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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.

Acute respiratory infections are a major public health concern because they are the leading cause of illness and death in many countries. Therefore, there is great interest in developing models and methods capable of modeling the spread of these infections within communities, with the aim of controlling outbreaks and preventing their spread. Agent-based models (ABM) are one of the most important tools in epidemiological research for modeling epidemic dynamics in realistic populations, but they face significant challenges in terms of computational complexity in their operation and calibration of epidemiological data, as parameter estimation typically requires repeated simulations across large parameter spaces to determine plausible values for key epidemiological parameters. This paper addresses the problem of alleviating computational constraints in the inverse problem of calibrating an ABM model for simulating the spread of respiratory infections in Saint Petersburg. The paper proposes the application of machine learning surrogate to link epidemic trajectories to underlying epidemiological parameters, enabling them to quickly infer parameter estimates from observed epidemic data. This is done by formulating the task of calibrating ABMs against epidemiological data as a supervised learning problem, where sequences extracted from epidemiological trajectories are associated with underlying epidemiological parameters. The research was based on evaluating the performance of attention-based sequence modeling, probabilistic deep learning, and distributional regression for inferring parameter estimates from truncated sequences of epidemic trajectories. Experimental evaluations have demonstrated the effectiveness of this approach and its practical and straightforward application. The results also indicated the superiority of attention-based sequence modeling, as it showed more consistent performance across metrics and horizons in accurate parameter estimation and credible uncertainty quantification. Distributional regression modeling also showed good performance with specific strengths in point accuracy while probabilistic deep learning performed poorly, especially at longer input horizons.

In the paper three characteristic scales of a biological system are proposed: microscopic (gene's size), mesoscopic (cell’s size) and macroscopic level (organism’s size). For each case the approach to modeling of circadian rhythms is discussed on the base of a time-delay model. At gene’s scale the stochastic description has been used. The robustness of rhythms mechanism to the fluctuations has been demonstrated. At the mesoscopic scale we propose the deterministic description within the spatially extended model. It was found the effect of collective synchronization of rhythms in cells. Macroscopic effects have been studied within the discrete model describing the collective behaviour of large amount of cells. The problem of cross-linking of results obtained at different scales is discussed. The comparison with experimental data is given.

The collective behavior of crowd leaving a room is modeled. The model is based on molecular dynamics approach with a mixture of socio-psychological and physical forces. The new algorithm for complicatedly branched space is proposed. It suggests that each individual develops its own plan of escape, which is stochastically transformed during the evolution. The algorithm includes also the separation of original space into rooms with possible exits selected by individuals according to their probability distribution. The model is calibrated on the base of empirical data provided by fire case in the nightclub “Lame Horse” (Perm, 2009). The algorithm is realized as an end-user Java software. It is assumed that this tool could help to test the buildings for their safety for humans.

In this article a method of legacy information systems handling is offered. During professional activities of specialists of various domains of industry they face with the problem that computer software that was involved in product development stage becomes obsolete much quickly than the product itself. At the same time switch to any modern software might be not possible due to various reasons. This problem is known as "legacy system" problem. It appears when product lifecycle is sufficiently longer than that of software systems that were used for product creation. In this article author offers an approach for solving this problem along with computer application based on this approach.

Recently many investigations have been devoted to theoretical models in new areas concerning description of different biological, sociological and historical processes. In the present paper we investigate the nazi Germany invasion in Poland, France and USSR from the kinetic theory point of view. We model this process with the Cauchy boundary problem for the two-element kinetic equations with spatial uniform initial conditions. The solution of the problem is given in the form of the traveling wave and the propagation velocity of a frontline depends on the quotient between initial forces concentrations. Moreover it is obtained that the general solution of the model can be obtained in terms of the quadratures and elementary functions. Finally it is shown that the frontline velocities are complied with the historical data.

This paper targets programmers and developers interested in utilizing parallel programming techniques to enhance application performance. The Oracle Solaris Studio software provides state-of-the-art optimizing and parallelizing compilers for C, C++ and Fortran, an advanced debugger, and optimized mathematical and performance libraries. Also included are an extremely powerful performance analysis tool for profiling serial and parallel applications, a thread analysis tool to detect data races and deadlock in memory parallel programs, and an Integrated Development Environment (IDE). The Oracle Message Passing Toolkit software provides the high-performance MPI libraries and associated run-time environment needed for message passing applications that can run on a single system or across multiple compute systems connected with high performance networking, including Gigabit Ethernet, 10 Gigabit Ethernet, InfiniBand and Myrinet. Examples of OpenMP and MPI are provided throughout the paper, including their usage via the Oracle Solaris Studio and Oracle Message Passing Toolkit products for development and deployment of both serial and parallel applications on SPARC and x86/x64 based systems. Throughout this paper it is demonstrated how to develop and deploy an application parallelized with OpenMP and/or MPI.

A three-dimensional (3D) hydrodynamic model to describe the spatial patterns of wind and turbulence characteristics in the atmospheric surface layer over inhomogeneous vegetation cover is presented. To describe the interaction of air flow with vegetation the theory of contrast structures is used. The numerical experiments provided by a developed model to assess the impact of small clear-cutting on wind and turbulent regime in the atmospheric surface layer showed a significant influence of heterogeneous vegetation on the wind field and the turbulent exchange processes between the land surface and the atmosphere. Obtained results give a reasonable agreement with field experimental data and results of numerical experiments provided using alternative models.

Heart rate (HR) is the most affordable indicator for measuring. In order to control the individual response to physical exercises of different load types heart rate is measured when the athletes perform different types of muscular work (strength machines, various types of training and competitive exercises). The magnitude of heart rate and its dynamics during muscular work and recovery can be objectively judged on the functional status of the cardiovascular system of an athlete, the level of its individual physical performance, as well as an adaptive response to a particular exercise. However, the heart rate is not an independent determinant of the physical condition of an athlete. HR size is formed by the interaction of the basic physiological mechanisms underlying cardiac hemodynamic ejection mode. Heart rate depends on one hand, on contractility of the heart, the venous return, the volumes of the atria and ventricles of the heart and from vascular heart load, the main components of which are elastic and peripheral resistance of the arterial system on the other hand. The values of arterial system vascular resistances depend on the power of muscular work and its duration. HR sensitivity to changes in heart load and vascular contraction was determined in athletes by pair regression analysis simultaneously recorded heart rate data, and peripheral $(R)$ and elastic $(E_a)$ resistance (heart vascular load), and the power $(W)$ of heartbeats (cardiac contractility). The coefficients of sensitivity and pair correlation between heart rate indicators and vascular load and contractility of left ventricle of the heart were determined in athletes at rest and during the muscular work on the cycle ergometer. It is shown that increase in both ergometer power load and heart rate is accompanied by the increase of correlation coefficients and coefficients of the heart rate sensitivity to $R$, $E_a$ and $W$.

The article deals with the modeling of the number of employed population by branches of the economy at the national and regional levels. The lack of targeted distribution of workers in a market economy requires the study of systemic processes in the labor market that lead to different dynamics of the number of employed in the sectors of the economy. In this case, personal strategies for choosing labor activity by economic agents become important. The presence of different strategies leads to the emergence of strata in the labor market with a dynamically changing number of employees, unevenly distributed among the sectors of the economy. As a result, non-linear fluctuations in the number of employed population can be observed, the toolkit of agentbased modeling is relevant for the study of the fluctuations. In the article, we examined in-phase and anti-phase fluctuations in the number of employees by economic activity on the example of the Jewish Autonomous Region in Russia. The fluctuations found in the time series of statistical data for 2008–2016. We show that such fluctuations appear by age groups of workers. In view of this, we put forward a hypothesis that the agent in the labor market chooses a place of work by a strategy, related with his age group. It directly affects the distribution of the number of employed for different cohorts and the total number of employed in the sectors of the economy. The agent determines the strategy taking into account the socio-economic characteristics of the branches of the economy (different levels of wages, working conditions, prestige of the profession). We construct a basic agentoriented model of a three-branch economy to test the hypothesis. The model takes into account various strategies of economic agents, including the choice of the highest wages, the highest prestige of the profession and the best working conditions by the agent. As a result of numerical experiments, we show that the availability of various industry selection strategies and the age preferences of employers within the industry lead to periodic and complex dynamics of the number of different-aged employees. Age preferences may be a consequence, for example, the requirements of employer for the existence of work experience and education. Also, significant changes in the age structure of the employed population may result from migration.