Journal of Personalized Medicine,
Journal Year:
2023,
Volume and Issue:
13(12), P. 1625 - 1625
Published: Nov. 21, 2023
Cardiovascular
disease
remains
a
leading
cause
of
morbidity
and
mortality
in
the
United
States
(US).
Although
high-quality
data
are
accessible
US
for
cardiovascular
research,
digital
literacy
(DL)
has
not
been
explored
as
potential
factor
influencing
mortality,
although
Social
Vulnerability
Index
(SVI)
used
previously
variable
predictive
modeling.
Utilizing
large
language
model,
ChatGPT4,
we
investigated
variability
CVD-specific
that
could
be
explained
by
DL
SVI
using
regression
We
fitted
two
models
to
calculate
crude
adjusted
CVD
rates.
Mortality
ICD-10
codes
were
retrieved
from
CDC
WONDER,
geographic
level
was
Department
Agriculture.
Both
datasets
merged
Federal
Information
Processing
Standards
code.
The
initial
exploration
involved
1999
through
2020
(n
=
65,791;
99.98%
complete
all
Counties)
(CCM).
Age-adjusted
(ACM)
had
3118
rows;
99%
Counties),
with
inclusion
model
(a
composite
internet
access).
By
leveraging
on
advanced
capabilities
ChatGPT4
linear
regression,
successfully
highlighted
importance
incorporating
predicting
mortality.
Our
findings
imply
just
availability
may
sufficient
without
significant
variables,
such
SVI,
predict
ACM.
Further,
our
approach
enable
future
researchers
consider
key
variables
study
other
health
outcomes
public-health
importance,
which
inform
clinical
practices
policies.
Ecological Indicators,
Journal Year:
2023,
Volume and Issue:
147, P. 109959 - 109959
Published: Jan. 30, 2023
Urban
flood
is
one
of
the
most
frequent
and
deadly
natural
disasters
in
world,
seriously
affecting
urban
sustainability
people's
well-being
China.
As
largest
developing
country
China
urgently
needs
to
improve
its
resilience.
Previous
studies
related
resilience
are
mostly
focused
on
assessment
method
simulation.
However,
few
directly
aim
reveal
influencing
factors
their
inner
relationships.
In
order
make
a
significant
contribution
long-term
improvement
context
global
climate
change
urbanization,
it
crucial
explore
mechanisms
This
study
aims
identify
key
interactions
To
this
end,
conceptual
framework
based
Pressure-State-Response
model
Social-Economic-Natural
Complex
Ecosystem
theory
(PSR-SENCE
model)
established
24
identified
within
three
dimensions.
The
relationships
between
tested
using
fuzzy-DEMATEL
method.
results
that
pressure
response
dimensions
have
greater
impact
whole
system,
while
state
dimension
more
influenced
by
other
two
14
critical
factors,
with
four
detailed
influence
paths
discussed
among
different
Accordingly,
implications
for
improving
paths.
provides
theoretical
basis
approach
how
proposes
specific
implications.
Ecological Indicators,
Journal Year:
2023,
Volume and Issue:
154, P. 110838 - 110838
Published: Aug. 23, 2023
In
recent
years,
the
analysis
of
social
vulnerability
to
floods
became
an
integrated
part
flood
risk
management
process,
strategies
and
policies
developed
focusing
on
reduction
methods
that
increase
resilience
vulnerable
communities.
Therefore,
reliable
robust
approaches
are
needed,
which
is
also
highlighted
by
increasing
socio-economic
growth
climate
change
related
effects
can
lead
unpredictable
consequences.
The
use
indices
most
widespread
methodology
allows
identification
communities
understanding
factors
floods.
However,
due
lack
a
standard
procedure,
existing
studies
often
characterized
uncertainties
subjective
selection
indicators,
inclusion
all
dimensions,
equal
or
weighting
methods,
reduced
number
variables
data
unavailability.
present
paper
addressing
these
gaps
developing
comprehensive
approach
which:
includes
large
set
indicators
selected
considering
local
context,
hazard
dimension
in
variables,
applies
objective
based
Principal
Component
Analysis
(PCA)
method.
Furthermore,
maps
using
Geographic
Information
System
(GIS)
tools,
provide
rapid
easy
way
identify
highly
areas.
results
showed
integration
statistical
GIS
tools
index
construction
provides
better
offers
overview
mitigation
adaptation
measures
must
be
implemented
authorities
order
improve
management.
Risk Analysis,
Journal Year:
2022,
Volume and Issue:
43(5), P. 1058 - 1078
Published: June 10, 2022
This
study
presents
the
first
nationwide
spatial
assessment
of
flood
risk
to
identify
social
vulnerability
and
exposure
hotspots
that
support
policies
aimed
at
protecting
high-risk
populations
geographical
regions
Canada.
The
used
a
national-scale
hazard
dataset
(pluvial,
fluvial,
coastal)
estimate
1-in-100-year
all
residential
properties
across
5721
census
tracts.
Residential
data
were
spatially
integrated
with
census-based
multidimensional
index
(SoVI)
included
demographic,
racial/ethnic,
socioeconomic
indicators
influencing
vulnerability.
Using
Bivariate
Local
Indicators
Spatial
Association
(BiLISA)
cluster
maps,
identified
geographic
concentration
where
high
coincided
exposure.
results
revealed
considerable
variations
in
tract-level
Flood
belonged
410
tracts,
21
metropolitan
areas,
eight
provinces
comprising
about
1.7
million
total
population
51%
half-a-million
Results
near
core
dense
urban
areas
predominantly
occupying
those
hotspots.
Recognizing
priority
locations
is
critically
important
for
government
interventions
mitigation
initiatives
considering
socio-physical
aspects
flooding.
Findings
reinforce
better
understanding
flood-disadvantaged
neighborhoods
Canada,
are
required
target
preparedness,
response,
recovery
resources
foster
socially
just
management
strategies.
Land,
Journal Year:
2023,
Volume and Issue:
12(6), P. 1200 - 1200
Published: June 9, 2023
With
global
climate
change
and
rapid
urbanization,
it
is
critical
to
assess
urban
flood
resilience
(UFR)
within
the
social-economic-natural
complex
ecosystem
in
dealing
with
disasters.
This
research
proposes
a
conceptual
framework
based
on
PSR-SENCE
model
for
evaluating
exploring
trends
over
time,
using
27
cities
Yangtze
River
Delta
(YRD)
of
China
as
case
studies.
For
overall
evaluation,
hybrid
weighting
method,
VIKOR,
sensitivity
analysis
were
used.
During
that
UFR
YRD
region
averaged
moderate
level
an
upward
trend.
distinguishes
between
levels
fluctuation
provinces
cities.
Jiangsu,
Zhejiang,
Anhui
all
displayed
trend
progressive
development;
however,
Shanghai
completely
opposite
pattern,
mainly
because
state
dimension.
81.41%
exhibited
varying,
resistance,
few
demonstrating
inverse
changes.
Regional,
provincial,
city-level
implications
are
proposed
future
enhancement.
The
contributes
better
understanding
under
conditions
provides
significant
insights
policymakers,
planners,
practitioners
other
similar
flood-prone
areas.
Journal of Urban Management,
Journal Year:
2024,
Volume and Issue:
13(3), P. 437 - 455
Published: June 19, 2024
Rapid
urbanization
and
climate
change
require
a
thorough
understanding
of
flood
vulnerability
in
order
to
assure
urban
safety
resilience.
Understanding
the
factors
that
contribute
vulnerability,
allows
us
develop
effective
initiatives
could
mitigate
destructive
consequences
flooding,
while
also
protecting
communities.
The
objective
this
research
is
identify
model
socio-economic
demographic
significantly
influence
floodplains
Jackson,
Mississippi,
Birmingham,
Alabama,
USA.
First
we
analyzed
correlation
between
then
employed
Principal
Component
Analysis
(PCA)
address
multicollinearity,
common
challenge
multivariate
statistical
modeling.
Subsequently,
PCs-based
global
regression
(PCR)
geographically
weighted
(PCGWR)
analysis
are
used
key
drivers
vulnerability.
findings
demonstrate
significant
proportion
variance
(>80%)
these
can
be
captured
by
first
two
three
Components
(PCs).
Consistent
with
existing
research,
African
American,
poverty,
seniors,
number
less
educated
people
positively
correlate
income
housing
prices
exhibit
negative
correlation.
Additionally,
PCGWR
outperformed
Regression
most
cases,
highlighting
spatial
heterogeneity
This
study
focuses
on
U.S.
cities,
methodology
applicable
other
cities
similar
characteristics.
identified
align
making
proposed
valuable
worldwide.
useful
for
local
governments,
policymakers,
developers
make
detailed
location
specific
plan
reduce
impact
improve
