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Since 1950s the field of city transport modelling has progressed rapidly. The first equilibrium distribution models of traffic flow appeared. The most popular model (which is still being widely used) was the Beckmann model, based on the two Wardrop principles. The core of the model could be briefly described as the search for the Nash equilibrium in a population demand game, in which losses of agents (drivers) are calculated based on the chosen path and demands of this path with correspondences being fixed. The demands (costs) of a path are calculated as the sum of the demands of different path segments (graph edges), that are included in the path. The costs of an edge (edge travel time) are determined by the amount of traffic on this edge (more traffic means larger travel time). The flow on a graph edge is determined by the sum of flows over all paths passing through the given edge. Thus, the cost of traveling along a path is determined not only by the choice of the path, but also by the paths other drivers have chosen. Thus, it is a standard game theory task. The way cost functions are constructed allows us to narrow the search for equilibrium to solving an optimization problem (game is potential in this case). If the cost functions are monotone and non-decreasing, the optimization problem is convex. Actually, different assumptions about the cost functions form different models. The most popular model is based on the BPR cost function. Such functions are massively used in calculations of real cities. However, in the beginning of the XXI century, Yu. E. Nesterov and A. de Palma showed that Beckmann-type models have serious weak points. Those could be fixed using the stable dynamics model, as it was called by the authors. The search for equilibrium here could be also reduced to an optimization problem, moreover, the problem of linear programming. In 2013, A.V.Gasnikov discovered that the stable dynamics model can be obtained by a passage to the limit in the Beckmann model. However, it was made only for several practically important, but still special cases. Generally, the question if this passage to the limit is possible remains open. In this paper, we provide the justification of the possibility of the above-mentioned passage to the limit in the general case, when the cost function for traveling along the edge as a function of the flow along the edge degenerates into a function equal to fixed costs until the capacity is reached and it is equal to plus infinity when the capacity is exceeded.

A method is proposed for analyzing the tremor of the earth’s surface, measured by means of space geodesy, in order to highlight the prognostic effects of seismicity activation. The method is illustrated by the example of a joint analysis of a set of synchronous time series of daily vertical displacements of the earth’s surface on the Japanese Islands for the time interval 2009–2023. The analysis is based on dividing the source data (1047 time series) into blocks (clusters of stations) and sequentially applying the principal component method. The station network is divided into clusters using the K-means method from the maximum pseudo-F-statistics criterion, and for Japan the optimal number of clusters was chosen to be 15. The Huang decomposition method into a sequence of independent empirical oscillation modes (EMD — Empirical Mode Decomposition) is applied to the time series of principal components from station blocks. To provide the stability of estimates of the waveforms of the EMD decomposition, averaging of 1000 independent additive realizations of white noise of limited amplitude was performed. Using the Cholesky decomposition of the covariance matrix of the waveforms of the first three EMD components in a sliding time window, indicators of abnormal tremor behavior were determined. By calculating the correlation function between the average indicators of anomalous behavior and the released seismic energy in the vicinity of the Japanese Islands, it was established that bursts in the measure of anomalous tremor behavior precede emissions of seismic energy. The purpose of the article is to clarify common hypotheses that movements of the earth’s crust recorded by space geodesy may contain predictive information. That displacements recorded by geodetic methods respond to the effects of earthquakes is widely known and has been demonstrated many times. But isolating geodetic effects that predict seismic events is much more challenging. In our paper, we propose one method for detecting predictive effects in space geodesy data.

Special actions (guerrilla, anti-guerrilla, reconnaissance and sabotage, subversive, counter-terrorist, counter-sabotage, etc.) are organized and conducted by law enforcement and armed forces and are aimed at protecting citizens and ensuring national security. Since the early 2000s, the problems of special actions have attracted the attention of specialists in the field of modeling, sociologists, physicists and representatives of other sciences. This article reviews and characterizes the works in the field of modeling special actions and counterterrorism. The works are classified by modeling methods (descriptive, optimization and game-theoretic), by types and stages of actions, and by phases of management (preparation and conduct of activities). The second section presents a classification of methods and models for special actions and counterterrorism, and gives a brief overview of descriptive models. The method of geographic profiling, network games, models of dynamics of special actions, the function of victory in combat and special actions (the dependence of the probability of victory on the correlation of forces and means of the parties) are considered. The third section considers the “attacker – defender” game and its extensions: the Stackelberg game and the Stackelberg security game, as well as issues of their application in security tasks In the “attacker – defender” game and security games, known works are classified on the following grounds: the sequence of moves, the number of players and their target functions, the time horizon of the game, the degree of rationality of the players and their attitude to risk, the degree of awareness of the players. The fourth section is devoted to the description of patrolling games on a graph with discrete time and simultaneous choice by the parties of their actions (Nash equilibrium is computed to find optimal strategies). The fifth section deals with game-theoretic models of transportation security as applications of Stackelberg security games. The last section is devoted to the review and characterization of a number of models of border security in two phases of management: preparation and conduct of activities. An example of effective interaction between Coast Guard units and university researchers is considered. Promising directions for further research are the following: first, modeling of counter-terrorist and special operations to neutralize terrorist and sabotage groups with the involvement of multidepartmental and heterogeneous forces and means, second, complexification of models by levels and stages of activity cycles, third, development of game-theoretic models of combating maritime terrorism and piracy.

The work presents an experimental study of the tree structure that occurs during a medical examination. At each meeting with a medical specialist, the patient receives a certain number of areas for consulting other specialists or for tests. A tree of directions arises, each branch of which the patient should pass. Depending on the branching of the tree, it can be as final — and in this case the examination can be completed — and endless when the patient’s goal cannot be achieved. In the work both experimentally and theoretically studied the critical properties of the transition of the system from the forest of the final trees to the forest endless, depending on the probabilistic characteristics of the tree.

For the description, a model is proposed in which a discrete function of the probability of the number of branches on the node repeats the dynamics of a continuous gaussian distribution. The characteristics of the distribution of the Gauss (mathematical expectation of $x_0$, the average quadratic deviation of $\sigma$) are model parameters. In the selected setting, the task refers to the problems of branching random processes (BRP) in the heterogeneous model of Galton – Watson.

Experimental study is carried out by numerical modeling on the final grilles. A phase diagram was built, the boundaries of areas of various phases are determined. A comparison was made with the phase diagram obtained from theoretical criteria for macrosystems, and an adequate correspondence was established. It is shown that on the final grilles the transition is blurry.

The description of the blurry phase transition was carried out using two approaches. In the first, standard approach, the transition is described using the so-called inclusion function, which makes the meaning of the share of one of the phases in the general set. It was established that such an approach in this system is ineffective, since the found position of the conditional boundary of the blurred transition is determined only by the size of the chosen experimental lattice and does not bear objective meaning.

The second, original approach is proposed, based on the introduction of an parameter of order equal to the reverse average tree height, and the analysis of its behavior. It was established that the dynamics of such an order parameter in the $\sigma = \text{const}$ section with very small differences has the type of distribution of Fermi – Dirac ($\sigma$ performs the same function as the temperature for the distribution of Fermi – Dirac, $x_0$ — energy function). An empirical expression has been selected for the order parameter, an analogue of the chemical potential is introduced and calculated, which makes sense of the characteristic scale of the order parameter — that is, the values of $x_0$, in which the order can be considered a disorder. This criterion is the basis for determining the boundary of the conditional transition in this approach. It was established that this boundary corresponds to the average height of a tree equal to two generations. Based on the found properties, recommendations for medical institutions are proposed to control the provision of limb of the path of patients.

The model discussed and its description using conditionally-infinite trees have applications to many hierarchical systems. These systems include: internet routing networks, bureaucratic networks, trade and logistics networks, citation networks, game strategies, population dynamics problems, and others.

Cognitive and regulatory processes in organism are ensured by the functioning of several different network systems — neural, endocrine, immune, and gene ones. These systems are, however, closely related and form a single integrated neurogenohumoral cognitive-regulatory dynamic system of organism. A review of publications is given which shows that it is possible to associate with this dynamic system a corresponding Lyapunov function (energy function, potential function) and that analyzing this function allows, due to its geometrical insight, to easily discover a set of general properties of cognitive and regulatory functioning of organism.

Automatic recognition of personal identity by biometric features is based on unique peculiarities or characteristics of people. Biometric identification process consist in making of reference templates and comparison with new input data. Iris pattern recognition algorithms presents high accuracy and low identification errors percent on practice. Iris pattern advantages over other biometric features are determined by its high degree of freedom (nearly 249), excessive density of unique features and constancy. High recognition reliability level is very important because it provides search in big databases. Unlike one-to-one check mode that is applicable only to small calculation count it allows to work in one-to-many identification mode. Every biometric identification system appears to be probabilistic and qualitative characteristics description utilizes such parameters as: recognition accuracy, false acceptance rate and false rejection rate. These characteristics allows to compare identity recognition methods and asses the system performance under any circumstances. This article explains the mathematical model of iris pattern biometric identification and its characteristics. Besides, there are analyzed results of comparison of model and real recognition process. To make such analysis there was carried out the review of existing iris pattern recognition methods based on different unique features vector. The Python-based software package is described below. It builds-up probabilistic distributions and generates large test data sets. Such data sets can be also used to educate the identification decision making neural network. Furthermore, synergy algorithm of several iris pattern identification methods was suggested to increase qualitative characteristics of system in comparison with the use of each method separately.

The proteins of the Major Histocompatibility Complex (MHC) play a key role in the functioning of the adaptive immune system, and the identification of peptides that bind to them is an important step in the development of vaccines and understanding the mechanisms of autoimmune diseases. Today, there are a number of methods for predicting the binding of a particular MHC allele to a peptide. One of the best such methods is NetMHCpan-4.0, which is based on an ensemble of artificial neural networks. This paper presents a methodology for qualitatively improving the underlying neural network underlying NetMHCpan-4.0. The proposed method uses the ensemble construction technique and adds as input an estimate of the Potts model taken from static mechanics, which is a generalization of the Ising model. In the general case, the model reflects the interaction of spins in the crystal lattice. Within the framework of the proposed method, the model is used to better represent the physical nature of the interaction of proteins included in the complex. To assess the interaction of the MHC + peptide complex, we use a two-dimensional Potts model with 20 states (corresponding to basic amino acids). Solving the inverse problem using data on experimentally confirmed interacting pairs, we obtain the values of the parameters of the Potts model, which we then use to evaluate a new pair of MHC + peptide, and supplement this value with the input data of the neural network. This approach, combined with the ensemble construction technique, allows for improved prediction accuracy, in terms of the positive predictive value (PPV) metric, compared to the baseline model.

Despite numerous achievements of modern science the problem of lightning initiation in an electrodeless thundercloud, the maximum electric field strength inside which is approximately an order of magnitude lower than the dielectric strength of air, remains unsolved. Although there is no doubt that discharge activity begins with the appearance of positive streamers, which can develop under approximately half the threshold electric field as compared to negative ones, it remains unexplored how cold weakly conducting streamer systems unite in a joint hot well-conducting leader channel capable of self-propagation due to effective polarization in a relatively small external field. In this study, we present a self-organizing transport model which is applied to the case of electric discharge tree formation in a thundercloud. So, the model is aimed at numerical simulation of the initial stage of lightning discharge development. Among the innovative features of the model are the absence of grid spacing, high spatiotemporal resolution, and consideration of temporal evolution of electrical parameters of transport channels. The model takes into account the widely known asymmetry between threshold fields needed for positive and negative streamers development. In our model, the resulting well-conducting leader channel forms due to collective effect of combining the currents of tens of thousands of interacting streamer channels each of which initially has negligible conductivity and temperature that does not differ from the ambient one. The model bipolar tree is a directed graph (it has both positive and negative parts). It has morphological and electrodynamic characteristics which are intermediate between laboratory long spark and developed lightning. The model has universal character which allows to use it in other tasks related to the study of transport (in the broad sense of the word) networks.

This article solves the problem of developing a technology for collecting initial data for building models for assessing the functional state of a person. This condition is assessed by the pupil response of a person to a change in illumination based on the pupillometry method. This method involves the collection and analysis of initial data (pupillograms), presented in the form of time series characterizing the dynamics of changes in the human pupils to a light impulse effect. The drawbacks of the traditional approach to the collection of initial data using the methods of computer vision and smoothing of time series are analyzed. Attention is focused on the importance of the quality of the initial data for the construction of adequate mathematical models. The need for manual marking of the iris and pupil circles is updated to improve the accuracy and quality of the initial data. The stages of the proposed technology for collecting initial data are described. An example of the obtained pupillogram is given, which has a smooth shape and does not contain outliers, noise, anomalies and missing values. Based on the presented technology, a software and hardware complex has been developed, which is a collection of special software with two main modules, and hardware implemented on the basis of a Raspberry Pi 4 Model B microcomputer, with peripheral equipment that implements the specified functionality. To evaluate the effectiveness of the developed technology, models of a single-layer perspetron and a collective of neural networks are used, for the construction of which the initial data on the functional state of intoxication of a person were used. The studies have shown that the use of manual marking of the initial data (in comparison with automatic methods of computer vision) leads to a decrease in the number of errors of the 1st and 2nd years of the kind and, accordingly, to an increase in the accuracy of assessing the functional state of a person. Thus, the presented technology for collecting initial data can be effectively used to build adequate models for assessing the functional state of a person by pupillary response to changes in illumination. The use of such models is relevant in solving individual problems of ensuring transport security, in particular, monitoring the functional state of drivers.

The numerical methods used to simulate the evolution of hydrodynamic systems require the considerable use of computational resources thus limiting the number of possible simulations. The data-driven simulation technique is one promising approach to the development of heuristic models, which may speed up the study of such models. In this approach, machine learning methods are used to tune the weights of an artificial neural network that predicts the state of a physical system at a given point in time based on initial conditions. This article describes an original neural network architecture and a novel multi-stage training procedure which create a heuristic model of a two-phase flow in a heterogeneous porous medium. The neural network-based model predicts the states of the grid cells at an arbitrary timestep (within the known constraints), taking in only the initial conditions: the properties of the heterogeneous permeability of the medium and the location of sources and sinks. The proposed model requires orders of magnitude less processor time in comparison with the classical numerical method, which served as a criterion for evaluating the effectiveness of the trained model. The proposed architecture includes a number of subnets trained in various combinations on several datasets. The techniques of adversarial training and weight transfer are utilized.