Global NEST Journal,
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 2, 2024
<p
style="text-align:justify"><span
style="font-size:12pt"><span
style="line-height:200%"><span
style="font-family:"Times
New
Roman","serif"">Wastewater
pollution
is
a
major
concern
due
to
organic
matter,
pesticides,
and
other
contaminants.
Untreated
discharge
of
this
wastewater
can
pollute
water
resources
harm
the
environment.
A
data-driven
approach
for
optimizing
treatment
systems
ensuring
recycled
water's
safety
effectiveness
by
calculating
energy,
chemical,
greenhouse
gas
emissions.
According
study,
process
system
optimization
decreases
negative
influence
on
This
suggested
research
looks
at
potential
reusing
purifying
it
so
be
used
in
coffee
plants.
variety
methods
cleaning
disinfecting
substances
are
detailed
article.
wide
range
physical,
biological
processes
utilized
these
treatments.
The
primary
objective
sewage
develop
effective
that
ensure
treated
reused
use
agriculture.
data
analysis
using
sensors
Connected
measure
nutrients,
pollutants,
salinity,
pH,
toxins
being
track
various
quality
measures.
Fuzzy-based
processing
utilizing
FRNNs
handle
uncertainties
inherent
sensor
through
fuzzy
logic
techniques.
Recurrent
neural
networks
capture
temporal
dependencies
data,
allowing
more
accurate
predictions.
Compared
with
existing
algorithms,
proposed
method
has
efficient
its
safe
reuse
cultivation,
promoting
conservation
sustainable
agricultural
practices.</span></span></span></p>
Environmental Chemistry Letters,
Год журнала:
2024,
Номер
22(5), С. 2293 - 2318
Опубликована: Май 21, 2024
Abstract
The
access
to
clean
and
drinkable
water
is
becoming
one
of
the
major
health
issues
because
most
natural
waters
are
now
polluted
in
context
rapid
industrialization
urbanization.
Moreover,
pollutants
such
as
antibiotics
escape
conventional
wastewater
treatments
thus
discharged
ecosystems,
requiring
advanced
techniques
for
treatment.
Here
we
review
use
artificial
intelligence
machine
learning
optimize
pharmaceutical
treatment
systems,
with
focus
on
quality,
disinfection,
renewable
energy,
biological
treatment,
blockchain
technology,
algorithms,
big
data,
cyber-physical
automated
smart
grid
power
distribution
networks.
Artificial
allows
monitoring
contaminants,
facilitating
data
analysis,
diagnosing
easing
autonomous
decision-making,
predicting
process
parameters.
We
discuss
advances
technical
reliability,
energy
resources
management,
cyber-resilience,
security
functionalities,
robust
multidimensional
performance
platform
distributed
consortium,
stabilization
abnormal
fluctuations
quality
Environmental Processes,
Год журнала:
2024,
Номер
11(3)
Опубликована: Авг. 1, 2024
Abstract
The
present
study
investigates
the
synergistic
performance
of
three-dimensional
electrochemical
process
to
decolourise
methyl
orange
(MO)
dye
pollutant
from
xenobiotic
textile
wastewater.
was
treated
using
technique
with
strong
oxidizing
potential,
and
additional
adsorption
technology
employed
effectively
remove
pollutants
Approximately
98%
MO
removal
efficiency
achieved
15
mA/cm
2
current
density,
3.62
kWh/kg
energy
consumption
79.53%
efficiency.
50
mg/L
rapidly
mineralized
a
half-life
4.66
min
at
density
.
Additionally,
graphite
intercalation
compound
(GIC)
electrically
polarized
in
reactor
enhance
direct
electrooxidation
OH
generation,
thereby
improving
treatment
Decolourisation
MO-polluted
wastewater
optimized
by
artificial
intelligence
(AI)
machine
learning
(ML)
techniques
such
as
Artificial
Neural
Networks
(ANN),
Support
Vector
Machine
(SVM),
random
forest
(RF)
algorithms.
Statistical
metrics
indicated
superiority
model
followed
this
order:
ANN
>
RF
SVM
Multiple
regression.
optimization
results
parameters
neural
network
(ANN)
approaches
showed
that
,
electrolysis
time
30
initial
concentration
were
best
operating
maintain
efficiencies
reactor.
Finally,
Monte
Carlo
simulations
sensitivity
analysis
yielded
prediction
lowest
uncertainty
variability
level,
whereas
predictive
outcome
slightly
better.
Highlights
•
In-depth
various
techniques.
Prediction
100%
regeneration
compound.
Advanced
statistical
targeted
responses
data
fitting
Analysis
uncertainties
simulation.
International Journal of Engineering Science and Technology,
Год журнала:
2025,
Номер
16(4), С. 11 - 19
Опубликована: Янв. 13, 2025
Groundwater
quality
variation
due
to
consequent
changes
in
the
standard
of
living
a
community
is
great
unease
owing
fact
that
groundwater
regarded
as
one
significant
water
supply
sources
available.
For
sustainable
use
resources
and
management,
monitoring
assessment
acts
catalyst
for
an
appropriate
judgment
on
quality.
In
this
study,
samples
were
collected
from
Lingayas
Institute
Management
Technology
(LIMAT),
Vijayawada
campus
Mudirajupalem,
Krishna
district,
Andhra
Pradesh,
India
assessing
alkalinity,
total
dissolved
solids
(TDS),
pH,
acidity
hardness
(TH)
using
methods.
Very
high
values
pH
TDS
obtained
which
within
vicinity
agricultural
fields.
Added
this,
Student’s
t
test
analysis
signposted
noteworthy
P
value
(<0.001)
mean
difference
was
substantial
statistically.
The
Mudirajupalem
further
affirmed
unfit
drinking,
evident
index
(WQI)
values.
This
study
emphasizes
implementing
various
locale
specific
rainwater
garnering
schemes,
solution
augmenting
recharge
maintaining
balance.
Computer Methods in Biomechanics & Biomedical Engineering,
Год журнала:
2025,
Номер
unknown, С. 1 - 14
Опубликована: Март 3, 2025
Lung
cancer
is
a
leading
cause
of
cancer-related
deaths,
often
diagnosed
late
due
to
its
aggressive
nature.
This
study
presents
novel
Adaptive
Dendritic
Neural
Model
(ADNM)
enhance
diagnostic
accuracy
in
high-dimensional
healthcare
data.
Utilizing
hyperparameter
optimization
and
activation
mechanisms,
ADNM
improves
scalability
feature
selection
for
multi-class
lung
prediction.
Using
Kaggle
dataset,
Particle
Swarm
Optimization
(PSO)
selected
features,
while
bootstrap
assessed
performance.
achieved
98.39%
accuracy,
99%
AUC,
Cohen's
kappa
96.95%,
with
rapid
convergence
via
the
Adam
optimizer,
demonstrating
potential
improving
early
diagnosis
personalized
treatment
oncology.
Hydrological Sciences Journal,
Год журнала:
2025,
Номер
unknown, С. 1 - 16
Опубликована: Март 18, 2025
Water
quality
assessment
is
crucial
for
environmental
health
and
of
life.
This
study
introduces
a
novel
water
index
(WQI)
model
reservoirs,
using
Wadi
Dayqah
Dam
in
Oman
as
case
study.
The
advances
data-driven
approach,
reducing
reliance
on
subjective
expert
opinions.
A
large
dataset
samples
was
analysed
machine
learning
(ML)
to
select
variables
(WQVs).
Using
bootstrapping
subsampling
the
proposed
WQI
then
calculated
through
sub-indexing,
weighting,
aggregating
sub-indices.
WQV
weights
were
estimated
gradient
boosting
rank
order
centroid
techniques,
while
aggregation
involved
scoring
data
envelopment
analysis
(DEA).
effectively
captures
uncertainty,
prioritizes
WQVs,
provides
solutions
issues
such
eclipsing,
ranking,
dealing
with
bad
variable
values.
results
validated
uncertainty
sensitivity
analyses,
highlighting
model's
potential
enhancing
decision
making
reservoir
management.
Karadeniz Fen Bilimleri Dergisi,
Год журнала:
2024,
Номер
14(2), С. 719 - 741
Опубликована: Июнь 18, 2024
Bu
çalışmada,
ülkemizde
deniz
kaplumbağalarının
yuvalama
alanı
olarak
koruma
altında
olan
Belek
Özel
Çevre
Koruma
Bölgesindeki
yüzey
sularının
uzun
yıllar
periyodundaki
kalite
değişimlerinin
değerlendirilmesinde
istatistiksel
metotların
kullanımı
hedeflenmiştir.
Çalışma
kapsamında
2005-2020
yılları
arasında
(15
yıl)
içinde
yer
alan
yüzeysel
su
kaynaklarına
ait
kalitesi
analiz
sonuçları
değerlendirilmeye
alınmıştır.
Yüzeysel
kalitesinin
sınıflandırılmasında
yürürlükte
Yerüstü
Su
Kalitesi
Yönetmeliği
standart
değerleri
çerçevesinde
fiziko-kimyasal
ve
biyolojik
parametre
verileri
edilmiş
sınıfları
belirlenmiştir.
Verilerin
çok
değişkenli
istatistiki
yöntemlerden
Kümeleme
Analizi
metodolojisi
kullanılmıştır.
analizi
sonucunda
manada
anlamlı
üç
küme
tespit
edilmiştir.
Kalitesine
göre
yapılan
sınıflandırması
Hiyerarşik
benzerlik
göstermiştir.
Oluşan
kümeler
neticesinde
genel
durumunun;
Acısu
Deresi’nin
II.
Sınıf
(İyi
Kalite),
Köprüçay
I.
(Çok
İyi
Sarısu
Kömürcüler
Kalite)
Ilıca
III.
(Orta
olduğu
çalışmalar
sonunda
görülmüştür.
İstatistiki
değerlendirmede
kullanılan
Temel
Bileşenler
Analizine
dört
faktör
belirlenmiş,
toplam
varyansın
%
91,04’ünü
açıklamıştır.
Sadece
birinci
59’unu
açıklamaktadır.
Özdeğeri
en
fazla
değişkenlerin;
Toplam
Koliform,
Kjehldal
Azotu,
Fekal
Azot,
Fosfor
temel
bileşenler
sonuçlarına
açıklanmıştır.
Genel
kirleticilerin
turizm
tesisleri,
evsel
kaynaklı
kirleticiler
yoğun
tarımsal
faaliyetlerden
kaynaklandığı
öngörülmektedir.
belirlenen
parametrelerin
sahadaki
izleme
çalışmalarında
öncelikli
kullanılabilecek
parametreler
