Alexandria Engineering Journal,
Год журнала:
2023,
Номер
85, С. 300 - 306
Опубликована: Ноя. 23, 2023
Titanium
matrix
composites
(TMCs)
offer
superior
specific
mechanical
properties
compared
to
monolithic
alloys.
However,
the
complex
interdependent
effects
of
composition
and
processing
on
resulting
microstructure
make
experimental
determination
optimal
TMC
formulations
challenging.
This
work
explored
a
materials
informatics
approach
integrating
machine
learning
(ML)
modeling
with
targeted
fabrication
characterization
for
accelerated
data-driven
design
TMCs.
A
dataset
368
data
points
composition,
method
various
TMCs
was
compiled
from
literature.
Five
ML
regression
algorithms
were
implemented
predict
density,
hardness
strength
composition-processing
features.
Among
models,
random
forest
achieved
highest
accuracy
R2
scores
above
0.93
low
errors.
Fabrication
Ti-6Al-4
V/SiC
using
ML-guided
parameters
showed
excellent
agreement
between
predicted
experimentally
measured
properties.
The
models
outperformed
conventional
empirical
predictions
by
structure-property
linkages
data.
integrated
computational-experimental
framework
can
guide
rapid
identification
property-optimized
reducing
trial-and-error.
Further
should
focus
physics-based
feature
engineering
active
learning.
demonstrated
here
shows
promise
accelerating
development
high-performance
Biosensors,
Год журнала:
2025,
Номер
15(4), С. 210 - 210
Опубликована: Март 25, 2025
In
this
literature
review,
methods
for
the
detection
of
breast
cancer
biomarkers
and
operation
electrochemical
sensors
are
considered.
The
work
in
determination
was
systematized,
a
comparative
table
with
other
compiled,
as
classification
depending
on
their
intended
use.
various
traditional
diagnosis
described,
including
mammography,
ultrasound,
magnetic
resonance
imaging,
positron
emission
computed
tomography,
single-photon
biopsy,
advantages
disadvantages
presented.
Key
sensor
parameters
compared,
such
limit,
linear
range,
response
time,
sensitivity,
characteristics
analyte
being
analyzed.
Based
reviewed
scientific
papers,
significance
detecting
is
demonstrated.
types
tumor
identified
by
biosensors
were
analyzed,
particular
focus
HER2.
Studies
HER2
using
compared
features
determining
biomarker
characterized.
Possible
interfering
agents
affecting
accuracy
under
experimental
conditions
considered,
mechanisms
action
ways
to
eliminate
them
proposed.
This
report
provides
summary
current
aspects
research
biomarkers.
development
opens
up
new
prospects
early
prognosis
treatment.
Sensors,
Год журнала:
2023,
Номер
23(21), С. 8813 - 8813
Опубликована: Окт. 30, 2023
Breast
cancer
has
garnered
global
attention
due
to
its
high
incidence
worldwide,
and
even
more
noteworthy
is
that
approximately
90%
deaths
breast
are
attributed
metastasis.
Therefore,
the
early
diagnosis
of
metastasis
holds
significant
importance
for
reducing
mortality
outcomes.
Biosensors
play
a
crucial
role
in
detection
metastatic
their
advantages,
such
as
ease
use,
portability,
real-time
analysis
capabilities.
This
review
primarily
described
various
types
sensors
detecting
based
on
biomarkers
cell
characteristics,
including
electrochemical,
optical,
microfluidic
chips.
We
offered
detailed
descriptions
performance
these
biosensors
made
comparisons
between
them.
Furthermore,
we
pathology
summarized
commonly
used
cancer.
Finally,
discussed
advantages
current-stage
challenges
need
be
addressed,
well
prospects
future
development.
Alexandria Engineering Journal,
Год журнала:
2023,
Номер
85, С. 300 - 306
Опубликована: Ноя. 23, 2023
Titanium
matrix
composites
(TMCs)
offer
superior
specific
mechanical
properties
compared
to
monolithic
alloys.
However,
the
complex
interdependent
effects
of
composition
and
processing
on
resulting
microstructure
make
experimental
determination
optimal
TMC
formulations
challenging.
This
work
explored
a
materials
informatics
approach
integrating
machine
learning
(ML)
modeling
with
targeted
fabrication
characterization
for
accelerated
data-driven
design
TMCs.
A
dataset
368
data
points
composition,
method
various
TMCs
was
compiled
from
literature.
Five
ML
regression
algorithms
were
implemented
predict
density,
hardness
strength
composition-processing
features.
Among
models,
random
forest
achieved
highest
accuracy
R2
scores
above
0.93
low
errors.
Fabrication
Ti-6Al-4
V/SiC
using
ML-guided
parameters
showed
excellent
agreement
between
predicted
experimentally
measured
properties.
The
models
outperformed
conventional
empirical
predictions
by
structure-property
linkages
data.
integrated
computational-experimental
framework
can
guide
rapid
identification
property-optimized
reducing
trial-and-error.
Further
should
focus
physics-based
feature
engineering
active
learning.
demonstrated
here
shows
promise
accelerating
development
high-performance
