Mixed reality is penetrating deeper and faster into all aspects of our modern life. The influence of new technologies has not spared modern architecture, which aims to create not only beautiful, but also functional spaces and thus combines art and science. In addition to exploring examples of successful collaborations of artistic concepts and scientific principles in the design of spaces, the article will also consider the introduction of mixed reality into architectural projects. Let's explore exactly how the fields of art and science interact with each other. These two areas complement each other, creating a harmonious and practical environment for living, working and relaxing. This article examines the collaboration of art and science in the context of mixed reality architectural solutions.

Keywords: architecture, art, science, collaboration, mixed reality, platform, Art&Science, interaction, benefits, digital objects, visual component, joint projects

The autonomy of architecture is presented in the article as an option for a new disclosure of the possibilities of an architectural project, endowing global mega-projects with a unique quality of self-sufficiency, which is especially important in the context of renovation, reconstruction of lost buildings or the need for rapid changes. Analyzing innovative strategies for integrating energy generation, water supply, waste management, and food production systems directly into the architectural structure of the complexes, the article examines in detail the design principles that ensure flexibility, adaptability, and social orientation of autonomous multifunctional complexes. The aim of the work is to substantiate the need to move to a new standard for urban development design, where autonomy is considered not as an additional option, but as a key factor in ensuring sustainable development and improving the quality of life. Examples of architectural solutions are presented that demonstrate the potential of autonomous architecture in creating self-sufficient and environmentally friendly complexes that can reduce the burden on infrastructure and increase the resilience of cities to external influences.

Keywords: autonomy, architecture, multifunctional complex, architectural design, sustainable development

In the modern world, mixed reality technologies are increasingly being used and implemented. The allow you to create virtual objects in a real environment and provide human interaction with them, using different devices, basic element of enteractive interaction and artificial intelligence. The term «mixed reality», which appeared in the 20th century, had a significant impact on architecture, graphic design and art. This study examines an innovative approach to interacting with architectural objects through technologies of mixed reality of artificial intelligence. Also revealed the essence of interactive interaction with mixed reality, the way of identifying methods and forms. Studying modern methods will improve the educational process and increase the level of accessibility of knowledge about architectural objects, contributing to the preservation and dissemination of knowledge about cultural heritage.

Keywords: mixed reality, architecture, art, artificial intelligence, interactive technology, interactive interaction, educational experience, knowledge, modern methods

The article reflects the basic principles of the application of artificial intelligence (AI) and machine learning (ML) technologies at oil refineries, with a particular focus on Russian industrial enterprises. Modern oil refineries are equipped with numerous sensors embedded in technological units, generating vast volumes of heterogeneous data in real time. Effective processing of this data is essential not only for maintaining the stable operation of equipment but also for optimizing energy consumption, which is especially relevant under the increasing global demand for energy resources. The study highlights how AI and ML methods are transforming data management in the oil industry by enabling predictive analytics and real-time decision-making. Python programming language plays a central role in this process due to its open-source ecosystem, flexibility, and extensive set of specialized libraries. Key libraries are categorized and discussed: for data preprocessing and manipulation (NumPy, SciPy, Pandas, Dask), for visualization (Matplotlib, Seaborn, Plotly), and for building predictive models (Scikit-learn, PyTorch, TensorFlow, Keras, Statsmodels). In addition, the article discusses the importance of model validation, hyperparameter tuning, and the automation of ML workflows using pipelines to improve the accuracy and adaptability of predictions under variable operating conditions. Through practical examples based on real industrial datasets, the authors demonstrate the capabilities of Python tools in creating interpretable and robust AI solutions that help improve energy efficiency and support digital transformation in the oil refining sector.

Keywords: machine learning (ML), artificial intelligence (AI), intelligent data analysis, Python, Scikit-learn, forecasting, energy consumption, oil refining, oil and gas industry, oil refinery

The high-level software environment of automated process control systems requires reproducible and predictable maintenance. This article outlines the requirements for automation tools that ensure a consistent execution environment and independence of procedures from its current state. An assessment of the architectural characteristics of common solutions is provided. The joint use of Terraform and Ansible is justified as the foundation of a formalized maintenance model.

Keywords: software environment, automated control system, maintenance, reproducibility, configuration, automation, life cycle, computing environment, change management, programming language.

Mathematical modeling of complex systems often requires the use of variable grouping methods to build effective models. This paper considers the problem of constructing a homogeneous nested piecewise linear regression with variable grouping for modeling the staffing of information protection units. A corresponding model for the Social Fund of Russia is constructed using spatial data for the year 2022. The data on the number of employees of the organization, electronic signatures, protected nodes, protected resources, the total number of structural units, individual buildings and IT service specialists are used as independent variables.

Keywords: information protection, regression model, homogeneous nested piecewise linear regression, parameter estimation, least modulus method, linear-Boolean programming problem, index set, set power, social fund

Differential-algebraic equations for describing the motion of a plane-parallel robot-manipulator are investigated. The dynamic model is constructed using the Lagrange equation and the substructure method. The design of a control system regulator using the sliding mode method is considered. The control accuracy is tested on a model of a 3-RRR plane-parallel robot . It consists of three kinematic chains, each of which has two links with three rotational joints. To study the efficiency of the controller, a circular trajectory is used as the target motion for the multibody system. The considered control system for a plane-parallel robot is capable of solving problems of movement and ensuring high positioning accuracy.

Keywords: control, plane-parallel robot, kinematic characteristics, dynamic model, differential-algebraic equations, constraint equation, controller, sliding mode, Lyapunov function, program trajectory

This article presents a structured approach to deploying and integrating Grafana, Loki, and Alloy in Kubernetes environments. The work was performed using a cluster managed via Kubespray. The architecture is focused on ensuring external availability, high fault tolerance, and universality of use.

Keywords: monitoring, ocestration, containerization, Grafana, Loki, Kubernetes, Alloy

The paper is about special questions in simulation of controlled electro drive with speed feedback. The incremental encoder, that is an angle sensor in fact, is widely used as a speed feedback sensor in such a drives. It has same special features as speed sensor because of discrete operation and this features are to be taken in account in control system development and simulation. The simulation model of incremental encoder and speed signal decoder is present. Model is realized in SimInTech simulation system using visual modeling and programming language based description approach.

Keywords: Incremental encoder, speed sensor, quadrature decoder, electro drive simulation, incremental encoder simulation, SimInTech

Changes in external conditions, parameters of object functioning, relationships between system elements and system connections with the supersystem lead to a decrease in the accuracy of the artificial intelligence models results, which is called model degradation. Reducing the risk of model degradation is relevant for electric power engineering tasks, the peculiarity of which is multifactor dependencies in complex technical systems and the influence of meteorological parameters. Therefore, automatic updating of models over time is a necessary condition for building user confidence in forecasting systems in power engineering tasks and industry implementations of such systems. There are various methods used to prevent degradation, including an algorithm for detecting data drift, an algorithm for updating models, their retraining, additional training, and fine-tuning. This article presents the results of a study of drift types, their systematization and classification by various features. The solution options that developers need to make when creating intelligent forecasting systems to determine a strategy for updating forecast models are formalized, including update trigger criteria, model selection, hyperparameter optimization, and the choice of an update method and data set formation. An algorithm for forming a strategy for automatic updating of artificial intelligence models is proposed and practical recommendations are given for developers of models in problems of forecasting time series in the power industry, such as forecasting electricity consumption, forecasting the output of solar, wind and hydroelectric power plants.

Keywords: time series forecasting, artificial intelligence, machine learning, trusted AI system, model degradation, data drift, concept drift

This study presents an effective vision -based method to accurately identify predator species from camera trap images in protected Uganda areas. To address the challenges of object detection in natural environments, we propose a new multiphase deep learning architecture that combines extraction of various features with concentrated edge detection. Compared to previous approaches, our method offers 90.9% classification accuracy, significantly requiring fewer manual advertising training samples. Background pixels were systematically filtered to improve model performance under various environmental conditions. This work advances in both biology and computational vision, demonstrating an effective and data-oriented approach to automated wildlife monitoring that supports science -based conservation measures.

Keywords: deep learning, camera trap, convolutional neural network, dataset, predator, kidepo national park, wildlife

The paper examines the case of IntraService incident management system implementation in an organization operating in the digital infrastructure segment. The study focuses on the assessment of changes that occurred in the functioning of the support service based on quantitative and qualitative indicators. The method of comparative analysis of operational parameters before and after the launch of the system is used, accompanied by expert interpretation of internal processes.

Keywords: implementation, system, incident, support, automation, platform, organization, infrastructure, process, integration

The article provides an overview of modern approaches to the study of digital twins and assesses the state of their implementation in transport logistics. The authors show features of the digitalization formation and identify barriers and prospects for the development of digital twins in the transport and logistics sector. The analysis and systematization of methods used to define the concept of a digital twin, the structure and typology of digital twins in logistics are carried out. Certain promising areas and links in product supply chains, in which digital twins are being implemented especially actively, are highlighted. The paper concludes that the implementation of digital technologies and digital twins in transport logistics can become an effective tool for its transformation in modern conditions if the development and implementation of digital twins is carried out within the framework of product supply chains based on cooperation between industrial companies and related companies, with the active support of the state.

Keywords: digital twins, transport and logistics systems, supply chains, intralogistics, digital chain

This study addresses the challenges of evaluating feature space dimensionality in the context of multi-label classification of cyber attacks. The research focuses on tabular data representations collected through a hardware-software simulation platform designed to emulate multi-label cyber attack scenarios. We investigate how multi-label dependencies — manifested through concurrent execution of multiple attack types on computer networks — influence both the informativeness of feature space assessments and classification accuracy. The Random Forest algorithm is employed as a representative model to quantify these effects. The practical relevance of this work lies in enhancing cyber attack detection and classification accuracy by explicitly accounting for multi-valued attribute dependencies. Experimental results demonstrate that incorporating such dependencies improves model performance, suggesting methodological refinements for security-focused machine learning pipelines.

Keywords: multivalued classification, attribute space, computer attacks, information security, classification of network traffic, attack detection, informative attributes, entropy

The purpose of this article is to analyze algorithms and models for conducting search and rescue operations in crowded areas during fires, as well as other man-made or natural emergencies. The chance of saving people's lives directly depends on the effectiveness of search and rescue operations. The article describes the brief essence of the Graph Model as a method of finding optimal solutions when building a route for conducting search operations in emergency situations, including fires at facilities with a mass presence of people.

Keywords: search and rescue operations, facilities with a mass presence of people, emergencies, rescue of people, fires, search for victims

The article considers the assessment of the suitability of solar radiation data from ERA5 atmospheric reanalysis for forecasting problems in the northern territories. The experimental site of the Mukhrino station (Khanty-Mansiysk Autonomous Okrug), equipped with an autonomous power supply system, was chosen as the object of analysis. A statistical analysis of the annual array of global horizontal insolation data obtained using the PVGIS platform has been carried out. Seasonal and diurnal features of changes in insolation are considered, distribution profiles are constructed, and emissions are estimated using the interquartile range method. It is established that the data are characterized by high variability and the presence of a large number of zero values due to polar nights and weather conditions. The identified features must be taken into account when building short-term forecasting models. The conclusion is made about the acceptable quality of ERA5 data for use in forecasting energy generation and consumption in heating systems.

Keywords: ERA5, solar radiation, horizontal insolation, the Far North, statistical analysis, forecasting, emissions analysis, renewable energy sources, energy supply to remote areas, time series, intelligent generation management

Processing of results the unequal measurements presented by a binary code and the rests is considered. The technique of increase of accuracy of results of telemeasurements is resulted at data transmission by a series from measurement by the rests together with a binary code. The rests are duplicated in half-words in a word of data. Results of application of a technique are shown at single distortions of bats of data for a series from three measurements: measurement by the rests, then measurement in a binary code and one more measurement by the rests. At processing a series from three measurements which is received with step on a scale, equal from unit up to half of module of comparison, accuracy of measurements raises at a single mistake in a bat in a word with a binary code and a word with the rests in comparison with transfer by a binary code.

Keywords: telemeasurements, unequal the measurements, the rests of data, a dispersion of an error, accuracy of measurements

Malicious actors often exploit undetected vulnerabilities in systems to carry out zero-day attacks. Existing traditional detection systems, based on deep learning and machine learning methods, are not effective at handling new zero-day attacks. These attacks often remain incorrectly classified, as they represent new and previously unknown threats. The expansion of the Internet of Things (IoT) networks only contributes to the increase in such attacks. This work analyzes approaches capable of detecting zero-day attacks in IoT networks, based on an unsupervised approach that does not require prior knowledge of the attacks or the need to train intrusion detection systems (IDS) on pre-labeled data.

Keywords: Internet of Things, zero-day attack, autoencoder, machine learning, neural network, network traffic

The article focuses on developing data clustering algorithms using asymmetric similarity measures, which are relevant in tasks involving directed interactions. Two algorithms are proposed: stepwise cluster formation and a modified version with iterative center refinement. Experiments were conducted, including a comparison with the k-medoids method. The results showed that the fixed-center algorithm is efficient for small datasets, while the center-recalculation algorithm provides more accurate clustering. The choice of algorithm depends on the requirements for speed and quality.

Keywords: clustering, asymmetric similarity measures, clustering algorithms, iterative refinement, k-medoids, directed interactions, adaptive methods

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 article addresses the challenges and proposes mathematical models for optimizing container freight transportation within complex logistics systems, emphasizing the growing importance of digital technologies and artificial intelligence in logistics by 2025. It highlights key industry issues such as decentralized global supply chains, environmental risks, infrastructure deficiencies, safety concerns, and notably, the costly problem of transporting empty containers, which accounts for a significant portion of operational expenses worldwide and in Russia. The core contribution is a modified three-dimensional transport optimization model that incorporates container types, cargo volumes, and transportation costs, including the cost variations due to partially filled or empty containers. The model extends classical transportation problem formulations by introducing a potentials method that accounts for the contributions of suppliers, recipients, and container costs to determine an optimal transport plan minimizing total costs. Constraints ensure that supply and demand conditions, container capacities, and route feasibility are respected. The model uniquely integrates the degree of container filling into cost calculations using a coefficient to adjust transportation costs accordingly. This approach enables more accurate and cost-effective freight planning. Additionally, the article discusses the development of a simulation model and a client-server application to automate the search for optimal transport plans, facilitating practical implementation. The proposed framework can be expanded to include various container types, cargo characteristics, and transport modes, offering a comprehensive tool for improving logistics efficiency in container freight transportation.

Keywords: diversification of management, production diversification, financial and economic purposes of a diversification, technological purposes of ensuring flexibility of production

The article presents a comparative analysis of the results obtained from the automatic determination of crack width in reinforced concrete structures using photogrammetric 3D targets and the manual method using a Brinell microscope. It also outlines the general conditions required to obtain accurate crack width measurements when performing photogrammetric surveys. The experience of using photogrammetric targets for determining crack width in reinforced concrete structures in Russia is limited due to the novelty of the method, the high cost of specialized equipment, and the complexity of data processing. Proper use of photogrammetric targets can significantly speed up the process of measuring crack width in monolithic reinforced concrete structures and improve measurement accuracy. This technology is particularly relevant for monitoring or field testing of structures that require regular crack width control.

Keywords: photogrammetric targets, monitoring, crack width, reinforced concrete, software suite, camera, focal length, lighting.