Electromagnetic Biology and Medicine,
Journal Year:
2025,
Volume and Issue:
unknown, P. 1 - 36
Published: Jan. 29, 2025
In
cardiovascular
research,
electromagnetic
fields
generated
by
Riga
plates
are
utilized
to
study
or
manipulate
blood
flow
dynamics,
which
is
particularly
crucial
in
developing
treatments
for
conditions
such
as
arterial
plaque
deposition
and
understanding
behavior
under
varied
conditions.
This
research
predicts
the
patterns
of
enhanced
with
gold
maghemite
nanoparticles
(gold-maghemite/blood)
an
microchannel
influenced
a
temperature
gradient
that
decays
exponentially,
sudden
changes
pressure
gradient.
The
modeling
includes
key
physical
influences
like
radiation
heat
emission
Darcy
drag
forces
porous
media,
mathematically
represented
through
unsteady
partial
differential
equations
solved
using
Laplace
transform
(LT)
method.
Results,
including
shear
stress
(SS)
rate
transfer
(RHT),
graphically
detailed,
demonstrating
velocity
profile
modifications
Hartmann
number
width
electrodes,
differences
RHT
between
hybrid
nano-blood
(HNB)
(NB).
results
indicate
increase
distribution
higher
modified
number,
decrease
wider
electrodes.
Temperature
elevated
both
Notably,
HNB
enhances
transmission
flow.
Furthermore,
artificial
intelligence-driven
methodology
employing
neural
network
(ANN)
has
been
incorporated
facilitate
rapid
precise
evaluations
SS
RHT,
remarkable
predictive
accuracy.
proposed
algorithm
exhibits
outstanding
accuracy,
achieving
99.998%
on
testing
dataset
96.843%
during
cross-validation
predicting
SS,
100%
dataset,
95.008%
RHT.
implementation
nanotechnology
intelligence
promises
new
tools
doctors
surgeons,
potentially
transforming
patient
care
oncology,
cardiology,
radiology.
model
also
facilitates
generation
guide
drug-loaded
magnetic
applications
targeted
drug
delivery,
hyperthermia
treatment,
MRI
contrast
enhancement,
monitoring,
cancer
controlled
release.
Modern Physics Letters B,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 24, 2025
The
implementation
of
artificial
intelligence
(AI)
to
predict
and
control
the
behavior
milk
enhanced
with
silver
zinc
oxide
nanoparticles
during
electromagnetic
heating
enhances
precision
energy
efficiency
pasteurization
sterilization
processes.
This
approach
ensures
precise
temperature
management,
reducing
risk
overheating
maintaining
milk’s
nutritional
sensory
integrity.
research
examines
flow
dynamics
(Ag-ZnO/milk)
in
a
suddenly
heated
movable
channel
under
sudden
pressure
variations.
It
incorporates
significant
physical
phenomena
such
as
radiant
heat
emission
Darcy
drag
forces,
Darcy’s
model
addressing
within
porous
medium.
are
thoroughly
defined
mathematically
physically,
solutions
succinctly
derived
using
Laplace
transform
(LT)
method.
findings,
including
analyses
shear
stress
(SS)
rate
transfer
(RHT),
presented
tabularly
graphically.
study
indicates
an
annex
momentum
higher
modified
Hartmann
numbers
enfeeblement
wider
electrode
widths.
Both
hybrid
nano-milk
(HNM)
(NM)
exhibit
thermal
degradation,
where
rising
Casson
parameters
amplify
SS,
whereas
elevated
radiation
lead
reduction
RHT.
Additionally,
AI-driven
neural
network
(ANN)
demonstrates
remarkable
predictive
precision,
achieving
95.175%
accuracy
testing
99.64%
cross-validation
for
SS
predictions,
while
attaining
perfect
100%
RHT
predictions
across
both
phases.
could
development
advanced
equipment
that
utilizes
technology
more
consistent
better
preservation
qualities.
