In
the
present
study,
piecewise
integrated
composite
(PIC)
bumper
beam
for
passenger
cars
was
proposed
and
design
optimisation
process
against
IIHS
test
carried
out
with
help
of
machine
learning.
Several
elements
in
FE
model
have
been
assigned
to
be
references,
order
collect
training
data
which,
allow
learning
study
method
predicting
loading
types
each
finite
element.
2-D
3-D
implementations
were
provided
by
models,
which
determined
stacking
sequences
element
PIC
beam.
It
found
that
beam,
designed
has
direct
impact
on
reducing
possibility
failure
as
well
increasing
bending
strength
effectively
than
conventional
Moreover,
implementation
produced
better
results
compared
since
it
preferable
choose
type
information
achieved
from
surroundings
when
target
located
either
at
corner
or
junction
planes
instead
using
came
same
plane
target.
Ecological Indicators,
Journal Year:
2023,
Volume and Issue:
147, P. 109968 - 109968
Published: Feb. 6, 2023
Landslide
susceptibility
mapping
is
a
meaningful
method
to
avoid
and
reduce
the
loss
from
landslide
hazard.
The
main
goal
of
current
paper
propose
hybrid
model
explore
effect
combining
Best-first
decision
tree
(BFT)
with
Bagging,
Cascade
generalization,
Decorate,
MultiboostAB,
Random
SubSpace
measure
achievement
each
combination
model.
Firstly,
inventory
map
was
produced
using
364
landslides
in
Yongxin
County
China,
then
non-landslide
data
were
generated
based
on
buffer
method.
Secondly,
255
non-landslides
randomly
chosen
for
training
rest
109
validation
data.
Then,
fifteen
environment
factors
chosen.
Thirdly,
Support
vector
machines
(SVM)
applied
analysis
most
useful
modeling.
result
demonstrated
that
all
Several
statistical
indexes
used
performance,
results
revealed
five
models
performed
better
than
single
BFT
BFT-D
BFT-B
best
effective
can
be
adapted
susceptibility.
maps
by
will
help
land
use
arrangement
groundwork
expansion
County.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Jan. 13, 2024
Abstract
Determining
the
degree
of
high
groundwater
arsenic
(As)
and
fluoride
(F
−
)
risk
is
crucial
for
successful
management
protection
public
health,
as
elevated
contamination
in
poses
a
to
environment
human
health.
It
fact
that
several
non-point
sources
pollutants
contaminate
multi-aquifers
Ganges
delta.
This
study
used
logistic
regression
(LR),
random
forest
(RF)
artificial
neural
network
(ANN)
machine
learning
algorithm
evaluate
vulnerability
Holocene
multi-layered
aquifers
delta,
which
part
Indo-Bangladesh
region.
Fifteen
hydro-chemical
data
were
modelling
purposes
sophisticated
statistical
tests
carried
out
check
dataset
regarding
their
dependent
relationships.
ANN
performed
best
with
an
AUC
0.902
validation
prepared
map
accordingly.
The
spatial
distribution
indicates
eastern
some
isolated
south-eastern
central
middle
portions
are
very
vulnerable
terms
As
F
concentration.
overall
prediction
demonstrates
29%
areal
coverage
delta
contents.
Finally,
this
discusses
major
categories,
rising
security
issues,
problems
related
quality
globally.
Henceforth,
monitoring
must
be
significantly
improved
successfully
detect
reduce
hazards
from
past,
present,
future
contamination.
Ecological Informatics,
Journal Year:
2024,
Volume and Issue:
82, P. 102769 - 102769
Published: Aug. 11, 2024
Desertification
is
one
of
the
most
significant
environmental
and
social
challenges
globally.
Monitoring
desertification
dynamics
quantitatively
identifying
contributions
its
driving
factors
are
crucial
for
land
restoration
sustainable
development.
This
study
develops
a
standardized
methodological
framework
that
combines
with
mechanisms
at
pixel
level,
applied
to
northern
China
from
2000
2020.
Using
multisource
data
employing
Time
Series
Segmentation
Residual
Trend
analysis
(TSS-RESTREND)
method
alongside
geographical
detector,
we
assessed
reversion,
expansion,
abrupt
change
processes,
along
impacts
interactions
natural
human
were
assessed.
Over
past
two
decades,
proportion
desertified
decreased
by
5.60%.
Notably,
32.88%
area
experienced
while
only
5.86%
underwent
expansion.
Abrupt
changes
in
both
reversed
expanding
areas
observed,
primarily
central
western
regions,
these
concentrated
periods
2009–2011
2014–2016.
The
various
different
sub-regions
exhibited
spatial
heterogeneity.
Increased
precipitation,
temperature,
evapotranspiration
contributed
reversion
area,
wind
speed
influenced
eastern
area.
Additionally,
population
density
afforestation
activities
also
promoted
reversion.
In
contrast,
precipitation
increased
temperature
expansion
areas,
respectively,
exacerbating
this
process.
Overall,
between
enhanced.
Future
control
ecological
engineering
planning
should
focus
on
coupling
effects
relevant
vegetation
changes.
