Choosing a programmable logic controller is one of the most important tasks when designing an automated system. The modern market offers many options, different in characteristics, which have different priorities for production. The paper proposes a method for evaluating the overall effectiveness of software logic controllers. When evaluating the selected characteristics, linear scaling and weight coefficients are introduced that take into account the importance of the parameter for the controller in question compared to others. The weight of the parameter in the calculation is set using a coefficient. The values of the weight coefficients may vary depending on the requirements of the technological process.

Keywords: programmable logic controller, efficiency evaluation method, weight ratio, petal diagram

The article presents numerical modeling of flow dividers (separators) with different hole diameters (3.5 mm, 7.0 mm, 14.0 mm) to prevent cavitation damage. The hole diameters, number, and rows in the separators have equivalent significance, as they determine the distribution of local velocities and pressures in the flow. This minimizes the risk of vapor bubble formation and subsequent collapse, which can lead to erosion of metal surfaces. For clarity, the simulation results are presented in the form of pictures of the distribution of pressure and velocities in each of the separators with different diameters. In order to prevent cavitation, the authors have presented a design of a "short-stroke" valve in which it is allowed to use flow dividers with enlarged holes.

Keywords: valve, cavitation, distribution pattern, separator, holes, rotation, simulation, flow divider

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

This paper considers the problem of task scheduling in manufacturing systems with multiple machines operating in parallel. Four approaches to solving this problem are proposed: pure Monte Carlo Tree Search (MCTS), a hybrid MCDDQ agent combining reinforcement learning based on Double Deep Q-Network (DDQN) and Monte Carlo Tree Search (MCTS), an improved MCDDQ-SA agent integrating the Simulated Annealing (SA) algorithm to improve the quality of solutions, and a greedy algorithm (Greedy). A model of the environment is developed that takes into account machine speeds and task durations. A comparative study of the effectiveness of methods based on the makespan (maximum completion time) and idle time metrics is conducted. The results demonstrate that MCDDQ-SA provides the best balance between scheduling quality and computational efficiency due to adaptive exploration of the solution space. Analytical tools for evaluating the dynamics of the algorithms are presented, which emphasizes their applicability to real manufacturing systems. The paper offers new perspectives for the application of hybrid methods in resource management problems.

Keywords: machine learning, Q-learning, deep neural networks, MCTS, DDQN, simulated annealing, scheduling, greedy algorithm

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

This article is devoted to the study of the possibilities of machine learning technology for forecasting the demand for goods. The study analyzes various models and the possibilities of their application as part of the task of predicting future sales. The greatest attention is focused on modern methods of time series analysis, in particular neural network and statistical approaches. The results obtained during the study clearly demonstrate the advantages and disadvantages of different models, the degree of influence of their parameters on the accuracy of the forecast within the framework of the demand forecasting task. The practical significance of the findings is determined by the possibility of using the results obtained in the analysis of a similar data set. The relevance of the study is due to the need for accurate forecasting of demand for goods to optimize inventory and reduce costs. The use of modern machine learning methods makes it possible to increase the accuracy of predictions, which is especially important in an unstable market and changing consumer demand.

Keywords: machine learning algorithms, demand estimation, forecasting accuracy, time sequence analysis, sales volume prediction, Python, autoregressive integrated moving average, random forest, gradient boosting, neural networks, long-term short-term memory

This paper explores the content-based filtering approach in modern recommender systems, focusing on its key principles, implementation methods, and evaluation metrics. The study highlights the advantages of content-based systems in scenarios that require deep object analysis and user preference modeling, especially when there is a lack of data for collaborative filtering.

Keywords: сontent - oriented filtering, recommendation systems, feature extraction, similarity metrics, personalization

The article presents the results of comparing numerical modeling of wooden structures with laboratory and full-scale tests. In the course of the work, numerical models of the material were created in the Ansys Workbench software package from volumetric finite elements with a variant set of physico-mechanical parameters simulating the behavior of real wood. The simulation parameters were based on the laboratory testing results of a solid wood beam. The simulation results were compared with the full-scale test results of a composite wood slab. Modeling of constructions was carried out in the form of linear, bilinear and multilinear models.

Keywords: solid wood beam, composite wood slab, bilinear finite element model, multilinear finite element model, stress-strain state

The article is devoted to the study of the influence of the choice of the calculation scheme on the accuracy of the engineering assessment of the behavior of monolithic reinforced concrete frame structures. Various types of models are considered: rod, plate and volumetric, taking into account both linear and physical nonlinearity. It is emphasized that the adequacy of accounting for the spatial interaction of elements, the reliability of the assessment of forces and stresses, as well as the possibility of optimizing design solutions, especially under seismic and wind loads, depend on the correctness of the adopted calculation scheme.
As part of the study, a single-span reinforced concrete frame was modeled, the load on which varied from 5 to 55 kN. A comparison of the calculated results with experimental data was carried out. It is shown that models that take into account physical nonlinearity and use more detailed modeling (for example, volumetric finite elements) provide the greatest accuracy in predicting deflections and stresses in the structure.
The obtained results confirm the necessity of a careful approach to the choice of the calculation scheme in design, especially in the design of high-rise buildings and structures in seismically dangerous areas. Recommendations are made on the rational use of models of different levels of detail in engineering practice.

Keywords: linear calculation, nonlinear calculation, frames, reinforced concrete, deflections, modeling

Introduction: Mobile Gaming Addiction (MGA) has emerged as a significant public health concern, with the World Health Organization recognizing it as a gaming disorder. Russia, with its growing mobile gaming market, is no exception. Aims and Objectives: This study aims to explore the feasibility of using neural networks for early MGA detection and intervention, with a focus on the Russian context. The primary objective is to develop and evaluate a neural network-based model for identifying behavioral patterns associated with MGA. Methods: A proof of concept study was conducted, employing a simplified neural network architecture and a dataset of 101 observations. The model's performance was evaluated using standard metrics, including accuracy, precision, recall, F1-score, and AUC-ROC score. Results: The study demonstrated the potential of neural networks in detecting MGA, achieving an F1-score of 0.75. However, the relatively low AUC-ROC score (0.58) highlights the need for addressing dataset limitations. Conclusion: This study contributes to the growing body of literature on MGA, emphasizing the importance of considering regional nuances and addressing dataset limitations. The findings suggest promising avenues for future research, including dataset expansion, advanced neural architectures, and region-specific mobile applications.

Keywords: neural networks, neural network architectures, autoencoder, digital addiction, gaming addiction, digital technologies, machine learning, artificial intelligence, mobile game addiction, gaming disorder

A system of expert-analytical methods for decision support at the design stage of multi-apartment residential buildings has been developed. The objective of the study is to economically evaluate the developed methods at the design stage of construction projects. Fuzzy logic is used as a mathematical basis for intelligent automated decision support systems. The method for assessing the investment attractiveness of residential complexes provides decision support based on the average area of ​​an apartment and the number of apartments per floor. The method for assessing the attractiveness of building a multi-storey residential building takes into account the class of housing, the height and area of ​​the building. An economic justification for the use of expert systems is provided and it is shown that calculating the construction attractiveness coefficient allows increasing the profit of a construction organization within 30% due to the use of empirical knowledge and specialized engineering solutions.

Keywords: intelligent automated system, design, multi-apartment residential building, economic assessment, investment attractiveness, fuzzy logic

A method has been developed for synthesizing antenna arrays with element failures using a convolutional artificial neural network with two encoders. A neural-network block architecture is proposed for computing the radiation pattern from the amplitude distribution of currents over the antenna-array aperture, enabling unsupervised training of the artificial neural network. The results obtained confirm the feasibility of the developed method.

Keywords: Antenna array synthesis, antenna element failures, radiation pattern, artificial neural network, unsupervised learning.

The article focuses on the application of machine learning methods for predicting failures in industrial equipment. A review of modern approaches such as Random Forest, SVM, and XGBoost is presented, with emphasis on their accuracy, robustness, and suitability for engineering tasks. Based on the analysis of real-world data (temperature, pressure, vibration, humidity), models were trained and compared, with XGBoost demonstrating the best performance. Key parameters influencing failures were identified, and a recommendation system was proposed, combining statistical analysis and predictive modeling. The developed solution enables timely detection of failure risks and optimization of maintenance processes.

Keywords: machine learning, predictive modeling, equipment management, failure prediction, data analysis

The article provides a comparative analysis of the approaches to forecasting rutting used in Russia and the USA. Mechanistic–Empirical Pavement Design Guide (MEPDG) and domestic regulatory documents are reviewed, and their key differences in forecast accuracy, applicability, and calculation complexity are identified.

Keywords: rutting, forecasting of road structures, MEPDG, monitoring of road conditions, regulatory methodologies

In the process of civil engineering, the role of the technical client is extremely important, since it is he who ensures control and coordination of all stages of construction, from the development of project documentation to commissioning of the facility. However, despite the importance of this role, technical client activities often face problems associated with ineffective management, high costs, schedule delays and quality deficiencies. Optimizing its activities can significantly increase the efficiency of the project and reduce risks. This article provides an analysis of possible ways to optimize the work of a technical client. Considered methods using modern software, training and improving the abilities of personnel, Total Quality Management and Lean Construction.

Keywords: technical client, project efficiency, civil engineering process management, lean construction