Ukraine’s Economy: Resilience Under War and Challenges for Post-War Recovery
Science and innovation,
Год журнала:
2024,
Номер
20(5), С. 16 - 34
Опубликована: Сен. 2, 2024
Introduction.
The
war
has
an
unprecedented
negative
effect
on
the
Ukrainian
economy
and
society,
socioeconomic
consequences
of
which
require
a
thorough
assessment
scientific
understanding.Problem
Statement.
Strategic,
programmatic
model
developments
regarding
post-war
reconstructive
recovery
Ukraine
should
take
into
account
main
determinants
national
resilience
challenges
facing
it
in
global
coordinates
hybrid
“peace—war”
system.Purpose.
To
identify
military
shocks
macroeconomic,
macro-financial,
social
aspects
for
period
February
2022
—
April
2024,
as
well
risks
Ukraine’s
recovery.Materials
Methods.
Materials
statistical
data
relevant
domestic
international
institutions
have
been
used.
methods
employed
are
follows:
dialectical
logical-historical,
statistical,
tabular-graphic,
institutional
methodology,
cyclical
world-system
analysis,
macroeconomic
aggregation,
time
series
analysis.Results.
caused
significant
damage
to
Ukraine,
but
overall,
over
two
years
war,
country
demonstrating
socio-economic
resilience.
However,
there
serious
economic
most
important
continuation
territory,
high
level
corruption,
dependence
external
financing,
growth
demo-economic
burden
poverty.Conclusions.
full-scale
is
component
global,
very
complex
long-term
process
reformatting
world
order
can
be
adequately
assessed
only
context
“peace—war"
system.
In
present-day
conditions
signifi
cant
uncertainty,
contextual
development,
based
activation
own
resource
potential,
ensure
our
country.
Язык: Английский
Prediction of the Behaviour from Discharge Points for Solid Waste Management
Machine Learning and Knowledge Extraction,
Год журнала:
2024,
Номер
6(3), С. 1389 - 1412
Опубликована: Июнь 24, 2024
This
research
investigates
the
behaviour
of
Discharge
Points
in
a
Municipal
Solid
Waste
Management
System
to
evaluate
feasibility
making
individual
predictions
every
Point.
Such
could
enhance
system
management
through
optimisation,
improving
their
ecological
and
economic
impact.
The
current
approaches
consider
installations
as
whole,
but
may
yield
better
results.
paper
follows
methodology
that
includes
analysing
data
from
200
different
over
period
four
years
applying
twelve
forecast
algorithms
found
more
commonly
used
for
these
literature,
including
Random
Forest,
Support
Vector
Machines,
Decision
Tree,
identify
predictive
patterns.
results
are
compared
evaluated
determine
accuracy
potential
improvements.
As
show
do
not
capture
behaviour,
alternative
suggested
further
development.
Язык: Английский