Name
Krivko Mikhail Sergeevich
Scholastic degree
—
Academic rank
—
Honorary rank
—
Organization, job position
Kuban State University
Web site url
—
Articles count: 2
This article focuses on crude oil trade development
between Russian Federation and European Union with
relation to port of Novorossiysk performance for the
period of 2010-2015 taking into consideration
restrictive measures imposed by European Union.
Literature review on the topic of economic sanctions
on Russia and other countries and impact of these
measures on economy of these countries is given in the
article. After analysis of crude oil trade volumes
between Russia and European Union through port of
Novorossiysk it is possible to conclude that decrease in
crude oil trade started two years before the
introduction of restrictive measures. Current values of
international trade indicators can be under significant
influence of sanctions, but the general trend of decline
started before that, and this trend can be connected
with other then political factors. Another conclusion is
the fact that sanctions made an impact on growth of
crude oil turnover, but at the same time this indicator
was negative for almost a decade before, making it
possible to consider the factor of sanctions as
significant, but not decisive
The article presents an algorithm for constructing an
expert system for quantitative bankruptcy risk
estimation of small agricultural enterprises. Fuzzy logic
analysis methodology in the form of fuzzy inference
system was put as a basis for this development,
classically including five steps: fuzzy rules base
forming, fuzzification, aggregation, intensification,
defuzzification. All the calculations were performed
using MATLAB 2012 software package including
Fuzzy module. Demand and costs of production were
proposed as main factors influencing bankruptcy risk.
Quantitative estimations of input parameters were
determined by 100-point scale on the basis of expert
estimations, and after that variables were fuzzificated in
the form of trapezoid numbers as most common in
fuzzy logic analysis (after triangular). Besides
quantitative estimation of bankruptcy risk a surface of
fuzzy inference was constructed, allowing to determine
dependence between output variable’s values and input
variables’ values of original bankruptcy risk model, as
well as necessary values of input variables values to
reach acceptable level by experts