Journal of ETA Maritime Science,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 16, 2024
This
study
presents
a
novel
approach
to
managing
disease
outbreaks
on
cruise
ships
by
integrating
Bluetooth
5.1
technology,
Binary
Contact
Detection
Model,
and
an
Alpha
Shape
algorithm.By
harnessing
the
precise
data
capture
capabilities
of
5.1,
this
accurately
tracks
interpersonal
interactions
delineated
high-risk
areas,
effectively
enhancing
close
contact
tracing
surveillance
efforts.The
Model
utilizes
In-phase
(I)
Quadrature
(Q)
identify
contacts
with
high
accuracy,
while
algorithm
helps
in
mapping
out
areas
most
susceptible
transmission.The
combined
use
these
technologies
represents
significant
advancement
public
health
surveillance,
offering
method
enhance
safety
mitigate
spread
infections
ships.
The
use
of
physical
dividers
as
control
measures
for
short-range
viral
transmission
in
indoor
settings
has
gained
increasing
attention.
However,
the
understanding
regarding
their
correct
usage
under
different
seating
arrangements
is
incomplete.
In
this
study,
we
focused
on
assessing
effectiveness
impeding
transient
cough
droplets
three
representative
layouts
using
large-eddy
simulation
technique
with
Eulerian–Lagrangian
model.
We
computationally
also
investigated
effects
ventilation
droplet
desk-divider
layouts.
approach
was
tested
two
rates
(1.0
and
1.8
m/s).
A
comparative
analysis
flow
fields,
topologies,
particle
directions
been
studied.
findings
indicate
that
sitting
arrangements,
rates,
partition
play
a
significant
role
designing
effective
infection
office
setup
considered.
protected
occupied
zone
(POV)
worked
best
situations
low
m/s)
cross-partition
protecting
healthy
persons.
addition,
POV
performed
well
high
(1.8
face-to-face
layout.
side-by-side
configuration
poorly
considered
person
seated
directly
opposite
infected
person.
numerical
predictions
may
be
used
to
validate
other
experimental
studies
educate
workers
engineers
airborne
control.
The
effects
of
ventilation
strategies
on
mitigating
airborne
virus
transmission
in
a
generic
indoor
space
representative
lobby
area
or
information
desk
found
hotel,
company,
cruise
ship
are
presented.
Multiphase
computational
fluid
dynamics
simulations
employed
conjunction
with
evaporation
modeling.
Four
different
flow
rates
examined
based
the
most
updated
post-COVID-19
pandemic
standards
from
health
organizations
and
recent
findings
research
studies.
Three
air
changes
per
hour
provide
best
option
for
minimizing
droplet
spreading
at
reasonable
energy
efficiency.
Thus,
higher
rate
is
not
solution
to
avoid
diseases.
Simultaneous
coughing
all
occupants
revealed
that
contagious
droplets
could
be
trapped
regions
low
airflow
furniture,
significantly
prolonging
their
time.
can
help
define
reduce
mitigate
while
maintaining
adequate
lower
consumption.
present
work
impacts
how
heat,
air-conditioning,
systems
designed
implemented.
This
paper
concerns
analyses
of
virus
droplet
dynamics
resulting
from
coughing
events
within
a
confined
environment
using,
as
an
example,
typical
cruiser's
cabin.
It
is
paramount
importance
to
be
able
comprehend
and
predict
dispersion
patterns
enclosed
spaces
under
varying
conditions.
Numerical
simulations
are
expensive
difficult
perform
in
real-time
situations.
Unsupervised
machine
learning
methods
proposed
study
patterns.
Data
multi-phase
computational
fluid
at
different
flow
rates
utilized
with
unsupervised
algorithm
identify
prevailing
trends
based
on
the
distance
traveled
by
droplets
their
sizes.
The
determines
optimal
clustering
introducing
novel
metrics
such
Clustering
Dominance
Index
Uncertainty.
Our
analysis
revealed
existence
three
distinct
stages
for
during
event,
irrespective
underlying
rates.
An
initial
stage
where
all
disperse
homogeneously,
intermediate
larger
overtake
smaller
ones,
final
ones.
first
time
coupled
particles'
understand
dynamic
behavior.
A
deep
learning
super-resolution
scheme
is
proposed
to
reconstruct
a
coarse,
turbulent
temperature
field
into
detailed,
continuous
field.
The
fluid
mechanics
application
here
refers
an
airflow
ventilation
process
in
indoor
setting.
Large
eddy
simulations
are
performed
from
dense
simulation
grid
and
provide
data
two-dimensional
images.
images
fed
flow
reconstruction
model
after
being
scaled
down
100
times.
Training
testing
on
these
images,
the
learns
map
such
highly
coarse
fields
their
high-resolution
counterparts.
This
computational,
approach
mimics
of
employing
sparse
sensor
measurements
trying
upscale
Notably,
achieves
high
performance
when
input
by
5–20
times
original
dimension,
acceptable
30,
poor
at
higher
scales.
peak
signal-to-noise
ratio,
structure
similarity
index,
relative
error
between
reconstructed
output
given
compared
common
image
processing
techniques,
as
linear
bicubic
interpolation.
pipeline
suggests
high-performance
platform
that
calculates
spatial
values
can
bypass
installation
wide
array,
making
it
cost-effective
solution
for
relevant
applications.
Developing
deep
learning
models
for
predicting
environmental
data
is
a
powerful
tool
that
can
significantly
enhance
equipment
design,
optimize
the
implementation
of
engineering
systems,
and
deepen
our
understanding
limitations
imposed
by
flow
physics.
This
study
unequivocally
demonstrates
accuracy
forecasting
based
on
popular
algorithms,
such
as
long-short-term
memory
model,
in
turbulent
mixing
regions
associated
with
physics
arising
from
ventilation.
contingent
two
essential
conditions.
First,
sparsity
sampling
consistent
model's
overall.
Second,
ensures
reasonable
regions.
The
investigation
combines
high-resolution
simulation
predictions
velocity,
temperature,
relative
humidity
ventilated
confined
space.
results
this
study,
their
high
accuracy,
not
only
help
to
understand
circulation
but
also
pave
way
developing
predictive
capabilities
data.
The
COVID-19
pandemic
highlighted
the
need
for
rapidly
deployable
healthcare
facilities,
leading
to
increased
use
of
modular
construction
methods.
Nonetheless,
knowledge
about
airflow
patterns
and
spread
bioaerosols
in
these
wards
remains
insufficient,
potentially
heightening
risk
cross-infection
among
workers
patients.
This
paper
presents
a
ventilation
design
negative-pressure
ward
aimed
at
reducing
infectious
particles.
We
investigate
effects
various
designs,
patient
postures
(sitting
supine),
air
changes
per
hour
(ACH)
on
cough
droplets
an
airborne
infection
isolation
room
using
large
eddy
simulation
Eulerian–Lagrangian
model.
Findings
show
that
ceiling
exhaust
grilles
(design
2)
resulted
lowest
radial
dispersion
(3.64
m)
12
ACH,
while
sidewall
exhausts
(baseline)
performed
best
higher
ACH
levels.
Seated
patients
had
quicker
droplet
evaporation
compared
those
supine
position.
setups
survival
included
bed's
bottom
ceiling,
maintaining
minimum
ACH.
Cases
5
13,
with
over
patient's
head
bottom,
showed
concentrations
DPM,
under
0.008
km−3
near
source
less
than
0.001
mid-room.
Sitting
posture
consistently
led
lower
DPM
concentrations.
research
emphasizes
critical
role
placement
re-circulation
transmission
risks,
ultimately
contributing
improved
strategies
control
AII
rooms.
This
study
investigates
the
effect
of
natural
ventilation
on
distribution
airborne
pathogens
in
narrow,
low-ceiling
corridors
typical
hotels,
offices,
or
cruise
ships.
Two
scenarios
are
examined:
a
milder
cough
at
6
m/s
and
stronger
12
m/s.
A
reference
baseline
case
with
no
airflow
is
compared
to
cases
featuring
an
incoming
velocity
1
(3.6
km/h),
examining
differences
dispersal
respiratory
droplets
from
two
individuals
coughing
spaced
5
meters
apart.
Both
direction
airflow,
assuming
one-way
traffic
minimize
pathogen
transmission.
Findings
indicate
that
accelerates
past
door,
exceeding
3
m/s,
gusts
reaching
4
due
interaction
recirculation
zones.
acceleration
affects
droplet
dispersal.
Larger
(>150
μm)
maintain
ballistic
trajectory,
traveling
2–4
m,
potentially
increasing
transmission
risk
but
suggesting
5-m
distancing
policy
could
suffice
for
protection.
Smaller
(<150
μm),
especially
those
<100μm,
spread
extensively
regardless
strength
while
containing
most
viral
mass
overall.
Thus,
alone
insufficient.
The
recommends
additional
safety
measures
be
enforced,
such
as
wearing
masks,
stricter
usage
protocols
by
limiting
corridor
use
one
person
every
20–30
s,
eliminating
when
feasible
effectively
mitigate
risks
environments.
Understanding
the
dispersion
of
particles
in
enclosed
spaces
is
crucial
for
controlling
spread
infectious
diseases.
This
study
introduces
an
innovative
approach
that
combines
unsupervised
learning
algorithm
with
a
Gaussian
mixture
model
to
analyze
behavior
saliva
droplets
emitted
from
coughing
individual.
The
effectively
clusters
data,
while
captures
distribution
these
clusters,
revealing
underlying
sub-populations
and
variations
particle
dispersion.
Using
computational
fluid
dynamics
simulation
this
integrated
method
offers
robust,
data-driven
perspective
on
dynamics,
unveiling
intricate
patterns
probabilistic
distributions
previously
unattainable.
combined
significantly
enhances
accuracy
interpretability
predictions,
providing
valuable
insights
public
health
strategies
prevent
virus
transmission
indoor
environments.
practical
implications
are
profound,
as
it
demonstrates
potential
advanced
techniques
addressing
complex
biomedical
engineering
challenges
underscores
importance
coupling
sophisticated
algorithms
statistical
models
comprehensive
data
analysis.
impact
findings
significant,
highlighting
relevance
research
real-world
applications.
The
development
of
the
global
COVID-19
pandemic
from
2020
onward
has
had
significant
impact
on
world
and
specifically
maritime
industry.
Striking
examples
were
outbreaks
onboard
Diamond
Princess
cruise
vessel
U.S.S.
Theodore
Roosevelt
aircraft
carrier
at
start
pandemic.
Contagious
disease
management
large
passenger
ships
remains
a
complex
issue,
amplified
by
international
character
industry,
confined
environment
shared
facilities.
This
paper
therefore
presents
an
integrated
infection
crowd
behavior
model
used
to
calculate
agent-specific
risk,
incorporating
guest
crew
circulation
through
ship
layout.
is
investigate
effect
layout
design,
capacity
reduction
mask
wearing
airborne
risk
vessels.