Three-dimensional	B-Process
digital	I-Process
subtraction	I-Process
angiographic	I-Process
(	O
3D-DSA	B-Process
)	O
images	O
from	O
diagnostic	O
cerebral	B-Process
angiography	I-Process
were	O
obtained	O
at	O
least	O
one	O
day	O
prior	O
to	O
embolization	B-Process
in	O
all	O
patients	O
.	O
The	O
raw	O
data	O
of	O
3D-DSA	B-Process
in	O
a	O
DICOM	B-Material
file	I-Material
were	O
used	O
for	O
creating	B-Task
a	I-Task
3D	I-Task
model	I-Task
of	I-Task
the	I-Task
target	I-Task
vessel	I-Task
segment	I-Task
.	O
These	O
data	O
were	O
converted	O
to	O
standard	B-Process
triangulation	I-Process
language	I-Process
(	O
STL	B-Process
)	O
surface	O
data	O
as	O
an	O
aggregation	O
of	O
fine	O
triangular	B-Material
meshes	I-Material
using	O
3D	B-Process
visualization	I-Process
and	O
measurement	B-Process
software	O
(	O
Amira	B-Material
version	I-Material
X	I-Material
,	O
FEI	O
,	O
Burlington	O
,	O
MA	O
,	O
USA	O
)	O
.	O
An	O
unstructured	O
computational	B-Material
volumetric	I-Material
mesh	I-Material
was	O
constructed	O
from	O
the	O
triangulated	B-Material
surface	I-Material
.	O
Smoothing	B-Process
and	O
remeshing	B-Process
followed	O
as	O
next	O
steps	O
.	O
The	O
STL	B-Material
file	I-Material
was	O
then	O
transferred	O
to	O
a	O
3D	B-Material
printer	I-Material
(	O
OBJET30	B-Material
Pro	I-Material
;	O
Stratasys	O
Ltd.	O
,	O
Eden	O
Prairie	O
,	O
MN	O
,	O
USA	O
)	O
.	O
The	O
resolution	O
of	O
the	O
build	O
layer	O
was	O
0.028mm	O
,	O
and	O
the	O
3D	O
printed	O
vessel	O
model	O
was	O
produced	O
using	O
acrylic	B-Material
resin	I-Material
(	O
Vero	B-Material
)	O
.	O
Following	O
immersion	B-Process
in	I-Process
water	I-Process
for	O
a	O
few	O
hours	O
,	O
the	O
surface	O
of	O
the	O
3D	B-Material
printed	I-Material
model	I-Material
was	O
smoothed	B-Process
by	O
manually	O
removing	O
spicule	O
.	O

Fig.	O
9	O
displays	O
the	O
growth	O
of	O
two	O
of	O
the	O
main	O
corrosion	B-Material
products	I-Material
that	O
develop	O
or	O
form	O
on	O
the	O
surface	O
of	O
Cu40Zn	B-Material
with	O
time	O
,	O
hydrozincite	B-Material
(	O
Fig.	O
9a	O
)	O
and	O
Cu2O	B-Material
(	O
Fig.	O
9b	O
)	O
.	O
It	O
should	O
be	O
remembered	O
that	O
both	O
phases	O
were	O
present	O
already	O
from	O
start	O
of	O
the	O
exposure	O
.	O
The	O
data	O
is	O
presented	O
in	O
absorbance	B-Process
units	I-Process
and	O
allows	O
comparisons	B-Task
to	I-Task
be	I-Task
made	I-Task
of	I-Task
the	I-Task
amounts	I-Task
of	I-Task
each	I-Task
species	I-Task
between	I-Task
the	I-Task
two	I-Task
Cu40Zn	I-Task
surfaces	I-Task
investigated	I-Task
,	O
DP	B-Material
and	O
HZ7	B-Material
.	O
The	O
tendency	O
is	O
very	O
clear	O
that	O
the	O
formation	B-Process
rates	I-Process
of	O
both	O
hydrozincite	B-Material
and	O
cuprite	B-Material
are	O
quite	O
suppressed	O
for	O
Cu40Zn	B-Material
with	O
preformed	O
hydrozincite	B-Material
(	O
HZ7	B-Material
)	O
compared	O
to	O
the	O
diamond	B-Material
polished	I-Material
surface	I-Material
(	O
DP	B-Material
)	O
.	O
In	O
summary	O
,	O
without	O
being	O
able	O
to	O
consider	O
the	O
formation	B-Process
of	I-Process
simonkolleite	I-Process
,	O
it	O
can	O
be	O
concluded	O
that	O
an	O
increased	O
surface	O
coverage	O
of	O
hydrozincite	B-Material
reduces	O
the	O
initial	B-Process
spreading	I-Process
ability	O
of	O
the	O
NaCl-containing	B-Material
droplets	I-Material
and	O
thereby	O
lowers	O
the	O
overall	O
formation	B-Process
rate	I-Process
of	O
hydrozincite	B-Material
and	O
cuprite	B-Material
.	O

AA	B-Material
2024-T3	I-Material
aluminium	I-Material
alloy	I-Material
is	O
widely	O
used	O
for	O
aerospace	B-Task
applications	I-Task
due	O
to	O
its	O
high	O
strength	O
to	O
weight	O
ratio	O
and	O
high	O
damage	O
tolerance	O
that	O
result	O
from	O
copper	B-Material
and	O
magnesium	B-Material
as	O
the	O
principal	O
alloying	B-Material
elements	I-Material
and	O
appropriate	O
thermomechanical	B-Process
processing	I-Process
.	O
The	O
microstructure	O
of	O
the	O
alloy	B-Material
is	O
relatively	O
complex	O
and	O
a	O
number	O
of	O
compositionally-distinct	B-Process
phases	I-Process
have	O
been	O
identified	O
[	O
1	O
]	O
.	O
Although	O
possessing	O
favourable	O
mechanical	O
properties	O
,	O
the	O
alloy	B-Material
is	O
relatively	O
susceptible	O
to	O
corrosion	B-Process
and	O
generally	O
requires	O
surface	B-Process
treatment	I-Process
in	O
practical	O
applications	O
.	O
The	O
corrosion	B-Process
behaviour	O
of	O
the	O
alloy	B-Material
is	O
particularly	O
affected	O
by	O
the	O
presence	O
of	O
the	O
intermetallic	B-Material
particles	I-Material
due	O
to	O
their	O
differing	O
potentials	O
with	O
respect	O
to	O
the	O
alloy	B-Material
matrix	I-Material
[	O
2	O
–	O
9	O
]	O
.	O
Copper-containing	B-Material
second	I-Material
phase	I-Material
particles	I-Material
at	O
the	O
alloy	B-Material
surface	O
are	O
particularly	O
detrimental	O
to	O
the	O
corrosion	B-Process
resistance	I-Process
as	O
they	O
provide	O
preferential	O
cathodic	O
sites	O
[	O
2,10	O
]	O
.	O
One	O
of	O
the	O
principle	O
types	O
of	O
second	B-Material
phase	I-Material
particle	I-Material
that	O
is	O
important	O
to	O
the	O
corrosion	B-Process
behaviour	I-Process
of	O
the	O
alloy	O
is	O
the	O
S	B-Material
phase	I-Material
(	O
Al2CuMg	B-Material
)	O
particle	O
[	O
1,11	O
]	O
.	O
Dealloying	O
of	O
S	B-Material
phase	I-Material
particles	I-Material
,	O
which	O
may	O
account	O
for	O
∼	O
60	O
%	O
of	O
the	O
constituent	B-Material
particles	I-Material
in	O
AA2024	B-Material
alloys	I-Material
[	O
11	O
]	O
,	O
is	O
commonly	O
observed	O
when	O
the	O
alloy	B-Material
is	O
exposed	O
to	O
an	O
aggressive	O
environment	O
.	O
The	O
particles	B-Material
are	O
considered	O
as	O
important	O
initiation	O
sites	O
for	O
severe	O
localized	O
corrosion	B-Process
in	O
the	O
alloy	B-Material
[	O
11	O
–	O
22	O
]	O
.	O
The	O
dealloying	B-Process
of	O
the	O
S	B-Material
phase	I-Material
particles	I-Material
and	O
the	O
resulting	O
enrichment	B-Process
of	O
copper	B-Material
result	O
in	O
a	O
decrease	B-Process
of	I-Process
the	I-Process
Volta	I-Process
potential	I-Process
with	O
respect	O
to	O
the	O
matrix	O
and	O
hence	O
the	O
dealloyed	B-Material
particles	I-Material
become	O
active	O
cathodic	B-Material
sites	I-Material
[	O
23	O
–	O
25	O
]	O
.	O

Measuring	B-Task
and	I-Task
analysing	I-Task
the	I-Task
hold	I-Task
time	I-Task
of	I-Task
the	I-Task
CPA	I-Task
pill	I-Task
allows	O
the	O
thermal	B-Process
boundary	I-Process
resistance	I-Process
within	O
the	O
pill	B-Material
to	O
be	O
assessed	O
;	O
the	O
thermal	B-Process
boundary	I-Process
dictates	O
the	O
actual	O
temperature	B-Process
of	O
the	O
CPA	B-Material
crystals	I-Material
in	O
comparison	O
to	O
the	O
temperature	O
of	O
the	O
cold	B-Material
finger	I-Material
,	O
which	O
is	O
maintained	O
at	O
a	O
constant	O
temperature	O
by	O
a	O
servo	B-Process
control	I-Process
program	I-Process
.	O
Fig.	O
17	O
shows	O
the	O
temperature	O
profile	O
during	O
the	O
recycling	B-Process
of	O
the	O
CPA	B-Material
pill	I-Material
and	O
subsequent	O
operation	B-Process
at	I-Process
200mK	I-Process
.	O
During	O
the	O
hold	O
time	O
,	O
the	O
servo	B-Process
control	I-Process
program	I-Process
maintained	O
the	O
CPA	B-Material
pill	I-Material
temperature	O
to	O
within	O
a	O
millikelvin	O
.	O
It	O
is	O
expected	O
that	O
microkelvin	B-Process
stability	I-Process
can	O
be	O
achieved	O
with	O
fast	B-Process
read-out	I-Process
thermometry	I-Process
(	O
which	O
was	O
not	O
available	O
at	O
the	O
time	O
of	O
testing	O
but	O
which	O
will	O
be	O
used	O
for	O
the	O
mKCC	B-Task
)	O
,	O
as	O
this	O
would	O
allow	O
for	O
temperature	O
control	O
on	O
much	O
faster	O
(	O
millisecond	O
)	O
timescales	O
than	O
the	O
current	O
(	O
approximately	O
1s	O
)	O
thermometry	O
readout	O
used	O
.	O

The	O
product	B-Process
change	I-Process
between	O
batches	B-Material
#	I-Material
1	I-Material
/#	I-Material
2	I-Material
and	O
the	O
others	O
is	O
the	O
most	O
influential	O
on	O
the	O
test	O
results	O
.	O
The	O
redesign	O
and	O
upgrade	O
to	O
110-nm	B-Process
process	I-Process
technology	I-Process
reduces	O
the	O
pass	O
rate	O
at	O
LNT	B-Material
by	O
approximately	O
half	O
.	O
This	O
is	O
mainly	O
caused	O
by	O
the	O
increased	O
incidence	O
of	O
erase	B-Process
and	I-Process
program	I-Process
timeouts	I-Process
with	O
some	O
contribution	O
from	O
long	B-Process
erase	I-Process
and	O
program	B-Process
times	I-Process
and	O
bit	B-Process
errors	I-Process
.	O
The	O
difference	O
in	O
pass	B-Process
rates	I-Process
at	O
88K	O
between	O
batches	B-Material
#	I-Material
3	I-Material
/#	I-Material
4	I-Material
and	O
#	B-Material
5	I-Material
/#	I-Material
6	I-Material
,	O
which	O
use	O
the	O
same	O
process	B-Process
technology	I-Process
with	O
the	O
same	O
dimensions	O
,	O
can	O
be	O
explained	O
by	O
the	O
fabrication	O
in	O
different	O
assembly	B-Process
lines	I-Process
,	O
where	O
other	O
processes	O
or	O
base	O
materials	O
may	O
have	O
been	O
changed	O
.	O
This	O
means	O
different	O
tolerances	B-Process
in	O
base	O
materials	O
and	O
production	O
process	O
,	O
which	O
are	O
more	O
pronounced	O
the	O
lower	O
the	O
temperature	O
.	O
Some	O
of	O
the	O
differences	O
of	O
technology	O
scale	O
may	O
reflect	O
shifts	O
in	O
transistor	B-Process
parameters	I-Process
such	O
as	O
transconductance	B-Process
/	I-Process
gain	I-Process
,	O
threshold	B-Process
voltage	I-Process
,	O
and	O
threshold	B-Process
slope	I-Process
[	O
7	O
]	O
.	O

Prior	O
to	O
assembling	B-Task
the	I-Task
miniature	I-Task
ADR	I-Task
,	O
the	O
mKCC	B-Material
MR	I-Material
heat	I-Material
switch	I-Material
could	O
not	O
be	O
fully	O
thermally	B-Process
characterised	I-Process
due	O
to	O
cryostat	B-Material
constraints	I-Material
.	O
However	O
,	O
based	O
on	O
experiments	O
and	O
research	O
conducted	O
at	O
MSSL	O
on	O
a	O
range	O
of	O
tungsten	B-Material
heat	I-Material
switches	I-Material
,	O
the	O
thermal	B-Process
conductivity	I-Process
has	O
been	O
estimated	O
.	O
In	O
Hills	O
et	O
al	O
.	O
[	O
8	O
]	O
,	O
an	O
equation	O
is	O
derived	O
which	O
allows	O
the	O
thermal	O
conductivity	O
(	O
κ	B-Process
)	O
below	O
6K	O
to	O
be	O
calculated	O
as	O
a	O
function	O
of	O
magnetic	B-Process
field	I-Process
(	O
B	B-Process
)	O
and	O
temperature	O
(	O
T	O
)	O
(	O
see	O
Eq.	O
(	O
1	O
))	O
.	O
To	O
estimate	O
the	O
performance	O
of	O
the	O
mKCC	B-Material
heat	I-Material
switch	I-Material
,	O
the	O
parameters	O
in	O
Eq	O
.	O
(	O
1	O
)	O
have	O
been	O
taken	O
from	O
the	O
measured	O
thermal	B-Process
conductivity	I-Process
of	O
another	O
MSSL	B-Material
heat	I-Material
switch	I-Material
with	O
the	O
same	O
1.5mm	O
square	O
cross	O
section	O
,	O
a	O
free	O
path	O
length	O
of	O
43cm	O
and	O
a	O
RRR	O
of	O
20,000	O
;	O
it	O
has	O
been	O
observed	O
from	O
experiments	O
conducted	O
at	O
MSSL	O
that	O
there	O
is	O
little	O
change	O
in	O
the	O
thermal	B-Process
performance	I-Process
for	O
tungsten	B-Material
heat	I-Material
switches	I-Material
with	O
a	O
RRR	O
between	O
20,000	O
and	O
32,000	O
(	O
subject	O
of	O
a	O
future	O
publication	O
)	O
and	O
therefore	O
the	O
performance	O
of	O
the	O
20,000	B-Material
RRR	I-Material
heat	I-Material
switch	I-Material
has	O
been	O
assumed	O
to	O
be	O
a	O
good	O
approximation	O
.	O
Fig.	O
5	O
gives	O
the	O
calculated	O
thermal	B-Process
conductivity	I-Process
of	O
the	O
mKCC	B-Material
switch	I-Material
at	O
0	O
,	O
1	O
,	O
2	O
and	O
3T	O
based	O
on	O
Eq	O
.	O
(	O
1	O
)	O
,	O
where	O
the	O
constants	O
b0	O
,	O
a1	O
,	O
a2	O
,	O
a3	O
,	O
a4	O
and	O
n	O
have	O
the	O
values	O
0.0328	O
,	O
1.19	O
×	O
10	O
−	O
4	O
,	O
3.57	O
×	O
10	O
−	O
6	O
,	O
1.36	O
,	O
0.000968	O
and	O
1.7	O
respectively	O
.	O
It	O
should	O
be	O
noted	O
that	O
the	O
calculated	O
thermal	B-Process
conductivity	I-Process
of	O
the	O
mKCC	B-Material
switch	I-Material
presented	O
in	O
Fig.	O
5	O
has	O
been	O
validated	O
by	O
comparing	O
the	O
experimental	O
results	O
of	O
the	O
miniature	B-Material
ADR	I-Material
with	O
modelled	O
predictions	O
(	O
this	O
is	O
discussed	O
in	O
Section	O
3.3	O
).(	O
1	O
)	O
κ	O
(	O
T	O
)=	O
b0T2	O
+	O
1a1	O
+	O
a2T2T	O
+	O
Bna3T	O
+	O
a4T4	O

An	O
early	O
attempt	O
to	O
combine	B-Task
sets	I-Task
and	I-Task
networks	I-Task
in	I-Task
a	I-Task
single	I-Task
visualization	I-Task
relied	O
on	O
first	O
drawing	O
an	O
Euler	B-Process
diagram	I-Process
then	O
placing	O
a	O
graph	O
inside	O
it	O
[	O
30	O
]	O
,	O
however	O
the	O
sets	O
were	O
often	O
visualized	O
with	O
convoluted	O
,	O
difficult	O
to	O
follow	O
curves	O
.	O
In	O
addition	O
,	O
only	O
limited	O
kinds	O
of	O
set	O
data	O
could	O
be	O
shown	O
as	O
the	O
system	O
was	O
limited	O
to	O
well-formed	O
Euler	B-Process
diagrams	I-Process
.	O
Compound	B-Material
graphs	I-Material
can	O
be	O
used	O
to	O
represent	O
restricted	O
kinds	O
of	O
grouped	O
network	B-Material
data	I-Material
[	O
8	O
]	O
.	O
Graph	B-Material
clusters	I-Material
are	O
visualized	O
with	O
transparent	B-Process
hulls	I-Process
by	O
Santamaria	O
and	O
Theron	O
[	O
39	O
]	O
.	O
However	O
,	O
the	O
technique	O
removes	O
edges	O
from	O
the	O
graph	O
and	O
it	O
is	O
not	O
sufficiently	O
sophisticated	O
for	O
arbitrary	O
overlapping	O
sets	O
.	O
Itoh	O
et	O
al	O
.	O
[	O
24	O
]	O
proposed	O
to	O
overlay	O
pie-like	O
glyphs	B-Material
over	O
the	O
nodes	O
in	O
a	O
graph	O
to	O
encode	O
multiple	O
categories	O
.	O
Each	O
set	O
is	O
hence	O
represented	O
using	O
disconnected	O
regions	O
that	O
are	O
linked	O
by	O
having	O
the	O
same	O
colour	O
.	O
This	O
causes	O
difficulties	O
with	O
tasks	O
that	O
involve	O
finding	B-Task
relations	I-Task
between	I-Task
sets	I-Task
such	O
as	O
T1	B-Process
,	I-Process
T3	I-Process
and	I-Process
T4	I-Process
in	O
Section	O
5.3	O
.	O
A	O
related	O
class	O
of	O
techniques	O
visualize	B-Process
grouping	I-Process
information	I-Process
over	I-Process
graphs	I-Process
using	O
convex	B-Process
hulls	I-Process
,	O
such	O
as	O
Vizster	B-Process
[	O
22	O
]	O
.	O
However	O
,	O
they	O
do	O
not	O
support	O
visualizing	O
set	O
overlaps	O
.	O

Moreover	O
,	O
one	O
observes	O
segregation	B-Process
effects	I-Process
by	O
the	O
XRD	B-Task
analysis	I-Task
,	O
which	O
probably	O
took	O
place	O
at	O
high	O
temperature	O
,	O
and	O
were	O
partially	O
quenched	B-Process
to	O
room	O
temperature	O
.	O
The	O
phase	O
analysis	O
showed	O
up	O
to	O
three	O
distinct	O
phases	O
,	O
which	O
should	O
have	O
hence	O
a	O
distinct	O
measurable	O
phase	O
transition	O
temperature	O
,	O
if	O
they	O
crystallise	B-Process
from	O
the	O
liquid	B-Material
on	O
the	O
surface	B-Material
.	O
In	O
the	O
thermograms	B-Process
these	O
effects	O
are	O
not	O
observable	O
as	O
different	O
solidification	B-Process
arrest	I-Process
or	O
clear	B-Process
inflections	I-Process
.	O
The	O
proportion	O
of	O
new	O
appearing	O
phases	O
is	O
small	O
and	O
therefore	O
the	O
latent	B-Process
heat	I-Process
released	O
by	O
this	O
new	O
phase	O
will	O
be	O
also	O
small	O
.	O
The	O
reflected	B-Process
light	I-Process
signal	I-Process
technique	I-Process
only	O
showed	O
one	O
phase	B-Process
change	I-Process
during	O
cooling	O
.	O
As	O
well	O
,	O
the	O
location	O
of	O
this	O
segregation	B-Process
cannot	O
be	O
determined	O
exactly	O
in	O
the	O
molten	B-Material
pool	I-Material
or	O
later	O
in	O
the	O
re-solidified	B-Material
material	I-Material
.	O
At	O
the	O
surface	O
,	O
where	O
the	O
temperature	O
is	O
measured	O
,	O
the	O
material	B-Task
analysis	I-Task
by	I-Task
Raman	I-Task
spectroscopy	I-Task
has	O
not	O
shown	O
signs	O
of	O
segregation	B-Process
,	O
so	O
that	O
also	O
the	O
uncertainties	O
in	O
composition	O
for	O
the	O
phase	B-Process
transition	I-Process
are	O
taken	O
from	O
the	O
uncertainties	O
from	O
the	O
XRD	B-Task
analysis	I-Task
for	O
the	O
most	O
abundant	O
phase	O
at	O
each	O
composition	O
in	O
re-solidified	B-Material
material	I-Material
.	O

Myocardial	B-Process
electrical	I-Process
propagation	I-Process
can	O
be	O
simulated	O
using	O
the	O
monodomain	B-Material
or	I-Material
bidomain	I-Material
PDEs	I-Material
[	O
5,6	O
]	O
.	O
Due	O
to	O
its	O
capacity	O
to	O
represent	O
complex	O
geometries	B-Material
with	O
ease	O
,	O
approximations	O
are	O
often	O
obtained	O
using	O
the	O
finite	B-Process
element	I-Process
method	I-Process
(	O
FEM	B-Process
)	O
to	O
discretise	O
the	O
PDEs	B-Material
in	O
space	O
on	O
realistic	O
cardiac	B-Material
geometry	I-Material
meshes	I-Material
;	O
this	O
results	O
in	O
very	O
large	O
(	O
up	O
to	O
forty-million	O
degrees	O
of	O
freedom	O
(	O
DOF	O
)	O
for	O
human	B-Task
heart	I-Task
geometries	I-Task
)	O
systems	B-Process
of	I-Process
linear	I-Process
equations	I-Process
which	O
must	O
be	O
solved	O
many	O
thousands	O
of	O
times	O
over	O
the	O
course	O
of	O
even	O
a	O
short	O
simulation	B-Process
.	O
Thus	O
,	O
they	O
are	O
extremely	O
computationally	O
demanding	O
,	O
presenting	O
taxing	O
problems	O
even	O
to	O
high-end	B-Process
supercomputing	I-Process
resources	I-Process
.	O
This	O
computational	O
demand	O
means	O
that	O
effort	O
has	O
been	O
invested	O
in	O
developing	B-Task
efficient	I-Task
solution	I-Task
techniques	I-Task
,	O
including	O
work	O
on	O
preconditioning	B-Task
,	O
parallelisation	B-Task
and	O
adaptivity	B-Task
in	I-Task
space	I-Task
and	I-Task
time	I-Task
[	O
7	O
–	O
12	O
]	O
.	O
In	O
this	O
study	O
,	O
we	O
investigate	B-Task
the	I-Task
potential	I-Task
of	I-Task
reducing	I-Task
the	I-Task
number	I-Task
of	I-Task
DOF	I-Task
by	O
using	O
a	O
high-order	B-Process
polynomial	I-Process
FEM	I-Process
[	O
13	O
–	O
15	O
]	O
to	O
approximate	O
the	O
monodomain	B-Material
PDE	I-Material
in	O
space	O
,	O
with	O
the	O
goal	O
of	O
significantly	B-Task
improving	I-Task
simulation	I-Task
efficiency	I-Task
over	O
the	O
piecewise-linear	B-Process
FEM	I-Process
approach	O
commonly	O
used	O
in	O
the	O
field	O
[	O
16	O
–	O
19	O
]	O
.	O
For	O
schemes	O
where	O
the	O
polynomial	O
degree	O
p	O
of	O
the	O
elements	O
is	O
adjusted	O
according	O
to	O
the	O
error	O
in	O
the	O
approximation	O
,	O
this	O
is	O
known	O
as	O
the	O
finite	B-Process
element	I-Process
p-version	I-Process
.	O
In	O
the	O
work	O
presented	O
here	O
,	O
we	O
work	O
with	O
schemes	O
which	O
keep	O
p	O
fixed	O
.	O

In	O
this	O
work	O
we	O
develop	B-Task
a	I-Task
new	I-Task
approach	I-Task
to	I-Task
DEA	I-Task
suitable	I-Task
for	I-Task
modelling	I-Task
three-dimensional	I-Task
problems	I-Task
.	O
The	O
present	O
DEA	B-Process
methods	I-Process
rely	O
on	O
the	O
fact	O
that	O
one	O
can	O
easily	O
parametrise	B-Process
the	I-Process
boundary	I-Process
of	I-Process
the	I-Process
region	I-Process
being	O
modelled	O
,	O
and	O
then	O
apply	O
an	O
orthonormal	B-Process
basis	I-Process
approximation	I-Process
over	O
the	O
resulting	O
boundary	B-Material
phase	I-Material
space	I-Material
coordinate	I-Material
system	I-Material
.	O
In	O
two	O
dimensions	O
this	O
is	O
simple	O
as	O
the	O
boundary	O
may	O
be	O
parametrised	O
along	O
its	O
arc-length	B-Material
and	O
the	O
associated	O
momentum	B-Material
(	I-Material
or	I-Material
direction	I-Material
)	I-Material
coordinate	I-Material
taken	O
tangential	O
to	O
the	O
boundary	O
.	O
The	O
basis	O
can	O
be	O
any	O
suitable	O
(	O
scaled	O
)	O
univariate	B-Material
basis	I-Material
in	O
both	O
position	O
and	O
momentum	O
,	O
such	O
as	O
a	O
Fourier	B-Material
basis	I-Material
[	O
8	O
]	O
or	O
Chebyshev	B-Material
polynomials	I-Material
[	O
9	O
]	O
.	O
Defining	O
a	O
suitable	O
parametrisation	O
for	O
the	O
spatial	O
coordinate	O
in	O
three-dimensions	O
becomes	O
much	O
more	O
difficult	O
.	O
In	O
momentum	O
space	O
spherical	B-Process
polar	I-Process
coordinates	I-Process
may	O
be	O
employed	O
and	O
so	O
these	O
problems	O
do	O
not	O
arise	O
.	O

We	O
order	B-Task
the	I-Task
discrete	I-Task
unknowns	I-Task
so	O
that	O
the	O
vector	B-Material
of	I-Material
unknowns	I-Material
,	O
xPS	B-Material
=[	I-Material
X,L	I-Material
]	I-Material
,	O
contains	O
the	O
nx	O
unknown	O
nodal	B-Material
coordinates	I-Material
,	O
followed	O
by	O
the	O
nb	O
unknown	O
discrete	O
Lagrange	B-Material
multipliers	I-Material
.	O
The	O
linear	O
systems	O
to	O
be	O
solved	O
in	O
the	O
course	O
of	O
the	O
Newton-based	B-Process
solution	I-Process
of	O
Eq	O
.	O
(	O
10	O
)	O
,	O
subject	O
to	O
the	O
displacement	B-Material
constraint	I-Material
(	O
9	O
)	O
,	O
then	O
have	O
saddle-point	B-Process
structure	I-Process
,(	O
15	O
)	O
where	O
E	O
is	O
the	O
tangent	B-Process
stiffness	I-Process
matrix	I-Process
of	O
the	O
unconstrained	B-Material
pseudo-solid	I-Material
problem	I-Material
,	O
and	O
the	O
two	B-Material
off-diagonal	I-Material
blocks	I-Material
Cxl	B-Material
and	O
Clx	B-Material
=	I-Material
CxlT	I-Material
arise	O
through	O
the	O
imposition	O
of	O
the	O
displacement	O
constraint	O
by	O
the	O
Lagrange	B-Material
multipliers	I-Material
.	O
We	O
refer	O
to	O
[	O
34	O
]	O
for	O
the	O
proof	O
of	O
the	O
LBB	B-Material
stability	O
of	O
this	O
discretisation	B-Process
;	O
see	O
also	O
[	O
35,36	O
]	O
for	O
a	O
discussion	O
of	O
the	O
LBB	B-Material
stability	O
of	O
the	O
Lagrange-multiplier-based	B-Process
imposition	I-Process
of	O
Dirichlet	B-Process
boundary	I-Process
conditions	I-Process
in	O
related	O
problems	O
.	O
We	O
note	O
that	O
during	O
the	O
first	O
step	O
of	O
the	O
Newton	B-Process
iteration	I-Process
,	O
E	O
is	O
symmetric	O
positive	O
definite	O
since	O
it	O
represents	O
the	O
tangent	B-Material
stiffness	I-Material
matrix	I-Material
relative	O
to	O
the	O
system	O
’s	O
equilibrium	B-Process
configuration	I-Process
.	O

Inequality	B-Process
(	O
22	O
)	O
indicates	O
that	O
the	O
maximum-norm	B-Material
is	O
the	O
loosest	O
among	O
all	O
p-norms	B-Material
.	O
Fortunately	O
,	O
this	O
loosest	O
constraint	O
would	O
not	O
seriously	O
affect	O
the	O
accuracy	O
since	O
the	O
value	O
of	O
||	B-Material
y	I-Material
||∞	I-Material
is	O
comparable	O
to	O
that	O
of	O
the	O
2-norm	B-Material
and	O
1-norm	B-Material
.	O
The	O
maximum-norm	B-Material
provides	O
us	O
with	O
the	O
largest	O
number	O
of	O
possible	O
solutions	O
under	O
a	O
given	O
error	O
limitation	O
[	O
24	O
]	O
.	O
This	O
would	O
greatly	O
enhance	O
the	O
possibility	O
of	O
finding	O
a	O
group	O
of	O
optimized	B-Material
coefficients	I-Material
when	O
scanning	O
a	O
vast	B-Process
solution	I-Process
set	I-Process
.	O
On	O
the	O
other	O
hand	O
,	O
checking	O
the	O
maximum	B-Material
deviation	I-Material
sounds	O
more	O
reasonable	O
than	O
checking	O
the	O
“	O
distance	O
”	O
between	O
the	O
accurate	O
and	O
approximated	O
wave	O
numbers	O
since	O
it	O
is	O
not	O
working	O
in	O
the	O
space	O
domain	O
.	O
Therefore	O
,	O
we	O
chose	O
the	O
maximum-norm	B-Material
as	O
our	O
criterion	O
for	O
designing	O
the	O
objective	B-Material
functions	I-Material
to	O
extend	B-Task
the	I-Task
accurate	I-Task
wave	I-Task
number	I-Task
coverage	I-Task
as	O
widely	O
as	O
possible	O
.	O

Similar	O
numerical	B-Material
oscillations	I-Material
to	O
those	O
described	O
above	O
also	O
emerge	O
in	O
the	O
ISPM	B-Material
when	O
utilising	O
classical	O
IBM	B-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	B-Task
new	I-Task
family	I-Task
of	I-Task
kernel	I-Task
functions	I-Task
which	I-Task
do	I-Task
not	I-Task
introduce	I-Task
these	I-Task
spurious	I-Task
oscillations	I-Task
.	O
The	O
kernel	O
functions	O
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	B-Process
derivatives	I-Process
.	O
A	O
Maple	B-Task
computer	I-Task
program	I-Task
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

Contact	B-Process
methods	I-Process
have	O
been	O
developed	O
and	O
used	O
in	O
Lagrangian	O
staggered-grid	B-Material
hydrodynamic	I-Material
(	O
SGH	B-Material
)	O
calculations	O
for	O
many	O
years	O
.	O
Early	O
examples	O
of	O
contact	B-Process
methods	I-Process
are	O
discussed	O
in	O
Wilkins	O
[	O
37	O
]	O
and	O
Cherry	O
et	O
al	O
.	O
[	O
7	O
]	O
.	O
Hallquist	O
et	O
al	O
.	O
[	O
17	O
]	O
provides	O
an	O
overview	O
of	O
multiple	O
contact	B-Process
algorithms	I-Process
used	O
in	O
various	O
Lagrangian	B-Process
SGH	I-Process
codes	O
dating	O
back	O
to	O
HEMP	B-Process
[	O
37	O
]	O
.	O
Of	O
particular	O
interest	O
,	O
Hallquist	O
et	O
al	O
.	O
[	O
17	O
]	O
describes	O
the	O
contact	B-Process
surface	I-Process
scheme	I-Process
used	O
in	O
TOODY	B-Process
[	O
31	O
]	O
and	O
later	O
implemented	O
in	O
DYNA2D	B-Process
[	O
36	O
]	O
.	O
The	O
contact	B-Process
method	I-Process
of	O
TOODY	B-Process
uses	O
a	O
master	B-Process
–	I-Process
slave	I-Process
approach	I-Process
.	O
The	O
goal	O
of	O
this	O
approach	O
is	O
to	O
treat	O
the	O
nodes	B-Material
on	O
the	O
contact	B-Material
surface	I-Material
in	O
a	O
manner	O
similar	O
to	O
an	O
internal	B-Material
node	I-Material
.	O
The	O
physical	O
properties	O
of	O
the	O
slave	B-Material
surface	I-Material
are	O
interpolated	O
to	O
a	O
ghost	B-Material
mesh	I-Material
(	O
termed	O
phony	B-Material
elements	I-Material
in	O
[	O
17	O
])	O
that	O
overlays	O
the	O
slave	B-Material
zones	I-Material
.	O
The	O
physical	O
properties	O
are	O
interpolated	O
from	O
the	O
slave	B-Material
surface	I-Material
to	O
the	O
ghost	B-Material
zones	I-Material
using	O
surface	O
area	O
weights	O
.	O
The	O
surface	O
area	O
weights	O
are	O
equal	O
to	O
the	O
ratio	O
of	O
the	O
ghost	B-Material
zone	I-Material
surface	I-Material
area	I-Material
to	O
the	O
surface	O
area	O
of	O
the	O
master	B-Material
surface	I-Material
.	O
The	O
contact	B-Task
surface	I-Task
method	I-Task
for	O
nodal-based	B-Process
Lagrangian	I-Process
cell-centered	I-Process
hydrodynamics	I-Process
(	O
CCH	B-Material
)	O
presented	O
in	O
this	O
paper	O
will	O
use	O
surface	O
area	O
weights	O
similar	O
in	O
concept	O
to	O
those	O
in	O
TOODY	B-Process
.	O
Following	O
the	O
area	B-Process
fraction	I-Process
approach	I-Process
of	O
TOODY	B-Process
may	O
seem	O
retrospective	O
;	O
however	O
,	O
using	O
surface	O
area	O
weights	O
naturally	O
extends	O
to	O
the	O
new	O
CCH	B-Material
methods	O
that	O
solve	B-Task
a	I-Task
Riemann-like	I-Task
problem	I-Task
at	I-Task
the	I-Task
node	I-Task
of	I-Task
a	I-Task
zone	I-Task
[	O
10,24,25,3	O
]	O
.	O

Three	O
Runge	B-Process
–	I-Process
Kutta	I-Process
IMEX	I-Process
schemes	I-Process
were	O
tested	O
by	O
Ullrich	O
and	O
Jablonowski	O
[	O
23	O
]	O
for	O
the	O
HEVI	B-Process
solution	I-Process
of	O
the	O
equations	O
governing	O
atmospheric	B-Process
motion	I-Process
.	O
They	O
tested	O
the	O
ARS	B-Process
(	I-Process
2,3,2	I-Process
)	I-Process
scheme	I-Process
of	O
Ascher	O
et	O
al	O
.	O
[	O
1	O
]	O
and	O
also	O
suggested	O
the	O
less	O
computationally	O
expensive	O
but	O
nearly	O
as	O
accurate	O
Strang	B-Process
carryover	I-Process
scheme	I-Process
.	O
This	O
involves	O
Strang	B-Process
splitting	I-Process
but	O
the	O
first	O
implicit	O
stage	O
is	O
cleverly	O
re-used	O
from	O
the	O
final	O
implicit	O
stage	O
of	O
the	O
previous	O
time-step	O
and	O
so	O
there	O
is	O
only	O
one	O
implicit	O
solution	O
per	O
time-step	O
.	O
Another	O
novel	O
approach	O
taken	O
by	O
Ullrich	O
and	O
Jablonowski	O
[	O
23	O
]	O
is	O
to	O
use	O
a	O
Rosenbrock	B-Process
solution	I-Process
in	O
order	O
to	O
treat	O
all	O
of	O
the	O
vertical	O
terms	O
implicitly	O
rather	O
than	O
just	O
the	O
terms	O
involved	O
in	O
wave	B-Process
propagation	I-Process
.	O
A	O
Rosenbrock	B-Process
solution	I-Process
is	O
one	B-Process
iteration	I-Process
of	I-Process
a	I-Process
Newton	I-Process
solver	I-Process
.	O
This	O
circumvents	O
the	O
time-step	B-Process
restriction	I-Process
associated	O
with	O
vertical	B-Process
advection	I-Process
at	O
the	O
cost	O
of	O
slowing	B-Process
the	I-Process
vertical	I-Process
advection	I-Process
.	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	O
element	O
is	O
divided	B-Process
into	I-Process
spatially-oriented	I-Process
bins	I-Process
in	O
the	O
y-direction	O
in	O
order	O
to	O
resolve	B-Process
the	I-Process
velocity	I-Process
and	I-Process
shear-stress	I-Process
profiles	I-Process
.	O
Velocity	O
in	O
each	O
bin	O
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-Task
noise	I-Task
further	O
.	O
The	O
fit	O
produces	O
a	O
continuous	B-Process
function	I-Process
that	O
avoids	O
stability	B-Process
issues	I-Process
arising	O
from	O
supplying	B-Process
highly	I-Process
fluctuating	I-Process
data	I-Process
to	I-Process
the	I-Process
macro	I-Process
solver	I-Process
.	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	O
region	O
,	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	O
of	O
the	O
polynomials	B-Material
used	O
in	O
the	O
core	O
micro	O
region	O
and	O
constrained	O
region	O
respectively	O
.	O
An	O
estimate	O
of	O
the	O
new	B-Material
slip	I-Material
velocity	I-Material
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	B-Task
the	I-Task
effects	I-Task
of	I-Task
the	I-Task
Schmidt	I-Task
number	I-Task
and	I-Task
the	I-Task
chemical	I-Task
reaction	I-Task
rate	I-Task
on	O
the	O
bulk-mean	B-Material
concentration	I-Material
of	I-Material
B	I-Material
in	O
water	O
.	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-Material
product	I-Material
of	O
B	O
,	O
as	O
well	O
as	O
acidification	B-Process
of	I-Process
water	I-Process
by	O
the	O
chemical	B-Process
reaction	I-Process
.	O
Here	O
,	O
the	O
bulk-mean	B-Material
concentration	I-Material
of	I-Material
B	I-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	B-Material
and	O
the	O
chemical	B-Process
reaction	I-Process
rate	I-Process
on	O
the	O
bulk-mean	B-Material
concentration	I-Material
CB	I-Material
⁎¯	I-Material
.	O
It	O
is	O
worth	O
to	O
mention	O
here	O
that	O
the	O
bulk-mean	B-Material
concentration	I-Material
of	I-Material
B	I-Material
reaches	O
approximately	O
0.6	O
as	O
the	O
chemical	B-Process
reaction	I-Process
rate	I-Process
and	O
the	O
Schmidt	B-Material
number	I-Material
increase	O
to	O
infinite	O
,	O
and	O
the	O
concentration	O
is	O
smaller	O
than	O
the	O
equilibrium	B-Material
concentration	I-Material
of	I-Material
A	I-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	O
,	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	O
will	O
be	O
a	O
function	O
of	O
the	O
Reynolds	B-Material
number	I-Material
of	O
the	O
water	O
flow	O
,	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	O
investigation	O
on	O
the	O
efficiency	O
of	O
the	O
aquarium	B-Process
chemical	I-Process
reaction	I-Process
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	O
practical	B-Process
modelling	I-Process
for	O
the	O
gas	B-Process
exchange	I-Process
between	O
air	O
and	O
water	O
.	O

Numerical	B-Task
simulation	I-Task
of	O
the	O
gas	B-Process
flow	I-Process
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-Process
continuum	I-Process
fluid	I-Process
dynamics	I-Process
,	O
which	O
assumes	O
that	O
locally	O
a	O
gas	O
is	O
close	O
to	O
a	O
state	O
of	O
thermodynamic	B-Process
equilibrium	I-Process
,	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	B-Process
and	I-Process
flexible	I-Process
modelling	I-Process
alternative	I-Process
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-Process
applications	I-Process
,	O
which	O
typically	O
have	O
a	O
geometry	O
of	O
high-aspect	B-Material
ratio	I-Material
(	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	O
multiscale	B-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	B-Process
channel	I-Process
network	I-Process
)	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	B-Process
convergence	I-Process
properties	I-Process
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	B-Material
solutions	I-Material
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-Process
solution	I-Process
polynomial	I-Process
orders	I-Process
.	O
Increasing	O
the	O
order	O
of	O
the	O
geometry	B-Process
parametrisation	I-Process
reduces	O
the	O
geometric	B-Process
error	I-Process
.	O
The	O
analytic	B-Task
test	I-Task
cases	I-Task
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	B-Process
features	I-Process
in	I-Process
the	I-Process
solution	I-Process
and	O
so	O
will	O
better	O
capture	B-Process
the	I-Process
geometry	I-Process
and	O
consequently	O
reduce	B-Process
this	I-Process
lower	I-Process
bound	I-Process
on	O
the	O
solution	B-Material
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	B-Material
surface	I-Material
is	O
not	O
,	O
then	O
exponential	B-Process
convergence	I-Process
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	B-Process
are	I-Process
aligned	I-Process
with	I-Process
element	I-Process
boundaries	I-Process
.	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	B-Material
surface	I-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

DPD	B-Process
was	O
first	O
proposed	O
in	O
order	O
to	O
recover	O
the	O
properties	O
of	O
isotropy	B-Process
(	O
and	O
Galilean	B-Process
invariance	I-Process
)	O
that	O
were	O
broken	O
in	O
the	O
so-called	O
lattice-gas	B-Process
automata	I-Process
method	O
[	O
5	O
]	O
.	O
In	O
DPD	B-Process
,	O
each	O
body	O
is	O
regarded	O
as	O
a	O
coarse-grained	B-Material
particle	I-Material
.	O
These	O
particles	B-Material
interact	O
in	O
a	O
soft	O
(	O
and	O
short-ranged	O
)	O
potential	O
,	O
allowing	O
larger	O
integration	O
timesteps	O
than	O
would	O
be	O
possible	O
in	O
MD	B-Process
,	O
while	O
simultaneously	O
decreasing	O
the	O
number	O
of	O
degrees	O
of	O
freedom	O
required	O
.	O
As	O
in	O
Langevin	B-Process
dynamics	I-Process
,	O
a	O
thermostat	B-Process
consisting	O
of	O
well-balanced	O
damping	O
and	O
stochastic	O
terms	O
is	O
applied	O
to	O
each	O
particle	O
.	O
However	O
,	O
unlike	O
in	O
Langevin	B-Process
dynamics	I-Process
,	O
both	O
terms	O
are	O
pairwise	O
and	O
the	O
damping	O
term	O
is	O
based	O
on	O
relative	O
velocities	O
,	O
leading	O
to	O
the	O
conservation	O
of	O
both	O
the	O
angular	B-Process
momentum	I-Process
and	O
the	O
linear	B-Process
momentum	I-Process
.	O
The	O
property	O
of	O
Galilean	B-Process
invariance	I-Process
(	O
i.e.	O
,	O
the	O
dependence	B-Process
on	I-Process
the	I-Process
relative	I-Process
velocity	I-Process
)	O
makes	O
DPD	B-Process
a	O
profile-unbiased	B-Process
thermostat	I-Process
(	O
PUT	B-Process
)	O
[	O
6,7	O
]	O
by	O
construction	O
and	O
thus	O
it	O
is	O
an	O
ideal	O
thermostat	O
for	O
nonequilibrium	B-Task
molecular	I-Task
dynamics	I-Task
(	I-Task
NEMD	I-Task
)	I-Task
[	O
8	O
]	O
.	O
The	O
momentum	O
is	O
expected	O
to	O
propagate	O
locally	O
(	O
while	O
global	O
momentum	O
is	O
conserved	O
)	O
and	O
thus	O
the	O
correct	O
hydrodynamics	O
is	O
expected	O
in	O
DPD	B-Process
[	O
8	O
]	O
,	O
as	O
demonstrated	O
previously	O
in	O
[	O
9	O
]	O
.	O
Due	O
to	O
the	O
aforementioned	O
properties	O
,	O
DPD	O
has	O
been	O
widely	O
used	O
to	O
recover	B-Task
thermodynamic	I-Task
,	I-Task
dynamical	I-Task
,	I-Task
and	I-Task
rheological	I-Task
properties	I-Task
of	O
complex	B-Material
fluids	I-Material
,	O
with	O
applications	O
in	O
polymer	B-Material
solutions	I-Material
[	O
10	O
]	O
,	O
colloidal	B-Material
suspensions	I-Material
[	O
11	O
]	O
,	O
multiphase	B-Material
flows	I-Material
[	O
12	O
]	O
,	O
and	O
biological	B-Material
systems	I-Material
[	O
13	O
]	O
.	O
DPD	B-Process
has	O
been	O
compared	O
with	O
Langevin	B-Process
dynamics	I-Process
for	O
out-of-equilibrium	O
simulations	O
of	O
polymeric	B-Process
systems	I-Process
in	O
[	O
14	O
]	O
,	O
where	O
as	O
expected	O
the	O
correct	O
dynamic	B-Process
fluctuations	I-Process
of	O
the	O
polymers	B-Material
were	O
obtained	O
with	O
the	O
former	O
but	O
not	O
with	O
the	O
latter	O
.	O

Copper-catalyzed	B-Material
Huisgen	I-Material
cycloadditions	I-Material
have	O
been	O
recently	O
extensively	O
studied	O
by	O
polymer	O
chemists	O
for	O
the	O
synthesis	B-Task
of	I-Task
functional	I-Task
polymers	I-Task
(	O
either	O
end-functional	O
or	O
side-functional	O
)	O
.	O
The	O
post-functionalization	O
of	O
synthetic	B-Material
polymers	I-Material
is	O
an	O
important	O
feature	O
of	O
macromolecular	B-Task
engineering	I-Task
as	O
many	O
polymerization	B-Process
mechanisms	O
are	O
rather	O
sensitive	O
to	O
the	O
presence	O
of	O
bulky	O
or	O
functional	O
groups	O
.	O
For	O
example	O
,	O
a	O
wide	O
variety	O
of	O
telechelic	B-Material
polymers	I-Material
(	O
i.e.	O
polymers	B-Material
with	I-Material
defined	I-Material
chain-ends	I-Material
)	O
can	O
be	O
efficiently	O
prepared	O
using	O
a	O
combination	O
of	O
atom	B-Process
transfer	I-Process
radical	I-Process
polymerization	I-Process
(	O
ATRP	B-Process
)	O
and	O
CuAAC	B-Process
.	O
This	O
strategy	O
was	O
independently	O
reported	O
in	O
early	O
2005	O
by	O
van	O
Hest	O
and	O
Opsteen	O
[	O
31	O
]	O
,	O
Lutz	O
et	O
al	O
.	O
[	O
32	O
]	O
,	O
and	O
Matyjaszewski	O
et	O
al	O
.	O
[	O
33	O
]	O
.	O
Such	O
step	O
was	O
important	O
since	O
ATRP	B-Process
is	O
a	O
very	O
popular	O
polymerization	B-Process
method	O
in	O
modern	O
materials	O
science	O
[	O
34,35	O
]	O
.	O
Indeed	O
,	O
ATRP	B-Process
is	O
a	O
facile	O
technique	O
,	O
which	O
allows	O
the	O
preparation	B-Process
of	I-Process
well-defined	I-Process
polymers	I-Process
with	O
narrow	O
molecular	O
weight	O
distribution	O
,	O
predictable	O
chain	O
length	O
,	O
controlled	O
microstructure	O
,	O
defined	O
chain-ends	O
and	O
controlled	O
architecture	O
[	O
36	O
–	O
41	O
]	O
.	O
However	O
,	O
the	O
range	O
of	O
possibilities	O
of	O
ATRP	B-Process
can	O
be	O
further	O
broadened	O
by	O
CuAAC	B-Process
.	O
For	O
instance	O
,	O
the	O
ω-bromine	B-Material
chain-ends	I-Material
of	I-Material
polymers	I-Material
prepared	O
by	O
ATRP	B-Process
can	O
be	O
transformed	O
into	O
azides	B-Material
by	O
nucleophilic	B-Process
substitution	I-Process
and	O
subsequently	O
reacted	O
with	O
functional	O
alkynes	B-Material
(	O
Scheme	O
3	O
)	O
[	O
32	O
]	O
.	O
Due	O
to	O
the	O
very	O
high	O
chemoselectivity	O
of	O
CuAAC	B-Process
,	O
this	O
method	O
is	O
highly	O
modular	O
and	O
may	O
be	O
used	O
to	O
synthesize	O
a	O
wide	O
range	O
of	O
ω-functional	B-Material
polymers	I-Material
.	O
Moreover	O
,	O
the	O
formed	O
triazole	B-Material
rings	I-Material
are	O
not	O
“	O
passive	O
”	O
spacers	O
but	O
interesting	O
functions	O
exhibiting	O
H-bonds	B-Process
capability	O
,	O
aromaticity	O
and	O
rigidity	O
.	O

The	O
viscoelastic	B-Process
behavior	I-Process
of	O
elastomers	B-Material
containing	O
small	O
amounts	O
of	O
unattached	O
chains	O
has	O
been	O
investigated	O
to	O
characterize	B-Task
the	I-Task
dynamics	I-Task
of	I-Task
the	I-Task
polymer	I-Task
chains	I-Task
trapped	O
in	O
fixed	O
networks	O
[	O
66	O
–	O
68	O
]	O
.	O
Polymer	B-Material
chains	I-Material
trapped	O
in	O
fixed	O
networks	O
constitute	O
a	O
simpler	O
system	O
for	O
the	O
study	O
of	O
the	O
polymer	B-Material
chain	I-Material
dynamics	O
than	O
the	O
corresponding	O
uncrosslinked	O
polymer	B-Material
melts	I-Material
.	O
This	O
is	O
because	O
the	O
complicated	O
effect	O
of	O
the	O
motion	O
of	O
the	O
surrounding	O
chains	O
on	O
the	O
dynamics	O
of	O
the	O
probe	B-Material
chain	I-Material
–	O
called	O
“	O
constraint	B-Process
release	I-Process
”	O
[	O
69	O
]	O
–	O
is	O
absent	O
in	O
the	O
fixed	O
network	O
systems	O
.	O
Most	O
of	O
the	O
earlier	O
studies	O
employed	O
randomly	O
crosslinked	B-Material
elastomers	I-Material
as	O
host	O
networks	O
.	O
In	O
this	O
case	O
,	O
precise	O
control	B-Task
of	I-Task
the	I-Task
mesh	I-Task
size	I-Task
of	O
the	O
host	O
networks	O
is	O
not	O
possible	O
,	O
and	O
the	O
mesh	O
size	O
has	O
a	O
broad	O
distribution	O
.	O
The	O
end-linking	O
systems	O
give	O
the	O
host	O
networks	O
a	O
more	O
uniform	O
mesh	O
size	O
,	O
and	O
they	O
can	O
control	O
the	O
mesh	O
size	O
by	O
the	O
size	O
of	O
the	O
precursor	B-Material
chains	I-Material
.	O
We	O
investigated	O
the	O
dynamic	O
viscoelasticity	O
of	O
end-linked	O
PDMS	B-Material
elastomers	I-Material
containing	O
unattached	O
linear	O
PDMS	O
as	O
functions	O
of	O
the	O
size	O
of	O
the	O
unattached	B-Material
chains	I-Material
(	O
Mg	B-Material
)	O
and	O
the	O
network	B-Material
mesh	I-Material
(	O
Mx	B-Material
)	O
(	O
Fig.	O
9a	O
)	O
[	O
70	O
]	O
.	O
We	O
employed	O
two	O
types	O
of	O
host	O
networks	O
with	O
Mx	B-Process
>	I-Process
Me	I-Process
and	O
Mx	B-Process
<	I-Process
Me	I-Process
where	O
Me	O
(≈	O
10,000	O
for	O
PDMS	B-Material
)	O
is	O
the	O
molecular	O
mass	O
between	O
adjacent	O
entanglements	O
in	O
the	O
molten	O
state	O
.	O
The	O
Mx	B-Process
>	I-Process
Me	I-Process
and	O
Mx	B-Process
<	I-Process
Me	I-Process
networks	O
(	O
designated	O
as	O
NL	B-Process
and	O
NS	B-Process
,	O
respectively	O
)	O
were	O
designed	O
by	O
end-linking	O
the	O
long	O
(	O
Mn	O
=	O
84,000	O
)	O
and	O
short	O
precursor	O
chains	O
(	O
Mn	O
=	O
4,550	O
)	O
,	O
respectively	O
.	O
The	O
mesh	B-Material
of	O
the	O
NL	O
networks	O
is	O
dominated	O
by	O
trapped	O
entanglements	O
,	O
while	O
that	O
of	O
the	O
NS	O
network	O
is	O
governed	O
by	O
chemical	O
cross-links	O
.	O

When	O
incompatible	O
three	O
component	O
polymer	B-Material
chains	I-Material
are	O
tethered	O
at	O
a	O
junction	O
point	O
,	O
the	O
resultant	O
star	B-Material
molecules	I-Material
of	O
the	O
ABC	O
type	O
are	O
in	O
a	O
very	O
frustrated	O
field	O
in	O
bulk	O
.	O
That	O
is	O
,	O
their	O
junction	O
points	O
cannot	O
be	O
aligned	O
on	O
two-dimensional	O
planes	O
but	O
on	O
one-dimensional	O
lines	O
,	O
as	O
schematically	O
shown	O
in	O
Fig.	O
1.	O
Furthermore	O
,	O
when	O
the	O
chain	O
length	O
difference	O
is	O
not	O
so	O
large	O
,	O
the	O
array	O
of	O
junction	O
points	O
tends	O
to	O
be	O
straight	O
and	O
long	O
one	O
.	O
Consequently	O
each	O
domain	O
with	O
mesoscopic	O
sizes	O
becomes	O
cylinders	B-Material
,	O
and	O
their	O
cross	O
sections	O
could	O
be	O
conformed	O
by	O
polygons	B-Material
[	O
28,29	O
]	O
.	O
This	O
is	O
because	O
three	B-Material
interfaces	I-Material
,	I-Material
A	I-Material
/	I-Material
B	I-Material
,	I-Material
B	I-Material
/	I-Material
C	I-Material
and	I-Material
C	I-Material
/	I-Material
A	I-Material
are	O
likely	O
to	O
be	O
flat	O
since	O
there	O
exist	O
no	O
junction	O
points	O
at	O
interfaces	O
and	O
therefore	O
chain	B-Process
entropy	I-Process
contribution	O
to	O
the	O
free	O
energy	O
of	O
structure	O
formation	O
is	O
considerably	O
small	O
comparing	O
with	O
regular	O
block	O
and	O
graft	O
copolymer	O
systems	O
.	O
As	O
a	O
matter	O
of	O
fact	O
,	O
Dotera	O
predicted	O
several	O
tiling	O
patterns	O
by	O
the	O
diagonal	B-Process
bond	I-Process
method	I-Process
,	O
a	O
new	O
Monte	B-Process
Carlo	I-Process
Simulation	I-Process
[	O
30	O
]	O
,	O
while	O
Gemma	O
and	O
Dotera	O
pointed	O
out	O
that	O
only	O
three	O
regular	O
tilings	O
,	O
i.e.	O
,	O
(	O
6.6.6	O
)	O
,	O
(	O
4.8.8	O
)	O
and	O
(	O
4.6.12	O
)	O
are	O
permitted	O
for	O
three-branched	B-Material
molecules	I-Material
proposed	O
as	O
the	O
“	O
even	B-Process
polygon	I-Process
theorem	I-Process
”	O
[	O
31	O
]	O
.	O

A	O
living	O
polymerization	B-Process
is	O
a	O
reaction	B-Process
without	O
transfer	O
and	O
termination	B-Process
reactions	O
that	O
can	O
proceed	O
up	O
to	O
complete	O
monomer	B-Process
conversion	I-Process
.	O
In	O
addition	O
,	O
when	O
initiation	O
is	O
quantitative	O
and	O
fast	O
compared	O
to	O
the	O
propagation	B-Process
reaction	O
,	O
polymers	B-Material
with	O
precisely	O
controlled	O
chain	O
length	O
and	O
narrow	O
molar	O
mass	O
distribution	O
can	O
be	O
obtained	O
.	O
In	O
the	O
case	O
of	O
an	O
industrial	B-Task
styrene	I-Task
polymerization	I-Task
this	O
would	O
permit	O
to	O
avoid	O
any	O
specific	O
washing	B-Process
or	O
degassing	B-Process
steps	O
,	O
which	O
are	O
necessary	O
in	O
the	O
radical	O
process	O
to	O
remove	O
residual	O
monomer	O
and	O
low	B-Material
molar	I-Material
mass	I-Material
oligomers	I-Material
.	O
Since	O
head-to-head	O
defects	O
along	O
the	O
chains	O
are	O
absent	O
,	O
anionic	B-Material
polystyrene	I-Material
would	O
exhibit	O
also	O
a	O
better	O
thermal	O
stability	O
than	O
radical	O
one	O
.	O
Therefore	O
,	O
production	B-Task
of	I-Task
anionic	I-Task
polystyrene	I-Task
(	I-Task
PS	I-Task
)	I-Task
would	O
be	O
of	O
interest	O
if	O
the	O
conditions	O
required	O
to	O
control	O
the	O
polymerization	B-Process
could	O
be	O
adapted	O
to	O
the	O
market	O
and	O
be	O
able	O
to	O
compete	O
economically	O
with	O
industrial	O
radical	O
processes	O
.	O
The	O
use	O
of	O
organic	B-Material
solvents	I-Material
and	O
of	O
expensive	O
alkyllithium	B-Process
initiators	I-Process
,	O
as	O
well	O
as	O
the	O
relatively	O
low	O
reaction	O
temperatures	O
required	O
,	O
was	O
some	O
important	O
limitation	O
to	O
overcome	O
.	O
The	O
possibilities	O
to	O
achieve	O
a	O
quantitative	O
living-like	O
anionic	B-Process
polymerization	I-Process
of	I-Process
styrene	I-Process
in	O
the	O
absence	O
of	O
solvent	B-Material
and	O
at	O
elevated	O
temperature	O
,	O
using	O
inexpensive	O
initiating	O
systems	O
,	O
were	O
the	O
main	O
targets	O
identified	O
to	O
tremendously	O
decrease	O
the	O
cost	O
of	O
the	O
anionic	O
process	O
.	O
This	O
implied	O
at	O
first	O
to	O
control	O
the	O
reactivity	O
and	O
stability	O
of	O
initiating	O
and	O
propagating	B-Process
active	I-Process
species	I-Process
in	O
such	O
unusual	O
operating	O
conditions	O
.	O

A	O
hydroxyl-functionalized	O
poly	O
(	O
butylene	O
succinate	O
)	O
based	O
polyester	B-Material
was	O
prepared	O
by	O
conventional	O
polycondensation	B-Process
of	O
benzyl-protected	O
dimethyl	B-Material
malonate	I-Material
and	O
1,4-butanediol	B-Material
(	O
Scheme	O
2	O
(	O
a	O
))	O
[	O
24a	O
]	O
.	O
Yao	O
et	O
al.	O
reported	O
on	O
the	O
direct	O
polycondensation	B-Process
of	O
l-lactic	B-Material
acid	I-Material
and	O
citric	B-Material
acid	I-Material
with	O
the	O
formation	O
of	O
poly	B-Material
[(	I-Material
l-lactic	I-Material
acid	I-Material
)-	I-Material
co	I-Material
-(	I-Material
citric	I-Material
acid	I-Material
)]	I-Material
,	O
obtaining	O
a	O
polyester	B-Material
oligomer	I-Material
with	O
both	O
pendant	O
carboxylic	O
and	O
hydroxyl	O
groups	O
[	O
24b	O
]	O
.	O
This	O
PLCA	B-Material
oligomer	I-Material
was	O
reacted	O
with	O
dihydroxylated	B-Material
PLLA	I-Material
as	O
a	O
macromonomer	O
,	O
yielding	O
a	O
PLCA	B-Material
–	I-Material
PLLA	I-Material
multiblock	I-Material
copolymer	I-Material
as	O
shown	O
in	O
Scheme	O
2	O
(	O
b	O
)	O
.	O
While	O
lipases	B-Material
have	O
been	O
investigated	O
for	O
the	O
ring-opening	B-Process
polymerization	I-Process
(	O
ROP	B-Process
)	O
of	O
cyclic	B-Material
ester	I-Material
monomers	I-Material
[	O
25,26	O
]	O
,	O
they	O
have	O
also	O
been	O
used	O
for	O
the	O
preparation	O
of	O
polyesters	B-Material
by	O
polycondensation	B-Process
reactions	O
.	O
The	O
advantage	O
of	O
this	O
technique	O
is	O
that	O
these	O
enzyme-catalyzed	B-Process
reactions	O
proceed	O
without	O
protection	O
of	O
the	O
pendant	O
functional	O
groups	O
.	O
In	O
this	O
field	O
,	O
hydroxyl-bearing	B-Material
polyesters	I-Material
have	O
been	O
synthesized	O
by	O
the	O
copolymerization	B-Process
of	O
divinyl	B-Material
adipate	I-Material
with	O
various	O
triols	B-Material
(	O
e.g.	O
glycerol	B-Material
,	O
1,2,4-butanetriol	B-Material
)	O
as	O
represented	O
in	O
Scheme	O
2	O
(	O
c	O
)	O
[	O
27	O
]	O
and	O
by	O
copolymerizations	B-Process
of	O
1,8-octanediol	B-Material
with	O
adipic	B-Material
acid	I-Material
and	O
several	O
alditols	B-Material
[	O
28	O
]	O
.	O
Very	O
recently	O
,	O
several	O
α-hydroxy	B-Material
acids	I-Material
derived	O
from	O
amino	B-Material
acids	I-Material
were	O
homo	O
-	O
and	O
copolymerized	O
with	O
lactic	B-Material
acid	I-Material
by	O
polycondensation	B-Process
in	O
bulk	O
without	O
protected	O
monomers	B-Material
(	O
Scheme	O
2	O
(	O
d	O
))	O
[	O
29	O
]	O
.	O
Biodegradable	B-Material
polyesters	I-Material
with	O
various	O
pendant	O
groups	O
were	O
obtained	O
,	O
although	O
the	O
molecular	O
weights	O
remained	O
low	O
(	O
1000	O
–	O
3000gmol	O
−	O
1	O
)	O
.	O

Despite	O
the	O
loss	O
of	O
directed	O
,	O
self-complementary	O
hydrogen	B-Process
bonding	I-Process
through	O
alkylation	B-Process
of	O
the	O
imidazole	O
ring	O
,	O
electrostatic	B-Process
aggregation	I-Process
of	O
imidazolium	B-Material
salts	I-Material
is	O
a	O
tunable	O
,	O
self-assembly	O
process	O
,	O
which	O
is	O
instrumental	O
to	O
several	O
applications	O
.	O
Imidazolium	B-Material
salts	I-Material
are	O
used	O
to	O
extract	O
metal	B-Material
ions	I-Material
from	O
aqueous	B-Material
solutions	I-Material
and	O
coat	O
metal	B-Material
nanoparticles	I-Material
[	O
15	O
]	O
,	O
dissolve	O
carbohydrates	B-Material
[	O
16	O
]	O
,	O
and	O
create	O
polyelectrolyte	B-Process
brushes	I-Process
on	O
surfaces	O
[	O
17	O
]	O
.	O
For	O
example	O
,	O
atom	B-Process
transfer	I-Process
radical	I-Process
polymerization	I-Process
(	O
ATRP	B-Process
)	O
was	O
used	O
to	O
graft	O
poly	B-Material
(	I-Material
1-ethyl	I-Material
3	I-Material
-(	I-Material
2-methacryloyloxy	I-Material
ethyl	I-Material
)	I-Material
imidazolium	I-Material
chloride	I-Material
)	O
brushes	O
onto	O
gold	B-Material
surfaces	I-Material
[	O
17	O
]	O
.	O
One	O
of	O
the	O
imidazolium	B-Material
salt	I-Material
’s	O
most	O
promising	O
attributes	O
is	O
its	O
antimicrobial	B-Process
action	I-Process
[	O
12,18	O
]	O
and	O
molecular	O
self-assembly	O
into	O
liquid	B-Material
crystals	I-Material
[	O
19,20	O
]	O
.	O
1-Alkyl-3-methylimidazolium	B-Material
chlorides	I-Material
and	O
bromides	B-Material
,	O
1-alkyl-2-methyl-3-hydroxyethylimidazolium	B-Material
chlorides	I-Material
,	O
and	O
N-alkyl-N-hydroxyethylpyrrolidinonium	B-Material
,	O
for	O
example	O
,	O
all	O
exhibit	O
strong	O
biocidal	B-Process
activity	I-Process
[	O
18	O
]	O
.	O
Hydrogels	B-Material
form	I-Material
from	O
polymerized	B-Material
methylimidazolium-based	I-Material
ionic	I-Material
liquids	I-Material
with	O
acryloyl	B-Material
groups	I-Material
;	O
the	O
polymer	B-Material
self-assembles	O
into	O
organized	B-Material
lamellae	I-Material
with	O
unique	O
swelling	O
properties	O
,	O
leading	O
to	O
bioactive	B-Task
applications	I-Task
[	O
19	O
]	O
.	O
Other	O
bioactive	O
applications	O
for	O
imidazolium	B-Material
salts	I-Material
include	O
antiarrhythmics	B-Material
[	O
21	O
]	O
,	O
anti-metastic	B-Material
agents	I-Material
[	O
22,23	O
]	O
,	O
and	O
imidazolium-based	B-Material
steroids	I-Material
[	O
24	O
]	O
.	O
Separation	B-Process
applications	I-Process
include	O
efficient	B-Process
absorption	I-Process
of	I-Process
CO2	I-Process
[	O
25	O
]	O
.	O
Imidazolium	B-Material
salts	I-Material
enhance	O
vesicle	B-Process
formation	I-Process
as	O
imidazolium	B-Material
surfactants	I-Material
[	O
26	O
]	O
,	O
and	O
they	O
also	O
find	O
application	O
in	O
polymeric	B-Process
actuators	I-Process
[	O
27	O
]	O
.	O

Although	O
the	O
basic	O
mechanisms	O
of	O
the	O
AD	B-Process
process	O
are	O
reasonably	O
well	O
understood	O
,	O
it	O
has	O
not	O
proved	O
simple	O
to	O
apply	O
existing	O
theories	O
to	O
the	O
interpretation	O
of	O
experimental	O
data	O
.	O
What	O
is	O
needed	O
is	O
a	O
combination	O
of	O
the	O
AD	O
theory	O
and	O
the	O
electronic	O
structure	O
of	O
realistic	O
systems	O
,	O
including	O
surface	B-Material
defects	I-Material
and	O
adsorbed	B-Material
species	I-Material
.	O
Such	O
electronic	O
structure	O
calculations	O
are	O
still	O
complex	O
and	O
time-consuming	O
.	O
In	O
many	O
cases	O
,	O
especially	O
for	O
insulating	O
surfaces	O
,	O
attempts	O
to	O
model	O
MIES	B-Material
spectra	I-Material
use	O
simple	O
or	O
intuitive	O
models	O
.	O
In	O
Refs	O
.	O
[	O
4,6,23	O
]	O
it	O
is	O
assumed	O
that	O
the	O
main	O
transition	O
mechanism	O
is	O
Auger	B-Process
de-excitation	I-Process
,	O
and	O
the	O
MIES	B-Material
spectra	I-Material
have	O
been	O
simulated	O
by	O
the	O
surface	O
density	B-Process
of	I-Process
states	I-Process
(	O
DOS	B-Process
)	O
projected	O
on	O
the	O
surface	B-Material
oxygen	I-Material
ions	I-Material
of	O
the	O
uppermost	O
surface	O
layer	O
using	O
a	O
Hartree	B-Process
–	I-Process
Fock	I-Process
method	I-Process
(	O
the	O
crystal	B-Process
code	I-Process
[	O
24,25	O
])	O
and	O
a	O
density	B-Process
functional	I-Process
theory	I-Process
(	O
DFT	B-Process
)	O
method	O
(	O
the	O
cetep	B-Process
code	I-Process
[	O
26	O
])	O
.	O
The	O
effect	O
of	O
the	O
overlap	O
between	O
the	O
surface	O
and	O
He	O
(	O
1s	O
)	O
wavefunctions	O
was	O
taken	O
into	O
account	O
only	O
approximately	O
by	O
applying	O
an	O
additional	O
z-dependent	O
exponential	O
factor	O
to	O
the	O
surface	O
DOS	B-Process
.	O
Other	O
workers	O
[	O
5,6	O
]	O
estimated	O
the	O
AD	B-Process
transition	I-Process
probability	O
using	O
a	O
DOS	B-Process
projected	O
on	O
to	O
the	O
projectile	O
1s	O
atomic	O
orbital	O
.	O
However	O
,	O
they	O
were	O
not	O
able	O
to	O
use	O
state-of-the-art	O
methods	O
for	O
the	O
surface	O
electronic	O
structure	O
.	O
Yet	O
the	O
success	O
of	O
the	O
simplified	O
treatments	O
[	O
4	O
–	O
6	O
]	O
,	O
especially	O
for	O
MIES	B-Material
features	O
such	O
as	O
relative	O
energies	O
of	O
the	O
different	O
peaks	O
,	O
suggests	O
that	O
real	O
spectra	O
are	O
indeed	O
related	O
to	O
the	O
projection	O
of	O
the	O
surface	O
DOS	B-Process
on	O
to	O
the	O
projectile	O
orbital	O
.	O

In	O
this	O
section	O
,	O
we	O
use	O
the	O
terrain	B-Process
data	I-Process
processing	I-Process
as	O
an	O
example	O
to	O
describe	B-Task
the	I-Task
geodetic	I-Task
data	I-Task
transformation	I-Task
method	I-Task
.	O
Since	O
Google	O
Maps	O
/	O
Earth	O
server	O
only	O
gives	O
the	O
terrain	O
data	O
in	O
graphical	O
display	O
,	O
we	O
have	O
to	O
get	O
terrain	B-Material
digital	I-Material
data	I-Material
from	O
other	O
sources	O
.	O
The	O
fine-resolution	B-Process
(	I-Process
3	I-Process
″	I-Process
or	I-Process
finer	I-Process
)	I-Process
terrain	I-Process
data	I-Process
bases	I-Process
such	O
as	O
SRTM	B-Process
(	O
Shuttle	B-Process
Radar	I-Process
Topographical	I-Process
Mission	I-Process
)	O
or	O
USGS	O
's	O
DEM	B-Process
(	O
Digital	B-Process
Elevation	I-Process
Model	I-Process
)	O
data	O
are	O
necessary	O
.	O
Moreover	O
,	O
since	O
3DWF	O
is	O
used	O
to	O
model	O
the	O
fine-scale	B-Process
(	I-Process
meters	I-Process
up	I-Process
to	I-Process
100m	I-Process
)	I-Process
atmospheric	I-Process
flow	I-Process
,	O
it	O
needs	O
fine	O
resolution	O
terrain	O
data	O
.	O
In	O
this	O
project	O
,	O
we	O
use	O
the	O
terrain	O
elevation	O
data	O
set	O
from	O
SRTM	B-Process
(	O
Farr	O
et	O
al	O
.	O
2007	O
)	O
with	O
3-arcsecond	O
(~	O
90m	O
resolution	O
at	O
the	O
equator	O
)	O
resolution	O
.	O
The	O
data	O
covers	O
the	O
land	O
area	O
,	O
nearly	O
global	O
from	O
56S	O
to	O
60N	O
latitudes	O
.	O
We	O
use	O
the	O
processed	O
version	O
4	O
SRTM	O
data	O
set	O
as	O
described	O
in	O
Gamache	O
(	O
2005	O
)	O
in	O
which	O
some	O
of	O
the	O
missing	O
data	O
holes	O
were	O
filled	O
.	O
The	O
original	O
data	O
is	O
organized	O
in	O
WGS84	B-Material
(	O
World	B-Material
Geodetic	I-Material
System	I-Material
84	I-Material
)	O
geodetic	O
coordinate	O
system	O
.	O
When	O
the	O
data	O
are	O
applied	O
to	O
the	O
3DWF	B-Process
model	O
,	O
they	O
are	O
transformed	O
to	O
the	O
local	O
East	B-Material
,	I-Material
North	I-Material
and	I-Material
Up	I-Material
(	O
ENU	B-Material
)	O
coordinate	O
(	O
see	O
Fig.	O
3	O
)	O
.	O
Since	O
the	O
3DWF	B-Process
is	O
a	O
fine	O
scale	O
wind	O
model	O
and	O
its	O
entire	O
model	O
domain	O
is	O
not	O
intended	O
to	O
be	O
larger	O
than	O
20	O
×	O
20km	O
,	O
this	O
Cartesian	B-Material
coordinate	I-Material
system	I-Material
is	O
a	O
good	O
choice	O
with	O
very	O
little	O
distortion	O
due	O
to	O
the	O
curvature	O
of	O
the	O
Earth	O
's	O
surface	O
.	O
The	O
transformation	O
from	O
the	O
WGS84	O
data	O
to	O
the	O
ENU	O
coordinate	O
is	O
performed	O
as	O
follows	O
(	O
Fukushima	O
,	O
2006	O
;	O
Featherstone	O
and	O
Claessens	O
,	O
2008	O
)	O
.	O

Apache	B-Process
Pig	I-Process
is	O
a	O
platform	O
for	O
creating	O
MapReduce	B-Process
workflows	I-Process
with	O
Hadoop	B-Material
.	O
These	O
workflows	O
are	O
expressed	O
as	O
directed	B-Material
acyclic	I-Material
graphs	I-Material
(	O
DAGs	B-Material
)	O
of	O
tasks	O
that	O
exist	O
at	O
a	O
conceptually	O
higher	O
level	O
than	O
their	O
implementations	O
as	O
series	O
of	O
MapReduce	B-Process
jobs	O
.	O
Pig	B-Process
Latin	I-Process
is	O
the	O
procedural	O
language	O
used	O
for	O
building	O
these	O
workflows	O
,	O
providing	O
syntax	O
similar	O
to	O
the	O
declarative	O
SQL	B-Process
commonly	O
used	O
for	O
relational	O
database	O
systems	O
.	O
In	O
addition	O
to	O
standard	O
SQL	B-Process
operations	I-Process
,	O
Pig	B-Process
can	O
be	O
extended	O
with	O
user-defined	B-Process
functions	I-Process
(	O
UDFs	B-Process
)	O
commonly	O
written	O
in	O
Java	O
.	O
We	O
adopted	O
Pig	B-Process
for	O
our	O
implementation	O
of	O
the	O
correlator	O
to	O
speed	O
up	O
development	O
time	O
,	O
allow	O
for	O
ad	O
hoc	O
workflow	O
changes	O
,	O
and	O
to	O
embrace	O
the	O
Hadoop	B-Material
community	O
׳	O
s	O
migration	O
away	O
from	O
MapReduce	B-Process
towards	O
more	O
generalized	O
DAG	B-Task
processing	I-Task
(	O
Mayer	O
,	O
2013	O
)	O
.	O
Specifically	O
,	O
in	O
the	O
event	O
that	O
future	O
versions	O
of	O
Hadoop	B-Material
are	O
optimized	O
to	O
support	O
paradigms	O
other	O
than	O
MapReduce	B-Process
,	O
Pig	B-Material
scripts	I-Material
could	O
take	O
advantage	O
of	O
these	O
advances	O
without	O
recoding	O
,	O
whereas	O
explicit	O
Java	B-Process
MapReduce	I-Process
jobs	O
would	O
need	O
to	O
be	O
rewritten	O
.	O

The	B-Task
threshold	I-Task
values	I-Task
for	I-Task
removing	I-Task
large	I-Task
caters	I-Task
were	I-Task
determined	I-Task
by	O
examining	O
the	O
craters	O
within	O
the	O
study	O
area	O
,	O
referencing	O
previous	O
studies	O
(	O
Molloy	O
and	O
Stepinski	O
,	O
2007	O
)	O
,	O
and	O
some	O
trial	O
and	O
error	O
.	O
After	O
the	O
parameter	O
values	O
are	O
determined	O
,	O
the	O
rest	O
of	O
the	O
process	O
is	O
automated	O
.	O
However	O
,	O
we	O
do	O
anticipate	O
some	O
minimum	O
manual	B-Process
editing	I-Process
may	O
be	O
needed	O
in	O
some	O
complicated	O
terrains	O
when	O
apply	O
it	O
to	O
all	O
of	O
Mars	O
.	O
To	O
minimize	O
the	O
distortion	O
resulted	O
from	O
map	O
projection	O
on	O
global	O
datasets	O
,	O
we	O
will	O
choose	O
an	O
equal	O
area	O
projection	O
by	O
evaluating	O
the	O
options	O
suggested	O
in	O
Steinwand	O
et	O
al	O
.	O
(	O
1995	O
)	O
or	O
conduct	O
geodesic	O
area	O
calculation	O
using	O
software	O
such	O
as	O
“	O
Tools	B-Process
for	I-Process
Graphics	I-Process
and	I-Process
Shapes	I-Process
”	O
(	O
http	O
://	O
www.jennessent.com	O
/	O
arcgis	O
/	O
shapes	O
_	O
graphics.htm	O
)	O
Although	O
post-formational	O
modification	O
to	O
the	O
valleys	O
may	O
be	O
minimum	O
(	O
Williams	O
and	O
Phillips	O
,	O
2001	O
)	O
,	O
there	O
may	O
nonetheless	O
be	O
modifications	O
such	O
as	O
eolian	O
fill	O
and	O
mass	O
wasting	O
(	O
e.g.	O
,	O
Grant	O
et	O
al.	O
,	O
2008	O
)	O
.	O
Thus	O
the	O
volume	O
estimates	O
derived	O
with	O
PBTH	B-Process
method	I-Process
represents	O
a	O
lower	O
bound	O
.	O
Comparing	O
the	O
estimates	O
from	O
MOLA	B-Material
and	O
HRSC	B-Material
data	O
reveals	O
that	O
MOLA	B-Material
estimate	O
is	O
about	O
91	O
%	O
of	O
HRSC	B-Material
value	O
.	O
However	O
,	O
MOLA	B-Material
has	O
global	O
coverage	O
whereas	O
HRSC	B-Material
does	O
not	O
.	O
Therefore	O
,	O
for	O
areas	O
where	O
there	O
is	O
only	O
MOLA	B-Material
coverage	O
,	O
the	O
estimate	O
may	O
be	O
scaled	O
upward	O
by	O
1.1	O
times	O
.	O
The	O
algorithm	O
has	O
been	O
tested	O
on	O
DEMs	B-Material
with	O
various	O
resolutions	O
(	O
2	O
m	O
for	O
simulated	O
DEM	B-Material
,	O
75m	O
for	O
HRSC	B-Material
,	O
and	O
463m	O
for	O
MOLA	B-Material
)	O
.	O
It	O
can	O
certainly	O
be	O
applied	O
to	O
higher	O
resolution	O
DEMs	B-Material
for	O
Mars	O
when	O
they	O
become	O
available	O
,	O
but	O
the	O
threshold	O
values	O
will	O
need	O
to	O
be	O
adjusted	O
.	O

This	O
research	O
traces	O
the	O
implementation	O
of	O
an	O
information	O
system	O
in	O
the	O
form	O
of	O
ERP	B-Material
modules	I-Material
covering	O
tenant	O
and	O
contract	O
management	O
in	O
a	O
Chinese	O
service	O
company	O
.	O
Misalignments	O
between	O
the	O
ERP	B-Process
system	I-Process
specification	O
and	O
user	O
needs	O
led	O
to	O
the	O
adoption	O
of	O
informal	O
processes	O
within	O
the	O
organisation	O
.	O
These	O
processes	O
are	O
facilitated	O
within	O
an	O
informal	O
organisational	O
structure	O
and	O
are	O
based	O
on	O
human	O
interactions	O
undertaken	O
within	O
the	O
formal	O
organisation	O
.	O
Rather	O
than	O
to	O
attempt	O
to	O
suppress	O
the	O
emergence	O
of	O
the	O
informal	O
organisation	O
the	O
company	O
decided	O
to	O
channel	O
the	O
energies	O
of	O
staff	O
involved	O
in	O
informal	O
processes	O
towards	O
organisational	O
goals	O
.	O
The	O
company	O
achieved	O
this	O
by	O
harnessing	O
the	O
capabilities	O
of	O
what	O
we	O
term	O
a	O
hybrid	B-Process
ERP	I-Process
system	I-Process
,	O
combining	O
the	O
functionality	O
of	O
a	O
traditional	O
(	O
formal	O
)	O
ERP	B-Process
installation	I-Process
with	O
the	O
capabilities	O
of	O
Enterprise	B-Process
Social	I-Process
Software	I-Process
(	O
ESS	B-Process
)	O
.	O
However	O
the	O
company	O
recognised	O
that	O
the	O
successful	O
operation	O
of	O
the	O
hybrid	O
ERP	B-Process
system	I-Process
would	O
require	O
a	O
number	O
of	O
changes	O
in	O
organisational	O
design	O
in	O
areas	O
such	O
as	O
reporting	O
structures	O
and	O
communication	O
channels	O
.	O
A	O
narrative	O
provided	O
by	O
interviews	O
with	O
company	O
personnel	O
is	O
thematised	O
around	O
the	O
formal	O
and	O
informal	O
characteristics	O
of	O
the	O
organisation	O
as	O
defined	O
in	O
the	O
literature	O
.	O
This	O
leads	O
to	O
a	O
definition	O
of	O
the	O
characteristics	O
of	O
the	O
hybrid	O
organisation	O
and	O
strategies	O
for	O
enabling	O
a	O
hybrid	O
organisation	O
,	O
facilitated	O
by	O
a	O
hybrid	B-Process
ERP	I-Process
system	I-Process
,	O
which	O
directs	O
formal	O
and	O
informal	O
behaviour	O
towards	O
organisational	O
goals	O
and	O
provides	O
a	O
template	O
for	O
future	O
hybrid	O
implementations	O
.	O

We	O
addressed	O
the	O
question	O
whether	B-Task
carbohydrate	I-Task
coupling	I-Task
increased	I-Task
antigen	I-Task
uptake	I-Task
by	O
DCs	O
via	O
C-type	B-Process
lectin	I-Process
receptor	I-Process
targeting	I-Process
.	O
Therefore	O
,	O
the	O
antigens	B-Material
were	O
labeled	O
with	O
pHrodo	B-Material
Red	I-Material
dye	I-Material
(	O
Invitrogen	B-Material
)	O
,	O
a	O
dye	O
that	O
specifically	O
fluoresces	B-Process
as	O
pH	O
decreases	O
from	O
neutral	O
to	O
acidic	O
,	O
as	O
provided	O
in	O
endosomes	B-Material
/	O
lysosomes	B-Material
of	O
cells	O
.	O
In	O
vitro	O
characterization	O
of	O
the	O
cellular	O
uptake	O
of	O
neoglycocomplexes	B-Material
using	O
bone	B-Material
marrow	I-Material
derived	I-Material
dendritic	I-Material
cells	I-Material
(	O
BMDCs	B-Material
)	O
demonstrated	O
superior	O
ingestion	B-Process
of	O
mannan-conjugates	B-Material
MN	I-Material
–	I-Material
Ova	I-Material
and	I-Material
MN	I-Material
–	I-Material
Pap	I-Material
(	O
Supplementary	O
Fig.	O
S4A-D,F	O
)	O
.	O
This	O
was	O
confirmed	O
in	O
vivo	O
by	O
intradermal	B-Task
needle-injection	I-Task
of	O
labeled	O
antigen	O
into	O
the	O
ear	O
pinnae	O
of	O
mice	O
.	O
Antigen	B-Material
uptake	O
and	O
transport	O
to	O
the	O
ear	O
dLNs	O
were	O
measured	O
after	O
24h	O
by	O
FACS	B-Process
analysis	I-Process
.	O
DCs	O
in	O
cervical	O
LNs	O
were	O
identified	O
according	O
to	O
their	O
high	O
expression	O
of	O
MHC	O
class	O
II	O
(	O
Fig.	O
3A	O
)	O
and	O
additionally	O
characterized	O
by	O
CD8α	B-Material
,	O
CD11b	B-Material
,	O
and	O
CD11c	B-Material
expression	O
and	O
uptake	O
of	O
pHrodo-labeled	B-Material
antigen	I-Material
(	O
Fig.	O
3B	O
,	O
D	O
–	O
F	O
)	O
.	O
The	O
results	O
showed	O
significantly	O
elevated	O
numbers	O
of	O
pHrodo	B-Material
+	I-Material
MHCIIhigh	I-Material
DCs	I-Material
for	O
mannan	O
conjugates	O
MN	B-Material
–	I-Material
Ova	I-Material
and	O
MN	B-Material
–	I-Material
Pap	I-Material
(	O
and	O
to	O
a	O
lesser	O
degree	O
for	O
MD	B-Material
–	I-Material
Pap	I-Material
)	O
in	O
comparison	O
to	O
the	O
unmodified	O
antigens	B-Material
(	O
Fig.	O
3C	O
)	O
.	O
Both	O
carbohydrates	B-Material
targeted	O
antigen	B-Material
preferentially	O
to	O
CD8α	B-Material
−	I-Material
DCs	I-Material
,	O
as	O
indicated	O
by	O
an	O
increase	O
in	O
CD8α	B-Material
−/	I-Material
pHrodo	I-Material
+	I-Material
DCs	I-Material
compared	O
to	O
unmodified	O
antigens	B-Material
(	O
Fig.	O
3E	O
and	O
F	O
)	O
.	O
Nevertheless	O
,	O
whether	O
the	O
antigens	O
were	O
taken	O
up	O
in	O
situ	O
by	O
dermal	O
DCs	O
or	O
by	O
LN	O
resident	O
APCs	O
via	O
the	O
afferent	O
lymphatics	O
could	O
not	O
be	O
fully	O
elucidated	O
.	O
Histology	O
revealed	O
that	O
antigen-loaded	B-Material
cells	I-Material
in	O
the	O
dLNs	O
were	O
already	O
present	O
30min	O
after	O
intradermal	B-Process
injection	I-Process
(	O
Supplementary	O
Fig.	O
S4G	O
)	O
,	O
suggesting	O
both	O
mechanisms	O
.	O

The	O
mesoporous	B-Material
silica	I-Material
particles	O
were	O
prepared	O
by	O
the	O
surfactant	B-Process
self-assembly	I-Process
method	O
described	O
previously	O
[	O
18,24	O
]	O
.	O
Briefly	O
,	O
a	O
homogeneous	B-Material
solution	I-Material
of	O
the	O
soluble	B-Material
silica	I-Material
precursor	I-Material
,	O
tetraethylorthosilicate	B-Material
(	O
TEOS	B-Material
;	O
Sigma-Aldrich	O
Corp.	O
,	O
St.	O
Louis	O
,	O
MO	O
)	O
,	O
and	O
hydrochloric	B-Material
acid	I-Material
was	O
mixed	O
in	O
ethanol	B-Material
and	O
water	B-Material
.	I-Material
A	O
surfactant	B-Material
,	O
cetyltrimethylammonium	B-Material
bromide	I-Material
(	O
CTAB	B-Material
;	O
Sigma-Aldrich	O
Corp.	O
,	O
St.	O
Louis	O
,	O
MO	O
)	O
,	O
with	O
an	O
initial	O
concentration	O
much	O
less	O
than	O
the	O
critical	O
micelle	O
concentration	O
was	O
added	O
to	O
lower	O
the	O
surface	O
tension	O
of	O
the	O
liquid	O
mixture	O
and	O
act	O
as	O
the	O
mesoporous	B-Material
structure-directing	I-Material
template	I-Material
.	O
Aerosol	B-Material
solutions	I-Material
of	O
soluble	B-Material
silica	I-Material
plus	O
surfactant	B-Material
were	O
then	O
generated	O
with	O
nitrogen	B-Process
as	O
a	O
carrier	O
atomizing	B-Material
gas	I-Material
using	O
a	O
commercially	O
available	O
atomizer	B-Process
(	O
Model	O
9392A	O
,	O
TSI	O
,	O
Inc.	O
,	O
St.	O
Paul	O
,	O
MN	O
)	O
.	O
The	O
aerosol	B-Material
droplets	I-Material
were	O
solidified	O
in	O
a	O
tube	B-Process
furnace	I-Process
at	O
400	O
°	O
C	O
until	O
dry	O
.	O
Once	O
dried	O
,	O
a	O
durapore	B-Material
membrane	I-Material
filter	I-Material
,	O
kept	O
at	O
80	O
°	O
C	O
,	O
was	O
used	O
to	O
collect	O
the	O
particles	O
.	O
As	O
a	O
final	O
step	O
,	O
the	O
surfactant	B-Material
was	O
removed	O
at	O
400	O
°	O
C	O
for	O
5h	O
via	O
calcination	B-Process
.	O
The	O
surface	O
of	O
the	O
mesoporous	B-Material
silica	I-Material
core	I-Material
in	O
these	O
studies	O
was	O
chemically	O
modified	O
with	O
10wt.	O
%	O
or	O
15wt.	O
%	O
by	O
aminopropyltriethoxysilane	B-Material
(	O
APTES	B-Material
;	O
Sigma-Aldrich	O
Corp.	O
,	O
St.	O
Louis	O
,	O
MO	O
)	O
conducted	O
identically	O
as	O
previously	O
described	O
[	O
17	O
]	O
to	O
create	O
a	O
positive	O
surface	O
charge	O
to	O
increase	O
loading	O
efficiency	O
of	O
negatively	O
charged	O
cargo	O
.	O
Further	O
,	O
Liu	O
and	O
colleagues	O
report	O
the	O
colloidal	O
stability	O
of	O
these	O
protocells	B-Material
with	O
lipid	O
bilayers	O
,	O
excess	O
amount	O
of	O
liposomes	B-Material
(	O
50μg	O
liposomes	O
per	O
0.5mg	O
silica	O
were	O
used	O
[	O
18	O
])	O
.	O

Ultrasound	B-Process
(	O
US	B-Process
)	O
can	O
initiate	O
the	O
release	O
of	O
drugs	O
from	O
liposomes	B-Material
via	O
an	O
event	O
called	O
inertial	B-Task
cavitation	I-Task
,	O
whereby	O
the	O
rarefactional	O
phase	O
of	O
an	O
ultrasound	O
wave	O
causes	O
the	O
expansion	O
of	O
a	O
gas	B-Material
bubble	I-Material
followed	O
by	O
a	O
violent	O
collapse	O
due	O
to	O
the	O
inertia	O
of	O
the	O
surrounding	O
media	O
.	O
This	O
collapse	O
creates	O
shock	O
waves	O
which	O
can	O
disrupt	O
the	O
stability	O
of	O
co-localised	O
liposomal	O
drug	O
carriers	O
.	O
To	O
date	O
,	O
studies	O
have	O
concentrated	O
on	O
the	O
use	O
of	O
low	O
frequency	O
or	O
high	O
intensity	O
US	B-Process
to	O
generate	O
gas	B-Material
bubbles	I-Material
in	O
situ	O
,	O
and	O
most	O
recently	O
such	O
parameters	O
have	O
been	O
used	O
to	O
achieve	O
a	O
variable	O
level	O
of	O
triggered	O
drug	O
release	O
following	O
an	O
intratumoral	B-Process
injection	I-Process
of	O
liposomes	O
[	O
14	O
]	O
.	O
However	O
,	O
concerns	O
persist	O
over	O
the	O
damage	O
to	O
non-target	O
tissue	O
that	O
such	O
US	B-Process
exposure	O
parameters	O
may	O
cause	O
and	O
whether	O
ultimately	O
they	O
will	O
be	O
widely	O
clinically	O
applicable	O
.	O
An	O
alternative	O
strategy	O
is	O
to	O
utilise	O
high-frequency	O
US	O
pulses	O
at	O
pressures	O
in	O
the	O
diagnostic	O
range	O
in	O
the	O
presence	O
of	O
pre-existing	O
gas	O
bubbles	O
.	O
This	O
provides	O
an	O
inertial	B-Task
cavitation	I-Task
stimulus	O
for	O
drug	O
release	O
using	O
safe	O
,	O
clinically	O
achievable	O
US	B-Process
exposure	O
conditions	O
and	O
approved	O
US	B-Material
contrast	I-Material
agents	I-Material
[	O
15	O
]	O
.	O
Indeed	O
,	O
in	O
the	O
context	O
of	O
improving	O
the	O
delivery	O
of	O
therapeutics	O
such	O
as	O
oncolytic	B-Material
viruses	I-Material
,	O
this	O
approach	O
has	O
already	O
shown	O
great	O
promise	O
[	O
16	O
]	O
.	O
A	O
further	O
advantage	O
of	O
this	O
approach	O
is	O
that	O
US-induced	B-Process
cavitation	I-Process
events	O
produce	O
distinct	O
acoustic	O
emissions	O
that	O
can	O
be	O
recorded	O
and	O
characterised	O
providing	O
non-invasive	O
feedback	O
,	O
a	O
feature	O
which	O
has	O
proven	O
useful	O
in	O
ablative	O
US	B-Process
applications	O
[	O
17	O
–	O
19	O
]	O
.	O

The	O
most	O
widely	O
used	O
ion	O
source	O
in	O
FIB	B-Process
instruments	I-Process
is	O
a	O
gallium	B-Material
(	O
Ga	B-Material
)	O
liquid	O
metal	O
ion	O
source	O
(	O
LMIS	O
)	O
[	O
1	O
]	O
.	O
Gallium	B-Material
is	O
attractive	O
as	O
an	O
ion	O
source	O
because	O
of	O
its	O
low	O
melting	O
temperature	O
(	O
29.8	O
°	O
C	O
at	O
standard	O
atmospheric	O
pressure	O
[	O
4	O
])	O
and	O
its	O
low	O
volatility	O
[	O
1	O
]	O
.	O
However	O
,	O
some	O
materials	O
show	O
sensitivity	O
to	O
the	O
Ga	B-Process
ion	I-Process
beam	I-Process
.	O
This	O
sensitivity	O
is	O
manifested	O
as	O
changes	O
in	O
the	O
structure	O
and	O
chemical	O
composition	O
of	O
the	O
starting	O
material	O
upon	O
exposure	O
to	O
the	O
Ga	O
ion	O
beam	O
[	O
5	O
]	O
.	O
Group	O
III	O
–	O
V	O
compound	O
semiconductors	B-Process
are	O
one	O
class	O
of	O
materials	O
that	O
show	O
such	O
sensitivity	O
.	O
Cryo-FIB	B-Task
milling	I-Task
has	O
recently	O
been	O
reported	O
to	O
suppress	O
the	O
reactions	O
between	O
the	O
Ga	B-Process
ion	I-Process
beam	I-Process
and	O
III	B-Material
–	I-Material
V	I-Material
materials	I-Material
[	O
6	O
]	O
.	O
The	O
suggested	O
advantage	O
of	O
cryo-FIB	B-Task
milling	I-Task
over	O
room	O
temperature	O
milling	O
of	O
Group	B-Material
III	I-Material
–	I-Material
V	I-Material
materials	I-Material
is	O
appealing	O
,	O
given	O
the	O
variety	O
of	O
present	O
and	O
potential	O
future	O
applications	O
for	O
these	O
materials	O
(	O
e.g.	O
,	O
as	O
electronic	O
or	O
photonic	O
devices	O
given	O
the	O
favorable	O
electron	O
transport	O
and	O
direct	O
band	O
gap	O
properties	O
associated	O
with	O
several	O
III	O
–	O
V	O
semiconductor	B-Process
systems	O
)	O
.	O

According	O
to	O
the	O
ellipsometric	B-Process
spectra	I-Process
,	O
optical	B-Process
constants	I-Process
and	O
other	O
physical	O
parameters	O
can	O
be	O
extracted	O
by	O
an	O
appropriate	O
fitting	O
model	O
.	O
In	O
order	O
to	O
estimate	O
the	O
optical	B-Process
constants	I-Process
/	I-Process
dielectric	I-Process
functions	O
of	O
Ni-doped	B-Material
TiO2	I-Material
films	I-Material
,	O
a	O
three-phase	B-Process
layered	I-Process
system	I-Process
(	O
air	B-Process
/	I-Process
film	I-Process
/	I-Process
substrate	I-Process
)	O
[	O
15	O
]	O
was	O
utilized	O
to	O
study	O
the	O
ellipsometric	O
spectra	O
.	O
TiO2	O
belongs	O
to	O
the	O
wide	O
band	O
gap	O
semiconductors	B-Process
.	O
Considering	O
the	O
contribution	O
of	O
the	O
M0	O
type	O
critical	O
point	O
with	O
the	O
lowest	O
three	O
dimensions	O
,	O
its	O
dielectric	O
function	O
can	O
be	O
calculated	O
by	O
Adachi	B-Process
's	I-Process
model	I-Process
[	O
15,22,23	O
]	O
:	O
ε	B-Process
(	I-Process
Ε	I-Process
)=	I-Process
ε	I-Process
∞+{	I-Process
A0	I-Process
[	I-Process
2	I-Process
−(	I-Process
1	I-Process
+	I-Process
χ0	I-Process
)	I-Process
1	I-Process
/	I-Process
2	I-Process
−(	I-Process
1	I-Process
−	I-Process
χ0	I-Process
)	I-Process
1	I-Process
/	I-Process
2	I-Process
]}/(	I-Process
EOBG2	I-Process
/	I-Process
3χ02	I-Process
)	I-Process
.	O
In	O
the	O
model	O
,	O
E	B-Process
is	O
the	O
incident	B-Process
photon	I-Process
energy	I-Process
,	O
ε	B-Process
∞	I-Process
is	O
the	O
high-frequency	B-Process
dielectric	I-Process
constant	I-Process
,	O
χ0	B-Process
=(	I-Process
E	I-Process
+	I-Process
iΓ	I-Process
)	I-Process
,	O
EOBG	B-Process
is	O
the	O
optical	O
gap	O
energy	O
,	O
and	O
A0	B-Process
and	I-Process
Γ	I-Process
are	O
the	O
strength	B-Process
and	I-Process
broadening	I-Process
parameters	I-Process
of	O
the	O
EOBG	B-Process
transition	O
,	O
respectively	O
.	O
As	O
an	O
example	O
,	O
the	O
experimental	O
SE	B-Process
of	O
the	O
film	O
TN1	O
at	O
an	O
incident	O
angle	O
70	O
°	O
by	O
dot	O
scatter	O
is	O
shown	O
in	O
Fig.	O
4.	O
The	O
Fabry	B-Material
–	I-Material
Pérot	I-Material
interference	I-Material
oscillations	O
due	O
to	O
multiple	O
reflections	O
within	O
the	O
film	O
have	O
been	O
found	O
in	O
the	O
photon	O
energy	O
from	O
1.5eV	O
to	O
3.5eV	O
(	O
354nm	O
–	O
826nm	O
)	O
,	O
which	O
indicates	O
that	O
the	O
films	O
are	O
transparent	O
in	O
this	O
region	O
.	O
Note	O
that	O
a	O
good	O
agreement	O
of	O
the	O
experimental	O
and	O
calculated	O
spectra	O
is	O
attained	O
in	O
the	O
whole	O
measured	O
photon	O
energy	O
range	O
.	O
The	O
fitting	O
thickness	O
for	O
film	O
TN2	O
is	O
159nm	O
,	O
which	O
is	O
very	O
near	O
to	O
the	O
value	O
obtained	O
by	O
SEM	B-Process
(	O
see	O
Fig.	O
1	O
(	O
b	O
))	O
.	O

Fig.	O
7	O
shows	O
the	O
relationship	O
between	O
the	O
testing	B-Process
time	I-Process
and	O
friction	B-Process
coefficients	I-Process
of	O
various	O
samples	O
under	O
dry	O
conditions	O
.	O
There	O
exist	O
running	O
in	O
and	O
steady	O
wear	O
period	O
in	O
the	O
wear	B-Process
process	I-Process
of	O
uncoated	B-Material
AZ31	I-Material
and	O
anodizing	B-Material
coating	I-Material
without	I-Material
Al2O3	I-Material
nanoparticles	I-Material
while	O
there	O
has	O
a	O
steady	O
wear	O
period	O
only	O
in	O
the	O
wear	B-Process
process	I-Process
of	O
composite	B-Material
anodizing	I-Material
coating	I-Material
with	O
Al2O3	B-Material
nanoparticles	I-Material
.	O
At	O
the	O
same	O
time	O
,	O
the	O
addition	O
of	O
nano-particles	B-Material
to	O
electrolyte	B-Material
led	O
to	O
reduction	B-Process
of	I-Process
friction	I-Process
coefficient	I-Process
.	O
The	O
friction	O
coefficient	O
of	O
composite	B-Process
coating	I-Process
is	O
relatively	O
lower	O
and	O
more	O
stable	O
than	O
what	O
has	O
been	O
reported	O
in	O
literature	O
[	O
24,25	O
]	O
for	O
anodizing	B-Material
coatings	I-Material
.	O
This	O
may	O
be	O
caused	O
by	O
“	B-Process
rolling	I-Process
effect	I-Process
”	I-Process
made	O
by	O
Al2O3	B-Material
nanoparticles	I-Material
on	O
the	O
surface	O
of	O
oxide	O
coating	O
.	O
Spherical	B-Material
nanoparticles	I-Material
change	O
sliding	O
into	O
rolling	O
,	O
which	O
reduce	B-Process
friction	I-Process
,	O
making	O
the	O
friction	B-Process
coefficient	I-Process
becomes	O
more	O
stable	O
.	O
The	O
friction	O
coefficient	O
of	O
anodizing	B-Material
coating	I-Material
without	I-Material
Al2O3	I-Material
nanoparticles	I-Material
has	O
large	O
fluctuation	O
maybe	O
for	O
the	O
damage	O
of	O
coating	O
.	O
In	O
contrast	O
to	O
the	O
uncoated	B-Material
AZ31	I-Material
magnesium	I-Material
alloy	I-Material
,	O
the	O
anodizing	B-Material
coatings	I-Material
show	O
slightly	O
lower	O
friction	B-Process
coefficient	I-Process
.	O
This	O
can	O
be	O
attributed	O
to	O
their	O
higher	O
load-bearing	O
capacity	O
for	O
high	O
hardness	O
.	O

Functionally	B-Material
Graded	I-Material
Materials	I-Material
(	O
FGMs	B-Material
)	O
,	O
described	O
in	O
detail	O
by	O
Suresh	O
and	O
Mortensen	O
[	O
1	O
]	O
,	O
are	O
a	O
type	O
of	O
heterogeneous	B-Material
composite	I-Material
materials	I-Material
exhibiting	O
gradual	O
variation	O
in	O
volume	O
fraction	O
of	O
their	O
constituents	O
from	O
one	O
surface	O
of	O
the	O
material	O
to	O
the	O
other	O
,	O
resulting	O
in	O
properties	O
which	O
vary	O
continuously	O
across	O
the	O
material	O
.	O
The	O
idea	O
of	O
a	O
Functionally	B-Material
Graded	I-Material
Material	I-Material
is	O
not	O
a	O
new	O
one	O
,	O
there	O
are	O
in	O
fact	O
many	O
natural	O
materials	O
which	O
exhibit	O
this	O
property	O
.	O
Study	B-Task
of	I-Task
bone	I-Task
,	I-Task
shell	I-Task
,	I-Task
balsawood	I-Task
and	I-Task
bamboo	I-Task
shows	O
that	O
they	O
are	O
all	O
graded	O
with	O
their	O
greatest	O
strength	O
on	O
the	O
outside	O
,	O
in	O
areas	O
where	O
the	O
greatest	O
protection	O
is	O
required	O
.	O
However	O
it	O
was	O
not	O
until	O
the	O
1980s	O
in	O
Japan	O
[	O
2	O
]	O
that	O
the	O
idea	O
of	O
a	O
Functionally	B-Material
Graded	I-Material
Material	I-Material
was	O
actively	O
researched	O
in	O
order	O
to	O
gain	O
advances	O
in	O
heat	B-Material
resistant	I-Material
materials	I-Material
for	O
use	O
in	O
aerospace	B-Task
and	O
nuclear	B-Task
fission	I-Task
reactors	I-Task
.	O

Recently	O
together	O
with	O
structural	B-Process
efficiency	I-Process
,	O
passenger	B-Task
safety	I-Task
is	O
also	O
an	O
important	O
issue	O
in	O
application	O
of	O
material	O
to	O
transportation	B-Process
industries	I-Process
.	O
Hence	O
,	O
the	O
crashworthiness	B-Material
parameters	I-Material
are	O
introducing	O
to	O
predict	O
the	O
capability	O
of	O
structure	O
to	O
prevent	B-Task
the	I-Task
massive	I-Task
damage	I-Task
and	O
protect	B-Task
the	I-Task
passenger	I-Task
in	O
the	O
event	O
of	O
a	O
crash	O
.	O
Crashworthiness	B-Task
parameters	I-Task
for	O
various	O
thin-walled	O
tubes	O
made	O
from	O
metal	B-Material
or	O
fibre	B-Material
/	I-Material
resin	I-Material
composites	I-Material
in	O
different	O
geometries	O
have	O
been	O
studied	O
.	O
A	O
critical	O
difference	O
of	O
tubular	B-Process
composites	I-Process
failure	I-Process
modes	O
compared	O
with	O
metallic	O
is	O
the	O
brittle	B-Process
collapse	I-Process
.	O
In	O
addition	O
,	O
in	O
composites	B-Material
,	O
tubular	B-Material
failure	I-Material
modes	I-Material
are	O
involved	O
with	O
micro-cracking	B-Process
development	I-Process
,	O
delamination	B-Process
,	O
fibre	B-Process
breakage	I-Process
,	O
etc.	O
,	O
instead	O
of	O
plastic	B-Process
deformation	I-Process
.	O
Implementation	B-Task
of	I-Task
composite	I-Task
materials	I-Task
in	O
the	O
field	O
of	O
crashworthiness	O
is	O
attributed	O
to	O
Hull	O
,	O
who	O
in	O
80s	O
and	O
90s	O
of	O
the	O
last	O
century	O
studied	O
extensively	O
the	O
crushing	B-Process
behaviour	I-Process
of	O
fibre	B-Material
reinforced	I-Material
composite	I-Material
material	I-Material
.	O
He	O
found	O
that	O
the	O
composite	B-Material
materials	I-Material
absorbed	O
high	O
energy	O
in	O
the	O
face	O
of	O
the	O
fracture	B-Process
surface	I-Process
energy	I-Process
mechanism	O
rather	O
than	O
plastic	B-Process
deformation	I-Process
as	O
observed	O
for	O
metals	B-Material
[	O
1	O
]	O
.	O
This	O
observation	O
has	O
inspired	O
others	O
to	O
further	O
investigation	O
about	O
crashworthiness	B-Process
characteristics	I-Process
of	O
composite	B-Material
materials	I-Material
.	O
Studies	O
have	O
examined	O
the	O
axial	B-Process
crushing	I-Process
behaviour	I-Process
of	O
fibre-reinforced	B-Material
tubes	I-Material
[	O
2	O
]	O
,	O
fibreglass	B-Material
tubes	I-Material
[	O
3,4	O
]	O
,	O
PVC	B-Material
tubes	I-Material
[	O
5	O
]	O
and	O
carbon	B-Material
fibre	I-Material
reinforced	I-Material
plastic	I-Material
(	O
CFRP	B-Material
)	O
tubes	O
[	O
6	O
]	O
.	O

Nanoparticle	B-Process
Tracking	I-Process
Analysis	I-Process
(	O
NTA	B-Process
)	O
has	O
been	O
applied	O
to	O
characterising	O
soot	B-Material
agglomerates	I-Material
of	O
particles	O
and	O
compared	O
with	O
Transmission	B-Process
Electron	I-Process
Microscoscopy	I-Process
(	O
TEM	B-Process
)	O
.	O
Soot	B-Material
nanoparticles	I-Material
were	O
extracted	O
from	O
used	O
oil	O
drawn	O
from	O
the	O
sump	O
of	O
a	O
light	O
duty	O
automotive	O
diesel	O
engine	O
.	O
The	O
samples	O
were	O
prepared	O
for	O
analysis	O
by	O
diluting	O
with	O
heptane	B-Material
.	O
Individual	O
tracking	O
of	O
soot	B-Material
agglomerates	I-Material
allows	O
for	O
size	B-Process
distribution	I-Process
analysis	I-Process
.	O
The	O
size	O
of	O
soot	O
was	O
compared	O
with	O
length	O
measurements	O
of	O
projected	O
two-dimensional	O
TEM	B-Process
images	O
of	O
agglomerates	B-Material
.	O
Both	O
the	O
techniques	O
show	O
that	O
soot-in-oil	O
exists	O
as	O
agglomerates	O
with	O
average	O
size	O
of	O
120nm	O
.	O
NTA	B-Process
is	O
able	O
to	O
measure	O
particles	O
in	O
polydisperse	B-Material
solutions	I-Material
and	O
reports	O
the	O
size	O
and	O
volume	O
distribution	O
of	O
soot-in-oil	B-Material
aggregates	I-Material
;	O
it	O
has	O
the	O
advantages	O
of	O
being	O
fast	O
and	O
relatively	O
low	O
cost	O
if	O
compared	O
with	O
TEM.Nanoparticle	O
Tracking	O
Analysis	O
(	O
NTA	O
)	O
has	O
been	O
applied	O
to	O
characterising	O
soot	O
agglomerates	O
of	O
particles	O
and	O
compared	O
with	O
Transmission	O
Electron	O
Microscoscopy	O
(	O
TEM	B-Process
)	O
.	O
Soot	O
nanoparticles	O
were	O
extracted	O
from	O
used	O
oil	O
drawn	O
from	O
the	O
sump	O
of	O
a	O
light	O
duty	O
automotive	O
diesel	O
engine	O
.	O
The	O
samples	O
were	O
prepared	O
for	O
analysis	O
by	O
diluting	O
with	O
heptane	O
.	O
Individual	O
tracking	O
of	O
soot	O
agglomerates	O
allows	O
for	O
size	O
distribution	O
analysis	O
.	O
The	O
size	O
of	O
soot	O
was	O
compared	O
with	O
length	O
measurements	O
of	O
projected	O
two-dimensional	O
TEM	O
images	O
of	O
agglomerates	O
.	O
Both	O
the	O
techniques	O
show	O
that	O
soot-in-oil	O
exists	O
as	O
agglomerates	O
with	O
average	O
size	O
of	O
120nm	O
.	O
NTA	O
is	O
able	O
to	O
measure	O
particles	O
in	O
polydisperse	O
solutions	O
and	O
reports	O
the	O
size	O
and	O
volume	O
distribution	O
of	O
soot-in-oil	O
aggregates	O
;	O
it	O
has	O
the	O
advantages	O
of	O
being	O
fast	O
and	O
relatively	O
low	O
cost	O
if	O
compared	O
with	O
TEM	O
.	O

Fig.	O
11	O
shows	O
the	O
wear-mode	B-Material
map	I-Material
of	O
RH	O
ceramics	O
,	O
in	O
which	O
the	O
early-stage	O
friction	O
coefficients	O
and	O
the	O
surface	O
roughness	O
of	O
the	O
pure	O
surface	O
were	O
chosen	O
.	O
The	O
value	O
of	O
the	O
fracture	O
toughness	O
of	O
RH	O
ceramics	O
was	O
calculated	O
based	O
on	O
the	O
reference	O
data	O
in	O
other	O
literature	O
[	O
22	O
]	O
.	O
The	O
Sc	O
of	O
RH	O
ceramics	O
was	O
smaller	O
than	O
Sc,critical	O
under	O
all	O
tested	O
conditions	O
during	O
the	O
initial	O
stage	O
of	O
friction	O
.	O
Thus	O
,	O
the	O
initial	O
wear	O
mode	O
of	O
RH	O
ceramics	O
was	O
powder	B-Process
formation	I-Process
or	O
plowing	B-Process
.	O
In	O
addition	O
,	O
powder	B-Process
formation	I-Process
and	O
plowing	B-Process
can	O
be	O
distinguished	O
using	O
a	O
dimensionless	B-Material
parameter	I-Material
(	O
Sc	O
⁎)	O
and	O
a	O
critical	O
parameter	O
(	O
Sc,critical	O
⁎).(	O
3	O
)	O
Sc	O
⁎=	O
HvRmaxKIc	O
(	O
4	O
)	O
Sc,critical	O
⁎=	O
51	O
+	O
10μwhere	O
Hv	O
is	O
the	O
Vickers	O
hardness	O
of	O
RH	O
ceramics	O
[	O
Pa	O
]	O
.	O
The	O
initial	O
wear	O
mode	O
of	O
RH	O
ceramics	O
was	O
determined	O
as	O
powder	O
formation	O
under	O
all	O
tested	O
conditions	O
,	O
as	O
demonstrated	O
in	O
Fig.	O
12	O
(	O
a	O
)	O
.	O
Furthermore	O
,	O
the	O
wear-mode	O
map	O
at	O
2	O
×	O
104	O
cycles	O
was	O
constructed	O
,	O
as	O
shown	O
in	O
Fig.	O
12	O
(	O
b	O
)	O
.	O
In	O
the	O
map	O
,	O
all	O
plots	O
moved	O
near	O
the	O
transition	O
curve	O
to	O
plowing	O
.	O
In	O
particular	O
,	O
the	O
plots	O
for	O
RH	O
ceramics	O
sliding	O
against	O
stainless	O
steel	O
or	O
Al2O3	O
balls	O
were	O
nearer	O
than	O
SiC	O
or	O
Si3N4	O
balls	O
.	O
Therefore	O
,	O
RH	O
ceramics	O
sliding	O
against	O
SiC	O
and	O
Si3N4	O
balls	O
showed	O
relatively	O
higher	O
wear	O
than	O
the	O
other	O
counterpart	O
materials	O
.	O
Nevertheless	O
,	O
these	O
results	O
from	O
the	O
wear-mode	O
maps	O
indicated	O
that	O
the	O
wear	O
mode	O
of	O
RH	O
ceramics	O
was	O
powder	O
formation	O
accompanied	O
with	O
microcracks	O
under	O
all	O
tested	O
conditions	O
in	O
this	O
study	O
,	O
resulting	O
in	O
low	O
wear	O
(<	O
5	O
×	O
10	O
−	O
9mm2	O
/	O
N	O
)	O
.	O
Indeed	O
,	O
the	O
observation	O
of	O
the	O
worn	O
surfaces	O
revealed	O
that	O
the	O
catastrophic	O
wear	O
of	O
RH	O
ceramics	O
accompanied	O
by	O
large	O
brittle	O
fracture	O
was	O
prevented	O
overall	O
,	O
as	O
shown	O
in	O
Fig.	O
13	O
.	O

The	O
lateral	B-Task
force	I-Task
,	I-Task
Q	I-Task
,	I-Task
is	I-Task
measured	I-Task
and	I-Task
recorded	I-Task
throughout	O
the	O
entire	O
test	O
by	O
a	O
piezoelectric	B-Process
load	I-Process
cell	I-Process
which	O
is	O
connected	O
to	O
the	O
quasi-stationary	O
LSMB	B-Process
.	O
The	O
LSMB	O
is	O
mounted	O
on	O
flexures	B-Material
which	O
provide	O
flexibility	O
in	O
the	O
horizontal	O
direction	O
so	O
that	O
the	O
majority	O
of	O
the	O
lateral	O
force	O
is	O
transmitted	O
though	O
the	O
much	O
stiffer	O
load	O
path	O
which	O
contains	O
the	O
load	B-Process
cell	I-Process
as	O
shown	O
in	O
Fig.	O
2.	O
Both	O
displacement	B-Process
and	I-Process
load	I-Process
sensors	I-Process
have	O
been	O
calibrated	O
(	O
both	O
externally	O
and	O
in-situ	O
)	O
in	O
static	O
conditions	O
.	O
The	O
load	O
and	O
displacement	O
signals	O
are	O
sampled	O
at	O
a	O
rate	O
of	O
two	O
hundred	O
measurements	O
per	O
fretting	O
cycle	O
at	O
all	O
fretting	O
frequencies	O
,	O
with	O
these	O
data	O
being	O
used	O
to	O
generate	O
fretting	B-Material
loops	I-Material
.	I-Material
The	O
loops	O
were	O
used	O
to	O
derive	O
the	O
contact	O
slip	O
amplitude	O
and	O
the	O
energy	O
coefficient	O
of	O
friction	O
in	O
each	O
cycle	O
according	O
to	O
the	O
method	O
suggested	O
by	O
Fouvry	O
et	O
al	O
.	O
[	O
17	O
]	O
.	O
Average	O
values	O
for	O
these	O
were	O
calculated	O
for	O
each	O
test	O
(	O
the	O
average	O
coefficient	O
of	O
friction	O
included	O
values	O
associated	O
with	O
the	O
initial	O
transients	O
in	O
the	O
tests	O
as	O
suggested	O
by	O
Hirsch	O
and	O
Neu	O
[	O
18	O
])	O
.	O

We	O
have	O
developed	O
the	O
theory	O
of	O
electrons	B-Process
carrying	I-Process
quantized	I-Process
orbital	I-Process
angular	I-Process
momentum	I-Process
.	I-Process
To	O
make	O
connection	O
to	O
realistic	O
situations	O
,	O
we	O
considered	O
a	O
plane	B-Material
wave	I-Material
moving	O
along	O
the	O
optic	O
axis	O
of	O
a	O
lens	B-Material
system	I-Material
,	O
intercepted	O
by	O
a	O
round	O
,	O
centered	O
aperture.88In	O
the	O
experiment	O
,	O
this	O
aperture	O
carries	O
the	O
holographic	O
mask	O
.	O
It	O
turns	O
out	O
that	O
the	O
movement	O
along	O
the	O
optic	O
axis	O
can	O
be	O
separated	O
off	O
;	O
the	O
reduced	O
Schrödinger	O
equation	O
operating	O
in	O
the	O
plane	O
of	O
the	O
aperture	O
can	O
be	O
mapped	O
onto	O
Bessel	O
's	O
differential	O
equation	O
.	O
The	O
ensuing	O
eigenfunctions	O
fall	O
into	O
families	O
with	O
discrete	O
orbital	O
angular	O
momentum	O
ℏm	O
along	O
the	O
optic	O
axis	O
where	O
m	O
is	O
a	O
magnetic	O
quantum	O
number	O
.	O
Those	O
vortices	O
can	O
be	O
produced	O
by	O
matching	O
a	O
plane	O
wave	O
after	O
passage	O
through	O
a	O
holographic	O
mask	O
with	O
a	O
fork	O
dislocation	O
to	O
the	O
eigenfunctions	O
of	O
the	O
cylindrical	O
problem	O
.	O
Vortices	O
can	O
be	O
focussed	O
by	O
magnetic	O
lenses	O
into	O
volcano-like	O
charge	O
distributions	O
with	O
very	O
narrow	O
angular	O
divergence	O
,	O
resembling	O
loop	O
currents	O
in	O
the	O
diffraction	O
plane	O
.	O
Inclusion	O
of	O
spherical	O
aberration	O
changes	O
the	O
ringlike	O
shape	O
but	O
does	O
not	O
destroy	O
the	O
central	O
zero	O
intensity	O
of	O
vortices	O
with	O
m	O
≠	O
0	O
.	O
Partial	O
coherence	O
of	O
the	O
incident	O
wave	O
leads	O
to	O
a	O
rise	O
of	O
the	O
central	O
intensity	O
minimum	O
.	O
It	O
is	O
shown	O
that	O
a	O
very	O
small	O
source	O
angle	O
(	O
i.e.	O
a	O
very	O
high	O
coherence	O
)	O
is	O
necessary	O
so	O
as	O
to	O
keep	O
the	O
volcano	O
structure	O
intact	O
.	O
Their	O
small	O
angular	O
width	O
in	O
the	O
far	O
field	O
may	O
allow	O
the	O
creation	O
of	O
nm-sized	O
or	O
smaller	O
electron	O
vortices	O
but	O
the	O
demand	O
for	O
extremely	O
high	O
coherence	O
of	O
the	O
source	O
poses	O
a	O
serious	O
difficulty	O
.	O

Some	O
methods	O
use	O
1D	B-Material
radial	I-Material
profiles	I-Material
obtained	O
from	O
circular	B-Process
averaging	I-Process
of	O
2D	B-Process
experimental	I-Process
PSD	I-Process
[	O
4,8,11	O
]	O
or	O
by	O
elliptical	B-Process
averaging	I-Process
[	O
17	O
]	O
.	O
An	O
inadequacy	O
of	O
circular	B-Process
averaging	I-Process
is	O
that	O
it	O
neglects	O
astigmatism	B-Process
.	O
Astigmatism	B-Process
distorts	O
the	O
circular	O
shape	O
of	O
the	O
Thon	B-Material
rings	I-Material
and	O
thus	O
decreases	O
their	O
modulation	O
depth	O
in	O
the	O
obtained	O
1D	B-Material
profile	I-Material
.	O
A	O
few	O
algorithms	O
that	O
consider	O
astigmatism	O
involve	O
concepts	O
such	O
as	O
dividing	O
the	O
PSD	B-Process
into	O
sectors	O
where	O
Thon	B-Material
rings	I-Material
are	O
approximated	O
by	O
circular	O
arcs	O
[	O
15,21	O
]	O
,	O
applying	O
Canny	B-Process
edge	I-Process
detection	I-Process
to	O
find	O
the	O
rings	O
[	O
17	O
]	O
prior	O
to	O
elliptical	B-Process
averaging	I-Process
,	O
determining	O
the	O
relationship	O
between	O
the	O
1D	O
circular	O
averages	O
with	O
and	O
without	O
astigmatism	B-Process
[	O
22	O
]	O
,	O
or	O
using	O
a	O
brute-force	O
scan	O
of	O
a	O
database	O
containing	O
precalculated	O
patterns	O
as	O
in	O
ATLAS	B-Process
[	O
23	O
]	O
.	O
Some	O
other	O
approaches	O
for	O
estimating	O
CTF	B-Process
parameters	O
do	O
a	O
fully	O
2D	B-Process
PSD	I-Process
optimization	I-Process
[	O
12,14,18,20	O
]	O
but	O
they	O
usually	O
regulate	O
and	O
fit	O
numerous	O
parameters	O
by	O
an	O
extensive	O
search	O
that	O
does	O
not	O
guarantee	O
convergence	O
.	O
Furthermore	O
,	O
only	O
a	O
few	O
schemes	O
that	O
were	O
developed	O
for	O
defocus	O
estimation	O
provide	O
an	O
error	O
analysis	O
[	O
23,24	O
]	O
.	O

Traditionally	O
,	O
archaeologists	O
have	O
recorded	B-Task
sites	I-Task
and	I-Task
artefacts	I-Task
via	O
a	O
combination	O
of	O
ordinary	B-Material
still	I-Material
photographs	I-Material
,	O
2D	B-Material
line	I-Material
drawings	I-Material
and	O
occasional	B-Material
cross-sections	I-Material
.	O
Given	O
these	O
constraints	O
,	O
the	O
attractions	O
of	O
3D	B-Process
models	I-Process
have	O
been	O
obvious	O
for	O
some	O
time	O
,	O
with	O
digital	B-Process
photogrammetry	I-Process
and	O
laser	B-Process
scanners	I-Process
offering	O
two	O
well-known	O
methods	O
for	O
data	B-Task
capture	I-Task
at	I-Task
close	I-Task
range	I-Task
(	O
e.g.	O
Bates	O
et	O
al.	O
,	O
2010	O
;	O
Hess	O
and	O
Robson	O
,	O
2010	O
)	O
.	O
The	O
highest	O
specification	O
laser	B-Process
scanners	I-Process
still	O
boast	O
better	O
positional	O
accuracy	O
and	O
greater	O
true	O
colour	O
fidelity	O
than	O
SfM	B-Process
–	I-Process
MVS	I-Process
methods	I-Process
(	O
James	O
and	O
Robson	O
,	O
2012	O
)	O
,	O
but	O
the	O
latter	O
produce	O
very	O
good	O
quality	O
models	O
nonetheless	O
and	O
have	O
many	O
unique	O
selling	O
points	O
.	O
Unlike	O
traditional	B-Process
digital	I-Process
photogrammetry	I-Process
,	O
little	O
or	O
no	O
prior	O
control	B-Process
of	I-Process
camera	I-Process
position	I-Process
is	O
necessary	O
,	O
and	O
unlike	O
laser	B-Process
scanning	I-Process
,	O
no	O
major	O
equipment	O
costs	O
or	O
setup	O
are	O
involved	O
.	O
However	O
,	O
the	O
key	O
attraction	O
of	O
SfM	B-Process
–	I-Process
MVS	I-Process
is	O
that	O
the	O
required	O
input	O
can	O
be	O
taken	O
by	O
anyone	O
with	O
a	O
digital	B-Material
camera	I-Material
and	O
modest	O
prior	B-Process
training	I-Process
about	I-Process
the	I-Process
required	I-Process
number	I-Process
and	I-Process
overlap	I-Process
of	I-Process
photographs	I-Process
.	O
A	O
whole	O
series	O
of	O
traditional	O
bottlenecks	O
are	O
thereby	O
removed	O
from	O
the	O
recording	B-Process
process	I-Process
and	O
large	O
numbers	O
of	O
archaeological	B-Task
landscapes	I-Task
,	O
sites	B-Task
or	O
artefacts	B-Task
can	O
now	O
be	O
captured	O
rapidly	O
,	O
in	O
the	O
field	O
,	O
in	O
the	O
laboratory	O
or	O
in	O
the	O
museum	O
.	O
Fig.	O
2a	O
–	O
c	O
shows	O
examples	O
of	O
terracotta	B-Process
warrior	I-Process
models	I-Process
for	O
which	O
the	O
level	O
of	O
surface	O
detail	O
is	O
considerable	O
.	O

Recent	O
astronomical	B-Task
observations	I-Task
of	O
high	B-Material
redshift	I-Material
type	I-Material
Ia	I-Material
supernovae	I-Material
performed	O
by	O
two	O
groups	O
[	O
1	O
–	O
3	O
]	O
as	O
well	O
as	O
the	O
power	B-Task
spectrum	I-Task
of	I-Task
the	I-Task
cosmic	I-Task
microwave	I-Task
background	I-Task
radiation	I-Task
obtained	O
by	O
the	O
BOOMERANG	B-Process
[	O
4	O
]	O
and	O
MAXIMA-1	B-Process
[	O
5	O
]	O
experiments	O
seem	O
to	O
indicate	O
that	O
at	O
present	O
the	O
Universe	O
is	O
in	O
a	O
state	O
of	O
accelerated	O
expansion	O
.	O
If	O
one	O
analyzes	O
these	O
data	O
within	O
the	O
Friedmann	B-Process
–	I-Process
Robertson	I-Process
–	I-Process
Walker	I-Process
(	I-Process
FRW	I-Process
)	I-Process
standard	I-Process
model	I-Process
of	O
cosmology	B-Task
their	O
most	O
natural	O
interpretation	O
is	O
that	O
the	O
Universe	O
is	O
spatially	O
flat	O
and	O
that	O
the	O
(	O
baryonic	O
plus	O
dark	O
)	O
matter	B-Process
density	I-Process
ρ	B-Process
is	O
about	O
one	O
third	O
of	O
the	O
critical	B-Process
density	I-Process
ρcrit	B-Process
.	O
Most	O
interestingly	O
,	O
the	O
dominant	O
contribution	O
to	O
the	O
energy	O
density	O
is	O
provided	O
by	O
the	O
cosmological	B-Material
constant	I-Material
Λ	B-Material
.	O
The	O
vacuum	B-Process
energy	I-Process
density	I-Process
(	O
1.1	O
)	O
ρΛ	B-Process
≡	I-Process
Λ	I-Process
/(	I-Process
8πG	I-Process
)	I-Process
is	O
about	O
twice	O
as	O
large	O
as	O
ρ	B-Process
,	O
i.e.	O
,	O
about	O
two	O
thirds	O
of	O
the	O
critical	B-Process
density	I-Process
.	O
With	O
ΩM	O
≡	O
ρ	O
/	O
ρcrit	O
,	O
ΩΛ	O
≡	O
ρΛ	O
/	O
ρcrit	O
and	O
Ωtot	O
≡	O
ΩM	O
+	O
ΩΛ	O
:	O
(	O
1.2	O
)	O
ΩM	O
≈	O
1	O
/	O
3,ΩΛ	O
≈	O
2	O
/	O
3,Ωtot	O
≈	O
1	O
.	O
This	O
implies	O
that	O
the	O
deceleration	B-Process
parameter	I-Process
q	B-Process
is	O
approximately	O
−	O
1	O
/	O
2	O
.	O
While	O
originally	O
the	O
cosmological	B-Task
constant	I-Task
problem	I-Task
[	O
6	O
]	O
was	O
related	O
to	O
the	O
question	B-Task
why	I-Task
Λ	I-Task
is	I-Task
so	I-Task
unnaturally	I-Task
small	I-Task
,	O
the	O
discovery	O
of	O
the	O
important	O
role	O
played	O
by	O
ρΛ	B-Material
has	O
shifted	O
the	O
emphasis	O
toward	O
the	O
“	O
coincidence	B-Task
problem	I-Task
”	O
,	O
the	O
question	O
why	B-Task
ρ	I-Task
and	I-Task
ρΛ	I-Task
happen	I-Task
to	I-Task
be	I-Task
of	I-Task
the	I-Task
same	I-Task
order	I-Task
of	I-Task
magnitude	I-Task
precisely	I-Task
at	I-Task
this	I-Task
very	I-Task
moment	I-Task
[	O
7	O
]	O
.	O

First	O
results	O
from	O
RHIC	O
on	O
charged	B-Task
multiplicities	I-Task
,	O
evolution	B-Task
of	I-Task
multiplicities	I-Task
with	I-Task
centrality	I-Task
,	O
particle	B-Task
ratios	I-Task
and	O
transverse	B-Task
momentum	I-Task
distributions	I-Task
in	O
central	O
and	O
minimum	O
bias	O
collisions	O
,	O
are	O
analyzed	O
in	O
a	O
string	B-Process
model	I-Process
which	O
includes	O
hard	B-Material
collisions	I-Material
,	O
collectivity	B-Material
in	I-Material
the	I-Material
initial	I-Material
state	I-Material
considered	O
as	O
string	O
fusion	O
,	O
and	O
rescattering	B-Material
of	I-Material
the	I-Material
produced	I-Material
secondaries	I-Material
.	O
Multiplicities	B-Task
and	O
their	B-Task
evolution	I-Task
with	O
centrality	O
are	O
successfully	O
reproduced	O
.	O
Transverse	B-Process
momentum	I-Process
distributions	I-Process
in	O
the	O
model	O
show	O
a	O
larger	O
pT-tail	O
than	O
experimental	O
data	O
,	O
disagreement	O
which	O
grows	O
with	O
increasing	O
centrality	O
.	O
Discrepancies	B-Process
with	I-Process
particle	I-Process
ratios	I-Process
appear	O
and	O
are	O
examined	O
comparing	O
with	O
previous	O
features	O
of	O
the	O
model	O
at	O
SPS	O
.	O

In	O
this	O
section	O
we	O
wish	O
to	O
calculate	B-Task
the	I-Task
cross	I-Task
section	I-Task
for	I-Task
the	I-Task
absorption	I-Task
of	I-Task
massless	I-Task
scalars	I-Task
by	O
the	O
self-dual	B-Process
string	I-Process
in	O
the	O
world	O
volume	O
of	O
the	O
M-theory	B-Material
five-brane	I-Material
.	O
We	O
will	O
adopt	O
an	O
entirely	B-Process
world	I-Process
volume	I-Process
approach	I-Process
similar	O
to	O
that	O
of	O
[	O
21	O
–	O
23	O
]	O
.	O
We	O
begin	O
by	O
writing	O
the	O
equation	O
satisfied	O
by	O
the	O
s-wave	B-Material
with	O
energy	O
ω	O
,	O
φ	O
(	O
r,t	O
)=	O
φ	O
(	O
r	O
)	O
eiωt	O
,	O
of	O
the	O
linear	B-Process
fluctuations	I-Process
of	O
the	O
four	O
overall	O
transverse	O
scalars	O
about	O
the	O
self-dual	O
string	O
,	O
(	O
it	O
is	O
known	O
that	O
there	O
are	O
problems	O
when	O
one	O
considers	O
higher	B-Process
angular	I-Process
momentum	I-Process
modes	I-Process
[	O
23	O
]	O
,	O
one	O
must	O
take	O
care	O
with	O
the	O
validity	O
of	O
the	O
linearized	B-Process
approximation	I-Process
,	O
this	O
is	O
discussed	O
in	O
[	O
13	O
])	O
:	O
(	O
15	O
)	O
ρ	O
−	O
3ddρρ3ddρ	O
+	O
1	O
+	O
R6ω6ρ6φ	O
(	O
ρ	O
)=	O
0	O
,	O
where	O
ρ	O
=	O
rω	O
,	O
R	O
=	O
Q1	O
/	O
3ℓp	O
.	O
Note	O
,	O
as	O
pointed	O
out	O
by	O
[	O
11	O
]	O
world	B-Process
volume	I-Process
solitons	I-Process
have	O
a	O
much	O
sharper	O
potential	O
than	O
the	O
Coulomb	O
type	O
potential	O
typical	O
of	O
brane	B-Process
solutions	I-Process
in	O
supergravity	O
;	O
thus	O
this	O
scattering	O
is	O
different	O
to	O
that	O
of	O
the	O
string	O
in	O
six-dimensional	O
supergravity	O
.	O
Nevertheless	O
,	O
for	O
small	B-Task
ωR	I-Task
one	O
may	O
solve	O
this	O
problem	O
by	O
matching	O
an	O
approximate	B-Process
solution	I-Process
in	O
the	O
inner	O
region	O
to	O
an	O
approximate	O
solution	O
in	O
the	O
outer	O
region	O
;	O
this	O
follows	O
closely	O
the	O
supergravity	B-Process
calculation	I-Process
[	O
24	O
]	O
.	O

We	O
consider	B-Task
cosmological	I-Task
consequences	I-Task
of	I-Task
a	I-Task
conformal-invariant	I-Task
formulation	I-Task
of	I-Task
Einstein	I-Task
's	I-Task
General	I-Task
Relativity	I-Task
where	O
instead	O
of	O
the	O
scale	B-Material
factor	I-Material
of	O
the	O
spatial	O
metrics	O
in	O
the	O
action	O
functional	O
a	O
massless	B-Material
scalar	I-Material
(	O
dilaton	B-Material
)	O
field	O
occurs	O
which	O
scales	O
all	O
masses	B-Material
including	O
the	O
Planck	B-Material
mass	I-Material
.	O
Instead	O
of	O
the	O
expansion	B-Process
of	I-Process
the	I-Process
universe	I-Process
we	O
obtain	O
the	O
Hoyle	B-Process
–	I-Process
Narlikar	I-Process
type	I-Process
of	I-Process
mass	I-Process
evolution	I-Process
,	O
where	O
the	O
temperature	B-Process
history	I-Process
of	I-Process
the	I-Process
universe	I-Process
is	O
replaced	O
by	O
the	O
mass	B-Process
history	I-Process
.	O
We	O
show	O
that	O
this	O
conformal-invariant	B-Process
cosmological	I-Process
model	I-Process
gives	O
a	O
satisfactory	O
description	O
of	O
the	O
new	O
supernova	B-Material
Ia	I-Material
data	I-Material
for	O
the	O
effective	B-Process
magnitude	I-Process
–	I-Process
redshift	I-Process
relation	I-Process
without	O
a	O
cosmological	B-Material
constant	I-Material
and	O
make	O
a	O
prediction	O
for	O
the	O
high-redshift	B-Process
behavior	I-Process
which	O
deviates	O
from	O
that	O
of	O
standard	B-Task
cosmology	I-Task
for	O
z	O
>	O
1.7	O
.	O

Production	B-Task
of	I-Task
charmonium	I-Task
states	I-Task
J	I-Task
/	I-Task
ψ	I-Task
and	I-Task
ψ′	I-Task
in	I-Task
nucleus	I-Task
–	I-Task
nucleus	I-Task
collisions	I-Task
has	O
been	O
studied	O
at	O
CERN	O
SPS	O
over	O
the	O
previous	O
15	O
years	O
by	O
the	O
NA38	O
and	O
NA50	O
Collaborations	O
.	O
This	O
experimental	B-Task
program	I-Task
was	O
mainly	O
motivated	O
by	O
the	O
suggestion	O
[	O
1	O
]	O
to	O
use	B-Task
the	I-Task
J	I-Task
/	I-Task
ψ	I-Task
as	I-Task
a	I-Task
probe	I-Task
of	I-Task
the	I-Task
state	I-Task
of	I-Task
matter	I-Task
created	I-Task
at	I-Task
the	I-Task
early	I-Task
stage	I-Task
of	I-Task
the	I-Task
collision	I-Task
.	O
The	O
original	B-Material
picture	I-Material
[	O
1	O
]	O
(	O
see	O
also	O
[	O
2	O
]	O
for	O
a	O
modern	O
review	O
)	O
assumes	O
that	O
charmonia	B-Material
are	O
created	O
exclusively	O
at	O
the	O
initial	O
stage	O
of	O
the	O
reaction	O
in	O
primary	B-Process
nucleon	I-Process
–	I-Process
nucleon	I-Process
collisions	I-Process
.	O
During	O
the	O
subsequent	B-Process
evolution	I-Process
of	I-Process
the	I-Process
system	I-Process
,	I-Process
the	O
number	O
of	O
hidden	B-Material
charm	I-Material
mesons	I-Material
is	O
reduced	O
because	O
of	O
:	O
(	O
a	O
)	O
absorption	B-Process
of	I-Process
pre-resonance	I-Process
charmonium	I-Process
states	I-Process
by	I-Process
nuclear	I-Process
nucleons	I-Process
(	O
normal	B-Process
nuclear	I-Process
suppression	I-Process
)	O
,	O
(	O
b	O
)	O
interactions	B-Process
of	I-Process
charmonia	I-Process
with	I-Process
secondary	I-Process
hadrons	I-Process
(	O
comovers	B-Process
)	O
,	O
(	O
c	O
)	O
dissociation	B-Process
of	I-Process
cc	I-Process
̄	I-Process
bound	I-Process
states	I-Process
in	I-Process
deconfined	I-Process
medium	I-Process
(	O
anomalous	B-Process
suppression	I-Process
)	O
.	O
It	O
was	O
found	O
[	O
3	O
]	O
that	O
J	B-Process
/	I-Process
ψ	I-Process
suppression	I-Process
with	O
respect	O
to	O
Drell	B-Material
–	I-Material
Yan	I-Material
muon	I-Material
pairs	I-Material
measured	O
in	O
proton	B-Material
–	I-Material
nucleus	I-Material
and	O
nucleus	B-Process
–	I-Process
nucleus	I-Process
collisions	I-Process
with	O
light	B-Material
projectiles	I-Material
can	O
be	O
explained	O
by	O
the	O
so-called	O
“	O
normal	O
”	O
(	O
due	O
to	O
sweeping	B-Material
nucleons	I-Material
)	O
nuclear	B-Process
suppression	I-Process
alone	O
.	O
In	O
contrast	O
,	O
the	O
NA50	B-Task
experiment	I-Task
with	O
a	O
heavy	B-Material
projectile	I-Material
and	I-Material
target	I-Material
(	O
Pb	B-Material
+	I-Material
Pb	I-Material
)	O
revealed	O
essentially	O
stronger	O
J	B-Material
/	I-Material
ψ	I-Material
suppression	O
for	O
central	B-Process
collisions	I-Process
[	O
4	O
–	O
7	O
]	O
.	O
This	O
anomalous	O
J	B-Process
/	I-Process
ψ	I-Process
suppression	I-Process
was	O
attributed	O
to	O
formation	B-Process
of	I-Process
quark	I-Process
–	I-Process
gluon	I-Process
plasma	I-Process
(	O
QGP	B-Material
)	O
[	O
7	O
]	O
,	O
but	O
a	O
comover	B-Process
scenario	I-Process
cannot	O
be	O
excluded	O
[	O
8	O
]	O
.	O

Brodsky	O
and	O
Lepage	O
[	O
8	O
]	O
have	O
proposed	O
a	O
formula	B-Task
for	I-Task
meson	I-Task
pair	I-Task
production	I-Task
which	O
looks	O
similar	O
to	O
(	O
25	O
)	O
,	O
except	O
for	O
a	O
different	O
charge	O
factor	O
and	O
the	O
appearance	O
of	O
the	O
timelike	O
electromagnetic	B-Material
meson	I-Material
form	O
factor	O
instead	O
of	O
the	O
annihilation	O
form	O
factor	O
R	O
(	O
s	O
)	O
.	O
This	O
formula	B-Task
was	O
obtained	O
from	O
the	O
leading-twist	O
result	O
by	O
neglecting	B-Process
part	I-Process
of	I-Process
the	I-Process
amplitudes	I-Process
with	I-Process
opposite	I-Process
photon	I-Process
helicities	I-Process
.	O
As	O
has	O
been	O
pointed	O
out	O
in	O
[	O
9	O
]	O
,	O
this	O
part	O
is	O
however	O
not	O
approximately	O
independent	O
of	O
the	O
pion	O
distribution	O
amplitude	O
and	O
not	O
generically	O
small	O
.	O
We	O
also	O
remark	O
that	O
the	O
appearance	O
of	O
Fπ	O
(	O
s	O
)	O
in	O
the	O
γγ	O
→	O
π	O
+	O
π	O
−	O
amplitude	O
is	O
no	O
longer	O
observed	O
if	O
corrections	O
from	O
partonic	B-Process
transverse	I-Process
momentum	I-Process
in	O
the	O
hard	B-Process
scattering	I-Process
process	I-Process
are	O
taken	O
into	O
account	O
,	O
and	O
that	O
these	O
corrections	O
are	O
not	O
numerically	O
small	O
for	O
the	O
values	O
of	O
s	O
we	O
are	O
dealing	O
with	O
[	O
13	O
]	O
.	O
Notice	O
further	O
that	O
two-photon	B-Process
annihilation	I-Process
produces	O
two	O
pions	O
in	O
a	O
C-even	O
state	O
,	O
whereas	O
the	O
electromagnetic	O
form	O
factor	O
projects	O
on	O
the	O
C-odd	O
state	O
of	O
a	O
pion	O
pair	O
.	O
In	O
contrast	O
,	O
our	O
annihilation	O
form	O
factor	O
R2π	O
(	O
s	O
)	O
is	O
C-even	O
as	O
discussed	O
after	O
(	O
24	O
)	O
.	O
Finally	O
,	O
due	O
to	O
a	O
particular	O
charge	O
factor	O
,	O
the	O
Brodsky	O
–	O
Lepage	O
formula	O
leads	O
to	O
a	O
vanishing	O
cross	O
section	O
for	O
γγ	O
annihilation	O
into	O
pairs	O
of	O
neutral	O
pseudoscalars	O
.	O

Since	O
perturbative	B-Process
expansion	I-Process
is	O
used	O
,	O
it	O
is	O
impossible	O
to	O
find	O
the	O
exact	B-Process
bounds	I-Process
;	O
instead	O
,	O
one	O
can	O
derive	O
tree-level	B-Process
unitarity	I-Process
bounds	I-Process
or	O
loop-improved	B-Process
unitarity	I-Process
bounds	I-Process
.	O
In	O
this	O
study	O
,	O
we	O
will	O
use	O
unitarity	O
bounds	O
coming	O
from	O
a	O
tree-level	B-Task
analysis	I-Task
[	O
20	O
]	O
.	O
This	O
tree	B-Task
level	I-Task
analysis	I-Task
is	O
derived	O
with	O
the	O
help	O
of	O
the	O
equivalence	B-Process
theorem	I-Process
[	O
21	O
]	O
,	O
which	O
itself	O
is	O
a	O
high-energy	B-Process
approximation	I-Process
where	O
it	O
is	O
assumed	O
that	O
the	O
energy	O
scale	O
is	O
much	O
larger	O
than	O
the	O
Z0	O
and	O
W	O
±	O
gauge-boson	B-Material
masses	I-Material
.	O
We	O
will	O
consider	O
here	O
this	O
“	O
high-energy	O
”	O
hypothesis	O
that	O
both	O
the	O
equivalence	B-Process
theorem	I-Process
and	O
the	O
decoupling	B-Process
regime	I-Process
are	O
well	O
settled	O
,	O
but	O
in	O
such	O
a	O
way	O
that	O
the	O
unitarity	B-Process
constraint	I-Process
is	O
also	O
fulfilled	O
.	O
Our	O
purpose	O
is	O
to	O
investigate	B-Task
the	I-Task
quantum	I-Task
effects	I-Task
in	I-Task
the	I-Task
decays	I-Task
of	I-Task
the	I-Task
light	I-Task
CP-even	I-Task
Higgs	I-Task
boson	I-Task
h0	I-Task
,	O
especially	O
looking	B-Task
for	I-Task
sizeable	I-Task
differences	I-Task
with	I-Task
respect	I-Task
to	I-Task
the	I-Task
SM	I-Task
in	I-Task
the	I-Task
decoupling	I-Task
regime	I-Task
.	O

In	O
the	O
bag	B-Process
model	I-Process
and	O
in	O
linear	O
or	O
harmonic	O
oscillator	B-Process
confining	I-Process
potentials	I-Process
,	O
the	O
first	O
excited	O
S-state	O
lies	O
above	O
the	O
lowest	O
P-state	O
,	O
making	O
the	O
predicted	O
Roper	O
mass	O
heavier	O
than	O
the	O
lightest	O
negative	O
parity	O
baryon	O
mass	O
.	O
Pairwise	B-Process
spin-dependent	I-Process
interactions	I-Process
must	O
reverse	O
the	O
level	B-Process
ordering	I-Process
.	O
As	O
mentioned	O
earlier	O
,	O
color-spin	B-Process
interactions	I-Process
fail	O
in	O
this	O
regard	O
[	O
29	O
]	O
,	O
while	O
flavor-spin	B-Process
interactions	I-Process
produce	O
the	O
desired	O
effect	O
.	O
Since	O
the	O
q3	B-Process
color	I-Process
wave	I-Process
function	I-Process
is	O
antisymmetric	O
,	O
the	O
flavor-spin-orbital	B-Process
wave	I-Process
function	I-Process
is	O
totally	O
symmetric	O
.	O
For	O
all	O
quarks	B-Material
in	O
an	O
S-state	O
,	O
the	O
flavor-spin	B-Process
wave	I-Process
function	I-Process
is	O
totally	O
symmetric	O
all	O
by	O
itself	O
and	O
leads	O
to	O
the	O
most	O
attractive	O
flavor-spin	B-Process
interaction	I-Process
.	O
If	O
one	O
quark	B-Material
is	O
in	O
a	O
P-state	O
,	O
the	O
orbital	B-Process
wave	I-Process
function	I-Process
is	O
mixed	O
symmetry	O
and	O
so	O
is	O
the	O
flavor-spin	B-Process
wave	I-Process
function	I-Process
,	O
and	O
the	O
flavor-spin	B-Process
interaction	I-Process
is	O
a	O
less	O
attractive	O
.	O
In	O
the	O
SU	O
(	O
3	O
)	O
F	O
symmetric	O
case	O
,	O
Eq	O
.	O
(	O
1	O
)	O
,	O
one	O
obtains	O
mass	B-Process
splittings	I-Process
(	O
2	O
)	O
ΔMχ	O
=−	O
14Cχ,N	O
(	O
939	O
)	O
,	O
N	O
∗(	O
1440	O
),−	O
4Cχ,Δ	O
(	O
1232	O
),−	O
2Cχ,N	O
∗(	O
1535	O
)	O
.	O
Here	O
we	O
have	O
approximated	O
the	O
N	O
∗(	O
1535	O
)	O
as	O
a	O
state	O
with	O
total	O
quark	B-Material
spin-1	O
/	O
2	O
.	O

The	O
measurements	O
presented	O
here	O
provide	O
evidence	O
for	O
the	O
existence	O
of	O
di-cluster	B-Material
structures	I-Material
in	O
10	O
–	O
12,14Be	O
.	O
Certainly	O
,	O
if	O
the	O
breakup	B-Process
process	I-Process
samples	O
the	O
overlap	O
between	O
the	O
wavefunctions	B-Process
of	I-Process
the	I-Process
ground	I-Process
state	I-Process
and	I-Process
the	I-Process
excited	I-Process
states	I-Process
,	O
the	O
first-chance	B-Process
cluster	I-Process
breakup	I-Process
cross-sections	I-Process
,	O
shown	O
in	O
Fig.	O
4	O
(	O
a	O
)	O
,	O
indicate	O
that	O
the	O
xHe	B-Material
+	I-Material
A	I-Material
−	I-Material
xHe	I-Material
cluster	I-Material
structure	I-Material
does	O
not	O
decrease	O
over	O
the	O
mass	O
range	O
A	O
=	O
10	O
,	O
12	O
and	O
14	O
.	O
Given	O
also	O
that	O
the	O
decay	B-Process
energy	I-Process
threshold	O
increases	O
with	O
mass	O
number	O
,	O
the	O
present	O
data	O
may	O
even	O
indicate	O
a	O
slight	O
increase	O
in	O
clustering	O
.	O
The	O
breakup	B-Process
cross-sections	I-Process
also	O
appear	O
to	O
demonstrate	O
that	O
these	O
nuclei	B-Material
possess	O
a	O
stronger	O
structural	O
overlap	O
with	O
an	O
α	O
–	O
Xn	O
–	O
α	O
configuration	O
,	O
although	O
the	O
reaction	B-Process
mechanics	I-Process
by	O
which	O
this	O
final	O
state	O
is	O
reached	O
may	O
be	O
complex	O
.	O
That	O
is	O
to	O
say	O
that	O
the	O
dominant	B-Process
structural	I-Process
mode	I-Process
of	O
the	O
neutron	B-Material
rich	I-Material
isotopes	I-Material
may	O
be	O
identified	O
with	O
two	O
alpha-particles	B-Material
plus	O
valence	B-Material
neutrons	I-Material
.	O
These	O
comprehensive	B-Task
measurements	I-Task
of	I-Task
the	I-Task
neutron-removal	I-Task
and	I-Task
cluster	I-Task
breakup	I-Task
for	O
the	O
first	O
time	O
provide	O
experimental	O
data	O
whereby	O
the	O
structure	O
of	O
the	O
most	O
neutron-rich	B-Material
Be	I-Material
isotopes	I-Material
can	O
be	O
modeled	O
via	O
their	O
reactions	O
.	O

Let	O
us	O
now	O
consider	O
the	O
case	O
of	O
a	O
beta-beam	B-Material
source	I-Material
.	O
Similarly	O
to	O
the	O
case	O
of	O
a	O
static	B-Material
tritium	I-Material
source	I-Material
,	O
an	O
advantage	O
of	O
the	O
beta-beams	B-Process
is	O
that	O
the	O
neutrino	B-Process
fluxes	I-Process
can	O
be	O
very	O
accurately	O
calculated	O
.	O
Fig.	O
3	O
shows	O
the	O
electron	B-Process
–	I-Process
neutrino	I-Process
scattering	I-Process
events	O
in	O
the	O
range	O
of	O
0.1	O
MeV	O
to	O
1	O
MeV	O
and	O
1	O
keV	O
to	O
10	O
keV	O
,	O
respectively	O
.	O
(	O
In	O
Fig.	O
3	O
(	O
b	O
)	O
we	O
have	O
rounded	O
to	O
the	O
nearest	O
integer	O
number	O
of	O
counts	O
.	O
)	O
The	O
shape	O
of	O
the	O
flux-averaged	O
cross	O
sections	O
is	O
very	O
similar	O
to	O
the	O
reactor	B-Material
case	I-Material
as	O
reflected	O
in	O
the	O
event	O
rates	O
shown	O
in	O
the	O
figures	O
.	O
As	O
can	O
be	O
seen	O
,	O
by	O
measuring	O
electron	B-Material
recoils	I-Material
in	O
the	O
keV	O
range	O
with	O
a	O
beta-beam	B-Material
source	I-Material
one	O
could	O
,	O
with	O
a	O
sufficiently	O
strong	O
source	O
,	O
have	O
a	O
very	O
clear	O
signature	O
for	O
a	O
neutrino	B-Process
magnetic	I-Process
moment	I-Process
of	O
5	O
×	O
10	O
−	O
11μB	O
.	O
These	O
figures	O
are	O
for	O
Helium-6	B-Material
ions	I-Material
,	O
however	O
,	O
similar	O
results	O
can	O
be	O
obtained	O
using	O
neutrinos	B-Material
from	O
18Ne	O
.	O
The	O
results	O
shown	O
are	O
obtained	O
for	O
an	O
intensity	O
of	O
1015	O
ν	O
/	O
s	O
(	O
i.e.	O
,	O
1015	O
ions	O
/	O
s	O
)	O
.	O
If	O
there	O
is	O
no	O
magnetic	O
moment	O
,	O
this	O
intensity	O
will	O
produce	O
about	O
170	O
events	O
in	O
the	O
0.1	O
MeV	O
to	O
1	O
MeV	O
range	O
per	O
year	O
and	O
3	O
events	O
in	O
the	O
1	O
keV	O
to	O
10	O
keV	O
range	O
per	O
year	O
.	O
These	O
numbers	O
increase	O
to	O
210	O
and	O
55	O
,	O
respectively	O
,	O
in	O
the	O
case	O
of	O
a	O
magnetic	O
moment	O
of	O
5	O
×	O
10	O
−	O
11μB	O
.	O

Each	O
hit	O
position	O
inside	O
the	O
drift	B-Material
chambers	I-Material
was	O
calculated	O
from	O
the	O
drift	O
time	O
digitized	O
by	O
a	O
flash	B-Material
analog-to-digital	I-Material
converter	I-Material
.	O
The	O
calculation	O
was	O
carried	O
out	O
based	O
on	O
a	O
relation	B-Process
between	I-Process
the	I-Process
hit	I-Process
position	I-Process
and	I-Process
the	I-Process
drift	I-Process
time	I-Process
(	O
x	B-Process
–	I-Process
t	I-Process
relation	I-Process
)	O
.	O
The	O
x	O
–	O
t	O
relation	O
was	O
precisely	O
calculated	O
by	O
a	O
drift	B-Material
chamber	I-Material
simulation	I-Material
package	I-Material
,	O
GARFIELD	B-Material
[	O
20	O
]	O
,	O
and	O
a	O
gas	B-Material
property	I-Material
simulation	I-Material
package	I-Material
,	O
MAGBOLTZ	B-Material
[	O
21	O
]	O
.	O
Although	O
the	O
chambers	B-Material
were	O
constructed	O
carefully	O
with	O
a	O
tolerance	O
of	O
100	O
μm	O
,	O
there	O
was	O
a	O
small	O
position	O
deviation	O
of	O
wires	O
and	O
field-shaping	O
patterns	O
,	O
which	O
could	O
locally	O
modify	O
the	O
electric	O
field	O
.	O
In	O
order	O
to	O
take	O
account	O
of	O
the	O
limited	O
accuracy	O
in	O
the	O
chamber	O
manufacturing	O
,	O
a	O
correction	O
was	O
commonly	O
applied	O
to	O
the	O
calculated	O
x	B-Process
–	I-Process
t	I-Process
relation	I-Process
throughout	O
the	O
experiments	O
.	O
The	O
correction	O
was	O
obtained	O
to	O
minimize	O
the	O
χ2	O
in	O
the	O
fitting	O
of	O
straight	O
tracks	O
of	O
clean	O
muon	O
events	O
observed	O
on	O
the	O
ground	O
without	O
magnetic	B-Process
field	I-Process
.	O
The	O
correction	O
was	O
as	O
small	O
as	O
expected	O
from	O
the	O
accuracy	O
in	O
the	O
chamber	B-Material
manufacturing	O
.	O
During	O
the	O
observations	O
,	O
the	O
x	B-Process
–	I-Process
t	I-Process
relation	I-Process
was	O
affected	O
by	O
the	O
variation	O
in	O
the	O
pressure	O
and	O
temperature	O
of	O
the	O
chamber	B-Material
gas	I-Material
.	O
In	O
order	O
to	O
take	O
account	O
of	O
these	O
time-dependent	O
variations	O
,	O
the	O
x	B-Process
–	I-Process
t	I-Process
relation	I-Process
was	O
calibrated	O
for	O
each	O
data-taking	O
run	O
.	O
Especially	O
in	O
calibrating	O
the	O
x	O
–	O
t	O
relation	O
of	O
ODCs	B-Material
,	O
an	O
absolute	O
reference	O
positions	O
were	O
provided	O
by	O
SciFi	O
,	O
which	O
are	O
not	O
affected	O
by	O
the	O
variation	O
in	O
the	O
pressure	O
nor	O
temperature	O
.	O

We	O
define	B-Task
a	I-Task
new	I-Task
multispecies	I-Task
model	I-Task
of	I-Task
Calogero	I-Task
type	I-Task
in	I-Task
D	I-Task
dimensions	I-Task
with	O
harmonic	B-Process
,	I-Process
two-body	I-Process
and	I-Process
three-body	I-Process
interactions	I-Process
.	O
Using	O
the	O
underlying	O
conformal	B-Process
SU	I-Process
(	I-Process
1,1	I-Process
)	I-Process
algebra	I-Process
,	O
we	O
indicate	O
how	O
to	O
find	O
the	O
complete	O
set	O
of	O
the	O
states	O
in	O
Bargmann	O
–	O
Fock	O
space	O
.	O
There	O
are	O
towers	O
of	O
states	O
,	O
with	O
equidistant	B-Process
energy	I-Process
spectra	I-Process
in	O
each	O
tower	O
.	O
We	O
explicitely	O
construct	B-Process
all	I-Process
polynomial	I-Process
eigenstates	I-Process
,	O
namely	O
the	O
center-of-mass	O
states	O
and	O
global	O
dilatation	O
modes	O
,	O
and	O
find	B-Process
their	I-Process
corresponding	I-Process
eigenenergies	I-Process
.	O
We	O
also	O
construct	B-Process
ladder	I-Process
operators	I-Process
for	O
these	O
global	O
collective	O
states	O
.	O
Analysing	B-Task
corresponding	I-Task
Fock	I-Task
space	I-Task
,	O
we	O
detect	O
the	O
universal	O
critical	O
point	O
at	O
which	O
the	O
model	O
exhibits	O
singular	O
behavior	O
.	O
The	O
above	O
results	O
are	O
universal	O
for	O
all	O
systems	O
with	O
underlying	O
conformal	O
SU	O
(	O
1,1	O
)	O
symmetry	O
.	O

The	O
expression	O
for	O
Pc	B-Material
is	O
also	O
easily	O
found	O
in	O
the	O
same	O
basis	O
,	O
where	O
it	O
becomes	O
apparent	O
that	O
the	O
dynamics	O
of	O
conversion	B-Process
in	I-Process
matter	I-Process
depends	O
only	O
on	O
the	O
relative	B-Process
orientation	I-Process
of	I-Process
the	I-Process
eigenstates	I-Process
of	O
the	O
vacuum	B-Process
and	O
matter	B-Process
Hamiltonians	I-Process
.	O
This	O
allows	O
to	O
directly	O
apply	O
the	O
known	O
analytical	B-Process
solutions	I-Process
for	I-Process
Pc	I-Process
,	O
and	O
,	O
upon	O
rotating	O
back	O
,	O
obtain	O
a	O
generalization	B-Process
of	I-Process
these	I-Process
results	I-Process
to	I-Process
the	I-Process
NSI	I-Process
case	I-Process
.	O
For	O
example	O
,	O
the	O
answer	O
for	O
the	O
infinite	O
exponential	O
profile	O
[	O
18,19	O
]	O
A	O
∝	O
exp	O
(−	O
r	O
/	O
r0	O
)	O
becomes	O
Pc	O
=	O
exp	O
[	O
γ	O
(	O
1	O
−	O
cos2θrel	O
)/	O
2	O
]−	O
1exp	O
(	O
γ	O
)−	O
1	O
,	O
where	O
γ	O
≡	O
4πr0Δ	O
=	O
πr0Δm2	O
/	O
Eν	O
.	O
We	O
further	O
observe	O
that	O
since	O
γ	O
⪢	O
1	O
the	O
adiabaticity	B-Process
violation	I-Process
occurs	O
only	O
when	O
|	O
θ	O
−	O
α	O
|⪡	O
1	O
and	O
φ	O
≃	O
π	O
/	O
2	O
,	O
which	O
is	O
the	O
analogue	O
of	O
the	O
small-angle	O
MSW	O
[	O
10,20	O
]	O
effect	O
in	O
the	O
rotated	O
basis	O
.	O
The	O
“	O
resonant	O
”	O
region	O
in	O
the	O
Sun	O
where	O
level	O
jumping	O
can	O
take	O
place	O
is	O
narrow	O
,	O
defined	O
by	O
A	O
≃	O
Δ	O
[	O
21	O
]	O
.	O
A	O
neutrino	B-Material
produced	O
at	O
a	O
lower	O
density	O
evolves	O
adiabatically	O
,	O
while	O
a	O
neutrino	O
produced	O
at	O
a	O
higher	O
density	O
may	O
undergo	O
level	O
crossing	O
.	O
The	O
probability	O
Pc	B-Material
in	O
the	O
latter	O
case	O
is	O
given	O
to	O
a	O
very	O
good	O
accuracy	O
by	O
the	O
formula	O
for	O
the	O
linear	O
profile	O
,	O
with	O
an	O
appropriate	O
gradient	O
taken	O
along	O
the	O
neutrino	B-Material
trajectory	O
,	O
(	O
12	O
)	O
Pc	O
≃	O
Θ	O
(	O
A	O
−	O
Δ	O
)	O
e	O
−	O
γ	O
(	O
cos2θrel	O
+	O
1	O
)/	O
2	O
,	O
where	O
Θ	O
(	O
x	O
)	O
is	O
the	O
step	O
function	O
,	O
Θ	O
(	O
x	O
)=	O
1	O
for	O
x	O
>	O
0	O
and	O
Θ	O
(	O
x	O
)=	O
0	O
otherwise	O
.	O
We	O
emphasize	O
that	O
our	O
results	O
differ	O
from	O
the	O
similar	O
ones	O
given	O
in	O
[	O
5,22	O
]	O
in	O
three	O
important	O
respects	O
:	O
(	O
i	O
)	O
they	O
are	O
valid	O
for	O
all	O
,	O
not	O
just	O
small	O
values	O
of	O
α	O
(	O
which	O
is	O
essential	O
for	O
our	O
application	O
)	O
,	O
(	O
ii	O
)	O
they	O
include	O
the	O
angle	O
φ	O
,	O
and	O
(	O
iii	O
)	O
the	O
argument	O
of	O
the	O
Θ	O
function	O
does	O
not	O
contain	O
cos2θ	O
,	O
as	O
follows	O
from	O
[	O
21	O
]	O
.	O
We	O
stress	O
that	O
for	O
large	O
values	O
of	O
α	O
and	O
φ	O
≃	O
π	O
/	O
2	O
adiabaticity	O
is	O
violated	O
for	O
large	O
values	O
of	O
θ	O
.	O

One	O
major	O
goal	O
of	O
current	O
nuclear	B-Task
physics	I-Task
is	O
the	O
observation	B-Task
of	I-Task
at	I-Task
least	I-Task
partial	I-Task
restoration	I-Task
of	I-Task
chiral	I-Task
symmetry	I-Task
.	O
Since	O
the	O
chiral	B-Material
order	I-Material
parameter	I-Material
〈	O
q	B-Material
̄	I-Material
q	I-Material
〉	O
is	O
expected	O
to	O
decrease	O
by	O
about	O
30	O
%	O
already	O
at	O
normal	O
nuclear	B-Material
matter	I-Material
density	O
[	O
1	O
–	O
4	O
]	O
,	O
any	O
in-medium	O
change	O
due	O
to	O
the	O
dropping	O
quark	B-Material
condensate	I-Material
should	O
in	O
principle	O
be	O
observable	O
in	O
photonuclear	B-Process
reactions	I-Process
.	O
The	O
conjecture	O
that	O
such	O
a	O
partial	B-Process
restoration	I-Process
of	I-Process
chiral	I-Process
symmetry	I-Process
causes	O
a	O
softening	O
and	O
narrowing	O
of	O
the	O
σ	B-Material
meson	I-Material
as	O
the	O
chiral	B-Material
partner	I-Material
of	O
the	O
pion	B-Material
in	O
the	O
nuclear	B-Material
medium	I-Material
[	O
5,6	O
]	O
has	O
led	O
to	O
the	O
idea	O
of	O
measuring	B-Process
the	I-Process
π0π0	I-Process
invariant	I-Process
mass	I-Process
distribution	I-Process
near	O
the	O
2π	O
threshold	O
in	O
photon	B-Material
induced	O
reactions	O
on	O
nuclei	B-Material
[	O
7	O
]	O
.	O
In	O
contrast	O
to	O
its	O
questionable	O
nature	O
as	O
a	O
proper	O
quasiparticle	B-Material
in	O
vacuum	O
,	O
the	O
σ	B-Material
meson	I-Material
might	O
develop	O
a	O
much	O
narrower	O
peak	O
at	O
finite	O
baryon	O
density	O
due	O
to	O
phase-space	O
suppression	O
for	O
the	O
σ	B-Process
→	I-Process
ππ	I-Process
decay	I-Process
,	O
hence	O
making	O
it	O
possible	O
to	O
explore	O
its	O
properties	O
when	O
embedded	O
in	O
a	O
nuclear	B-Process
many-body	I-Process
system	I-Process
[	O
8	O
–	O
11	O
]	O
.	O
Measuring	B-Process
a	I-Process
threshold	I-Process
enhancement	I-Process
of	I-Process
the	I-Process
π0π0	I-Process
invariant	I-Process
mass	I-Process
spectrum	I-Process
might	O
serve	O
as	O
a	O
signal	O
for	O
the	O
partial	B-Task
restoration	I-Task
of	I-Task
chiral	I-Task
symmetry	I-Task
inside	O
nuclei	B-Material
and	O
,	O
therefore	O
,	O
give	O
information	O
about	O
one	O
of	O
the	O
most	O
fundamental	O
features	O
of	O
QCD	B-Material
.	O

An	O
OPE	B-Material
of	O
VQCD	B-Material
(	I-Material
r	I-Material
)	I-Material
was	O
developed	O
in	O
[	O
3	O
]	O
.	O
In	O
this	O
and	O
the	O
next	O
paragraph	O
,	O
we	O
review	O
the	O
content	O
of	O
that	O
paper	O
relevant	O
to	O
our	O
analysis	O
.	O
Within	O
this	O
framework	O
,	O
short-distance	O
contributions	O
are	O
contained	O
in	O
the	O
potentials	O
,	O
which	O
are	O
in	O
fact	O
the	O
Wilson	O
coefficients	O
,	O
while	O
non-perturbative	O
contributions	O
are	O
contained	O
in	O
the	O
matrix	O
elements	O
that	O
are	O
organized	O
in	O
multipole	B-Process
expansion	I-Process
in	O
r	O
→	O
at	O
r	O
≪	O
ΛQCD	O
−	O
1	O
.	O
The	O
following	O
relation	O
was	O
derived	O
:	O
(	O
16	O
)	O
VQCD	O
(	O
r	O
)=	O
VS	O
(	O
r	O
)+	O
δEUS	O
(	O
r	O
),(	O
17	O
)	O
δEUS	O
=−	O
ig2TFNC	O
∫	O
0	O
∞	O
dte	O
−	O
iΔV	O
(	O
r	O
)	O
t	O
×〈	O
r	O
→⋅	O
E	O
→	O
a	O
(	O
t	O
)	O
φadj	O
(	O
t,0	O
)	O
abr	O
→⋅	O
E	O
→	O
b	O
(	O
0	O
)〉+	O
O	O
(	O
r3	O
)	O
.	O
VS	O
(	O
r	O
)	O
denotes	O
the	O
singlet	O
potential	O
.	O
δEUS	O
(	O
r	O
)	O
denotes	O
the	O
non-perturbative	O
contribution	O
to	O
the	O
QCD	B-Material
potential	I-Material
,	O
which	O
starts	O
at	O
O	O
(	O
ΛQCD3r2	O
)	O
in	O
the	O
multipole	B-Process
expansion	I-Process
.	O
ΔV	O
(	O
r	O
)=	O
VO	O
(	O
r	O
)−	O
VS	O
(	O
r	O
)	O
denotes	O
the	O
difference	O
between	O
the	O
octet	O
and	O
singlet	O
potentials	O
;	O
see	O
[	O
3	O
]	O
for	O
details	O
.	O
Intuitively	O
VS	O
(	O
r	O
)	O
corresponds	O
to	O
VUV	O
(	O
r	O
;	O
μf	O
)	O
and	O
δEUS	O
(	O
r	O
)	O
to	O
VIR	O
(	O
r	O
;	O
μf	O
)	O
.	O
We	O
adopt	O
dimensional	B-Process
regularization	I-Process
in	O
our	O
analysis	O
;	O
we	O
also	O
refer	O
to	O
hard	B-Process
cutoff	I-Process
schemes	I-Process
when	O
discussing	O
conceptual	O
aspects	O
.	O

It	O
has	O
recently	O
been	O
demonstrated	O
[	O
15	O
]	O
(	O
see	O
also	O
[	O
13	O
]	O
and	O
references	O
therein	O
)	O
that	O
for	O
a	O
self-dual	O
background	O
the	O
two-loop	B-Material
QED	I-Material
effective	O
action	O
takes	O
a	O
remarkably	O
simple	O
form	O
that	O
is	O
very	O
similar	O
to	O
the	O
one-loop	B-Process
action	I-Process
in	O
the	O
same	O
background	O
.	O
There	O
are	O
expectations	O
that	O
this	O
similarity	O
persists	O
at	O
higher	B-Process
loops	I-Process
,	O
and	O
therefore	O
there	O
should	O
be	O
some	O
remarkable	O
structure	O
encoded	O
in	O
the	O
all-loop	B-Process
effective	I-Process
action	I-Process
for	O
gauge	O
theories	O
.	O
In	O
the	O
supersymmetric	O
case	O
,	O
one	O
has	O
to	O
replace	O
the	O
requirement	O
of	O
self-duality	O
by	O
that	O
of	O
relaxed	O
super	O
self-duality	O
[	O
16	O
]	O
in	O
order	O
to	O
arrive	O
at	O
conclusions	O
similar	O
to	O
those	O
given	O
in	O
[	O
15	O
]	O
.	O
Further	O
progress	O
in	O
this	O
direction	O
may	O
be	O
achieved	O
through	O
the	O
analysis	B-Task
of	I-Task
N	I-Task
=	I-Task
2	I-Task
covariant	I-Task
supergraphs	I-Task
.	O
Finally	O
,	O
we	O
believe	O
that	O
the	O
results	O
of	O
this	O
Letter	O
may	O
be	O
helpful	O
in	O
the	O
context	O
of	O
the	O
conjectured	O
correspondence	O
[	O
17	O
–	O
19	O
]	O
between	O
the	O
D3-brane	B-Process
action	I-Process
in	O
AdS5	B-Material
×	I-Material
S5	I-Material
and	O
the	O
low-energy	O
action	O
for	O
N	O
=	O
4	O
SU	B-Material
(	I-Material
N	I-Material
)	I-Material
SYM	I-Material
on	O
its	O
Coulomb	B-Material
branch	I-Material
,	O
with	O
the	O
gauge	B-Material
group	I-Material
SU	B-Material
(	I-Material
N	I-Material
)	I-Material
spontaneously	O
broken	O
to	O
SU	B-Material
(	I-Material
N	I-Material
−	I-Material
1	I-Material
)×	I-Material
U	I-Material
(	I-Material
1	I-Material
)	I-Material
.	O
There	O
have	O
appeared	O
two	O
independent	O
F6	B-Task
tests	I-Task
of	I-Task
this	I-Task
conjecture	I-Task
[	O
19,20	O
]	O
,	O
with	O
conflicting	O
conclusions	O
.	O
The	O
approach	O
advocated	O
here	O
provides	O
the	O
opportunity	O
for	O
a	O
further	O
test	O
.	O

The	O
Substrate	B-Task
Induced	I-Task
Coagulation	I-Task
(	O
SIC	B-Task
)	O
coating	B-Process
process	O
provides	O
a	O
self	O
assembled	O
and	O
almost	O
binder	O
free	O
coating	O
with	O
small	O
particles	O
.	O
Most	O
research	O
so	O
far	O
has	O
been	O
used	O
to	O
coat	O
a	O
variety	O
of	O
surfaces	O
with	O
highly	O
conductive	O
carbon	B-Material
blacks	I-Material
[	O
34,35,36	O
]	O
.	O
Layers	O
deposited	O
by	O
this	O
technique	O
have	O
been	O
used	O
in	O
electromagnetic	B-Process
wave	I-Process
shielding	I-Process
,	O
in	O
the	O
metallization	B-Process
process	O
of	O
through-holes	O
in	O
printed	B-Material
wiring	I-Material
boards	I-Material
,	O
and	O
in	O
the	O
manufacture	O
of	O
conducting	B-Material
polymers	I-Material
(	O
such	O
as	O
Teflon	B-Material
)	O
[	O
37,38,39	O
]	O
.	O
An	O
advantage	O
of	O
this	O
dip-coating	B-Process
process	O
is	O
that	O
it	O
can	O
be	O
used	O
for	O
any	O
kind	O
of	O
surface	O
,	O
provided	O
the	O
substrate	O
is	O
stable	O
in	O
water	O
and	O
that	O
the	O
particles	O
used	O
for	O
the	O
coating	O
form	O
a	O
meta-stable	B-Process
dispersion	I-Process
.	O
Recently	O
,	O
a	O
non-aqueous	O
SIC	B-Process
coating	I-Process
process	O
of	O
carbon	B-Material
black	I-Material
was	O
developed	O
by	O
investigating	O
the	O
stabilities	O
of	O
non-aqueous	O
dispersions	O
[	O
36	O
]	O
.	O
These	O
dispersions	O
were	O
used	O
to	O
prepare	O
LiCoO2-composite	B-Process
electrodes	I-Process
for	O
Li-ion	B-Process
batteries	I-Process
with	O
an	O
improved	O
conductivity	O
while	O
keeping	O
the	O
content	O
of	O
active	O
battery	O
material	O
high	O
[	O
35	O
]	O
.	O

This	O
paper	O
proposes	O
a	O
sentence	B-Process
stress	I-Process
feedback	I-Process
system	I-Process
in	O
which	O
sentence	O
stress	O
prediction	O
,	O
detection	O
,	O
and	O
feedback	O
provision	O
models	O
are	O
combined	O
.	O
This	O
system	O
provides	B-Task
non-native	I-Task
learners	I-Task
with	I-Task
feedback	I-Task
on	I-Task
sentence	I-Task
stress	I-Task
errors	I-Task
so	O
that	O
they	O
can	O
improve	O
their	O
English	O
rhythm	O
and	O
fluency	O
in	O
a	O
self-study	O
setting	O
.	O
The	O
sentence	O
stress	O
feedback	O
system	O
was	O
devised	O
to	O
predict	B-Task
and	I-Task
detect	I-Task
the	I-Task
sentence	I-Task
stress	I-Task
of	O
any	O
practice	O
sentence	O
.	O
The	O
accuracy	O
of	O
the	O
prediction	B-Process
and	I-Process
detection	I-Process
models	I-Process
was	O
96.6	O
%	O
and	O
84.1	O
%	O
,	O
respectively	O
.	O
The	O
stress	B-Process
feedback	I-Process
provision	I-Process
model	I-Process
offers	O
positive	O
or	O
negative	O
stress	O
feedback	O
for	O
each	O
spoken	O
word	O
by	O
comparing	O
the	O
probability	O
of	O
the	O
predicted	O
stress	O
pattern	O
with	O
that	O
of	O
the	O
detected	O
stress	O
pattern	O
.	O
In	O
an	O
experiment	O
that	O
evaluated	O
the	O
educational	O
effect	O
of	O
the	O
proposed	O
system	O
incorporated	O
in	O
our	O
CALL	B-Material
system	I-Material
,	O
significant	O
improvements	O
in	O
accentedness	O
and	O
rhythm	O
were	O
seen	O
with	O
the	O
students	O
who	O
trained	O
with	O
our	O
system	O
but	O
not	O
with	O
those	O
in	O
the	O
control	O
group	O
.	O

Plastically	O
deformed	O
MGs	B-Material
develop	O
inhomogeneity	O
and	O
show	O
harder	O
and	O
softer	O
regions	O
[	O
16	O
]	O
.	O
While	O
this	O
could	O
in	O
principle	O
be	O
associated	O
with	O
a	O
BE	O
according	O
to	O
the	O
composite	O
model	O
,	O
a	O
MG	B-Material
provides	O
no	O
basis	O
for	O
a	O
dislocation-based	O
theory	O
.	O
The	O
search	B-Task
for	I-Task
a	I-Task
BE	I-Task
in	I-Task
plastic	I-Task
flow	I-Task
is	O
hindered	O
by	O
the	O
softening	O
of	O
MGs	B-Material
associated	O
with	O
shear-banding	B-Process
(	O
in	O
contrast	O
to	O
the	O
work-hardening	O
familiar	O
in	O
conventional	O
alloys	O
)	O
.	O
Anelastic	B-Process
deformation	I-Process
is	O
,	O
however	O
,	O
of	O
interest	O
as	O
its	O
time-dependence	O
must	O
relate	O
to	O
relaxation	O
processes	O
in	O
the	O
MG	O
structure	O
that	O
in	O
turn	O
should	O
be	O
connected	O
to	O
the	O
onset	O
of	O
plasticity	O
.	O
In	O
particular	O
,	O
anelasticity	O
may	O
offer	O
a	O
way	O
to	O
study	O
the	O
operation	O
of	O
the	O
shear	B-Process
transformation	I-Process
zones	I-Process
(	O
STZs	B-Process
[	O
17	O
])	O
often	O
used	O
to	O
interpret	O
the	O
deformation	O
of	O
MGs	B-Material
.	O
Fujita	O
et	O
al.	O
have	O
used	O
torsion	O
tests	O
to	O
observe	B-Task
anelasticity	I-Task
in	I-Task
MGs	I-Task
loaded	O
(	O
at	O
maximum	O
,	O
on	O
the	O
cylindrical	O
sample	O
surface	O
)	O
to	O
30	O
%	O
,	O
16	O
%	O
and	O
just	O
4	O
%	O
of	O
the	O
shear	O
yield	O
stress	O
τy	O
[	O
18	O
]	O
.	O
In	O
the	O
present	O
work	O
we	O
apply	O
torsion	O
to	O
MG	B-Material
samples	O
to	O
reach	O
stresses	O
up	O
to	O
24	O
%	O
of	O
τy	O
,	O
and	O
for	O
the	O
first	O
time	O
in	O
the	O
elastic	O
regime	O
investigate	O
the	O
effects	O
of	O
torque	O
reversal	O
.	O

SPS	B-Material
has	O
been	O
utilized	O
in	O
several	O
studies	O
to	O
retain	B-Process
the	I-Process
nanostructure	I-Process
of	I-Process
aluminum	I-Process
alloy	I-Process
powders	I-Process
during	I-Process
consolidation	I-Process
.	O
Ye	O
et	O
al.	O
investigated	B-Task
the	I-Task
effect	I-Task
of	I-Task
processing	I-Task
of	I-Task
cryomilled	I-Task
Al	I-Task
5083	I-Task
powder	I-Task
via	O
SPS	B-Process
[	O
13	O
]	O
.	O
X-ray	B-Process
Diffraction	I-Process
(	I-Process
XRD	I-Process
)	I-Process
grain	I-Process
size	I-Process
calculations	I-Process
before	O
and	O
after	O
SPS	B-Process
showed	O
that	O
the	O
average	O
grain	O
size	O
of	O
the	O
alloy	B-Material
only	O
increased	O
from	O
25nm	O
to	O
50nm	O
(	O
from	O
powder	O
to	O
bulk	O
state	O
)	O
.	O
Subsequently	O
,	O
the	O
hardness	O
values	O
obtained	O
through	O
nanoindentation	B-Process
for	O
specimens	O
of	O
AA5083	B-Material
produced	O
via	O
SPS	B-Material
were	O
highly	O
improved	O
in	O
comparison	O
to	O
conventional	O
sintering	O
methods	O
were	O
grain	O
coarsening	O
takes	O
place	O
on	O
a	O
larger	O
scale	O
.	O
In	O
another	O
study	O
the	O
combination	B-Process
of	I-Process
cryomilling	I-Process
and	I-Process
SPS	I-Process
of	I-Process
AA-5356	I-Process
/	I-Process
B4C	I-Process
nanocomposites	I-Process
powder	I-Process
was	O
found	O
to	O
largely	O
improve	O
the	O
microhardness	O
and	O
flexural	O
strengths	O
of	O
the	O
bulk	B-Material
nanocomposite	I-Material
.	O
Rana	O
et	O
al	O
.	O
[	O
14	O
]	O
investigated	B-Task
the	I-Task
effect	I-Task
of	I-Task
SPS	I-Task
on	I-Task
mechanically	I-Task
milled	I-Task
AA6061	I-Task
(	I-Task
Al	I-Task
–	I-Task
Mg	I-Task
–	I-Task
Si	I-Task
)	I-Task
micro-alloy	I-Task
powder	I-Task
.	O
The	O
average	O
grain	O
size	O
after	O
20h	O
of	O
milling	O
was	O
∼	O
35nm	O
and	O
increased	O
to	O
only	O
∼	O
85nm	O
after	O
processing	O
with	O
SPS	B-Material
at	O
500	O
°	O
C	O
.	O
Microhardness	O
and	O
compressive	O
tests	O
were	O
carried	O
out	O
on	O
the	O
consolidated	O
near	O
full	O
density	O
specimens	O
of	O
both	O
unmilled	B-Material
and	I-Material
milled	I-Material
powders	I-Material
and	O
the	O
results	O
showed	O
significant	O
increase	O
in	O
both	O
hardness	O
and	O
compressive	O
strengths	O
for	O
the	O
milled	B-Material
nanocrystalline	I-Material
powders	I-Material
as	O
a	O
result	O
of	O
the	O
very	O
fine	O
grain	O
size	O
.	O

A	O
principle	O
of	O
high-throughput	B-Task
materials	I-Task
science	I-Task
is	O
that	O
one	O
does	O
not	O
know	O
a	O
priori	O
where	O
the	O
value	O
of	O
the	O
data	O
lies	O
for	O
any	O
specific	O
application	O
.	O
Trends	O
and	O
insights	O
are	O
deduced	O
a	O
posteriori	O
.	O
This	O
requires	O
efficient	B-Task
interfaces	I-Task
to	I-Task
interrogate	I-Task
available	I-Task
data	I-Task
on	I-Task
various	I-Task
levels	I-Task
.	O
We	O
have	O
developed	O
a	O
simple	O
WEB-based	B-Process
API	I-Process
to	O
greatly	O
improve	B-Task
the	I-Task
accessibility	I-Task
and	I-Task
utility	I-Task
of	I-Task
the	I-Task
AFLOWLIB	I-Task
database	I-Task
[	O
14	O
]	O
to	O
the	O
scientific	O
community	O
.	O
Through	O
it	O
,	O
the	O
client	O
can	O
access	O
calculated	O
physical	B-Material
properties	I-Material
(	O
thermodynamic	B-Material
,	I-Material
crystallographic	I-Material
,	I-Material
or	I-Material
mechanical	I-Material
properties	I-Material
)	O
,	O
as	O
well	O
as	O
simulation	B-Material
provenance	I-Material
and	O
runtime	B-Material
properties	I-Material
of	O
the	O
included	O
systems	O
.	O
The	O
data	O
may	O
be	O
used	O
directly	O
(	O
e.g.	O
,	O
to	O
browse	B-Process
a	I-Process
class	I-Process
of	I-Process
materials	I-Process
with	I-Process
a	I-Process
desired	I-Process
property	I-Process
)	O
or	O
integrated	O
into	O
higher	B-Process
level	I-Process
work-flows	I-Process
.	O
The	O
interface	B-Process
also	O
allows	O
for	O
the	O
sharing	B-Process
of	I-Process
updates	I-Process
of	I-Process
data	I-Process
used	O
in	O
previous	O
published	O
works	O
,	O
e.g.	O
,	O
previously	O
calculated	O
alloy	B-Material
phase	I-Material
diagrams	I-Material
[	O
19	O
–	O
31	O
]	O
,	O
thus	O
the	O
database	O
can	O
be	O
expanded	O
systematically	O
.	O

The	O
Discrete	B-Process
Element	I-Process
Method	I-Process
applied	O
to	O
spheres	B-Material
is	O
well	O
established	O
as	O
a	O
reasonably	O
realistic	O
tool	O
,	O
in	O
a	O
wide	O
range	O
of	O
engineering	O
disciplines	O
,	O
for	O
modelling	B-Task
packing	I-Task
and	I-Task
flow	I-Task
of	I-Task
granular	I-Task
materials	I-Task
;	O
Asmar	O
et	O
al	O
.	O
[	O
8	O
]	O
describes	O
the	O
fundamentals	O
of	O
this	O
method	O
as	O
applied	O
by	O
code	O
developed	O
in-house	O
at	O
Nottingham	O
;	O
since	O
these	O
are	O
widely	O
documented	O
the	O
details	O
are	O
not	O
reproduced	O
here	O
,	O
simply	O
a	O
summary	O
.	O
It	O
applies	O
an	O
explicit	B-Process
time	I-Process
stepping	I-Process
approach	I-Process
to	O
numerically	B-Process
integrate	I-Process
the	O
translational	B-Process
and	I-Process
rotational	I-Process
motion	I-Process
of	O
each	O
particle	O
from	O
the	O
resulting	O
forces	B-Process
and	O
moments	B-Process
acting	O
on	O
them	O
at	O
each	O
timestep	O
.	O
The	O
inter-particle	B-Material
and	I-Material
particle	I-Material
wall	I-Material
contacts	I-Material
are	O
modelled	O
using	O
the	O
linear	B-Process
spring	I-Process
–	I-Process
dashpot	I-Process
–	I-Process
slider	I-Process
analogy	I-Process
.	O
Contact	B-Process
forces	I-Process
are	O
modelled	O
in	O
the	O
normal	O
and	O
tangential	O
directions	O
with	O
respect	O
to	O
the	O
line	O
connecting	O
the	O
particles	B-Material
centres	I-Material
.	O
Particle	B-Material
elastic	I-Material
stiffness	O
is	O
set	O
so	O
sphere	B-Process
“	I-Process
overlap	I-Process
”	I-Process
is	O
not	O
significant	O
and	O
moderate	O
contact	B-Process
damping	I-Process
is	O
applied	O
.	O
Particle	B-Process
cohesion	I-Process
can	O
also	O
be	O
modelled	O
but	O
is	O
assumed	O
to	O
be	O
negligible	O
in	O
the	O
current	O
study	O
.	O
The	O
translational	B-Process
and	I-Process
rotational	I-Process
motion	I-Process
of	O
each	O
particle	B-Material
is	O
modelled	O
using	O
a	O
half	B-Process
step	I-Process
leap-frog	I-Process
Verlet	I-Process
numerical	I-Process
integration	I-Process
scheme	I-Process
to	O
update	O
particle	B-Material
positions	I-Material
and	O
velocities	B-Material
.	O
Near-neighbour	B-Process
lists	I-Process
are	O
used	O
to	O
increase	O
the	O
computational	O
efficiency	O
of	O
determining	O
particle	B-Material
contacts	I-Material
and	O
a	O
zoning	B-Process
method	I-Process
is	O
used	O
each	O
time	O
the	O
list	O
is	O
composed	O
;	O
that	O
is	O
the	O
system	O
is	O
divided	O
into	O
cubic	O
regions	O
,	O
each	O
particle	B-Material
centre	I-Material
is	O
within	O
one	O
zone	O
,	O
and	O
potential	O
contacting	B-Material
particles	I-Material
are	O
within	O
the	O
same	O
or	O
next-door	O
neighbour	O
zones	O
.	O
Full	O
details	O
are	O
given	O
in	O
Asmar	O
et	O
al	O
.	O
[	O
8	O
]	O
.	O

In	O
this	O
paper	O
,	O
crystal	B-Process
plasticity	I-Process
model	I-Process
,	O
in	O
combination	O
with	O
XFEM	B-Process
,	O
has	O
been	O
applied	O
to	O
study	B-Task
cyclic	I-Task
deformation	I-Task
and	I-Task
fatigue	I-Task
crack	I-Task
growth	I-Task
in	O
a	O
nickel-based	B-Material
superalloy	I-Material
LSHR	I-Material
(	O
Low	B-Material
Solvus	I-Material
High	I-Material
Refractory	I-Material
)	O
at	O
high	O
temperature	O
.	O
The	O
first	O
objective	O
of	O
this	O
research	O
was	O
to	O
develop	B-Task
and	I-Task
evaluate	I-Task
a	I-Task
RVE-based	I-Task
finite	I-Task
element	I-Task
model	I-Task
with	O
the	O
incorporation	O
of	O
a	O
realistic	O
material	O
microstructure	O
.	O
The	O
second	O
objective	O
of	O
this	O
work	O
was	O
to	O
determine	B-Task
the	I-Task
parameters	I-Task
of	I-Task
a	I-Task
crystal	I-Task
plasticity	I-Task
constitutive	I-Task
model	I-Task
to	O
describe	O
the	O
cyclic	B-Process
deformation	I-Process
behaviour	O
of	O
the	O
material	O
by	O
using	O
a	O
user-defined	B-Material
material	I-Material
subroutine	I-Material
(	O
UMAT	B-Material
)	O
interfaced	O
with	O
the	O
finite	O
element	O
package	O
ABAQUS	B-Material
.	O
The	O
model	O
parameters	O
were	O
calibrated	O
from	O
extensive	O
finite	B-Process
element	I-Process
analyses	I-Process
to	O
fit	O
the	O
monotonic	B-Material
,	I-Material
stress	I-Material
relaxation	I-Material
and	I-Material
cyclic	I-Material
test	I-Material
data	I-Material
.	O
The	O
third	O
objective	O
was	O
to	O
predict	B-Task
crack	I-Task
growth	I-Task
by	O
combining	O
the	O
XFEM	B-Process
technique	I-Process
and	O
the	O
calibrated	B-Process
crystal	I-Process
plasticity	I-Process
UMAT	I-Process
,	O
for	O
which	O
accumulated	O
plastic	B-Process
strain	I-Process
was	O
used	O
as	O
the	O
fracture	B-Process
criterion	I-Process
.	O

This	O
paper	O
has	O
highlighted	B-Task
a	I-Task
band	I-Task
of	I-Task
frequencies	I-Task
,	O
outside	O
the	O
conventional	O
operation	O
range	O
,	O
and	O
close	O
to	O
electrical	B-Process
resonance	I-Process
of	I-Process
an	I-Process
eddy	I-Process
current	I-Process
probe	I-Process
,	O
where	O
the	O
magnitude	O
of	O
impedance	B-Material
SNR	I-Material
reaches	O
a	O
peak	O
.	O
The	O
SNR	O
of	O
scans	O
of	O
three	O
slots	O
of	O
varying	O
depth	O
were	O
enhanced	O
by	O
a	O
factor	O
of	O
up	O
to	O
3.7	O
,	O
from	O
the	O
SNR	O
measured	O
at	O
1MHz	O
.	O
This	O
is	O
a	O
result	O
of	O
a	O
defect-decoupling	B-Process
resonance-shift	I-Process
effect	I-Process
and	O
is	O
referred	O
to	O
as	O
the	O
near	B-Process
electrical	I-Process
resonance	I-Process
signal	I-Process
enhancement	I-Process
(	O
NERSE	B-Process
)	O
phenomenon	O
.	O
NERSE	O
frequency	O
operation	O
has	O
significant	O
potential	O
for	O
ECT	B-Process
inspection	I-Process
,	O
and	O
opens	O
up	O
a	O
range	O
of	O
investigative	O
possibilities	O
.	O
Within	O
this	O
investigation	O
,	O
only	O
the	O
magnitude	O
of	O
the	O
electrical	B-Material
impedance	I-Material
has	O
been	O
analyzed	O
.	O
An	O
immediate	O
extension	O
of	O
this	O
investigation	O
will	O
be	O
to	O
consider	O
phase	O
information	O
,	O
and	O
determine	O
whether	O
a	O
similar	O
exploitable	O
NERSE	B-Process
effect	O
exists	O
.	O

There	O
are	O
a	O
number	O
of	O
avenues	O
to	O
explore	O
for	O
future	O
work	O
,	O
in	O
particular	O
the	O
use	B-Task
of	I-Task
other	I-Task
time	I-Task
–	I-Task
frequency	I-Task
analysis	I-Task
methods	I-Task
.	O
The	O
STFT	B-Process
spectrogram	I-Process
was	O
utilised	O
here	O
,	O
as	O
it	O
is	O
the	O
simplest	O
to	O
implement	O
.	O
Whilst	O
all	O
of	O
the	O
echoes	O
could	O
be	O
clearly	O
resolved	O
in	O
both	O
time	O
and	O
frequency	O
,	O
the	O
spectrogram	O
suffers	O
from	O
a	O
fixed	B-Material
resolution	I-Material
,	O
i.e.	O
an	O
increase	O
of	O
time	B-Material
resolution	I-Material
necessarily	O
leads	O
to	O
a	O
decrease	O
in	O
frequency	B-Material
resolution	I-Material
.	O
Other	O
methods	O
of	O
time	B-Process
–	I-Process
frequency	I-Process
analysis	I-Process
,	O
such	O
as	O
discrete	B-Process
wavelet	I-Process
analysis	I-Process
,	O
benefit	O
from	O
advantage	O
of	O
multi-resolution	B-Process
analysis	I-Process
,	O
which	O
offers	O
improved	O
temporal	B-Material
resolution	I-Material
of	O
the	O
high	O
frequency	O
components	O
,	O
and	O
frequency	B-Material
resolution	I-Material
of	O
the	O
low	B-Material
frequency	I-Material
components	I-Material
[	O
25,18,19	O
]	O
.	O
Also	O
,	O
whilst	O
the	O
current	O
work	O
has	O
utilised	O
SH	B-Material
waves	I-Material
that	O
are	O
generated	O
by	O
EMATs	B-Process
,	O
the	O
physics	O
that	O
describes	O
the	O
pulsed	B-Material
array	I-Material
system	I-Material
is	O
universal	O
to	O
other	O
types	O
of	O
waves	O
.	O
Future	O
work	O
will	O
include	O
demonstrating	B-Task
this	I-Task
phenomenon	I-Task
with	I-Task
a	I-Task
number	I-Task
of	I-Task
other	I-Task
systems	I-Task
,	O
for	O
example	O
using	O
longitudinal	B-Material
ultrasonic	I-Material
waves	I-Material
or	O
electromagnetic	B-Material
waves	I-Material
.	O

Global	B-Process
optimisation	I-Process
algorithms	I-Process
are	O
used	O
in	O
this	O
study	O
to	O
solve	B-Task
the	I-Task
optimisation	I-Task
problem	I-Task
as	O
they	O
are	O
known	O
to	O
be	O
efficient	O
in	O
incorporating	B-Process
statistical	I-Process
information	I-Process
and	O
dealing	O
with	O
complicated	O
objective	B-Process
functions	I-Process
that	O
have	O
multiple	O
local	B-Material
minima	I-Material
/	I-Material
maxima	I-Material
.	O
The	O
genetic	B-Process
algorithm	I-Process
(	O
GA	B-Process
)	O
is	O
such	O
a	O
global	B-Process
optimisation	I-Process
technique	I-Process
that	O
mimics	B-Process
biological	I-Process
evolution	I-Process
processes	I-Process
and	O
is	O
used	O
in	O
this	O
particular	O
study	O
.	O
The	O
algorithm	O
starts	O
with	O
a	O
random	B-Process
selection	I-Process
of	I-Process
a	I-Process
population	I-Process
from	O
the	O
decision	B-Material
variable	I-Material
domain	I-Material
(	O
X	B-Material
)	O
.	O
The	O
genetic	B-Process
algorithm	I-Process
repeatedly	O
modifies	O
this	O
population	O
.	O
At	O
each	O
step	O
,	O
the	O
algorithm	O
selects	B-Process
a	I-Process
group	I-Process
of	I-Process
individual	I-Process
values	I-Process
from	O
the	O
population	B-Material
(	O
parent	B-Material
)	O
which	O
are	O
evolved	O
through	O
crossover	B-Process
or	O
mutation	B-Process
to	O
produce	O
members	O
of	O
the	O
next	O
generation	O
.	O
This	O
process	O
is	O
repeated	O
for	O
several	O
generations	O
until	O
an	O
optimum	O
solution	O
is	O
reached	O
.	O
See	O
[	O
19	O
]	O
for	O
a	O
fuller	O
description	O
of	O
the	O
GA	B-Process
.	O

In	O
the	O
Total	B-Process
Focusing	I-Process
Method	I-Process
(	O
TFM	B-Process
)	O
the	O
beam	O
is	O
synthetically	O
focused	O
at	O
every	O
point	O
in	O
the	O
target	O
region	O
[	O
7	O
]	O
as	O
follows	O
.	O
After	O
obtaining	O
the	O
FMC	B-Material
data	I-Material
,	O
the	O
target	O
region	O
,	O
which	O
is	O
in	O
the	O
x	B-Material
–	I-Material
z	I-Material
plane	I-Material
in	O
2D	O
(	O
Fig.	O
1	O
)	O
,	O
is	O
discretized	B-Process
into	I-Process
a	I-Process
grid	I-Process
.	O
The	O
signals	O
from	O
all	O
elements	O
in	O
the	O
array	O
are	O
then	O
summed	B-Process
to	O
synthesize	B-Process
a	I-Process
focus	I-Process
at	I-Process
every	I-Process
point	I-Process
in	I-Process
this	I-Process
grid	I-Process
.	O
Linear	B-Process
interpolation	I-Process
of	O
the	O
time	O
domain	O
signals	O
is	O
necessary	O
since	O
they	O
are	O
discretely	B-Process
sampled	I-Process
.	O
The	O
intensity	O
of	O
the	O
TFM	B-Material
image	I-Material
ITFM	O
at	O
any	O
point	O
(	O
x,z	O
)	O
is	O
given	O
by	O
:(	O
10	O
)	O
ITFM	O
(	O
x,z	O
)=|∑	O
HTR	O
(	O
1c	O
((	O
xT	O
−	O
x	O
)	O
2	O
+	O
z2	O
+(	O
xR	O
−	O
x	O
)	O
2	O
+	O
z2	O
))|	O
forallT,Rwhere	O
HTR	O
(	O
t	O
)	O
is	O
the	O
Hilbert	B-Process
transform	I-Process
of	O
a	O
signal	O
uTR	O
(	O
t	O
)	O
in	O
the	O
FMC	B-Material
data	I-Material
,	O
xT	O
is	O
the	O
x-position	O
of	O
the	O
transmitting	B-Material
element	I-Material
(	O
T	B-Material
)	O
and	O
xR	O
is	O
the	O
x-position	O
of	O
the	O
receiving	B-Material
element	I-Material
(	O
R	B-Material
)	O
.	O
Note	O
that	O
the	O
z-position	O
of	O
all	O
elements	O
is	O
zero	O
(	O
Fig.	O
3a	O
)	O
.	O
The	O
summation	B-Process
is	O
carried	O
out	O
for	O
all	O
possible	O
transmitter	B-Material
–	I-Material
receiver	I-Material
pairs	I-Material
and	O
therefore	O
uses	O
all	O
the	O
information	O
captured	O
with	O
FMC	B-Material
.	O
This	O
algorithm	O
is	O
referred	O
to	O
as	O
‘	O
conventional	O
TFM’	O
in	O
this	O
paper	O
.	O

It	O
is	O
known	O
that	O
as	O
the	O
temperature	O
of	O
the	O
sample	O
rises	O
,	O
the	O
Lorentz	B-Process
mechanism	I-Process
remains	O
dominant	O
until	O
Tc	O
of	O
steel	B-Material
is	O
reached	O
(	O
770	O
°	O
C	O
for	O
a	O
low	B-Material
carbon	I-Material
steel	I-Material
)	O
,	O
when	O
the	O
magnetostrictive	B-Process
mechanism	I-Process
becomes	O
more	O
efficient	O
[	O
15	O
]	O
.	O
Previously	O
this	O
has	O
been	O
thought	O
due	O
to	O
a	O
thin	O
ferromagnetic	B-Material
oxide	I-Material
layer	O
on	O
the	O
sample	O
surface	O
,	O
the	O
surface	O
being	O
cooler	O
than	O
the	O
bulk	O
of	O
the	O
material	O
[	O
16,17	O
]	O
.	O
This	O
layer	O
concentrates	O
the	O
magnetic	B-Material
field	I-Material
,	O
increasing	O
generation	B-Process
efficiency	I-Process
.	O
Recent	O
studies	O
also	O
show	O
that	O
rearrangement	O
of	O
the	O
magnetic	O
moments	O
from	O
ordered	O
domains	O
to	O
a	O
disordered	O
state	O
at	O
a	O
magnetic	B-Process
phase	I-Process
transition	I-Process
lowers	O
the	O
magnetostrictive	O
constant	O
.	O
This	O
ferromagnetic	B-Process
to	I-Process
paramagnetic	I-Process
transition	I-Process
is	O
accompanied	O
by	O
large	O
changes	O
in	O
the	O
efficiency	O
of	O
electromagnetic	B-Process
ultrasound	I-Process
generation	I-Process
leading	O
to	O
the	O
use	O
of	O
EMATs	B-Material
as	O
a	O
method	O
of	O
studying	B-Task
phase	I-Task
transitions	I-Task
in	O
magnetic	B-Material
alloys	I-Material
[	O
18	O
]	O
.	O

Shear	B-Material
horizontal	I-Material
(	I-Material
SH	I-Material
)	I-Material
ultrasound	I-Material
waves	I-Material
are	O
guided	B-Material
waves	I-Material
(	O
they	O
have	O
propagation	B-Process
properties	O
affected	O
by	O
the	O
geometry	O
of	O
the	O
propagation	O
medium	O
)	O
,	O
with	O
symmetric	O
and	O
anti-symmetric	O
modes	O
;	O
phase	O
and	O
group	O
speeds	O
are	O
dependent	O
on	O
frequency	O
,	O
sample	O
thickness	O
,	O
and	O
the	O
bulk	B-Process
shear	I-Process
wave	I-Process
speed	I-Process
[	O
11,12	O
]	O
.	O
The	O
properties	O
of	O
the	O
different	O
modes	O
can	O
be	O
very	O
useful	O
,	O
such	O
as	O
in	O
thickness	O
measurement	O
[	O
13	O
]	O
,	O
but	O
in	O
this	O
case	O
they	O
are	O
a	O
complication	O
.	O
SH0	B-Material
has	O
a	O
thickness	O
independent	O
speed	O
,	O
equal	O
to	O
the	O
shear	B-Process
wave	I-Process
speed	I-Process
,	O
and	O
is	O
non-dispersive	O
(	O
the	O
phase	B-Process
and	I-Process
group	I-Process
speed	I-Process
are	O
equal	O
to	O
the	O
shear	B-Process
wave	I-Process
speed	I-Process
for	O
all	O
frequencies	O
)	O
.	O
The	O
oscillation	B-Process
direction	O
of	O
SH	B-Process
ultrasound	I-Process
is	O
in	O
the	O
plane	O
of	O
the	O
surface	O
where	O
the	O
wave	B-Material
was	O
generated	O
,	O
and	O
perpendicular	O
to	O
the	O
propagation	B-Process
direction	O
,	O
as	O
shown	O
in	O
Fig.	O
1	O
,	O
with	O
respect	O
to	O
a	O
reference	O
interface	O
,	O
which	O
is	O
typically	O
a	O
sample	O
surface	O
.	O
Under	O
certain	O
conditions	O
,	O
such	O
as	O
over	O
short	O
propagation	O
distances	O
,	O
SH	B-Material
waves	I-Material
can	O
be	O
treated	O
as	O
bulk	B-Material
waves	I-Material
.	O

Volume	B-Task
EM	I-Task
can	O
be	O
performed	O
using	O
transmission	B-Process
or	O
scanning	B-Material
electron	I-Material
microscopes	I-Material
.	O
Each	O
approach	O
has	O
its	O
own	O
strengths	O
and	O
weaknesses	O
,	O
and	O
the	O
choice	O
is	O
dependant	O
on	O
the	O
required	O
lateral	O
(	O
x	O
,	O
y	O
)	O
and	O
axial	O
(	O
z	O
)	O
resolution	O
,	O
and	O
the	O
size	O
of	O
the	O
structure	O
of	O
interest	O
.	O
Historically	O
,	O
transmission	B-Material
electron	I-Material
microscopy	I-Material
(	O
TEM	B-Material
)	O
was	O
the	O
tool	O
of	O
choice	O
for	O
ultrastructural	B-Task
examination	I-Task
of	I-Task
biomedical	I-Task
specimens	I-Task
at	O
sub-nanometer	O
resolution	O
.	O
However	O
,	O
for	O
many	O
cell	B-Task
biology	I-Task
studies	I-Task
structural	O
resolution	O
is	O
actually	O
limited	O
by	O
the	O
deposition	B-Process
of	I-Process
heavy	I-Process
metals	I-Process
onto	O
membranes	O
during	O
sample	O
preparation	O
.	O
In	O
addition	O
,	O
voxel	O
dimensions	O
may	O
only	O
need	O
to	O
be	O
half	O
that	O
of	O
the	O
smallest	O
expected	O
feature	O
of	O
interest	O
(	O
Briggman	O
and	O
Bock	O
,	O
2012	O
)	O
.	O
Advances	O
in	O
scanning	B-Process
electron	I-Process
microscopy	I-Process
(	I-Process
SEM	I-Process
)	I-Process
technology	I-Process
are	O
now	O
driving	O
a	O
paradigm	O
shift	O
in	O
electron	B-Task
imaging	I-Task
.	O
SEMs	B-Process
with	O
field	B-Material
emission	I-Material
electron	I-Material
sources	I-Material
and	O
high	B-Material
efficiency	I-Material
electron	I-Material
detectors	I-Material
can	O
achieve	O
lateral	O
resolutions	O
in	O
the	O
order	O
of	O
3nm	O
,	O
allowing	O
visualisation	B-Task
of	I-Task
structures	I-Task
such	O
as	O
synaptic	B-Material
vesicles	I-Material
and	O
membranes	B-Material
(	O
De	O
Winter	O
et	O
al.	O
,	O
2009	O
;	O
Knott	O
et	O
al.	O
,	O
2008	O
;	O
Vihinen	O
et	O
al.	O
,	O
2013	O
;	O
Villinger	O
et	O
al.	O
,	O
2012	O
)	O
,	O
though	O
resolving	B-Task
individual	I-Task
leaflets	I-Task
of	I-Task
membrane	I-Task
bilayers	I-Task
remains	O
a	O
challenge	O
(	O
Vihinen	O
et	O
al.	O
,	O
2013	O
)	O
.	O
The	O
use	O
of	O
low	B-Material
beam	I-Material
energies	I-Material
also	O
limits	O
the	O
interaction	B-Process
volume	I-Process
,	O
enhancing	B-Process
axial	I-Process
resolution	I-Process
(	O
Hennig	O
and	O
Denk	O
,	O
2007	O
)	O
.	O
In	O
this	O
review	O
,	O
volume	B-Task
imaging	I-Task
in	O
both	O
transmission	B-Process
and	I-Process
scanning	I-Process
EMs	I-Process
will	O
be	O
explored	O
,	O
moving	O
from	O
traditional	B-Process
manual	I-Process
techniques	I-Process
,	O
through	O
to	O
the	O
latest	B-Process
systems	I-Process
where	O
aspects	O
of	O
both	O
sample	B-Task
preparation	I-Task
and	O
imaging	B-Task
have	O
been	O
automated	O
.	O

In	O
this	O
paper	O
,	O
we	O
present	O
our	O
experimental	B-Task
observations	I-Task
on	I-Task
how	I-Task
solvents	I-Task
can	I-Task
vary	I-Task
the	I-Task
TPA	I-Task
and	I-Task
TPF	I-Task
properties	I-Task
of	I-Task
fluorescent	I-Task
rhodamine	I-Task
(	I-Task
Rh	I-Task
)	I-Task
dyes	I-Task
Rh6G	B-Material
,	O
RhB	B-Material
and	O
Rh101	B-Material
.	O
Rhodamines	B-Material
are	O
well-known	O
xanthenes	B-Material
dyes	I-Material
,	O
which	O
have	O
been	O
extensively	O
used	O
for	O
many	O
widespread	O
applications	O
in	O
single-molecule	B-Process
detection	I-Process
[	O
24	O
]	O
,	O
DNA-sequence	B-Process
determination	I-Process
[	O
25	O
]	O
,	O
fluorescence	B-Process
labelling	I-Process
[	O
26	O
]	O
,	O
etc	O
.	O
due	O
to	O
their	O
strong	O
fluorescence	O
over	O
the	O
visible	O
spectral	O
region	O
.	O
Molecular	O
geometries	O
of	O
rhodamine	B-Material
dyes	I-Material
are	O
well-known	O
[	O
27,28	O
]	O
and	O
indicate	O
that	O
all	O
the	O
structures	O
are	O
non-centrosymmetric	O
.	O
In	O
general	O
,	O
for	O
centrosymmetric	B-Material
molecules	I-Material
,	O
TPA	B-Process
is	O
forbidden	O
when	O
tuned	O
to	O
the	O
transitions	O
at	O
one-half	O
of	O
the	O
excitation	O
frequencies	O
.	O
However	O
,	O
for	O
non-centrosymmetric	B-Material
molecules	I-Material
due	O
to	O
symmetry	B-Process
relaxations	I-Process
,	O
the	O
single-photon	B-Process
absorption	I-Process
(	O
SPA	B-Process
)	O
peaks	O
and	O
TPA	B-Process
peaks	I-Process
may	O
coincide	O
.	O
So	O
we	O
set	O
our	O
primary	O
aim	O
to	O
find	O
the	O
effect	O
of	O
solvent	O
polarity	O
on	O
the	O
correlation	O
of	O
SPA	B-Process
and	O
TPA	B-Process
peaks	O
for	O
all	O
the	O
dyes	B-Material
.	O

The	O
other	O
methods	O
for	O
enhancement	B-Task
of	I-Task
photocatalytic	I-Task
activity	I-Task
are	O
grafting	O
co-catalysts	B-Material
.	O
There	O
are	O
two	O
kinds	O
of	O
co-catalysts	O
in	O
terms	O
of	O
its	O
function	O
:	O
one	O
is	O
for	O
separation	B-Process
of	I-Process
electrons	I-Process
and	O
the	O
other	O
is	O
for	O
separation	B-Process
of	I-Process
holes	I-Process
.	O
The	O
former	O
representative	O
co-catalysts	B-Material
are	O
Pt	B-Material
,	O
Fe3	B-Material
+	I-Material
,	O
and	O
Cu2	B-Material
+	I-Material
[	O
9	O
–	O
12	O
]	O
.	O
It	O
was	O
reported	O
that	O
Fe3	B-Material
+	I-Material
and	O
Cu2	B-Material
+	I-Material
were	O
grafted	O
as	O
amorphous	B-Material
oxide	I-Material
cluster	I-Material
[	O
9,10	O
]	O
,	O
and	O
reduced	O
into	O
Fe2	B-Material
+	I-Material
and	O
Cu	B-Material
+	I-Material
by	O
receiving	O
one	O
electron	B-Material
,	O
respectively	O
[	O
11,12	O
]	O
.	O
The	O
reduced	B-Material
metal	I-Material
oxide	I-Material
cluster	I-Material
with	O
reduced	B-Material
ions	I-Material
could	O
return	O
into	O
the	O
original	O
state	O
by	O
giving	O
more	O
than	O
one	O
electron	B-Material
to	O
molecular	O
oxygen	B-Material
.	O
The	O
latter	O
ones	O
are	O
CoOx	B-Material
,	O
CoPi	B-Material
(	O
CoPOx	B-Material
)	O
,	O
IrOx	B-Material
,	O
and	O
RuOx	B-Material
which	O
are	O
used	O
for	O
water	B-Process
oxidation	I-Process
,	O
among	O
which	O
CoPi	B-Material
is	O
reported	O
to	O
be	O
the	O
most	O
effective	O
co-catalyst	B-Material
for	O
water	B-Process
oxidation	I-Process
[	O
13	O
]	O
.	O
However	O
,	O
there	O
were	O
few	O
reports	O
concerning	O
co-grafting	B-Process
effects	O
on	O
photocatalytic	B-Process
activity	I-Process
especially	O
in	O
gaseous	B-Process
phase	I-Process
.	O
We	O
expected	O
that	O
by	O
co-grafting	B-Process
of	O
both	O
co-catalysts	B-Material
for	O
separations	O
of	O
electrons	B-Material
and	O
holes	B-Material
,	O
photocatalytic	B-Process
activity	I-Process
in	O
gaseous	B-Process
phase	I-Process
would	O
be	O
further	O
enhanced	O
.	O
Moreover	O
,	O
complex	O
of	O
BiVO4	B-Material
with	O
the	O
other	O
materials	O
of	O
p-type	B-Material
semiconductor	I-Material
is	O
also	O
effective	O
for	O
enhancing	O
photocatalytic	B-Process
activity	I-Process
.	O

An	O
obvious	O
metric	O
to	O
measure	O
the	O
monitoring	O
performance	O
between	O
the	O
different	O
conditions	O
would	O
be	O
to	O
compare	B-Task
how	I-Task
many	I-Task
clicks	I-Task
the	I-Task
users	I-Task
made	I-Task
in	I-Task
average	I-Task
for	I-Task
each	I-Task
condition	I-Task
.	O
Furthermore	O
of	O
interest	O
are	O
the	O
buffer	B-Material
values	O
of	O
the	O
respective	O
buffers	B-Material
at	O
the	O
time	O
of	O
the	O
user	O
's	O
interaction	O
with	O
the	O
simulation	B-Process
(	O
e.g.	O
,	O
the	O
input	B-Material
buffer	I-Material
of	O
a	O
certain	O
machine	O
at	O
the	O
time	O
of	O
refilling	O
it	O
)	O
.	O
A	O
relatively	O
high	O
average	O
buffer	O
value	O
can	O
e.g.	O
signify	O
that	O
the	O
users	O
do	O
not	O
trust	O
that	O
the	O
respective	O
mode	O
of	O
process	O
monitoring	O
conveys	O
the	O
need	O
for	O
interaction	O
in	O
time	O
,	O
leading	O
the	O
users	O
to	O
switching	O
their	O
attention	O
to	O
the	O
process	O
simulation	O
in	O
regular	O
intervals	O
,	O
and	O
performing	O
interactions	O
just	O
in	O
case	O
.	O
A	O
low	O
average	O
buffer	O
can	O
,	O
on	O
the	O
other	O
hand	O
,	O
signify	O
that	O
the	O
users	O
rely	O
on	O
the	O
respective	O
conditions’	O
ability	O
to	O
signal	O
interaction	O
needs	O
.	O
On	O
the	O
other	O
hand	O
,	O
if	O
e.g.	O
an	O
input	O
buffer	O
had	O
already	O
been	O
completely	O
depleted	O
at	O
the	O
time	O
of	O
intervention	O
,	O
this	O
may	O
signify	O
that	O
the	O
respective	O
condition	O
has	O
failed	O
to	O
inform	O
the	O
users	O
in	O
time	O
.	O
In	O
many	O
cases	O
,	O
participants	O
used	O
double	O
clicks	O
for	O
their	O
interactions	O
,	O
while	O
a	O
single	O
click	O
would	O
have	O
been	O
sufficient	O
,	O
a	O
fact	O
that	O
was	O
perhaps	O
not	O
communicated	O
clearly	O
enough	O
to	O
the	O
participants	O
.	O
Therefore	O
,	O
if	O
several	O
clicks	O
were	O
performed	O
directly	O
one	O
after	O
another	O
,	O
only	O
the	O
first	O
click	O
was	O
taken	O
into	O
account	O
.	O

The	O
first-principles	B-Task
calculations	I-Task
are	O
performed	O
using	O
the	O
Cambridge	B-Material
Serial	I-Material
Total	I-Material
Energy	I-Material
Package	I-Material
(	O
CASTEP	B-Material
)	O
[	O
21	O
]	O
which	O
implements	O
the	O
plane-wave	B-Process
pseudopotential	I-Process
DFT	I-Process
method	I-Process
.	O
The	O
exchange	B-Process
correlation	I-Process
functional	I-Process
is	O
approximated	O
using	O
the	O
generalized	B-Process
gradient	I-Process
approximation	I-Process
(	O
PBE-GGA	B-Process
)	O
[	O
22	O
]	O
,	O
and	O
the	O
electron	B-Material
–	O
ion	B-Material
interactions	O
are	O
described	O
by	O
Vanderbilt-type	B-Process
ultrasoft	I-Process
pseudopotentials	I-Process
[	O
23	O
]	O
.	O
The	O
plane	B-Material
wave	I-Material
basis	O
set	O
is	O
truncated	O
at	O
a	O
cutoff	O
of	O
400eV	O
,	O
and	O
the	O
Brillouin-zone	B-Process
sampling	I-Process
was	O
performed	O
using	O
the	O
Monkhorst-Pack	B-Process
scheme	I-Process
with	O
a	O
k-point	B-Process
spacing	I-Process
in	O
reciprocal	O
space	O
of	O
0.04Å	O
−	O
1	O
.	O
Tests	O
show	O
that	O
these	O
computational	O
parameters	O
give	O
results	O
that	O
are	O
sufficiently	O
accurate	O
for	O
present	O
purposes	O
.	O
The	O
ferromagnetism	B-Process
of	O
nickel	B-Material
is	O
accounted	O
for	O
by	O
performing	O
all	O
calculations	O
using	O
spin	B-Process
polarization	I-Process
,	O
starting	O
at	O
a	O
ferromagnetic	O
initial	O
configuration	O
and	O
relaxing	O
towards	O
its	O
ground	O
state	O
.	O
However	O
,	O
for	O
all	O
compositions	O
considered	O
,	O
the	O
ground	O
state	O
electronic	O
structure	O
of	O
each	O
alloy	B-Material
is	O
found	O
to	O
exhibit	O
only	O
very	O
weak	O
ferromagnetism	B-Process
,	O
and	O
the	O
effect	O
is	O
not	O
thought	O
to	O
influence	O
their	O
phase	O
stability	O
.	O
Table	O
1	O
shows	O
the	O
calculated	O
equilibrium	O
lattice	O
constants	O
of	O
the	O
η	O
phase	O
at	O
various	O
Ti	B-Material
concentrations	O
,	O
using	O
partially	O
ordered	O
ηP	B-Material
structures	I-Material
.	O
The	O
change	O
in	O
lattice	O
constant	O
upon	O
Ti	B-Process
alloying	I-Process
is	O
relatively	O
small	O
,	O
but	O
can	O
be	O
related	O
to	O
the	O
∼	O
10	O
%	O
larger	O
covalent	O
radius	O
of	O
Ti	O
.	O
The	O
calculated	O
lattice	O
constants	O
are	O
in	O
good	O
agreement	O
with	O
the	O
experimental	O
values	O
,	O
which	O
relate	O
to	O
an	O
alloy	B-Material
with	O
a	O
Al	B-Material
/	O
Ti	B-Material
ratio	O
of	O
∼	O
2.75	O
.	O

When	O
we	O
formulate	O
the	O
downscaling	O
problem	O
as	O
a	O
multi-objective	B-Process
optimization	I-Process
problem	O
,	O
we	O
face	O
,	O
however	O
,	O
the	O
following	O
problems	O
.	O
Minimizing	O
the	O
sum	O
of	O
different	O
objectives	O
is	O
problematic	O
,	O
since	O
they	O
may	O
have	O
different	O
units	O
and	O
ranges	O
.	O
Even	O
with	O
an	O
appropriate	O
scaling	B-Process
procedure	I-Process
there	O
is	O
a	O
risk	O
of	O
treating	O
the	O
objectives	O
unequally	O
or	O
getting	O
trapped	O
in	O
a	O
local	O
minimum	O
.	O
Firstly	O
,	O
we	O
can	O
never	O
know	O
,	O
what	O
is	O
the	O
minimum	O
value	O
of	O
each	O
objective	O
that	O
can	O
be	O
achieved	O
by	O
the	O
regression	B-Process
.	O
Thus	O
,	O
designing	O
an	O
appropriate	O
scaling	B-Process
procedure	I-Process
is	O
difficult	O
and	O
one	O
would	O
need	O
to	O
decide	O
on	O
the	O
relative	O
importance	O
of	O
the	O
different	O
objectives	O
in	O
advance	O
.	O
Secondly	O
,	O
adding	O
multiple	O
,	O
conflicting	O
objectives	O
very	O
likely	O
results	O
in	O
a	O
fitness	O
function	O
with	O
multiple	O
local	O
minima	O
,	O
which	O
makes	O
optimization	B-Process
more	O
difficult	O
.	O
To	O
avoid	O
these	O
problems	O
,	O
we	O
have	O
implemented	O
fitness	O
calculation	O
according	O
to	O
the	O
Strength	B-Process
Pareto	I-Process
Evolutionary	I-Process
Algorithm	I-Process
(	O
SPEA	B-Process
)	O
by	O
Zitzler	O
and	O
Thiele	O
(	O
1999	O
)	O
,	O
instead	O
of	O
using	B-Process
a	I-Process
single	I-Process
(	I-Process
weighted	I-Process
)	I-Process
fitness	I-Process
or	I-Process
cost	I-Process
function	I-Process
.	O
Approaches	O
for	O
multi-objective	B-Process
optimization	I-Process
like	O
SPEA	B-Process
are	O
widely	O
used	O
in	O
evolutionary	B-Task
computation	I-Task
.	O
In	O
SPEA	B-Process
the	O
fitness	B-Process
calculation	I-Process
during	O
the	O
fitting	O
procedure	O
is	O
based	O
on	O
an	O
intercomparison	O
of	O
the	O
different	O
models	O
.	O
Further	O
,	O
a	O
finite	O
set	O
of	O
so	O
called	O
Pareto	B-Process
optimal	I-Process
models	I-Process
(	O
downscaling	B-Process
rules	I-Process
)	O
is	O
returned	O
.	O

The	O
main	O
objective	O
of	O
this	O
manuscript	O
is	O
to	O
present	B-Task
and	I-Task
discuss	I-Task
the	I-Task
application	I-Task
of	I-Task
SLAMM	I-Task
to	I-Task
the	I-Task
New	I-Task
York	I-Task
coast	I-Task
.	O
Although	O
the	O
base	O
analysis	O
considers	O
a	O
range	O
of	O
different	O
possible	O
SLR	B-Process
scenarios	O
,	O
the	O
effects	O
of	O
various	O
sources	O
of	O
uncertainties	O
such	O
as	O
input	O
parameters	O
and	O
driving	O
data	O
are	O
not	O
accounted	O
for	O
.	O
In	O
addition	O
,	O
refined	O
and	O
site-specific	O
data	O
are	O
often	O
not	O
available	O
requiring	O
the	O
use	O
of	O
regional	O
data	O
collected	O
from	O
literature	O
and	O
professional	O
judgement	O
in	O
order	O
to	O
run	O
the	O
model	O
.	O
To	O
ignore	O
the	O
effects	O
of	O
these	O
uncertainties	O
on	O
predictions	O
may	O
make	O
interpretation	O
of	O
the	O
results	O
and	O
subsequent	O
decision	O
making	O
misleading	O
since	O
the	O
likelihood	O
and	O
probabilities	O
of	O
predicted	O
outcomes	O
would	O
be	O
unknown	O
.	O
A	O
unique	O
capability	O
of	O
the	O
current	O
version	O
of	O
SLAMM	B-Process
is	O
the	O
ability	O
to	O
aggregate	O
multiple	O
types	O
of	O
input-data	O
uncertainty	O
to	O
create	O
outputs	O
accompanied	O
by	O
probability	O
statements	O
and	O
confidence	O
intervals	O
.	O
Uncertainty	O
in	O
elevation	O
data	O
layers	O
have	O
been	O
considered	O
by	O
several	O
modeling	O
groups	O
to	O
various	O
extents	O
(	O
Gesch	O
,	O
2009	O
;	O
Gilmer	O
and	O
Ferdaña	O
,	O
2012	O
;	O
Schmid	O
et	O
al.	O
,	O
2014	O
)	O
.	O
However	O
,	O
to	O
the	O
best	O
of	O
our	O
knowledge	O
,	O
no	O
other	O
marsh	B-Material
migration	I-Material
model	I-Material
simultaneously	O
accounts	O
for	O
the	O
combined	O
effects	O
of	O
uncertainty	O
in	O
spatial	B-Process
inputs	I-Process
(	O
DEM	B-Process
,	O
VDATUM	B-Process
,	O
etc.	O
)	O
and	O
parameter	B-Process
choices	I-Process
(	O
accretion	B-Process
rates	I-Process
,	O
tide	B-Process
ranges	I-Process
,	O
etc.	O
)	O
on	O
landcover	B-Process
projections	I-Process
.	O
This	O
added	O
feature	O
of	O
SLAMM	B-Process
allows	O
results	O
to	O
be	O
evaluated	O
in	O
terms	O
of	O
their	O
likelihood	O
of	O
occurrence	O
with	O
respect	O
to	O
input-data	O
and	O
parameter	O
uncertainties	O
.	O
Further	O
,	O
by	O
assigning	O
wide	O
ranges	O
of	O
uncertainty	O
when	O
appropriate	O
,	O
it	O
permits	O
the	O
production	O
of	O
meaningful	O
projections	O
in	O
areas	O
where	O
high-quality	B-Material
local	I-Material
data	I-Material
are	O
not	O
available	O
.	O

Using	O
measured	O
data	O
from	O
two	O
arable	O
sites	O
in	O
the	O
UK	O
we	O
have	O
shown	O
that	O
lags	O
can	O
have	O
significant	O
impact	O
on	O
model	B-Task
evaluation	I-Task
and	O
can	O
affect	O
both	O
the	O
level	O
of	O
correlation	O
between	O
measured	O
and	O
simulated	O
data	O
and	O
the	O
magnitude	O
of	O
the	O
sums	O
of	O
the	O
residuals	O
.	O
Also	O
,	O
we	O
used	O
the	O
division	O
of	O
MSE	B-Process
to	O
three	O
constituent	B-Process
statistics	I-Process
(	O
SB	B-Process
,	O
SDSD	B-Process
and	O
LCS	B-Process
)	O
to	O
show	O
how	O
the	O
level	O
of	O
correlation	O
can	O
affect	O
the	O
sum	O
of	O
residuals	O
.	O
By	O
dividing	O
the	O
algorithm-predicted	O
series	O
of	O
lag	O
values	O
into	O
monthly	O
sets	O
and	O
examining	O
the	O
frequency	O
distribution	O
of	O
the	O
lags	O
,	O
certain	O
patterns	O
in	O
these	O
temporally	O
patchy	O
series	O
have	O
been	O
identified	O
.	O
A	O
challenging	O
task	O
in	O
relation	O
to	O
time	O
lags	O
between	O
observed	O
and	O
simulated	O
daily	O
data	O
,	O
is	O
to	O
determine	O
their	O
cause	O
.	O
This	O
task	O
becomes	O
more	O
difficult	O
for	O
model	O
outputs	O
such	O
as	O
soil	O
N2O	B-Material
emissions	I-Material
that	O
are	O
driven	O
by	O
various	O
interacting	O
variables	O
.	O
Even	O
more	O
so	O
,	O
because	O
the	O
measured	O
N2O	B-Material
datasets	I-Material
and	O
the	O
measured	O
datasets	O
of	O
their	O
drivers	O
(	O
e.g.	O
soil	O
moisture	O
,	O
soil	O
N	O
content	O
)	O
cover	O
small	O
time	O
periods	O
,	O
they	O
are	O
not	O
continuous	O
and	O
can	O
vary	O
widely	O
in	O
size	O
.	O
In	O
this	O
study	O
we	O
implemented	O
the	O
algorithm	O
using	O
measured	B-Material
and	I-Material
simulated	I-Material
data	I-Material
for	I-Material
soil	I-Material
moisture	I-Material
(	O
first	O
and	O
second	O
example	O
)	O
and	O
soil	B-Material
mineral	I-Material
N	I-Material
(	O
second	O
example	O
)	O
,	O
and	O
compared	O
its	O
results	O
with	O
the	O
respective	O
results	O
for	O
N2O	B-Material
.	O
In	O
our	O
first	O
example	O
,	O
we	O
showed	O
that	O
the	O
estimated	O
lags	O
in	O
N2O	B-Task
prediction	I-Task
are	O
related	O
to	O
the	O
lags	O
in	O
soil	B-Task
moisture	I-Task
prediction	I-Task
in	O
a	O
way	O
that	O
changes	O
gradually	O
through	O
time	O
.	O
In	O
our	O
second	O
example	O
,	O
the	O
lags	O
in	O
N2O	B-Task
prediction	I-Task
were	O
explained	O
by	O
the	O
lags	O
in	O
soil	B-Material
moisture	I-Material
and	O
soil	B-Task
mineral	I-Task
N	I-Task
prediction	I-Task
,	O
with	O
which	O
they	O
had	O
a	O
positive	O
relationship	O
.	O

In	O
representing	B-Task
wetland-river	I-Task
interactions	I-Task
involving	I-Task
GIWs	I-Task
,	O
many	O
models	O
assume	O
that	O
the	O
wetland	O
can	O
discharge	O
into	O
a	O
river	O
but	O
cannot	O
receive	O
overbank	O
flows	O
from	O
it	O
.	O
In	O
such	O
models	O
,	O
the	O
volume	O
of	O
water	O
(	O
or	O
water	O
level	O
elevation	O
)	O
in	O
a	O
wetland	O
and	O
its	O
corresponding	O
threshold	O
value	O
(	O
predominantly	O
controlled	O
by	O
outlet	B-Process
elevation	I-Process
)	O
are	O
the	O
prime	O
determinants	O
of	O
wetland	O
outflow	O
(	O
Feng	O
et	O
al.	O
,	O
2012	O
;	O
Hammer	O
and	O
Kadlec	O
,	O
1986	O
;	O
Johnson	O
et	O
al.	O
,	O
2010	O
;	O
Kadlec	O
and	O
Wallace	O
,	O
2009	O
;	O
Powell	O
et	O
al.	O
,	O
2008	O
;	O
Voldseth	O
et	O
al.	O
,	O
2007	O
;	O
Wen	O
et	O
al.	O
,	O
2013	O
;	O
Zhang	O
and	O
Mitsch	O
,	O
2005	O
)	O
.	O
However	O
,	O
in	O
regions	O
characterised	O
by	O
widespread	O
riparian	O
wetlands	O
that	O
are	O
hydraulically	O
connected	O
with	O
adjacent	O
rivers	O
,	O
wetland-river	O
interaction	O
is	O
likely	O
to	O
be	O
bidirectional	O
.	O
Such	O
interactions	B-Task
should	I-Task
be	I-Task
quantified	I-Task
according	I-Task
to	I-Task
hydraulic	I-Task
principles	I-Task
involving	O
relative	B-Material
river	I-Material
and	I-Material
wetland	I-Material
water	I-Material
level	I-Material
elevations	I-Material
as	O
well	O
as	O
the	O
properties	B-Material
of	I-Material
the	I-Material
connection	I-Material
between	I-Material
the	I-Material
two	I-Material
(	O
Kouwen	O
,	O
2013	O
;	O
Liu	O
et	O
al.	O
,	O
2008	O
;	O
Min	O
et	O
al.	O
,	O
2010	O
;	O
Nyarko	O
,	O
2007	O
;	O
Restrepo	O
et	O
al.	O
,	O
1998	O
)	O
.	O
In	O
the	O
WATFLOOD	B-Process
model	O
,	O
for	O
instance	O
,	O
riparian	B-Task
wetland-river	I-Task
interaction	I-Task
is	I-Task
modelled	I-Task
using	O
the	O
principle	B-Material
of	I-Material
Dupuit-Forchheimer	I-Material
lateral	I-Material
/	I-Material
radial	I-Material
groundwater	I-Material
flow	I-Material
(	O
Kouwen	O
,	O
2013	O
)	O
.	O
Since	O
exchange	O
between	O
riparian	O
wetlands	O
and	O
rivers	O
can	O
occur	O
over	O
the	O
surface	O
and	O
/	O
or	O
through	O
the	O
subsurface	O
,	O
Restrepo	O
et	O
al	O
.	O
(	O
1998	O
)	O
incorporated	O
an	O
equivalent	B-Process
transmissivity	I-Process
expression	I-Process
,	O
obtained	O
for	O
wetland	B-Material
vegetation	I-Material
and	O
the	O
subsurface	B-Material
soil	I-Material
,	O
into	O
the	O
Darcy	B-Process
flow	I-Process
equation	I-Process
of	O
the	O
MODFLOW	B-Process
model	O
.	O

Typical	O
physically-based	B-Process
2D	I-Process
flood	I-Process
models	I-Process
solve	B-Task
the	I-Task
Shallow	I-Task
Water	I-Task
Equations	I-Task
(	I-Task
SWEs	I-Task
)	I-Task
,	O
requiring	O
high	O
computational	O
resources	O
.	O
Many	O
of	O
these	O
models	O
have	O
been	O
developed	O
to	O
obtain	O
better	O
performance	O
,	O
while	O
maintaining	O
the	O
required	O
accuracy	O
,	O
by	O
reducing	B-Process
the	I-Process
complexity	I-Process
of	I-Process
the	I-Process
SWEs	I-Process
.	O
This	O
reduction	O
is	O
usually	O
achieved	O
by	O
approximating	O
or	O
neglecting	O
less	O
significant	O
terms	O
of	O
the	O
equations	O
(	O
Hunter	O
et	O
al.	O
,	O
2007	O
;	O
Yen	O
and	O
Tsai	O
,	O
2001	O
)	O
.	O
The	O
JFLOW	B-Process
model	O
(	O
Bradbrook	O
et	O
al.	O
,	O
2004	O
)	O
,	O
Urban	B-Process
Inundation	I-Process
Model	I-Process
(	O
UIM	B-Process
)	O
(	O
Chen	O
et	O
al.	O
,	O
2007	O
)	O
,	O
and	O
the	O
diffusive	O
version	O
of	O
LISFLOOD-FP	B-Process
(	O
Hunter	O
et	O
al.	O
,	O
2005	O
)	O
solve	O
the	O
2D	B-Process
diffusion	I-Process
wave	I-Process
equations	I-Process
that	O
neglect	O
the	O
inertial	O
(	O
local	O
acceleration	O
)	O
and	O
advection	O
(	O
convective	O
acceleration	O
)	O
terms	O
(	O
Yen	O
and	O
Tsai	O
,	O
2001	O
)	O
.	O
The	O
inertial	O
version	O
of	O
LISFLOOD-FP	B-Process
(	O
Bates	O
et	O
al.	O
,	O
2010	O
)	O
solves	O
the	O
SWEs	B-Process
without	O
the	O
advection	O
term	O
.	O
In	O
either	O
version	O
of	O
LISFLOOD-FP	B-Process
the	O
flow	O
is	O
decoupled	O
in	O
the	O
Cartesian	B-Process
directions	I-Process
.	O
Other	O
models	O
use	O
the	O
full	O
SWEs	B-Process
but	O
focus	O
on	O
the	O
use	O
of	O
multi	B-Material
resolution	I-Material
grids	I-Material
or	I-Material
irregular	I-Material
mesh	I-Material
,	O
like	O
InfoWorks	B-Material
ICM	I-Material
(	O
Innovyze	O
,	O
2012	O
)	O
and	O
MIKE	B-Material
FLOOD	I-Material
(	O
DHI	O
Software	O
,	O
2014	O
;	O
Hénonin	O
et	O
al.	O
,	O
2013	O
)	O
.	O
These	O
last	O
two	O
models	O
are	O
commercial	O
packages	O
,	O
and	O
the	O
code	O
applied	O
in	O
the	O
optimisation	O
techniques	O
is	O
not	O
in	O
the	O
public	O
domain	O
.	O

The	O
purported	O
advantages	O
of	O
EMR	B-Task
implementation	I-Task
in	O
urban	O
slums	O
are	O
widely	O
promoted	O
.	O
Increasingly	O
capable	O
health	B-Task
information	I-Task
systems	I-Task
could	O
facilitate	O
communication	O
,	O
help	O
coordinate	O
care	O
,	O
and	O
improve	O
the	O
continuity	O
of	O
care	O
in	O
disadvantaged	O
communities	O
like	O
Kibera	O
.	O
However	O
,	O
available	O
systems	O
may	O
not	O
have	O
the	O
ability	O
to	O
simplify	O
care	O
or	O
improve	O
efficiency	O
where	O
funding	O
and	O
human	O
resources	O
are	O
scarce	O
,	O
infrastructure	O
is	O
unreliable	O
and	O
health	O
data	O
demands	O
are	O
opportunistic	O
,	O
not	O
strategic	O
.	O
This	O
study	O
described	O
perceptions	O
of	O
local	O
EMR	B-Process
stakeholders	O
in	O
two	O
urban	O
slum	O
clinics	O
.	O
They	O
shared	O
many	O
observations	O
that	O
may	O
be	O
important	O
for	O
other	O
EMR	O
initiatives	O
to	O
heed	O
,	O
and	O
worried	O
most	O
about	O
the	O
sustainability	O
of	O
EMR	O
initiatives	O
in	O
like	O
communities	O
.	O
The	O
future	O
for	O
EMR	O
use	O
in	O
urban	O
slums	O
is	O
promising	O
.	O
Innovative	O
new	O
technologies	O
,	O
such	O
as	O
mobile	B-Material
applications	I-Material
and	O
point-of-care	O
laboratory	O
tests	O
,	O
could	O
extend	O
the	O
reach	O
of	O
EMRs	B-Process
where	O
infrastructure	O
is	O
wanting	O
.	O
New	O
cloud-based	B-Process
EMR	I-Process
ecosystems	I-Process
,	O
where	O
data	O
is	O
collected	O
and	O
stored	O
centrally	O
could	O
leverage	O
cell	O
phone	O
networks	O
to	O
promote	O
more	O
health	O
information	O
sharing	O
,	O
coordination	O
of	O
care	O
and	O
ultimately	O
better	O
outcomes	O
for	O
vulnerable	O
populations.Summary	O
pointsWhat	O
was	O
already	O
known	O
on	O
the	O
topic	O
?•	O
Rapid	O
urbanization	O
is	O
associated	O
with	O
growth	O
in	O
the	O
number	O
and	O
size	O
of	O
urban	O
slums	O
and	O
an	O
associated	O
rise	O
in	O
the	O
burden	O
of	O
disease	O
,	O
further	O
worsening	O
an	O
already	O
fragmented	O
and	O
inefficient	O
health	O
care	O
system	O
.	O

As	O
future	O
work	O
on	O
the	O
protocol	B-Task
,	O
we	O
would	O
promote	O
two	O
items	O
.	O
Firstly	O
,	O
the	O
two	O
mobility	B-Process
models	I-Process
that	O
we	O
have	O
considered	O
in	O
this	O
work	O
propose	O
possible	O
way	O
to	O
capture	O
social	O
context	O
in	O
the	O
way	O
nodes	B-Material
move	O
in	O
the	O
physical	B-Material
space	I-Material
,	O
yet	O
still	O
potentially	O
allowing	O
nodes	B-Material
to	O
explore	O
the	O
geographical	B-Material
regions	I-Material
considered	O
in	O
its	O
entirety	O
.	O
Further	O
insights	O
to	O
the	O
performance	B-Material
potential	I-Material
could	O
be	O
given	O
through	O
the	O
assessment	O
of	O
the	O
protocol	O
with	O
other	O
mobilities	O
that	O
can	O
extend	B-Process
the	I-Process
physical	I-Process
region	I-Process
of	I-Process
movement	I-Process
as	O
well	O
as	O
impose	B-Process
potential	I-Process
restrictions	I-Process
on	O
the	O
nodes	B-Material
mobility	O
,	O
for	O
example	O
by	O
forcing	B-Process
similar	I-Process
nodes	I-Process
to	I-Process
move	I-Process
within	I-Process
specifically	I-Process
defined	I-Process
areas	I-Process
.	O
Secondly	O
,	O
the	O
different	O
forwarding	B-Process
modes	I-Process
introduced	O
in	O
Section	O
3.3	O
express	O
different	O
levels	O
of	O
cooperation	O
across	O
the	O
network	B-Process
.	O
The	O
push-community	B-Process
mode	I-Process
,	O
for	O
example	O
,	O
is	O
a	O
form	O
of	O
interest-community	O
selfishness	O
and	O
assumes	O
reciprocation	O
in	O
the	O
nodes’	O
behaviour	O
.	O
The	O
vulnerability	O
(	O
resp.	O
resilience	O
)	O
of	O
the	O
protocol	O
to	O
different	O
instances	O
of	O
node	O
misbehaviours	O
is	O
a	O
research	O
item	O
worth	O
exploring	O
.	O

The	O
proposed	O
multihop	B-Process
routing	I-Process
protocol	I-Process
,	O
PHASeR	B-Process
,	O
applies	O
the	O
technique	O
of	O
blind	O
forwarding	O
in	O
a	O
MWSN	B-Material
,	O
which	O
increases	O
the	O
reliability	O
of	O
data	O
delivery	O
through	O
its	O
inherent	O
use	O
of	O
multiple	O
routes	O
.	O
This	O
approach	O
requires	O
a	O
gradient	B-Process
metric	I-Process
to	O
be	O
continuously	O
maintained	O
,	O
which	O
is	O
problematic	O
in	O
a	O
dynamic	O
topology	O
.	O
The	O
literature	O
commonly	O
uses	O
either	O
flooding	O
or	O
location	O
awareness	O
,	O
however	O
flooding	O
creates	O
large	O
amounts	O
of	O
overhead	O
and	O
location	O
determination	O
schemes	O
can	O
often	O
be	O
inaccurate	O
,	O
power	O
hungry	O
and	O
create	O
the	O
issue	O
of	O
the	O
dead	O
end	O
problem	O
.	O
PHASeR	B-Process
uses	O
a	O
novel	O
method	O
of	O
gradient	B-Process
maintenance	I-Process
in	O
a	O
mobile	B-Material
network	I-Material
,	O
which	O
requires	O
the	O
proactive	O
sharing	O
of	O
only	O
local	O
topology	O
information	O
.	O
This	O
is	O
facilitated	O
by	O
a	O
global	O
TDMA	B-Process
(	O
time	B-Process
division	I-Process
multiple	I-Process
access	I-Process
)	O
MAC	B-Process
(	O
medium	B-Process
access	I-Process
control	I-Process
)	O
layer	O
and	O
further	O
reduces	O
the	O
amount	O
of	O
overhead	O
,	O
which	O
in	O
turn	O
will	O
decrease	O
packet	B-Material
latency	I-Material
.	O
PHASeR	B-Process
is	O
also	O
set	O
apart	O
by	O
its	O
use	O
of	O
encapsulation	B-Process
,	O
which	O
allows	O
data	O
from	O
multiple	O
nodes	B-Material
to	O
be	O
transmitted	O
in	O
the	O
same	O
packet	O
in	O
order	O
to	O
handle	O
high	O
volumes	O
of	O
traffic	O
.	O
It	O
utilises	O
node	B-Process
cooperation	I-Process
to	O
create	O
a	O
robust	O
multipath	O
routing	O
solution	O
.	O
As	O
such	O
,	O
the	O
contribution	O
of	O
this	O
paper	O
is	O
a	O
cross-layer	B-Process
routing	I-Process
protocol	I-Process
for	O
MWSNs	B-Material
that	O
can	O
handle	O
the	O
constant	O
flow	O
of	O
data	O
from	O
sensors	B-Material
in	O
highly	O
mobile	O
situations	O
.	O

Superconductivity	B-Process
in	O
actinides	B-Material
was	O
first	O
observed	O
in	O
thorium	B-Material
metal	I-Material
in	O
1929	O
[	O
7	O
]	O
,	O
then	O
in	O
elemental	O
uranium	B-Material
in	O
1942	O
[	O
8	O
]	O
,	O
and	O
in	O
uranium	B-Material
compounds	I-Material
in	O
1958	O
[	O
9	O
]	O
.	O
A	O
new	O
class	O
of	O
uranium	B-Material
superconductors	I-Material
emerged	O
in	O
the	O
1980	O
's	O
with	O
the	O
discovery	O
of	O
uranium	B-Material
heavy	I-Material
fermion	I-Material
superconductors	I-Material
[	O
10	O
]	O
.	O
Further	O
surprises	O
came	O
at	O
the	O
beginning	O
of	O
the	O
century	O
with	O
the	O
discovery	O
of	O
ferromagnetic	B-Material
superconductors	I-Material
in	O
uranium	B-Material
systems	O
[	O
11	O
]	O
and	O
the	O
first	O
observation	O
of	O
superconductivity	B-Process
in	O
plutonium	B-Material
[	O
12	O
]	O
and	O
neptunium	B-Material
[	O
13	O
]	O
compounds	O
.	O
The	O
actinides	B-Material
(	O
or	O
actinoids	B-Material
)	O
are	O
located	O
at	O
the	O
end	O
of	O
the	O
periodic	B-Process
table	I-Process
(	O
N	O
=	O
89	O
(	O
Ac	B-Material
)	O
or	O
90	O
(	O
Th	B-Material
)	O
to	O
103	O
(	O
Lr	B-Material
))	O
.	O
Transuranium	B-Material
elements	I-Material
(	O
or	O
transuranics	B-Material
)	O
are	O
the	O
chemical	O
elements	O
with	O
atomic	O
number	O
(	O
Z	O
)	O
greater	O
than	O
92	O
(	O
uranium	B-Material
)	O
and	O
due	O
to	O
their	O
short	O
half-life	O
on	O
a	O
geological	O
timescale	O
,	O
they	O
are	O
essentially	O
synthetic	B-Material
elements	I-Material
.	O
Above	O
Z	O
=	O
103	O
(	O
Lr	B-Material
)	O
,	O
one	O
talks	O
about	O
transactinides	B-Material
(	O
or	O
superactinides	B-Material
)	O
elements	O
.	O
These	O
latter	O
elements	O
have	O
extremely	O
short	O
half-lives	O
and	O
no	O
macroscopic	O
quantity	O
is	O
available	O
for	O
the	O
study	B-Task
of	I-Task
condensed-matter	I-Task
properties	I-Task
.	O

PV	B-Task
cells	I-Task
are	O
one	O
of	O
the	O
most	O
promising	O
technologies	O
for	O
conversion	B-Process
of	I-Process
incident	I-Process
solar	I-Process
radiation	I-Process
into	I-Process
electric	I-Process
power	I-Process
.	O
However	O
,	O
this	O
technology	O
is	O
still	O
far	O
from	O
being	O
able	O
to	O
compete	O
with	O
fossil	B-Process
fuel-based	I-Process
energy	I-Process
conversion	I-Process
technologies	O
because	O
of	O
its	O
relatively	O
low	O
efficiency	O
and	O
energy	O
density	O
.	O
Theoretically	O
,	O
there	O
are	O
three	O
unavoidable	O
losses	O
that	O
limit	O
the	O
solar	B-Process
conversion	I-Process
efficiency	O
of	O
a	O
device	O
with	O
a	O
single	O
absorption	B-Process
threshold	O
or	O
band	O
gap	O
Eg	O
:	O
(	O
1	O
)	O
incomplete	B-Process
absorption	I-Process
,	O
where	O
photons	B-Material
with	O
energies	O
below	O
Eg	O
are	O
not	O
absorbed	O
;	O
(	O
2	O
)	O
thermalization	B-Process
or	O
carrier	B-Process
cooling	I-Process
,	O
where	O
solar	B-Material
photons	I-Material
with	O
sufficient	O
energy	O
generate	O
electron-hole	B-Material
pairs	I-Material
and	O
then	O
immediately	O
lose	O
almost	O
all	O
energy	O
in	O
excess	O
of	O
Eg	O
in	O
the	O
form	O
of	O
heat	O
;	O
and	O
(	O
3	O
)	O
radiative	B-Process
recombination	I-Process
,	O
where	O
a	O
small	O
fraction	O
of	O
the	O
excited	O
states	O
radioactively	O
recombine	O
with	O
the	O
ground	O
state	O
at	O
the	O
maximum	O
power	O
output	O
(	O
Hanna	O
&	O
Nozik	O
,	O
2006	O
;	O
Henry	O
,	O
1980	O
)	O
.	O
Taking	O
an	O
air	O
mass	O
of	O
1.5	O
as	O
an	O
example	O
,	O
for	O
different	O
band	O
gap	O
Eg	O
these	O
three	O
losses	O
can	O
be	O
calculated	O
and	O
the	O
results	O
are	O
indicated	O
by	O
areas	O
S1	O
,	O
S2	O
,	O
and	O
S3	O
in	O
Fig.	O
1.	O
Note	O
that	O
the	O
area	O
under	O
the	O
outer	O
curve	O
is	O
the	O
solar	B-Process
power	I-Process
per	O
unit	O
area	O
,	O
and	O
that	O
only	O
S4	O
can	O
be	O
delivered	O
to	O
the	O
load	O
.	O

Xylanases	B-Material
have	O
potential	O
applications	O
in	O
various	O
fields	O
.	O
Some	O
of	O
the	O
important	O
applications	O
are	O
as	O
fallows	O
.	O
Xylanases	O
are	O
used	O
as	O
bleaching	B-Material
agent	I-Material
in	O
the	O
pulp	B-Task
and	I-Task
paper	I-Task
industry	I-Task
.	O
Mostly	O
they	O
are	O
used	O
to	O
hydrolyzed	B-Process
the	I-Process
xylan	I-Process
component	I-Process
from	O
wood	B-Material
which	O
facilitate	O
in	O
removal	B-Process
of	I-Process
lignin	I-Process
(	O
Viikari	O
,	O
Kantelinen	O
,	O
Buchert	O
,	O
&	O
Puls	O
,	O
1994	O
)	O
.	O
It	O
also	O
helps	O
in	O
brightening	B-Process
of	I-Process
the	I-Process
pulp	I-Process
to	O
avoid	O
the	O
chlorine	B-Process
free	I-Process
bleaching	I-Process
operations	I-Process
(	O
Paice	O
,	O
Jurasek	O
,	O
Ho	O
,	O
Bourbonnais	O
,	O
&	O
Archibald	O
,	O
1989	O
)	O
.	O
In	O
bakeries	O
the	O
xylanase	B-Material
act	O
on	O
the	O
gluten	O
fraction	O
of	O
the	O
dough	O
and	O
help	O
in	O
the	O
even	B-Process
redistribution	I-Process
of	I-Process
the	I-Process
water	I-Process
content	I-Process
of	I-Process
the	I-Process
bread	I-Process
(	O
Wong	O
&	O
Saddler	O
,	O
1992	O
)	O
.	O
Xylanases	B-Material
also	O
have	O
potential	O
application	O
in	O
animal	B-Task
feed	I-Task
industry	I-Task
.	O
They	O
are	O
used	O
for	O
the	O
hydrolysis	B-Task
of	I-Task
non-starchy	I-Task
polysaccharides	I-Task
such	O
as	O
arabinoxylan	B-Material
in	O
monogastric	O
diets	O
(	O
Walsh	O
,	O
Power	O
,	O
&	O
Headon	O
,	O
1993	O
)	O
.	O
Xylanases	B-Material
also	O
play	O
a	O
key	O
role	O
in	O
the	O
maceration	O
of	O
vegetable	O
matter	O
(	O
Beck	O
&	O
Scoot	O
,	O
1974	O
)	O
,	O
protoplastation	O
of	O
plant	O
cells	O
,	O
clarification	O
of	O
juices	O
and	O
wine	O
(	O
Biely	O
,	O
1985	O
)	O
liquefaction	O
of	O
coffee	O
mucilage	O
for	O
making	O
liquid	O
coffee	O
,	O
recovery	O
of	O
oil	O
from	O
subterranian	O
mines	O
,	O
extraction	O
of	O
flavors	O
and	O
pigments	O
,	O
plant	O
oils	O
and	O
starch	O
(	O
McCleary	O
,	O
1986	O
)	O
and	O
to	O
improve	O
the	O
efficiency	O
of	O
agricultural	O
silage	O
production	O
(	O
Wong	O
&	O
Saddler	O
,	O
1992	O
)	O
.	O

ObjectiveElectrically	O
evoked	O
auditory	O
steady-state	O
responses	O
(	O
EASSRs	O
)	O
are	O
neural	O
potentials	O
measured	O
in	O
the	O
electroencephalogram	O
(	O
EEG	O
)	O
in	O
response	O
to	O
periodic	O
pulse	O
trains	O
presented	O
,	O
for	O
example	O
,	O
through	O
a	O
cochlear	O
implant	O
(	O
CI	O
)	O
.	O
EASSRs	O
could	O
potentially	O
be	O
used	O
for	O
objective	O
CI	O
fitting	O
.	O
However	O
,	O
EEG	O
signals	O
are	O
contaminated	O
with	O
electrical	O
CI	O
artifacts	O
.	O
In	O
this	O
paper	O
,	O
we	O
characterized	O
the	O
CI	O
artifacts	O
for	O
monopolar	O
mode	O
stimulation	O
and	O
evaluated	O
at	O
which	O
pulse	O
rate	O
,	O
linear	O
interpolation	O
over	O
the	O
signal	O
part	O
contaminated	O
with	O
CI	O
artifact	O
is	O
successful.MethodsCI	O
artifacts	O
were	O
characterized	O
by	O
means	O
of	O
their	O
amplitude	O
growth	O
functions	O
and	O
duration.ResultsCI	O
artifact	O
durations	O
were	O
between	O
0.7	O
and	O
1.7ms	O
,	O
at	O
contralateral	O
recording	O
electrodes	O
.	O
At	O
ipsilateral	O
recording	O
electrodes	O
,	O
CI	O
artifact	O
durations	O
are	O
range	O
from	O
0.7	O
to	O
larger	O
than	O
2ms.ConclusionAt	O
contralateral	O
recording	O
electrodes	O
,	O
the	O
artifact	O
was	O
shorter	O
than	O
the	O
interpulse	O
interval	O
across	O
subjects	O
for	O
500pps	O
,	O
which	O
was	O
not	O
always	O
the	O
case	O
for	O
900pps.SignificanceCI	O
artifact-free	O
EASSRs	O
are	O
crucial	O
for	O
reliable	O
CI	O
fitting	O
and	O
neuroscience	O
research	O
.	O
The	O
CI	O
artifact	O
has	O
been	O
characterized	O
and	O
linear	O
interpolation	O
allows	O
to	O
remove	O
it	O
at	O
contralateral	O
recording	O
electrodes	O
for	O
stimulation	O
at	O
500pps	O
.	O

One	O
way	O
to	O
enforce	O
this	O
ratio	O
is	O
to	O
use	O
a	O
probabilistic	B-Process
,	I-Process
‘	I-Process
roulette	I-Process
wheel’	I-Process
style	I-Process
lane	B-Task
selection	I-Task
policy	I-Task
.	O
VISSIM	B-Process
,	O
along	O
with	O
most	O
simulation	B-Process
toolkits	I-Process
,	O
offers	O
methods	O
to	O
specify	O
probabilistic	B-Process
routing	I-Process
whereby	O
a	O
defined	O
percentage	O
of	O
vehicles	O
are	O
sent	O
down	O
unique	O
routes	O
.	O
This	O
is	O
a	O
piecewise	B-Process
technique	I-Process
that	O
can	O
be	O
reapplied	O
at	O
various	O
locations	O
around	O
a	O
simulation	B-Process
.	O
While	O
these	O
methods	O
are	O
attractive	O
from	O
a	O
calibration	O
perspective	O
as	O
exact	O
representations	O
of	O
existing	O
statistics	O
can	O
be	O
ensured	O
,	O
the	O
process	O
is	O
an	O
unrealistic	O
one	O
as	O
it	O
assumes	O
that	O
drivers	B-Material
make	O
probabilistic	O
decisions	O
at	O
precise	O
locations	O
.	O
So	O
in	O
this	O
case	O
when	O
a	O
vehicle	B-Material
arrives	O
at	O
a	O
point	O
prior	O
to	O
the	O
weighbridges	B-Material
it	O
is	O
allocated	O
one	O
of	O
the	O
lanes	O
based	O
on	O
the	O
respective	O
probabilities	O
.	O
It	O
turns	O
out	O
that	O
this	O
method	O
leads	O
to	O
significant	O
variations	O
in	O
trip	O
times	O
depending	O
on	O
the	O
initial	O
random	O
number	O
seed	O
,	O
this	O
can	O
be	O
seen	O
in	O
a	O
graphic	O
of	O
the	O
key	O
areas	O
of	O
the	O
simulation	O
for	O
the	O
2	O
different	O
runs	O
(	O
Fig.	O
7	O
)	O
.	O
One	O
of	O
the	O
benefits	O
of	O
graphical	B-Process
microsimulation	I-Process
is	O
that	O
the	O
2D	B-Process
and	I-Process
3D	I-Process
simulations	I-Process
help	O
the	O
researcher	O
to	O
visualise	O
a	O
new	O
scheme	O
and	O
its	O
potential	O
benefits	O
but	O
also	O
to	O
highlight	O
unrealistic	O
behaviour	O
.	O
Fig.	O
7	O
shows	O
the	O
congestion	O
at	O
the	O
decision	O
point	O
for	O
2	O
different	O
runs	O
.	O
Using	O
probabilistic	B-Process
routing	I-Process
to	O
enforce	O
correct	O
routing	O
percentages	O
is	O
a	O
clear	O
case	O
of	O
overcalibration	O
affecting	O
simulation	B-Process
brittleness	O
.	O

A	O
few	O
studies	O
within	O
the	O
physiological	O
domain	O
are	O
of	O
special	O
relevance	O
to	O
this	O
work	O
.	O
These	O
include	O
a	O
performance	O
analysis	O
of	O
a	O
blood-flow	B-Process
LB	I-Process
solver	I-Process
using	O
a	O
range	O
of	O
sparse	O
and	O
non-sparse	O
geometries	O
[	O
21	O
]	O
and	O
a	O
performance	B-Process
prediction	I-Process
model	I-Process
for	O
lattice-Boltzmann	B-Process
solvers	I-Process
[	O
22,23	O
]	O
.	O
This	O
performance	B-Process
prediction	I-Process
model	I-Process
can	O
be	O
applied	O
largely	O
to	O
our	O
HemeLB	B-Process
application	I-Process
,	O
although	O
HemeLB	O
uses	O
a	O
different	O
decomposition	O
technique	O
and	O
performs	O
real-time	O
rendering	O
and	O
visualisation	O
tasks	O
during	O
the	O
LB	B-Process
simulations	I-Process
.	O
Mazzeo	O
and	O
Coveney	O
[	O
1	O
]	O
studied	O
the	O
scalability	O
of	O
an	O
earlier	O
version	O
of	O
HemeLB	B-Process
.	O
However	O
,	O
the	O
current	O
performance	O
characteristics	O
of	O
HemeLB	O
are	O
substantially	O
enhanced	O
due	O
to	O
numerous	O
subsequent	O
advances	O
in	O
the	O
code	O
,	O
amongst	O
others	O
:	O
an	O
improved	O
hierarchical	O
,	O
compressed	O
file	O
format	O
;	O
the	O
use	O
of	O
ParMETIS	B-Material
to	O
ensure	O
good	O
load-balance	O
;	O
the	O
coalesced	B-Process
communication	I-Process
patterns	I-Process
to	O
reduce	O
the	O
overhead	O
of	O
rendering	B-Process
;	O
use	O
of	O
compile-time	B-Process
polymorphism	I-Process
to	O
avoid	O
virtual	O
function	O
calls	O
in	O
inner	O
loops	O
.	O

Although	O
mean-field	B-Process
models	I-Process
have	O
been	O
used	O
in	O
all	O
these	O
settings	O
,	O
little	O
analysis	O
has	O
been	O
done	O
on	O
their	O
behaviour	O
as	O
spatially	B-Task
extended	I-Task
dynamical	I-Task
systems	I-Task
.	O
In	O
part	O
,	O
this	O
is	O
due	O
to	O
their	O
staggering	O
complexity	O
.	O
The	O
Liley	B-Process
model	I-Process
[	O
15	O
]	O
considered	O
here	O
,	O
for	O
instance	O
,	O
consists	O
of	O
fourteen	O
coupled	O
Partial	B-Material
Differential	I-Material
Equations	I-Material
(	O
PDEs	B-Material
)	O
with	O
strong	O
nonlinearities	O
,	O
imposed	O
by	O
coupling	O
between	O
the	O
mean	O
membrane	B-Material
potentials	I-Material
and	O
the	O
mean	O
synaptic	B-Material
inputs	I-Material
.	O
The	O
model	O
can	O
be	O
reduced	O
to	O
a	O
system	O
of	O
Ordinary	B-Material
Differential	I-Material
Equations	I-Material
(	O
ODEs	B-Material
)	O
by	O
considering	O
only	O
spatially	O
homogeneous	O
solutions	O
,	O
and	O
the	O
resulting	O
system	O
has	O
been	O
examined	O
in	O
detail	O
using	O
numerical	B-Process
bifurcation	I-Process
analysis	I-Process
(	O
see	O
[	O
16	O
]	O
and	O
references	O
therein	O
)	O
.	O
In	O
order	O
to	O
compute	O
equilibria	O
,	O
periodic	O
orbits	O
and	O
such	O
objects	O
for	O
the	O
PDE	B-Material
model	I-Material
,	O
we	O
need	O
a	O
flexible	O
,	O
stable	O
simulation	O
code	O
for	O
the	O
model	O
and	O
its	O
linearization	O
that	O
can	O
run	O
in	O
parallel	O
to	O
scale	O
up	O
to	O
a	O
domain	O
size	O
of	O
about	O
2500cm2	O
,	O
the	O
size	O
of	O
a	O
full-grown	O
human	O
cortex	O
.	O
We	O
also	O
need	O
efficient	B-Task
,	I-Task
iterative	I-Task
solvers	I-Task
for	I-Task
linear	I-Task
problems	I-Task
with	I-Task
large	I-Task
,	I-Task
sparse	I-Task
matrices	I-Task
.	O
In	O
this	O
paper	O
,	O
we	O
will	O
show	O
that	O
all	O
this	O
can	O
be	O
accomplished	O
in	O
the	O
open-source	O
software	O
package	O
PETSc	B-Material
[	O
17	O
]	O
.	O
Our	O
implementation	O
consists	O
of	O
a	O
number	O
of	O
functions	O
in	O
C	O
that	O
are	O
available	O
publicly	O
[	O
18	O
]	O
.	O

While	O
virtualization	B-Process
technologies	I-Process
certainly	O
reduce	O
the	O
complexity	O
of	O
using	O
a	O
system	O
,	O
and	O
especially	O
when	O
working	O
across	O
multiple	O
heterogeneous	O
computing	O
environments	O
,	O
they	O
are	O
not	O
widely	O
deployed	O
in	O
high	B-Process
performance	I-Process
computing	I-Process
scenarios	O
.	O
As	O
its	O
name	O
suggest	O
,	O
HPC	B-Process
seeks	O
to	O
obtain	O
maximum	O
performance	O
from	O
computing	O
platforms	O
.	O
Extra	O
software	O
layers	O
impact	O
detrimentally	O
on	O
performance	O
,	O
meaning	O
that	O
in	O
HPC	O
scenarios	O
users	O
typically	O
run	O
the	O
applications	B-Material
as	O
close	O
to	O
the	O
‘	O
bare	O
metal’	O
as	O
possible	O
.	O
In	O
addition	O
to	O
the	O
performance	O
degradation	O
introduced	O
by	O
virtualization	B-Process
technologies	I-Process
,	O
choosing	O
what	O
details	O
to	O
abstract	O
in	O
a	O
virtualized	B-Material
interface	I-Material
is	O
itself	O
very	O
important	O
.	O
Grid	B-Process
and	I-Process
cloud	I-Process
computing	I-Process
support	O
different	O
interaction	B-Process
models	I-Process
.	O
In	O
grid	B-Process
computing	I-Process
,	O
the	O
user	O
interacts	O
with	O
an	O
individual	O
resource	O
(	O
or	O
sometimes	O
a	O
broker	B-Material
)	O
in	O
order	O
to	O
launch	O
jobs	O
into	O
a	O
queuing	B-Process
system	I-Process
.	O
In	O
cloud	B-Process
computing	I-Process
,	O
users	O
interact	O
with	O
a	O
virtual	B-Material
server	I-Material
,	O
in	O
effect	O
putting	O
them	O
in	O
control	O
of	O
their	O
own	O
complete	O
operating	O
system	O
.	O
Both	O
of	O
these	O
interaction	B-Process
models	I-Process
put	O
the	O
onus	O
on	O
the	O
user	O
to	O
understand	O
very	O
specific	O
details	O
of	O
the	O
system	O
that	O
they	O
are	O
dealing	O
with	O
,	O
making	O
life	O
difficult	O
for	O
the	O
end	O
user	O
,	O
typically	O
a	O
scientist	O
who	O
wants	O
to	O
progress	O
his	O
or	O
her	O
scientific	O
investigations	O
without	O
any	O
specific	O
usability	O
hurdles	O
obstructing	O
the	O
pathway	O
.	O

FabHemeLB	B-Material
is	O
a	O
Python	B-Material
tool	I-Material
which	O
helps	O
automate	B-Task
the	I-Task
construction	I-Task
and	I-Task
management	I-Task
of	I-Task
ensemble	I-Task
simulation	I-Task
workflows	I-Task
.	O
FabHemeLB	B-Material
is	O
an	O
extended	O
version	O
of	O
FabSim	B-Material
[	O
27	O
]	O
configured	O
to	O
handle	O
HemeLB	B-Material
operations	O
.	O
Both	O
FabSim	B-Material
and	O
FabHemeLB	B-Material
help	O
to	O
automate	B-Task
application	I-Task
deployment	I-Task
,	I-Task
execution	I-Task
and	I-Task
data	I-Task
analysis	I-Task
on	I-Task
remote	I-Task
resources	I-Task
.	O
FabHemeLB	B-Material
can	O
be	O
used	O
to	O
compile	O
and	O
build	O
HemeLB	B-Material
on	O
any	O
remote	O
resource	O
,	O
to	O
reuse	O
machine-specific	O
configurations	O
,	O
and	O
to	O
organize	B-Process
and	I-Process
curate	I-Process
simulation	I-Process
data	I-Process
.	O
It	O
can	O
also	O
submit	O
HemeLB	B-Material
jobs	O
to	O
a	O
remote	O
resource	O
specifying	O
the	O
number	O
of	O
cores	O
and	O
the	O
wall	O
clock	O
time	O
limit	O
for	O
completing	O
a	O
simulation	B-Process
.	O
The	O
tool	O
is	O
also	O
able	O
to	O
monitor	B-Process
the	I-Process
queue	I-Process
status	I-Process
on	I-Process
remote	I-Process
resources	I-Process
,	O
fetch	B-Process
results	I-Process
of	I-Process
completed	I-Process
jobs	I-Process
,	O
and	O
can	O
conveniently	O
combine	B-Process
functionalities	I-Process
into	I-Process
single	I-Process
one-line	I-Process
commands	I-Process
.	O
In	O
general	O
,	O
the	O
FabHemeLB	B-Material
commands	O
have	O
the	O
following	O
structure	O
:	O

In	O
this	O
paper	O
,	O
an	O
implementation	O
of	O
a	O
LBP	B-Material
(	O
local	B-Material
binary	I-Material
pattern	I-Material
)	O
based	O
fast	O
face	B-Task
recognition	I-Task
system	O
on	O
symbian	B-Material
platform	I-Material
is	O
presented	O
.	O
First	O
,	O
face	O
in	O
picture	O
taken	O
from	O
camera	O
is	O
detected	O
using	O
AdaBoost	B-Process
algorithm	I-Process
.	O
Second	O
,	O
the	O
pre-processing	O
of	O
the	O
face	O
is	O
done	O
,	O
including	O
eye	B-Task
location	I-Task
,	I-Task
geometric	I-Task
normalization	I-Task
,	I-Task
illumination	I-Task
normalization	I-Task
.	O
During	O
the	O
face	B-Task
preprocessing	I-Task
,	O
a	O
rapid	O
eye	B-Task
location	I-Task
method	O
named	O
ER	B-Process
(	O
Eyeball	B-Process
Search	I-Process
)	O
is	O
proposed	O
and	O
implemented	O
.	O
Last	O
,	O
the	O
improved	O
LBP	B-Material
is	O
adopted	O
for	O
recognition	B-Task
.	O
Although	O
the	O
computational	O
capability	O
of	O
the	O
symbian	B-Material
platform	I-Material
is	O
limited	O
,	O
the	O
experimental	O
results	O
show	O
good	O
performance	O
for	O
recognition	O
rate	O
and	O
time	O
.	O
in	O
pressIn	O
this	O
paper	O
,	O
an	O
implementation	O
of	O
a	O
LBP	B-Material
(	O
local	B-Material
binary	I-Material
pattern	I-Material
)	O
based	O
fast	O
face	O
recognition	O
system	O
on	O
symbian	B-Material
platform	I-Material
is	O
presented	O
.	O
First	O
,	O
face	O
in	O
picture	O
taken	O
from	O
camera	O
is	O
detected	O
using	O
AdaBoost	B-Process
algorithm	I-Process
.	O
Second	O
,	O
the	O
pre-processing	B-Task
of	I-Task
the	I-Task
face	I-Task
is	O
done	O
,	O
including	O
eye	B-Task
location	I-Task
,	O
geometric	B-Task
normalization	I-Task
,	O
illumination	B-Task
normalization	I-Task
.	O
During	O
the	O
face	O
preprocessing	B-Process
,	O
a	O
rapid	O
eye	B-Process
location	I-Process
method	I-Process
named	O
ER	B-Process
(	O
Eyeball	B-Process
Search	I-Process
)	O
is	O
proposed	O
and	O
implemented	O
.	O
Last	O
,	O
the	O
improved	O
LBP	B-Material
is	O
adopted	O
for	O
recognition	B-Process
.	O
Although	O
the	O
computational	O
capability	O
of	O
the	O
symbian	B-Material
platform	I-Material
is	O
limited	O
,	O
the	O
experimental	O
results	O
show	O
good	O
performance	O
for	O
recognition	B-Task
rate	O
and	O
time	O
.	O
in	O
press	O

A	O
sentence	B-Task
alignment	I-Task
model	O
based	O
on	O
combined	O
clues	O
and	O
Kernel	B-Process
Extensional	I-Process
Matrix	I-Process
Matching	I-Process
(	O
KEMM	B-Process
)	O
method	O
is	O
proposed	O
.	O
In	O
this	O
model	O
,	O
a	O
similarity	B-Material
matrix	I-Material
for	O
sentence	B-Task
aligning	I-Task
is	O
formed	O
by	O
the	O
similarities	O
of	O
bilingual	O
sentences	O
calculated	O
by	O
the	O
combined	O
clues	O
,	O
such	O
as	O
lexicon	O
,	O
morphology	O
,	O
length	O
and	O
special	O
symbols	O
,	O
etc.	O
;	O
then	O
this	O
similarity	B-Material
matrix	I-Material
is	O
used	O
to	O
construct	B-Process
a	I-Process
select	I-Process
matrix	I-Process
for	O
sentence	B-Task
aligning	I-Task
;	O
finally	O
,	O
obtains	O
the	O
sentence	O
alignments	O
by	O
KEMM	B-Process
.	O
Experimental	O
results	O
illustrated	O
that	O
our	O
model	O
outperforms	O
over	O
the	O
Gale	B-Process
's	I-Process
system	I-Process
on	O
handling	O
any	O
types	O
of	O
sentence	O
alignments	O
,	O
with	O
30	O
%	O
total	O
sentence	B-Task
alignment	I-Task
error	O
rate	O
decreasing	O
.	O

In	O
this	O
paper	O
a	O
comparison	B-Task
between	I-Task
two	I-Task
popular	I-Task
feature	I-Task
extraction	I-Task
methods	I-Task
is	O
presented	O
.	O
Scale-invariant	B-Process
feature	I-Process
transform	I-Process
(	O
or	O
SIFT	B-Process
)	O
is	O
the	O
first	O
method	O
.	O
The	O
Speeded	B-Process
up	I-Process
robust	I-Process
features	I-Process
(	O
or	O
SURF	B-Process
)	O
is	O
presented	O
as	O
second	O
.	O
These	O
two	O
methods	O
are	O
tested	O
on	O
set	O
of	O
depth	B-Material
maps	I-Material
.	O
Ten	O
defined	O
gestures	O
of	O
left	O
hand	O
are	O
in	O
these	O
depth	O
maps	O
.	O
The	O
Microsoft	B-Process
Kinect	I-Process
camera	I-Process
is	O
used	O
for	O
capturing	O
the	O
images	O
[	O
1	O
]	O
.	O
The	O
Support	B-Process
vector	I-Process
machine	I-Process
(	O
or	O
SVM	B-Process
)	O
is	O
used	O
as	O
classification	B-Process
method	O
.	O
The	O
results	O
are	O
accuracy	O
of	O
SVM	B-Process
prediction	O
on	O
selected	O
images	O
.	O

This	O
figure	O
demonstrates	O
that	O
changes	B-Process
in	I-Process
the	I-Process
measure	I-Process
of	I-Process
bitumen	I-Process
content	I-Process
create	O
sizable	O
differences	B-Process
in	I-Process
the	I-Process
stiffness	I-Process
modulus	I-Process
of	I-Process
asphaltic	I-Process
samples	I-Process
that	O
include	O
waste	B-Material
glass	I-Material
cullet	I-Material
.	O
As	O
the	O
percentage	O
of	O
glass	O
increases	O
,	O
the	O
measure	O
of	O
the	O
stiffness	B-Process
modulus	I-Process
of	O
modified	B-Material
asphalt	I-Material
increases	O
too	O
.	O
But	O
with	O
pass	O
of	O
optimum	O
measure	O
of	O
glass	B-Material
the	O
stiffness	B-Process
modulus	I-Process
of	O
asphaltic	B-Material
samples	I-Material
decrease	O
.	O
This	O
trend	O
in	O
total	O
of	O
percentages	O
of	O
bitumen	B-Material
content	O
is	O
existing	O
.	O
Due	O
to	O
that	O
waste	B-Material
glass	I-Material
cullet	I-Material
has	O
no	O
suction	O
;	O
the	O
trend	O
does	O
not	O
extend	O
to	O
measuring	O
the	O
stiffness	B-Process
modulus	I-Process
of	O
asphaltic	B-Material
samples	I-Material
including	O
waste	B-Material
glass	I-Material
cullet	I-Material
with	O
different	O
percentage	O
of	O
bitumen	B-Material
content	O
.	O
Glass	B-Material
particles	I-Material
do	O
not	O
absorb	O
any	O
bituminous	B-Material
material	I-Material
,	O
so	O
it	O
is	O
necessary	O
to	O
decrease	O
the	O
bitumen	B-Material
content	O
with	O
the	O
addition	O
of	O
glass	B-Material
cullet	I-Material
.	O
According	O
to	O
Fig.	O
2	O
and	O
the	O
results	O
of	O
the	O
Marshall	B-Process
tests	I-Process
,	O
the	O
optimum	O
bitumen	B-Material
measures	O
decrease	O
significantly	O
in	O
samples	O
that	O
include	O
higher	O
percentages	O
of	O
waste	B-Material
glass	I-Material
cullet	I-Material
.	O
As	O
the	O
percentage	O
of	O
optimum	O
bitumen	B-Material
content	O
is	O
1	O
%	O
more	O
in	O
samples	O
without	O
waste	B-Material
glass	I-Material
cullet	I-Material
in	O
comparison	O
with	O
saphaltic	B-Material
samples	I-Material
that	O
include	O
20	O
%	O
waste	B-Material
glass	I-Material
cullet	I-Material
.	O
The	O
stiffness	B-Process
modulus	I-Process
of	O
asphaltic	B-Material
samples	I-Material
that	O
include	O
waste	B-Material
glass	I-Material
cullet	I-Material
increased	O
due	O
to	O
additional	O
interlocking	B-Process
between	O
the	O
aggregate	O
and	O
the	O
angularity	O
of	O
particles	O
of	O
glass	B-Material
cullet	I-Material
content	O
.	O
The	O
increase	O
in	O
the	O
intrusive	O
friction	O
angle	O
because	O
of	O
the	O
glass	B-Material
particles’	I-Material
increased	O
angularity	O
is	O
the	O
main	O
reason	O
for	O
the	O
addition	O
of	O
the	O
stiffness	B-Process
modulus	I-Process
of	O
asphaltic	B-Material
samples	I-Material
that	O
include	O
waste	B-Material
glass	I-Material
cullet	I-Material
.	O
But	O
as	O
the	O
percentage	O
of	O
glass	O
content	O
reaches	O
greater	O
than	O
15	O
%	O
,	O
the	O
particles’	O
abundance	O
cause	O
slip	O
these	O
particles	B-Material
on	O
together	O
.	O
The	O
stiffness	B-Process
modulus	I-Process
of	O
samples	O
decreases	O
as	O
the	O
percentage	O
of	O
glass	B-Material
cullet	I-Material
increases	O
.	O
The	O
variations	O
in	O
the	O
stiffness	B-Process
modulus	I-Process
of	O
asphaltic	B-Material
samples	I-Material
that	O
include	O
different	O
percentages	O
of	O
waste	B-Material
glass	I-Material
cullet	I-Material
at	O
different	O
temperature	O
are	O
shown	O
in	O
Fig.	O
3	O
.	O