The paper analyzes South Korea’s strategy in the global oil market. South Korean oil cooperation is characterized by the creation or termination of joint projects in the oil sector, as well as the Republic of Korea’s national project for the diversification of state-energy suppliers. Oil cooperation currently has great potential, and the conditions that have developed at the highest level allow open discussions about positive dynamics for short-term and medium-term prospects in the field of oil cooperation. The analysis presented here includes export and import connections in the oil market. The authorities of the current administration of the Republic of Korea have adopted a new political stance towards the north, in accordance with which the state is actively developing and establishing relations with the Democratic People’s Republic of Korea (DPRK) and the Russian Federation. In the coming years, South Korea aims to renew and revise potential projects in the field of oil cooperation. The main result of this is that the political climate of the Republic of Korea is currently concentrated on the development of an oil cooperation strategy.

The main goal of this study is to evaluate the impact of restrictive measures introduced in connection with COVID-19 on consumption in renewable energy markets. The study will be based on the hypothesis that similar changes in human behavior can be expected in the future with the further spread of COVID-19 and/or the introduction of additional quarantine measures around the world. The analysis also yielded additional results. The strongest reductions in energy generation occurred in countries with a high percentage (more than 80%) of urban population (Brazil, USA, the United Kingdom and Germany). This study uses two models created with the Keras Long Short-Term Memory (Keras LSTM) Model, and 76 and 10 parameters are involved. This article suggests that various restrictive strategies reduced the sustainable demand for renewable energy and led to a drop in economic growth, slowing the growth of COVID-19 infections in 2020. It is unknown to what extent the observed slowdown in the spread from March 2020 to September 2020 due to the policy’s impact and not the interaction between the virus and the external environment. All renewable energy producers decreased the volume of renewable energy market supply in 2020 (except China).

This article presents a machine learning approach in a heterogeneous group of algorithms in a transport type model for the optimal distribution of tasks in safety-critical systems (SCS). Applied systems in the working area identify the determination of their parameters. Accordingly, in this article, machine learning models are implemented on various subsets of our transformed data and repeatedly calculated the bounds for 90 percent tolerance intervals, each time noting whether or not they contained the actual value of X. This approach considers the features of algorithms for solving such important classes of problem management as the allocation of limited resources in multi-agent SCS and their most important properties. Modeling for the error was normally distributed. The results are obtained, including the situation requiring solutions, recorded and a sample is made out of the observations. This paper summarizes the literature review on the machine learning approach into new implication research. The empirical research shows the effect of the optimal algorithm for transport safety-critical systems.

The main goal of this study is to evaluate the impact of population mobility on electricity generation in Russian cities in the conditions of the spread of COVID-19, and identify hotspots. Furthermore, the evaluation is also conducted using hybrid fuzzy decision-making modelling. In this context, q-ROF DEMATEL and TOPSIS methods are taken into consideration. Additionally, a comparative evaluation is also performed with the help of Intuitionistic and Pythagorean fuzzy sets. The results are quite similar that allows to conclude that the findings are reliable and coherent. The study proves the hypothesis that human behavior changed during the COVID-19 pandemic, and electricity consumption is declining in major cities around the world. The biggest fall in energy generation was in Moscow and Yekaterinburg. In St. Petersburg and Nizhny Novgorod, the fall in energy generation is no so crucial because these cities have low building density. The study uses Long Short-Term Memory models with many different parameters. The Q-Rung Orthopair Fuzzy Sets model forecasts new COVID-19 using ten parameters. This study identifies factors influencing the spread of COVID-19 based on the theory of "broken windows" and outlines directions in limiting population mobility, which can form the basis of state policy. According to the analysis the air temperature is the variable that most affects this process.