Process
which	O
was	O
developed	O
our	O
latest	O
work	O
.	O
Moreover	O
,	O
a	O
healthy	B-Material
volunteer	I-Material
was	O
introduced	O
to	O
do	O
sinusoidal	B-Task
tracking	I-Task
test	I-Task
to	O
evaluate	B-Task
the	I-Task
pupil	I-Task
location	I-Task
method	I-Task
.	O
Experimental	O
results	O
showed	O
that	O
the	O
method	O
could	O
well	O
trace	B-Process
eye	I-Process
movement	I-Process
and	O
meet	O
the	O
anticipated	O
results	O
with	O
stimulation	B-Process
.	O

The	O
Hamiltonian	B-Process
approach	I-Process
and	O
the	O
variational	B-Process
approach	I-Process
are	O
utilized	O
to	O
treat	B-Task
the	I-Task
relativistic	I-Task
harmonic	I-Task
oscillator	I-Task
for	O
the	O
amplitude-frequency	B-Process
relationship	I-Process
.	O
The	O
nice	O
reliability	O
is	O
shown	O
by	O
the	O
result	B-Process
comparison	I-Process
with	O
that	O
from	O
open	B-Material
literature	I-Material
.	O
The	O
simplicity	O
and	O
efficiency	O
of	O
the	O
methods	O
are	O
also	O
disclosed	O
for	O
different	O
range	O
of	O
the	O
initial	B-Process
amplitude	I-Process
during	O
looking	O
for	O
the	O
amplitude-frequency	B-Process
relationship	I-Process
for	O
the	O
nonlinear	B-Process
relativistic	I-Process
harmonic	I-Process
oscillator	I-Process
.	O

This	O
paper	O
make	O
the	O
explained	O
variables	O
our	O
financial	B-Process
stress	I-Process
index	I-Process
consist	O
of	O
the	O
synchronous	B-Process
variables	I-Process
financial	I-Process
systemic	I-Process
risk	I-Process
,	O
and	O
make	O
the	O
explanatory	O
variables	O
the	O
macroeconomic	B-Process
variable	I-Process
,	O
currency	B-Process
credit	I-Process
variable	I-Process
,	O
asset	B-Process
price	I-Process
variable	I-Process
and	O
the	O
macroeconomic	B-Process
variable	I-Process
of	I-Process
correlative	I-Process
economic	I-Process
powers	I-Process
,	O
then	O
use	O
stepwise	B-Process
regression	I-Process
method	I-Process
to	O
establish	B-Task
the	I-Task
financial	I-Task
systemic	I-Task
risk	I-Task
best	I-Task
predict	I-Task
equation	I-Task
,	O
thus	O
set	B-Task
up	I-Task
the	I-Task
reasonable	I-Task
and	I-Task
practical	I-Task
financial	I-Task
systemic	I-Task
risk	I-Task
early-warning	I-Task
index	I-Task
system	I-Task
;	O
besides	O
,	O
use	O
the	O
best	B-Process
prediction	I-Process
equations	I-Process
predicts	O
the	O
financial	O
systemic	O
risk	O
status	O
in	O
2011	O
.	O
The	O
predicted	O
results	O
show	O
that	O
Chinese	O
financial	O
systemic	O
risk	O
is	O
on	O
the	O
rise	O
in	O
the	O
first	O
three	O
quarters	O
and	O
higher	O
than	O
the	O
peak	O
of	O
2008	O
;	O
financial	O
systemic	O
risk	O
start	O
to	O
decline	O
since	O
the	O
fourth	O
quarter	O
.	O

In	O
Obstacle	B-Task
detection	I-Task
is	O
based	O
on	O
inverse	B-Process
perspective	I-Process
mapping	I-Process
and	I-Process
homography	I-Process
.	O
Obstacle	B-Task
classification	I-Task
is	O
based	O
on	O
fuzzy	B-Process
neural	I-Process
network	I-Process
.	O
The	O
estimation	B-Task
of	I-Task
the	I-Task
vanishing	I-Task
point	I-Task
relies	O
on	O
feature	B-Process
extraction	I-Process
strategy	I-Process
.	O
The	O
method	O
exploits	O
the	O
geometrical	B-Process
relations	I-Process
between	O
the	O
elements	B-Material
in	O
the	O
scene	B-Material
so	O
that	O
obstacle	B-Material
can	O
be	O
detected	B-Process
.	O
The	O
estimated	B-Process
homography	I-Process
of	O
the	O
road	B-Material
plane	I-Material
between	O
successive	O
images	B-Material
is	O
used	O
for	O
image	B-Task
alignment	I-Task
.	O
A	O
new	O
fuzzy	B-Process
decision	I-Process
fusion	I-Process
method	I-Process
with	O
fuzzy	B-Process
attribution	I-Process
for	I-Process
obstacle	I-Process
detection	I-Process
and	I-Process
classification	I-Process
application	I-Process
is	O
described	O
The	B-Process
fuzzy	I-Process
decision	I-Process
function	I-Process
modifies	O
parameters	O
with	O
auto-adapted	B-Process
algorithm	I-Process
to	O
get	O
better	O
classification	B-Process
probability	O
It	O
is	O
shown	O
that	O
the	O
method	O
can	O
achieve	O
better	O
classification	O
result	O
.	O

The	O
load	O
of	O
beam	B-Material
pumping	I-Material
unit	I-Material
is	O
changeable	O
,	O
which	O
is	O
often	O
in	O
a	O
state	O
of	O
light	B-Process
load	I-Process
.	O
Reducing	B-Process
a	I-Process
certain	I-Process
voltage	I-Process
can	O
improve	B-Process
the	I-Process
power	I-Process
factor	I-Process
and	I-Process
efficiency	I-Process
of	I-Process
the	I-Process
beam	I-Process
pumping	I-Process
unit	I-Process
when	O
in	O
light	B-Process
load	I-Process
.	O
We	O
can	O
change	B-Process
the	I-Process
voltage	I-Process
by	O
changing	B-Process
the	I-Process
thyristor	I-Process
trigger	I-Process
angle	I-Process
.	O
It	O
is	O
complex	O
and	O
unacceptable	O
to	O
analyze	B-Process
the	I-Process
change	I-Process
of	I-Process
the	I-Process
cycles	I-Process
of	I-Process
the	I-Process
load	I-Process
overall	I-Process
.	O
So	O
we	O
can	O
divide	B-Process
the	I-Process
load	I-Process
of	O
the	O
whole	O
cycle	O
into	O
several	O
equal	B-Material
parts	I-Material
,	O
each	O
can	O
be	O
thought	O
of	O
as	O
a	O
constant	B-Process
load	I-Process
.	O
The	O
most	O
optimal	B-Process
voltage	I-Process
for	O
the	O
current	B-Process
load	I-Process
can	O
be	O
calculated	B-Task
by	I-Task
genetic	I-Task
algorithm	I-Task
.	O
When	O
each	O
load	O
is	O
in	O
the	O
most	O
optimal	B-Process
voltage	I-Process
,	O
we	O
can	O
get	O
the	O
whole	B-Process
optimal	I-Process
voltage	I-Process
changeable	I-Process
rule	I-Process
.	I-Process
Then	O
it	O
produces	O
the	O
result	O
of	O
energy	B-Task
saving	I-Task
.	O

Based	O
on	O
the	O
description	B-Process
model	I-Process
of	O
object-orientation-based	B-Process
direction	I-Process
relation	I-Process
in	O
two-dimensional	B-Material
space	I-Material
,	O
the	O
description	B-Task
mode	I-Task
of	I-Task
object-orientation-based	I-Task
direction	I-Task
relation	I-Task
in	I-Task
three-dimensional	I-Task
space	I-Task
is	I-Task
proposed	I-Task
.	O
The	O
basic	O
idea	O
is	O
that	O
the	O
actual	O
direction	B-Process
region	I-Process
is	O
modeled	O
as	O
an	O
open	B-Material
shape	I-Material
.	O
The	O
computation	B-Process
related	O
to	O
the	O
world	B-Material
boundary	I-Material
of	O
spatial	B-Process
direction	I-Process
region	I-Process
is	O
eliminated	O
,	O
and	O
the	O
processing	B-Process
of	I-Process
the	I-Process
direction	I-Process
predicates	I-Process
is	O
converted	O
into	O
the	O
processing	B-Process
of	I-Process
topological	I-Process
operations	I-Process
between	O
open	B-Material
shapes	I-Material
and	O
closed	O
geometry	B-Material
objects	I-Material
.	O
The	O
algorithms	B-Process
of	I-Process
topological	I-Process
operations	I-Process
between	O
open	B-Material
shapes	I-Material
and	O
closed	O
geometry	B-Material
objects	I-Material
are	O
presented	O
and	O
the	O
theoretical	B-Task
proof	I-Task
for	I-Task
the	I-Task
correctness	I-Task
and	I-Task
completeness	I-Task
of	I-Task
the	I-Task
algorithms	I-Task
is	O
performed	O
.	O

The	O
paper	O
deals	O
with	O
the	O
computation	B-Task
of	I-Task
distribution	I-Task
network	I-Task
components	I-Task
reliability	I-Task
parameters	I-Task
.	O
Knowledge	O
of	O
the	O
component	B-Process
reliability	I-Process
parameters	I-Process
in	O
power	B-Material
networks	I-Material
is	O
necessary	O
for	O
the	O
reliability	B-Process
computation	I-Process
and	O
also	O
for	O
reliability-centered	B-Process
maintenance	I-Process
system	I-Process
.	O
Component	B-Process
reliability	I-Process
parameters	I-Process
are	O
possible	O
to	O
retrieve	B-Process
only	O
with	O
accurate	B-Material
databases	I-Material
of	I-Material
distribution	I-Material
companies	I-Material
.	O
Such	O
a	O
database	B-Material
includes	O
records	B-Process
of	I-Process
outages	I-Process
and	I-Process
interruptions	I-Process
in	O
power	B-Material
networks	I-Material
.	O
It	O
is	O
impossible	O
to	O
retrieve	B-Process
reliability	I-Process
parameters	I-Process
from	O
this	O
data	B-Material
in	O
a	O
direct	O
way	O
because	O
of	O
heterogeneity	B-Process
.	O
In	O
this	O
paper	O
,	O
we	O
introduce	B-Task
some	I-Task
results	I-Task
of	I-Task
databases	I-Task
calculations	I-Task
.	O
We	O
apply	O
this	O
framework	B-Process
for	I-Process
the	I-Process
retrieving	I-Process
of	I-Process
parameters	I-Process
from	I-Process
outage	I-Process
data	I-Process
in	O
the	O
Czech	O
and	O
Slovak	O
republics	O
.	O
There	O
are	O
also	O
actual	O
results	O
.	O

In	O
the	O
paper	O
we	O
propose	O
a	O
conceptual	B-Task
methodology	I-Task
to	I-Task
control	I-Task
liquid	I-Task
state	I-Task
of	O
Al-Si	B-Material
alloys	I-Material
in	O
melting	B-Process
and	I-Process
holding	I-Process
sub-process	I-Process
of	O
the	O
pressure	B-Process
die-casting	I-Process
process	I-Process
.	O
Given	O
that	O
,	O
we	O
determine	B-Task
the	I-Task
characteristic	I-Task
of	I-Task
the	I-Task
holding	I-Task
furnace	I-Task
based	O
on	O
weight	B-Material
percent	I-Material
(	O
wt	O
%)	O
of	O
the	O
certain	O
alloys	O
and	O
their	O
elements	O
.	O
Subsequently	O
the	O
paper	O
introduces	O
an	O
application	O
of	O
methodology	O
of	O
research	O
for	O
establishing	O
characteristic	O
of	O
holding	O
furnace	O
.	O
The	O
application	O
was	O
realized	O
under	O
real	O
conditions	O
in	O
foundry	O
that	O
uses	O
horizontal	O
cold	O
chamber	O
machine	O
CLH	O
400.1	O
.	O
The	O
chemical	O
analysis	O
was	O
performed	O
by	O
spectrophotometer	O
SPECTROLAB	O
JR.CCD	O
2000	O
.	O
Finally	O
the	O
last	O
part	O
of	O
the	O
paper	O
lists	O
overall	O
findings	O
with	O
possible	O
future	O
direction	O
to	O
extend	O
this	O
methodology	O
in	O
practice	O
.	O

Our	O
country	O
is	O
rich	O
of	O
line	B-Process
galloping	I-Process
,	O
there	O
are	O
many	O
important	O
galloping	B-Material
data	I-Material
failed	O
to	O
collect	B-Task
systematically	I-Task
and	I-Task
completely	I-Task
because	O
there	O
is	O
no	O
unified	B-Process
management	I-Process
platform	I-Process
.	O
After	O
the	O
galloping	B-Process
occurrence	I-Process
in	O
2009	O
–	O
2010	O
's	O
winter	O
the	O
department	O
of	O
productive	O
of	O
the	O
State	O
Grid	O
Corporation	O
organized	O
a	O
lot	O
of	O
human	O
to	O
carry	B-Process
out	I-Process
the	I-Process
research	I-Process
of	I-Process
galloping	I-Process
information	I-Process
,	I-Process
this	O
work	O
is	O
time	O
–	O
consuming	O
and	O
inefficient	O
.	O
The	O
State	O
Grid	O
Corporation	O
has	O
used	O
the	O
production	B-Material
management	I-Material
system	I-Material
(	O
PMS	B-Material
)	O
which	O
is	O
a	O
powerful	O
and	O
easy	O
to	O
use	O
.	O
With	O
the	O
help	O
of	O
the	O
system	O
we	O
can	O
create	B-Task
a	I-Task
galloping	I-Task
database	I-Task
which	O
can	O
save	B-Process
resources	I-Process
and	I-Process
storage	I-Process
the	I-Process
galloping	I-Process
data	I-Process
.	O
To	O
build	O
and	O
put	O
it	O
into	O
application	B-Process
of	I-Process
database	I-Process
can	O
provide	O
technical	B-Process
support	I-Process
for	O
line	B-Process
galloping	I-Process
prevention	I-Process
and	O
galloping	B-Process
research	I-Process
work	I-Process
.	O

This	O
paper	O
presents	O
a	O
non-fragile	B-Process
controller	I-Process
design	I-Process
method	I-Process
based	O
on	O
system	B-Process
quadratic	I-Process
performance	I-Process
optimization	I-Process
.	O
For	O
the	O
additive	B-Process
controller	I-Process
gain	I-Process
variations	I-Process
,	O
the	O
necessary	O
and	O
sufficient	O
conditions	O
for	O
the	O
existence	O
of	O
non-fragile	B-Process
state	I-Process
feedback	I-Process
controller	I-Process
are	O
given	O
and	O
transformed	B-Process
to	O
the	O
LMI	B-Task
problems	I-Task
,	O
which	O
simplifies	B-Process
the	I-Process
solutions	I-Process
to	O
obtain	O
non-fragile	B-Process
state	I-Process
feedback	I-Process
controllers	I-Process
.	O
The	O
flight	B-Process
control	I-Process
simulation	I-Process
results	O
prove	O
the	O
reliability	O
and	O
validity	O
of	O
the	O
method	O
.	O

A	O
fuzzy-Hammerstein	B-Task
model	I-Task
predictive	I-Task
control	I-Task
method	I-Task
is	O
proposed	O
for	O
a	O
continuous	B-Material
stirred-tank	I-Material
reactor	I-Material
(	O
CSTR	B-Material
)	O
.	O
In	O
this	O
paper	O
T-S	B-Process
fuzzy	I-Process
model	I-Process
is	O
used	O
to	O
approximate	O
the	O
static	B-Task
nonlinear	I-Task
characteristics	I-Task
of	I-Task
Hammerstein	I-Task
model	I-Task
,	O
and	O
a	O
linear	O
autoregressive	O
model	O
is	O
used	O
to	O
solve	O
the	O
results	O
of	O
optimal	O
control	O
.	O
The	O
designed	O
nonlinear	O
predictive	O
controller	O
using	O
Hammerstein	O
model	O
make	O
good	O
use	O
of	O
the	O
ability	O
of	O
universal	O
approach	O
nonlinear	O
of	O
T-S	O
model	O
,	O
and	O
divide	O
the	O
question	O
of	O
nonlinear	O
predictive	O
control	O
into	O
the	O
nonlinear	O
model	O
recongnization	O
and	O
the	O
question	O
of	O
linear	O
predictive	O
control	O
.	O
The	O
application	O
results	O
of	O
CSTR	O
process	O
show	O
the	O
proposed	O
control	O
method	O
has	O
good	O
control	O
performance	O
compared	O
to	O
PID	O
controller	O
.	O

The	O
key	O
point	O
of	O
robot	B-Task
dynamics	I-Task
is	O
optimal	O
design	O
and	O
control	O
.	O
The	O
efficiency	B-Task
of	I-Task
robot	I-Task
dynamics	I-Task
has	O
been	O
the	O
goal	O
of	O
researchers	O
in	O
recent	O
years	O
.	O
Screws	B-Material
are	O
used	O
to	O
describe	B-Task
dynamic	I-Task
problems	I-Task
in	O
this	O
paper	O
,	O
and	O
an	O
O	B-Material
(	I-Material
N	I-Material
)	I-Material
recursive	I-Material
robot	I-Material
forward	O
dynamic	O
algorithm	O
is	O
given	O
on	O
this	O
.	O
It	O
can	O
be	O
easily	O
extended	O
to	O
tree	B-Process
topology	I-Process
,	I-Process
closed	I-Process
loop	I-Process
and	I-Process
spatial	I-Process
robot	I-Process
systems	I-Process
.	O
And	O
three	O
classic	O
methods	O
of	O
robot	B-Task
dynamics	I-Task
are	O
compared	O
for	O
easy	O
of	O
use	O
.	O
The	O
results	O
show	O
that	O
dynamics	B-Process
described	I-Process
with	I-Process
screws	I-Process
are	O
helpful	O
in	O
high	B-Process
efficient	I-Process
dynamics	I-Process
modelling	I-Process
.	I-Process
The	O
dynamical	B-Process
expressions	I-Process
based	O
on	O
screws	B-Material
are	O
concise	O
and	O
clear	O
.	O
It	O
's	O
efficiency	O
is	O
high	O
of	O
O	O
(	O
N	O
)	O
and	O
is	O
linear	B-Process
to	I-Process
the	I-Process
degree	I-Process
of	I-Process
freedom	I-Process
.	O
With	O
the	O
improvement	B-Task
of	I-Task
computation	I-Task
efficiency	I-Task
,	O
it	O
will	O
make	O
the	O
real-time	B-Process
dynamics	I-Process
control	I-Process
become	O
possible	O
.	O

An	O
algorithm	B-Process
of	I-Process
multi-axis	I-Process
NC	I-Process
tool-path	I-Process
generation	I-Process
for	O
subdivision	B-Material
surfaces	I-Material
is	O
proposed	O
.	O
The	O
algorithm	B-Process
includes	O
two	O
steps	O
:	O
model	B-Process
building	I-Process
and	O
tool	B-Process
path	I-Process
generation	I-Process
.	O
In	O
the	O
section	O
of	O
model	B-Process
building	I-Process
,	O
in	O
order	O
to	O
obtain	B-Task
the	I-Task
deformed	I-Task
surface	I-Task
,	O
the	O
deformation	B-Process
vector	I-Process
is	I-Process
computed	I-Process
which	O
is	O
associated	O
with	O
the	O
curvature	B-Process
and	I-Process
the	I-Process
slope	I-Process
of	I-Process
cutter	I-Process
location	I-Process
surface	I-Process
.	O
In	O
the	O
procedure	O
of	O
tool	B-Process
path	I-Process
generation	I-Process
,	O
the	O
slicing	B-Process
procedure	I-Process
is	O
adopted	O
to	O
get	O
the	O
CL	B-Material
points	I-Material
.	O
In	O
addition	O
,	O
the	O
inversely	B-Process
converted	I-Process
method	I-Process
is	O
used	O
.	O
The	O
method	O
is	O
tested	B-Task
by	I-Task
some	I-Task
examples	I-Task
with	I-Task
actual	I-Task
machining	I-Task
.	O
The	O
results	O
show	O
that	O
the	O
method	O
can	O
effectively	O
reduce	O
the	O
error	O
of	O
the	O
scallop	B-Material
height	I-Material
for	O
subdivision	B-Material
surface	I-Material
and	O
obtain	O
the	O
better	O
shape	O
and	O
quality	O
.	O
In	O
addition	O
,	O
the	O
computational	O
complexity	O
and	O
is	O
scalable	O
and	O
robust	O
.	O

Modeling	B-Task
or	I-Task
approximating	I-Task
high	I-Task
dimensional	I-Task
,	I-Task
computationally-expensive	I-Task
problems	I-Task
faces	O
an	O
exponentially	O
increasing	O
difficulty	O
,	O
the	O
“	O
curse	B-Process
of	I-Process
dimensionality	I-Process
”	O
.	O
This	O
paper	O
proposes	O
a	O
new	B-Process
form	I-Process
of	I-Process
high	I-Process
dimensional	I-Process
model	I-Process
representation	I-Process
(	O
HDMR	B-Process
)	O
by	O
utilizing	O
the	O
support	B-Process
vector	I-Process
regression	I-Process
(	O
SVR	B-Process
)	O
,	O
termed	O
as	O
adaptive	B-Process
SVR-HMDR	I-Process
,	O
to	O
conquer	B-Task
this	I-Task
dilemma	I-Task
.	O
The	O
proposed	O
model	O
could	O
reveal	O
explicit	B-Process
correlations	I-Process
among	O
different	O
input	B-Material
variables	I-Material
of	O
the	O
underlying	O
function	O
which	O
is	O
unknown	O
or	O
expensive	O
for	O
computation	B-Process
.	O
Taking	O
advantage	O
of	O
HDMR	B-Process
's	O
hierarchical	O
structure	O
,	O
it	O
could	O
alleviate	B-Task
the	I-Task
exponential	I-Task
increasing	I-Task
difficulty	I-Task
,	O
and	O
gain	O
satisfying	O
accuracy	O
with	O
small	O
set	O
of	O
samples	O
by	O
SVR	B-Process
.	O
Numerical	O
examples	O
of	O
different	O
dimensionality	O
are	O
given	O
to	O
illustrate	B-Task
the	I-Task
principle	I-Task
,	I-Task
procedure	I-Task
and	I-Task
performance	I-Task
of	O
SVR-HDMR	B-Process
.	O

Metal	B-Material
–	I-Material
intermetallic	I-Material
laminated	I-Material
(	O
MIL	B-Material
)	O
composites	O
are	O
fabricated	B-Process
upon	I-Process
reaction	I-Process
sintering	I-Process
of	O
titanium	B-Material
and	I-Material
aluminum	I-Material
foils	I-Material
of	O
various	O
thicknesses	O
.	O
The	O
intermetallic	B-Process
phase	I-Process
of	O
Al3Ti	B-Process
forming	I-Process
during	O
the	O
above	O
processing	B-Process
gives	O
high	O
hardness	O
and	O
stiffness	O
to	O
the	O
composite	B-Material
,	O
while	O
unreacted	B-Material
titanium	I-Material
provides	O
the	O
necessary	O
high	O
strength	O
and	O
ductility	O
.	O
Some	O
results	O
of	O
studies	B-Process
of	I-Process
microstructure	I-Process
and	O
some	O
mechanical	O
properties	O
of	O
layered	B-Material
composites	I-Material
are	O
presented	B-Task
on	I-Task
the	I-Task
example	I-Task
of	I-Task
Ti-Al	I-Task
system	I-Task
.	O
Static	B-Task
and	I-Task
dynamic	I-Task
tests	I-Task
results	I-Task
are	I-Task
discussed	I-Task
for	O
the	O
case	O
when	O
the	O
intermetallic	B-Process
reaction	I-Process
was	O
interrupted	B-Process
in	O
the	O
course	O
of	O
intermetallic	B-Process
sintering	I-Process
and	O
also	O
for	O
the	O
case	O
when	O
it	O
was	O
completed	O
.	O

A	O
design	B-Process
method	I-Process
for	O
network	B-Process
attack	I-Process
and	O
defense	B-Process
simulation	I-Process
platform	I-Process
is	O
discussed	O
in	O
this	O
paper	O
.	O
Firstly	O
the	O
component	O
and	O
function	O
of	O
the	O
platform	O
are	O
analyzed	O
.	O
Then	O
Visio	B-Process
second	I-Process
development	I-Process
method	I-Process
is	O
used	O
to	O
construct	B-Task
the	I-Task
virtual	I-Task
network	I-Task
topology	I-Task
.	O
The	O
parsing	B-Task
of	I-Task
virtual	I-Task
network	I-Task
topology	I-Task
is	O
also	O
researched	O
and	O
the	O
relative	B-Material
flow	I-Material
sheet	I-Material
is	O
described	O
.	O
Lastly	O
an	O
example	O
is	O
carried	O
out	O
to	O
test	B-Task
performance	I-Task
of	I-Task
the	I-Task
platform	I-Task
.	O
Simulation	O
results	O
show	O
the	O
effectiveness	O
of	O
the	O
proposed	O
method	O
.	O

The	O
existing	O
GO	B-Process
methodology	I-Process
algorithm	I-Process
is	O
theoretical	O
,	O
and	O
hard	O
to	O
solve	O
with	O
computer	O
.	O
In	O
this	O
paper	O
,	O
we	O
research	O
a	O
new	B-Task
method	I-Task
to	I-Task
get	I-Task
the	I-Task
reliability	I-Task
of	I-Task
system	I-Task
based	I-Task
on	I-Task
GO	I-Task
methodology	I-Task
.	O
According	O
to	O
some	O
properties	O
of	O
the	O
operators	O
in	O
GO	B-Material
chart	I-Material
,	O
GO	O
chart	O
can	O
be	O
transformed	O
into	O
series	B-Process
structure	I-Process
,	O
then	O
the	O
minimal	B-Process
path	I-Process
sets	I-Process
are	I-Process
induced	I-Process
based	O
on	O
Enumeration	B-Process
method	I-Process
from	O
first	O
operator	O
to	O
last	O
one	O
.	O
It	O
is	O
very	O
convenient	O
for	O
computer	O
to	O
calculate	B-Task
the	I-Task
system	I-Task
reliability	I-Task
with	O
the	O
new	O
method	O
based	O
on	O
minimal	B-Process
path	I-Process
sets	I-Process
.	O
The	O
case	O
study	O
indicates	O
the	O
method	O
is	O
suitable	O
for	O
practical	B-Task
engineering	I-Task
,	O
which	O
can	O
be	O
used	O
to	O
possess	O
the	O
quantitative	B-Process
analysis	I-Process
of	O
complex	B-Process
GO	I-Process
methodology	I-Process
models	I-Process
.	O

The	O
number	O
of	O
hidden	B-Material
nodes	I-Material
is	O
a	O
critical	O
factor	O
for	O
the	O
generalization	B-Task
of	I-Task
ELM	I-Task
.	O
Generally	O
,	O
it	O
is	O
heavy	O
for	O
time	B-Process
consumption	I-Process
to	O
obtain	B-Process
the	I-Process
optimal	I-Process
number	I-Process
of	I-Process
hidden	I-Process
nodes	I-Process
with	O
trial-and-error	B-Process
.	O
A	O
novel	B-Task
algorithm	I-Task
is	I-Task
proposed	I-Task
to	O
optimize	B-Task
the	I-Task
hidden	I-Task
node	I-Task
number	I-Task
to	O
guarantee	O
good	O
generalization	B-Process
,	O
which	O
employs	O
the	O
PSO	B-Process
in	O
the	O
optimization	B-Process
process	I-Process
with	O
structural	B-Process
risk	I-Process
minimization	I-Process
principle	I-Process
.	O
The	O
simulation	O
results	O
indicate	O
our	O
algorithm	B-Process
for	O
the	O
optimal	O
number	O
of	O
hidden	B-Material
nodes	I-Material
is	O
reasonable	O
and	O
feasible	O
with	O
6	B-Material
datasets	I-Material
on	O
benchmark	O
problems	O
by	O
the	O
accuracy	B-Process
comparisons	I-Process
.	O

For	O
providing	O
the	O
government	O
with	O
effective	B-Task
monitoring	I-Task
of	O
the	O
trends	B-Process
of	I-Process
the	I-Process
economic	I-Process
variables	I-Process
in	O
the	O
future	O
and	O
good	O
reference	O
for	O
developing	B-Process
a	I-Process
reasonable	I-Process
policy	I-Process
,	O
in	O
this	O
paper	O
,	O
we	O
establish	B-Task
a	I-Task
time	I-Task
series	I-Task
model	I-Task
on	O
China	O
's	O
Foreign	B-Process
Direct	I-Process
Investment	I-Process
(	O
FDI	B-Process
)	O
by	O
using	O
wavelet	B-Process
analysis	I-Process
and	O
intervention	B-Process
analysis	I-Process
and	O
time	B-Process
series	I-Process
analysis	I-Process
and	O
predict	B-Task
the	I-Task
trend	I-Task
of	I-Task
FDI	I-Task
in	O
the	O
next	O
several	O
years	O
.	O
This	O
model	O
eliminates	O
the	O
interference	B-Process
of	I-Process
noise	I-Process
for	O
predicting	O
by	O
using	O
wavelet	B-Process
analysis	I-Process
,	O
and	O
describes	O
the	O
autocorrelation	B-Process
and	O
time-varying	B-Process
volatility	I-Process
of	O
the	O
financial	B-Material
time	I-Material
series	I-Material
by	O
using	O
ARIMA	B-Process
-	I-Process
GARCH-M	I-Process
model	I-Process
.	O
The	O
simulation	O
results	O
show	O
that	O
this	O
model	O
explains	O
the	O
dynamic	O
structure	O
of	O
China	O
's	O
FDI	B-Process
trends	O
well	O
.	O

Monitoring	B-Task
the	I-Task
wear	I-Task
condition	I-Task
of	O
the	O
tramway	B-Material
superstructure	I-Material
is	O
one	O
of	O
the	O
key	O
points	O
to	O
guarantee	O
an	O
adequate	O
safety	B-Process
level	I-Process
of	O
the	O
light	B-Material
rail	I-Material
transport	I-Material
system	I-Material
.	O
The	O
purpose	O
of	O
this	O
paper	O
is	O
to	O
suggest	O
a	O
new	B-Task
non-conventionalprocedure	I-Task
for	O
measuring	O
the	O
transverse	B-Process
profile	I-Process
of	O
rails	B-Material
in	O
operation	O
by	O
means	O
of	O
image-processing	B-Process
technique	I-Process
.	O
This	O
methodological	O
approach	O
is	O
based	O
on	O
the	O
“	O
information	B-Material
”	I-Material
contained	O
in	O
high-resolution	B-Material
photographic	I-Material
images	I-Material
of	O
tracks	B-Material
and	O
on	O
specific	B-Process
algorithms	I-Process
which	O
allow	O
to	O
obtain	O
the	O
exact	O
geometric	O
profile	O
of	O
the	O
rails	B-Material
and	O
therefore	O
to	O
measure	O
the	O
state	O
of	O
the	O
rail-head	O
extrados	O
wear	O
.	O

Robust	B-Task
and	I-Task
automatic	I-Task
thresholding	I-Task
of	O
gray	B-Material
level	I-Material
images	I-Material
has	O
been	O
commonly	O
used	O
in	O
the	O
field	O
of	O
pattern	B-Task
recognition	I-Task
and	O
computer	B-Task
vision	I-Task
for	O
objects	B-Task
detecting	I-Task
,	I-Task
tracking	I-Task
and	I-Task
recognizing	I-Task
.	O
The	O
Otsu	B-Process
scheme	I-Process
,	O
a	O
widely	O
used	O
image	B-Process
thresholding	I-Process
technique	I-Process
,	O
provides	O
approving	O
results	O
for	O
segmenting	B-Process
a	O
gray	B-Material
level	I-Material
image	I-Material
with	O
only	O
one	O
modal	B-Process
distribution	I-Process
in	O
gray	B-Material
level	I-Material
histogram	I-Material
.	I-Material
However	O
,	O
it	O
provides	O
poor	O
results	O
if	O
the	O
histogram	O
of	O
a	O
gray	O
level	O
is	O
non-bimodal	O
.	O
For	O
enhancing	B-Task
the	I-Task
performance	I-Task
of	O
the	O
Otsu	B-Process
algorithm	I-Process
further	O
,	O
in	O
this	O
work	O
,	O
an	O
improved	O
median-based	B-Process
Otsu	I-Process
image	I-Process
thresholding	I-Process
algorithm	I-Process
is	O
presented	O
.	O
Finally	O
extensive	B-Task
tests	I-Task
are	O
performed	O
and	O
the	O
experiments	O
show	O
that	O
our	O
method	O
obtain	O
more	O
satisfactory	O
results	O
than	O
the	O
original	O
Otsu	B-Process
thresholding	I-Process
algorithm	I-Process
.	O

In	O
this	O
paper	O
,	O
a	O
novel	O
position	B-Task
estimation	I-Task
method	I-Task
of	O
prism	B-Material
was	O
proposed	O
for	O
single-lens	B-Material
stereovision	I-Material
system	I-Material
.	O
The	O
prism	B-Material
with	O
multi	O
faces	O
was	O
considered	O
as	O
a	O
single	B-Material
optical	I-Material
system	I-Material
composed	O
of	O
some	O
refractive	B-Material
planes	I-Material
.	O
A	O
transformation	B-Material
matrix	I-Material
which	O
can	O
express	B-Task
the	I-Task
relationship	I-Task
between	O
an	O
object	B-Material
point	I-Material
and	O
its	O
image	B-Material
by	O
the	O
refraction	B-Process
of	I-Process
prism	I-Process
was	O
derived	O
based	O
on	O
geometrical	B-Process
optics	I-Process
,	O
and	O
a	O
mathematical	B-Process
model	I-Process
was	O
introduced	O
which	O
can	O
denote	O
the	O
position	O
of	O
prism	B-Material
with	O
arbitrary	O
faces	O
only	O
by	O
7	O
parameters	O
.	O
This	O
model	O
can	O
extend	B-Process
the	I-Process
application	I-Process
of	I-Process
single-lens	I-Process
stereovision	I-Process
system	I-Process
using	O
prism	B-Material
to	O
a	O
more	O
widely	O
area	O
.	O
Experimentation	O
results	O
are	O
presented	O
to	O
prove	O
the	O
effectiveness	B-Process
and	O
robustness	B-Process
of	O
our	O
proposed	O
model	O
.	O

Power	B-Process
Grid	I-Process
reasoning	I-Process
expert	I-Process
system	I-Process
is	O
a	O
complex	O
system	O
.	O
To	O
solve	O
knowledge	B-Task
sharing	I-Task
of	I-Task
knowledge	I-Task
Base	I-Task
in	I-Task
expert	I-Task
system	I-Task
,	O
we	O
abstract	B-Task
and	I-Task
analyze	I-Task
the	I-Task
power	I-Task
grid	I-Task
security	I-Task
investigation	I-Task
procedure	I-Task
by	O
using	O
ontology	B-Process
Technology	I-Process
.	O
With	O
ontology-based	B-Material
Power	I-Material
Grid	I-Material
knowledge	I-Material
base	I-Material
,	O
we	O
establish	O
associated	B-Task
relationship	I-Task
of	I-Task
procedure	I-Task
vocabularies	I-Task
.	O
In	O
this	O
paper	O
,	O
we	O
introduce	B-Task
and	I-Task
analyze	I-Task
of	I-Task
semantic	I-Task
reasoning	I-Task
tools	I-Task
such	O
as	O
Jena	B-Material
.	O
The	O
reasoner	B-Process
mechanism	I-Process
and	O
inference	B-Material
rules	I-Material
of	I-Material
grammar	I-Material
has	O
been	O
included	O
and	O
explained	O
.	O
At	O
last	O
we	O
give	O
a	O
specific	B-Task
application	I-Task
of	I-Task
security	I-Task
investigation	I-Task
procedure	I-Task
ontology	I-Task
and	I-Task
reasoning	I-Task
.	O

This	O
phase	O
was	O
completed	O
in	O
2005	O
.	O
Previous	O
contracts	O
had	O
been	O
procured	O
with	O
the	O
contractor	O
providing	O
the	O
detailed	B-Material
design	I-Material
.	O
For	O
this	O
system	O
the	O
design	O
was	O
undertaken	O
by	O
Mott	O
MacDonald	O
.	O
It	O
was	O
developed	O
by	O
looking	B-Process
at	I-Process
the	I-Process
systems	I-Process
installed	I-Process
previously	I-Process
and	I-Process
calculating	I-Process
what	O
was	O
actually	O
required	O
to	O
achieve	O
cathodic	B-Task
protection	I-Task
of	I-Task
the	I-Task
piers	I-Task
.	O
This	O
resulted	O
in	O
a	O
significant	B-Process
reduction	I-Process
in	I-Process
the	I-Process
number	I-Process
of	I-Process
zones	I-Process
and	I-Process
monitoring	I-Process
probes	I-Process
.	O
The	O
varying	B-Process
amounts	I-Process
of	I-Process
steelwork	I-Process
in	O
the	O
beams	O
had	O
previously	O
lead	O
to	O
up	O
to	O
5	O
zones	O
per	O
beam	O
,	O
with	O
multiple	B-Process
layers	I-Process
of	I-Process
mesh	I-Process
to	O
achieve	O
the	O
design	O
current	O
density	O
.	O
On	O
review	O
of	O
the	O
data	B-Material
the	O
operating	O
current	O
density	O
was	O
similar	O
in	O
all	O
zones	O
and	O
so	O
this	O
was	O
reduced	O
to	O
a	O
single	O
zone	O
per	O
beam	O
.	O
The	O
encapsulation	B-Process
was	O
susceptible	O
to	O
ASR	B-Process
and	O
contained	O
post	O
tensioning	O
and	O
so	O
it	O
was	O
decided	O
to	O
use	O
a	O
galvanic	B-Material
system	I-Material
based	O
on	O
Galvashield	B-Material
CC	I-Material
anodes	I-Material
from	O
Fosroc	O
.	O
Our	O
design	O
did	O
not	O
include	O
an	O
option	O
to	O
allow	O
depolarization	B-Task
of	I-Task
the	I-Task
galvanic	I-Task
system	I-Task
,	O
but	O
the	O
contractor	O
supplied	O
one	O
,	O
such	O
that	O
the	O
anodes	O
could	O
be	O
remotely	O
disconnected	O
.	O
The	O
control	O
unit	O
was	O
from	O
Electrotech	O
CP	O
and	O
operated	O
via	O
a	O
broadband	B-Process
connection	I-Process
provided	O
by	O
the	O
contractor	O
.	O

An	O
innovative	O
sound	B-Material
wall	I-Material
system	I-Material
was	O
developed	O
in	O
the	O
University	O
of	O
Western	O
Ontario	O
,	O
and	O
was	O
examined	O
to	O
serve	O
as	O
a	O
vertical	O
extension	O
to	O
the	O
existing	B-Material
sound	I-Material
walls	I-Material
.	O
The	O
wall	B-Material
system	I-Material
(	O
denoted	O
as	O
flexi-wall	B-Material
)	O
consists	O
of	O
stay-in-place	B-Material
poly-blocks	I-Material
as	O
formwork	O
,	O
light	B-Material
polyurethane	I-Material
foam	I-Material
(	O
LPF	B-Material
)	O
reinforced	O
with	O
steel	B-Material
rebars	I-Material
as	O
structural	B-Material
cores	I-Material
and	O
polyurea	B-Material
as	O
a	O
coating	B-Process
of	O
the	O
wall	B-Material
surfaces	I-Material
(	O
Fig.	O
1	O
)	O
.	O
Poly-blocks	B-Material
are	O
interlocking	B-Material
light-weight	I-Material
blocks	I-Material
which	O
are	O
stacked	O
up	O
layer	O
by	O
layer	O
and	O
act	O
as	O
formwork	O
for	O
the	O
LPF	B-Material
cores	I-Material
.	O
The	O
poly-block	B-Material
is	O
20	O
×	O
20	O
×	O
80cm3	O
and	O
includes	O
four	O
cylindrical	B-Material
voids	I-Material
with	O
14cm	O
diameter	O
.	O
It	O
is	O
made	O
of	O
molded	B-Material
low-density	I-Material
polyurethane	I-Material
and	O
weighs	O
approximately	O
1kg	O
.	O
The	O
poly-blocks	B-Material
are	O
fire-resistant	B-Material
blocks	I-Material
and	O
have	O
an	O
excellent	O
capability	O
to	O
absorb	B-Process
,	O
mitigate	B-Process
and	O
reflect	B-Process
a	O
wide	O
range	O
of	O
noises	O
with	O
unmatched	O
frequency	O
of	O
reflective	O
noise	O
.	O
Polyurea	B-Material
coating	I-Material
is	O
an	O
abrasion-resistant	B-Material
finishing	I-Material
layer	I-Material
,	O
which	O
is	O
sprayed	B-Process
on	O
the	O
surfaces	B-Material
of	I-Material
the	I-Material
wall	I-Material
and	O
sets	O
within	O
2	O
–	O
3min	O
.	O
This	O
layer	B-Material
also	O
enhances	B-Process
the	I-Process
surface	I-Process
resistance	I-Process
of	O
poly-blocks	B-Material
against	O
stone	B-Process
impact	I-Process
,	O
weathering	B-Process
,	O
fire	B-Process
development	I-Process
,	O
chemicals	B-Material
and	O
penetration	B-Process
.	O
LPF	B-Material
is	O
an	O
expanding	B-Material
liquid	I-Material
mixture	I-Material
which	O
is	O
injected	O
into	O
the	O
poly-block	B-Material
voids	I-Material
and	O
cures	B-Material
within	O
10min	O
.	O
Steel	B-Material
rebars	I-Material
are	O
epoxied	O
into	O
holes	B-Material
drilled	O
in	O
the	O
existing	B-Material
sound	I-Material
wall	I-Material
to	O
connect	O
the	O
wall	B-Material
extension	I-Material
to	O
its	O
base	B-Material
.	O

Another	O
important	O
reason	O
for	O
the	O
damages	O
incurred	O
by	O
the	O
RC	O
buildings	O
is	O
workmanship	B-Process
defects	I-Process
.	O
It	O
is	O
understood	O
that	O
granulometry	B-Process
of	I-Process
the	I-Process
handmade	I-Process
concretes	I-Process
was	O
not	O
in	O
compliance	O
with	O
the	O
standards	O
since	O
the	O
aggregate	O
utilized	O
in	O
them	O
was	O
not	O
sieved	O
.	O
Also	O
the	O
compaction	B-Process
process	I-Process
was	O
not	O
properly	O
implemented	O
in	O
general	O
in	O
the	O
installment	B-Process
of	I-Process
concrete	I-Process
in	O
RC	O
buildings	O
.	O
This	O
situation	O
resulted	O
in	O
the	O
concrete	O
to	O
exhibit	O
an	O
excessively	O
porous	O
structure	O
.	O
The	O
most	O
fundamental	O
rules	O
of	O
thumb	O
of	O
construction	O
,	O
namely	O
concrete	B-Material
cover	I-Material
,	O
was	O
not	O
taken	O
care	O
of	O
in	O
formwork	O
workmanship	O
.	O
Faults	B-Process
in	I-Process
the	I-Process
connections	I-Process
of	I-Process
stirrups	I-Process
to	O
the	O
longitudinal	O
bars	O
,	O
unstaggered	B-Process
formation	I-Process
of	I-Process
stirrup	I-Process
hooks	B-Material
in	O
beams	B-Material
and	O
columns	B-Material
,	O
the	O
perpendicular	B-Process
angles	I-Process
of	O
the	O
hooks	B-Material
,	O
inadequately	B-Process
anchorage	I-Process
lengths	I-Process
of	O
the	O
stirrup	B-Material
hooks	I-Material
and	O
longitudinal	B-Material
bars	I-Material
,	O
and	O
the	O
use	B-Process
of	I-Process
cold	I-Process
joints	I-Process
were	O
the	O
other	O
frequently	O
encountered	O
workmanship	B-Process
defects	I-Process
(	O
Figs.	O
19	O
–	O
22	O
)	O
.	O

The	O
exponential	B-Process
relationships	I-Process
reported	O
in	O
the	O
plots	O
may	O
be	O
used	O
to	O
convert	B-Task
the	I-Task
dielectric	I-Task
values	I-Task
to	I-Task
air	I-Task
void	I-Task
values	I-Task
as	O
prescribed	O
in	O
previous	O
studies	O
[	O
1	O
–	O
3	O
]	O
.	O
The	O
AC	B-Process
pavement	I-Process
composite	I-Process
permittivity	I-Process
reduces	O
,	O
along	O
with	O
the	O
reflection	O
coefficient	O
,	O
as	O
the	O
volumetric	B-Process
proportion	I-Process
of	I-Process
air	I-Process
increases	O
as	O
compared	O
to	O
the	O
remaining	O
components	O
.	O
However	O
,	O
the	O
method	O
relies	O
on	O
an	O
empirical	O
fit	O
,	O
determined	O
on	O
a	O
case-by-case	O
basis	O
,	O
since	O
the	O
permittivity	O
of	O
the	O
remaining	O
components	O
depends	O
on	O
the	O
mix	B-Material
design	I-Material
(	O
aggregate	O
type	O
,	O
binder	O
content	O
,	O
etc.	O
)	O
.	O
Long	B-Process
term	I-Process
studies	I-Process
in	I-Process
Finland	I-Process
concluded	O
that	O
this	O
empirical	O
fit	O
is	O
an	O
exponential	B-Task
relationship	I-Task
[	O
1	O
]	O
.	O
The	O
exponential	O
fits	O
,	O
using	O
a	O
sufficient	B-Process
amount	I-Process
of	I-Process
cores	I-Process
,	O
can	O
be	O
used	O
to	O
map	B-Task
the	I-Task
air	I-Task
void	I-Task
content	I-Task
variation	I-Task
in	O
a	O
similar	O
manner	O
to	O
the	O
dielectric	B-Material
maps	I-Material
shown	O
in	O
Fig.	O
4.	O
Only	O
4	O
cores	O
were	O
feasible	O
due	O
to	O
various	O
factors	O
involved	O
with	O
testing	B-Task
the	I-Task
final	I-Task
lift	I-Task
of	O
an	O
in-service	O
pavement	O
.	O
More	O
cores	O
are	O
needed	O
for	O
more	B-Task
stable	I-Task
exponential	I-Task
coefficients	I-Task
,	O
although	O
the	O
limited	O
cores	O
show	O
that	O
the	O
predicted	B-Process
relationships	I-Process
are	O
similar	O
for	O
the	O
measured	B-Process
dielectric	I-Process
range	I-Process
in	O
this	O
case-study	O
.	O
Since	O
both	O
regressions	B-Process
predict	O
air	B-Task
void	I-Task
content	I-Task
at	O
a	O
maximum	O
difference	O
of	O
0.56	O
%	O
,	O
which	O
is	O
within	O
the	O
uncertainty	O
of	O
the	O
core	B-Process
measurement	I-Process
precision	I-Process
of	O
0.7	O
%	O
,	O
use	O
of	O
either	O
the	O
initial	B-Process
or	I-Process
repeat	I-Process
run	I-Process
regression	I-Process
predictions	I-Process
are	O
appropriate	O
.	O

MicroCT	B-Process
has	O
been	O
applied	O
to	O
AM	B-Material
parts	I-Material
in	O
various	O
forms	O
.	O
Some	O
preliminary	B-Material
results	I-Material
demonstrating	O
the	O
visualization	B-Task
of	I-Task
defects	I-Task
including	O
porosity	B-Process
in	I-Process
AM	I-Process
components	I-Process
were	O
reported	O
in	O
[	O
6	O
]	O
.	O
In	O
another	O
study	O
,	O
the	O
porosity	B-Task
structures	I-Task
in	I-Task
parts	I-Task
built	O
with	O
improper	O
settings	O
were	O
investigated	O
[	O
7	O
]	O
.	O
In	O
this	O
work	O
,	O
the	O
average	B-Process
porosity	I-Process
ranged	O
from	O
0.1	O
–	O
0.5	O
%	O
,	O
and	O
large	O
pores	O
were	O
observed	O
which	O
followed	O
the	O
build	B-Process
direction	I-Process
and	O
may	O
be	O
attributed	O
to	O
the	O
electron	B-Process
beam	I-Process
raster	I-Process
and	I-Process
overlap	I-Process
pattern	I-Process
.	I-Process
This	O
was	O
followed	O
by	O
more	O
recent	B-Material
reports	I-Material
of	O
the	O
porosity	B-Task
distribution	I-Task
as	O
a	O
function	O
of	O
build	O
strategy	O
for	O
electron	B-Material
beam	I-Material
melted	I-Material
samples	I-Material
with	O
average	O
porosity	O
<	O
0.2	O
%	O
[	O
8	O
]	O
.	O
In	O
another	O
study	O
,	O
similar	O
porosity	B-Material
images	I-Material
from	O
microCT	B-Material
were	O
reported	O
at	O
levels	O
above	O
0.2	O
%	O
average	O
porosity	O
[	O
9,10	O
]	O
.	O
Very	O
recent	O
work	O
reports	O
similar	O
images	O
and	O
may	O
indicate	O
that	O
the	O
porosity	O
structure	O
depends	O
on	O
the	O
build	O
direction	O
[	O
11	O
]	O
.	O
Other	O
applications	O
of	O
the	O
use	O
of	O
microCT	O
to	O
characterize	B-Task
AM	I-Task
parts	I-Task
include	O
the	O
comparison	B-Process
of	I-Process
the	I-Process
part	I-Process
to	I-Process
its	I-Process
design	I-Process
model	I-Process
[	O
12	O
]	O
and	O
the	O
characterization	B-Process
of	I-Process
surface	I-Process
roughness	I-Process
of	O
such	O
parts	O
[	O
13	O
]	O
.	O
In	O
the	O
present	O
work	O
,	O
the	O
aim	O
is	O
to	O
demonstrate	B-Task
a	I-Task
specific	I-Task
type	I-Task
of	I-Task
defect	I-Task
present	O
at	O
very	B-Process
low	I-Process
average	I-Process
porosity	I-Process
levels	I-Process
below	O
0.01	O
%	O
,	O
and	O
which	O
does	O
not	B-Process
follow	I-Process
the	I-Process
build	I-Process
direction	I-Process
as	O
in	O
some	O
other	O
reported	O
examples	O
.	O
We	O
also	O
demonstrate	O
how	O
this	O
porosity	B-Task
structure	I-Task
changes	I-Task
after	O
Hot	B-Process
Isostatic	I-Process
Pressing	I-Process
(	O
HIP	B-Process
)	O
treatment	O
of	O
the	O
same	O
sample	O
.	O

Aeroengine	B-Task
turbine	I-Task
disks	I-Task
often	O
consist	O
of	O
paramagnetic	B-Material
,	O
that	O
means	O
non-ferromagnetic	B-Material
Nickel	I-Material
based	I-Material
alloys	I-Material
.	O
Sometimes	O
,	O
parasitic	B-Material
small	I-Material
ferromagnetic	I-Material
particles	I-Material
can	O
be	O
included	O
in	O
these	O
disks	O
that	O
may	O
decrease	B-Task
the	I-Task
mechanical	I-Task
stability	I-Task
.	O
For	O
this	O
reason	O
,	O
in	O
case	O
of	O
a	O
suspicion	O
disks	O
are	O
to	O
be	O
analysed	O
with	O
respect	O
to	O
ferromagnetic	B-Process
inclusions	I-Process
.	O
These	O
inclusions	O
generate	O
a	O
magnetic	B-Task
density	I-Task
which	O
can	O
be	O
measured	O
by	O
a	O
flux	B-Material
gate	I-Material
magnetometer	I-Material
using	O
the	O
magnetic	B-Process
remanence	I-Process
method	I-Process
[	O
1	O
]	O
.	O
The	O
detection	B-Process
principle	I-Process
of	I-Process
ferromagnetic	I-Process
impurities	I-Process
in	O
non-magnetic	O
metallic	O
materials	O
is	O
based	O
on	O
their	O
remanence	O
.	O
Before	O
such	O
a	O
measurement	B-Task
can	O
be	O
carried	O
out	O
,	O
the	O
aeroengine	O
turbine	O
disks	O
are	O
premagnetised	B-Process
in	O
axial	O
direction	O
.	O
As	O
ferromagnetic	O
materials	O
show	O
the	O
well-known	O
hysteresis	B-Process
behaviour	I-Process
,	O
those	O
materials	O
can	O
be	O
magnetised	O
by	O
a	O
strong	B-Process
magnetic	I-Process
field	I-Process
which	O
drives	O
the	O
magnetic	O
material	O
into	O
saturation	O
.	O
When	O
removing	B-Process
the	I-Process
magnetic	I-Process
field	I-Process
,	O
the	O
remanence	O
is	O
left	O
.	O
This	O
remaining	B-Process
flux	I-Process
density	I-Process
is	O
used	O
to	O
detect	O
them	O
in	O
non-magnetic	O
materials	O
.	O

Although	O
the	O
presented	O
model	O
is	O
developed	O
and	O
tested	O
with	O
a-C	B-Material
:	I-Material
H	I-Material
layers	I-Material
in	O
mind	O
,	O
it	O
is	O
not	O
necessarily	O
limited	O
to	O
them	O
.	O
Moreover	O
,	O
the	O
only	O
assumptions	O
are	O
chemical	B-Process
reactions	I-Process
between	O
the	O
gas	B-Material
and	O
the	O
solid	B-Material
forming	I-Material
volatiles	I-Material
,	O
the	O
loss	O
of	O
these	O
volatiles	O
from	O
the	O
material	O
and	O
the	O
two	O
stated	O
boundary	O
conditions	O
of	O
gas	B-Material
influx	O
at	O
a	O
single	B-Material
outer	I-Material
surface	I-Material
and	O
the	O
possibility	O
of	O
reactions	B-Process
throughout	O
the	O
bulk	O
.	O
Porosity	B-Process
and	O
significant	O
gas	B-Material
inventories	I-Material
were	O
observed	O
not	O
only	O
for	O
carbon	B-Material
[	O
12	O
]	O
but	O
,	O
e.g.	O
also	O
for	O
beryllium	B-Material
co-deposits	I-Material
[	O
25	O
]	O
and	O
can	O
be	O
expected	O
for	O
other	O
co-deposits	O
formed	O
in	O
plasma	B-Material
devices	I-Material
[	O
1	O
]	O
.	O
Thus	O
,	O
TCR	B-Material
and	O
its	O
description	O
by	O
the	O
presented	O
model	O
may	O
be	O
applicable	O
to	O
all	O
deposits	O
.	O
If	O
a	O
layer	O
has	O
constituents	B-Process
that	I-Process
are	I-Process
not	I-Process
forming	I-Process
volatiles	I-Process
with	I-Process
the	I-Process
reactive	I-Process
gas	I-Process
,	O
e.g.	O
W	B-Process
and	I-Process
Be	I-Process
with	I-Process
O2	I-Process
,	O
these	O
constituents	O
cannot	O
be	O
removed	O
by	O
TCR	B-Material
,	O
as	O
they	O
will	O
not	O
be	O
removed	O
from	O
the	O
deposit	O
.	O
This	O
can	O
influence	O
the	O
removal	O
of	O
other	O
deposit	B-Material
constituents	I-Material
and	O
the	O
time	O
evolution	O
of	O
the	O
process	O
can	O
change	O
.	O
The	O
new	O
understanding	O
of	O
TCR	B-Material
may	O
,	O
for	O
the	O
first	O
time	O
,	O
allow	O
applying	O
the	O
method	O
in	O
a	O
controlled	O
way	O
to	O
nuclear	B-Material
fusion	I-Material
devices	I-Material
,	O
possibly	O
solving	O
the	O
tritium	B-Process
retention	I-Process
issue	O
especially	O
related	O
to	O
carbon	B-Material
based	I-Material
materials	I-Material
.	O

Power	B-Material
and	I-Material
particle	I-Material
exhaust	I-Material
are	O
crucial	O
for	O
the	O
viability	O
of	O
any	O
future	O
fusion	B-Material
power	I-Material
plant	I-Material
concept	O
.	O
Heat	O
in	O
fusion	B-Process
reactors	I-Process
must	O
be	O
extracted	O
through	O
a	O
wall	O
and	O
cannot	O
be	O
exhausted	O
volumetrically	O
,	O
which	O
limits	O
the	O
allowed	O
power	O
density	O
in	O
fusion	O
reactors	O
[	O
1	O
]	O
and	O
is	O
a	O
severe	B-Task
technical	I-Task
challenge	I-Task
in	O
itself	O
[	O
2	O
]	O
.	O
In	O
addition	O
,	O
structural	B-Process
material	I-Process
changes	I-Process
resulting	O
from	O
neutron	B-Process
irradiation	I-Process
cause	O
degradation	O
in	O
the	O
heat	O
exhaust	O
capabilities	O
of	O
existing	O
designs	O
[	O
3	O
]	O
and	O
static	B-Process
surfaces	I-Process
can	O
suffer	O
severely	O
from	O
erosion	O
due	O
to	O
impinging	B-Material
plasma	I-Material
particles	I-Material
[	O
4,5	O
]	O
.	O
It	O
is	O
concluded	O
that	O
conventional	O
concepts	O
and	O
materials	O
for	O
plasma	B-Task
facing	I-Task
components	I-Task
(	O
PFCs	B-Task
)	O
reach	O
their	O
limits	O
in	O
terms	O
of	O
material	O
lifetime	O
and	O
power	O
exhaust	O
at	O
approximately	O
20MW	O
/	O
m2	O
,	O
which	O
is	O
presumably	O
dramatically	O
reduced	O
to	O
<	O
10MW	O
/	O
m2	O
due	O
to	O
neutron	B-Material
damage	O
in	O
a	O
D-T	B-Material
reactor	I-Material
[	O
6	O
]	O
or	O
even	O
only	O
half	O
that	O
value	O
[	O
7	O
]	O
.	O