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This article solves the problem of constructing a neuro-fuzzy model of fuzzy rules formation and using them for objects state evaluation in conditions of uncertainty. Traditional mathematical statistics or simulation modeling methods do not allow building adequate models of objects in the specified conditions. Therefore, at present, the solution of many problems is based on the use of intelligent modeling technologies applying fuzzy logic methods. The traditional approach of fuzzy systems construction is associated with an expert attraction need to formulate fuzzy rules and specify the membership functions used in them. To eliminate this drawback, the automation of fuzzy rules formation, based on the machine learning methods and algorithms, is relevant. One of the approaches to solve this problem is to build a fuzzy neural network and train it on the data characterizing the object under study. This approach implementation required fuzzy rules type choice, taking into account the processed data specificity. In addition, it required logical inference algorithm development on the rules of the selected type. The algorithm steps determine the number and functionality of layers in the fuzzy neural network structure. The fuzzy neural network training algorithm developed. After network training the formation fuzzyproduction rules system is carried out. Based on developed mathematical tool, a software package has been implemented. On its basis, studies to assess the classifying ability of the fuzzy rules being formed have been conducted using the data analysis example from the UCI Machine Learning Repository. The research results showed that the formed fuzzy rules classifying ability is not inferior in accuracy to other classification methods. In addition, the logic inference algorithm on fuzzy rules allows successful classification in the absence of a part of the initial data. In order to test, to solve the problem of assessing oil industry water lines state fuzzy rules were generated. Based on the 303 water lines initial data, the base of 342 fuzzy rules was formed. Their practical approbation has shown high efficiency in solving the problem.

The reconstruction of charged particle trajectories in tracking detectors is a key problem in the analysis of experimental data for high energy and nuclear physics.

The amount of data in modern experiments is so large that classical tracking methods such as Kalman filter can not process them fast enough. To solve this problem, we have developed two neural network algorithms of track recognition, based on deep learning architectures, for local (track by track) and global (all tracks in an event) tracking in the GEM tracker of the BM@N experiment at JINR (Dubna). The advantage of deep neural networks is the ability to detect hidden nonlinear dependencies in data and the capability of parallel execution of underlying linear algebra operations.

In this work we generalize these algorithms to the cylindrical GEM inner tracker of BESIII experiment. The neural network model RDGraphNet for global track finding, based on the reverse directed graph, has been successfully adapted. After training on Monte Carlo data, testing showed encouraging results: recall of 98% and precision of 86% for track finding.

The local neural network model TrackNETv2 was also adapted to BESIII CGEM successfully. Since the tracker has only three detecting layers, an additional neuro-classifier to filter out false tracks have been introduced. Preliminary tests demonstrated the recall value at the first stage of 99%. After applying the neuro-classifier, the precision was 77% with a slight decrease of the recall to 94%. This result can be improved after the further model optimization.

The paper is devoted to the secondary use of spectrum in telecommunication networks. It is emphasized that one of the solutions to this problem is the use of cognitive radio technologies and dynamic spectrum access for the successful functioning of which a large amount of information is required, including the parameters of base stations and network subscribers. Storage and processing of information should be carried out using a radio environment map, which is a spatio-temporal database of all activity in the network and allows you to determine the frequencies available for use at a given time. The paper presents a two-level model for forming a map of the radio environment of a cellular communication system LTE, in which the local and global levels are highlighted, which is described by the following parameters: a set of frequencies, signal attenuation, signal propagation map, grid step, current time count. The key objects of the model are the base station and the subscriber unit. The main parameters of the base station include: name, identifier, cell coordinates, range number, radiation power, numbers of connected subscriber devices, dedicated resource blocks. For subscriber devices, the following parameters are used: name, identifier, location, current coordinates of the device cell, base station identifier, frequency range, numbers of resource blocks for communication with the station, radiation power, data transmission status, list of numbers of the nearest stations, schedules movement and communication sessions of devices. An algorithm for the implementation of the model is presented, taking into account the scenarios of movement and communication sessions of subscriber devices. A method for calculating a map of the radio environment at a point on a coordinate grid, taking into account losses during the propagation of radio signals from emitting devices, is presented. The software implementation of the model is performed using the MatLab package. The approaches are described that allow to increase the speed of its work. In the simulation, the choice of parameters was carried out taking into account the data of the existing communication systems and the economy of computing resources. The experimental results of the algorithm for the formation of a radio environment map are demonstrated, confirming the correctness of the developed model.

In the last decade along with classical cytotoxic agents, antiangiogenic drugs have been actively used in cancer chemotherapy. They are not aimed at killing malignant cells, but at blocking the process of angiogenesis, i.e., the growth of new vessels in the tumor and its surrounding tissues. Agents that stimulate angiogenesis, in particular, vascular endothelial growth factor, are actively produced by tumor cells in the state of metabolic stress. It is believed that blocking of tumor neovascularization should lead to a shortage of nutrients flow to the tumor, and thus can stop, or at least significantly slow down its growth. Clinical practice on the use of first antiangiogenic drug bevacizumab has shown that in some cases such therapy does not influence the growth rate of the tumor, whereas for other types of malignant neoplasms antiangiogenic therapy has a high antitumor effect. However, it has been shown that along with successful slowing of tumor growth, therapy with bevacizumab can induce directed tumor progression to a more invasive, and therefore more lethal, type. These data require theoretical analysis and rationale for the evolutionary factors that lead to the observation of epithelial-mesenchymal transition. For this purpose we have developed a spatially distributed mathematical model of growth and antiangiogenic therapy of heterogeneous tumor consisting of two subpopulations of malignant cells. One of subpopulations possesses inherent characteristics of epithelial phenotype, i.e., low motility and high proliferation rate, the other one corresponds to mesenchymal phenotype having high motility and low proliferation rate. We have performed the investigation of competition between these subpopulations of heterogeneous tumor in the cases of tumor growth without therapy and under bevacizumab monotherapy. It is shown that constant use of antiangiogenic drug leads to an increase of the region in parameter space, where the dominance of mesenchymal phenotype takes place, i.e., within a certain range of parameters in the absence of therapy epithelial phenotype is dominant but during bevacizumab administration mesenchymal phenotype begins to dominate. This result provides a theoretical basis of the clinically observed directed tumor progression to more invasive type under antiangiogenic therapy.

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.

Finding PageRank vector is of great scientific and practical interest due to its applicability to modern search engines. Despite the fact that this problem is reduced to finding the eigenvector of the stochastic matrix $P$, the need for new algorithms is justified by a large size of the input data. To achieve no more than linear execution time, various randomized methods have been proposed, returning the expected result only with some probability close enough to one. We will consider two of them by reducing the problem of calculating the PageRank vector to the problem of finding equilibrium in an antagonistic matrix game, which is then solved using the Grigoriadis – Khachiyan algorithm. This implementation works effectively under the assumption of sparsity of the input matrix. As far as we know, there are no successful implementations of neither the Grigoriadis – Khachiyan algorithm nor its application to the task of calculating the PageRank vector. The purpose of this paper is to fill this gap. The article describes an algorithm giving pseudocode and some details of the implementation. In addition, it discusses another randomized method of calculating the PageRank vector, namely, Markov chain Monte Carlo (MCMC), in order to compare the results of these algorithms on matrices with different values of the spectral gap. The latter is of particular interest, since the magnitude of the spectral gap strongly affects the convergence rate of MCMC and does not affect the other two approaches at all. The comparison was carried out on two types of generated graphs: chains and $d$-dimensional cubes. The experiments, as predicted by the theory, demonstrated the effectiveness of the Grigoriadis – Khachiyan algorithm in comparison with MCMC for sparse graphs with a small spectral gap value. The written code is publicly available, so everyone can reproduce the results themselves or use this implementation for their own needs. The work has a purely practical orientation, no theoretical results were obtained.

For the three-axis stabilization of the spacecraft in the orbital coordinate system, including in the indirect equilibrium position, an electrodynamic control method is used based on the simultaneous use of two control torques that affect the dynamics of the spacecraft’s rotational motion in the Earth’s magnetic field (EMF), namely, the Lorentz torque and the torque of magnetic interaction. It is assumed that the spacecraft, equipped with an electric charge with a controlled vector of static moment of charge of the first order and a controlled intrinsic magnetic moment, moves in a Keplerian circular Earth orbit of arbitrary inclination. It was previously shown that combining two control systems, magnetic and Lorentz control, into a single electrodynamic control system (EDCS) makes it possible to successfully solve various problems of controlling the angular motion of spacecraft. Unlike many well-known studies performed for one or another approximate EMF model, this work does not impose restrictions on the accuracy of the EMF approximation. Previous studies have shown the limited capabilities of the EDCS for spacecraft moving in orbits close to the polar ones, due to the presence in this case of such points on the spacecraft trajectory in which it is possible for the lines of action of the geomagnetic induction vector and the spacecraft velocity vector relative to the EMF. Therefore, in this paper, the problem of overcoming these difficulties is posed and solved. A modification of the EDCS is proposed, based, firstly, on optimizing the control of the angular motion of the spacecraft and, secondly, on limiting the maximum value of the modulus of the vector of the center of charge relative to the center of mass of the spacecraft, which must be created during control. A method for selecting parameters for a modified EMF is recommended. The presented results of numerical experiments for spacecraft located in polar and circumpolar orbits not only demonstrate the operability of the proposed modification of the EDCS, but also indicate the possibility of technical implementation of the modified electrodynamic method of three-axis spacecraft stabilization.

In modern cardiology, accurate assessment of the functional significance of coronary artery stenoses is a critical factor for selecting treatment strategies and making informed clinical decisions. This paper presents an automated algorithm for processing dynamic X-ray angiographic image sequences aimed at estimating blood flow velocity. This parameter serves as the basis for determining the Quantitative Flow Ratio (QFR), which acts as an effective noninvasive alternative to traditional invasive fractional flow reserve (FFR) measurements. The proposed methodology successfully overcomes classic challenges of angiographic analysis, such as vessel motion artifacts during the cardio-respiratory cycle, variable contrast opacification, and the geometric complexity of the vascular tree in two-dimensional projections.

The presented processing workflow includes several key stages. Initially, frame preprocessing is performed to suppress noise and filter out the anatomical background. Subsequently, segmentation is implemented using a Sato filter and Otsu thresholding, followed by skeletonization to extract vessel centerlines. Particular attention is paid to the algorithm for automated identification of bifurcation points and the filtration of artifactual intersections caused by vessel overlapping. To ensure data continuity, a temporal tracking method for the target segment based on template correlation is applied, which is especially important during phases with low contrast agent concentration. The mathematical core of the algorithm is based on solving a 1D inverse problem for the advection-diffusion equation, allowing for the recovery of blood flow velocity from temporal intensity curves.

As part of the study, a detailed validation of the method was conducted by comparing automated calculation results with manual expert measurements across ten clinical datasets. The results confirm the robustness of the computational scheme within physiologically relevant ranges and its ability to significantly reduce inter-observer variability. The developed approach minimizes the need for physician intervention in the data processing stage, opening up prospects for creating real-time clinical decision support systems in the catheterization laboratory setting.

Formation of adequate behavioral patterns in condition of the unknown environment carried out through exploratory behavior. At the same time the rapid formation of an acceptable pattern is more preferable than a long elaboration perfect pattern through repeat play learning situation. In extreme situations, phenomenon of imprinting is observed — instant imprinting of behavior pattern, which ensure the survival of individuals. In this paper we propose a hypothesis and imprint model when trained on a single successful pattern of virtual robot's neural network demonstrates the effective functioning. Realism of the model is estimated by checking the stability of playback behavior pattern to perturbations situation imprint run.

In recent decades, the introduction of biophotonics and quantum electronics advance into medical practice led to the development of new diagnostic and therapeutic approaches for many diseases. In the field of oncology, photodynamic therapy (PDT) is successfully used today in the treatment of various types of cancer. Along with further improvement of PDT, the development of direct laser therapy is currently underway, in which the generation of singlet oxygen molecules ($^{1}$О$_2^{}$) in cancer cells occurs under NIR laser irradiation with a wavelength of $\lambda=1267$ nm without the need to introduce photosensitizers into the patient's body. For the purpose of a~theoretical investigation of the direct effect of NIR laser irradiation on cancer cells and the description of a~large set of experimental data, a mathematical model has been developed. The model includes the main cellular processes activated in cancer cells by NIR laser irradiation that determine the effectiveness of its cytotoxic effect on cancer cells. As a result of modeling, the rate of $^{1}$О$_2^{}$ generation under NIR laser irradiation was estimated, and the kinetics of active oxygen species (ROS) molecules was described. The ROS degradation due to the action of the antioxidant system of cell protection was taken into account in the model. It was shown that NIR laser irradiation induces lipid peroxidation that leads to cellular membrane damage and cell death through ferroptosis. As a result of modeling, it was established that a cascade of free-radical and enzymatic reactions of ROS transformation and accumulation leads to a prolonged response of cervical adenocarcinoma cells HeLa to the action of laser irradiation with $\lambda=1267$ nm, during which oxidative stress develops, causing cancer cell death through apoptosis and ferroptosis.