ACS Applied Electronic Materials,
Journal Year:
2024,
Volume and Issue:
7(1), P. 1 - 12
Published: Dec. 23, 2024
The
liquid–liquid
triboelectric
nanogenerator
(L-L
TENG)
is
an
emerging
nanogeneration
technology
that
converts
weak
mechanical
energy,
tidal
and
other
forms
of
energy
into
electricity
through
the
frictional
interactions
between
liquids.
This
paper
reviews
research
progress
L-L
TENG.
First,
it
provides
overview
working
principles
TENG,
analyzes
its
basic
mechanisms,
summarizes
fundamental
operation
modes
while
organizing
materials
currently
used
for
charge
transfer.
Additionally,
this
outlines
applications
TENG
in
harvesting,
medicine,
fields,
offering
insights
performance
enhancement
expansion
application
scenarios.
Finally,
discusses
challenges
facing
development
as
well
future
direction
potential
applications.
Overall,
conversion
technology,
has
attracted
widespread
interest
from
scientists
worldwide.
review
aims
to
provide
scientists,
engineers,
researchers
related
fields
with
a
comprehensive
perspective
further
advance
area
research.
Protection and Control of Modern Power Systems,
Journal Year:
2024,
Volume and Issue:
9(6), P. 1 - 18
Published: Nov. 1, 2024
Supercapacitors
(SCs)
are
widely
recognized
as
excellent
clean
energy
storage
devices.
Accurate
state
of
health
(SOH)
estimation
and
remaining
useful
life
(RUL)
prediction
essential
for
ensuring
their
safe
reliable
operation.
This
paper
introduces
a
novel
method
SOH
RUL
prediction,
based
on
hybrid
neural
network
optimized
by
an
improved
honey
badger
algorithm
(HBA).
The
combines
the
advantages
convolutional
(CNN)
bidirectional
long-short-term
memory
(BiLSTM)
network.
HBA
optimizes
hyperparameters
CNN
automatically
extracts
deep
features
from
time
series
data
reduces
dimensionality,
which
then
used
input
BiLSTM.
Additionally,
recurrent
dropout
is
introduced
in
layer
to
reduce
overfitting
facilitate
learning
process.
approach
not
only
improves
accuracy
estimates
forecasts
but
also
significantly
processing
time.
SCs
under
different
working
conditions
validate
proposed
method.
results
show
that
model
effectively
features,
enriches
local
details,
enhances
global
perception
capabilities.
outperforms
single
models,
reducing
root
mean
square
error
below
1%,
offers
higher
robustness
compared
other
methods.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Oct. 9, 2024
Accurate
runoff
forecasting
is
of
great
significance
for
water
resource
allocation
flood
control
and
disaster
reduction.
However,
due
to
the
inherent
strong
randomness
sequences,
this
task
faces
significant
challenges.
To
address
challenge,
study
proposes
a
new
SMGformer
forecast
model.
The
model
integrates
Seasonal
Trend
decomposition
using
Loess
(STL),
Informer's
Encoder
layer,
Bidirectional
Gated
Recurrent
Unit
(BiGRU),
Multi-head
self-attention
(MHSA).
Firstly,
in
response
nonlinear
non-stationary
characteristics
sequence,
STL
used
extract
sequence's
trend,
period,
residual
terms,
multi-feature
set
based
on
'sequence-sequence'
constructed
as
input
model,
providing
foundation
subsequent
models
capture
evolution
runoff.
key
features
are
then
captured
layer.
Next,
BiGRU
layer
learn
temporal
information
these
features.
further
optimize
output
MHSA
mechanism
introduced
emphasize
impact
important
information.
Finally,
accurate
achieved
by
transforming
through
Fully
connected
verify
effectiveness
proposed
monthly
data
from
two
hydrological
stations
China
selected,
eight
compare
performance
results
show
that
compared
with
Informer
1th
step
MAE
decreases
42.2%
36.6%,
respectively;
RMSE
37.9%
43.6%
NSE
increases
0.936
0.975
0.487
0.837,
respectively.
In
addition,
KGE
at
3th
0.960
0.805,
both
which
can
maintain
above
0.8.
Therefore,
accurately
sequence
extend
effective
period
