Land,
Год журнала:
2024,
Номер
13(10), С. 1617 - 1617
Опубликована: Окт. 5, 2024
In
the
context
of
rapid
urbanization
and
digitalization,
scientifically
assessing
spatio-temporal
interaction
between
digital
inclusive
finance
(DIF)
urban
ecological
resilience
(UER)
is
crucial
for
promoting
coordinated
development
regional
ecology
economy.
This
study
investigates
spatiotemporal
evolution
coupled
coordination
degree
(CCD),
decoupling
phenomenon,
its
hindering
factors
in
Yangtze
River
Economic
Belt
(YREB)
by
utilizing
kernel
density
analysis,
standard
deviation
ellipse,
model,
obstacle
analysis.
Through
systematic
analyses,
this
paper
aims
to
elucidate
disparities
among
regions
within
YREB,
identify
problematic
areas,
propose
targeted
improvement
measures.
The
results
show
that
(1)
CCD
DIF
UER
YREB
has
increased
annually
from
2011
2020.
However,
there
are
persistent
imbalances,
with
an
overall
low
level
uneven
spatial
development,
a
trend
“higher
east
lower
west”.
(2)
reached
at
least
primary
level,
coupling
enhancement
speed
ranked
as
“downstream
>
midstream
upstream”,
differences
decreasing.
(3)
analysis
reveals
predominant
UER,
indicating
digitization
financial
services
not
concurrently
pressures.
(4)
identifies
digitalization
major
barriers
CCD.
provides
scientific
basis
analytical
framework
understanding
current
offering
important
reference
formulating
more
effective
policies.
Remote Sensing,
Год журнала:
2022,
Номер
14(14), С. 3253 - 3253
Опубликована: Июль 6, 2022
Remote
sensing
(RS)
plays
an
important
role
gathering
data
in
many
critical
domains
(e.g.,
global
climate
change,
risk
assessment
and
vulnerability
reduction
of
natural
hazards,
resilience
ecosystems,
urban
planning).
Retrieving,
managing,
analyzing
large
amounts
RS
imagery
poses
substantial
challenges.
Google
Earth
Engine
(GEE)
provides
a
scalable,
cloud-based,
geospatial
retrieval
processing
platform.
GEE
also
access
to
the
vast
majority
freely
available,
public,
multi-temporal
offers
free
cloud-based
computational
power
for
analysis.
Artificial
intelligence
(AI)
methods
are
enabling
technology
automating
interpretation
imagery,
particularly
on
object-based
domains,
so
integration
AI
into
represents
promising
path
towards
operationalizing
automated
RS-based
monitoring
programs.
In
this
article,
we
provide
systematic
review
relevant
literature
identify
recent
research
that
incorporates
GEE.
We
then
discuss
some
major
challenges
integrating
several
priorities
future
research.
developed
interactive
web
application
designed
allow
readers
intuitively
dynamically
publications
included
review.
NJAS Impact in Agricultural and Life Sciences,
Год журнала:
2023,
Номер
95(1)
Опубликована: Янв. 30, 2023
Innovations
in
digital
technologies,
especially
artificial
intelligence
(AI),
promise
substantial
benefits
to
the
agricultural
sector.
Agriculture
is
increasingly
expected
ensure
food
security
and
safety
while
at
same
time
considering
environmental
aspects.
AI
sector
offers
potential
feed
a
continuously
growing
global
population
still
contribute
achieving
UN’s
Sustainable
Development
Goals
(SDGs).
Despite
its
promises,
use
of
agriculture
limited.
We
argue
that
slow
uptake
due
diverse
ways
which
impacts
agri-food
industry,
diversity
foods,
supply
chains,
climates,
land
propose
this
also
exacerbated
by
ethical
concerns
arising
from
use,
varying
degrees
technological
development
skills,
economic
AI.
A
literature
review
multiple
disciplines
(economic,
environmental,
social,
ethical,
technological)
focus
group
experts.
AI-powered
systems
raise
various
sets
need
be
aligned
provide
sustainable
solutions
for
domain.
Our
research
proposes
it
important
adopt
an
interdisciplinary
approach
when
developing
agriculture.
should
developed
collaboration
because
has
greater
chance
robust,
economically-valuable
socially
desirable,
may
lead
acceptance
trust
among
farmers
using
it.
Results in Engineering,
Год журнала:
2024,
Номер
22, С. 102132 - 102132
Опубликована: Апрель 21, 2024
Multiple
industries
have
been
revolutionized
by
the
incorporation
of
data
science
advancements
into
intelligent
environment
technologies,
specifically
in
context
smart
grids.
Smart
grids
offer
a
dynamic
and
efficient
framework
for
management
optimization
electricity
generation,
distribution,
consumption,
thanks
to
developments
big
analytics.
This
review
delves
integration
Grid
applications
Big
Data
analytics
reviewing
25
papers
screened
with
PRISMA
standard.
The
paper
matter
encompasses
critical
domains
including
adaptive
energy
management,
canonical
correlation
analysis,
novel
methodologies
blockchain
machine
learning.
emphasizes
contributions
efficiency,
security,
sustainability
means
rigorous
methodology.
Genes,
Год журнала:
2021,
Номер
12(5), С. 783 - 783
Опубликована: Май 20, 2021
Warming
and
drought
are
reducing
global
crop
production
with
a
potential
to
substantially
worsen
malnutrition.
As
the
green
revolution
in
last
century,
plant
genetics
may
offer
concrete
opportunities
increase
yield
adaptability.
However,
rate
at
which
threat
is
happening
requires
powering
new
strategies
order
meet
food
demand.
In
this
review,
we
highlight
major
recent
‘big
data’
developments
from
both
empirical
theoretical
genomics
that
speed
up
identification,
conservation,
breeding
of
exotic
elite
varieties
feed
humans.
We
first
emphasize
bottlenecks
capture
utilize
novel
sources
variation
abiotic
stress
(i.e.,
heat
drought)
tolerance.
argue
adaptation
wild
relatives
dry
environments
could
be
informative
on
how
phenotypes
react
drier
climate
because
natural
selection
has
already
tested
more
options
than
humans
ever
will.
Because
isolated
pockets
cryptic
diversity
still
persist
remote
semi-arid
regions,
encourage
habitat-based
population-guided
collections
for
genebanks.
continue
discussing
systematically
study
tolerance
these
landraces
using
geo-referencing
extensive
environmental
data.
By
uncovering
genes
underlie
adaptive
trait,
introgressed
into
cultivars.
unlocking
genetic
hidden
related
species
early
remains
challenge
complex
traits
that,
as
tolerance,
polygenic
regulated
by
many
low-effect
genes).
Therefore,
finish
prospecting
modern
analytical
approaches
will
serve
overcome
issue.
Concretely,
genomic
prediction,
machine
learning,
multi-trait
gene
editing,
all
innovative
alternatives
accurate
pre-
efforts
toward
adaptability
yield,
while
matching
future
demands
face
increased
drought.
succeed,
advocate
trans-disciplinary
approach
open-source
data
long-term
funding.
The
perspectives
discussed
throughout
review
ultimately
aim
contribute
waves
events.
Industrial Marketing Management,
Год журнала:
2024,
Номер
117, С. 92 - 113
Опубликована: Янв. 2, 2024
Artificial
Intelligence
(AI)
is
a
game-changing
capability
in
industrial
markets
that
can
accelerate
humanity's
race
against
climate
change.
Positioned
resource-hungry
and
pollution-intensive
industry,
this
study
explores
AI-powered
service
innovation
capabilities
their
overall
effects.
The
develops
validates
an
AI
model,
identifying
three
primary
dimensions
nine
subdimensions.
Based
on
dataset
the
fast
fashion
findings
show
significantly
influence
both
environmental
market
performance,
which
performance
acts
as
partial
mediator.
Specifically,
results
identify
key
elements
of
AI-informed
framework
for
action
how
be
used
to
develop
range
mitigation,
adaptation
resilience
initiatives
response
Abstract
Big
climate
change
data
have
become
a
pressing
issue
that
organizations
face
with
methods
to
analyze
generated
from
various
types.
Moreover,
storage,
processing,
and
analysis
of
activities
are
becoming
very
massive,
challenging
for
the
current
algorithms
handle.
Therefore,
big
analytics
designed
significantly
large
amounts
required
enhance
seasonal
monitoring
understand
ascertain
health
risks
change.
In
addition,
would
improve
allocation,
utilisation
natural
resources.
This
paper
provides
an
extensive
discussion
analytic
investigates
how
sustainability
issues
can
be
analyzed
through
these
approaches.
We
further
present
methods,
strengths,
weaknesses,
essence
analyzing
using
methods.
The
common
datasets,
implementation
frameworks
modeling,
future
research
directions
were
also
presented
clarity
compelling
challenges.
method
is
well-timed
solve
inherent
easy
realization
sustainable
development
goals.
Mesopotamian Journal of Big Data,
Год журнала:
2023,
Номер
2023, С. 125 - 137
Опубликована: Дек. 2, 2023
Climate
change
represents
an
urgent
environmental
crisis
with
far-reaching
risks
to
ecosystems
and
human
communities
worldwide.
Rapid
development
of
mitigation
strategies
solutions
is
imperative
but
relies
profoundly
on
advancements
in
detection,
attribution,
prediction
derived
from
climate
data
analytics.
This
paper
examines
the
growing
role
science
not
only
quantifying
anthropogenic
also
informing
impact
assessment
targeted
intervention
across
climate-sensitive
sectors.
First,
we
survey
established
emerging
techniques
for
characterization,
including
machine
learning
applications
Earth
systems
data.
Next,
discuss
how
sophisticated
models
alongside
statistical
analysis
multi-domain
datasets—from
migration
patterns
crop
yields—deepens
scientific
comprehension
repercussions.
Building
these
insights,
spotlight
data-enabled
solution
paradigms
enabling
smart
action,
ranging
high-resolution
risk
mapping,
emissions
reductions
via
optimized
renewable
energy
infrastructure,
global
warming
suppression
solar
radiation
management.
However,
carefully
examine
practical
limitations
hindering
deployment
ethical
concerns
posed
by
certain
proposals.
Ultimately,
while
delivers
powerful
tools
response,
this
underscores
continued
gathering
cross-disciplinary
collaboration
vital
overcome
analytical
uncertainties,
implementation
barriers,
moral
objections
as
work
avert
profound
breakdown.
