All issues

The article is devoted to description the author's method of multidimensional analysis for generate an predictive assessment of organizations’ credit risk, based on the credit history information, which taking into account value and period of credit. An example of credit risk assessment is given.

Transport of respiratory gases by respiratory and circulatory systems is one of the most important processes associated with living conditions of the human body. Significant and/or long-term deviations of oxygen and carbon dioxide concentrations from the normal values in blood can be a reason of significant pathological changes with irreversible consequences: lack of oxygen (hypoxia and ischemic events), the change in the acidbase balance of blood (acidosis or alkalosis), and others. In the context of a changing external environment and internal conditions of the body the action of its regulatory systems aimed at maintaining homeostasis. One of the major mechanisms for maintaining concentrations (partial pressures) of oxygen and carbon dioxide in the blood at a normal level is the regulation of minute ventilation, respiratory rate and depth of respiration, which is caused by the activity of the central and peripheral regulators.

In this paper we propose a mathematical model of the regulation of pulmonary ventilation parameter. The model is used to calculate the minute ventilation adaptation during hypoxia and hypercapnia. The model is developed using a single-component model of the lungs, and biochemical equilibrium conditions of oxygen and carbon dioxide in the blood and the alveolar lung volume. A comparison with laboratory data is performed during hypoxia and hypercapnia. Analysis of the results shows that the model reproduces the dynamics of minute ventilation during hypercapnia with sufficient accuracy. Another result is that more accurate model of regulation of minute ventilation during hypoxia should be developed. The factors preventing from satisfactory accuracy are analysed in the final section.

Respiratory function is one of the main limiting factors of the organism during intense physical activities. Thus, it is important characteristic of high performance sport and extreme physical activity conditions. Therefore, the results of this study have significant application value in the field of mathematical modeling in sport. The considered conditions of hypoxia and hypercapnia are partly reproduce training at high altitude and at hypoxia conditions. The purpose of these conditions is to increase the level of hemoglobin in the blood of highly qualified athletes. These conditions are the only admitted by sport committees.

This paper studies minimum volume elastoplastic bodies. One part of the boundary of every reviewed body is fixed to the same space points while stresses are set for the remaining part of the boundary surface (loaded surface). The shape of the loaded surface can change in space but the limit load factor calculated based on the assumption that the bodies are filled with elastoplastic medium must not be less than a fixed value. Besides, all varying bodies are supposed to have some type of a limited volume sample manifold inside of them.

The following problem has been set: what is the maximum number of cavities (or holes in a two-dimensional case) that a minimum volume body (plate) can have under the above limitations? It is established that in order to define a mathematically correct problem, two extra conditions have to be met: the areas of the holes must be bigger than the small constant while the total length of the internal hole contour lines within the optimum figure must be minimum among the varying bodies. Thus, unlike most articles on optimum design of elastoplastic structures where parametric analysis of acceptable solutions is done with the set topology, this paper looks for the topological parameter of the design connectivity.

The paper covers the case when the load limit factor for the sample manifold is quite large while the areas of acceptable holes in the varying plates are bigger than the small constant. The arguments are brought forward that prove the Maxwell and Michell beam system to be the optimum figure under these conditions. As an example, microphotographs of the standard biological bone tissues are presented. It is demonstrated that internal holes with large areas cannot be a part of the Michell system. At the same the Maxwell beam system can include holes with significant areas. The sufficient conditions are given for the hole formation within the solid plate of optimum volume. The results permit generalization for three-dimensional elastoplastic structures.

The paper concludes with the setting of mathematical problems arising from the new problem optimally designed elastoplastic systems.

The article deals with the process of creating an effective algorithm for determining the amount of emitted quanta from an X-ray tube in computer tomography (CT) studies. An analysis of domestic and foreign literature showed that most of the work in the field of radiometry and radiography takes the tabulated values of X-ray absorption coefficients into account, while individual dose factors are not taken into account at all since many studies are lacking the Dose Report. Instead, an average value is used to simplify the calculation of statistics. In this regard, it was decided to develop a method to detect the amount of ionizing quanta by analyzing the noise of CT data. As the basis of the algorithm, we used Poisson and Gauss distribution mathematical model of owns’ design of logarithmic value. The resulting mathematical model was tested on the CT data of a calibration phantom consisting of three plastic cylinders filled with water, the X-ray absorption coefficient of which is known from the table values. The data were obtained from several CT devices from different manufacturers (Siemens, Toshiba, GE, Phillips). The developed algorithm made it possible to calculate the number of emitted X-ray quanta per unit time. These data, taking into account the noise level and the radiuses of the cylinders, were converted to X-ray absorption values, after which a comparison was made with tabulated values. As a result of this operation, the algorithm used with CT data of various configurations, experimental data were obtained, consistent with the theoretical part and the mathematical model. The results showed good accuracy of the algorithm and mathematical apparatus, which shows reliability of the obtained data. This mathematical model is already used in the noise reduction program of the CT of own design, where it participates as a method of creating a dynamic threshold of noise reduction. At the moment, the algorithm is being processed to work with real data from computer tomography of patients.

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.

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.

Execution or violation of the principle of competitive exclusion in communities is the subject of many studies. The principle of competitive exclusion means that coexistence of species in community is impossible if the number of species exceeds the number of controlling mutually independent factors. At that time there are many examples displaying the violations of this principle in the natural systems. The explanations for this paradox vary from inexact identification of the set of factors to various types of spatial and temporal heterogeneities. One of the factors breaking the principle of competitive exclusion is intraspecific competition. This study holds the model of community with two species and one influencing factor with density-dependent mortality and spatial heterogeneity. For such models possibility of the existence of stable equilibrium is proved in case of spatial homogeneity and negative effect of the species on the factor. Our purpose is analysis of possible variants of dynamics of the system with spatial heterogeneity under the various directions of the species effect on the influencing factor. Numerical analysis showed that there is stable coexistence of the species agreed with homogenous spatial distributions of the species if the species effects on the influencing factor are negative. Density-dependent mortality and spatial heterogeneity lead to violation of the principle of competitive exclusion when equilibriums are Turing unstable. In this case stable spatial heterogeneous patterns can arise. It is shown that Turing instability is possible if at least one of the species effects is positive. Model nonlinearity and spatial heterogeneity cause violation of the principle of competitive exclusion in terms of both stable spatial homogenous states and quasistable spatial heterogeneous patterns.

A simplified implicit Euler method was analyzed as an alternative to the explicit Euler method, which is a commonly used method in numerical modeling in electrophysiology. The majority of electrophysiological models are quite stiff, since the dynamics they describe includes a wide spectrum of time scales: a fast depolarization, that lasts milliseconds, precedes a considerably slow repolarization, with both being the fractions of the action potential observed in excitable cells. In this work we estimate stiffness by a formula that does not require calculation of eigenvalues of the Jacobian matrix of the studied ODEs. The efficiency of the numerical methods was compared on the case of typical representatives of detailed and conceptual type models of excitable cells: Hodgkin–Huxley model of a neuron and Aliev–Panfilov model of a cardiomyocyte. The comparison of the efficiency of the numerical methods was carried out via norms that were widely used in biomedical applications. The stiffness ratio’s impact on the speedup of simplified implicit method was studied: a real gain in speed was obtained for the Hodgkin–Huxley model. The benefits of the usage of simple and high-order methods for electrophysiological models are discussed along with the discussion of one method’s stability issues. The reasons for using simplified instead of high-order methods during practical simulations were discussed in the corresponding section. We calculated higher order derivatives of the solutions of Hodgkin-Huxley model with various stiffness ratios; their maximum absolute values appeared to be quite large. A numerical method’s approximation constant’s formula contains the latter and hence ruins the effect of the other term (a small factor which depends on the order of approximation). This leads to the large value of global error. We committed a qualitative stability analysis of the explicit Euler method and were able to estimate the model’s parameters influence on the border of the region of absolute stability. The latter is used when setting the value of the timestep for simulations a priori.

The article substantiates the need to develop and analyze mathematical models that take into account the mutual influence of long (Kondratiev) waves of economic development. The analysis of the available publications shows that at the model level, the direct and inverse relationships between intersecting long waves are still insufficiently studied. As practice shows, the production of the current long wave can receive an additional impetus for growth from the technologies of the next long wave. The technologies of the next industrial revolution often serve as improving innovations for the industries born of the previous industrial revolution. As a result, the new long wave increases the amplitude of the oscillations of the trajectory of the previous long wave. Such results of the interaction of long waves in the economy are similar to the effects of interference of physical waves. The mutual influence of the recessions and booms of the economies of different countries gives even more grounds for comparing the consequences of this mutual influence with the interference of physical waves. The article presents a model for the development of the technological base of production, taking into account the possibilities of combining old and new technologies. The model consists of several sub-models. The use of a different mathematical description for the individual stages of updating the technological base of production allows us to take into account the significant differences between the successive phases of the life cycle of general purpose technologies, considered in modern literature as the technological basis of industrial revolutions. One of these phases is the period of formation of the appropriate infrastructure necessary for the intensive diffusion of new general purpose technology, for the rapid development of industries using this technology. The model is used for illustrative calculations with the values of exogenous parameters corresponding to the logic of changing long waves. Despite all the conditionality of the illustrative calculations, the configuration of the curve representing the change in the return on capital in the simulated period is close to the configuration of the real trajectory of the return on private fixed assets of the US economy in the period 1982-2019. The factors that remained outside the scope of the presented model, but which are advisable to take into account when describing the interference of long waves of economic development, are indicated.

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.