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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.

In this paper, the system of equations of magnetic hydrodynamics (MHD) is considered. The exact solutions found describe fluid flows in a porous medium and are related to the development of a core simulator and are aimed at creating a domestic technology «digital deposit» and the tasks of controlling the parameters of incompressible fluid. The central problem associated with the use of computer technology is large-dimensional grid approximations and high-performance supercomputers with a large number of parallel microprocessors. Kinetic methods for solving differential equations and methods for «gluing» exact solutions on coarse grids are being developed as possible alternatives to large-dimensional grid approximations. A comparative analysis of the efficiency of computing systems allows us to conclude that it is necessary to develop the organization of calculations based on integer arithmetic in combination with universal approximate methods. A class of exact solutions of the Navier – Stokes system is proposed, describing three-dimensional flows for an incompressible fluid, as well as exact solutions of nonstationary three-dimensional magnetic hydrodynamics. These solutions are important for practical problems of controlled dynamics of mineralized fluids, as well as for creating test libraries for verification of approximate methods. A number of phenomena associated with the formation of macroscopic structures due to the high intensity of interaction of elements of spatially homogeneous systems, as well as their occurrence due to linear spatial transfer in spatially inhomogeneous systems, are highlighted. It is fundamental that the emergence of structures is a consequence of the discontinuity of operators in the norms of conservation laws. The most developed and universal is the theory of computational methods for linear problems. Therefore, from this point of view, the procedures of «immersion» of nonlinear problems into general linear classes by changing the initial dimension of the description and expanding the functional spaces are important. Identification of functional solutions with functions makes it possible to calculate integral averages of an unknown, but at the same time its nonlinear superpositions, generally speaking, are not weak limits of nonlinear superpositions of approximations of the method, i.e. there are functional solutions that are not generalized in the sense of S. L. Sobolev.

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.

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.

Certifying a transport airplane for the flights under icing conditions in Russia was carried out within the framework of the requirements of Annex С to the AP-25 Aviation Rules. In force since 2023 to replace AP-25 the new Russian certification document “Airworthiness Standards” (NLG-25) proposes the introduction of Appendix O. A feature of Appendix O is the need to carry out calculations in conditions of high liquid water content and with large water drops (500 microns or more). With such parameters of the dispersed flow, such physical processes as the disruption and splashing of a water film when large drops enter it become decisive. The flow of a dispersed medium under such conditions is essentially polydisperse. This paper describes the modifications of the IceVision technique implemented on the basis of the FlowVision software package for the ice accretion calculations within the framework of Appendix O.

The main difference between the IceVision method and the known approaches is the use of the Volume of fluid (VOF) technology to the shape of ice changes tracking. The external flow around the aircraft is calculated simultaneously with the growth of ice and its heating. Ice is explicitly incorporated in the computational domain; the heat transfer equation is solved in it. Unlike the Lagrangian approaches, the Euler computational grid is not completely rebuilt in the IceVision technique: only the cells containing the contact surface are changed.

The IceVision 2.0 version accounts for stripping the film, as well as bouncing and splashing of falling drops at the surfaces of the aircraft and ice. The diameter of secondary droplets is calculated using known empirical correlations. The speed of the water film flow over the surface is determined taking into account the action of aerodynamic forces, gravity, hydrostatic pressure gradient and surface tension force. The result of taking into account surface tension is the effect of contraction of the film, which leads to the formation of water flows in the form of rivulets and ice deposits in the form of comb-like growths. An energy balance relation is fulfilled on the ice surface that takes into account the energy of falling drops, heat exchange between ice and air, the heat of crystallization, evaporation, sublimation and condensation. The paper presents the results of solving benchmark and model problems, demonstrating the effectiveness of the IceVision technique and the reliability of the obtained results.

The creation of a virtual laboratory stand that allows one to obtain reliable characteristics that can be proven as actual, taking into account errors and noises (which is the main distinguishing feature of a computational experiment from model studies) is one of the main problems of this work. It considers the following task: there is a rectangular waveguide in the single operating mode, on the wide wall of which a technological hole is cut, through which a sample for research is placed into the cavity of the transmission line. The recovery algorithm is as follows: the laboratory measures the network parameters (S₁₁ and/or S₂₁) in the transmission line with the sample. In the computer model of the laboratory stand, the sample geometry is reconstructed and an iterative process of optimization (or sweeping) of the electrophysical parameters is started, the mask of this process is the experimental data, and the stop criterion is the interpretive estimate of proximity (or residual). It is important to note that the developed computer model, along with its apparent simplicity, is initially ill-conditioned. To set up a computational experiment, the Comsol modeling environment is used. The results of the computational experiment with a good degree of accuracy coincided with the results of laboratory studies. Thus, experimental verification was carried out for several significant components, both the computer model in particular and the algorithm for restoring the target parameters in general. It is important to note that the computer model developed and described in this work may be effectively used for a computational experiment to restore the full dielectric parameters of a complex geometry target. Weak bianisotropy effects can also be detected, including chirality, gyrotropy, and material nonreciprocity. The resulting model is, by definition, incomplete, but its completeness is the highest of the considered options, while at the same time, the resulting model is well conditioned. Particular attention in this work is paid to the modeling of a coaxial-waveguide transition, it is shown that the use of a discrete-element approach is preferable to the direct modeling of the geometry of a microwave device.

One of problems in developing the gas condensate fields lies on the fact that the condensed hydrocarbons in the gas-bearing layer can get stuck in the pores of the formation and hence cannot be extracted. In this regard, research is underway to increase the recoverability of hydrocarbons in such fields. This research includes a wide range of studies on mathematical simulations of the passage of gas condensate mixtures through a porous medium under various conditions.

In the present work, within the classical approach based on the Darcy law and the law of continuity of flows, we formulate an initial-boundary value problem for a system of nonlinear differential equations that describes a depletion of a multicomponent gas-condensate mixture in porous reservoir. A computational scheme is developed on the basis of the finite-difference approximation and the fourth order Runge .Kutta method. The scheme can be used for simulations both in the spatially one-dimensional case, corresponding to the conditions of the laboratory experiment, and in the two-dimensional case, when it comes to modeling a flat gas-bearing formation with circular symmetry.

The computer implementation is based on the combination of C++ and Maple tools, using the MPI parallel programming technique to speed up the calculations. The calculations were performed on the HybriLIT cluster of the Multifunctional Information and Computing Complex of the Laboratory of Information Technologies of the Joint Institute for Nuclear Research.

Numerical results are compared with the experimental data on the pressure dependence of output of a ninecomponent hydrocarbon mixture obtained at a laboratory facility (VNIIGAZ, Ukhta). The calculations were performed for two types of porous filler in the laboratory model of the formation: terrigenous filler at 25 .„R and carbonate one at 60 .„R. It is shown that the approach developed ensures an agreement of the numerical results with experimental data. By fitting of numerical results to experimental data on the depletion of the laboratory reservoir, we obtained the values of the parameters that determine the inter-phase transition coefficient for the simulated system. Using the same parameters, a computer simulation of the depletion of a thin gas-bearing layer in the circular symmetry approximation was carried out.

Industrial imaging or “machine vision” is currently a key technology in many industries as it can be used to optimize various processes. The purpose of this work is to create a software and hardware complex for measuring the overall and weight characteristics of cargo based on an intelligent system using neural network identification methods that allow one to overcome the technological limitations of similar complexes implemented on ultrasonic and infrared measuring sensors. The complex to be developed will measure cargo without restrictions on the volume and weight characteristics of cargo to be tariffed and sorted within the framework of the warehouse complexes. The system will include an intelligent computer program that determines the volume and weight characteristics of cargo using the machine vision technology and an experimental sample of the stand for measuring the volume and weight of cargo.

We analyzed the solutions to similar problems. We noted that the disadvantages of the studied methods are very high requirements for the location of the camera, as well as the need for manual operations when calculating the dimensions, which cannot be automated without significant modifications. In the course of the work, we investigated various methods of object recognition in images to carry out subject filtering by the presence of cargo and measure its overall dimensions. We obtained satisfactory results when using cameras that combine both an optical method of image capture and infrared sensors. As a result of the work, we developed a computer program allowing one to capture a continuous stream from Intel RealSense video cameras with subsequent extraction of a three-dimensional object from the designated area and to calculate the overall dimensions of the object. At this stage, we analyzed computer vision techniques; developed an algorithm to implement the task of automatic measurement of goods using special cameras and the software allowing one to obtain the overall dimensions of objects in automatic mode.

Upon completion of the work, this development can be used as a ready-made solution for transport companies, logistics centers, warehouses of large industrial and commercial enterprises.

A crucial component of distributed computing systems is network infrastructure. While networking forms the backbone of such systems, it is often the invisible partner to storage and computing resources. We propose to integrate Network Elements directly into distributed systems through the workload management layer. There are many reasons for this approach. As the complexity and demand for distributed systems grow, it is important to use existing infrastructure efficiently. For example, one could use network performance measurements in the decision making mechanisms of workload management systems. New advanced technologies allow one to programmatically define network configuration, for example SDN — Software Defined Networks. We will describe how these methods are being used within the PanDA workload management system of the ATLAS collaboration.

One of the important direction of cloud technologies development nowadays is a creation of methods for integration of various cloud infrastructures. An actuality of such direction in academic field is caused by a frequent lack of own computing resources and a necessity to attract additional ones. This article is dedicated to existing approaches to cloud infrastructures integration with each other: federations and so called ‘cloud bursting’. A ‘federation’ in terms of OpenNebula cloud platform is built on a ‘one master zone and several slave ones’ schema. A term ‘zone’ means a separate cloud infrastructure in the federation. All zones in such kind of integration have a common database of users and the whole federation is managed via master zone only. Such approach is most suitable for a case when cloud infrastructures of geographically distributed branches of a single organization need to be integrated. But due to its high centralization it's not appropriate when one needs to join cloud infrastructures of different organizations. Moreover it's not acceptable at all in case of clouds based on different software platforms. A model of federative integration implemented in EGI Federated Cloud allows to connect clouds based on different software platforms but it requires a deployment of sufficient amount of additional services which are specific for EGI Federated Cloud only. It makes such approach is one-purpose and uncommon one. A ‘cloud bursting’ model has no limitations listed above but in case of OpenNebula platform what the Laboratory of Information Technologies of Joint Institute for Nuclear Research (LIT JINR) cloud infrastructure is based on such model was implemented for an integration with a certain set of commercial cloud resources providers. Taking into account an article authors’ experience in joining clouds of organizations they represent as well as with EGI Federation Cloud a ‘cloud bursting’ driver was developed by LIT JINR cloud team for OpenNebula-based clouds integration with each other as well as with OpenStack-based ones. The driver's architecture, technologies and protocols it relies on and an experience of its usage are described in the article.