Currently, one of the main factors influencing the formation of architecture is the functional purpose of the building, since it determines the essence of the architectural object. The purpose of the scientific work is to study the influence of building functions on the historical architecture of Europe and their impact on the development of modern architecture. This article sets the objectives of studying the classification of functional purposes of buildings, conducting a retrospective analysis of the development and formation of architectural styles in Europe, based on world design experience and the conducted research to identify the influence of the building function on its planning and volumetric-spatial solutions in the process of architecture development. The research method is the analysis of the historical architecture of Europe from the time of the inception of architecture to the present day, carried out on the basis of world design experience in different eras. In the course of the study, four main trends in the development of the functions of modern architecture were identified: integration with nature, creation of adaptive spaces, multifunctionality and development of new functions. It is concluded that the building function played the most important role throughout the entire period of architecture formation, which led to the emergence of a huge variety of building types today and made a significant contribution to the development of architecture of the XXI century.

Keywords: architecture, historical architecture, architectural style, functional purpose, European architecture, building type, retrospective analysis, function, influence, development

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

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

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

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

In this article, we examined the permeability of concrete and the effect of corrosion processes on the durability and reliability of reinforced concrete structures. Attention is paid not only to the causes and mechanisms of corrosion, but also modern methods and strategies for protecting concrete and reinforced concrete structures from it are provided, aimed at extending their service life and ensuring operational safety. This knowledge will allow engineers and builders to plan and implement projects more efficiently, reducing the risks and economic losses associated with corrosion processes.

Keywords: corrosion of concrete, corrosion of steel reinforcement, permeability, reinforced concrete, durability, strength, reliability

The article discusses the use of a recurrent neural network in the task of predicting pollutants in the air based on simulated data in the form of a time series. Neural recurrent network models with long Short-Term Memory (LSTM) are used to build the forecast. Unidirectional LSTM (hereinafter simply LSTM), as well as bidirectional LSTM (Bidirectional LSTM, hereinafter Bi-LSTM). Both algorithms were applied for temperature, humidity, pollutant concentration, and other parameters, taking into account both seasonal and short-term changes. The Bi-LSTM network showed the best performance and the least errors.

Keywords: environmental monitoring, data analysis, forecasting, recurrent neural networks, long-term short-term memory, unidirectional, bidirectional

Steel hoisting ropes play an important role in metallurgical equipment, ensuring reliability and efficiency of lifting operations. One of the key features of their operation is the high level of contamination typical of metallurgical operations. Metallurgical processes are often accompanied by dust, metal chips and other abrasive particles that can significantly degrade ropes, causing wear and corrosion. To maintain the efficient operation of equipment it is necessary to monitor the condition of hoisting ropes in real time, which makes the task of improving automatic systems for monitoring the condition of ropes urgent. The paper reviews the methods of optical control of defects in hoisting steel ropes, the advantages and limitations of different approaches are considered. The aim of the work is to justify the effectiveness of the authors' developed method of analyzing rope defect images using neural networks in relation to the method based on the discrete Fourier transform. It is revealed that one of the most promising in terms of technical and economic efficiency of inspection methods is the application of vision system with image processing based on convolutional neural network technology, which allows to effectively detect defects in complex and changing operating conditions, such as metallurgical and mining production, where the background of the image may be non-uniform, and the distance between the camera and the rope varies.

Keywords: lifting ropes, vision systems, optical control methods, fast Fourier transform, hidden Markov models, convolutional neural networks

The article considers the issues of imitation modeling of fibrous material mixing processes using Markov processes. The correct combination and redistribution of components in a two-component mixture significantly affects their physical properties, and the developed model makes it possible to optimize this process. The authors propose an algorithm for modeling transitions between mixture states based on Markov processes.

Keywords: modeling, imitation, mixture, mixing, fibrous materials

This article discusses the basic concepts and practical aspects of programming using the actor model on the Akka platform. The actor model is a powerful tool for creating parallel and distributed systems, providing high performance, fault tolerance and scalability. The article describes in detail the basic principles of how actors work, their lifecycle, and messaging mechanisms, as well as provides examples of typical patterns such as Master/Worker and Proxy. Special attention is paid to clustering and remote interaction of actors, which makes the article useful for developers working on distributed systems.

Keywords: actor model, akka, parallel programming, distributed systems, messaging, clustering, fault tolerance, actor lifecycle, programming patterns, master worker, proxy actor, synchronization, asynchrony, scalability, error handling

This article is devoted to a comparative analysis of the resilience of ResNet18 and ResNet50 neural networks to adversarial attacks on training sets. The issue of the importance of ensuring the safety of learning sets is considered, taking into account the growing scope of artificial intelligence applications. The process of conducting an adversarial attack is described using the example of an animal recognition task. The results of two experiments are analyzed. The purpose of the first experiment was to identify the dependence of the number of epochs required for the successful execution of an adversarial attack on the training set on the neural network version of the ResNet architecture using the example of ResNet18 and ResNet50. The purpose of the second experiment was to get an answer to the question: how successful are attacks on one neural network using modified images of the second neural network. An analysis of the experimental results showed that ResNet50 is more resistant to competitive attacks, but further improvement is still necessary.

Keywords: artificial intelligence, computer vision, Reset, ResNet18, ResNet50, adversarial attacks, learning set, learning set security, neural networks, comparative analysis

The article presents an analysis of the application of the Socratic method for selecting machine learning models in corporate information systems. The study aims to explore the potential of utilizing the modular architecture of Socratic Models for integrating pretrained models without the need for additional training. The methodology relies on linguistic interactions between modules, enabling the combination of data from various domains, including text, images, and audio, to address multimodal tasks. The results demonstrate that the proposed approach holds significant potential for optimizing model selection, accelerating decision-making processes, and reducing the costs associated with implementing artificial intelligence in corporate environments.

Keywords: Socratic method, machine learning, corporate information systems, multimodal data, linguistic interaction, business process optimization, artificial intelligence