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The article presents the numerical modeling and study of the kinetic model of propane pyrolysis. The study of the reaction kinetics is a necessary stage in modeling the dynamics of the gas flow in the reactor.

The kinetic model of propane pyrolysis is a nonlinear system of ordinary differential equations of the first order with parameters, the role of which is played by the reaction rate constants. Math modeling of processes is based on the use of the mass conservation law. To solve an initial (forward) problem, implicit methods for solving stiff ordinary differential equation systems are used. The model contains 60 input kinetic parameters and 17 output parameters corresponding to the reaction substances, of which only 9 are observable. In the process of solving the problem of estimating parameters (inverse problem), there is a question of non-uniqueness of the set of parameters that satisfy the experimental data. Therefore, before solving the inverse problem, the possibility of determining the parameters of the model is analyzed (analysis of identifiability).

To analyze identifiability, we use the orthogonal method, which has proven itself well for analyzing models with a large number of parameters. The algorithm is based on the analysis of the sensitivity matrix by the methods of differential and linear algebra, which shows the degree of dependence of the unknown parameters of the models on the given measurements. The analysis of sensitivity and identifiability showed that the parameters of the model are stably determined from a given set of experimental data. The article presents a list of model parameters from most to least identifiable. Taking into account the analysis of the identifiability of the mathematical model, restrictions were introduced on the search for less identifiable parameters when solving the inverse problem.

The inverse problem of estimating the parameters was solved using a genetic algorithm. The article presents the found optimal values of the kinetic parameters. A comparison of the experimental and calculated dependences of the concentrations of propane, main and by-products of the reaction on temperature for different flow rates of the mixture is presented. The conclusion about the adequacy of the constructed mathematical model is made on the basis of the correspondence of the results obtained to physicochemical laws and experimental data.

In our research, we focus on the problem of classification for silent speech recognition to develop a brain– computer interface (BCI) based on electroencephalographic (EEG) data, which will be capable of assisting people with mental and physical disabilities and expanding human capabilities in everyday life. Our previous research has shown that the silent pronouncing of some words results in almost identical distributions of electroencephalographic signal data. Such a phenomenon has a suppressive impact on the quality of neural network model behavior. This paper proposes a data processing technique that distinguishes between statistically remote and inseparable classes in the dataset. Applying the proposed approach helps us reach the goal of maximizing the semantic load of the dictionary used in BCI.

Furthermore, we propose the existence of a statistical predictive criterion for the accuracy of binary classification of the words in a dictionary. Such a criterion aims to estimate the lower and the upper bounds of classifiers’ behavior only by measuring quantitative statistical properties of the data (in particular, using the Kolmogorov – Smirnov method). We show that higher levels of classification accuracy can be achieved by means of applying the proposed predictive criterion, making it possible to form an optimized dictionary in terms of semantic load for the EEG-based BCIs. Furthermore, using such a dictionary as a training dataset for classification problems grants the statistical remoteness of the classes by taking into account the semantic and phonetic properties of the corresponding words and improves the classification behavior of silent speech recognition models.

Analysis of regional economic indicators plays a crucial role in management and development planning, with Gross Regional Product (GRP) serving as one of the key indicators of economic activity. The application of artificial intelligence, including neural network technologies, enables significant improvements in the accuracy and reliability of forecasts of economic processes. This study compares three neural network algorithm models for predicting the GRP of a typical region of the Russian Federation — the Udmurt Republic — based on time series data from 2000 to 2023. The selected models include a neural network with the Bat Algorithm (BA-LSTM), a neural network model based on backpropagation error optimized with a Genetic Algorithm (GA-BPNN), and a neural network model of Elman optimized using the Particle Swarm Optimization algorithm (PSO-Elman). The research involved stages of neural network modeling such as data preprocessing, training model, and comparative analysis based on accuracy and forecast quality metrics. This approach allows for evaluating the advantages and limitations of each model in the context of GRP forecasting, as well as identifying the most promising directions for further research. The utilization of modern neural network methods opens new opportunities for automating regional economic analysis and improving the quality of forecast assessments, which is especially relevant when data are limited and for rapid decision-making. The study uses factors such as the amount of production capital, the average annual number of labor resources, the share of high-tech and knowledge-intensive industries in GRP, and an inflation indicator as input data for predicting GRP. The high accuracy of the predictions achieved by including these factors in the neural network models confirms the strong correlation between these factors and GRP. The results demonstrate the exceptional accuracy of the BA-LSTM neural network model on validation data: the coefficient of determination was 0.82, and the mean absolute percentage error was 4.19%. The high performance and reliability of this model confirm its capacity to predict effectively the dynamics of the GRP. During the forecast period up to 2030, the Udmurt Republic is expected to experience an annual increase in Gross Regional Product (GRP) of +4.6% in current prices or +2.5% in comparable 2023 prices. By 2030, the GRP is projected to reach 1264.5 billion rubles.

This article presents a model of a fully automated warehouse with deep storage racks designed for boxed goods storage. The study focuses on optimizing warehouse operations through discrete multiagent simulation of shuttle movements for pallet loading and unloading tasks. The authors investigate various product placement strategies, including the Nearest Channel Positioning Algorithm (NCPA), Most Empty Channel Group Placement (MECGP), andMost Filled Channel Group Placement (MFCGP), while analyzing optimal routing schemes for the given warehouse topology.

A key contribution is determining the optimal number of shuttles to maximize warehouse throughput. Simulation results demonstrate that increasing the number of robots beyond 15 does not significantly improve efficiency due to increased route collisions. The study also examines 24-hour warehouse occupancy dynamics, revealing optimal storage utilization levels.

The developed model enables performance evaluation and optimization of task distribution among robots to minimize order processing time. Future research directions include implementing machine learning techniques to further enhance warehouse management systems.

Optimal management of fuel supply system boils down to choosing an energy development strategy which provides consumers with the most efficient and reliable fuel and energy supply. As a part of the program on switching the heat supply distributed management system of the Udmurt Republic to renewable energy sources, an “Information-analytical system of regional alternative fuel supply management” was developed. The paper presents the mathematical model of optimal management of fuel supply logistic system consisting of three interconnected levels: raw material accumulation points, fuel preparation points and fuel consumption points, which are heat sources. In order to increase effective the performance of regional fuel supply system a modification of information-analytical system and extension of its set of functions using the methods of quick responding when emergency occurs are required. Emergencies which occur on any one of these levels demand the management of the whole system to reconfigure. The paper demonstrates models and algorithms of optimal management in case of emergency involving break down of such production links of logistic system as raw material accumulation points and fuel preparation points. In mathematical models, the target criterion is minimization of costs associated with the functioning of logistic system in case of emergency. The implementation of the developed algorithms is based on the usage of genetic optimization algorithms, which made it possible to obtain a more accurate solution in less time. The developed models and algorithms are integrated into the information-analytical system that enables to provide effective management of alternative fuel supply of the Udmurt Republic in case of emergency.

This paper is devoted to some variants of improving the convergence rate guarantees of the gradient-type algorithms for relatively smooth and relatively Lipschitz-continuous problems in the case of additional information about some analogues of the strong convexity of the objective function. We consider two classes of problems, namely, convex problems with a relative functional growth condition, and problems (generally, non-convex) with an analogue of the Polyak – Lojasiewicz gradient dominance condition with respect to Bregman divergence. For the first type of problems, we propose two restart schemes for the gradient type methods and justify theoretical estimates of the convergence of two algorithms with adaptively chosen parameters corresponding to the relative smoothness or Lipschitz property of the objective function. The first of these algorithms is simpler in terms of the stopping criterion from the iteration, but for this algorithm, the near-optimal computational guarantees are justified only on the class of relatively Lipschitz-continuous problems. The restart procedure of another algorithm, in its turn, allowed us to obtain more universal theoretical results. We proved a near-optimal estimate of the complexity on the class of convex relatively Lipschitz continuous problems with a functional growth condition. We also obtained linear convergence rate guarantees on the class of relatively smooth problems with a functional growth condition. For a class of problems with an analogue of the gradient dominance condition with respect to the Bregman divergence, estimates of the quality of the output solution were obtained using adaptively selected parameters. We also present the results of some computational experiments illustrating the performance of the methods for the second approach at the conclusion of the paper. As examples, we considered a linear inverse Poisson problem (minimizing the Kullback – Leibler divergence), its regularized version which allows guaranteeing a relative strong convexity of the objective function, as well as an example of a relatively smooth and relatively strongly convex problem. In particular, calculations show that a relatively strongly convex function may not satisfy the relative variant of the gradient dominance condition.

This work deals with numerical modeling of motion of the multibody systems consisting of rigid bodies with arbitrary masses and inertial properties. We consider both planar and spatial systems which may contain kinematic loops.

The numerical modeling is fully automatic and its computational algorithm contains three principal steps. On step one a graph of the considered mechanical system is formed from the userinput data. This graph represents the hierarchical structure of the mechanical system. On step two the differential-algebraic equations of motion of the system are derived using the so-called Joint Coordinate Method. This method allows to minimize the redundancy and lower the number of the equations of motion and thus optimize the calculations. On step three the equations of motion are integrated numerically and the resulting laws of motion are presented via user interface or files.

The aforementioned algorithm is implemented in the software complex that contains a computer algebra system, a graph library, a mechanical solver, a library of numerical methods and a user interface.

A Large Language Model (LLM) is an advanced artificial intelligence algorithm that utilizes deep learning methodologies and extensive datasets to process, understand, and generate humanlike text. These models are capable of performing various tasks, such as summarization, content creation, translation, and predictive text generation, making them highly versatile in applications involving natural language understanding. Generative AI, often associated with LLMs, specifically focuses on creating new content, particularly text, by leveraging the capabilities of these models. Developers can harness LLMs to automate complex processes, such as extracting relevant information from system requirement documents and translating them into a structured database schema. This capability has the potential to streamline the database design phase, saving significant time and effort while ensuring that the resulting schema aligns closely with the given requirements. By integrating LLM technology with Natural Language Processing (NLP) techniques, the efficiency and accuracy of generating database schemas based on textual requirement specifications can be significantly enhanced. The proposed tool will utilize these capabilities to read system requirement specifications, which may be provided as text descriptions or as Entity-Relationship Diagrams (ERDs). It will then analyze the input and automatically generate a relational database schema in the form of SQL commands. This innovation eliminates much of the manual effort involved in database design, reduces human errors, and accelerates development timelines. The aim of this work is to provide a tool can be invaluable for software developers, database architects, and organizations aiming to optimize their workflow and align technical deliverables with business requirements seamlessly.

One of the principles of forming a competitive market environment is to create conditions for economic agents to implement Nash – Cournot optimal strategies. With the standard approach to determining Nash – Cournot optimal market strategies, economic agents must have complete information about the indicators and dynamic characteristics of all market participants. Which is not true.

In this regard, to find Nash – Cournot optimal solutions in dynamic models, it is necessary to have a coordinator who has complete information about the participants. However, in the case of a large number of game participants, even if the coordinator has the necessary information, computational difficulties arise associated with the need to solve a large number of coupled equations (in the case of linear dynamic games — Riccati matrix equations).

In this regard, there is a need to decompose the general problem of determining optimal strategies for market participants into private (local) problems. Approaches based on the iterative decomposition of coupled matrix Riccati equations and the solution of local Riccati equations were studied for linear dynamic games with a quadratic criterion. This article considers a simpler approach to the iterative determination of the Nash – Cournot equilibrium in an oligopoly, by decomposition using operational calculus (operator method).

The proposed approach is based on the following procedure. A virtual coordinator, which has information about the parameters of the inverse demand function, forms prices for the prospective period. Oligopolists, given fixed price dynamics, determine their strategies in accordance with a slightly modified optimality criterion. The optimal volumes of production of the oligopolists are sent to the coordinator, who, based on the iterative algorithm, adjusts the price dynamics at the previous step.

The proposed procedure is illustrated by the example of a static and dynamic model of rational behavior of oligopoly participants who maximize the net present value (NPV). Using the methods of operational calculus (and in particular, the inverse Z-transformation), conditions are found under which the iterative procedure leads to equilibrium levels of price and production volumes in the case of linear dynamic games with both quadratic and nonlinear (concave) optimization criteria.

The approach considered is used in relation to examples of duopoly, triopoly, duopoly on the market with a differentiated product, duopoly with interacting oligopolists with a linear inverse demand function. Comparison of the results of calculating the dynamics of price and production volumes of oligopolists for the considered examples based on coupled equations of the matrix Riccati equations in Matlab (in the table — Riccati), as well as in accordance with the proposed iterative method in the widely available Excel system shows their practical identity.

In addition, the application of the proposed iterative procedure is illustrated by the example of a duopoly with a nonlinear demand function.

We consider the optimal motion control problem for a mobile device with an external rigid shell moving along a prescribed trajectory in a viscous fluid. The mobile robot under consideration possesses the property of self-locomotion. Self-locomotion is implemented due to back-and-forth motion of an internal material point. The optimal motion control is based on the Sugeno fuzzy inference system. An approach based on constructing decision trees using the genetic algorithm for structural and parametric synthesis has been proposed to obtain the base of fuzzy rules.