Advances in educational technologies and instructional design book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 59 - 76
Published: Dec. 20, 2024
In
the
evolving
landscape
of
smart
education,
need
for
robust
and
efficient
network
connectivity
is
paramount
delivering
seamless
learning
experiences.
This
chapter
explores
integration
AI-driven
optimization
techniques
aimed
at
enhancing
improving
user
experiences
in
educational
environments.
By
leveraging
intelligent
design
principles,
this
work
examines
how
AI
algorithms
can
dynamically
manage
resources,
predict
traffic
patterns,
facilitate
adaptive
environments
that
respond
to
real-time
demands.
Key
topics
include
application
machine
performance
monitoring,
role
optimizing
bandwidth
allocation,
strategies
ensuring
reliability
scalability
networks.
The
also
discusses
case
studies
illustrating
practical
implementation
optimization,
showcasing
improvements
student
engagement,
accessibility,
overall
outcomes.
Frontiers of Urban and Rural Planning,
Journal Year:
2025,
Volume and Issue:
3(1)
Published: March 7, 2025
This
paper
explores
the
evolution
of
Geodesign
in
addressing
spatial
and
environmental
challenges
from
its
early
foundations
to
recent
integration
artificial
intelligence
(AI).
AI
enhances
existing
methods
by
automating
data
analysis,
improving
land
use
classification,
refining
heat
island
effect
assessment,
optimizing
energy
use,
facilitating
green
infrastructure
planning,
generating
design
scenarios.
Despite
transformative
potential
Geodesign,
related
quality,
model
interpretability,
ethical
concerns
such
as
privacy
bias
persist.
highlights
case
studies
that
demonstrate
application
offering
insights
into
role
understanding
systems
designing
future
changes.
The
concludes
advocating
for
responsible
transparent
ensure
equitable
effective
outcomes.
Sustainability,
Journal Year:
2025,
Volume and Issue:
17(7), P. 3099 - 3099
Published: March 31, 2025
Adopting
innovative,
systematically
structured,
and
sustainable
human
resource
management
(SHRM)
practices
is
essential
for
enhancing
logistics
agility
deriving
development
in
operations.
This
study
examines
the
influence
of
resources
on
with
a
mediating
role
artificial
intelligence
(AI)
China’s
industry.
Given
rapid
growth
technological
advancements
sector,
this
employed
quantitative
research
convenience
sampling
techniques
to
collect
data
from
341
employees
working
Smart
PLS
was
used
test
proposed
hypotheses
through
structural
equation
modeling
(SEM).
The
study’s
findings
reveal
that
reward
management,
training
development,
job
appraisal,
teamwork
significantly
enhance
agility,
while
recruitment
selection
show
an
insignificant
impact.
Similarly,
results
HRM
positively
agility.
In
addition,
substantially
mediates
relationship
between
These
offer
valuable
insights
highlighting
how
AI
can
strengthen
foster
improve
performance.
are
particularly
relevant
practitioners
policymakers
aiming
sustainability
efficiency
sector.
World Electric Vehicle Journal,
Journal Year:
2025,
Volume and Issue:
16(4), P. 240 - 240
Published: April 20, 2025
Reducing
carbon
dioxide
emissions
in
transportation
has
become
a
priority
for
achieving
emission
targets.
Transitioning
to
electric
vehicles
significantly
decreases
global
CO2
and
reduces
urban
noise
air
pollution.
The
selection
of
efficient
charging
strategies
bus
fleets
substantially
influences
their
environmental
impact.
This
study
analyzes
the
strategy
based
on
real
operational
data
from
Győr,
Hungary.
It
evaluates
impact
different
times
emissions,
considering
energy
mixes
Hungary,
Poland,
Germany,
Sweden.
A
methodology
been
developed
defining
sustainable
environmentally
friendly
by
incorporating
conditions
as
well
daily,
monthly,
seasonal
fluctuations
factors.
Results
indicate
substantial
potential
reduction
through
recommended
alternative
strategies,
although
further
studies
regarding
battery
lifespan
economic
feasibility
infrastructure
investments
are
recommended.
novelty
this
work
lies
integrating
with
hourly
country-specific
intensity
values
assess
impacts
dynamically.
comparative
framework
four
provides
quantifiable
insights
into
under
diverse
national
mixes.
Advances in computational intelligence and robotics book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 435 - 462
Published: March 7, 2025
Due
to
increasing
urbanization,
smart
cities
have
developed
rapidly,
and
they
focus
on
technology
driven
infrastructure
sustainable
development.
With
becoming
more
digital,
Corporate
Social
Responsibility
(CSR)
Artificial
Intelligence
(AI)
are
key
issues
in
determining
the
urban
habitat
of
future.
This
work
investigates
relationship
between
CSR,
AI
cities,
their
implications
for
Aiming
from
perspective
role
city
making
responsibility
corporations
enhancing
environment,
this
chapter
discusses
opportunities
difficulties
combining
CSR
building
liveable,
efficient,
cities.
More
specifically,
study
aims
help
extend
understanding
entanglement
corporate
responsibility,
technological
innovation,
sustainability
guide
development
resilient
just
Deleted Journal,
Journal Year:
2025,
Volume and Issue:
7(4)
Published: March 29, 2025
Abstract
Cloud
computing
environments
encounter
significant
challenges
in
resource
management
through
queueing
and
scheduling
systems,
as
traditional
methods
struggle
with
dynamic
workload
optimization.
This
research
introduces
an
innovative
AI-enhanced
framework
combining
deep
prediction
reinforcement
learning
for
scheduling.
The
features
a
dual-layer
neural
network
architecture
hybrid
decision
engine
that
merges
conventional
metrics
learned
policies.
Experimental
results
simulated
cloud
environment
showcase
remarkable
improvements:
30%
decrease
average
waiting
time,
25%
optimization
queue
length,
91%
peak
utilization
compared
to
approaches.
model
demonstrates
20–35%
higher
throughput
rates
across
various
intensities,
the
scheduler
maintaining
steady
performance
under
high
loads.
Statistical
confidence
levels
exceed
95%,
validating
approach's
effectiveness.
provides
practical
solutions
service
providers,
enabling
implementation
of
efficient,
adaptive
systems
reduce
operational
costs
while
enhancing
quality.
modular
ensures
scalability
seamless
integration
existing
infrastructure,
making
it
particularly
valuable
large-scale
production
environments.
Sustainability,
Journal Year:
2025,
Volume and Issue:
17(8), P. 3383 - 3383
Published: April 10, 2025
Efficient
traffic
management
in
urban
areas
represents
a
key
challenge
for
modern
cities,
particularly
the
context
of
sustainable
development
and
reducing
negative
environmental
impacts.
This
paper
explores
application
artificial
intelligence
(AI)
optimizing
through
combination
reinforcement
learning
(RL)
predictive
analytics.
The
focus
is
on
simulating
network
Belgrade
(Serbia,
Europe),
where
RL
algorithms,
such
as
Deep
Q-Learning
Proximal
Policy
Optimization,
are
used
dynamic
signal
control.
model
optimized
operations
at
intersections
with
high
volumes
using
real-time
data
from
IoT
sensors,
computer
vision-enabled
cameras,
third-party
mobile
usage
connected
vehicles.
In
addition,
implemented
analytics
leverage
time
series
models
(LSTM,
ARIMA)
graph
neural
networks
(GNNs)
to
anticipate
congestion
bottlenecks,
enabling
initiative-taking
decision-making.
Special
attention
given
challenges
transmission
delays,
system
scalability,
ethical
implications,
proposed
solutions
including
edge
computing
distributed
models.
Results
simulation
demonstrate
significant
advantages
AI
370
control
devices
installed
fixed
timing
systems
adaptive
systems,
an
average
reduction
waiting
times
by
33%,
resulting
16%
decrease
greenhouse
gas
emissions
improved
safety
(measured
number
accidents).
A
limitation
this
that
it
does
not
offer
system’s
adaptability
temporary
surges
during
mass
events
or
severe
weather
conditions.
finding
integrating
into
consists
fixed-timing
lights
approach
improving
quality
life
large
cities
like
achieving
smart
city
objectives.
