This paper provides a survey of metrics used to assess the quality of images generated by generative models. Specialized metrics are required to objectively evaluate image quality. A comparative analysis showed that a combination of different metrics is necessary for a comprehensive evaluation of generation quality. Perceptual metrics are effective for assessing image quality from the perspective of machine systems, while metrics evaluating structure and details are useful for analyzing human perception. Text-based metrics allow for the assessment of image-text alignment but cannot replace metrics focused on visual or structural evaluation. The results of this study will be beneficial for specialists in machine learning and computer vision, as well as contribute to the improvement of generative algorithms and the expansion of diffusion model applications.

Keywords: deep learning, metric, generative model, image quality, image

The article provides a justification for the concept of a folding system for a prefabricated residential module based on wooden structures. An analysis of foreign analogues of prefabricated transformable wooden buildings and an assessment of the possibility of their use in northern climatic conditions has been performed. A transformation system for a prefabricated wooden module for use in northern and Arctic conditions is proposed and substantiated.

Keywords: low-rise housing construction, transformation, transformation of low-rise residential buildings, prefabricated transformable buildings, pre-manufactured at the factory, high degree of factory readiness

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

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

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

For neural network algorithms to work successfully when processing 3D point clouds, it is necessary to provide a detailed point cloud of the external environment. A similar task arises when a manipulative robot is operating in a new environment, where before processing a cloud of scene points, it is necessary to obtain a detailed representation of the external environment using an RGB-D camera mounted on the end link of the robot. To solve this problem, this study proposes an algorithm for adaptive control of a manipulative robot to build a model of the external environment. By applying an adaptive approach, during the research of the external environment, the manipulative robot moves the RGB-D camera, taking into account the changes in the current environment model introduced by the previous RGB-D image. The results obtained allow us to judge the effectiveness of the proposed approach, showing that due to adaptability, it allows us to achieve high scene coverage rates.

Keywords: environment model, manipulative robot, adaptive control algorithm, surface reconstruction, RGB-D camera, visual information processing, TSDF volume

A non-stationary system of automatic speed control of a DC motor with an adaptive controller is considered. Comparative simulation modeling in Simulink of the system with and without an adapter is performed. The results of the modeling confirm the stability of the adaptive system in a larger range of change of the non-stationary parameter compared to the conventional system. At the same time, the speed and quality of transient processes are maintained at the level recommended for such systems.

Keywords: automatic control system, non-stationarity, adaptive controller, subordinate control system, electromechanical object, DC motor

The method of synthesis of control of a territorially distributed complex technical system with metrological support is presented. The synthesis method is based on the method for identifying the parameters of a stationary semi-Markov model of operation of a complex technical system, developed by the author, based on solving a system of algebraic equations, which includes the linear invariants of the semi-Markov stationary model identified in the article. The results of modeling changes in the parameters of a complex technical system are presented, taking into account the current state of the fleet of complex technical systems with an optimal choice of the interval between checks, rational use of redundancy and stationary maintenance. The obtained results can find application in the decision support system for managing a fleet of complex technical systems. by choosing the optimal interval between checks, using redundancy and carrying out stationary maintenance.

Keywords: park of complex technical systems, control synthesis method, system invariants

The paper presents a simulation of flight control of an unmanned aerial vehicle (UAV). A distributed control system is proposed that sequentially includes internal and external circuits to control the state of motion of the aircraft. The control efficiency of a cascade PD controller (proportional-differential) is higher than that of a traditional PID controller (proportional-integral-differentiating). A new cascade control algorithm with a PD controller is proposed. First, the dynamics of the UAV is modeled based on the Newton-Euler method, then the state of motion of the device is controlled by a distributed control system based on cascaded levels of proportional derivatives of the internal and external contours. The simulation results show that the controller, developed on the basis of proportional-derivative control speed of internal and external circuits, is able to achieve fast tracking of the position and orientation of the UAV in case of external disturbances and has good control quality. The developed algorithm has increased the control efficiency by 5-7% compared to the traditional PID algorithm.

Keywords: Unmanned Aerial Vehicle, PID controller, Cascade PD controller, Algorithm Optimization, UAV Control Algorithm