Fermentation,
Год журнала:
2025,
Номер
11(3), С. 130 - 130
Опубликована: Март 7, 2025
This
study
provides
a
comparative
evaluation
of
several
ensemble
model
constructions
for
the
prediction
specific
methane
yield
(SMY)
from
anaerobic
digestion.
From
authors’
knowledge
based
on
existing
research,
present
their
accuracy
and
utilization
in
digestion
modeling
relative
to
individual
machine
learning
methods
is
incomplete.
Three
input
datasets
compiled
samples
using
agricultural
forestry
lignocellulosic
residues
previous
studies
were
used
this
study.
A
total
six
five
evaluated
per
dataset,
whose
was
assessed
robust
10-fold
cross-validation
100
repetitions.
Ensemble
models
outperformed
one
out
three
terms
accuracy.
They
also
produced
notably
lower
coefficients
variation
root-mean-square
error
(RMSE)
than
most
accurate
(0.031
0.393
dataset
A,
0.026
0.272
B,
0.021
0.217
AB),
being
much
less
prone
randomness
training
test
data
split.
The
optimal
generally
benefited
higher
number
included,
as
well
diversity
principles.
Since
reporting
final
fitting
single
split-sample
approach
highly
randomness,
adoption
multiple
repetitions
proposed
standard
future
studies.
International Journal of Computational and Experimental Science and Engineering,
Год журнала:
2025,
Номер
11(1)
Опубликована: Янв. 9, 2025
The
rapid
advancement
of
computational
intelligence
(CI)
techniques
has
enabled
the
development
highly
efficient
frameworks
for
solving
complex
optimization
problems
across
various
domains,
including
engineering,
healthcare,
and
industrial
systems.
This
paper
presents
innovative
that
integrate
advanced
algorithms
such
as
Quantum-Inspired
Evolutionary
Algorithms
(QIEA),
Hybrid
Metaheuristics,
Deep
Learning-based
models.
These
aim
to
address
challenges
by
improving
convergence
rates,
solution
accuracy,
efficiency.
In
context
a
framework
was
successfully
used
predict
optimal
treatment
plans
cancer
patients,
achieving
92%
accuracy
rate
in
classification
tasks.
proposed
demonstrate
potential
addressing
broad
spectrum
problems,
from
resource
allocation
smart
grids
dynamic
scheduling
manufacturing
integration
cutting-edge
CI
methods
offers
promising
future
optimizing
performance
real-world
wide
range
industries.
International Journal of Computational and Experimental Science and Engineering,
Год журнала:
2025,
Номер
11(1)
Опубликована: Фев. 5, 2025
In
this
study,
we
propose
the
Adaptive
Learning
Path
Optimization
Algorithm
(ALPOA)
to
enhance
personalized
e-learning
experiences
by
tailoring
content
delivery
based
on
individual
learner
profiles.
ALPOA
employs
a
hybrid
optimization
framework
combining
Genetic
(GA)
and
Particle
Swarm
(PSO)
dynamically
adjust
learning
paths.
The
algorithm
considers
multiple
factors
such
as
proficiency,
speed,
engagement
level,
difficulty.
Experimental
results
demonstrate
that
outperforms
traditional
static
models,
achieving
25%
improvement
in
efficiency,
30%
increase
engagement,
20%
reduction
redundancy.
model
was
tested
dataset
of
1,500
learners,
showing
97%
accuracy
predicting
optimal
paths
15%
higher
knowledge
retention
rate
compared
benchmark
algorithms.
ALPOA’s
scalability
adaptability
make
it
promising
solution
for
education
systems,
fostering
improved
outcomes
satisfaction.
Future
work
will
focus
integrating
real-time
feedback
mechanisms
expanding
support
diverse
environments.
Processes,
Год журнала:
2025,
Номер
13(2), С. 294 - 294
Опубликована: Янв. 21, 2025
Anaerobic
digestion
(AD)
is
a
biotechnological
process
in
which
the
microorganisms
degrade
complex
organic
matter
to
simpler
components
under
anaerobic
conditions
produce
biogas
and
fertilizer.
This
has
many
environmental
benefits,
such
as
green
energy
production,
waste
treatment,
protection,
greenhouse
gas
emissions
reduction.
It
long
been
known
that
two
main
species
(acidogenic
bacteria
methanogenic
archaea)
community
of
AD
differ
aspects,
optimal
for
their
growth
development
are
different.
Therefore,
if
performed
single
bioreactor
(single-phase
process),
selected
taking
into
account
slow-growing
methanogens
at
expense
fast-growing
acidogens,
affecting
efficiency
whole
process.
led
two-stage
(TSAD)
recent
years,
where
processes
divided
cascade
separate
bioreactors
(BRs).
division
consecutive
BRs
leads
significantly
higher
yields
two-phase
system
(H2
+
CH4)
compared
traditional
single-stage
CH4
production
review
presents
state
art,
advantages
disadvantages,
some
perspectives
(based
on
more
than
210
references
from
2002
2024
our
own
studies),
including
all
aspects
TSAD—different
parameters’
influences,
types
bioreactors,
microbiology,
mathematical
modeling,
automatic
control,
energetical
considerations
TSAD
processes.
