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A previously developed model that describes the dynamics of social tension in a society divided into two groups: the elite and the people was considered. This model took into account the impact of economic situation changes and the elite–people interaction. The model has been modified by including in the equation describing the tension of the people, a term that takes into account the adaptation of the people to the current situation.

The model coefficients estimation is an important task, the solution of which allows obtaining information about the nature of the interaction between elite and people. We believe that the solution of the system of model equations with optimal coefficients is closest to the values of the indicator characterizing social tension. We used the normalized level of homicide rate as an indicator of social tension.

The model contains seven coefficients. Two coefficients characterizing the influence of economic situation changes on elite and people are taken equal to each other and the same for all countries. We obtained their estimations using a simplified model that takes into account only the change in the economic situation and allows an analytical solution.

The Bayesian approach was used to estimate the remaining five coefficients of model for post-Soviet countries. The prior probability densities of the four coefficients for all countries under consideration were taken to be the same. The prior probability density of fifth coefficient was considered to depend on the regime of government (authoritarian or «transitional»). We assumed that the calculated tension matches with the corresponding indicator of tension in cases where the difference between them does not exceed 5%.

The calculations showed that for the post-Soviet countries, a good coincidence was obtained between the calculated values of the people tension and the normalized level of homicide rate. The coincidence is satisfactory only on average.

The following main results was obtained at the work: under the influence of some «significant» events in 40% of post-Soviet countries, there was a rapid change in the nature of interaction between the elite and the people; regional feature have some influence on the elite–people interaction; the type of government does not significantly affect the elite–people interaction; the method for assessing the stability of the country by the value of the model coefficients is proposed.

The background social strain of a society can be quantitatively estimated using various statistical indicators. Mathematical models, allowing to forecast the dynamics of social strain, are successful in describing various social processes. If the number of interacting groups is small, the dynamics of the corresponding indicators can be modelled with a system of ordinary differential equations. The increase in the number of interacting components leads to the growth of complexity, which makes the analysis of such models a challenging task. A continuous social stratification model can be considered as a result of the transition from a discrete number of interacting social groups to their continuous distribution in some finite interval. In such a model, social strain naturally spreads locally between neighbouring groups, while in reality, the social elite influences the whole society via news media, and the Internet allows non-local interaction between social groups. These factors, however, can be taken into account to some extent using the term of the model, describing negative external influence on the society. In this paper, we develop a continuous social stratification model, describing the dynamics of two societies connected through migration. We assume that people migrate from the social group of donor society with the highest strain level to poorer social layers of the acceptor society, transferring the social strain at the same time. We assume that all model parameters are constants, which is a realistic assumption for small societies only. By using the finite volume method, we construct the spatial discretization for the problem, capable of reproducing finite propagation speed of social strain. We verify the discretization by comparing the results of numerical simulations with the exact solutions of the auxiliary non-linear diffusion equation. We perform the numerical analysis of the proposed model for different values of model parameters, study the impact of migration intensity on the stability of acceptor society, and find the destabilization conditions. The results, obtained in this work, can be used in further analysis of the model in the more realistic case of inhomogeneous coefficients.

The most important principle of military science and border security is the principle of concentrating the main efforts on the main directions and tasks. At the tactical level, there are many mathematical models for computing the optimal resource allocation by directions and objects, whereas at the state level there are no corresponding models. Using the statistical data on the results of the protection of the US border, an exponential type border production function parameter is calculated that reflects the organizational and technological capabilities of the border guard. The production function determines the dependence of the probability of detaining offenders from the density of border guards per kilometer of the border. Financial indicators in the production function are not taken into account, as the border maintenance budget and border equipment correlate with the number of border agents. The objective function of the border guards is defined — the total prevented damage from detained violators taking into account their expected danger for the state and society, which is to be maximized. Using Slater's condition, the solution of the problem was found — optimal density of border guard was calculated for the regions of the state. Having a model of resource allocation, the example of the three border regions of the United States has also solved the reverse problem — threats in the regions have been assessed based on the known allocation of resources. The expected danger from an individual offender on the US-Canada border is 2–5 times higher than from an offender on the US-Mexican border. The results of the calculations are consistent with the views of US security experts: illegal migrants are mostly detained on the US-Mexican border, while potential terrorists prefer to use other channels of penetration into the US (including the US-Canadian border), where the risks of being detained are minimal. Also, the results of the calculations are consistent with the established practice of border protection: in 2013 the number of border guards outside the checkpoints on the US-Mexican border increased by 2 times compared with 2001, while on the American-Canadian border — 4 times. The practice of border protection and the views of specialists give grounds for approval of the verification of the model.

The article deals with the development of a methodological approach to forecasting and modeling the socioeconomic consequences of viral epidemics in conditions of heterogeneous economic development of territorial systems. The relevance of the research stems from the need for rapid mechanisms of public management and stabilization of adverse epidemiological situation, taking into account the spatial heterogeneity of the spread of COVID-19, accompanied by a concentration of infection in large metropolitan areas and territories with high economic activity. The aim of the work is to substantiate a methodology to assess the spatial heterogeneity of the spread of coronavirus infection, find poles of its growth, emerging spatial clusters and zones of their influence with the assessment of inter-territorial relationships, as well as simulate the effects of worsening epidemiological situation on the dynamics of economic development of regional systems. The peculiarity of the developed approach is the spatial clustering of regional systems by the level of COVID-19 incidence, conducted using global and local spatial autocorrelation indices, various spatial weight matrices, and L.Anselin mutual influence matrix based on the statistical information of the Russian Federal State Statistics Service. The study revealed a spatial cluster characterized by high levels of infection with COVID-19 with a strong zone of influence and stable interregional relationships with surrounding regions, as well as formed growth poles which are potential poles of further spread of coronavirus infection. Regression analysis using panel data not only confirmed the impact of COVID-19 incidence on the average number of employees in enterprises, the level of average monthly nominal wages, but also allowed to form a model for scenario prediction of the consequences of the spread of coronavirus infection. The results of this study can be used to form mechanisms to contain the coronavirus infection and stabilize socio-economic at macroeconomic and regional level and restore the economy of territorial systems, depending on the depth of the spread of infection and the level of economic damage caused.

The paper considers the adequacy of the model developed earlier by the author for the analysis of income inequality and based on an empirically confirmed hypothesis that the relative (to the income of the richest group) income values of 20% population groups in total income can be represented as a finite functional sequence, each member of which depends on one parameter — a specially defined indicator of inequality. It is shown that in addition to the existing methods of inequality analysis, the model makes it possible to estimate with the help of analytical expressions the income shares of 20%, 10% and smaller groups of the population for different levels of inequality, as well as to identify how they change with the growth of inequality, to estimate the level of inequality for known ratios between the incomes of different groups of the population, etc.

The paper provides a more detailed confirmation of the proposed model adequacy in comparison with the previously obtained results of statistical analysis of empirical data on the distribution of income between the 20% and 10% population groups. It is based on the analysis of certain ratios between the values of quintiles and deciles according to the proposed model. The verification of these ratios was carried out using a set of data for a large number of countries and the estimates obtained confirm the sufficiently high accuracy of the model.

Data are presented that confirm the possibility of using the model to analyze the dependence of income distribution by population groups on the level of inequality, as well as to estimate the inequality indicator for income ratios between different groups, including variants when the income of the richest 20% is equal to the income of the poor 60 %, income of the middle class 40% or income of the rest 80% of the population, as well as when the income of the richest 10% is equal to the income of the poor 40 %, 50% or 60%, to the income of various middle class groups, etc., as well as for cases, when the distribution of income obeys harmonic proportions and when the quintiles and deciles corresponding to the middle class reach a maximum. It is shown that the income shares of the richest middle class groups are relatively stable and have a maximum at certain levels of inequality.

The results obtained with the help of the model can be used to determine the standards for developing a policy of gradually increasing the level of progressive taxation in order to move to the level of inequality typical of countries with social oriented economy.

When person`s diseases is result of bacterial infection, various characteristics of the organism are used for observation the course of the disease. Currently, one of these indicators is dynamics of cytokine concentrations are produced, mainly by cells of the immune system. There are many types of these low molecular weight proteins in human body and many species of animals. The study of cytokines is important for the interpretation of functional disorders of the body's immune system, assessment of the severity, monitoring the effectiveness of therapy, predicting of the course and outcome of treatment. Cytokine response of the body indicating characteristics of course of disease. For research regularities of such indication, experiments were conducted on laboratory mice. Experimental data are analyzed on the development of pneumonia and treatment with several drugs for bacterial infection of mice. As drugs used immunomodulatory drugs “Roncoleukin”, “Leikinferon” and “Tinrostim”. The data are presented by two types cytokines` concentration in lung tissue and animal blood. Multy-sided statistical ana non statistical analysis of the data allowed us to find common patterns of changes in the “cytokine profile” of the body and to link them with the properties of therapeutic preparations. The studies cytokine “Interleukin-10” (IL-10) and “Interferon Gamma” (IFN$\gamma$) in infected mice deviate from the normal level of infact animals indicating the development of the disease. Changes in cytokine concentrations in groups of treated mice are compared with those in a group of healthy (not infected) mice and a group of infected untreated mice. The comparison is made for groups of individuals, since the concentrations of cytokines are individual and differ significantly in different individuals. Under these conditions, only groups of individuals can indicate the regularities of the processes of the course of the disease. These groups of mice were being observed for two weeks. The dynamics of cytokine concentrations indicates characteristics of the disease course and efficiency of used therapeutic drugs. The effect of a medicinal product on organisms is monitored by the location of these groups of individuals in the space of cytokine concentrations. The Hausdorff distance between the sets of vectors of cytokine concentrations of individuals is used in this space. This is based on the Euclidean distance between the elements of these sets. It was found that the drug “Roncoleukin” and “Leukinferon” have a generally similar and different from the drug “Tinrostim” effect on the course of the disease.

Social media is a crucial indicator of the position of assets in the financial market. The paper describes the rigid solution for the classification problem to determine the influence of social media activity on financial market movements. Reputable crypto traders influencers are selected. Twitter posts packages are used as data. The methods of text, which are characterized by the numerous use of slang words and abbreviations, and preprocessing consist in lemmatization of Stanza and the use of regular expressions. A word is considered as an element of a vector of a data unit in the course of solving the problem of binary classification. The best markup parameters for processing Binance candles are searched for. Methods of feature selection, which is necessary for a precise description of text data and the subsequent process of establishing dependence, are represented by machine learning and statistical analysis. First, the feature selection is used based on the information criterion. This approach is implemented in a random forest model and is relevant for the task of feature selection for splitting nodes in a decision tree. The second one is based on the rigid compilation of a binary vector during a rough check of the presence or absence of a word in the package and counting the sum of the elements of this vector. Then a decision is made depending on the superiority of this sum over the threshold value that is predetermined previously by analyzing the frequency distribution of mentions of the word. The algorithm used to solve the problem was named benchmark and analyzed as a tool. Similar algorithms are often used in automated trading strategies. In the course of the study, observations of the influence of frequently occurring words, which are used as a basis of dimension 2 and 3 in vectorization, are described as well.

The paper presents a comparative analysis of the enterprises of the Arctic Zone of the Russian Federation (AZ RF) on economic indicators in accordance with the rating of the Polar index. This study includes numerical data of 193 enterprises located in the AZ RF. Machine learning methods are applied, both standard, from open source, and own original methods — the method of Optimally Reliable Partitions (ORP), the method of Statistically Weighted Syndromes (SWS). Held split, indicating the maximum value of the functional quality, this study used the simplest family of different one-dimensional partition with a single boundary point, as well as a collection of different two-dimensional partition with one boundary point on each of the two combining variables. Permutation tests allow not only to evaluate the reliability of the data of the revealed regularities, but also to exclude partitions with excessive complexity from the set of the revealed regularities. Patterns connected the class number and economic indicators are revealed using the SDT method on one-dimensional indicators. The regularities which are revealed within the framework of the simplest one-dimensional model with one boundary point and with significance not worse than p < 0.001 are also presented in the given study. The so-called sliding control method was used for reliable evaluation of such diagnostic ability. As a result of these studies, a set of methods that had sufficient effectiveness was identified. The collective method based on the results of several machine learning methods showed the high importance of economic indicators for the division of enterprises in accordance with the rating of the Polar index. Our study proved and showed that those companies that entered the top Rating of the Polar index are generally recognized by financial indicators among all companies in the Arctic Zone. However it would be useful to supplement the list of indicators with ecological and social criteria.

The method of the stochastic matrix spectrum analysis is used to build an indicator that allows to determine the subject of scientific texts without keywords usage. This matrix is a matrix of conditional probabilities of bigrams, built on the statistics of the alphabet characters in the text without spaces, numbers and punctuation marks. Scientific texts are classified according to the mutual arrangement of invariant subspaces of the matrix of conditional probabilities of pairs of letter combinations. The separation indicator is the value of the cosine of the angle between the right and left eigenvectors corresponding to the maximum and minimum eigenvalues. The computational algorithm uses a special representation of the dichotomy parameter, which is the integral of the square norm of the resolvent of the stochastic matrix of bigrams along the circumference of a given radius in the complex plane. The tendency of the integral to infinity testifies to the approximation of the integration circuit to the eigenvalue of the matrix. The paper presents the typical distribution of the indicator of identification of specialties. For statistical analysis were analyzed dissertations on the main 19 specialties without taking into account the classification within the specialty, 20 texts for the specialty. It was found that the empirical distributions of the cosine of the angle for the mathematical and Humanities specialties do not have a common domain, so they can be formally divided by the value of this indicator without errors. Although the body of texts was not particularly large, nevertheless, in the case of arbitrary selection of dissertations, the identification error at the level of 2 % seems to be a very good result compared to the methods based on semantic analysis. It was also found that it is possible to make a text pattern for each of the specialties in the form of a reference matrix of bigrams, in the vicinity of which in the norm of summable functions it is possible to accurately identify the theme of the written scientific work, without using keywords. The proposed method can be used as a comparative indicator of greater or lesser severity of the scientific text or as an indicator of compliance of the text to a certain scientific level.

Modeling the fight against terrorist, pirate and robbery acts at sea is an urgent scientific task due to the prevalence of force acts and the insufficient number of works on this issue. The actions of pirates and terrorists are diverse. Using a base ship, they can attack ships up to 450–500 miles from the coast. Having chosen the target, they pursue it and use the weapons to board the ship. Actions to free a ship captured by pirates or terrorists include: blocking the ship, predicting where pirates might be on the ship, penetrating (from board to board, by air or from under water) and cleaning up the ship’s premises. An analysis of the special literature on the actions of pirates and terrorists showed that the act of force (and actions to neutralize it) consists of two stages: firstly, blocking the vessel, which consists in forcing it to stop, and secondly, neutralizing the team (terrorist groups, pirates), including penetration of a ship (ship) and its cleaning. The stages of the cycle are matched by indicators — the probability of blocking and the probability of neutralization. The variables of the act of force model are the number of ships (ships, boats) of the attackers and defenders, as well as the strength of the capture group of the attackers and the crew of the ship - the victim of the attack. Model parameters (indicators of naval and combat superiority) were estimated using the maximum likelihood method using an international database of incidents at sea. The values of these parameters are 7.6–8.5. Such high values of superiority parameters reflect the parties' ability to act in force acts. An analytical method for calculating excellence parameters is proposed and statistically substantiated. The following indicators are taken into account in the model: the ability of the parties to detect the enemy, the speed and maneuverability characteristics of the vessels, the height of the vessel and the characteristics of the boarding equipment, the characteristics of weapons and protective equipment, etc. Using the Becker model and the theory of discrete choice, the probability of failure of the force act is estimated. The significance of the obtained models for combating acts of force in the sea space lies in the possibility of quantitative substantiation of measures to protect the ship from pirate and terrorist attacks and deterrence measures aimed at preventing attacks (the presence of armed guards on board the ship, assistance from warships and helicopters).