To create an autonomously moving vessel, it is necessary to know exactly how to determine the current coordinates of the vessel in the selected coordinate system, determine the actual trajectory of the vessel, estimate the motion trend to predict the current coordinates, and calculate the course correction to return to the line of the specified path. The navigational and hydrographic conditions of navigation on each section of the route determine the requirements for the accuracy of observations and the time spent on locating the vessel. The problem of predicting the trajectory of the vessel’s motion in automatic mode is especially important for river vessels or river-sea vessels, predicting the trajectory of the route sections during the maneuvering of the vessel. At the moment, one of the most accurate ways of determining the coordinates of the vessel is by reading the satellite signal. However, when a vessel is near hydraulic structures, problems may arise connected with obtaining a satellite signal due to interference and, therefore, the error in measuring the coordinates of the vessel increases. The likelihood of collisions and various kinds of incidents increases. In such cases, it is possible to correct the trajectory of the movement using an autonomous navigation system. In this work, opportunities of the possible application of artificial neural networks to create such a corrective system using only the coordinates of the ship’s position are discussed. It was found that this is possible on sections of the route where the ship does not maneuver.

In a modern economy, international trade is an important factor in the development of various regions. Shipping is one of the most important elements of the global supply chain. However, after the economic crisis of 2008, global shipping revenues plummeted. One way to restore profitability is the consolidation of shipping routes and the globalization of shipping lines. As container transport lines move to larger ships, the structure of the delivery route becomes a structure with intermediate points. This trend put forward higher demands on the port infrastructure, which aggravated the competition between regional ports, as well as ports that could degrade into a large cargo consolidation port. The economic advantage is enhanced by cooperation between shipping lines and ports. Thus, ports and shipping lines in the same supply chain can be mutually beneficial partners. The study analyses the effectiveness of horizontal and vertical cooperation between ports and carriers. As a source of information, a review of the literature on this issue, expert opinions, and statistical data is taken. Next, a mathematical model is built on the basis of cooperative game theory, and numerical analysis is carried out. The results show that the strategy of cooperation of shipping lines strongly depends on the situation with the supply and demand of vessels. A port that interacts with shipping lines will significantly reduce port charges, which creates the advantage of receiving more port requests. However, cooperation may lead to losses for the port, so a redistribution of profits is necessary to maintain the coalition.

Because of the lack of reliable information on the spread parameters of COVID-19, there is an increasing demand for new approaches to efficiently predict the dynamics of new virus spread under uncertainty. The study presented in this paper is based on the Case-Based Reasoning method used in statistical analysis, forecasting and decision making in the field of public health and epidemiology. A new mathematical Case-Based Rate Reasoning model (CBRR) has been built for the short-term forecasting of coronavirus spread dynamics under uncertainty. The model allows for predicting future values of the increase in the percentage of new cases for a period of 2–3 weeks. Information on the dynamics of the total number of infected people in previous periods in Italy, Spain, France, and the United Kingdom was used. Simulation results confirmed the possibility of using the proposed approach for constructing short-term forecasts of coronavirus spread dynamics. The main finding of this study is that using the proposed approach for Russia showed that the deviation of the predicted total number of confirmed cases from the actual one was within 0.3%. For the USA, the deviation was 0.23%.

The consideration of infectious diseases from a mathematical point of view can reveal possible options for epidemic control and fighting the spread of infection. However, predicting and modeling the spread of a new, previously unexplored virus is still difficult. The present paper examines the possibility of using a new approach to predicting the statistical indicators of the epidemic of a new type of virus based on the example of COVID-19. The important result of the study is the description of the principle of dynamic balance of epidemiological processes, which has not been previously used by other researchers for epidemic modeling. The new approach is also based on solving the problem of predicting the future dynamics of precisely random values of model parameters, which is used for defining the future values of the total number of: cases (C); recovered and dead (R); and active cases (I). Intelligent heuristic algorithms are proposed for calculating the future trajectories of stochastic parameters, which are called the percentage increase in the total number of confirmed cases of the disease and the dynamic characteristics of epidemiological processes. Examples are given of the application of the proposed approach for making forecasts of the considered indicators of the COVID-19 epidemic, in Russia and European countries, during the first wave of the epidemic.

Relevance. Assessment of the burden of disease provides information on the economic consequences of the disease, allows you to assess the social significance, identify areas that require additional clinical and economic research, changes in methodological approaches to the organization of measures for the prevention, early detection and treatment of diseases.The aim. Assessment of the socioeconomic and global burden of COVID-19 in the Russian Federation (RF).Materials and methods. Identification and assessment of direct medical, direct non-medical, and indirect costs associated with the development of the coronavirus epidemic. When calculating the socioeconomic burden, the variant of calculations was chosen taking into account the prevalence of the disease. The sources of data on the epidemiology of the disease were data from the Ministry of Health and data from the Government of the RF.Results. The socioeconomic burden of COVID-19 in 2020 in the RF amounted to about 5.4 trillion rubles (5 % of nominal GDP in 2020) and was largely due to indirect costs due to GDP losses due to a 1.5-month period of self-isolation. The estimated global burden of disease is more than 4 million YLLs globally, of which in the RF 2,486.30 among men and 1,378.22 YLL among women.Conclusion. The epidemic of the new coronavirus infection has led to colossal economic losses in Russian society. The data presented underscore not only the clinical, but also the economic importance of investing in the development of strategies for the treatment and prevention of new coronavirus infection.

The social-economic burden of spinal muscular atrophy in Russia