The article is devoted to the development and application of system-dynamic models for managing the process of liquidation of emergency situations at oil-refining and chemical enterprises. A complex of heterogeneous system-dynamic models has been developed, allowing enterprise management according to a criterion that minimizes the deviation of relevant indicators of safe functioning from the values recommended by the decision-maker. A formulation of the problem of managing the process of liquidation of emergency situations is presented, and a comprehensive methodology is proposed, including the construction of cause-and-effect graphs, regression analysis of functional dependencies, numerical solution of a system of nonlinear differential equations, as well as a procedure for correcting the system-dynamic model. The complex of models makes it possible to take into account key safety indicators, external factors, and nonlinear effects, ensuring high accuracy of forecasting and risk analysis. The obtained results can be used in the development of control systems for the process of liquidation of consequences of emergency situations at oil-refining and chemical enterprises of the country, as well as in training systems for facility-level units of the Ministry of Emergency Situations.

Forecasting methodologies are widely applied in the analysis of socio-economic systems. Employing robust forecasting techniques enables organizations to anticipate future developments, optimize resource allocation, and mitigate potential risks. In the energy sector, accurate forecasting of supply and demand is essential for maintaining grid stability, reducing operational costs, and enhancing reliability. This study aims to assess the effectiveness of statistical and neural network modeling methods in forecasting macroeconomic indicators within the energy sector of the Russian Federation.

This paper addresses the problems of managing a multi-project program under soft dependencies between some program projects. As a rule, the use of soft dependencies saves the time and/or cost of executing the next project, which decreases the time and/or cost of implementing the entire program. An example shows how rarely dealing with soft dependencies can be reduced to simple schemes without losing the problem essence. Consideration of stochastic uncertainty in project implementation is introduced. According to the conclusion, the effect from considering soft dependencies depends on the probability of realizing them.

Based on the sequential probability ratio test (likelihood ratio) method, a controlled random search algorithm is proposed for the approximate solution of large-scale discrete programming problems. The reduction in the exhaustive search of feasible sets of the decision variables of the problem is achieved by introducing non-zero probabilities of false recognition of the optimal solution. As a practical application of the algorithm, the problem of forming optimal well placement patterns in oil and gas reservoirs is considered. The results of computational experiments are presented, the purpose of which was to study the accuracy of the problem’s solution depending on its dimension (the number of blocks where well placement is possible and the number of wells to be placed were varied). The optimal solution of the problem obtained by one of the exact methods of discrete programming was used as a reference solution, against which the accuracy of the approximate solution generated by the proposed algorithm was evaluated.

Encoding of temporal information (also termed time or temporal encoding in the literature) is a key function of biological systems underlying perception, learning, decisionmaking, and behavior synchronization. Despite a large amount of empirical research data and many theoretical models, a unified concept of temporal encoding has not yet been formulated. This paper overviews research works devoted to time encoding in living organisms. It examines current views on the neural correlates of time encoding. The evolution of approaches and models is traced from classical scalar timing models to more complex network, population, and Bayesian concepts. The main trends and paradigm shifts in modeling time memorization and prediction are highlighted. The key properties of time encoding observed in most vertebrate species are indicated. The first fundamental phenomenological models—the internal clock model and the Scalar Expectancy Theory (SET)—are described in detail. Their significance for control theory, artificial intelligence, and robotics is substantiated.

It is proposed to design concurrent error-detection (CED) circuits for discrete automation and computing devices using Boolean signal correction (BSC) with weighted sum codes. Within this approach, a CED circuit corrects signals from all outputs of an object under diagnosis and involves a particular subset of codewords of a preselected weighted sum code. An algorithm is developed for selecting codewords used to design a CED circuit based on BSC; this algorithm allows choosing the best variant to cover faults at the object’s outputs and ensures the self-checking property of the circuit. The features of organizing CED circuits based on the above method are shown.