The article offers a variant of the development of lighting design projects for outdoor architectural lighting. Based on the modeling of light distribution in the DIALux 4.13 program, brushes have been created using specific lighting devices that simulate lighting effects from real lighting devices. A variant of the sketch of outdoor architectural lighting using Adobe Photoshop has been created with the implementation of local lighting techniques using the example of a drama theater building. Using a three-dimensional model of the object, a light design project was created in the DIALux EVO program. The proposed method of creating sketches is useful in professional activities related to the development of sketches of lighting design projects based on their high-quality photographs without the need to develop three-dimensional models, for conceptual proposals of fragments of the urban light environment and landscape territories. Having developed a base of brushes (based on real light distributions of lighting devices), it is possible to create sketches of architectural lighting of buildings that implement various lighting techniques.

Keywords: adobe photoshop, dialux 4.13, dialux evo, sketch, brush, building facade, outdoor architectural lighting, lighting effect, lighting technique, architectural lighting concept

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 stability calculation of a П-shaped hinged frame is considered. The concept of r-like frames is introduced as frames with the same ratio r of the linear stiffnesses of the transom and the strut. It is shown that the parameter vcr , which determines the critical load on the frame, is the same for r-like frames. Approximate formulas allowing to determine the critical load parameter vcr and design lengths of compressed bars with an error not exceeding 2% are obtained.

Keywords: flat frame, stability, critical force, reduced length coefficient, r-like frames, approximation, least squares method

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

The article presents results of self-compacting concrete with microsilica and grinded concrete crush additive study. The work presents concrete mix designes and mechanical tests results.Found that with the use of complex additive it is possible to obtain concrete of class B80 with a strength of at least 60 MPa after 7 days of hardening under normal conditions. High frost resistance of such concrete is noted. Concluded the possibility of complex additive application and potential advantages of its usage in self-compacting concrete.

Keywords: self-compacting concrete, silica, active mineral additives, concrete crushing screening, concrete, concrete scrap, finely ground filler, secondary filler, construction waste, determination of mechanical characteristics of concrete, high-strength concrete

The paper presents the results of experiments on the production of a composite material using small-lump wood waste based on soft wood varieties and waste products from non-plasticized polyvinyl chloride. An additional component of the raw material mixture was methylene chloride, in which polymer waste was dissolved. The solution was mixed with the filler, and then the solvent was pressed and removed at its boiling point. It is established that an increase in the degree of filling of the studied material contributes to the formation of a developed porous structure. The effect of increased porosity on the main operational properties, primarily thermal conductivity and water resistance, has been revealed. It is determined that the degree of filling, equal to 55 wt. %, makes it possible to obtain a material with the most effective combination of values of basic properties for use as a building thermal insulation. The practical application of the results of the work makes it possible to produce a low-cost thermal insulation material due to the joint disposal of heterogeneous waste.

Keywords: polymer composite material, thermal insulation material, polymer waste, wood waste, polyvinyl chloride, methylene chloride

The possibility of using expanded perlite dust (RUNWAY) in cement binder systems is being considered. A method for modifying the binder by joint mechanical activation of Portland cement, alumosilicate waste and hyperplasticizer is presented. The physico-chemical features of the modified runway make it possible to regulate the processes of hydration, phase formation, as well as the formation of the structural matrix of the composite. The compressive strength of the binder using RUNWAY as an active mineral additive increases by 36% with a decrease in density.

Keywords: technogenic raw materials, perlite, binder, dispersion, modification, mechanical activation, hydration, structure formation, phase formation, resource conservation