Polymers for Advanced Technologies,
Год журнала:
2024,
Номер
35(12)
Опубликована: Дек. 1, 2024
ABSTRACT
This
review
provides
a
comprehensive
overview
of
the
emerging
applications
stimuli‐responsive
hydrogels
in
3D
printing,
emphasizing
their
transformative
potential
creating
adaptive
and
multifunctional
structures.
Stimuli‐responsive
hydrogels,
including
magneto‐,
thermo‐,
pH‐,
moisture‐,
solvent‐,
photo‐responsive
varieties,
have
gained
significant
attention
due
to
ability
undergo
dynamic
changes
response
specific
environmental
stimuli.
The
begins
by
exploring
fundamental
characteristics
fabrication
methods
used
additive
manufacturing,
highlighting
exceptional
adaptability
programmability.
It
then
delves
into
various
across
diverse
fields,
soft
robotics,
tissue
engineering,
drug
delivery
systems,
wearable
electronics,
food
technology,
electromagnetic
interference
shielding,
anti‐counterfeiting
technologies.
By
integrating
latest
advancements
printing
techniques,
this
aims
offer
insights
how
are
enabling
development
innovative,
intelligent,
environmentally
responsive
systems.
future
perspectives
section
discusses
challenges
opportunities
for
advancing
use
suggesting
directions
research
that
could
push
boundaries
functional
materials
programmable
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Фев. 12, 2024
Abstract
Promoting
renewable
energy
sources,
particularly
in
the
solar
industry,
has
potential
to
address
shortfall
Central
Africa.
Nevertheless,
a
difficulty
occurs
due
erratic
characteristics
of
irradiance
data,
which
is
influenced
by
climatic
fluctuations
and
challenging
regulate.
The
current
investigation
focuses
on
predicting
an
inclined
surface,
taking
into
consideration
impact
variables
such
as
temperature,
wind
speed,
humidity,
air
pressure.
used
methodology
for
this
objective
Artificial
Neural
Network
(ANN),
inquiry
carried
out
metropolitan
region
Douala.
data
collection
device
research
meteorological
station
located
at
IUT
This
was
built
component
Douala
sustainable
city
effort,
partnership
with
CUD
IRD.
Data
collected
30-min
intervals
duration
around
2
years,
namely
from
January
17,
2019,
October
30,
2020.
aforementioned
been
saved
database
that
underwent
pre-processing
Excel
later
employed
MATLAB
creation
artificial
neural
network
model.
80%
available
utilized
training
network,
15%
allotted
validation,
remaining
5%
testing.
Different
combinations
input
were
evaluated
ascertain
their
individual
degrees
accuracy.
logistic
Sigmoid
function,
50
hidden
layer
neurons,
yielded
correlation
coefficient
98.883%
between
observed
estimated
sun
irradiation.
function
suggested
evaluating
intensities
radiation
place
being
researched
other
sites
have
similar
conditions.
IEEE Access,
Год журнала:
2024,
Номер
12, С. 53497 - 53516
Опубликована: Янв. 1, 2024
This
research
paper
intends
to
provide
real-life
applications
of
Generative
AI
(GAI)
in
the
cybersecurity
domain.
The
frequency,
sophistication
and
impact
cyber
threats
have
continued
rise
today's
world.
ever-evolving
threat
landscape
poses
challenges
for
organizations
security
professionals
who
continue
looking
better
solutions
tackle
these
threats.
GAI
technology
provides
an
effective
way
them
address
issues
automated
manner
with
increasing
efficiency.
It
enables
work
on
more
critical
aspects
which
require
human
intervention,
while
systems
deal
general
situations.
Further,
can
detect
novel
malware
threatening
situations
than
humans.
feature
GAI,
when
leveraged,
lead
higher
robustness
system.
Many
tech
giants
like
Google,
Microsoft
etc.,
are
motivated
by
this
idea
incorporating
elements
their
make
efficient
dealing
tools
Google
Cloud
Security
Workbench,
Copilot,
SentinelOne
Purple
come
into
picture,
leverage
develop
straightforward
robust
ways
emerging
perils.
With
advent
domain,
one
also
needs
take
account
limitations
drawbacks
that
such
have.
some
periodically
giving
wrong
results,
costly
training,
potential
being
used
malicious
actors
illicit
activities
etc.
Frontiers in Neurorobotics,
Год журнала:
2024,
Номер
18
Опубликована: Май 20, 2024
During
the
last
few
years,
a
heightened
interest
has
been
shown
in
classifying
scene
images
depicting
diverse
robotic
environments.
The
surge
can
be
attributed
to
significant
improvements
visual
sensor
technology,
which
enhanced
image
analysis
capabilities.
Since
two-dimensionalal
(2D)
materials
have
distinct
chemical
and
physical
properties,
they
are
widely
used
in
various
sectors
of
modern
technologies.
In
the
domain
diagnostic
biodevices,
particularly
for
point-of-care
(PoC)
biomedical
diagnostics,
2D-based
field-effect
transistor
biosensors
(bio-FETs)
demonstrate
substantial
potential.
Here,
this
review
article,
operational
mechanisms
detection
capabilities
biosensing
devices
utilizing
graphene,
transition
metal
dichalcogenides
(TMDCs),
black
phosphorus,
other
2D
addressed
detail.
The
incorporation
these
into
FET-based
offers
significant
advantages,
including
low
limits
(LOD),
real-time
monitoring,
label-free
diagnosis,
exceptional
selectivity.
also
highlights
diverse
applications
biosensors,
ranging
from
conventional
to
wearable
devices,
underscoring
versatility
material-based
FET
devices.
Additionally,
provides
a
comprehensive
assessment
limitations
challenges
faced
by
along
with
insights
future
prospects
advancements.
Notably,
detailed
comparison
is
tabulated
platforms
their
working
mechanisms.
Ultimately,
aims
stimulate
further
research
innovation
field
while
educating
scientific
community
about
latest
advancements
materials-based
biosensors.
Sensors,
Год журнала:
2024,
Номер
24(10), С. 3032 - 3032
Опубликована: Май 10, 2024
The
domain
of
human
locomotion
identification
through
smartphone
sensors
is
witnessing
rapid
expansion
within
the
realm
research.
This
boasts
significant
potential
across
various
sectors,
including
healthcare,
sports,
security
systems,
home
automation,
and
real-time
location
tracking.
Despite
considerable
volume
existing
research,
greater
portion
it
has
primarily
concentrated
on
activities.
Comparatively
less
emphasis
been
placed
recognition
localization
patterns.
In
current
study,
we
introduce
a
system
by
facilitating
both
physical
location-based
utilizes
capabilities
to
achieve
its
objectives.
Our
goal
develop
that
can
accurately
identify
different
activities,
such
as
walking,
running,
jumping,
indoor,
outdoor
To
this,
perform
preprocessing
raw
sensor
data
using
Butterworth
filter
for
inertial
Median
Filter
Global
Positioning
System
(GPS)
then
applying
Hamming
windowing
techniques
segment
filtered
data.
We
extract
features
from
GPS
select
relevant
variance
threshold
feature
selection
method.
extrasensory
dataset
exhibits
an
imbalanced
number
samples
certain
address
this
issue,
permutation-based
augmentation
technique
employed.
augmented
are
optimized
Yeo–Johnson
power
transformation
algorithm
before
being
sent
multi-layer
perceptron
classification.
evaluate
our
K-fold
cross-validation
technique.
datasets
used
in
study
Extrasensory
Sussex
Huawei
Locomotion
(SHL),
which
contain
experiments
demonstrate
achieves
high
accuracy
with
96%
94%
over
SHL
activities
91%
outperforming
previous
state-of-the-art
methods
recognizing
types
