Fucoxanthin
is
a
carotenoid
that
possesses
various
beneficial
medicinal
properties
for
human
well-being.
However,
the
current
extraction
technologies
and
quantification
techniques
are
still
lacking
in
terms
of
cost
validation,
high
energy
consumption,
long
time,
low
yield
production.
To
date,
artificial
intelligence
(AI)
models
can
assist
improvise
bottleneck
fucoxanthin
process
by
establishing
new
processes
which
involve
big
data,
digitalization,
automation
efficiency
This
review
highlights
application
AI
such
as
neural
network
(ANN)
adaptive
neuro
fuzzy
inference
system
(ANFIS),
capable
learning
patterns
relationships
from
large
datasets,
capturing
non-linearity,
predicting
optimal
conditions
significantly
impact
yield.
On
top
that,
combining
metaheuristic
algorithm
genetic
(GA)
further
improve
parameter
space
discovery
ANN
ANFIS
models,
results
R2
accuracy
ranging
98.28%
to
99.60%
after
optimization.
Besides,
support
vector
machine
(SVM),
convolutional
networks
(CNNs),
have
been
leveraged
fucoxanthin,
either
computer
vision
based
on
color
images
or
regression
analysis
statistical
data.
The
findings
reliable
when
modeling
concentration
pigments
with
66.0%
−
99.2%.
paper
has
reviewed
feasibility
potential
purposes,
reduce
cost,
accelerate
yields,
development
fucoxanthin-based
products.
Environmental Chemistry Letters,
Год журнала:
2023,
Номер
21(5), С. 2525 - 2557
Опубликована: Июнь 13, 2023
Abstract
Climate
change
is
a
major
threat
already
causing
system
damage
to
urban
and
natural
systems,
inducing
global
economic
losses
of
over
$500
billion.
These
issues
may
be
partly
solved
by
artificial
intelligence
because
integrates
internet
resources
make
prompt
suggestions
based
on
accurate
climate
predictions.
Here
we
review
recent
research
applications
in
mitigating
the
adverse
effects
change,
with
focus
energy
efficiency,
carbon
sequestration
storage,
weather
renewable
forecasting,
grid
management,
building
design,
transportation,
precision
agriculture,
industrial
processes,
reducing
deforestation,
resilient
cities.
We
found
that
enhancing
efficiency
can
significantly
contribute
impact
change.
Smart
manufacturing
reduce
consumption,
waste,
emissions
30–50%
and,
particular,
consumption
buildings
30–50%.
About
70%
gas
industry
utilizes
technologies
enhance
accuracy
reliability
forecasts.
Combining
smart
grids
optimize
power
thereby
electricity
bills
10–20%.
Intelligent
transportation
systems
dioxide
approximately
60%.
Moreover,
management
design
cities
through
application
further
promote
sustainability.
Waste Management Bulletin,
Год журнала:
2024,
Номер
2(2), С. 244 - 263
Опубликована: Май 9, 2024
Waste
management
poses
a
pressing
global
challenge,
necessitating
innovative
solutions
for
resource
optimization
and
sustainability.
Traditional
practices
often
prove
insufficient
in
addressing
the
escalating
volume
of
waste
its
environmental
impact.
However,
advent
Artificial
Intelligence
(AI)
technologies
offers
promising
avenues
tackling
complexities
systems.
This
review
provides
comprehensive
examination
AI's
role
management,
encompassing
collection,
sorting,
recycling,
monitoring.
It
delineates
potential
benefits
challenges
associated
with
each
application
while
emphasizing
imperative
improved
data
quality,
privacy
measures,
cost-effectiveness,
ethical
considerations.
Furthermore,
future
prospects
AI
integration
Internet
Things
(IoT),
advancements
machine
learning,
importance
collaborative
frameworks
policy
initiatives
were
discussed.
In
conclusion,
holds
significant
promise
enhancing
practices,
such
as
concerns,
cost
implications
is
paramount.
Through
concerted
efforts
ongoing
research
endeavors,
transformative
can
be
fully
harnessed
to
drive
sustainable
efficient
practices.
Environmental Science & Technology,
Год журнала:
2023,
Номер
57(46), С. 18203 - 18214
Опубликована: Июль 3, 2023
The
increasing
prevalence
of
nanoplastics
in
the
environment
underscores
need
for
effective
detection
and
monitoring
techniques.
Current
methods
mainly
focus
on
microplastics,
while
accurate
identification
is
challenging
due
to
their
small
size
complex
composition.
In
this
work,
we
combined
highly
reflective
substrates
machine
learning
accurately
identify
using
Raman
spectroscopy.
Our
approach
established
spectroscopy
data
sets
nanoplastics,
incorporated
peak
extraction
retention
processing,
constructed
a
random
forest
model
that
achieved
an
average
accuracy
98.8%
identifying
nanoplastics.
We
validated
our
method
with
tap
water
spiked
samples,
achieving
over
97%
accuracy,
demonstrated
applicability
algorithm
real-world
environmental
samples
through
experiments
rainwater,
detecting
nanoscale
polystyrene
(PS)
polyvinyl
chloride
(PVC).
Despite
challenges
processing
low-quality
nanoplastic
spectra
study
potential
forests
distinguish
from
other
particles.
results
suggest
combination
holds
promise
developing
particle
strategies.
Biocatalysis and Agricultural Biotechnology,
Год журнала:
2024,
Номер
58, С. 103224 - 103224
Опубликована: Май 11, 2024
Micro-
and
macroalgal
biomass
conversion
into
valuable
products
is
central
to
biorefinery
research,
addressing
global
challenges
in
energy
sustainability.
This
review
details
recent
advancements
algal
conversion,
focusing
on
the
enhancement
of
technologies
increase
commercial
value.
Methods
such
as
pyrolysis
catalytic
bioconversion
are
examined
for
their
efficiency
producing
biofuels
biochemicals.
The
utilization
specialized
strains
effective
bioremediation
role
algal-derived
biofertilizers
biostimulants
providing
sustainable
alternatives
chemical
fertilizers
discussed.
These
significantly
improve
soil
health
plant
growth.
also
highlights
advanced
extraction
techniques,
including
supercritical
CO2
enzyme-assisted
extraction,
expanding
application
derivatives
beyond
traditional
agricultural
uses.
Despite
potential,
high
cultivation
processing
costs
remain,
necessitating
further
optimization
algae-based
viability.
crucial
roles
genetic
engineering
synthetic
biology
emphasized
enhancing
productivity
tailoring
bioproduct
synthesis.
stresses
need
continued
research
technological
overcome
these
barriers,
thus
promoting
broader
adoption
biorefineries
contributing
practices
enhanced
security.
IEEE Internet of Things Journal,
Год журнала:
2024,
Номер
11(15), С. 25757 - 25770
Опубликована: Апрель 12, 2024
Effective
data
management
schemes
have
always
been
the
major
demand
in
universal
industrial
Internet
of
Things
(IoT)
systems,
especially
resource-constrained
scenarios.
In
realistic
wastewater
treatment
process
(WTP),
only
limited
monitoring
resource
can
be
available
due
to
some
digital
constraint.
Aiming
at
this
practical
issue,
work
explores
utilization
deep
neural
network
deal
with
such
issue
objective
situation.
Therefore,
a
learning-based
scheme
for
intelligent
control
WTP
under
IoT
is
proposed
paper.
Firstly,
specific
encoding
and
preprocessing
approach
developed
business
scenario.
Then,
detailed
workflow
structure
applied
predict
key
intermediate
parameters
which
further
guide
decision.
Finally,
comprehensive
series
experiments
are
conducted
on
real-world
dataset
covers
range
one
year.
Both
efficiency
robustness
proposal
tested
by
introducing
several
performance
metrics.
The
results
show
that
it
proper
prediction
effect
environment,
facilitate
following
operations.
Microbial Cell Factories,
Год журнала:
2025,
Номер
24(1)
Опубликована: Янв. 14, 2025
Abstract
Extensive
anthropogenic
activity
has
led
to
the
accumulation
of
organic
and
inorganic
contaminants
in
diverse
ecosystems,
which
presents
significant
challenges
for
environment
its
inhabitants.
Utilizing
microalgae
as
a
bioremediation
tool
can
present
potential
solution
these
challenges.
Microalgae
have
gained
attention
promising
biotechnological
detoxifying
environmental
pollutants.
This
is
due
their
advantages,
such
rapid
growth
rate,
cost-effectiveness,
high
oil-rich
biomass
production,
ease
implementation.
Moreover,
microalgae-based
remediation
more
environmentally
sustainable
not
generating
additional
waste
sludge,
capturing
atmospheric
CO
2
,
being
efficient
nutrient
recycling
algal
production
biofuels
high-value-added
products
generation.
Hence,
achieve
sustainability's
three
main
pillars
(environmental,
economic,
social).
Microalgal
mediate
contaminated
wastewater
effectively
through
accumulation,
adsorption,
metabolism.
These
mechanisms
enable
reduce
concentration
heavy
metals
levels
that
are
considered
non-toxic.
However,
several
factors,
microalgal
strain,
cultivation
technique,
type
pollutants,
limit
understanding
removal
mechanism
efficiency.
Furthermore,
adopting
novel
technological
advancements
(e.g.,
nanotechnology)
may
serve
viable
approach
address
challenge
refractory
pollutants
process
sustainability.
Therefore,
this
review
discusses
ability
different
species
mitigate
persistent
industrial
effluents,
dyes,
pesticides,
pharmaceuticals.
Also,
paper
provided
insight
into
nanomaterials,
nanoparticles,
nanoparticle-based
biosensors
from
immobilization
on
nanomaterials
enhance
open
new
avenue
future
advancing
research
regarding
biodegradation
