The article describes the methodology for developing a client-server application intended for constructing a virtual museum. The creation of the server part of the application with the functions of processing and executing requests from the client part, as well as the creation of a database and interaction with it, is discussed in detail. The client part is developed using the Angular framework and the TypeScript language; the three-dimensional implementation is based on the three.js library, which is an add-on to WebGL technology. The server part is developed on the ASP.NET Core platform in C#. The database schema is based on a Code-First approach using Entity Framework Core. Microsoft SQL Server is used as the database management system.

Keywords: client-server application, virtual tour designer, virtual museum, three.js library, framework, Angular, ASP.NET Core, Entity Framework Core, Code-First, WebGL

The purpose of the study is to focus on the need to introduce the concept of a cyber attack as an emergency situation to reduce the number of cyber threats in institutions, organizations and enterprises when using modern information technologies. The study is devoted to the analysis of cyber attacks on people’s life support facilities in the world and in Russia, in particular. Currently, the media in different countries of the modern world are talking about the avalanche growth in the number of cyber-attacks; this makes information security one of the most important issues in the activities of government agencies and production facilities. The problem of information security is studied in detail, taking into account the consequences of cyber attacks on technological systems in all spheres of human activity.

Keywords: cybersecurity, cyber emergency, monitoring, Zero Trust model, cyber insurance

This article presents a study aimed at evaluating the use of the Matlab Simulink software environment for the development of microcontroller systems of the STM32 family. The possibilities of Simulink in the field of modeling and testing control algorithms, as well as in generating code that can be directly applied to microcontrollers, are analyzed. The article describes in detail the process of creating conceptual models and their dynamic modeling. The advantages of using Simulink include speeding up the development process through automated assembly and the ability to adjust model parameters in real time. In addition, Simulink allows you to generate processor-optimized code, which significantly increases the efficiency of microcontroller systems. However, attention is also drawn to some limitations associated with using Simulink, such as the need to create a configuration file in STM32CubeMX and potential difficulties in configuring it. The article provides an in-depth analysis of the application of Simulink in the context of the development of STM32 microcontrollers and can become a key material for those who want to deepen their knowledge in this area.

Keywords: model-oriented programming, MatLab, Simulink, STM32, microcontroller, code generation, automatic control system, DC motor

The article proposes the use of intelligent methods for predicting the reliability of contract execution as a key element of the system for ensuring information security of the critical infrastructure of financial sector organizations. Based on the analysis of historical data and the use of machine learning methods, a comprehensive model for assessing and predicting the risks of failure or poor performance of contracts by suppliers has been developed. It is shown how the use of predictive analytics can improve the efficiency of information security risk management, optimize planning and resource allocation, and make informed decisions when interacting with suppliers of critical services and equipment.

Keywords: intelligent system, predictive analytics, information security, critical infrastructure, financial sector, contract execution, machine learning

Since 2017, EVRAZ ZSMK JSC has been developing and operating a mathematical model covering all processing stages from ore extraction to final products – SMM Forecast. The model will be used to calculate technical cases, plans, and parity prices for iron ore and coal, and its use brought more than 200 million rubles of economic effect in 2020 alone. The use of a universal mathematical model made it possible in 2023 to begin the development of a module for daily optimization of an agglomeration factory and blast furnace production. The article discusses the experience of EVRAZ ZSMK JSC in the development and implementation of a daily planning system based on the monthly planning model of SMM Forecast, as well as methods for achieving an acceptable speed of multi-period optimization. The SMM Forecast system was originally designed for end-to-end, scenario-based calculation of the main raw materials from ore and coal to finished products in a volumetric monthly planning. The system uses optimization algorithms to search for a global target function to maximize margin income under specified constraints. The mathematical model of redistribution uses the norms and technologies specified in the company's regulatory documents. At the same time, the model is universal and the transfer of algorithms from monthly to daily mode was carried out with minimal modifications. The article also discusses the difficulties encountered and various methods of solving these problems. The first problem faced by the developers was the low speed of optimization of the model in daily dynamics due to the strong complication of the optimization load. The calculation time has increased significantly, and to solve this problem, it took the introduction of a number of optimization cycles aimed at reducing the speed of solving equations, introducing variable boundaries, and determining starting points. As a result, the calculation time for one month was about 40 minutes. The second problem was the need to develop a complex supply management algorithm and optimize stacking at the sinter plant. As a result of solving this problem, a working tool has been developed that brings additional income to the enterprise.

Keywords: metallurgy, modeling, planning, daily planning, sintering plant, blast furnace shop, stacking

This article is dedicated to developing a method for diagnosing depression using the analysis of user behavior in a video game on the Unity platform. The method involves employing machine learning to train classification models based on data from gaming sessions of users with confirmed diagnoses of depression. As part of the research, users are engaged in playing a video game, during which their in-game behavior is analyzed using specific depression criteria taken from the DSM-5 diagnostic guidelines. Subsequently, this data is used to train and evaluate machine learning models capable of classifying users based on their in-game behavior. Gaming session data is serialized and stored in the Firebase Realtime Database in text format for further use by the classification model. Classification methods such as decision trees, k-nearest neighbors, support vector machines, and random forest methods have been applied. The diagnostic method in the virtual space demonstrates prospects for remote depression diagnosis using video games. Machine learning models trained based on gaming session data show the ability to effectively distinguish users with and without depression, confirming the potential of this approach for early identification of depressive states. Using video games as a diagnostic tool enables a more accessible and engaging approach to detecting mental disorders, which can increase awareness and aid in combating depression in society.

Keywords: videogame, unity, psychiatric diagnosis, depression, machine learning, classification, behavior analysis, in-game behavior, diagnosis, virtual space

The use of simulation analysis requires a large number of models and computational time. Reduce the calculation time in complex complex simulation and statistical modeling, allowing the implementation of parallel programming technologies in the implemented models. This paper sets the task of parallelizing the algorithmization of simulation modeling of the dynamics of a certain indicator (using the example of a model of the dynamics of cargo volume in a storage warehouse). The model is presented in the form of lines for calculating input and output flows, specified as: a moving average autoregressive model with trend components; flows of the described processes, specified according to the principle of limiting the limitation on the volume (size) of the limiting parameter, with strong stationarity of each of them. A parallelization algorithm using OpenMP technology is proposed. The efficiency indicators of the parallel algorithm are estimated: speedup, calculated as the ratio of the execution time of the sequential and parallel algorithm, and efficiency, reflecting the proportion of time that computational threads spend in calculations, and representing the ratio of the speedup to the sequential result of the processors. The dependence of the execution of the sequential and parallel algorithm on the number of simulations has been constructed. The efficiency of the parallel algorithm for the main stages of the simulation implementation was obtained at the level of 73%, the speedup is 4.38 with the number of processors 6. Computational experiments demonstrate a fairly high efficiency of the proposed parallel algorithm.

Keywords: simulation modeling, parallel programming, parallel algorithm efficiency, warehouse loading model, OpenMP technology

The paper presents a mathematical model, algorithm and simulation results of the steam pressure control process in a steam curtain of a tubular furnace of a diesel fuel hydrotreating technological installation based on a PID-controller with filtration of the current control error of a double moving average.

Keywords: automation, subsystem, control, control, steam curtain of a tubular furnace, steam pressure, moving average

The article discusses the possibilities of using virtual reality technologies to organize fire safety training for schoolchildren. The requirements for the virtual simulator are formulated from the point of view of ensuring the possibility of conducting classes on practicing evacuation skills from the building of a specific educational organization. A functional model of a virtual simulator is presented, built on the basis of the methodology of structural analysis and design, describing the process of developing a virtual space with interactive elements and organizing training for the evacuation of students based on it. A semantic description of the control signals of the functional model, its inputs, mechanisms and outputs is given. The contents of the model subsystems are revealed. Requirements for software, hardware and methodological support for training using virtual reality technologies when conducting fire training are formulated. The concept of creating a digital twin of a building of a general education organization in virtual space is substantiated. Examples of improving virtual space by using the results of mathematical modeling of fire are given. The use of visualization of smoke and flame in virtual space is justified to avoid the occurrence of panic in children during evacuation in fire conditions. Conclusions are drawn about the advantages of the proposed virtual simulator. The prospects for further research and solution to the problem of developing skills for evacuating students from a building of a general education organization in case of fire are listed.

Keywords: virtual reality, virtual simulator, virtual space, fire safety, evacuation, fire training, mathematical modeling of fire, educational technologies, functional modeling

This article is devoted to the development of a method for detecting defects on the surface of a product based on anomaly detection methods using a feature extractor based on a convolutional neural network. The method involves the use of machine learning to train classification models based on the obtained features from a layer of a pre-trained U-Net neural network. As part of the study, an autoencoder is trained based on the U-Net model on data that does not contain images of defects. The features obtained from the neural network are classified using classical algorithms for identifying anomalies in the data. This method allows you to localize areas of anomalies in a test data set when only samples without anomalies are available for training. The proposed method not only provides anomaly detection capabilities, but also has high potential for automating quality control processes in various industries, including manufacturing, medicine, and information security. Due to the advantages of unsupervised machine learning models, such as robustness to unknown forms of anomalies, this method can significantly improve the efficiency of quality control and diagnostics, which in turn will reduce costs and increase productivity. It is expected that further research in this area will lead to even more accurate and reliable methods for detecting anomalies, which will contribute to the development of industry and science.

Keywords: U-Net, neural network, classification, anomaly, defect, novelty detection, autoencoder, machine learning, image, product quality, performance

In this paper, the problem of an equalizer design for high-speed receiver channel which is designed to compensate for the uneven frequency response of the input differential signal. Using special design methods, as well as modeling tools for frequency and transient characteristics, an equalizer with the ability to digitally adjust the gain was developed. This adjustment also reduces the impact of the spread of process parameters, which is inevitable during the production of the chip.

Keywords: attenuation, transceiver, equalizer, IP block, equalization, gain, amplitude

5G wireless networks are of great interest for research. Network Slicing is one of the key technologies that allows efficient use of resources in fifth-generation networks. This paper considers a method of resource allocation in 5G wireless networks using Network Slicing technology. The paper examined a model for accessing radio network resources, which includes several solutions to improve service efficiency by configuring the logical part of the network. This model uses network slicing technology and elastic traffic. In the practical part of the work, transition intensity matrices were constructed for two different configurations.

Keywords: queuing system, 5G, two - service queuing system, resource allocation, Network Slicing, elastic traffic, minimum guaranteed bitrate

The article is devoted to the development of a new mathematical method for modeling radial plain bearings having a polymer coating with an axial groove on the bearing surface. For the calculation evaluation of technical solutions for wear resistance, the compressibility of a truly viscous lubricant under laminar flow conditions is taken into account. As a result, new mathematical models were obtained that make it possible to estimate the duration of the hydrodynamic flow regime of the lubricant, to prove the stability and possibility of changing lubrication modes from boundary to hydrodynamic, as well as to make a calculated assessment of the effect of compressibility of the lubricant and wear resistance on operational characteristics.

Keywords: modeling, mathematical method development, modified design, compressibility impact assessment

With the rapid development of technology and the widespread use of video surveillance, modeling the architecture of neural networks for human recognition in video is attracting increasing attention from researchers. This article presents a study of the use of neural networks (NN) as an interdisciplinary model for classifying objects in video, including solving the problem of face search. This highlights the versatility of neural networks in integrating trained data and accurately classifying objects, which is critical for ensuring security and efficiency of video surveillance. The study uses an analysis of various neural network architectures, as well as a study of their operating algorithms. Data obtained from a literature review and experimental results allow us to evaluate the effectiveness of solving the task of classifying objects in video using various architectures, without tying the study to a specific data set. The study confirms the possibility of using modern neural network architectures for human recognition in real-time video based on the experience of experts in the field of computer vision and machine learning. The active use of neural networks as a tool for video surveillance increases the safety of infrastructure facilities and the efficiency of security services. Ultimately, this article presents an analysis of neural network architectures for facial recognition in video streams, advocating their use as a key element in the development of modern video surveillance systems and ensuring public safety.

Keywords: neural networks, neural network architectures, video surveillance systems, real-time recognition, improving security, social well-being

One of the most important points in increasing the conversion component of a web resource is identifying the most attractive places for the site user. To identify these locations, a site user activity data visualization tool was created that provides a visual representation of each user action on a site page.

Keywords: heat map, site, oculograph, fixation, priority area