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The traditional approach to computing optimal game strategies of firms on oligopolistic markets and of indicators of such markets consists in studying linear dynamical games with quadratic criteria and solving generalized matrix Riccati equations.

The other approach proposed by the author is based on methods of operational calculus (in particular, Z-transform). This approach makes it possible to achieve economic meaningful decisions under wider field of parameter values. It characterizes by simplicity of computations and by necessary for economic analysis visibility. One of its advantages is that in many cases important for economic practice, it, in contrast to the traditional approach, provides the ability to make calculations using widespread spreadsheets, which allows to study the prospects for the development of oligopolistic markets to a wide range of professionals and consumers.

The article deals with the practical aspects of determining the optimal Nash–Cournot strategies of participants in oligopolistic markets on the basis of operational calculus, in particular the technique of computing the optimal Nash–Cournot strategies in Excel. As an illustration of the opportinities of the proposed methods of calculation, examples close to the practical problems of forecasting indicators of the markets of high-tech products are studied.

The results of calculations obtained by the author for numerous examples and real economic systems, both using the obtained relations on the basis of spreadsheets and using extended Riccati equations, are very close. In most of the considered practical problems, the deviation of the indicators calculated in accordance with the two approaches, as a rule, does not exceed 1.5–2%. The highest value of relative deviations (up to 3–5%) is observed at the beginning of the forecasting period. In typical cases, the period of relatively noticeable deviations is 3–5 moments of time. After the transition period, there is almost complete agreement of the values of the required indicators using both approaches.

Metal hydrides are an interesting class of chemical compounds that can reversibly bind a large amount of hydrogen and are, therefore, of interest for energy applications. Understanding the factors affecting the kinetics of hydride formation and decomposition is especially important. Features of the material, experimental setup and conditions affect the mathematical description of the processes, which can undergo significant changes during the processing of experimental data. The article proposes a general approach to numerical modeling of the formation and decomposition of metal hydrides and solving inverse problems of estimating material parameters from measurement data. The models are divided into two classes: diffusive ones, that take into account the gradient of hydrogen concentration in the metal lattice, and models with fast diffusion. The former are more complex and take the form of non-classical boundary value problems of parabolic type. A rather general approach to the grid solution of such problems is described. The second ones are solved relatively simply, but can change greatly when model assumptions change. Our experience in processing experimental data shows that a flexible software tool is needed; a tool that allows, on the one hand, building models from standard blocks, freely changing them if necessary, and, on the other hand, avoiding the implementation of routine algorithms. It also should be adapted for high-performance systems of different paradigms. These conditions are satisfied by the HIMICOS library presented in the paper, which has been tested on a large number of experimental data. It allows simulating the kinetics of formation and decomposition of metal hydrides, as well as related tasks, at three levels of abstraction. At the low level, the user defines the interface procedures, such as calculating the time layer based on the previous layer or the entire history, calculating the observed value and the independent variable from the task variables, comparing the curve with the reference. Special algorithms can be used for solving quite general parabolic-type boundary value problems with free boundaries and with various quasilinear (i.e., linear with respect to the derivative only) boundary conditions, as well as calculating the distance between the curves in different metric spaces and with different normalization. This is the middle level of abstraction. At the high level, it is enough to choose a ready tested model for a particular material and modify it in relation to the experimental conditions.

Our purpose is to study the spatio-temporal population wave behavior observed in the predator-prey system. It is assumed that predators move both directionally and randomly, and prey spread only diffusely. The model does not take into account demographic processes in the predator population; it’s total number is constant and is a parameter. The variables of the model are the prey and predator densities and the predator speed, which are connected by a system of three reaction – diffusion – advection equations. The system is considered on an annular range, that is the periodic conditions are set at the boundaries of the interval. We have studied the bifurcations of wave modes arising in the system when two parameters are changed — the total number of predators and their taxis acceleration coefficient.

The main research method is a numerical analysis. The spatial approximation of the problem in partial derivatives is performed by the finite difference method. Integration of the obtained system of ordinary differential equations in time is carried out by the Runge –Kutta method. The construction of the Poincare map, calculation of Lyapunov exponents, and Fourier analysis are used for a qualitative analysis of dynamic regimes.

It is shown that, population waves can arise as a result of existence of directional movement of predators. The population dynamics in the system changes qualitatively as the total predator number increases. А stationary homogeneous regime is stable at low value of parameter, then it is replaced by self-oscillations in the form of traveling waves. The waveform becomes more complicated as the bifurcation parameter increases; its complexity occurs due to an increase in the number of temporal vibrational modes. A large taxis acceleration coefficient leads to the possibility of a transition from multi-frequency to chaotic and hyperchaotic population waves. A stationary regime without preys becomes stable with a large number of predators.

In this work, the problem of constructing an electronic analogue of heterogeneous DNA is solved with the help of the methods of mathematical modeling. Electronic analogs of that type, along with other physical models of living systems, are widely used as a tool for studying the dynamic and functional properties of these systems. The solution to the problem is based on an algorithm previously developed for homogeneous (synthetic) DNA and modified in such a way that it can be used for the case of inhomogeneous (native) DNA. The algorithm includes the following steps: selection of a model that simulates the internal mobility of DNA; construction of a transformation that allows you to move from the DNA model to its electronic analogue; search for conditions that provide an analogy of DNA equations and electronic analogue equations; calculation of the parameters of the equivalent electrical circuit. To describe inhomogeneous DNA, the model was chosen that is a system of discrete nonlinear differential equations simulating the angular deviations of nitrogenous bases, and Hamiltonian corresponding to these equations. The values of the coefficients in the model equations are completely determined by the dynamic parameters of the DNA molecule, including the moments of inertia of nitrous bases, the rigidity of the sugar-phosphate chain, and the constants characterizing the interactions between complementary bases in pairs. The inhomogeneous Josephson line was used as a basis for constructing an electronic model, the equivalent circuit of which contains four types of cells: A-, T-, G-, and C-cells. Each cell, in turn, consists of three elements: capacitance, inductance, and Josephson junction. It is important that the A-, T-, G- and C-cells of the Josephson line are arranged in a specific order, which is similar to the order of the nitrogenous bases (A, T, G and C) in the DNA sequence. The transition from DNA to an electronic analog was carried out with the help of the A-transformation which made it possible to calculate the values of the capacitance, inductance, and Josephson junction in the A-cells. The parameter values for the T-, G-, and C-cells of the equivalent electrical circuit were obtained from the conditions imposed on the coefficients of the model equations and providing an analogy between DNA and the electronic model.

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.

Laser damage to transparent solids is a major limiting factor output power of laser systems. For laser rangefinders, the most likely destruction cause of elements of the optical system (lenses, mirrors) actually, as a rule, somewhat dusty, is not an optical breakdown as a result of avalanche, but such a thermal effect on the dust speck deposited on an element of the optical system (EOS), which leads to its ignition. It is the ignition of a speck of dust that initiates the process of EOS damage.

The corresponding model of this process leading to the ignition of a speck of dust takes into account the nonlinear Stefan –Boltzmann law of thermal radiation and the infinite thermal effect of periodic radiation on the EOS and the speck of dust. This model is described by a nonlinear system of differential equations for two functions: the EOS temperature and the dust particle temperature. It is proved that due to the accumulating effect of periodic thermal action, the process of reaching the dust speck ignition temperature occurs almost at any a priori possible changes in this process of the thermophysical parameters of the EOS and the dust speck, as well as the heat exchange coefficients between them and the surrounding air. Averaging these parameters over the variables related to both the volume and the surfaces of the dust speck and the EOS is correct under the natural constraints specified in the paper. The entire really significant spectrum of thermophysical parameters is covered thanks to the use of dimensionless units in the problem (including numerical results).

A thorough mathematical study of the corresponding nonlinear system of differential equations made it possible for the first time for the general case of thermophysical parameters and characteristics of the thermal effect of periodic laser radiation to find a formula for the value of the permissible radiation intensity that does not lead to the destruction of the EOS as a result of the ignition of a speck of dust deposited on the EOS. The theoretical value of the permissible intensity found in the general case in the special case of the data from the Grasse laser ranging station (south of France) almost matches that experimentally observed in the observatory.

In parallel with the solution of the main problem, we derive a formula for the power absorption coefficient of laser radiation by an EOS expressed in terms of four dimensionless parameters: the relative intensity of laser radiation, the relative illumination of the EOS, the relative heat transfer coefficient from the EOS to the surrounding air, and the relative steady-state temperature of the EOS.

This study proposes and analyzes a discrete-time mathematical model of population dynamics with seasonal reproduction, taking into account the density-dependent regulation and sex structure. In the model, population birth rate depends on the number of females, while density is regulated through juvenile survival, which decreases exponentially with increasing total population size. Analytical and numerical investigations of the model demonstrate that when more than half of both females and males survive, the population exhibits stable dynamics even at relatively high birth rates. Oscillations arise when the limitation of female survival exceeds that of male survival. Increasing the intensity of male survival limitation can stabilize population dynamics, an effect particularly evident when the proportion of female offspring is low. Depending on parameter values, the model exhibits stable, periodic, or irregular dynamics, including multistability, where changes in current population size driven by external factors can shift the system between coexisting dynamic modes. To apply the model to real populations, we propose an approach for estimating demographic parameters based on total abundance data. The key idea is to reduce the two-component discrete model with sex structure to a delay equation dependent only on total population size. In this formulation, the initial sex structure is expressed through total abundance and depends on demographic parameters. The resulting one-dimensional equation was applied to describe and estimate demographic characteristics of ungulate populations in the Jewish Autonomous Region. The delay equation provides a good fit to the observed dynamics of ungulate populations, capturing long-term trends in abundance. Point estimates of parameters fall within biologically meaningful ranges and produce population dynamics consistent with field observations. For moose, roe deer, and musk deer, the model suggests predominantly stable dynamics, while annual fluctuations are primarily driven by external factors and represent deviations from equilibrium. Overall, these estimates enable the analysis of structured population dynamics alongside short-term forecasting based on total abundance data.

The paper presents a computational model of the thermal state of the breast with an interstitial tumor. The model is based on the modified Pennes biothermal equation and describes a five-layered biological area including skin, subcutaneous fat, glandular and muscular tissues, as well as a neoplasm zone. Convective heat exchange with the environment is taken into account at the outer boundary, and body temperature is maintained at the internal boundary. In addition, the fabric surface is exposed to exponentially attenuating effects of spatial heating, such a heating scheme is actually based on the Bouguer – Lambert – Baer law. Tissue thermal conductivity and blood perfusion are modeled by linear functions of temperature, reflecting physiological thermoregulation. The boundary-value problem for the partial differential equation has been solved numerically using an explicit-implicit finite difference scheme; the system of algebraic equations getting after an approximation of the mentioned boundary-value problem is solved by the Thomas procedure. Numerical experiments have shown that even a small tumor increases the local temperature of tissues by half a degree due to increased metabolism and delayed blood perfusion. This anomaly is clearly manifested in tumors larger than ten millimeters. It was found that the depth of occurrence critically affects the thermal response: when the tumor is located closer to the surface, the maximum temperature shifts to the skin, whereas at a deeper position, a thermal peak forms inside the glandular tissue. The effectiveness of hyperthermic exposure was assessed by the integral criterion of thermal necrosis based on the Arrhenius law. At a radiation intensity that creates a surface thermal load of about five kilowatts per square meter and an attenuation factor of one hundred, tumor destruction begins after two to three minutes of exposure, while the surrounding healthy tissues remain within safe temperatures. Reducing the attenuation coefficient leads to the opposite effect: heat spreads deeper, and the glandular tissue is damaged first, which limits the therapeutic window. Additionally, maps of the distribution of temperature, time to necrosis, and the depth of thermal damage were constructed depending on the irradiation power, diameter, and position of the tumor.

We study the stochastic dynamics of the two-dimensional Hindmarsh–Rose model in the parametrical zone of coexisting stable equilibria and limit cycles. The phenomenon of noise-induced transitions between the attractors is investigated. Under the random disturbances, equilibrium and periodic regimes combine in bursting regime: the system demonstrates an alternation of small fluctuations near the equilibrium with high amplitude oscillations. This effect is analysed using the stochastic sensitivity function technique and a method of estimation of critical values for noise intensity is proposed.

The paper deals with a prey-predator model, which describes the spatiotemporal dynamics of plankton community and the nutrients. The system is described by reaction-diffusion-advection equations in a onedimensional vertical column of water in the surface layer. Advective term of the predator equation represents the vertical movements of zooplankton with velocity, which is assumed to be proportional to the gradient of phytoplankton density. This study aimed to determine the conditions under which these movements (taxis) lead to the spatially heterogeneous structures generated by the system. Assuming diffusion coefficients of all model components to be equal the instability of the system in the vicinity of stationary homogeneous state with respect to small inhomogeneous perturbations is analyzed.

Necessary conditions for the flow-induced instability were obtained through linear stability analysis. Depending on the local kinetics parameters, increasing the taxis rate leads to Turing or wave instability. This fact is in good agreement with conditions for the emergence of spatial and spatiotemporal patterns in a minimal phytoplankton–zooplankton model after flow-induced instabilities derived by other authors. This mechanism of generating patchiness is more general than the Turing mechanism, which depends on strong conditions on the diffusion coefficients.

While the taxis exceeding a certain critical value, the wave number corresponding to the fastest growing mode remains unchanged. This value determines the type of spatial structure. In support of obtained results, the paper presents the spatiotemporal dynamics of the model components demonstrating Turing-type pattern and standing wave pattern.