The aim of this study is mathematical modelling of the southern part of the Siberian craton using the finite element method. The task of the study is to carry out mathematical modelling of the stress-strain state of the visco-elastic-plastic medium of the Yakutsk-Vilyui large eruptive province on the basis of the boundary value problem. Hypothesis of the study: the possibility of using the results of numerical study to determine the zones of mineral dislocations. In the southern part of the craton, on the territory of the Republic of Sakha-Yakutia, there are the richest oil and gas fields, the largest of which is the Ust-Vilyuyskoye field located in the southern part of the craton. Research method: numerical experiment carried out by the method of mathematical modelling. Results achieved: finite element studies were carried out, the possibility of using numerical methods was determined, the stress-strain state of the plate was analysed, and the locations of anomalies of dislocations of craton rocks were determined to identify potential oil and gas bearing fields.

Keywords: craton, mathematical modeling, stress-strain state, geophysics, geotectonics, stretching, igneous province, material models, Hardening Soil model, finite element method, mineral dislocation

Today, the problem of global climate change and the associated degradation of permafrost is a priority area of ​ ​ research. In the regions of the Far North, a change in temperature by half a degree contributes to the emergence of geocryological dangers: the appearance of ravines, thermokarst subsidence, heaving, the formation of residential ice and, as a result, the destruction of houses and infrastructure. In Russia, the permafrost zone occupies about 63-65% of its territory and extends for thousands of kilometers. Various engineering and geological impacts, including man-made ones, represent a serious geocryological hazard and can lead to degradation of frozen soils in various regions. Due to the rapid thawing of ice and climate change, collapses and voids form on frozen soils. Also, when thawing permafrost, a large amount of groundwater is formed and there is a risk of salinization of nearby water and coastal zones. This can lead to the loss of resources that are necessary for the life of the local population, as well as for the economy of the region. Despite the fact that there are numerous programs and studies on this topic, a huge amount of work has not yet been carried out in Russia to eliminate geocryological threats.

Keywords: Geocryology, permafrost, ground, frozen soil degradation, thawing, monitoring, Yakutia, cryolithozone, geocryological hazard

In the context of stable demand for consumer electronics, current methods of automated software testing in production often prove to be inefficient, leading to an increase in software errors. This paper examines an enhanced method of automated testing using Remote Procedure Call (RPC) and cloud technologies. The main objective of the research is to create a universal and effective system for automated software testing, capable of scaling and adapting to various platforms and libraries. The results of the experiment confirmed the possibility of integrating the described method with existing testing systems without significant modifications, ensuring a higher efficiency of the testing process and a reduction in its duration.

Keywords: automated testing, consumer electronics devices, consumer electronics, software, remote procedure call, software quality, software testing, cloud devices, software production, testing task manager

The article studies the peculiarities of architecture of enterprises and small-scale energy facilities in the urban environment. A number of problems arising in the interaction of small-scale energy facilities with urban development and their placement in the urban space are described. The prerequisites of architectural and spatial classification of small-scale energy facilities are emphasized. Classification of types of small-scale energy facilities by functional, urban planning, volume-spatial and compositional features is proposed. The requirements to the architecture of small-scale energy objects and their integration into the architectural and spatial context at the present stage are formed. The identified requirements and types of small-scale energy objects create a methodological basis for the formation of adaptive approaches and promising directions for the harmonization of these objects in the structure of urban spaces.

Keywords: architecture, small-scale energy facilities, urban environment, integration, harmonization, classification features

The study is aimed at studying the corrosion processes of a metal waterstop inside technological seams in order to ensure its tightness and maintain performance qualities, since if corrosion occurs, the waterstop may lose its functional properties, which can lead to groundwater leaks. At the moment, there is no single source containing a complete set of information on corrosion processes in relation to metal waterstops, which leads to design errors, since it is extremely difficult to make the correct choice of metal waterstops, because it is impossible to analyze data and determine the parameters that determine the corrosion rate of metal waterstops in the technological concreting seam, it is necessary to take into account a large number of parameters related to the study of groundwater.

Keywords: waterproofing, technological seam, metal waterstop, corrosion processes, concrete, waterproofing system, ground water, carbonization, galvanized waterstops, copper waterstops, stainless steel waterstops

The possibility of detection of steganography in digital images based on the classification of stegocontainers is investigated. The obtained results demonstrate the effectiveness of using deep neural networks for solving this problem. The LSB method can be detected using EfficientNet b3 architecture. The achieved classification accuracy is above 97%. Using of steganography methods in frequency domain can be effectively detected by classifying their representation in the form of a digital YCrBr model, with augmentation (vertical and horizontal rotations). The classification accuracy is above 77%.

Keywords: Steganography, stegocontainer, machine learning, classification, digital image, deep learning, CNN, EfficientNet b3, confidentiality, information protection

A new mathematical apparatus is proposed for monitoring the adequacy of the choice of signal sampling interval from the point of view of taking into account the main high-frequency components and identifying the possibilities of increasing it. It is based on the construction of special aliasing grams based on measured signal samples. Aliasing grams are graphs of standard deviations of the amplitude spectra of a conventionally reference discrete signal, specified with the highest sampling frequency, and auxiliary discrete signals obtained over the same observation interval, but with lower sampling frequencies. By analyzing such graphs, it is easy to identify sampling frequencies that lead to the appearance of the aliasing effect in the case of sampling, and, consequently, to distortion of the signal spectrum. To speed up and simplify the construction of aliasinggrams, it is proposed to use as auxiliary signals obtained from the reference one by thinning. It has been shown that this device is also effective in the case of the spectrum spreading effect. It can be used in self-learning measuring systems.

Keywords: sampling interval, aliasing, amplitude spectrum, aliasing-gram, sample decimation, spectrum spreading

The quality of asphalt concrete mixture at the output of an asphalt concrete plant is unstable due to disturbances that we cannot control or control with significant delay. Disturbances may include factors such as inaccuracies in the existing relationships between the properties of asphalt concrete mixture components and the parameters of the technological process with the quality of the finished product. Disturbances can also be attributed to our lack of knowledge about the relationships between individual indicators and the quality of the mixture. Forecasting these disturbances to determine the actual quality at the output becomes a key task. Previously, determining the optimal length of data series for forecasting was a challenging task. Nowadays, with the use of modern technologies, this problem has been successfully solved. In this article, the authors propose an adaptive forecasting method to determine the optimal length of data series. The research results include forecasting error values with and without adaptation. The adaptive forecasting method demonstrated smaller values of mean absolute error (MAE) compared to the non-adaptive forecasting method (where the length of the time series is always equal to 100). This allows for more efficient and accurate prediction of cumulative disturbances, which is critically important for ensuring high and stable quality of asphalt concrete mixture.

Keywords: asphalt concrete, asphalt concrete mixture, disturbance, control system, autoregressive model, forecasting, adaptive forecasting method, optimal length of series, forecast accuracy, mean absolute error

In this work, an experimental model of a circuit diagram with pulsating circulation of a liquid coolant in a heated circuit of a plate heat exchanger was assembled and tested. As a result of hydraulic and energy calculations of the circuit, the optimal parameters for flow, pressure, and temperature of the coolant were selected at maximum efficiency of the impact unit. It has been established that with an increase in the operating frequency of the impact unit, the heat transfer coefficient of the heat exchanger first decreases and reaches a minimum of 452.47 W/(m2*0C) at a frequency of 0.5 Hz, and then begins to increase and reaches a maximum of 482.31 W/(m2* 0C) at a frequency of 2 Hz, after which it gradually decreases. It has also been experimentally established that the temperature at the outlet of the heat exchanger of the heated circuit increases with increasing frequency of the shock unit and reaches a maximum at a frequency of 2 Hz, after which it begins to gradually decrease. It has been established that the change in temperature at the outlet of the heat exchanger of the heated circuit exceeds the change in temperature at the outlet of the heat exchanger of the heating circuit at operating frequencies above 1 Hz, which is due to the stronger influence of cavitation at these frequencies.

Keywords: heat exchanger, heat transfer coefficient, impact unit, frequency, heat transfer

This scientific article examines the application of artificial intelligence and machine learning in the textile industry with an emphasis on the automation of the design of weaving weaves. The article discusses research and approaches using neural networks, genetic algorithms, deep learning methods and computer vision to create, optimize and analyze weaves. The main attention is paid to the comparison of existing software solutions that allow automating the design process and significantly improving its productivity, accuracy and quality. The importance of integrating AI and machine learning into the textile industry is emphasized, as this opens up new opportunities for automating processes, improving product quality and increasing the competitiveness of the textile industry at the global level.

Keywords: automation, modern systems, design technologies, computer-aided design, information systems, software, fabric drawings, computer-aided design of weaving patterns, innovations in the textile industry, process optimization, digitalization

The article is devoted to the study of crowd behavior in public buildings during a fire. A method for evaluating the effectiveness of organizing the evacuation of people from a public building is proposed, which makes it possible to take into account the spread of panic among evacuees. The method is based on the development of an evacuation simulation model that takes into account the impact of certain factors on the degree of people's panic, which implements an agent-based approach. The proposed method allows, when describing the psycho-emotional behavior of each agent separately in the process of evacuation, to evaluate the effectiveness of organizing the evacuation of the crowd as a whole. The simulation results on the example of a shopping and entertainment center show that possible panic conditions of evacuees can affect the efficiency of evacuation.

Keywords: evacuation, panic, simulation model, efficiency assessment method, shopping and entertainment complex, agent-based approach

The paper considers the possibility of using waste from the forest industry, construction waste and ash and slag waste from boiler heating as a basis for the creation of biofertilizers and components that improve the structure of heavy soils. The analysis of experience on the impact of crushed bricks, coniferous and leaf litter with the inclusion of wood components, and ash and slag waste of boiler heating on the growth functions of red clover and watercress over various time intervals is presented. The possibility of using the presented waste in the process of self-infestation of territories is considered. A number of experiments have been conducted to identify the phytotoxic effect, as well as the reaction of general inhibition or stimulation of growth indicators of higher plants when used as a substrate in pure form, or a mixture with natural soil, wood components, samples of ash and slag waste of various shelf life, brick fighting.

Keywords: forest industry waste, construction waste, broken bricks, ash and slag waste, self-fouling, phytotoxicity, soil structure, morphological changes of plants

The influence of the ratio of fly ash and blast furnace slag in a geopolymer binder on the properties of concrete hardening during heat and humidity treatment was studied. The article obtained data on the influence of the binder composition on the workability of the concrete mixture, the strength and shrinkage of concrete. The dependences of the influence of hardening temperature and the proportion of slag in the binder on the strength of geopolymer concrete were established. The results obtained made it possible to recommend the studied binder and concrete based on it for pilot industrial production of prefabricated reinforced concrete.

Keywords: geopolymer binder, fly ash, blast furnace slag, concrete, strength, workability, water absorption, shrinkage

This article presents a research study dedicated to the application of the YOLOv8 neural network model for road sign detection. During the study, a model based on YOLOv8 was developed and trained, which successfully detects road signs in real-time. The article also presents the results of experiments in which the YOLOv8 model is compared to other widely used methods for sign detection. The obtained results have practical significance in the field of road traffic safety, offering an innovative approach to automatic road sign detection, which contributes to improving speed control, attentiveness, and reducing accidents on the roads.

Keywords: machine learning, road signs, convolutional neural networks, image recognition

Outlier detection is an important area of data research in various fields. The aim of the study is to provide a non-exhaustive overview of the features of using methods for detecting outliers in data based on various machine learning techniques: supervised, unsupervised, semi-supervised. The article outlines the features of the application of certain methods, their advantages and limitations. It has been established that there is no universal method for detecting outliers suitable for various data, therefore, the choice of a particular method for the implementation of research should be made based on an analysis of the advantages and limitations inherent in the chosen method, with the obligatory consideration of the capabilities of the available computing power and the characteristics of the available data, in including those including their classification into outliers and normal data, as well as their volume.

Keywords: outliers, machine learning, outlier detection, data analysis, data mining, big data, principal component analysis, regression, isolating forest, support vector machine