npj Urban Sustainability,
Год журнала:
2024,
Номер
4(1)
Опубликована: Янв. 13, 2024
Abstract
A
post-disaster
recovery
process
necessitates
significant
financial
and
time
investment.
Previous
studies
have
found
the
importance
of
spatial
heterogeneity,
but
heterogeneity
has
not
been
extended
to
directed
relationships
despite
significance
sequential
plans.
Identifying
a
causal
structure
between
county-level
series
data
can
reveal
in
process.
This
study
uses
discovery
method
spatiotemporal
counties
before,
during,
after
Hurricane
Irma
2017.
proposes
node
aggregation
methods
at
different
scales
obtain
internally
validated
links.
paper
utilizes
points
interest
with
daily
location
information
from
mobile
phones
nighttime
light
data.
We
find
intra-regional
homogeneity,
inter-regional
hierarchical
among
urban,
suburban,
rural
based
on
network
motif
analysis.
Subsequently,
this
article
suggests
plans
using
graph
methods.
These
results
help
policymakers
develop
scenarios
estimate
corresponding
impacts.
International Journal of Disaster Risk Reduction,
Год журнала:
2023,
Номер
96, С. 103893 - 103893
Опубликована: Июль 28, 2023
We
present
a
comprehensive
resilience
glossary,
comprising
set
of
93
definitions
resilience-related
terms
used
in
the
context
critical
infrastructures.
The
definition
and
use
many
these
terms,
as
well
term
itself,
shows
an
enormous
variability
literature.
Therefore,
we
draw
from
diverse
pool
published
definitions,
integrate
multiple
contrasting
views,
compare
individual
provide
references
to
adjoining
or
contesting
create
clear
terminology.
This
terminology
outlines
specific
understanding
which
supports
effective
assessment
management
two
central
elements
this
are
that
(1)
is
ability
system
deal
with
impacts
unspecific
possibly
unforeseen
disruptive
events,
(2)
comprises
three
pillar
capacities
whose
quality
can
be
extracted
performance
curves.
Nature Communications,
Год журнала:
2024,
Номер
15(1)
Опубликована: Март 12, 2024
Forecasting
all
components
in
complex
systems
is
an
open
and
challenging
task,
possibly
due
to
high
dimensionality
undesirable
predictors.
We
bridge
this
gap
by
proposing
a
data-driven
model-free
framework,
namely,
feature-and-reconstructed
manifold
mapping
(FRMM),
which
combination
of
feature
embedding
delay
embedding.
For
high-dimensional
dynamical
system,
FRMM
finds
its
topologically
equivalent
manifolds
with
low
dimensions
from
then
sets
the
low-dimensional
as
generalized
predictor
achieve
predictions
components.
The
substantial
potential
shown
for
both
representative
models
real-world
data
involving
Indian
monsoon,
electroencephalogram
(EEG)
signals,
foreign
exchange
market,
traffic
speed
Los
Angeles
Country.
overcomes
curse
predictor,
thus
has
applications
many
other
systems.
