Contemporary Agriculture,
Год журнала:
2024,
Номер
73(3-4), С. 238 - 249
Опубликована: Дек. 1, 2024
Summary
The
primary
objective
of
this
study
was
to
forecast
the
labour
productivity
in
Algeria's
agricultural
sector
by
year
2030
using
seasonal
autoregressive
integrated
moving
average
(SARIMA)
model.
Quarterly
data
spanning
from
first
quarter
1991
fourth
2021
were
analyzed,
identifying
SARIMA
model
(1,
1,
1)
x
4)
as
most
suitable
for
capturing
variations
and
accurately
fitting
historical
data.
methodology
utilized
Python
3.11.5
processing
modelling,
thus
enabling
a
comprehensive
analysis
trends
patterns
Algerian
productivity.
results
obtained
demonstrate
robust
steady
growth
attributable
advancements
farming
techniques,
technological
innovations,
evolving
market
conditions.
These
findings
highlight
critical
role
accurate
forecasting
effective
policy-making
resource
allocation.
By
providing
insights
into
future
trends,
research
supports
development
strategies
aimed
at
enhancing
resilience
sustainability
sector,
particularly
face
challenges
posed
climate
change
geopolitical
tensions.
conclusion
underscores
importance
leveraging
predictive
models
such
informing
policies
ensuring
long-term
food
security
economic
stability
Algeria.
Sustainability,
Год журнала:
2024,
Номер
16(19), С. 8336 - 8336
Опубликована: Сен. 25, 2024
This
paper
explores
new
sensor
technologies
and
their
integration
within
Connected
Autonomous
Vehicles
(CAVs)
for
real-time
road
condition
monitoring.
Sensors
like
accelerometers,
gyroscopes,
LiDAR,
cameras,
radar
that
have
been
made
available
on
CAVs
are
able
to
detect
anomalies
roads,
including
potholes,
surface
cracks,
or
roughness.
also
describes
advanced
data
processing
techniques
of
detected
with
sensors,
machine
learning
algorithms,
fusion,
edge
computing,
which
enhance
accuracy
reliability
in
assessment.
Together,
these
support
instant
safety
long-term
maintenance
cost
reduction
proactive
strategies.
Finally,
this
article
provides
a
comprehensive
review
the
state-of-the-art
future
directions
monitoring
systems
traditional
smart
roads.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Апрель 21, 2025
Abstract
Multi-channel
sensor
data
often
suffer
from
missing
or
corrupted
values
due
to
failures,
communication
disruptions,
environmental
interference.
These
issues
severely
limit
the
accuracy
of
intelligent
systems
relying
on
integration.
Existing
restoration
techniques
fail
capture
complex
correlations
among
channels,
especially
when
losses
occur
randomly
and
continuously.
To
overcome
these
limitations,
we
propose
an
autoencoder-based
recovery
algorithm
that
recursively
feeds
reconstructed
outputs
back
into
model
progressively
refine
estimates.
A
dynamic
termination
criterion
monitors
reconstruction
improvements,
automatically
stopping
iterations
further
refinements
become
negligible.
This
recursive
input
strategy
significantly
enhances
computational
efficiency
compared
conventional
single-step
methods.
Experiments
multivariate
datasets
show
proposed
method
outperforms
one-time
autoencoder
maintains
robust
performance
across
diverse
scenarios.
approach
provides
a
scalable
adaptable
solution
ensure
integrity
in
networks,
enabling
improved
reliability
operational
industrial
technological
applications.
The International Journal of Advanced Manufacturing Technology,
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 29, 2024
Abstract
High-pressure
die
casting
(HPDC)
is
a
permanent
mold-based
production
technology
that
facilitates
the
of
near
net
shape
components
from
nonferrous
alloys.
The
pressure
and
temperature
conditions
within
cavity
impact
cast
product
quality
during
after
conclusion
filling
process.
Die
surface
sensors
can
deliver
information
describing
at
die-casting
interface.
They
are
associated
with
high
costs
limited
service
lifetimes
below
achievable
total
cycle
count
inserts
therefore
ill-suited
for
industrial
use
cases.
In
this
work,
suitability
long
short-term
memory
(LSTM)
recurrent
neural
networks
(RNN)
substituting
physical
virtually
ramp-up
or
end
sensor
life
investigated.
Training
LSTMs
data
233
cycles
different
process
parameters
provides
which
then
applied
to
99
further
cycles.
prediction
accuracy
investigated
time
interval
lengths
in
solidification
cooling
phase.
For
longer
intervals,
deteriorates,
potentially
due
highly
individual
hardly
ascertainable
buildup
distortion
internal
stresses.
Overall,
however,
developed
excellent
temperatures
good
pressures.
Sensors,
Год журнала:
2024,
Номер
24(4), С. 1236 - 1236
Опубликована: Фев. 15, 2024
In
the
era
of
Industry
4.0
and
5.0,
a
transformative
wave
softwarisation
has
surged.
This
shift
towards
software-centric
frameworks
been
cornerstone
highlighted
need
to
comprehend
software
applications.
research
introduces
novel
agent-based
architecture
designed
sense
predict
application
metrics
in
industrial
scenarios
using
AI
techniques.
It
comprises
interconnected
agents
that
aim
enhance
operational
insights
decision-making
processes.
The
forecaster
component
uses
random
forest
regressor
known
aggregated
metrics.
Further
analysis
demonstrates
overall
robust
predictive
capabilities.
Visual
representations
an
error
underscore
forecasting
accuracy
limitations.
work
establishes
foundational
understanding
for
behaviours,
charting
course
future
advancements
components
within
evolving
landscapes.
