Micromachines,
Год журнала:
2024,
Номер
16(1), С. 15 - 15
Опубликована: Дек. 26, 2024
In
recent
years,
metal
nanomaterials
and
nanoproducts
have
been
developed
intensively,
they
are
now
widely
applied
across
various
sectors,
including
energy,
aerospace,
agriculture,
industry,
biomedicine.
However,
identified
as
potentially
toxic,
with
the
toxicity
of
nanoparticles
posing
significant
risks
to
both
human
health
environment.
Therefore,
toxicological
risk
assessment
is
essential
identify
mitigate
potential
adverse
effects.
This
review
provides
a
comprehensive
analysis
safety
sustainability
metallic
(such
Au
NPs,
Ag
etc.)
in
key
domains
such
medicine,
environmental
protection.
Using
dual-perspective
approach,
it
highlights
unique
advantages
machine
learning
data
processing,
predictive
modeling,
optimization.
At
same
time,
underscores
importance
traditional
methods,
particularly
their
ability
offer
greater
interpretability
more
intuitive
results
specific
contexts.
Finally,
comparative
methods
techniques
for
detecting
presented,
emphasizing
challenges
that
need
be
addressed
future
research.
PLoS ONE,
Год журнала:
2025,
Номер
20(2), С. e0317914 - e0317914
Опубликована: Фев. 6, 2025
Regarding
the
transportation
of
people,
commodities,
and
other
items,
aeroplanes
are
an
essential
need
for
society.
Despite
generally
low
danger
associated
with
various
modes
transportation,
some
accidents
may
occur.
The
creation
a
machine
learning
model
employing
data
from
autonomous-reliant
surveillance
transmissions
is
detection
prediction
commercial
aircraft
accidents.
This
research
included
development
abnormal
categorisation
models,
assessment
recognition
quality,
anomalies.
methodology
consisted
following
steps:
formulation
problem,
selection
labelling,
construction
prediction,
installation,
testing.
tagging
technique
was
based
on
requirements
set
by
Global
Aviation
Organisation
business
jet-engine
aircraft,
which
expert
pilots
then
validated.
93%
precision
demonstrated
excellent
match
most
effective
model,
linear
dipole
Furthermore,
"good
fit"
verified
its
achieved
area-under-the-curve
ratios
0.97
identification
0.96
daily
detection.
Advanced Materials,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 20, 2024
Abstract
Machine
learning
(ML)
has
emerged
as
a
pioneering
tool
in
advancing
the
research
application
of
high‐performance
solid‐state
hydrogen
storage
materials
(HSMs).
This
review
summarizes
state‐of‐the‐art
ML
resolving
crucial
issues
such
low
capacity
and
unfavorable
de‐/hydrogenation
cycling
conditions.
First,
datasets,
feature
descriptors,
prevalent
models
tailored
for
HSMs
are
described.
Specific
examples
include
successful
titanium‐based,
rare‐earth‐based,
solid
solution,
magnesium‐based,
complex
HSMs,
showcasing
its
role
exploiting
composition–structure–property
relationships
designing
novel
specific
applications.
One
representative
works
is
single‐phase
Ti‐based
HSM
with
superior
cost‐effective
comprehensive
properties,
to
fuel
cell
feeding
system
at
ambient
temperature
pressure
through
high‐throughput
composition‐performance
scanning.
More
importantly,
this
also
identifies
critically
analyzes
key
challenges
faced
by
domain,
including
poor
data
quality
availability,
balance
between
model
interpretability
accuracy,
together
feasible
countermeasures
suggested
ameliorate
these
problems.
In
summary,
work
outlines
roadmap
enhancing
ML's
utilization
research,
promoting
more
efficient
sustainable
energy
solutions.
