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The paper considers an earlier proposed by the author discrete modification of the A. P. Mikhailov “power – society” model. The modification is based on a stochastic cellular automaton, it’s microdynamics being completely different from the c continuous model based on differential equations. However, the macrodynamics of the discrete modification is shown in previous works to be equivalent to one of the continuous model. This is important, but at the same time raises the question why use the discrete model. The answer lies in its flexibility, which allows adding a variety of factors, the consideration of which in a continuous model either leads to a significant increase in computational complexity or is simply impossible.

This paper considers several examples of such applicability expansion of the model, with the help of which a number of applied problems are solved.

One of the modifications of the model takes into account economic ties between regions and municipalities, which could not be studied in the basic model. Computational experiments confirmed the improvement of the socio-economic indicators of the system under the influence of the ties.

The second modification allows internal migration in the system. Using it we studied the socio-economic development of a more prosperous region that attracts migrants.

Next we studied the dynamics of the system while the number of regions and municipalities changes. The negative impact of this process on the socio-economic indicators of the system was shown and possible control was found to overcome this negative impact.

The results of this study, therefore, include both the solution of some applied problems and the demonstration of the broader applicability of the discrete model compared with the continuous one.

In this paper we propose a mathematical model of cancer tumour occurrence in a quasi twodimensional epithelial tissue. Basic model of the epithelium growth describes the appearance of intensive movement and growth of tissue when it is damaged. The model includes the effects of division of cells and intercalation. It is assumed that the movement of cells is caused by the wave of mitogen-activated protein kinase (MAPK), which in turn activated by the chemo-mechanical signal propagating along tissue due to its local damage. In this paper it is assumed that cancer cells arise from local failure of spatial synchronization of circadian rhythms. The study of the evolutionary dynamics of the model could determine the chemo-physical properties of a tumour, and spatial relationship between the occurrence of cancer cells and development of the entire tissue parameters coordinating its evolution through the exchange of chemical and mechanical signals.

In this paper construct the mathematical model for consensus in technical committees for standardization (TC), based on the consensus model proposed DeGroot. The basic problems of achieving consensus in the development of consensus standards in terms of the proposed model are discussed. The results of statistical modeling characterizing the dependence of time to reach consensus on the number of members of the TC and their authoritarianism are presented. It has been shown that increasing the number of TC experts and authoritarianism negative impact on the time to reach a consensus and increase fragmentation of the TC.

This work analyzes the problem of traffic signs recognition in intelligent transport systems. The basic concepts of computer vision and image recognition tasks are considered. The most effective approach for solving the problem of analyzing and recognizing images now is the neural network method. Among all kinds of neural networks, the convolutional neural network has proven itself best. Activation functions such as Relu and SoftMax are used to solve the classification problem when recognizing traffic signs. This article proposes a technology for recognizing traffic signs. The choice of an approach for solving the problem based on a convolutional neural network due to the ability to effectively solve the problem of identifying essential features and classification. The initial data for the neural network model were prepared and a training sample was formed. The Google Colaboratory cloud service with the external libraries for deep learning TensorFlow and Keras was used as a platform for the intelligent system development. The convolutional part of the network is designed to highlight characteristic features in the image. The first layer includes 512 neurons with the Relu activation function. Then there is the Dropout layer, which is used to reduce the effect of overfitting the network. The output fully connected layer includes four neurons, which corresponds to the problem of recognizing four types of traffic signs. An intelligent traffic sign recognition system has been developed and tested. The used convolutional neural network included four stages of convolution and subsampling. Evaluation of the efficiency of the traffic sign recognition system using the three-block cross-validation method showed that the error of the neural network model is minimal, therefore, in most cases, new images will be recognized correctly. In addition, the model has no errors of the first kind, and the error of the second kind has a low value and only when the input image is very noisy.

In the last quarter of the twentieth century, the nature of global demographic and economic development began to change rapidly: the continuously accelerating growth of the main characteristics that took place over the previous two hundred years was replaced by a sharp slowdown. In the context of these changes, the role of a long-term forecast of global dynamics is increasing. At the same time, the forecast should be based not on inertial projection of past trends into future periods, but on mathematical modeling of fundamental patterns of historical development. The article presents preliminary results of research on mathematical modeling and forecasting of global demographic and economic dynamics based on this approach. The basic dynamic equations reflecting this dynamics are proposed, the modification of these equations in relation to different historical epochs is justified. For each historical epoch, based on the analysis of the corresponding system of equations, a phase portrait was determined and its features were analyzed. Based on this analysis, conclusions were drawn about the patterns of world development in the period under review.

It is shown that mathematical description of technology development is important for modeling historical dynamics. A method for describing technological dynamics is proposed, on the basis of which the corresponding mathematical equations are proposed.

Three stages of historical development are considered: the stage of agrarian society (before the beginning of the XIX century), the stage of industrial society (XIX–XX centuries) and the modern era. The proposed mathematical model shows that an agrarian society is characterized by cyclical demographic and economic dynamics, while an industrial society is characterized by an increase in demographic and economic characteristics close to hyperbolic.

The results of mathematical modeling have shown that humanity is currently moving to a fundamentally new phase of historical development. There is a slowdown in growth and the transition of human society into a new phase state, the shape of which has not yet been determined. Various options for further development are considered.

Hemostasis system is one of the key body’s defense systems, which is presented in all the liquid tissues and especially important in blood. Hemostatic response is triggered as a result of the vessel injury. The interaction between specialized cells and humoral systems leads to the formation of the initial hemostatic clot, which stops bleeding. After that the slow process of clot dissolution occurs. The formation of hemostatic plug is a unique physiological process, because during several minutes the hemostatic system generates complex structures on a scale ranging from microns for microvessel injury or damaged endothelial cell-cell contacts, to centimeters for damaged systemic arteries. Hemostatic response depends on the numerous coordinated processes, which include platelet adhesion and aggregation, granule secretion, platelet shape change, modification of the chemical composition of the lipid bilayer, clot contraction, and formation of the fibrin mesh due to activation of blood coagulation cascade. Computer modeling is a powerful tool, which is used to study this complex system at different levels of organization. This includes study of intracellular signaling in platelets, modelling humoral systems of blood coagulation and fibrinolysis, and development of the multiscale models of thrombus growth. There are two key issues of the computer modeling in biology: absence of the adequate physico-mathematical description of the existing experimental data due to the complexity of the biological processes, and high computational complexity of the models, which doesn’t allow to use them to test physiologically relevant scenarios. Here we discuss some key unresolved problems in the field, as well as the current progress in experimental research of hemostasis and thrombosis. New findings lead to reevaluation of the existing concepts and development of the novel computer models. We focus on the arterial thrombosis, venous thrombosis, thrombosis in microcirculation and the problems of fibrinolysis and thrombolysis. We also briefly discuss basic types of the existing mathematical models, their computational complexity, and principal issues in simulation of thrombus growth in arteries.

Epidemics severely destabilize economies by reducing productivity, weakening consumer spending, and overwhelming public infrastructure, often culminating in economic recessions. The COVID-19 pandemic underscored the critical role of nonpharmaceutical interventions, such as lockdowns, in containing infectious disease transmission. This study investigates how the progression of epidemics and the implementation of lockdown policies shape the economic well-being of populations. By integrating compartmental ordinary differential equation (ODE) models, the research analyzes the interplay between epidemic dynamics and economic outcomes, particularly focusing on how varying lockdown intensities influence both disease spread and population wealth. Findings reveal that epidemics inflict significant economic damage, but timely and stringent lockdowns can mitigate healthcare system overload by sharply reducing infection peaks and delaying the epidemic’s trajectory. However, carefully timed lockdown relaxation is equally vital to prevent resurgent outbreaks. The study identifies key epidemiological thresholds—such as transmission rates, recovery rates, and the basic reproduction number $(\mathfrak{R}0)$ — that determine the effectiveness of lockdowns. Analytically, it pinpoints the optimal proportion of isolated individuals required to minimize total infections in scenarios where permanent immunity is assumed. Economically, the analysis quantifies lockdown impacts by tracking population wealth, demonstrating that economic outcomes depend heavily on the fraction of isolated individuals who remain economically productive. Higher proportions of productive individuals during lockdowns correlate with better wealth retention, even under fixed epidemic conditions. These insights equip policymakers with actionable frameworks to design balanced lockdown strategies that curb disease spread while safeguarding economic stability during future health crises.

Using mathematical modeling, geopolitical, historical and natural science approach, the model of national security. Security model reflects the dichotomy of values development and conservation, being the product of the corresponding functions. In this paper we evaluated the basic parameters of the model and discusses some of its applications in the field of geopolitics and national security.

In the first section of the paper a definition of presentation (perception) functions — components of individual’s subjective view of the world — are proposed. Using the basic psychophysical law formulated by S. Stevens, and relying on the hypotheses of socialization, rationality, individual choice, complexity of informational influences, dynamics of ideas and perceptions, and accessibility, formal dependence was derived allowing to calculate the function of presentation (perception) for probabilistic indicators (with known distribution function or subjective probability) and of interval type. In the second and third sections parameters of the presentation function according to surveys of the U.S. population related to the war in Korea, Vietnam, and Iraq are estimated.

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 $W$.