Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Feb. 26, 2025
A
new
technique
has
been
developed
to
identify
ACL
tears
in
sports
injuries.
This
method
utilizes
a
Convolutional
Neural
Network
(CNN)
combination
with
modified
Political
Optimizer
(IPO)
algorithm,
resulting
major
breakthrough
detecting
tears.
The
study
provides
an
innovative
approach
this
type
of
injury.
CNN/IPO
surpasses
traditional
optimization
techniques,
ensuring
precise
and
timely
detection
the
potential
significantly
improve
treatment
results,
enabling
clinicians
intervene
promptly
effectively,
leading
enhanced
recovery
rehabilitation
for
athletes.
integration
CNN
IPO
algorithm
unparalleled
level
accuracy
efficiency
identifying
tears,
facilitating
more
tailored
strategies
sports-related
findings
have
revolutionize
way
medical
professionals
musculoskeletal
injuries,
enhancing
overall
well-being
athletic
performance.
research's
significance
extends
beyond
medicine,
illuminating
avenues
management
paving
advancements
injury
diagnosis
treatment.
Energy Reports,
Journal Year:
2024,
Volume and Issue:
11, P. 5504 - 5531
Published: May 22, 2024
Electricity
consumption
is
increasing
rapidly,
and
the
limited
availability
of
natural
resources
necessitates
efficient
energy
usage.
Predicting
managing
electricity
costs
challenging,
leading
to
delays
in
pricing.
Smart
appliances
Internet
Things
(IoT)
networks
offer
a
solution
by
enabling
monitoring
control
from
broadcaster
side.
Green
IoT,
also
known
as
Things,
emerges
sustainable
approach
for
communication,
data
management,
device
utilization.
It
leverages
technologies
such
Wireless
Sensor
Networks
(WSN),
Cloud
Computing
(CC),
Machine-to-Machine
(M2M)
Communication,
Data
Centres
(DC),
advanced
metering
infrastructure
reduce
promote
environmentally
friendly
practices
design,
manufacturing,
IoT
optimizes
processing
through
enhanced
signal
bandwidth,
faster
more
communication.
This
comprehensive
review
explores
advancements
smart
grids,
paving
path
sustainability.
covers
energy-efficient
communication
protocols,
intelligent
renewable
integration,
demand
response,
predictive
analytics,
real-time
monitoring.
The
importance
edge
computing
fog
allowing
distributed
intelligence
emphasized.
addresses
challenges,
opportunities
presents
successful
case
studies.
Finally,
concludes
outlining
future
research
avenues
providing
policy
recommendations
foster
advancement
IoT.
Heliyon,
Journal Year:
2024,
Volume and Issue:
10(6), P. e27353 - e27353
Published: Feb. 29, 2024
Predicting
the
electricity
demand
is
a
key
responsibility
for
energy
industry
and
governments
in
order
to
provide
an
effective
dependable
supply.
Traditional
projection
techniques
frequently
rely
on
mathematical
models,
which
are
limited
their
ability
recognize
complex
patterns
correlations
data.
Machine
learning
has
emerged
as
viable
tool
estimating
last
decade.
In
this
study,
Modified
War
Strategy
Optimization-Based
Convolutional
Neural
Network
(MWSO-CNN)
been
provided
prediction.
To
increase
precision
of
prediction,
MWSO-CNN
approach
integrates
benefits
modified
war
strategy
optimization
technique
convolutional
neural
network.
improve
efficiency,
employed
adjust
hyperparameters
CNN
algorithm.
The
suggested
tested
real-world
dataset,
findings
show
that
it
outperforms
many
state-of-the-art
machine
predicting
demand.
general,
could
offer
successful
cost-effective
consumption,
will
benefit
both
business
society
whole.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: June 17, 2024
Abstract
Renewable
microgrids
enhance
security,
reliability,
and
power
quality
in
systems
by
integrating
solar
wind
sources,
reducing
greenhouse
gas
emissions.
This
paper
proposes
a
machine
learning
approach,
leveraging
Gaussian
Process
(GP)
Krill
Herd
Algorithm
(KHA),
for
energy
management
renewable
with
reconfigurable
structure
based
on
remote
switching
of
tie
sectionalizing.
The
method
utilizes
modeling
hybrid
electric
vehicle
(HEV)
charging
demand.
To
counteract
HEV
effects,
two
scenarios
are
explored:
coordinated
intelligent
charging.
A
novel
optimization
inspired
the
(KHA)
is
introduced
complex
problem,
along
self-adaptive
modification
to
tailor
solutions
specific
situations.
Simulation
an
IEEE
microgrid
demonstrates
efficiency
both
scenarios.
predictive
model
yields
remarkably
low
Mean
Absolute
Percentage
Error
(MAPE)
1.02381
total
Results
also
reveal
reduction
operation
cost
scenario
compared
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Jan. 6, 2025
The
microgrid
(MG)
faces
significant
security
issues
due
to
the
two-way
power
and
information
flow.
Integrating
an
Energy
Management
System
(EMS)
balance
energy
supply
demand
in
Malaysian
microgrids,
this
study
designs
a
Fuzzy
Logic
Controller
(FLC)
that
considers
intermittent
renewable
sources
fluctuating
patterns.
FLC
offers
flexible
solution
scheduling
effectively
assessed
by
MATLAB/Simulink
simulations.
consists
of
PV,
battery,
grid,
load.
A
Maximum
Power
Point
Tracking
(MPPT)
controller
helps
extract
maximum
PV
output
manages
storage
providing
or
absorbing
excess
power.
analysis
is
performed
observing
State
Charge
(SoC)of
battery
for
each
source.
grid
supplies
additional
if
fail
meet
load
demand.
Total
Harmonic
Distortion
(THD)
compares
performance
Proportional-Integral
(PIC)
FLC.
results
show
PI
design
reduces
THD
current
signal,
while
does
not
reduce
when
used
EMS.
However,
better
control
over
battery's
SOC,
preventing
overcharging
over-discharging.
While
THD,
provides
superior
SOC
system
comprising
findings
demonstrate
zero
higher
than
80%
lower
20%,
signifying
no
charging
discharging
takes
place
avoid
third
goal
was
accomplished
comparing
confirming
current's
EMS
designed
with
both
maintained
below
5%,
following
IEEE
519
harmonic
standard,
using
block
MATLAB
Simulink.
This
highlights
FLC's
potential
address
demand-supply
mismatches
variability,
which
crucial
optimizing
performance.
