The article presents a brief description of the thermal power plant Berezovskaya GRES. Based on the results of the analysis, it was concluded that the entire workshop of the electrolysis plant needs to be retrofitted and equipment needs to be replaced or upgraded using modern industrial automation systems for technological processes. The necessary equipment has been selected for the designed process control system. An architectural solution of the system has been created that ensures the integration of controllers with the dispatch control and data collection system based on the Astra Linux operating system, as well as efficient operation and a high level of reliability. Electrical wiring diagrams for analog and digital inputs have been developed. The interface of the operator's automated workplace has been developed and the system has been modeled.

Keywords: industrial protocol, import substitution, controller, dispatch control and data collection

The rapid electrification of transport and energy systems imposes extreme and often conflicting requirements on the performance of lithium-ion batteries. The classical paradigm of step-by-step optimization of individual components (materials and designs) has reached its limits, facing the challenge of negative synergistic effects. Despite the availability of advanced methods, ranging from detailed physical and chemical models to machine learning algorithms, the field of energy storage system design remains fragmented. This article provides a critical analysis of three isolated domains: the empirical-synthetic approach, physical and mathematical modeling, and software methods. Systemic shortcomings have been identified, including the lack of end-to-end methodologies, the "black box" problem of ML solutions, extreme requirements for data and computational resources, and limited portability of solutions. The concept of a hybrid predictive platform is proposed, which purposefully integrates fast regression models for deterministic parameters and specialized neural networks for predicting complex nonlinear degradation processes. This integration allows for the consideration of a battery cell as a single entity, optimizing the trade-offs between key characteristics (capacity, power, lifespan, and safety) during the virtual design phase, resulting in reduced time and cost.

Keywords: energy storage systems, system approach, electrode materials, optimization, system design, machine learning, hybrid models, degradation prediction, and performance optimization

The increasing complexity of cyberattacks, often involving multiple vectors and aimed at achieving various goals, necessitates advanced modeling techniques to understand and predict attacker behavior. This paper proposes a formal approach to describe such attacks using a weakly connected oriented tree model that satisfies specific conditions. The model is designed to represent the attack surface and a collection of attack vectors, allowing for the analysis of possible attack scenarios. We introduce a sequential composition operation that combines sets of attack vectors, enabling the modeling of combined attacks. The study includes an example of an attack on an information system through a vulnerability that allows brute-force password guessing and phishing emails, with the goals of either obtaining a database or causing a denial of service. We investigate the set of attack scenarios generated by the model and formulate a rule for estimating the number of possible scenarios for an arbitrary number of attack vector sets. The proposed method facilitates preliminary analysis of attack scenarios, aiding cybersecurity professionals in making informed decisions about implementing additional defense mechanisms at various stages of an attack. The results demonstrate the applicability of the model for evaluating attack scenarios and provide a foundation for further research into more complex attack structures.

Keywords: attack modeling, information security, attack trajectory, attack scenario, attack vector, cybersecurity

The article discusses the problems of generating and updating software documentation using large language models. An overview of existing approaches is presented, including code summarization, systems using augmented generation approaches, assistants embedded in the development environment, and their limitations in terms of loss of architectural context and the occurrence of structural hallucinations. The concept of a graphically augmented documentation system is proposed, where the "source of truth" is a directed graph of knowledge about the code, built by static code analysis and analysis of library dependencies. An algorithm for constructing a graph is described, including node extraction, library bytecode analysis, and semantic link classification. The effectiveness of the approach was confirmed by experimental implementation on an industrial microservice, where the system demonstrated the ability to correctly restore the context and generate meaningful documentation without distorting the facts.

Keywords: automatic documentation, large language models, knowledge graph, augmented text generation, static analysis, semantic search, vector representation, microservice architecture, program structure interface, bytecode, technical documentation

A carbon fiber reinforced steel beam was tested, which was subjected to cyclic loads of varying intensity during bending during glue hardening and periodically tested in static mode to determine the increase in stiffness. Tests have shown that adhesion occurs at higher loads, the adhesion strength decreases, and when the shear stress in the adhesive layer is exceeded, adhesion does not occur. The flexibility of the adhesive layer also reduces the cross-section characteristics, but by no more than 7%. Lap shear tests performed on samples cut from reinforced beams confirmed the results of bending tests, showing that the greatest decrease in adhesion strength occurs at the ends of the beams, where the sliding and shear stresses are greatest.

Keywords: carbon fiber reinforcement, cyclic load, adhesive joint strength, lap shear test

The article investigates the formation of professional competencies among students of a technical university (from bachelors to postgraduates) in the context of the digital transformation of higher education. The necessity of transitioning from traditional pedagogical models to a methodology integrating digital educational technologies and psychological-pedagogical support is substantiated. Central attention is paid to the potential of the digital environment for developing practice-oriented skills, critical thinking, and academic autonomy. The results of a pedagogical experiment are presented, within which a blended learning model based on reciprocal interaction and content co-creation was tested: students acted as knowledge constructors, developing criterion-referenced test tasks, while instructors performed the function of metacognitive experts. It is shown that the implementation of this model leads to a statistically significant increase in the level of professional and meta-professional competencies. The conclusion is made that digital tools, used as a platform for co-authorship, serve as a meaning-forming framework of pedagogical design, contributing to the training of a new type of engineers – reflective practitioners and co-authors of the educational process.

Keywords: construction and technical forensic examination, special knowledge, forensic expert, examination production, examination procedure

The article highlights the problems associated with the accuracy of calculations and measurements of the thermal protection properties of multilayer building envelopes. The authors consider options aimed at improving the accuracy of calculations, such as using numerical modeling to calculate the temperature fields of structures, using hot-box testing equipment to determine the heat transfer coefficients of structural fragments, and improving the accuracy of measurements. The article provides a description of the hot-box setup and explains why the measurement accuracy is three times higher than in conventional climate chambers.

Keywords: measurement of the heat transfer coefficient, thermal protection of buildings, energy saving, measurement accuracy, calorimetric measurement method, hot box installation

The article discusses the problem of synthesizing adaptive control systems for robotic complexes operating under conditions of uncertainty and variable external influences. A methodology for building control systems based on deep reinforcement learning algorithms integrated with traditional management methods is proposed. The scientific novelty lies in the development of a hybrid architecture combining a deterministic dynamic robot model that provides basic stability and an adaptive neural network module based on Deep Deterministic Gradient Policy  that compensates for unaccounted-for disturbances and parametric uncertainties. The practical significance is confirmed by the results of computational experiments on a manipulator model with six degrees of freedom, where the proposed system showed a 67% reduction in positioning error under variable loads compared to the optimal PID controller, as well as the ability to adapt online when the weight of the load changes. The implemented approach opens up prospects for the creation of autonomous robotic systems capable of efficiently performing tasks in unstructured environments.

Keywords: deep reinforcement learning, adaptive control, robotic complexes, hybrid control systems, dynamic modeling, neural network controllers

The article discusses the task of detecting malicious attachments in emails used in targeted cyber attacks. An approach based on the combined use of text and file attributes of messages using machine learning methods is proposed. The models of logistic regression and the random forest method are compared according to the main classification quality metrics. Experiments on a synthetic dataset have shown that logistic regression provides a higher completeness of detection of malicious attachments, whereas a random forest is characterized by a higher classification accuracy. The results obtained confirm the effectiveness of the hybrid approach and the possibility of its integration into email protection systems.

Keywords: machine learning, targeted attack, email, phishing, malicious attachment, attack detection, information security

The article discusses the issues of investigating the efficiency of separation of droplet moisture of a working fluid from a vertically ascending gas-liquid stream in intensive wet dust and gas purification devices, depending on the relative gas content, the velocity of the gas-liquid stream and the design characteristics of a radial inertial louver separator, which is part of the gas purification equipment.  Within the investigated range of hydraulic parameters of the separation mode, optimal ratios have been established between the key design parameters of the proposed separation device to implement an effective drip extraction process.

Keywords: intensive wet dust and gas cleaning devices, radial inertia louver separator, gas-liquid flow, efficiency of droplet collection

With the rapid growth of information on the internet, the accumulation of large databases, and the constant influx of data from various sensors and intelligent systems, it is becoming increasingly difficult for users to find what they are really looking for. Therefore, the development of automatic summarization methods is considered a crucial task in natural language processing. These needs have motivated the development of various methods and approaches for extracting semantic and semantic information from documents, classifying it, and systematizing it. This article develops the architecture of a hybrid-syntactic fuzzy system for extracting semantic features from text and presents its mathematical formalization. The author's method enables a transition from empirical assessments of word importance to a rigorous formalized calculation of their semantic weight.

Keywords: semantics, sentence, extraction, fuzzy logic, comparison, data

The development of digital learning platforms, electronic document management systems, and web-based systems that process natural language text information has led to an increase in the volume of content and/or arrays of processed full-text documents. This, in turn, has increased the demand for highly effective natural language processing methods capable of capturing text semantics. This article proposes a hybrid architecture based on the integration of probabilistic and fuzzy logic that effectively addresses semantic ambiguity issues by integrating stochastic and fuzzy logic channels that take into account both statistical patterns and linguistic uncertainty.

Keywords: integration, semantics, interpretation, natural language, uncertainty, patterns

The coefficients of determination and the absolute values of forecast assessment results based on the use of linear trends for different samples of initial data, varied by increasing amplitude over time intervals, are considered. A new linear method of forecast boundaries for forecast assessment (data extrapolation) is proposed.

Keywords: system analysis, statistical data, mathematical trend assessment, forecast evaluation, confidence interval, method of forecast limits