Buildings,
Journal Year:
2024,
Volume and Issue:
14(12), P. 4024 - 4024
Published: Dec. 18, 2024
The
relationship
between
coronary
heart
disease
(CHD)
and
complex
urban
built
environments
remains
a
subject
of
considerable
uncertainty.
development
predictive
models
via
machine
learning
to
explore
the
underlying
mechanisms
this
association,
as
well
formulation
intervention
policies
planning
strategies,
has
emerged
pivotal
area
research.
A
cross-sectional
dataset
hospital
admissions
for
CHD
over
course
year
from
in
Dalian
City,
China,
was
assembled
matched
with
multi-source
environment
data
residential
addresses.
This
study
evaluates
five
models,
including
decision
tree
(DT),
random
forest
(RF),
eXtreme
gradient
boosting
(XGBoost),
multi-layer
perceptron
(MLP),
support
vector
(SVM),
compares
them
multiple
linear
regression
models.
results
show
that
DT,
RF,
XGBoost
exhibit
superior
capabilities,
all
R2
values
exceeding
0.70.
DT
model
performed
best,
an
value
0.818,
best
performance
based
on
metrics
such
MAE
MSE.
Additionally,
using
explainable
AI
techniques,
reveals
contribution
different
factors
identifies
significant
influencing
cold
regions,
ranked
age,
Digital
Elevation
Model
(DEM),
house
price
(HP),
sky
view
factor
(SVF),
interaction
factors.
Stratified
analyses
by
age
gender
variations
groups:
those
under
60
years
old,
Road
Density
is
most
influential
factor;
61–70
group,
top
71–80
81
building
height
leading
males,
GDP
females,
factor.
explores
feasibility
predicting
risk
regions
provides
comprehensive
methodology
workflow
cardiovascular
refined
neighborhood-level
factors,
offering
scientific
construction
sustainable
healthy
cities.
Sustainability,
Journal Year:
2025,
Volume and Issue:
17(8), P. 3747 - 3747
Published: April 21, 2025
As
climate
change
intensifies,
urban
populations
face
growing
threats
from
frequent
and
severe
heatwaves,
underscoring
the
urgent
need
for
advanced
risk
assessment
frameworks
to
inform
adaptation
strategies.
This
systematic
review
synthesizes
methodological
innovations
in
heatwave
(2007–2024),
analyzing
259
studies
through
bibliometric
analysis
(CiteSpace
6.4.R1)
multi-criteria
evaluation.
We
propose
hazard–exposure–vulnerability–adaptability
(HEVA)
framework,
an
extension
of
Crichton’s
triangle
that
integrates
dynamic
adaptability
metrics
supports
high-resolution
spatial
assessment.
Our
reveals
three
key
gaps:
(1)
Inconsistent
indicator
selection
across
studies;
(2)
limited
microclimatic
variations;
(3)
sparse
integration
IoT-
or
satellite-based
monitoring.
The
study
offers
practical
solutions
enhancing
accuracy,
including
refined
weighting
methodologies
techniques.
conclude
by
proposing
a
research
agenda
prioritizes
interdisciplinary
approaches—bridging
planning,
science,
public
health—while
advocating
policy
tools
address
inequities
heat
exposure.
These
insights
advance
development
more
precise,
actionable
systems
support
climate-resilient
development.
PLoS ONE,
Journal Year:
2025,
Volume and Issue:
20(5), P. e0323566 - e0323566
Published: May 15, 2025
Natural
landscapes
are
crucial
resources
for
enhancing
visitor
experiences
in
ecotourism
destinations.
Previous
research
indicates
that
high
temperatures
may
impact
tourists’
perception
of
and
emotions.
Still,
the
potential
value
natural
landscape
regulating
emotions
under
high-temperature
conditions
remains
unclear.
In
this
study,
we
employed
machine
learning
models
such
as
LSTM-CNN,
Hrnet,
XGBoost,
combined
with
hotspot
analysis
SHAP
methods,
to
compare
reveal
impacts
elements
on
different
temperature
conditions.
The
results
indicate:
(1)
Emotion
prediction
spatial
a
significant
increase
proportion
negative
conditions,
reaching
30.1%,
emotion
hotspots
concentrated
downtown
area,
whereas,
non-high
accounted
14.1%,
more
uniform
distribution.
(2)
Under
four
most
influential
factors
were
Color
complexity
(0.73),
Visual
entropy
(0.71),
Greenness
(0.68),
Aquatic
rate
(0.6).
contrast,
(0.6),
Openness
(0.56),
(0.55),
(0.55).
(3)
Compared
enhanced
positive
effects
environmental
emotions,
(0.94),
(0.84),
Enclosure
(0.71)
showing
stable
impacts.
Additionally,
aquatic
had
emotional
regulation
effect
(contribution
1.05),
effectively
improving
overall
experience.
This
study
provides
data
foundation
optimizing
destinations,
integrating
advantages
various
proposing
framework
collection,
comparison,
evaluation
It
thoroughly
explores
enhance
sustainable
planning
recommendations
conservation
ecosystems
ecotourism.