This article explores the opportunities and challenges of integrating cloud, fog, and edge computing in the context of digital transformation. The analysis reveals that the synergy of these technologies enables optimization of big data processing, enhances system adaptability, and ensures information security. Special attention is given to hybrid architectures that combine the advantages of centralized and decentralized approaches. Practical aspects are addressed, such as the use of the ENIGMA simulator for modeling scalable infrastructures and the EC-CC architecture for smart grids and IoT systems. The role of specialized frameworks in optimizing routing and improving infrastructure reliability is also highlighted. The integration of these technologies drives advancements in key industries, including energy, healthcare, and the Internet of Things, despite challenges related to data security.

Keywords: cloud computing, fog computing, edge computing, hybrid architectures, Internet of Things, digital transformation, big data, decentralized systems, computing integration, distributed computing, data security, resource optimization, data transfer speed

The paper analyzes existing effective technologies of waste recycling and utilization. The authors consider various approaches in the international practice of recycling production and consumption waste. An assessment is given of the possibilities of using effective technologies for waste recycling and disposal and the necessary costs for their implementation in relation to the conditions of an industrial enterprise. The types and volumes of waste that can be recycled and disposed of irrevocably are considered, for which the carbon footprint parameters are calculated using the materials management model. A statistical regression analysis of data on the production, processing, disposal and incineration of polyethylene waste, solid municipal waste and paper was carried out. The principles of building a system for reducing technogenic risks and managing production and consumption waste were determined.

Keywords: waste processing; waste disposal; carbon footprint; carbon footprint calculation methods; man-made risk management system; hazardous impact factors; industrial waste management

The paper investigates the use of architectural combinatorics to solve the problems of multifunctional residential complexes in the conditions of digital transformation. The main methods of combinatorics, including conceptual and formal approaches, are considered. The main stages of evolution of the method, starting from constructivism, and the role of modern digital technologies such as BIM, parametric modeling, machine learning and artificial intelligence in the implementation of combinatorial approaches are described. Attention is given to sustainable architecture and optimization of spatial solutions. Successful and problematic project examples are analyzed. Limitations of the application of the technologies are analyzed, as well as ethical and social aspects of their use. The conclusions substantiate the significance of the method in the context of contemporary challenges.

Keywords: architectural combinatorics, combinatorial methods, multifunctional residential complex, sustainable development, sustainable architecture, adaptive architecture, digital technologies, BIM, parametric modeling, machine learning, artificial intelligence

The paper analyzes existing 3D printing technologies in the context of application in construction. The experience of 3D printing application in commercial projects is considered. Scientific research on the improvement of various technologies is summarized. 3D printing technologies promising for construction - wire-arc and ultrasonic additive manufacturing - are identified.

Keywords: 3D printing, construction, additive technologies

With the rapid development of mobile technologies and increasing risks of data leakage, providing reliable user authentication becomes one of the key tasks of information security. This paper is devoted to the study of application of neural network technologies for biometric authentication in modern mobile systems. The paper provides a comprehensive analysis of existing biometric authentication methods such as face recognition, voice and fingerprint analysis. Special attention is paid to the peculiarities of the methods' operation, accuracy and resistance to attacks. The main advantages and disadvantages of each of the considered authentication methods are given. At the end of the article is presented the practical application of the developed algorithm of neural network authentication based on fingerprint analysis, integrated into the SIM-card. This innovative approach not only increases the security level of mobile devices, but also provides convenience to the user. The implementation of this case study will form the basis for further research presented in this thesis work, which emphasizes the importance of integrating neural network technologies into authentication processes. The results of the research will be useful for both scientists and developers in the field of information security, opening new horizons for the improvement of biometric systems in the mobile environment.

Keywords: authentication, neural networks, biometrics, mobile systems, information security, deepfake, GDPR, hybrid technologies, sim card

Mathematical modeling, numerical methods and program complexes (technical sciences). Geopolitical situation analysis of a number of episodes of the American Revolution in the context of applying structural balance and mathematical modeling methods. Structural balance management can help to find the most optimal strategies for interacting parties. This approach is used in a set of disciplines. In this article, the author analyzes examples of actors' interaction in the context of the American Revolution, which allows us to evaluate the state of affairs at this historical stage in an illustrative form. This approach is universal and is able to emphasize the management of structural balance in systems with actors, each of which has its own features and interests. A number of specific historical episodes serves as an example of the balanced and unbalanced systems. Each episode has its explanation in the frame of history. During the American Revolution, actors (countries and specific politicians, as well as indigenous peoples) had their own goals and interests, and their positive or negative interactions shaped the course of history in many ways.

Keywords: mathematical modeling, structural balance, discrete models, sign graph, U.S. history

The article is devoted to the development of a tool for automated generation of time constraints in the context of circuit development in the basis of programmable logic integrated circuits (FPGAs). The paper analyzes current solutions in the field of interface tools for generating design constraints. The data structure for the means of generating design constraints and algorithms for reading and writing Synopsys Design Constraints format files have been developed. Based on the developed structures and algorithms, a software module was implemented, which was subsequently implemented into the circuit design flow in the FPGA basis - X-CAD.

Keywords: computer-aided design, field programmable gate array, automation, design constraints, development, design route, interface, algorithm, tool, static timing 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

The article examines the modular structure of interactions between various models based on the Socratic dialogue. The research aims to explore the possibilities of synthesizing neural networks and system analysis using Socratic methods for managing corporate IT projects. The application of these methods enables the integration of knowledge stored in pre – trained models without additional training, facilitating the resolution of complex management tasks. The research methodology is based on analyzing the capabilities of multimodal models, their integration through linguistic interactions, and system analysis of key aspects of IT project management. The results include the development of a structured framework for selecting suitable models and generating recommendations, thereby improving the efficiency of project management in corporate environments. The scientific significance of the study lies in the integration of modern artificial intelligence approaches to implement system analysis using multi – agent solutions.

Keywords: neural networks, system analysis, Socratic method, corporate IT projects, multimodal models, project management

This article is devoted to a comparative analysis of methods for extracting knowledge from texts used to build ontologies. Various extraction approaches are reviewed, such as lexical, statistical, machine learning and deep learning methods, as well as ontology-oriented methods. As a result of the study, recommendations are formulated for choosing the most effective methods depending on the specifics of the task and the type of data being processed.

Keywords: ontology, knowledge extraction, text classification, named entities, machine learning, semantic analysis, model

The article analyzes traditional and flexible project management methodologies, their key features, advantages and limitations. Traditional methodologies such as the waterfall model and the critical path method rely on sequential planning that is suitable for projects with fixed requirements. Agile methodologies (Agile, Scrum) are highly adaptable, which is important for projects with frequent changes. The authors compare the conditions of application of both approaches and describe the criteria for choosing a methodology depending on the type and dynamics of the project. The article will be useful for both practitioners and researchers in the field of project management.

Keywords: project management, project management methodology, cascade model, critical path method, program evaluation and review technique, agile software development, critical path method

The article discusses options for solving the problem of slowing down the operating cycle of programmable logic controllers that occurs when implementing complex algorithms. For most modern programmable logic controllers, the operating cycle directly depends on the volume of the user program. The program is executed pseudo-parallel, so the complexity of the algorithm affects the slowdown of the cycle indirectly, through an increase in the code volume. The growth of the program leads to a slowdown in the controller's response to changes in the state of the inputs. In some cases, controller developers provide programmers with options that allow them to disrupt the natural order of the operating cycle, thereby reducing the response time. The advantages and disadvantages of such methods are discussed in detail in this article. As an alternative way to reduce the controller's response time, the possibility of transferring the execution of a part of the algorithm to the operator's touch panel is considered. Modern touch panels, in addition to their main task - implementing the human-machine interface, have many additional functions, including the ability to use macros. The main functionality of macros is given and the possibility of exchanging data between the controller and the panel is demonstrated. This feature is a prerequisite for delegating some of the functions of the panel controller. The limitations and risks that arise when using this approach are discussed in detail, and situations in which it is preferable to use this method are identified.

Keywords: programmable logic controller, operator touch panel, controller operation cycle, distribution of computing resources, macros

The article provides an overview of existing technical solutions, considered from the point of view of two approaches: complete replacement of the electrolysis plant with a modern analog; replacement of only the measuring part of the system. Based on the results of a review of existing technical solutions, it was concluded that the purchase and replacement of an entire electrolysis plant or a software measuring part is economically unprofitable, and Berezovskaya GRES can implement its own SEU-20 plant management system using modern Russian-made automation software, but its own development will bring significant economic benefits. due to the lower cost compared to foreign counterparts. Для проектируемой системы управления технологическим процессом выбрано необходимое оборудование. Рассчитано время реакции системы при аварийной ситуации. Разработаны электрическая структурная и функциональная схемы автоматизации электролизной установки СЭУ-20. Разработан интерфейс АРМ оператора и произведено моделирование разработанной программы с отработкой различных ситуаций.

Keywords: electrolysis plant, import substitution, remote control, remote control, PLC programming, APM operator

The article is devoted to the automation of the process of managing road construction works at a manufacturing enterprise. Among the means of communication in Russia, highways are in the first place in terms of length. Construction of new roads, repair and bringing the existing roads to regulatory requirements is a complex process that can be characterized as a project. The process of project-oriented management of road construction works is formalized, project limitations are defined. The enlarged milestones of project-oriented management of road construction works are highlighted, including the stages of initialization and implementation. The categories of system users and their functions are defined. A class diagram of the information system for managing road construction works is provided. An algorithm for the operation of an automated system for managing road construction works based on a project-oriented approach is developed and described in detail. Formalization of the calculation of the percentage of project readiness is carried out based on the significance coefficient. Examples of implementing the algorithm stages in the information system and generating analytical reports in the system are given. The reports generated in the system are described in detail. The economic efficiency of the proposed automation system is substantiated.

Keywords: road construction works, project-oriented management, highway, automation, reporting, significance coefficient, project, project resources, performance indicator, construction, repair

The article presents the results of a numerical experiment comparing the accuracy of neural network recognition of objects in images using various types of data set extensions. It describes the need to expand data sets using adaptive approaches in order to minimize the use of image transformations that may reduce the accuracy of object recognition. The author considers such approaches to data set expansion as random and automatic augmentation, as they are common, as well as the developed method of adaptive data set expansion using a reinforcement learning algorithm. The algorithms of operation of each of the approaches, their advantages and disadvantages of the methods are given. The work and main parameters of the developed method of expanding the dataset using the Deep-Q-Network algorithm are described from the point of view of the algorithm and the main module of the software package. Attention is being paid to one of the machine learning approaches, namely reinforcement learning. The application of a neural network for approximating the Q-function and updating it in the learning process, which is based on the developed method, is described. The experimental results show the advantage of using data set expansion using a reinforcement learning algorithm using the example of the Squeezenet v1.1 classification model. The comparison of recognition accuracy using data set expansion methods was carried out using the same parameters of a neural network classifier with and without the use of pre-trained weights. Thus, the increase in accuracy in comparison with other methods varies from 2.91% to 6.635%.

Keywords: dataset, extension, neural network models, classification, image transformation, data replacement

The article presents an analysis of the network stability of modern banking systems from the point of view of graph theory. The use of graph models makes it possible to effectively describe complex network structures, identify bottlenecks, and predict system behavior during failures or attacks. Algorithms based on graph theory, such as Dijkstra's Algorithm, have been proposed to ensure minimal transaction processing time and improve system reliability. A comparative analysis of various optimization methods through modeling on abstract graphs and real banking network data was carried out. As a result of the study, solutions were proposed to protect the banking system, as well as improve its connectivity and fault tolerance.

Keywords: banking system, graph theory, Dijkstra's algorithm, blockchain, transactions, cyber attack, network stability analysis, banking infrastructure, cyber security, DDoS attack

Distributed data storage systems (DSS) are multi-parameter, complexly configurable systems. Fault tolerance and reliability of DSS data storage are ensured by a set of different methods. To assess the efficiency of new methods, it is convenient to use software tools that simulate the operation of DSS. The purpose of this work is to study the existing software simulators of DSS to assess the potential of their use. The study is based on the analysis of several software simulators that model the operation of DSS. The analysis took into account such parameters as the choice of the redundancy introduction method, the data placement method, the data recovery algorithm after failure, and the choice of storage architecture. The results of the study show that simulators offer a wide range of options for modeling fault tolerance, but some of them demonstrate greater efficiency in some scenarios. A generalized structural diagram of the simulators is built, revealing the features of the architecture and principles of operation. The CR-SIM simulator has the greatest functionality, but its source codes and executable file are not available. Simulators with open source code do not have a flexible architecture for their expansion with new methods. The сonclusion is made about the need to develop a new simulator in the form of an open source software tool, the architecture of which is designed for its expansion. Such a simulator will allow testing new developments in the field of fault tolerance enhancement technologies.

Keywords: data storage system, fault tolerance, erasure codes, software simulator, dependability, simulation model

The technology of applying the variational principle in problems of development and testing of complex technical systems is described. Let there be a certain set of restrictions imposed on random variables in the form of given statistical moments and/or in the form of a restriction by some estimates from above and below the range of possible values of these random variables. The task is set: without knowing anything except these restrictions, to construct for further research, ultimately, for assessing the efficiency of the complex technical system being developed, the probability distribution function of its determining parameter. By varying the functional, including Shannon entropy and typical restrictions on the distribution density function of the determining parameter of a complex technical system, the main stages of constructing the distribution density function are described. It is shown that, depending on the type of restriction, the constructed distribution density function can have an analytical form, be expressed through special mathematical functions, or be calculated numerically. Examples of applying the variational principle to find the distribution density function are given. It is demonstrated that the variational principle allows obtaining both the distribution laws widely used in probability theory and mathematical statistics, and specific distributions characteristic of the problems of developing and testing complex technical systems. The technology of applying the variational principle presented in the article can be used in the model of managing the self-diagnostics process of intelligent control systems with machine consciousness.

Keywords: variational principle, distribution density function, Shannon entropy, complex technical system

This study presents a comparative analysis of machine learning models used for driver classification based on microelectromechanical system (MEMS) sensor data. The research utilizes the “UAH-DriveSet” open dataset, which includes over 500 minutes of driving data with annotations for aggressive driving events, such as sudden braking, sharp turns, and rapid acceleration. The models evaluated in this study include gradient boosting algorithms, a recurrent neural network and a convolutional neural network. Special attention is given to the impact of data segmentation parameters, specifically window size and overlap, on classification performance using the sliding window method. The effectiveness of each model was assessed based on classification metrics such as accuracy, precision, and F1 score. The results show that gradient boosting “LightGBM” outperforms the other models in terms of accuracy and F1 score, while long short-term memory model demonstrates good performance with time-series data but requires larger datasets for better generalization. Convolutional neural network, while effective for identifying short-term patterns, faced difficulties with class imbalances. This research provides valuable insights into selecting the most appropriate machine learning models for driver behavior classification and offers directions for future work in developing intelligent systems using MEMS sensor data.

Keywords: driver behavior analysis, microelectromechanical system sensors, machine learning, aggressive driving, gradient boosting, recurrent neural networks, convolutional neural networks, sliding window, driver classification

The results of fire dynamics simulation based on the FDS software kernel are a large amount of data describing the dynamics of various parameters in the space of the studied object. Solving various research problems based on them may require quite complex processing, which goes beyond the functionality of existing software solutions. The article is devoted to the method of efficiency increasing for numerical fire dynamics simulation results processing by automating the implementation of relevant operations. The article describes the functional model of the developed technology and its main stages. Approbation of the proposed method was carried out using the example of solving the problem of forming initial data arrays in high spatial and time resolution for the subsequent study of enclosing tunnel structures heating in case of fire. Graphs of the gas medium temperature at various points under the roof of the tunnel structure from the coordinate are presented, as well as temperature fields in the vertical section of the investigated structure in the plane passing through the fire focus at different times. Based on the comparative analysis, it was shown that the speed of calculation results automated processing is several orders of magnitude higher compared to methods that use the functionality of existing software solutions designed to view the output of the fire dynamics simulation.

Keywords: fire dynamics simulation, automation, data processing, tunnel structures, mathematical model, FDS

The article discusses current issues related to the design of a smart home wireless local area network based on splitter-repeater modules. Special attention in the study is paid to the modules of wired and wireless hubs and switches. The results of the comparative characteristics of PLC and FBT splitter-repeaters are also presented. Particular emphasis is placed on the network topology and its main components.

Keywords: wireless network, topology, data, transmission, power, traffic, packet, failures, adapter, cable, connection

The article explores the actor model as implemented in the Elixir programming language, which builds upon the principles of the Erlang language. The actor model is an approach to parallel programming where independent entities, called actors, communicate with each other through asynchronous messages. The article details the main concepts of Elixir, such as comparison with a sample, data immutability, types and collections, and mechanisms for working with the actors. Special attention is paid to the practical aspects of creating and managing actors, their interaction and maintenance. This article will be valuable for researchers and developers interested in parallel programming and functional programming languages.

Keywords: actor model, elixir, parallel programming, pattern matching, data immutability, processes, messages, mailbox, state, recursion, asynchrony, distributed systems, functional programming, fault tolerance, scalability

The purpose of this article is to create a convolutional neural network model for identifying and predicting audio deepfakes by classifying voice content using deep machine learning algorithms and python programming language libraries. The audio content datasets are basic for the neural network learning process and are represented by mel spectrograms. The processing of graphic images of the audio signal in the heatmap format forms the knowledge base of the convolutional neural network. The results of the visualization of mel spectrograms in the ratio of the measurement of the frequency of sound and chalk determine the key characteristics of the audio signal and provide a comparison procedure between a real voice and artificial speech. Modern speech synthesizers use a complex selection and generate synthetic speech based on the recording of a person's voice and a language model. We note the importance of mel spectrograms, including for speech synthesis models, where this type of spectrograms is used to record the timbre of a voice and encode the speaker's original speech. Convolutional neural networks allow you to automate the processing of mel spectrograms and classify voice content: original or fake. The experiments conducted on test voice sets proved the success of learning and using convolutional neural networks using images of MFCC spectral coefficients to classify and study audio content, and the use of this type of neural networks in the field of information security to identify audio deepfakes.

Keywords: neural networks, detection of voice deepfakes, information security, speech synthesis models, deep machine learning, categorical cross-entropy, loss function, algorithms for detecting voice deepfakes, convolutional neural networks, mel-spectrograms