Geopolymer
is
regarded
as
a
novel
type
of
eco-friendly
material
that
may
replace
cement.
To
improve
the
prediction
accuracy
mechanical
properties
fly
ash-slag-based
geopolymer
(FASGG),
well
optimize
composition
and
mix
design,
this
study
utilizes
seven
key
parameters
variables,
compressive
flexural
strengths
were
outputs.
Deep
learning
techniques
applied
to
train
predict
600
sets
experimental
data,
developing
predictive
model
MK-CNN-GRU,
which
integrated
Maximal
Information
Coefficient-K-median
algorithm,
Convolutional
Neural
Network,
Gated
Recurrent
Unit
algorithms.
Results
indicated
ranking
input
related
with
strength
was
curing
age,
Ca/Si
ratio,
ash-to-slag
Si/Al
water-to-binder
alkali
activator
modulus,
equivalent.
Three
classical
models
selected
benchmarks
for
predicting
at
different
ages.
The
MK-CNN-GRU
could
fully
exploit
internal
features
data
learn
its
variation
patterns,
resulting
in
more
stable
performance.
An
ablation
submodels
confirms
considers
temporal
dependencies,
long-
short-term
features,
local
dependencies
hierarchical
feature
representations
within
data.
Experimental
suggested
an
exponential
relationship
between
FASGG.
predictions
effectively
captured
variations,
demonstrating
good
generalization
ability
applicability.
This
enhances
estimation
regarding
behavior
FASGG,
offering
theoretical
framework
refining
design.
Sustainable Development,
Год журнала:
2023,
Номер
32(3), С. 1700 - 1722
Опубликована: Сен. 5, 2023
Abstract
Environmental
Kuznets
curve
(EKC)
is
one
of
the
key
theories
economic
and
environmentally
sustainable
development.
Has
change
in
geopolitics
recent
years
affected
international
collaboration
study
EKC?
Based
on
publications
EKC
included
Web
Science,
a
case
changes
China–US
conducted
to
explore
impact
geopolitical
collaborative
research
The
results
show
that
(1)
global
study,
countries
around
world
have
made
more
efforts,
among
which
China,
United
States,
Turkey,
Malaysia,
England
other
most
contributions
literature
database
are
with
development
potential
this
field.
(2)
International
between
China
States
field
has
gradually
increased,
scientific
two
increased
under
influence
conflicts,
shown
characteristics
lasting
stability.
(3)
exhibit
divergent
patterns,
Pakistan
being
China's
foremost
partner
domain,
while
serves
as
US’
primary
collaborator.
Furthermore,
demonstrates
significantly
higher
volume
independently
published
works
compared
highlighting
strengthening
capabilities.
number
collaborating
internationally
shows
different
upward
trend
than
across
three
data
sets
set
paper.
(4)
In
hot
research,
maintained
good
partnerships
countries,
preferred
for
world.
It
can
be
seen
from
represented
by
relative
stability,
not
had
significant
Heliyon,
Год журнала:
2024,
Номер
10(2), С. e24321 - e24321
Опубликована: Янв. 1, 2024
When
it
comes
to
the
environmental
costs,
economists
have
tried
study
effects
of
foreign
direct
investment-growth
nexus,
but
they
ignored
crucial
role
that
financial
development
and
technical
innovation
play.
Massive
increases
in
energy
consumption
contributed
degradation
BRICS
nations,
which
experienced
rapid
IND
due
their
robust
economies.
This
uses
data
from
1990
2021
examine
relationship
between
carbon
emissions
member
nations
factors
such
as
FDI,
technological
innovation,
economic
growth.
Within
panel
results
confirm
a
high
cross-sectional
reliance.
The
countries'
development,
investment
all
negative
statistically
significant
long-run
association
with
CO2
emissions,
according
Augmented
Mean
Group
(AMG)
estimator.
On
other
hand,
growth,
TI,
IND,
use
positive
associations
emissions.
study's
researchers
choose
Dumitrescu
Hurlin
causality
test
look
at
way
around.
Economic
growth
(EG),
Digital
(DEG),
Financial
efficiency
(FE),
(CO2),
Industrialization
(IND),
Technological
Innovation
(TI),
Foreign
(FDI)
Inflation
are
identified
having
bidirectional
causative
relationship.
In
contrast,
unidirectional
causal
is
observed
FDI
To
entice
high-quality
must
advance
industries,
institutions,
innovation.
addition,
these
need
immediate
legislative
solutions
because
major
cause
damage.
Frontiers of Engineering Management,
Год журнала:
2024,
Номер
unknown
Опубликована: Июнь 21, 2024
Abstract
The
rapid
increase
in
global
urbanization,
along
with
the
growth
of
construction
industry,
highlights
urgent
need
for
effective
management
and
demolition
(C&D)
waste.
Intelligent
technologies
offer
a
viable
solution
to
this
critical
challenge.
However,
there
remains
significant
challenge
integrating
these
into
cohesive
framework.
This
study
conducts
quantitative
analysis
214
papers
from
2000
2023,
highlighting
extensive
use
artificial
intelligence
(AI)
building
information
modeling
(BIM),
geographic
systems
(GIS)
big
data
(BD).
A
further
qualitative
73
selected
investigates
seven
different
intelligent
context
C&D
waste
(CDWM).
To
overcome
current
limitations
knowledge,
future
research
should
concentrate
on
(1)
comprehensive
integration
technology,
(2)
inclusive
studies
throughout
all
lifecycle
phases
CDWM,
(3)
continued
examination
new
technologies,
such
as
blockchain.
Based
insights,
suggests
strategic
framework
implementation
CDWM.
aims
assist
professionals
merging
various
undertaking
lifecycle-wide
research,
narrowing
divide
between
existing
technologies.
It
also
lays
solid
foundation
academic
work
examine
specific
conduct
comparative
studies,
refine
decisions.
Regular
updates
technological
developments
are
essential
stakeholders
consistently
enhance
CDWM
standards.
