This article is devoted to the issues of implementation of automated control system of regional electric networks based on intelligent technologies. The significance of the issue under consideration is expressed in the fact that along with the development of technologies and their active use, electric loads in networks where large losses of electric power occur are increasing. Some issues of increasing the efficiency of production and consumption of energy resources at the regional level are considered. The main elements of the system approach to the analysis of the automated process control system of the regional energy system (APCS RES) are described. An example of the design and implementation of a pilot project for the introduction of intelligent technologies in the electrical networks of the Chechen Republic, in particular in the electrical networks of the city of Argun, is given. The most significant entities of the regional electric power industry are considered, which determine the process of functioning of the automated process control system of the power grid, as well as those that have or are capable of having an impact on the structure and process of functioning of the automated process control system of the power grid, where the degree of influence, situations and moments of influence for all the entities given are absolutely different. The conducted methodology of forming entities represents their dependence on each other to varying degrees.

Keywords: electricity metering, electrical network, intelligent technologies, automated process control system, regional energy system, electricity tariff

This study examines the control of the cooling fan speed as an effective means of reducing emissions of volatile organic compounds during three-dimensional layer-by-layer printing. The high extrusion temperatures used in modern high-speed printers lead to emissions of harmful volatile organic compounds, which poses health risks in poorly ventilated rooms. A mathematical model has been developed to establish a quadratic relationship between the fan speed and the volumetric air flow, which directly affects the deposition of volatile organic compounds on the melt surface. The experimental setup uses relay control of the motor current and proportional-integral-differentiating speed control, ensuring rapid stabilization of the air flow with minimal overshoot. From the analysis of transient characteristics, including motor current, fan speed, airflow velocity, and power consumption, it is shown that precise control of fan speed creates stable and predictable airflow movement, significantly reducing emissions of volatile organic compounds. In addition, the results show that integrating the feedback of the volatile organic compounds sensor in real time with the control of the extrusion rate can offer an even more adaptive and effective strategy for reducing emissions. This research lays the foundation for safer and more efficient 3D printing processes with layer-by-layer deposition modeling through improved temperature and emission management.

Keywords: volatile organic compounds, three-dimensional printing, adaptive control, layer-by-layer deposition, regulation

The paper addresses the problem of the technical diagnostics of hump control devices, such as wagon retarders. The current analytical methods of monitoring and technical diagnostics of wagon retarder conditions are reviewed. The factors that are used in the existing diagnostics systems are analyzed and new factors to be taken into account, including specific pathway peculiarities, wagon group lengths, breaking curve styles, initial wagon group speed and environment conditions, are suggested. The suggested set of factors are characterized from the point of regression analysis. The replacement of some continuous factors with lexical ones are suggested. Decision tree-based classificators are suggested to perform the classification of hump retarder conditions. The decision tree-based classificators can be built with the means of Data Mining on a training set. An improved method of building decision trees is suggested. It’s advantage over the existing algorithms is shown on evaluation sets.

Keywords: hump yard, wagon retarders, regression, decision trees, classification, data mining, multi-factor analysis, soft computations

The article explores the implementation of digital and mathematical technologies in decision support systems (DSS) aimed at enhancing the efficiency of livestock enterprises. In the context of digital transformation and increasing uncertainty in agriculture, the authors emphasize the importance of intelligent DSS capable of processing large datasets and supporting rapid, evidence-based decision-making. The purpose of the study is to identify effective technological and methodological approaches for optimizing livestock management, particularly in the area of animal feeding. Methods include the use of mathematical models, predictive algorithms, automated control systems, and big data analytics. The proposed DSS architecture enables real-time monitoring, adaptive ration formulation, and integration of physiological, environmental, and economic data. The paper provides practical examples of successful DSS applications, such as automated milking systems and health monitoring technologies, and analyzes their impact on productivity and cost reduction. A set of methodological recommendations is formulated to enhance management efficiency, including modular system design, staff training, and integration of IoT and AI technologies. The article concludes that intelligent DSS not only reduce feeding costs but also improve animal health, optimize resource use, and support sustainable agricultural practices. The results are of practical significance for researchers, developers, and farm managers aiming to implement data-driven solutions in livestock production.

Keywords: diversification of management, production diversification, financial and economic purposes of a diversification, technological purposes of ensuring flexibility of production

The article introduces a methodology for verifying BIM models of capital construction facilities. This approach focuses on dynamic assessment of intersection collision weights, combining geometric analysis, statistical methods, and adaptive metric weighting. Key metrics considered include granularity, geometry errors, tessellation complexity, and fill factor. The proposed methodology utilizes Python implementation with IfcOpenShell, leveraging a multithreaded architecture to significantly reduce data processing time. Testing on 20 multidisciplinary models highlights critical problematic elements such as walls, beams, and air ducts. The results demonstrate that adaptive weight distribution effectively identifies and prioritizes potential errors, improving the accuracy and reliability of BIM models. The study's findings are crucial for enhancing design and construction processes. By accurately assessing and mitigating errors, the methodology reduces project delays, cost overruns, and safety risks. It also promotes better coordination among project stakeholders, streamlining workflows and improving project outcomes. In conclusion, the proposed methodology is a valuable tool for verifying BIM models, ensuring the integrity and quality of capital construction projects. Its application can lead to more efficient, cost-effective, and reliable construction processes, benefiting both developers and end-users.

Keywords: TIM, collisions, verification, dynamic weights, adaptive metrics, algorithms, IfcOpenShell, python, standard deviation

The article discusses approaches to the systematic analysis of historical data collected from water treatment facilities. By using tools from mathematical statistics, machine learning methods, and visual analysis techniques, the article proposes a formalized approach to assessing the efficiency of water treatment equipment. This approach makes it possible to identify hidden patterns in the data, build robust models of interdependencies, and develop recommendations for optimizing the technological process.

Keywords: water treatment, telemetry data, time series analysis, machine learning, equipment efficiency

The article considers a variant of constructing a model of a solar battery orientation drive based on a DC motor and PID control. Orientation in space is performed along two axes: azimuth and zenith. The model is used for optimal adjustment of PID controller parameters when processing the required orientation angles under gusty wind conditions. The following are used as the main adjustment criteria: small overshoot when processing the angle, aperiodic (non-oscillatory) nature of transient processes, minimum dynamic error in compensating for wind effects when processing the angle, minimum settling time when processing the effect. The controller was optimized using the coordinate descent method. A variant of controller adjustment for the optimal mode is given with process graphs confirming its practical optimality. The constructed drive model can be used to implement a digital twin of the solar battery panel orientation drive monitoring and control system.

Keywords: mathematical model of the drive, PID controller, solar panel, gusty wind effects, azimuth and zenith orientation, optimization by complex criterion

This paper presents a highly technical implementation of an ECG-based biometric identification system utilizing deep learning models for both verification and closed-set identification. We propose a dual-model architecture comprising a Siamese neural network for one-to-one verification and a deep convolutional neural network (CNN) for one-to-many classification. The methodology includes comprehensive signal preprocessing, data augmentation to simulate physiological variability, and feature extraction tailored to ECG characteristics. Experimental evaluation on benchmark ECG datasets demonstrates the effectiveness of the proposed system. The Siamese network achieves high verification accuracy with low equal error rates, while the CNN classifier attains state-of-the-art identification accuracy (exceeding 98% on average) across enrolled subjects. Key performance metrics—accuracy, precision, recall, and F1-score—indicate robust performance, outperforming several existing biometric methods. The results highlight the viability of ECG-based authentication in real-world applications.

Keywords: biometric authentication, electrocardiogram (ECG), siamese neural network, convolutional neural network, qrs complex, signal processing

When measuring the intensity of light, the size of the light source should be small compared to the photometric distance. In this case, the law of squares of distance is fulfilled, which can be applied in practice and obtain high measurement accuracy if the photometry distance exceeds the largest size of the light source by at least 10 times. For light sources with finite dimensions at small distances to the illuminated surface, this law must be amended. This paper presents the results of calculations of errors when using the law of squares of distance for light sources of finite sizes of various shapes and various light distributions.

Keywords: the law of squares of distance, luminous intensity, measurement error, photometry distance.

The paper describes the programs developed by the author, designed for automatic control of calculations carried out in the Gaussian package, as well as processing of the obtained results. Gaussian is a powerful quantum chemical program that allows solving a wide variety of problems related to the study of chemical compounds. However, this program has its own features, which in some problems create the need for multiple restarts of the calculation. In addition, in some cases, additional calculations may be required. For example, if we are talking about studying a reaction, and not a separate molecule, the characteristics of the reaction must be calculated using the results of calculations of the molecules involved in it. By automating the research process and performing calculations that are not directly available in Gaussian, shell programs can significantly save the user's time and relieve him of routine work. The approaches described are especially relevant when it comes to studying a whole set of molecules (for example, when studying reactions within a certain class of chemical compounds). Thus, provided there are no emergency situations, the use of a shell program eliminates the need for user intervention at all stages of the study that occur between the creation of initial tasks and obtaining the final results.

Keywords: mathematical modeling of chemical reactions, quantum chemical calculation, quantum chemical methods, software, Gaussian, shell program, automation of research

It's analyzed the dependence of the melt pressure value from other extrusion process parameters in polymer recycling and justified the importance of pressure monitoring. Structural scheme of developed automatic control system, not including pressure control, is presented and the ways of its improving are shown, taking into account also the influence of viscosity characteristics of processed polymers and constructional features of the extruder on melt pressure value.

Keywords: polymer extrusion, melt pressure, automatic control system of extrusion process

The article proposes a methodology for design an attribute space to detect behavioral anomalies of users in CRM systems. It describes methods for recording actions through integrated trackers that capture user activity, clicks, cursor movements, and keystrokes. The aggregation of this data into feature vectors enables the application of machine learning algorithms to detect anomalies and enhance information security in the CRM system.

Keywords: information security, CRM system, behavioral analysis, anomaly detection, user identification, behavioral analytics, activity monitoring, digital footprint, insider threats, attribute space

In the era of transition to Industry 4.0, industrial equipment requires highly efficient solutions to analyze its performance. Modern monitoring and diagnostic systems should ensure reliable identification of possible problems at an early stage, even in difficult operating conditions, with minimal errors. At the same time, it is important that such equipment is affordable and compact for mass use. In this regard, the study compares the capabilities of piezoaccelerometers and MEMS accelerometers for vibration analysis and fault detection of stepper motors. In the course of the work, the main existing methods of vibration analysis of the equipment condition were analyzed, software was developed that implements variants of mathematical models for processing data from inertial orientation sensors. The design of the stand and the software for the MPU-9250 and AP2037-100-03 sensors are described. The results of the implementation and testing of the MPU-9250 and AP2037-100-03 sensors are presented. The purpose of this study was to compare piezoaccelerometers and MEMS accelerometers to evaluate their effectiveness in vibration diagnostics of stepper motors.

Keywords: vibration monitoring, MEMS sensors, piezoaccelerometers, industry 4.0, predictive analytics, vibration analysis

The article presents the results of a study devoted to the development of an identification subsystem for an industrial process operator in a mobile simulator used for training and monitoring professional skills. The functional requirements for the operator identification subsystem based on neural network technologies, the processes of user interaction with the personality recognition subsystem, and loading a reference image for further identification of the operator during training and monitoring on the simulator are formalized using visual models in UML notation. A prototype of the subsystem has been developed based on the Kotlin programming language and the TensorFlow library. The developed image analysis subsystem has a high speed of face detection and initialization, reaching less than 0.5 s, which makes it especially effective for real-time tasks where performance plays a key role. Local data processing on mobile devices ensures protection of user privacy by eliminating data transfer to remote servers, which minimizes the risks of information leaks. Optimization of power consumption ensures long-term operation on devices with limited battery capacity, which makes the system convenient and practical to use. The considered subsystem is planned to be adapted for monitoring the formation of skills for working on equipment during operator training on mobile simulators. The subsystem, based on VR/AR technologies, as well as a trained neural network, will analyze data on user reactions in real time.

Keywords: mobile simulators, neural networks, user identification, professional training, UML diagrams, TensorFlow

The study presents an approach to modelling multivariate time series using parameterisation, using yield curve as an example. The effectiveness of adding parameterisation coefficients to predicates is evaluated, and new loss functions are proposed that focus on modelling the shape of the curve. Prediction models including LSTM, Prophet and hybrid combinations were applied. A Python-based system was developed to automate data processing and evaluation. The method improves the accuracy and interpretability of forecasts, offering a promising tool for financial modelling.

Keywords: machine learning, financial engineering, stock market modeling, bond market

This paper addresses the challenge of assuring data quality in high-frequency Internet-of-Things (IoT) streams while migrating to a hybrid edge–cloud architecture. We demonstrate that moving a subset of data-quality procedures—trust-metric calculation, outlier detection, and data-contract validation—from the cloud to edge devices markedly lowers end-to-end latency and reduces cloud load. After surveying existing cloud-centric and edge-centric quality-control solutions, we reveal their limitations: static placement of analytic modules and lack of support for dynamic workload drift. We introduce the concept of edge-oriented data-quality control, in which validation tasks are continuously re-assigned according to real-time network bandwidth and CPU utilisation. A prototype based on Apache Flink implements the proposed scheduler. Experiments with an industrial testbed (300 000 messages/s) show a 37 % reduction in alert latency and a 46 % decrease in cloud CPU consumption compared with a fully cloud-based pipeline. The paper discusses strengths, weaknesses, applicability boundaries, and security threats, and outlines future work on adaptive model selection at the edge, multimodal stream support, and formalised data-quality contracts.

Keywords: data quality control, streaming processing, Internet of Things, cloud computing, Apache Flink, Apache Kafka, anomaly detection, dynamic offloading

A method of power and kinematic analysis of the differential drive of vehicle wheels is proposed, in which uncertainty is eliminated by using the principle of minimum potential energy.

Keywords: external load modeling, differential drive, vehicle, driver, optimization problem