Buildings,
Год журнала:
2024,
Номер
15(1), С. 16 - 16
Опубликована: Дек. 24, 2024
This
study
presents
a
framework
for
integrating
digital
twins
and
knowledge
graphs
to
enhance
heritage
building
conservation,
addressing
challenges
in
environmental
stress
management,
material
degradation,
structural
integrity
while
preserving
historical
authenticity.
Using
validated
synthetic
data,
the
enables
real-time
monitoring,
predictive
maintenance,
emergency
response
through
twin
connected
graph.
Four
scenarios
were
simulated
evaluate
system
performance:
high
humidity
exceeding
75%
threshold
triggered
alerts
limestone
maintenance;
temperature
fluctuations
caused
strain
levels
up
0.009
units
load-bearing
components
at
35
°C,
necessitating
inspection;
cumulative
degradation
monitoring
projected
re-plastering
needs
by
month
eight
as
plaster
index
approached
85%;
sudden
impact
events
responses,
with
spikes
over
0.004
prompting
within
2.5
s.
Response
times
averaged
50
ms
under
normal
conditions,
peaking
150
during
high-frequency
updates,
showing
robust
Application
Programming
Interface
(API)
performance
data
synchronization.
SPARQL
(SPARQL
Protocol
RDF
Query
Language)
queries
graph
facilitated
proactive
maintenance
scheduling,
reducing
reactive
interventions
supporting
sustainable
especially
suited
humid–temperate
climates.
offers
novel,
structured
approach
that
bridges
modern
technology
preservation
needs,
both
urgent
conservation
long-term
sustainability
ensure
resilience
of
buildings.
Mathematics,
Год журнала:
2025,
Номер
13(6), С. 981 - 981
Опубликована: Март 17, 2025
In
Industry
4.0,
predictive
maintenance
(PdM)
is
key
to
optimising
production
processes.
While
its
popularity
among
companies
grows,
most
studies
highlight
theoretical
benefits,
with
few
providing
empirical
evidence
on
economic
impact.
This
study
aims
fill
this
gap
by
quantifying
the
performance
of
manufacturing
in
Visegrad
Group
countries
through
PdM
algorithms.
The
purpose
our
research
assess
whether
these
generate
higher
operational
profits
and
lower
sales
costs.
Using
descriptive
statistics,
non-parametric
tests,
Hodges–Lehmann
median
difference
estimate,
linear
regression,
authors
analysed
data
1094
enterprises.
Results
show
that
significantly
improves
performance,
variations
based
geographic
scope.
Regression
analysis
confirmed
as
an
essential
predictor
even
after
considering
factors
like
company
size,
legal
structure,
Enterprises
more
effective
cost
management
net
were
likely
adopt
PdM,
revealed
decision
tree
analysis.
Our
findings
provide
benefits
algorithms
their
potential
enhance
competitiveness,
offering
a
valuable
foundation
for
business
managers
make
informed
investment
decisions
encouraging
further
other
industries.
Advances in computational intelligence and robotics book series,
Год журнала:
2025,
Номер
unknown, С. 463 - 494
Опубликована: Март 7, 2025
This
chapter
provides
a
comprehensive
overview
of
the
integration
artificial
intelligence
(AI)
and
related
technologies
in
manufacturing
sector,
detonating
Smart
Manufacturing.
It
discusses
pivotal
role
AI
enhancing
operational
efficiency,
optimizing
production
processes,
improving
product
quality
through
data-driven
decision-making,
including
IoT
Big
Data
integration.
All
these
are
applied
in.
predictive
maintenance,
control,
supply
chain
optimization,
showcasing
real-world
case
studies
examples.
Additionally,
it
addresses
challenges
opportunities
associated
with
implementing
interaction
of.
AI,
IoT,
Industry
4.0,
data
manufacturing,
emphasizing
importance
fostering
culture
innovation
continuous
improvement.
The
findings
underscore
transformative
potential
driving
evolution
smart
ultimately
contributing
to
increased
competitiveness
rapidly
changing
global
economy.
Advances in computational intelligence and robotics book series,
Год журнала:
2025,
Номер
unknown, С. 281 - 310
Опубликована: Апрель 18, 2025
AI
is
now
an
everyday
aspect
of
many
people's
daily
lives
worldwide.
Businesses
have
more
potential
to
use
optimise
some
risky
or
repetitive
processes
that
were
previously
handled
by
humans,
but
people
are
often
afraid
due
concerns
about
privacy
and
lost
job
opportunities.
Meanwhile,
has
also
become
a
cross-cultural
phenomenon.
Perceptions
thought
change
between
cultures,
advanced
emerging
economies
may
different
ideas
how
will
develop
in
the
future.
Therefore,
order
comprehend
relationship
culture
usage
AI,
it
crucial
look
into
individual
organisational
variances
attitudes
towards
trust
based
on
cultural
differences.
Applied Sciences,
Год журнала:
2025,
Номер
15(10), С. 5235 - 5235
Опубликована: Май 8, 2025
Background:
Accurate
estrus
identification
in
dairy
cows
is
essential
for
enhancing
reproductive
efficiency
and
economic
performance.
The
dispersed
nature
of
data
individual
cow
differences
pose
significant
challenges
accurate
identification.
Methods:
This
study
gathered
from
812
literature
sources
using
Python
3.9
crawler
technology.
were
then
preprocessed
CiteSpace
6.4.
We
constructed
a
knowledge
graph
depicting
physiological,
behavioral,
appearance
changes
during
through
entity
relationship
extraction.
To
uncover
potential
relationships
within
the
graph,
we
applied
compared
two
association
rule
algorithms:
FP-Growth
Apriori.
utilized
Boolean
functions
derived
learning
to
validate
ability
rules
identify
normal
estrus.
Additionally,
employed
an
enhanced
Iforest-OCSVM
anomaly
detection
model
assess
performance
detecting
abnormal
Furthermore,
optimized
Incremental
Algorithm
Dynamic
Knowledge
Expansion.
Results:
Based
on
initial
with
86
entities
9
relationships,
mining
added
17
new
strong
marked
by
‘with’,
its
completeness
providing
deeper
insights
into
behaviors
physiological
changes.
these
exhibited
notable
effectiveness
both
detection,
validating
their
robustness
practical
applications.
algorithm’s
optimization
bolstered
scalability,
making
it
more
adaptable
future
expansions
complex
integrations.
Conclusions:
By
constructing
that
integrates
estrus,
established
comprehensive
framework
understanding
Association
mining,
particularly
algorithm,
enriching
content
offering
demonstrated
utility
accuracy,
robust
foundation
multi-dimensional
research.
