Thermal Science,
Год журнала:
2024,
Номер
28(6 Part B), С. 5217 - 5229
Опубликована: Янв. 1, 2024
Multiple
sclerosis
impacts
the
central
nervous
system,
causing
symptoms
like
fatigue,
pain,
and
motor
impairments.
Diagnosing
multiple
often
requires
complex
tests,
MRI
analysis
is
critical
for
accuracy.
Machine
learning
has
emerged
as
a
key
tool
in
neurological
disease
diagnosis.
This
paper
introduces
diagnosis
network
(MSDNet),
stacked
ensemble
of
deep
classifiers
detection.
The
MSDNet
uses
min-max
normalization,
artificial
hummingbird
algorithm
feature
selection,
combination
LSTM,
DNN,
CNN
models.
Hyperparameters
are
optimized
using
enhanced
walrus
optimization
algorithm.
Experimental
results
show
MSDNet's
superior
performance
compared
to
recent
methods.
Journal of Adhesion Science and Technology,
Год журнала:
2025,
Номер
unknown, С. 1 - 26
Опубликована: Янв. 16, 2025
Fibre-reinforced
polymer
(FRP)
composites
are
increasingly
favoured
for
strengthening
existing
structures
due
to
their
numerous
structural
benefits.
Nevertheless,
the
performance
of
such
technology
is
strongly
affected
by
behaviour
epoxy
resin
adhesive
layer,
which
largely
dependent
on
its
curing
conditions.
This
study
introduces
a
deep
learning
(DL)
framework
that
leverages
eXtreme
Gradient
Boosting
(XGBoost)
and
genetic
programming
(GP)
comprehensively
influence
scenarios
vitreous
transition
adhesive.
An
experimental
dataset
comprising
160
data
points
was
used
develop
predictive
models.
The
XGBoost
models
exhibited
high
accuracy
both
onset
temperature
peak
tan
δ
temperature,
achieving
R2
values
0.982
0.993
unseen
test
set,
respectively.
While
GP
lower
with
0.834
0.842,
they
provided
explicit
equations
enhance
interpretability
DL
model
facilitate
practical
application.
To
make
these
insights
accessible
engineers
without
expertise,
web-based
graphical
user
interface
software
developed,
incorporating
all
Additionally,
feature
assessment
conducted,
providing
visual
representations
impact
each
output
results,
thus
enhancing
engineering
applications.
PLoS ONE,
Год журнала:
2025,
Номер
20(3), С. e0319540 - e0319540
Опубликована: Март 20, 2025
Under
the
increasing
pressure
of
global
climate
change,
water
conservation
(WC)
in
semi-arid
regions
is
experiencing
unprecedented
levels
stress.
WC
involves
complex,
nonlinear
interactions
among
ecosystem
components
like
vegetation,
soil
structure,
and
topography,
complicating
research.
This
study
introduces
a
novel
approach
combining
InVEST
modeling,
spatiotemporal
transfer
Water
Conservation
Reserves
(WCR),
deep
learning
to
uncover
regional
patterns
driving
mechanisms.
The
model
evaluates
Xiong’an
New
Area’s
characteristics
from
2000
2020,
showing
74%
average
increase
depth
with
an
inverted
“V”
spatial
distribution.
Spatiotemporal
analysis
identifies
temporal
changes,
WCR
land
use,
key
protection
areas,
revealing
that
Area
primarily
shifts
lowest
areas
lower
areas.
potential
enhancement
are
concentrated
northern
region.
Deep
quantifies
data
complexity,
highlighting
critical
factors
precipitation,
drought
influencing
WC.
detailed
enables
development
personalized
zones
strategies,
offering
new
insights
into
managing
complex
data.
