The article describes the features of using a two-layer membrane with the use of injection control fittings in the installation of underground waterproofing. The circumstances preventing the mass application of this technology have been identified, the main part of which is related to the increase in the cost of work at the initial stage. However, the use of the technology is justified because it allows you to localize the location and period of leakage, has increased maintainability and durability.

Keywords: waterproofing, modern waterproofing technologies, double-layer membrane, injection control fittings

This paper considers the modeling of user work with a multi-server database developed on the basis of microservice architecture. The subject area was analyzed, the main entities of the system were described, and the mechanisms of data transfer and service interaction using Docker and Apache Kafka were implemented. It was revealed that the development of a multi-server database allowed to achieve high scalability and fault tolerance of the system. The implementation of replication and sharding mechanisms provided even load distribution, and the use of Kafka message broker facilitated efficient data exchange between services. The testing confirmed the system's reliability under high load, as well as revealed its strengths and potential improvements.

Keywords: modeling, load balancing, Docker, Apache Kafka, microservice architecture, distributed systems, query optimization

The paper considers the issue of choosing a data transmission protocol through telecommunication networks for the implementation of a distributed monitoring and diagnostic system for hydrogen solid polymer fuel cells. It has been established that the organization of such systems is potentially possible on the basis of protocols: HTTP, Websockets and UDP, however, to ensure maximum efficiency in making diagnostic decisions, the use of the UDP protocol is preferable. Experimental estimates show that the maximum time to receive a diagnostic message will be no more than 250 ms, and the average is about 125 ms.

Keywords: solid polymer fuel cell, monitoring, diagnostics, distributed system, data transmission protocols, UDP, message delays, telecommunication networks, hydrogen energy, remote control

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

Modern predictive analytics methods significantly enhance the capabilities of network monitoring systems by enabling early detection of anomalies and potential failures. This article presents the results of a study on approaches to building a proactive network monitoring system using machine learning and statistical analysis methods. It is demonstrated that the use of combined models based on recurrent neural networks and autoregressive models provides the most accurate network traffic forecasting with a prediction horizon of up to 10 time intervals. The practical implementation of the proposed approach allows for a 27% reduction in unplanned downtime and a 35% decrease in incident response time compared to traditional reactive monitoring systems.

Keywords: predictive analytics, network monitoring, machine learning, statistical analysis, anomaly detection, traffic forecasting, recurrent neural networks, autoregressive models, proactive systems, fault tolerance

The article discusses the issues of controlling a pulse voltage stabilizer, which has the potential to provide high specific characteristics, efficiency and quality of the output voltage. It is shown that it is advisable to use a digital computing device to control this stabilizer. The oscillograms of the processes in the layout of a pulse voltage stabilizer with a digital control device are presented and an intermediate conclusion is made about an increase in the duration of transients in the stabilizer layout in comparison with the minimum possible due to the lack of time required to accurately calculate the duration of the control signals of the stabilizer's power transistors. It is proposed to calculate in advance the necessary durations of the control signals of the stabilizer's power transistors with a step-by-step change in the external operating conditions of the stabilizer and to store the results in the memory of the digital control device. The time diagrams of processes in the stabilizer simulation model are presented, in which control is implemented according to the proposed option.

Keywords: autonomous power supply system, digital twin, pulse voltage stabilizer, digital computing device

The article examines the problem of global network optimization, as well as currently existing software and hardware solutions. The purpose of the study is to determine the technological basis for developing a prototype of a domestic WAN optimizer. When studying the subject area, it turned out that there are no domestic solutions in this area that are freely available. The resulting solution can be adapted to the specific requirements of the customer company by adding the necessary modifications to the prototype.

Keywords: global network, data deduplication, WAN optimizer, bandwidth

This study addresses the technical bottlenecks of generative AI in architectural style control by proposing a nodular workflow construction method based on ComfyUI (A graphical user interface for working with the Stable Diffusion model, simplifying the management of image generation parameters.), aiming to achieve precise and controllable generation of functionalist architectural renderings. Through deconstructing the technical characteristics of the Stable Diffusion (A generative AI model based on diffusion processes that transforms noise into images through iterative noise removal.) model, neural network components such as ControlNet (A neural network architecture used for precise control of image generation via additional input data.) edge constraints and LoRA(Low-Rank Adaptation. A method for fine - tuning neural networks using low - rank matrices, enabling modification of large models with minimal computational costs.) module enhancements are encapsulated into visual nodes, establishing a three-phase generation path of "case analysis - parameter encoding - dynamic adjustment". Experiments involving 10 classical functionalist architectural cases employed orthogonal experimental methods to validate node combination strategies, revealing that the optimal workflow incorporating MLSD (Multi-Level Semantic Diffusion. An algorithm that combines semantic segmentation and diffusion models to generate structurally consistent images.) straight-line detection and LoRA prefabricated component reinforcement significantly improves architectural style transfer effectiveness. The research demonstrates: 1) The nodular system overcomes the "black box" limitations of traditional AI tools by exposing latent space(A multi - dimensional space where neural networks encode semantic features of data.) parameters, enabling architects to directionally configure professional elements; 2) Workflow templates support rapid recombination within 4 nodes, enhancing cross-project adaptability while further compressing single-image generation time; 3) Strict architectural typology matching (e.g., residential-to-residential, office-to-office) is critical for successful style transfer, as typological deviations cause structural logic error rates to surge. This research holds significant implications in architectural design by leveraging ComfyUI to develop workflows that transform how architects visualize and communicate ideas, thereby improving project outcomes. It demonstrates practical applications of this technology, proving its potential to accelerate design processes and expand architects' creative possibilities.

Keywords: comfyui, functionalist architecture, style transfer, node-based workflow, artificial intelligence, architectural design, generative design

The article is devoted to the study of the problem of estimating unknown parameters of linear regression models using the least absolute deviations method. Two well-known approaches to identifying regression models are considered: the first is based on solving a linear programming problem; the second, known as the iterative least-squares method, allows one to obtain an approximate solution to the problem. To test this method, a special program was developed using the Gretl software package. A dataset of house prices and factors influencing them, consisting of 20640 observations, was used for computational experiments. The best results were obtained using the quantreg function built into Gretl, which implements the Frisch-Newton algorithm; the second result was obtained using an iterative method; and the third result was achieved by solving a linear program using the LPSolve software package.

Keywords: regression analysis, least absolute deviations method, linear programming, iterative least squares method, variational weighted quadratic approximation method

The article presents aspects of designing an artificial intelligence module for analyzing video streams from surveillance cameras in order to classify objects and interpret their actions as part of the task of collecting statistical information and recording information about abnormal activity of surveillance objects. A diagram of the sequence of the user's process with active monitoring using a Telegram bot and a conceptual diagram of the interaction of the information and analytical system of a pedigree dog kennel on the platform "1С:Enterprise" with external services.

Keywords: computer vision, machine learning, neural networks, artificial intelligence, action recognition, object classification, YOLO, LSTM model, behavioral patterns, keyword search, 1C:Enterprise, Telegram bot

A set of techniques for obtaining retrospective, statistical, expert information, data integration, competence deficit assessment and knowledge management to compensate for competence deficit in organisational systems is presented. For the purpose of practical implementation of an integrated approach to improving the management of organisational systems, a model and an algorithm for obtaining data by applying a set of techniques have been developed. In the future, the proposed methodological solutions will significantly improve the efficiency of organisational systems management through the rational application of automated management systems with components of trusted artificial intelligence.

Keywords: algorithm, critical events, integration, information resources, recommendations, systematisation, efficiency

The article discusses machine learning methods, their application areas, limitations and application possibilities. Additionally highlighted achievements in deep learning, which allow obtaining accurate results with optimal time and effort. The promising architecture of neural networks of transformers is also described in detail. As an alternative approach, it is proposed to use a generative adversarial network in the process of converting a scan into elements of a digital information model.

Keywords: scanning, point cloud, information model, construction, objects, representation, neural network, machine learning

The article formulates the task of developing a fire safety management procedure based on a risk-based approach, taking into account the preferences of the decision maker in the multi–criteria system "result - cost – time". A multi-criteria mathematical model of the procedure under consideration has been developed, as well as an algorithm for its implementation with the development of information technology and a test case.

Keywords: risk-based approach, fire risk, decision support systems, Pareto-optimal solutions

This paper describes a virtualized environment designed to conduct comprehensive experiments involving peer-to-peer networks and information security algorithms. The architecture is based on integrating the VMware hypervisor with the EVE-NG network device emulation platform, providing flexible resource allocation and realistic topology simulation. A MikroTik router serves as the central node, enabling a “star-shaped” scheme of interaction among virtual machines running various operating systems (Windows 7, 10, 11, Linux Debian). The chosen configuration simplifies testing the multiple initial connections and multi-level cryptography algorithms, ensures stable routing, and supports further automation of software installation using Bash or PowerShell scripts.

Keywords: information security, virtualized environment, multiple initial connections, peer-to-peer network, virtual private network

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