Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Окт. 28, 2024
Optimizing
agricultural
water
resource
management
is
crucial
for
food
production,
as
effective
can
significantly
improve
irrigation
efficiency
and
crop
yields.
Currently,
precise
demand
forecasting
have
become
key
research
focuses;
however,
existing
methods
often
fail
to
capture
complex
spatial
temporal
dependencies.
To
address
this,
we
propose
a
novel
deep
learning
framework
that
combines
remote
sensing
technology
with
the
UNet-ConvLSTM
(UCL)
model
effectively
integrate
features
from
MODIS
GLDAS
datasets.
Our
leverages
high-resolution
data
UNet
dependencies
captured
by
ConvLSTM
prediction
accuracy.
Experimental
results
demonstrate
our
UCL
achieves
best
$$R^2$$
compared
methods,
reaching
0.927
on
dataset
0.935
dataset.
This
approach
highlights
potential
of
AI
technologies
in
addressing
critical
challenges
management,
contributing
more
sustainable
efficient
production
systems.
Frontiers in Ecology and Evolution,
Год журнала:
2023,
Номер
11
Опубликована: Март 13, 2023
Carbon
neutrality
and
carbon
peak
are
two
important
measures
to
control
climate
change.
They
have
a
huge
impact
on
many
companies
in
the
fields
of
energy,
industry,
construction,
transportation,
etc.
can
change
development
pattern
related
industries
increase
new
investment
opportunities.
This
paper
proposes
path
analysis
standardization
energy
economic
management
under
background
peak,
aiming
study
forecast
low-carbon
conditions.
The
algorithm
proposed
this
is
an
consumption
based
IPAT
model,
which
be
combined
with
model
analyze
process
data.
In
addition,
by
analyzing
evaluating
contribution
various
factors,
people
better
understand
environment
formulate
corresponding
solutions.
experimental
results
show
that,
from
2013
2017,
baseline
scenario,
emissions
increased
year
year,
9.25
billion
tons
10.48
tons.
Under
neutral
its
9.22
tons,
9.24
9.19
9.21
respectively.
Obviously,
controlled
through
strategies.
Through
these
prediction
results,
it
proved
that
peaking
excellent
effects
promoting
management.
At
same
time,
also
provides
valuable
reference
information
for
further
research
peaks.
Frontiers in Neuroscience,
Год журнала:
2023,
Номер
17
Опубликована: Сен. 20, 2023
In
the
medical
field,
electronic
records
contain
a
large
amount
of
textual
information,
and
unstructured
nature
this
information
makes
data
extraction
analysis
challenging.
Therefore,
automatic
entity
from
has
become
significant
issue
in
healthcare
domain.To
address
problem,
paper
proposes
deep
learning-based
model
called
Entity-BERT.
The
aims
to
leverage
powerful
feature
capabilities
learning
pre-training
language
representation
BERT(Bidirectional
Encoder
Representations
Transformers),
enabling
it
automatically
learn
recognize
various
types
records,
including
terminologies,
disease
names,
drug
more,
providing
more
effective
support
for
research
clinical
practices.
Entity-BERT
utilizes
multi-layer
neural
network
cross-attention
mechanism
process
fuse
at
different
levels
types,
resembling
hierarchical
distributed
processing
human
brain.
Additionally,
employs
pre-trained
sequence
models
data,
sharing
similarities
with
semantic
understanding
Furthermore,
can
capture
contextual
long-term
dependencies,
combining
handle
complex
diverse
expressions
method
brain
many
aspects.
exploring
how
utilize
competitive
learning,
adaptive
regulation,
synaptic
plasticity
optimize
model's
prediction
results,
adjust
its
parameters,
achieve
dynamic
adjustments
perspective
neuroscience
brain-like
cognition
is
interest.Experimental
results
demonstrate
that
achieves
outstanding
performance
recognition
tasks
within
surpassing
other
existing
models.
This
not
only
provides
efficient
accurate
natural
technology
health
field
but
also
introduces
new
ideas
directions
design
optimization
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Окт. 28, 2024
Optimizing
agricultural
water
resource
management
is
crucial
for
food
production,
as
effective
can
significantly
improve
irrigation
efficiency
and
crop
yields.
Currently,
precise
demand
forecasting
have
become
key
research
focuses;
however,
existing
methods
often
fail
to
capture
complex
spatial
temporal
dependencies.
To
address
this,
we
propose
a
novel
deep
learning
framework
that
combines
remote
sensing
technology
with
the
UNet-ConvLSTM
(UCL)
model
effectively
integrate
features
from
MODIS
GLDAS
datasets.
Our
leverages
high-resolution
data
UNet
dependencies
captured
by
ConvLSTM
prediction
accuracy.
Experimental
results
demonstrate
our
UCL
achieves
best
$$R^2$$
compared
methods,
reaching
0.927
on
dataset
0.935
dataset.
This
approach
highlights
potential
of
AI
technologies
in
addressing
critical
challenges
management,
contributing
more
sustainable
efficient
production
systems.