Journal of Population Therapeutics and Clinical Pharmacology,
Год журнала:
2023,
Номер
unknown, С. 1464 - 1478
Опубликована: Дек. 28, 2023
This
paper
focuses
on
utilizing
Electroencephalography
(EEG)
signals
and
machine
learning
techniques
in
developing
an
objective
stress
assessment
framework.
The
study
aimed
to
investigate
the
correlation
between
EEG
Perceived
Stress
Scale
(PSS)
by
data
segmentation
technique.
PSS
scores
are
employed
record
perceived
levels
of
individuals.
These
serve
as
basis
for
categorizing
into
three
classes:
i)
two
class:
stressed
non-stressed
ii)
stressed,
mildly
non-stressed,
iii)
four
highly
moderately
non-stressed.
recordings
captured
from
40
participants
using
4
channels
Inter
axon
Muse
headband,
equipped
with
dry
electrodes.
is
segmented
units
10
seconds.
processed
extract
five
feature
sets
including
Power
Spectrum,
Rational
Asymmetry,
Differential
Correlation
Spectral
Density.
success
accessed
classifiers
(Naive
Bayes,
Support
Vector
Machine,
Logistic
Regression,
Simple
Random
Tree,
K-Nearest
Neighbor,
Bagging,
Forest,
Multilayer
Perceptron,
AdaBoost).
highest
accuracies
achieved
two-,
three-,
four-class
classification
91.52%,
88.47%,
87.36%,
respectively.
obtained
Adaboost
classifier
two-class
classification,
Forest
three-class
again
classification.
findings
underline
importance
chosen
features
increasing
prediction
accuracy
while
contributing
existing
knowledge
detection
Signals.
Nanoparticles
(NPs)
are
synthesized
in
different
methods
such
as
physical
or
chemical
which
costly
and
harmful
to
the
environment.
However,
green
synthesis
is
a
cost-effective
environment-friendly
NPs
approach.
Green-synthesized
highly
effective
against
various
bacterial
strains.
Zirconium
dioxide
nanoparticles
([Formula:
see
text])
have
been
this
research
using
approach
for
improved
antimicrobial
activity.
For
of
NPs,
Alocasia
indica
has
used
reducing
agent.
The
extract
was
prepared
reacted
with
light
solution
ZrOCl
2
⋅
8H
O.
successful
formation
[Formula:
text]
confirmed
by
color
change
solution.
Some
analytical
techniques,
including
Ultraviolet-visible
(UV)
spectroscopy,
Fourier
Transformed
Infrared
Spectroscopy
(FTIR),
Scanning
Electron
Microscopy
(SEM),
Transmission
(TEM),
Energy
Dispersive
X-ray
(EDX),
diffraction
(XRD),
inhibition
analyses
performed
analyze
performance
NPs.
peaks
formed
UV
analysis
also
presence
functional
groups
FTIR
analysis.
TEM
revealed
that
were
almost
circular.
peak
crystalline
structure
Impressively;
99.99%
rate
obtained
gram-positive
bacteria
strain.
great
potential
biomedical
applications
medicine
implants
its
excellent
applications.
The Open Biotechnology Journal,
Год журнала:
2023,
Номер
17(1)
Опубликована: Дек. 29, 2023
Background:
Phyto-fabrication
of
nanoparticles
has
gained
attention
in
recent
times
owing
to
its
simple
mode,
cost-effective
and
eco-friendly
nature.
Objective:
Hence,
the
present
study
aimed
synthesize
cobalt
oxide
from
methanol
extracts
Ocimum
gratissimum
flower
leaf
evaluate
their
antimicrobial
action
towards
pathogenic
bacteria
fungi.
Methods:
Cobalt
(CoONPs)
was
achieved
using
chloride
hydrate
solution
as
a
precursor.
Characterization
fabricated
CoONPs
performed
Ultra
Violet-Visible
spectrometry
(UV-Vis),
X-ray
diffractometer
(XRD),
Fourier
Transform-Infrared
spectroscopy
(FTIR).
The
property
tested
against
two
(
Staphylococcus
aureus
Escherichia
coli
)
fungi
Cryptococcus
albidus
Candida
globasa
by
agar
disc
diffusion
technique
measurement
Minimum
Inhibitory
Concentration
(MIC).
Results:
Initial
confirmation
synthesis
observed
colour
change
light
pink
reddish
pink.
Further,
UV-Vis
spectrophotometry
validated
with
peak
at
509
nm.
XRD
authenticated
crystal
nature
synthesized
extract
2θ
angles
an
average
size
54.9
nm
55.02
FTIR
confirmed
functional
groups
plant
extracts,
which
are
believed
reduce
stabilize
CoONPs.
findings
antibacterial
activity
showed
that
higher
inhibition
zone
E.
(20.00
±
2.00
mm)
than
S.
.
Relating
fungi,
displayed
significantly
highest
C.
(28.67±0.57
(25.0
0.00
mm).
lowest
MIC
(MIC
7.5
µg/ml).
For
smallest
found
2.5
μg/ml).
Conclusion:
current
research
established
efficacy
phytochemical
constituents
O.
for
enhancement
effectiveness
both
Journal of Population Therapeutics and Clinical Pharmacology,
Год журнала:
2023,
Номер
unknown, С. 1464 - 1478
Опубликована: Дек. 28, 2023
This
paper
focuses
on
utilizing
Electroencephalography
(EEG)
signals
and
machine
learning
techniques
in
developing
an
objective
stress
assessment
framework.
The
study
aimed
to
investigate
the
correlation
between
EEG
Perceived
Stress
Scale
(PSS)
by
data
segmentation
technique.
PSS
scores
are
employed
record
perceived
levels
of
individuals.
These
serve
as
basis
for
categorizing
into
three
classes:
i)
two
class:
stressed
non-stressed
ii)
stressed,
mildly
non-stressed,
iii)
four
highly
moderately
non-stressed.
recordings
captured
from
40
participants
using
4
channels
Inter
axon
Muse
headband,
equipped
with
dry
electrodes.
is
segmented
units
10
seconds.
processed
extract
five
feature
sets
including
Power
Spectrum,
Rational
Asymmetry,
Differential
Correlation
Spectral
Density.
success
accessed
classifiers
(Naive
Bayes,
Support
Vector
Machine,
Logistic
Regression,
Simple
Random
Tree,
K-Nearest
Neighbor,
Bagging,
Forest,
Multilayer
Perceptron,
AdaBoost).
highest
accuracies
achieved
two-,
three-,
four-class
classification
91.52%,
88.47%,
87.36%,
respectively.
obtained
Adaboost
classifier
two-class
classification,
Forest
three-class
again
classification.
findings
underline
importance
chosen
features
increasing
prediction
accuracy
while
contributing
existing
knowledge
detection
Signals.
