COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION

Medical oxygen flowmeter calibration

2025-09-23T17:07:06+00:00

The article solves the problem of calibrating an oxygen flowmeter for subsequent accurate and reliable accounting of medical oxygen in medical institutions, where oxygen is transported through pipelines in a gaseous state. The feasibility of using thermal mass flow converters is described, which provide high sensitivity, a wide measurement range and the ability to determine the flow rate under standard conditions without additional pressure and temperature sensors. A method for calibrating a flowmeter on a specialized stand is proposed, which allows increasing the measurement accuracy to 2%. The article describes in detail the construction of a mathematical model of the oxygen compressibility coefficient. An algorithm for constructing and implementing a corrective function of a flowmeter that takes into account the deviation of its readings from real values is also given. The effectiveness of the proposed approach is demonstrated, which allows integrating the obtained results into the microprocessor system of the flow meter and achieving high accuracy in measuring and calculating the consumed oxygen under standard conditions.

User Interface Model for Sorting Structured Data.

2025-09-23T17:07:05+00:00

Sorting tabular data is a critical stage in processing structured information, applied in the analysis of technical lists, such as security sensor inventories, or the preparation of analytical materials. This article presents a methodology and model for developing an interactive web interface for sorting tabular structures, enabling file upload (e.g., Excel) and in-browser sorting without additional software. The interface supports manual, step-by-step (with intermediate result display), and automatic sorting modes, allowing prioritization by ascending, descending, alphabetical order, or text-based filtering. The interface consists of a main window, file selection, parameter settings, priority settings, partial match, sorting mode selection, and final result display windows. Each window is described as an ordered n-tuple with control elements (buttons, checkboxes, text fields). The model details window transition logic, variable initialization, and error handling, ensuring intuitive user interaction. The technical implementation uses HTML, CSS, JavaScript, and the XLSX library, with the sorting algorithm based on dynamic-constrained data movement principles. The practical section demonstrates interface window screenshots and the system's sequential operation on security sensor data, confirming its effectiveness for numeric and text fields. Testing revealed the solution's flexibility and performance, suitable for educational, office, and applied use. The scientific novelty lies in formalizing user interaction with the sorting interface through sets and ordered tuples, describing system states and user actions, ensuring complete logical determinacy of transitions. The formal model provides a theoretical basis for scalability, while web technologies ensure accessibility and ease of integration. Compared to analogs, the developed interface stands out for its interactivity, support for text queries, and clear formalization of interaction logic, enhancing its competitiveness.

A method for choosing a strategy for redesigning interfaces to ensure effective human-machine interaction in web applications

2025-09-23T17:07:03+00:00

The article examines the problem of selecting an optimal strategy for redesigning web application interfaces, which is particularly relevant in the context of digital transformation, increasing user demands for service quality, and intensified competition in the field of information technology. It is well known that interface redesign can have both positive and negative consequences: on the one hand, it may improve system ergonomics, enhance user experience, and simplify the work of development teams; on the other hand, it carries significant risks related to possible failures in the production environment, team overload, and decreased user loyalty in the case of unsuccessful changes. Traditionally, such decisions are made based on experience or intuition, which reduces the justification and transparency of the process. The aim of the study is to formalize the selection of a redesign strategy using a multi-criteria analysis that considers technical, organizational, and ergonomic aspects. For this purpose, the Analytic Hierarchy Process (AHP) was applied, enabling comparison of alternatives across multiple criteria and determination of their priorities. The study modeled four redesign approaches: “Big Bang”, gradual migration, parallel development, and the use of Feature Flags. Pairwise comparisons of criteria were conducted using the SuperDecisions software, which allowed the calculation of global priorities and sensitivity analysis of the results. The findings revealed that the most effective strategy is the use of Feature Flags, as this approach ensures controlled implementation of changes, minimizes the risk of critical failures, simplifies testing, and reduces psychological pressure on the team. The scientific novelty of the study lies in applying AHP specifically to web interface redesign, where engineering and human factors intersect. The practical significance lies in the potential use of this methodology as a universal decision-making tool in UI/UX design, project management, and human–machine interaction. Future research may focus on automating model construction, integrating AHP into DevOps processes, and applying real UX metrics to refine evaluation criteria.

Information technology for processing and analysis of electrocardiogram signals considering its morphological and rhythmic features

2025-09-23T17:07:02+00:00

The article presents a comprehensive two-level information technology for processing and analysis of electrocardiogram signalі, which integrates an express method of cardiodiagnostics based on amplitude and time variability functions for use in extreme conditions and a detailed mathematical apparatus based on cyclic random processes for complete analysis of morphological and rhythmic signal features. The developed technology includes a structural scheme with blocks of preprocessing, segmentation, rhythm function formation, statistical processing and classification, which allows its use both for rapid diagnosis of critical conditions (extrasystole, fibrillation) and for planned detailed cardiodiagnostics. A systematization and classification of existing methods for analyzing the amplitude variability of electrocardiogram signal has also been conducted.

Adaptive behaviour tuning of a neural network-based method for moving object recognition in video streams

2025-09-23T17:07:01+00:00

The article presents an improvement of the method for searching and recognizing moving objects in video streams in real time, which is based on calculating interframe differences (deltas) and using a neural classifier. A mechanism for adaptive behaviour tuning of the method depending on the characteristics of the input data is proposed, which makes it possible to increase the recognition accuracy and processing speed under changing background conditions and limited computational resources. The developed method is an evolutionary adaptive mechanism, that allows the algorithm to gradually change its processing strategies based on the collected data, forming a heat map and optimising its performance for the specifics of a particular environment. To evaluate the effectiveness, an experimental comparison of the improved method with its basic version [5] was carried out, analyzing indicators such as average frame processing time, RAM and video memory usage, CPU and GPU load, and recognition accuracy. The optimization resulted in up to a 20% increase in processing speed and a slight improvement in accuracy (~0.8%) without increasing the use of key computational resources. The experimental results confirm the feasibility of integrating the adaptation mechanism into the delta-classification method to improve its efficiency for real-time operation.

Prototypes of Intelligent Security and Infrastructure Management Systems Based on IoT Platforms

2025-09-24T18:28:19+00:00

In modern engineering projects, the development of intelligent security and infrastructure management systems is becoming increasingly important due to growing demands for efficiency, reliability, and adaptability of engineering solutions in technologically complex environments. Rapid urbanization, increased traffic flows, and the growing complexity of building architectures require the implementation of advanced monitoring and control technologies. In this context, the integration of the Internet of Things (IoT) concept, software platforms, and algorithmic data processing methods is key to creating next-generation comprehensive engineering solutions. The research focuses on developing a hardware–software complex that combines sensor devices, communication protocols, and data processing algorithms. Particular attention is given to the development of methods for real-time data collection, analysis, and interpretation, enabling effective engineering decision-making. An analysis of Ukrainian studies has shown that existing systems are mainly focused on specific aspects—such as energy efficiency of sensor networks, routing algorithms, or local IoT device protection—and often lack comprehensive validation. This highlights the need for integrated solutions that provide real-time data stream processing, multi-level information protection, scalability, and system reliability. The experimental part includes a prototype system for room monitoring and parking management using sensors and cameras, where data are aggregated and processed by artificial intelligence algorithms. System performance is evaluated based on detection accuracy, processing latency, data stream throughput, average energy consumption, and reliability. The construction of synchronized datasets with timestamps and unique identifiers ensures data coherence and allows optimization of the balance between reaction speed and energy efficiency. Future research prospects include expanding the sensor network, integrating various data types, implementing adaptive load-balancing algorithms, and developing AI models for anomaly prediction and automatic system configuration, which will enhance the reliability and efficiency of IoT solutions in critical and long-term operational scenarios

Researching solutions and modernizing home server infrastructure.

2025-09-23T17:06:58+00:00

The article considers the possibilities of building and optimizing a home server infrastructure using various hardware and software solutions. A comparative study of three server configurations based on the Windows 10, Xubuntu and Raspberry Pi OS operating systems was conducted. To ensure the main functions of the server, media streaming, game server and file storage were implemented. The Samba (file storage), MiniDLNA (media streaming) and Minecraft Server (game server) services were used on the Xubuntu platform. A similar set of services adapted to the ARM architecture was used on the Raspberry Pi OS. The configuration on Windows 10 was implemented using Serviio (media streaming), sharing (file storage) and Minecraft Server (game server). Each of the configurations was tested in three operating modes: idle, at average and maximum load. For each mode, the load on the processor and RAM, power consumption, startup and boot time, temperature and network connection speed were measured. The measurement results were systematized and analyzed to determine the most effective solution for building a home server, taking into account energy efficiency, stability and system performance. The results obtained can be used to select the optimal configuration of server hardware and software when developing and upgrading home server systems.

Construction of Wi-Fi coverage heat map using the ESP8266 IoT module

2025-09-23T17:06:57+00:00

The article proposes a concept of a hardware-software solution to build the Wi-Fi heat map based on the IoT-module ESP8266. Based on the analysis of existing publications and open projects, it was found that existing tools and solutions do not allow building WiFi heat maps with sufficient accuracy and artifact level. There was created a method to build an indoor WiFi heat map coverage based on the RSSI of a wireless network. Scanning wireless channels includes a search by SSID and optionally by BSSID to avoid hopping between radio modules with the same network. The initial position is defined as the closest position to the AP and can be arbitrary. After positioning fix the room scanning is started with the fixed step square spiral . The obtained data is stored in CSV format and simultaneously transmitted to the server as JSON via MQTT. Data visualization uses Python and NumPy/SciPy/Matplotlib with the edge masking griddata method. Also it is possible to build a BSSID WiFi heat map. The method and hardware-software solution were tested successfully to build a heat map. The influence on the heat map is significant from outsiders and metal objects. The highest quality result was obtained by BSSID scanning.

Multilevel organization of rendering in modeling interactive maps

2025-09-23T17:06:55+00:00

The paper considers the problem of optimizing the level of detail LOD in interactive 3D maps used for real-time visualization of spatial data. A mathematical sigmoid logistic model is proposed that takes into account key parameters: distance to the object, camera viewing angle, current CPU/GPU load, and device resource capacity. It is shown that the use of the logistic function allows adaptively and smoothly adjusting the level of detail, ensuring the optimal balance between image quality and rendering performance. Formulas describing the dependence of LOD on input parameters are theoretically substantiated, and a method for discretizing levels of detail is also proposed. An analysis of the influence of individual factors on the value of the parameter φφ, which determines the degree of detail, is carried out. The correctness of the model is experimentally confirmed using the example of a complex polygon mesh with over 400 thousand edges. The results obtained demonstrate that the model allows to significantly reduce the load on the graphics system without losing important visual details on the map. The proposed approach is universal and can be applied for adaptive rendering in various geographic information systems. The work opens up prospects for further development of models taking into account additional parameters and integration with modern optimization algorithms.

Development of Information Consolidation Methods in Situation Awareness Systems

2025-09-23T17:06:54+00:00

The scientific field of information consolidation and the development of situation awareness systems is gaining increasing relevance due to the rapid advancement of intelligent autonomous agents and systems based on artificial intelligence. The development of effective methods for integrating, harmonizing, and representing information plays a crucial role in enhancing the adaptability, accuracy, and contextual awareness of modern digital systems. For the further advancement of this field, it is essential to conduct a comprehensive analysis of previous achievements, as most recent developments are based on already established methods, architectures, and practical implementations. This paper presents a review of scientific approaches and analyzes the main trends in the development of information consolidation methods in situation awareness systems. In particular, it addresses key terminology, stages in the evolution of the situation awareness concept, approaches to knowledge representation, the role of ontological models, and methods for processing fuzzy and contextual information. The study also identifies current challenges, open issues, and outlines promising directions for future research

Adaptive pseudo-random reconfiguration of the operating frequency with information-extreme control for protected unmanned networks

2025-09-23T17:06:53+00:00

The article explores the issue of the vulnerability of unmanned aerial vehicle (UAV) communication channels under conditions of active use of electronic warfare (EW) systems. Modern unmanned systems are increasingly becoming targets of deliberate attacks, including traffic interception and modification, jamming of control signals, data spoofing, or launching destructive cyberattacks on communication channels. To ensure the reliability, security, and continuity of communication between the UAV and the ground control station, the authors propose a multi-level adaptive protection architecture that includes several complementary components: adaptive pseudo-random frequency hopping (FHSS), software-defined networks (SDN/NFV), embedded IDS/IPS attack detection systems, and hardware trust modules. The use of logistic mapping and a normalized information criterion of information-extreme learning allows for dynamic control of FHSS sequences, taking into account the channel state and threat level. The SDN controller, through flexible network management, monitors the channels, detects signs of attacks or instability, and reconfigures data transmission routes in real time. The results of experimental studies confirm the effectiveness of the proposed solution — signal processing delay was only 8–12 ms, which is acceptable for critical control tasks, and fluctuations in the packet delivery ratio (PDR) during adaptive channel switching did not exceed 5%, indicating the system’s stability and resilience even under active electronic interference. The combination of information-extreme control, adaptive FHSS, and flexible SDN/NFV architecture creates an effective system for countering EW and cyber threats. The proposed approach not only increases the survivability and availability of communication channels but also helps preserve the accuracy of machine learning (ML) recognition models operating onboard the UAV, which is critically important for carrying out combat or reconnaissance missions in hostile environments.

Implementation of a Hybrid Password Generation Algorithm as Web Application

2025-09-23T17:06:51+00:00

The article addresses the problem of ensuring password reliability as a key tool for user authentication and access control in digital environments. Despite the development of biometric and multifactor authentication technologies, passwords remain the most common security mechanism due to their simplicity, universality, and cost-effectiveness. However, weak or reused passwords are the cause of the majority of account breaches, which highlights the need for efficient mechanisms of password generation and management. The paper presents a web application for automated generation of strong passwords with customizable parameters, including length, symbol composition, and optional dictionary-based word insertion to improve usability. The proposed system implements a hybrid algorithm that combines random symbol generation with dictionary elements, providing a balance between resistance to brute-force attacks and practical memorability. An additional feature is the integration of modern encryption methods (SHA-256, MD5, Base64, etc.), enabling adaptability to various security scenarios. The client-side architecture, based on HTML5, CSS, and JavaScript, ensures confidentiality and autonomy without requiring server-side processing. This development aims to enhance information security by reducing human-related risks in password creation. Future research perspectives include the use of machine learning for adaptive complexity adjustment, integration with corporate systems, and the development of mobile and cross-platform solutions

Formalized method for RAM optimization in content-based image retrieval systems

2025-09-23T17:06:50+00:00

This paper presents a formalized method for optimizing RAM usage in content-based image retrieval systems implemented in Java. The method combines two approaches: low-level optimization of data structures and compilation of the application into native code using GraalVM. At its core, the method analyzes commonly used structures for storing image descriptors and replaces them with memory-efficient equivalents at the programming language level. Additionally, native compilation reduces runtime overhead and overall memory consumption during program execution. The method is evaluated using the Multidimensional Cube model, which stores vector descriptors entirely in memory. The paper provides theoretical formulas for estimating memory usage based on data representation structures and presents results of experimental profiling. It also discusses the limitations of the proposed method, including increased compilation time and resource consumption, as well as incompatibility with some software libraries. The optimized implementation shows up to a threefold reduction in RAM usage and improved processing speed, while GraalVM provides an additional runtime memory reduction of up to 73%. The method can be applied to other CBIR systems and broader classes of problems that involve storing large volumes of vector structures in memory, especially when using programming languages with similar data structures and/or GraalVM support.

A system for analyzing groups of social network users based on graph databases

2025-09-23T17:06:49+00:00

The article examines approaches and tools for social network analysis using graph databases. The study explores methods for analysis and implements the detection and assessment of characteristics of social communities, including its dynamic. The developed toolkit enables: detecting potential bot farms (groups of accounts with high internal connection density, almost no external contacts, and synchronized publication of highly similar content); identifying supporters and opponents of a specific narrative, idea, party, or person (clusters of accounts that disseminate positive/negative messages about the target, comparing the average polarity and top keywords in each cluster, and identifying key influential nodes in each group); assessing the structural integration and cohesion of communities (cluster size, clustering coefficient, connection density, detection of bridges and break points to evaluate the resilience of information dissemination). The obtained results confirm the relevance of using graph databases, particularly Neo4j, for the analysis of complex social structures. The developed software can be adapted to other topics, sources, or platforms, and its functionality can be extended for various types of social networks

Mathematical modeling of viral hepatitis B

2025-09-23T17:06:48+00:00

Studies of spatiotemporal models of viral infections have become an indispensable tool for biological researchers, as they can improve the understanding of the dynamics of the virus inside the target, in particular the population of viral particles in interaction with other populations, the mathematical models of which are described by the corresponding boundary value problems of mathematical physics for partial differential equations. Existing publications on spatiotemporal models of hepatitis B populations are limited to the formulation of mathematical models. Therefore, our goal is to develop numerical-analytical methods for the study of nonlinear spatiotemporal models with the subsequent use of these solutions for the synthesis of optimal control of doses of picaric drugs, with the aim of reducing (or completely neutralizing) the impact of viruses on the target organ. The application of the numerical-analytic iterative method, based on the use of the method of integral transformations, made it possible to obtain approximate solutions of the corresponding boundary value problems for systems of nonlinear partial differential equations obtained in quadratures, which makes it possible to simulate the synthesis of optimal control in real time (in contrast to the possible use of the corresponding difference schemes for this purpose

Method for combining pseudo-labeling and model compression to optimize self-training in object Identification tasks

2025-09-23T17:06:47+00:00

This scientific work examines a combined approach to optimizing training by means of self-training for object identification models, integrating a pseudo-labeling mechanism with a model compression technique through knowledge distillation. The main objective of the proposed method is to improve object classification accuracy under limited labeled data while simultaneously reducing the computational costs associated with training deep neural networks. The methodology involves automatically including unlabeled samples in the training set when the teacher model demonstrates high confidence, followed by using these samples to train a more compact student model. The training process employs soft probabilistic labels, enabling better transfer of generalized knowledge from teacher to student. Experimental results on the CIFAR-100 and ImageNet datasets show that the proposed method outperforms traditional self-training techniques across key metrics such as accuracy, precision, recall, and F1-score, and achieves higher training stability and efficiency. The findings confirm the potential of the combined approach for scalable deployment in real-world computer vision tasks, especially when labeled data is scarce or when deploying models on resource-constrained devices. An additional advantage is the reduced rate of incorrect pseudo-labels due to the application of a confidence threshold. The proposed training framework also improves generalization to unseen samples. These results can be used in future research aimed at enhancing training methods with partially or entirely unlabeled data.

Utilization of Bionic Principles in the Deployment of Kubernetes Orchestration Systems

2025-09-23T17:06:46+00:00

This article explores the possibility of applying bionic principles in the design of container orchestration deployment systems based on Kubernetes. The key components of the Kubernetes architecture are considered as an example of a complex dynamic system characterized by self-regulation, scalability, and adaptability. A modulation of the deployment of Kubernetes components is presented. Analogies are drawn between the functional modules of Kubernetes and the structures of living organisms, which makes it possible to identify similarities in organizational principles and functioning.

The study proposes the application of bionic concepts such as homeostasis, emergent behavior, and subjective systems, and outlines their implementation through modern technologies — in particular KEDA, Prometheus, Grafana, and Kubecost. Examples of configurations are provided to demonstrate the reactivity of the system to external events and workload changes, as well as its ability to optimize resource usage. Special attention is given to the prospects of Kubernetes evolving into an adaptive and self-protective platform through integration with machine learning methods and bio-inspired algorithms.

A comparative analysis of the organs of biological organisms and the corresponding structures of Kubernetes is conducted, along with an examination of the potential for building subjective systems based on its components. Since subjective systems can provide greater autonomy to Kubernetes components in the future, the study concludes that the bionic approach is a promising foundation for enhancing the resilience and flexibility of Kubernetes under unstable environmental conditions.

A method of resource management in cloud environments

2025-09-23T17:06:45+00:00

The article presents a comprehensive method of adaptive management of computing resources in cloud environments, aimed at increasing the efficiency of the use of infrastructures under conditions of dynamic and resource-intensive load. The proposed approach combines the mechanisms of hybrid time series forecasting using deep learning models (LSTM), statistical methods (ARIMA, Prophet) and multi-criteria optimization of the process of scaling and distributing tasks. Particular attention is paid to SLA-oriented classification of requests, which allows you to take into account the priority, criticality to latency, and computational intensity of tasks, increasing the level of quality assurance of service. The developed architecture of the method has a modular construction and provides for the integration of a predictive component, a context-oriented scheduler and a mechanism of adaptive load balancing that operates in real time. A feature is the use of a dynamic choice of a forecasting model depending on the characteristics of the load, which provides an increase in the accuracy of estimation of future resource needs. The task assignment algorithm implements multi-factor ranking of compute nodes based on latency, geographic proximity, load level, and power consumption, which helps to achieve a compromise between performance and economy. Practical confirmation of the effectiveness of the method was carried out by simulation modeling in the Kubernetes environment using real scenarios of variable query intensity. The experiments conducted demonstrated a significant reduction in the average response time and the number of SLA violations compared to basic reactive autoscaling strategies. Also, there was an improvement in the indicators of the level of resource use and a decrease in total energy consumption due to the flexible shutdown of excess capacities. The results of the study confirm the possibility of applying the proposed method in the conditions of highly dynamic traffic characteristic of service-oriented and edge-cloud architectures. Due to its ability to self-adapt and a hybrid approach to forecasting and resource management, the method can be effectively implemented in distributed computing systems that work with strict SLA requirements and increased requirements for energy efficiency. Based on the results obtained, the proposed solution is a promising basis for further research and practical use in the field of cloud technologies, IoT infrastructures and federated computing platforms.

Method for constructing neural network tools for recognizing the emotional tone of texts in universal computing devices

2025-09-23T17:06:43+00:00

This paper proposes a method for constructing neural-network tools to recognize the emotional coloring of Ukrainian-language texts on “universal” computing platforms (CPU/mobile devices/GPU). The method standardizes the stages of input encoding and neural-network output decoding into probabilistic estimates, and formalizes an algorithm for selecting the architecture based on resource constraints and accuracy requirements. Three branches are provided: lightweight models for tight computational budgets, sequential recurrent models for moderate resources, and Transformer-based models for quality-first scenarios. A unified pipeline for processing text fragments is presented: tokenization/vectorization, neural transformation, normalization, and selection of the dominant emotion. Experimental evaluation of two prototypes confirms the “quality–efficiency” trade-off: RoBERTa outperforms FastText in accuracy, but requires more parameters and exhibits higher inference latency (milliseconds versus microseconds). The method targets Ukrainian and mixed social-media content, and supports optimizations for resource-constrained platforms. Its practical value lies in a guided architecture choice and a reproducible development process that balance accuracy, speed, and energy consumption. Future directions include ensembles/hybrids, handling sarcasm, and expanding the set of emotions. The results show that a systematized approach enables adaptive solutions for real-time mood monitoring and integration into mobile and embedded systems. The proposed methodology is compatible with existing corpora, metrics, and the standards of reproducibility and open science.

Development of approaches to the choice of methodology for designing a smart system interface

2025-09-23T17:06:42+00:00

The article addresses one of the key problems of modern digital design – the integration of UX/UI into smart systems, since in today’s digital world high-quality user interface (UI) design is a crucial factor in the success of software products and services, while usability and positive user experience (UX) directly influence customer satisfaction and the competitiveness of companies. Due to the integration of computing and communication tools, the number of everyday objects acquiring “smart” characteristics is growing exponentially. This, in turn, significantly increases the importance of creating intuitive user interfaces for managing “smart” objects and systems operating in dynamic, context-dependent environments. The most popular methodological approaches to interface design are analyzed, their comparison carried out, and the advantages and disadvantages of each outlined. A number of criteria for selecting interface design methods for smart systems are identified and substantiated, including user-centeredness, development flexibility, rapid prototyping, scalability, and integration capabilities. The application of the analytic hierarchy process (AHP) is proposed to select the most appropriate interface design methodology, and its use is demonstrated with the example of a smart thermostat as a representative element of a “smart home.” The expediency of a combined approach in choosing methodologies for designing interfaces of intelligent systems is substantiated, which makes it possible to effectively take into account the specifics of different stages of the project life cycle. It is concluded that the further development of smart system interfaces will be determined by the increasing role of artificial intelligence, personalization, and multimodal interaction, while integration with augmented and mixed reality technologies will open new opportunities for ensuring more natural and convenient user interaction with digital services.

Interpreted sentiment analysis for the Ukrainian language based on rules

2025-09-23T17:06:41+00:00

Sentiment analysis plays a vital role in natural language processing (NLP), enabling automated evaluation of emotional tone in text across multiple languages. Yet, popular tools like VADER often fall short when applied to languages with complex morphology and syntax, such as Ukrainian. This study presents an enhanced rule-based sentiment analysis algorithm tailored specifically for Ukrainian-language content, addressing the limitations of generic, English-centric models. The algorithm incorporates an expanded lexicon that includes the EMOLEX sentiment dictionary, polarity scores, emoji sentiment mappings, and intensity modifiers to improve sentiment detection. It also leverages advanced techniques such as dependency parsing and position-aware scoring to better capture contextual nuances. These enhancements are necessary for correctly deciphering Ukrainian's distinctive linguistic structures, which frequently present difficulties for conventional sentiment analysis systems. The algorithm was evaluated using datasets in the Ukrainian language and compared to VADER. The custom model performs noticeably better than VADER, according to the results, especially when it comes to detecting strongly positive or negative sentiments. Because they provide greater accuracy and contextual awareness, these results emphasise the value of language-specific tools for non-English content. Even though the results are encouraging, more work is necessary. In order to create a hybrid system that can handle increasingly complex and ambiguous expressions with even higher accuracy, future research may investigate integrating AI techniques, such as machine learning and deep learning.

Identification of representative energy consumption patterns: development and comparative analysis of methodologies

2025-09-23T17:06:40+00:00

This paper presents the development and empirical evaluation of a methodology for identifying the most representative daily energy consumption pattern, a key task for optimizing Smart Grid systems, load forecasting, and demand management. The proposed approach focuses on optimizing data preprocessing and utilizing a multi-factor similarity metric. It includes a two-stage adaptive filtering of daily profiles based on peak and average power, which allows for the effective exclusion of anomalous and inactive days through dynamic thresholds calculated based on statistical indicators (median, percentiles), adapting to individual consumption characteristics. To select the representative pattern, a composite metric was developed that comprehensively integrates similarities in shape, power level, and the temporal characteristics of active periods. The scientific novelty lies in the hybrid filtering and direct medoid selection without full-fledged clustering, which simplifies computation and eliminates the need to predefine the number of clusters. The methodology provides a fast and justified determination of the most typical consumption profile, which can serve as a reliable baseline model for anomaly detection, consumer segmentation, or synthetic load data generation. Testing results on real data from the "ECO data set" confirm the approach's effectiveness for various electrical appliances. This research makes a significant contribution to the advancement of energy management methods. Prospects include the integration of "elastic" metrics, formalized clustering, and the development of sophisticated anomaly detection systems.

Integration of Sensor Data Collection IoT Systems Based on Piezoсeramic Sensors into the AWS Cloud Infrastructure

2025-09-23T17:06:39+00:00

The article explores the methods and tools for integrating IoT systems for sensor data collection based on piezoelectric ceramic sensors into the Amazon Web Services (AWS) cloud infrastructure. An architectural model of the IoT system is presented, taking into account the choice of communication protocols, standard interfaces, data transmission formats, and AWS services (IoT Core, Lambda, Timestream, DynamoDB, S3, CloudWatch). A comparative analysis is conducted on the applicability of various data transmission protocols in the context of limited computational resources and the specific characteristics of piezoelectric sensor signals. Special attention is given to the secure authentication of devices using X.509 certificates, as well as to the protection of data during transmission. A resilient cloud architecture approach is proposed using AWS availability zones. An example of an experimental implementation is described based on the ESP32 platform and muRata piezo elements, with data transmitted to the cloud and stored in Timestream. The research results demonstrate the effectiveness of the integrated system for vibration monitoring tasks and highlight the potential of such solutions in industrial and engineering applications.

Comparative Analysis of the Implementation of Classical Statistical Models for Univariate Time Series Forecasting in Python, R, and Julia

2025-09-23T17:06:37+00:00

This study presents a comparative analysis of the implementation of classical statistical models for univariate time series forecasting in the programming environments Python, R, and Julia. The research examines naïve and seasonal naïve methods, exponential smoothing (ETS), as well as the automatic parameter selection of autoregressive integrated moving average models (Auto-ARIMA). Two datasets were used for the experiments: daily Brent crude oil prices and monthly average wage indicators in Ukraine. Forecast accuracy was evaluated using three metrics — RMSE, MASE, and sMAPE — across different forecasting horizons. The results demonstrate that modeling outcomes depend not only on the mathematical nature of the algorithms but also on the specifics of their implementation in the respective ecosystems. It was found that Python and R provide full support for all the models under consideration, whereas Julia, at the time of the study, lacked stable implementations of ETS and Auto-ARIMA, which partially limited the possibilities for comparison. The findings are relevant both for assessing the maturity of software ecosystems in the field of time series analysis and for guiding the practical selection of tools in applied forecasting tasks.

esign of an Information System for Supporting Educational Processes in Higher Education Institutions

2025-09-23T17:06:36+00:00

The article explores topical issues of designing a modern information system designed to effectively support educational processes in higher education institutions (HEIs). In the context of increasing digitalization of the educational environment, continuous development of information infrastructure, and growing requirements for transparency and efficiency of educational management, the construction of flexible and scalable information platforms is becoming increasingly important. The author analyzes existing approaches to the organization of information systems in higher education and proposes a model based on the principles of Domain-Driven Design (DDD). The proposed model allows to clearly structure educational processes, reflect the internal logic of the institution's activities and ensure convenient interaction between different categories of users. The main components of the proposed information system are identified: modules for managing the contingent of students, administering curricula and programs, an evaluation module and the formation of official documents (including diploma supplements). Particular emphasis is placed on ensuring a high level of information security and protection of personal data through centralized authorization based on the OAuth2 protocol and the use of JWT tokens. In addition, the advantages of the microservice architecture of the system are described, which involves the use of separate databases for each module, which ensures the independence of components, ease of scaling and effective support of the system as a whole. The author outlines the prospects for further development of the information system through the integration of additional services, the use of analytics and intelligent systems, which will significantly expand the functionality and improve the quality of management decisions in higher education institutions.

Data aggregation in wireless sensor networks using computational intelligence technologies

2025-09-23T17:06:35+00:00

. The article considers the problem of effective data aggregation in wireless sensor networks (WSNs), which is important procedure for reducing energy consumption, minimizing excess transmission, and extending the network lifetime. Given the limited resources of WSN nodes, traditional data aggregation methods are not always able to function effectively in a heterogeneous environment. In this context, special attention is paid to the application of computational intelligence technologies, such as neural networks, genetic algorithms, fuzzy logic, which allow for adaptive aggregation depending on the characteristics of the environment and network. At the same time, the combination of fuzzy logic, evolutionary algorithms, and artificial neural networks in a single hybrid system makes it possible to use the strengths of each technology, ensuring high adaptability and accuracy of data aggregation. As part of the research, a model of a fuzzy inference system for data aggregation was developed, which takes into account such node parameters as residual energy, load, and distance to the base station. The model was implemented in the MATLAB environment using the Fuzzy Logic Designer tool. To increase the efficiency of the system, it was optimized using a genetic algorithm, which made it possible to adjust the membership functions. At the final stage, the model was transformed into an adaptive neuro-fuzzy inference system ANFIS, which was trained on the basis of simulated data in MATLAB. The simulation results confirm that the proposed approach provides flexible and energy-efficient control of the aggregation process in the dynamic environment of the WSN

Method for predicting web server load based on wavelet analysis of web logs and web traffic

2025-09-23T17:06:34+00:00

The article is devoted to the study and development of a method for web server load prediction using wavelet analysis as a tool for preprocessing weblog and web traffic data. The relevance of the research lies in the need to ensure stable performance of web services under the conditions of increasing intensity and uneven distribution of workload. The proposed approach is based on the application of the discrete wavelet transform (DWT), which provides a multilevel decomposition of time series and makes it possible to capture both global trends and local fluctuations in traffic. Such decomposition increases noise resistance and facilitates the detection of hidden patterns in the data. The preprocessed time series are further applied in deep learning models, particularly in recurrent neural networks such as LSTM or the Prophet framework, which demonstrate high capability in modeling nonlinear dependencies and long-term correlations. An experimental comparison was carried out to evaluate the effectiveness of load prediction with and without wavelet-based filtering. The results confirmed a reduction in the mean prediction error, improved request balancing within the cluster, and increased overall fault tolerance of the system. The obtained findings have practical value for the design of intelligent resource management systems and can be integrated into monitoring solutions to enhance the efficiency and reliability of web services operating under dynamic load conditions

Energy-efficient autonomous greenhouse ventilation system with remote intelligent control

2025-09-23T17:06:33+00:00

The article develops an autonomous ventilation system for a greenhouse system powered by solar energy and remote smart control, which includes a solar panel, a battery and a charge controller, a fan/heater as actuators, temperature and light sensors, and a controller. At the same time, the software control logic is integrated into the Node-RED environment, implementing the reading of temperature and light indicators, an algorithm for automatically turning on/off ventilation when a given temperature threshold is exceeded, and analysis of the light level as a conditional indicator of the intensity of solar energy generation. The system provides autonomous power supply for the system by optimizing electricity consumption according to the available charge and lighting conditions. For this, it is necessary to develop a graphical interface for monitoring parameters in Node-RED, which allows the user to monitor the temperature, ventilation and power status in real time and perform system testing in a real greenhouse, assess the stability of operation, energy efficiency, and adaptability to changes in climatic and energy conditions.

Functional representation of 3D objects as a method of data generalization in generative machine learning models

2025-09-23T17:06:32+00:00

The article proposes a new method for representing three-dimensional objects through scalar functions of a vector argument. This method of representation allows reducing the function to a series of normalized vectors, which is a key requirement for building machine learning models of Transformer class. The need to develop such a method is based on a general conceptual hypothesis about the possibility of using the currently most successful type of generative neural networks, which is central in the field of large language models (LLM), to generate 3D objects with specific physical properties. This hypothesis follows from the fact that Transformer-like models (in particular, GPT) have already been applied to generate two-dimensional images, therefore, with correct representation of 3D object’s shapes, it is possible to extend their application to 3D modeling. The results of testing the proposed method showed high accuracy in collapsing the “point cloud” of the 3D model surface into a series of vectors (embeddings) and restoring the original figure from this series. The demonstrational implementation of the method has the drawback of inaccuracy in function interpolation, but this is not a limitation of the method itself, but only of its technical implementation. The real limitation of the method is its narrowing down to only one class of topological spaces. Further research will be aimed at generalizing this method to all possible types of topologies

Interactive scoreboard on addressable LEDs

2025-09-29T15:05:10+00:00

The article addresses the urgent problem of timely public notification during emergencies using modern IoT technologies. The author analyzes the limitations of traditional alert systems, such as mechanical sirens and broadcast networks, which often lack flexibility and fail to provide detailed information. The paper substantiates the development of an interactive display board based on addressable LEDs, capable of presenting visual signals, graphical messages, and complementary audio alerts. The hardware design employs an ESP32 microcontroller with integrated Wi-Fi and Bluetooth modules, enabling real-time data retrieval through the UkraineAlarm API. Visual indication is implemented with a WS2812B LED matrix, while audio feedback is supported by the MAX98357 sound module. The article provides a detailed description of the software architecture, including modules for network management, API communication, data visualization, and device configuration. Prototype testing demonstrated fast response times of approximately two seconds and stable operation even under unstable network conditions. The obtained results confirm the effectiveness of the interactive board as a tool for civil protection and emergency communication. Future research directions include expanding functionality with autonomous power supply, integrating mobile applications, and applying artificial intelligence algorithms for multi-source data analysis and threat prediction, thereby enhancing resilience and public safety.

Adaptive routing in OSPF networks with distrustful nodes

2025-09-23T17:06:29+00:00

This article addresses the problem of ensuring QoS (Quality of Service) in networks using the OSPF (Open Shortest Path First) protocol while considering trust in relay nodes. A routing approach is proposed that takes into account traffic transmission requirements regarding delays and bandwidth while considering the presence of untrusted nodes in the network environment. The method developed based on this approach allows route adaptation depending on traffic type, ensuring security for confidential data and efficiency for real-time processed data. Simulation showed that in the case of the presence of nodes in the network connected by enhanced quality channels but with low trust, the proposed method ensures traffic delivery to such nodes but does not use them as transit nodes when alternative paths to the destination node exist. The advantages of the method include consideration of bandwidth and delays during routing and selection of secure routes that do not include untrusted nodes if such routes exist. This allows its use in computer networks based on the OSPF protocol for creating adaptive routes.

Developing responsive web design based on Design Patterns

2025-09-24T06:00:31+00:00

The article explores the use of design patterns in the development of adaptive web design for corporate landing pages. Landing pages are an effective tool for a concise and understandable presentation of a company on the Internet. Landing combines static and interactive elements, which increases user interest and improves the impression of the company. Adaptive design ensures correct display of the page on different devices (smartphones, tablets, PCs), which helps to expand the customer base. Design patterns increase the structure, organization and ease of maintenance of sites, promote the reuse of components and the implementation of complex behavior. The use of 8 patterns is researched: the facade creates a simplified interface for interacting with complex subsystems of the site, hiding technical details; the observer is responsible for the interface's reaction to user events or data changes, which increases the dynamism of the page; the factory method allows you to flexibly create interface elements, which ensures extensibility and adaptability; the command encapsulates user actions, facilitating their processing and reuse; the state controls the site's operating modes, responsible for changing the interface behavior depending on the context; The wrapper creates an abstraction around complex objects, hiding implementation details and simplifying work with components on different devices; the decorator allows you to dynamically add functions or styles to interface elements without changing the main code, which is useful for adapting the appearance to different screens; the strategy provides selection and dynamic change of display and interaction with the user depending on the context. This approach ensures high quality architecture, optimal performance and flexibility of the site.

Methods and tools for modeling Wi-Fi coverage areas and the impact of interference on signal quality

2025-09-23T17:06:27+00:00

Methods and means of modeling Wi-Fi coverage areas and the influence of interference in local wireless computer networks based on Wi-Fi technology have been studied. The relevance of the problem is determined by the growing requirements for signal quality under conditions of intensive Wi-Fi use in offices, industrial facilities, and urban environments. To address this issue, an analysis of modern methods for modeling coverage areas has been carried out, which are classified into physical (transmitter power control, use of directional antennas, repeaters) and software-based approaches (NS-3 and OMNeT++ simulators, Ekahau and AirMagnet CAD tools, mathematical models of signal propagation). Particular attention was given to modeling with the NS-3 and OMNeT++ simulators, which enable reproduction of PHY- and MAC-layer protocol behavior for Wi-Fi standards 802.11a/b/g/n/ac/ax/be. Practical modeling of the coverage area for a segment of the Petro Mohyla Black Sea National University’s network was performed using the ITU-R P.1238 mathematical model, with signal heat map visualization in MATLAB. The results confirmed the importance of optimal access point placement for minimizing losses and improving signal quality. In addition, a simulation in the NS-3 environment was conducted, allowing assessment of the impact of interference and distance on the quality of service. The findings highlight the effectiveness of a combined modeling approach and confirm the need for further research into next-generation Wi-Fi 6 & Wi-Fi 7 technologies with MU-MIMO and OFDMA support.

Analysis of approaches to minimize false results in the process of detecting anomalies by cyber defense systems

2025-09-23T17:06:26+00:00

In the context of researching the problem of detecting unknown (new) cyberattacks on information and communication systems (ICS), the feasibility of an anomaly detection approach, which demonstrates the greatest potential among alternative methods, is substantiated. The types of errors that occur during anomaly detection using the most common methods, as well as the causes of their occurrence, are examined: low data quality and incompleteness, concept drift, class imbalance, and contextual exploitation factors. A comparative analysis of approaches aimed at reducing the number of false results in the process of anomaly detection by cybersecurity systems is presented, and their advantages and disadvantages are described. A domain-oriented scheme for assessing the threat of anomalies is proposed: an event is analyzed according to a relevant domain (memory, file system, network, access, database) with predefined feature pools. Three components are evaluated: the predictive consistency of the event chain (the discrepancy between the expected and actual trajectory), contextual consistency based on the "subject-action-object" architecture, and changes in the ICS state before and after the anomaly. The combination of predictive, semantic, and system state information makes it possible to reasonably distinguish technical failures from targeted attacks and reduce the probability of false conclusions in scenarios involving unknown cyberattacks. Additionally, a contextual history of suspicious events is maintained to track potential cyberattacks distributed over time. The proposed scheme is considered a basis for formalizing criteria, algorithms for selecting domains and their corresponding features, decision-making rules, and verification procedures aimed at the development of intelligent cybersecurity systems for critically important ICS

Feedback control of nonlinear objects

2025-09-23T17:06:24+00:00

Automatic feedback control of nonlinear objects is a relevant direction in the control of such objects. Optimal control of linear dynamic objects is widespread. For nonlinear objects, there is a known approach based on the use of the maximum principle, the so-called program control, when the problem of determining optimal motion trajectories is solved without taking into account the deviation of the object's state variables from the response of the control system (signals at the output of the control system). An analytical review of literature sources is performed, which offer approaches to modeling the problems of optimal control of nonlinear objects, in particular, the solution of the Riccati matrix equation. In the first section of the article, a method for solving the Riccati matrix equation is proposed, based on an iterative numerical-analytic approach, which provides an opportunity to obtain a solution of the Riccati matrix equation in quadratures with further use to determine the optimal trajectories of an object subject to automatic control. In the second section of the article, a method for optimal control of a biomedical object, which are inherently nonlinear objects, is proposed using the example of optimal control for determining the optimal dose of drugs. To determine the optimal trajectory of the corresponding system of nonlinear ordinary differential equations, an iterative numerical-analytic method for solving the corresponding Cauchy problem is used. The results of mathematical modeling are presented.

Federated learning in IoT networks

2025-09-23T17:06:23+00:00

The development of the Internet of Things (IoT) creates specific requirements for machine learning related to the distributed nature of data, device resource constraints, and information privacy. Federated Learning (FL) is a distributed machine learning paradigm that enables model training on edge devices without centralizing raw data. The article provides a systematic review of federated learning methodologies for IoT networks. Adaptive optimization algorithms (EAFO, FedEAFO), communication compression techniques, privacy preservation methods (FedHDPrivacy), and architectural solutions for heterogeneous IoT environments are analyzed. Specific IoT-FL challenges are considered: statistical heterogeneity (non-IID data), system heterogeneity of devices, energy constraints, and network connectivity dynamics. A multidimensional classification of FL methods by architecture, optimization, and privacy protection criteria is presented. Experimental results demonstrate achieving communication compression up to 100x with minimal accuracy loss. Open problems of scalability to millions of devices and adaptation to dynamic topologies are identified. Promising directions are defined: continual learning, integration with 5G/6G networks, and neuromorphic computing for ultra-low-power IoT devices.

A method for detecting intrusions in wireless sensor networks based on specification rules and an artificial neural network

2025-09-23T17:06:22+00:00

A probabilistic (stochastic) monitoring strategy based on Nash's Bayesian equilibrium was used, which allowed BB agents to automatically optimize their behavior depending on the current threat level and their own resources, as well as dynamically change their strategies, ensuring the flexibility and adaptability of the system to changes in the behavior of malicious nodes. Specification rules have been developed based on a combination of sensor node indicators that affect the quality of information transmission. The status of nodes is tracked by intrusion detection agents that passively monitor the network. A multilayer neural network is used to dynamically update the threshold values of sensor node indicators in the specification rule database used by intrusion detection agents. A method for detecting intrusions in wireless sensor networks has been developed and formalized by combining a probabilistic strategy for monitoring the status of a sensor node by intrusion detection agents based on Nash equilibrium, rules for combining sensor node indicators, and detection of anomalous nodes using a multilayer neural network. This made it possible to adaptively track anomalies and detect network-level attacks, preventing the dangerous influence of malicious nodes by blocking them.

6G-IoT technology application scenarios in next-generation infocommunication networks

2025-09-23T17:06:21+00:00

The article investigates the architectural, functional, and technological aspects of shaping an intelligent urban environment based on the Internet of Things (IoT) and sixth-generation (6G) mobile communication technologies. A conceptual model of a smart city is presented, taking into account the features of scalable IoT infrastructure, decentralized data processing, and the application of artificial intelligence (AI) to optimize urban processes. Key vectors of IoT implementation in smart cities are analyzed, including environmental monitoring, energy management, transportation logistics, waste management, public safety, and citizen communication. The potential of 6G technologies is revealed as the foundation of an ultra-fast, scalable, and intelligent communication platform supporting the implementation of augmented/virtual reality (AR/VR), holographic communication, autonomous transport, telemedicine, and remote education. Special attention is paid to the impact of 6G networks on the development of next-generation medical services, including remote consultations, real-time surgery, continuous patient monitoring via the Internet of Medical Things (IoMT), the use of AI in diagnostics and treatment planning, and the protection of sensitive medical data. The prospects of integrating autonomous transport with 6G network infrastructure are described, aimed at enhancing safety, mobility efficiency, and environmental sustainability amid urbanization. The paper highlights the synergy of AI, edge/fog computing, quantum cryptography, blockchain, and virtualized networks in urban governance and digital control. The challenges of implementation are identified, including the technical limitations of high-frequency communications, ensuring cybersecurity, regulatory adaptation, digital inequality, complexity of inter-network integration, and the need for standardization. Based on the results obtained, strategic approaches are proposed for developing a sustainable, adaptive, high-tech, and innovation-oriented urban infrastructure capable of supporting intelligent services and sustainable development.

6G physical layer technologies in telecommunication networks

2025-09-23T17:06:19+00:00

The article explores the conceptual foundations and technical solutions aimed at designing Next-Generation Air Interfaces (NGAI) capable of meeting the target performance indicators of sixth-generation (6G) networks. These targets include enhanced spectral efficiency, ultra-low latency, scalability, energy efficiency, cybersecurity, resilience to dynamic transmission environments, and the integration of intelligent control functions. The evolution of access technologies up to 5G is analyzed, identifying key architectural, functional, and protocol-level limitations that hinder the full realization of 6G requirements. The study summarizes the main development directions for NGAI, including the implementation of cognitive radio with intelligent spectrum analysis, non-orthogonal multiple access (NOMA), rate-splitting multiple access (RSMA), massive MIMO with narrow-beamforming, terahertz-band communication, and the use of reconfigurable intelligent surfaces (RIS) for adaptive channel control. The role of machine learning (ML) and deep learning (DL) algorithms in these areas is emphasized. Special attention is given to the influence of AI/ML on dynamic radio resource management functions such as spectral environment adaptation, traffic load prediction, self-optimization, and real-time cognitive decision-making. The article examines innovative modulation and coding schemes, including geometric QAM, LDPC codes, Polar codes, compressive sensing, and orbital angular momentum (OAM)-based multiplexing. An architectural model of an integrated air interface is proposed, supporting Fog/Edge/Mist/Cloud computing infrastructures, XR services, the Internet of Everything (IoE), autonomous systems, and mobile agents. The potential of Cell-Free Massive MIMO and non-terrestrial network (NTN) components is explored within the 6G-SAGIN (Space–Air–Ground Integrated Network) architecture. Systemic approaches to implementing energy-efficient, adaptive, secure, and intelligent NGAI are substantiated, positioning it as a key enabler of the 6G infocommunication ecosystem, capable of merging physical, digital, and cyber spaces into a unified functional platform of the future.

Scientific and technical foundations for the efficient operation of 6G telecommunication networks

2025-09-23T17:06:18+00:00

The article presents a comprehensive analysis of the key development vectors of sixth-generation (6G) mobile communication technologies, which are considered the foundation for building a high-performance, secure, adaptive, and energy-efficient infocommunication infrastructure of the future. The study reviews contemporary scientific publications highlighting the strategic directions of wireless network evolution in the context of increasing demands for data transmission speed, latency, scalability, reliability, and security. The core functional objectives of 6G are substantiated, including achieving terabit-level data rates, sub-millisecond latency, support for massive hyperconnectivity, integration of artificial intelligence (AI) into network management, energy efficiency, and data confidentiality protection. Particular attention is paid to promising technologies that form the technical foundation of 6G: terahertz communications, massive MIMO, holographic communication, cognitive spectrum management, blockchain solutions, quantum-secure protocols, energy-autonomous devices, and edge/fog computing. The primary challenges are systematized, including the complexity of THz band utilization, growing cyber threats, lack of standardization for ultra-high frequencies, increasing energy consumption demands, and ethical considerations related to AI deployment. The research outlines architectural approaches to 6G implementation, including AI-based orchestration, service decentralization, support for emerging scenarios (AR/VR/XR, IoNT, BCI, holographic communication), flexible resource management, and the integration of terrestrial and non-terrestrial segments. Strategic directions for further research are identified, such as the development of nanoscale components for antennas and sensors, modeling of intelligent network management, enhancement of secure communication protocols, and the development of standardized performance validation methods. The proposed analysis provides a conceptual basis for designing a reliable and scalable sixth-generation network architecture.

6G mobile network infrastructure optimization

2025-09-23T17:06:17+00:00

The article presents an in-depth analysis of a comprehensive approach to ensuring the energy efficiency of hardware for sixth-generation (6G) networks as one of the key factors in the sustainable development of telecommunications infrastructure under the growing demand for high-speed, reliable, and environmentally balanced communication services. The study examines concepts and practical solutions aimed at reducing energy consumption while maintaining or improving the performance of network components, including base stations, user terminals, and data transmission and processing systems. The necessity of employing artificial intelligence (AI) and machine learning (ML) algorithms for dynamic equipment operation management, load forecasting, and adaptive real-time power consumption regulation is substantiated. Prospects for integrating renewable energy sources, energy harvesting technologies, and wireless power transfer (WPT) systems are outlined, enhancing node autonomy and reducing dependence on conventional energy resources. The role of 6G in enabling high-precision environmental parameter monitoring, ecological mapping, and public environmental oversight through large-scale sensor networks and holographic communications is highlighted. A review of state-of-the-art channel modeling methods and signal propagation characteristics in the terahertz band is provided, taking into account multipath effects, massive MIMO utilization, intelligent beamforming, hybrid antenna arrays, and adaptive modulation and coding schemes. Strategies for optimizing performance and quality of service (QoS) indicators are defined, including the integration of AI/ML solutions, edge/fog computing, cognitive spectrum management, network slicing, and service-oriented architectures. The paper analyzes technical, regulatory, and environmental challenges of 6G deployment, such as standardization, cybersecurity resilience, frequency resource harmonization, carbon footprint reduction, and life-cycle energy efficiency of equipment. Directions for interdisciplinary research are identified, focusing on the development of adaptive, fault-tolerant, scalable, and energy-efficient next-generation telecommunication systems capable of meeting the growing needs of the digital economy and society. The obtained results can be applied in the formation of green communication concepts, the design of energy-efficient network architectures, the development of power consumption management strategies, and the implementation of technologies that combine high throughput, low latency, flexible resource management, and environmental responsibility.

Tropospheric Communication with UAV Relay: Impact of Meteorological Conditions and SDR Validation

2025-09-23T17:06:16+00:00

The paper considers the possibilities of using unmanned aerial vehicles as high-altitude relays for temporary deployment of tropospheric communication networks in emergency conditions. Modern approaches to long-range radio communication organization are analyzed and their limitations are outlined. A concept of a hybrid system at 5 GHz frequency with UAV at 200-1000 m altitude is proposed, combining the advantages of tropospheric scattering and line of sight. A methodology for assessing the impact of precipitation, fog and snow on maximum communication range based on link energy balance is developed. Simulation results are compared with experimental data obtained on an LTE training model based on the LimeSDR SDR platform. It is shown that moderate rain reduces the range by almost half, but increasing the UAV altitude partially compensates for losses. Recommendations for adaptive system operation in various meteorological conditions are proposed. The practical value of the work lies in creating a methodology for designing communication systems with UAV relays, taking into account real operational limitations. The obtained results can be used in the design of emergency communication systems for rescue services, military units and humanitarian missions.

A system for destroying FPV drones controlled via fiber optic communication lines using barriers with pulsed electric discharge

2025-09-23T17:06:15+00:00

This article describes an innovative approach to countering FPV drones equipped with fiber-optic communication systems by creating high-temperature electro-thermal barriers. The methods for destroying the fiber-optic cable are based on heating or pulsed discharges, which cause the fiber to burn upon contact with a conductive wire. Two technologies are compared: constant heating of a nichrome wire and pulsed high-current discharges of steel wire. The technical implementation of the system is provided, including energy storage, high-voltage switches, and controllers, enabling the creation of modular protection networks for rear facilities. An analysis demonstrates the advantages of pulsed systems, particularly their masking, efficiency, and the potential for long sections. The choice of materials and circuit design for creating localized high-temperature destruction zones of optical fibers is justified. The proposed technology offers promising prospects for developing passive, safe, and scalable means of protecting objects from next-generation unmanned systems that utilize fiber-optic control channels.

Model of a crewless boat

2025-09-23T17:06:11+00:00

At this time, in order to conduct hostilities on the fronts of the Russian-Ukrainian war, the share of autonomous controlled systems for defeating the enemy on the ground, in the air and on the water is significantly increasing. This work is devoted to the basics of the construction of such systems, which presents the principles of design and construction of unmanned surface vehicles (USV) using available technologies and materials. Based on the review of modern USVs in the form of single and multi-housing structures from various manufacturers, the advantages of the trimaran body that can be used to fulfill the set goals are shown. Therefore, the work proposes the option of creating a light three-hull apparatus from available materials. For its implementation, sketches of hull parts are given, taking into account the peculiarities of hydrodynamics of small swimming facilities, and can be implemented without the use of metalworking machines. The presented procedure for the manufacture and assembly of the UVS corps allows not only to replicate them by the forces of the student community, but also to complete the product with the necessary sufficiently complex equipment for performing real combat missions. In addition, sufficient attention is paid to the description of the design and features of the system for controlling the direction of movement of the device and sealing the hull in the area of the propeller shaft.

Mathematical Model of an Information and Control System for Detecting Unmanned Aerial Vehicles and Loitering Munitions

2025-09-23T17:06:10+00:00

The article addresses the urgent scientific task of increasing the efficiency of detecting unmanned aerial vehicles (UAVs) and loitering munitions (LMs) under combat conditions by employing adaptive information-control systems (ICS) that integrate various types of sensors within wireless sensor networks (WSNs). The study analyzes modern approaches to the use of radar, electro-optical, and acoustic surveillance tools, particularly considering the results of deep learning and fusion algorithms. It is demonstrated that the integration of data from heterogeneous sensors ensures a high reliability of target detection even in the presence of interference, limited situational awareness, and maneuvering targets.

In the current context of countering high-tech threats, the application of fusion algorithms is especially relevant, as they enable the effective combination of information from diverse sensors. Such algorithms are a crucial component of ICS, as they allow for synergistic data processing, improve recognition reliability, and minimize false alarm rates even in complex combat environments.

A formalized probabilistic-adaptive model for detecting aerial objects is presented, which accounts for time constraints, sensor beam steering, network connectivity, and false alarm minimization. The proposed objective function enables optimization of the trade-off between detection speed and accuracy. Additionally, the article presents a mathematical framework that considers spatial constraints of the area of responsibility, predicted UAV trajectories, and their motion characteristics. The use of probability thresholds for decision-making enables the system to adapt to dynamic changes in the combat situation.

This article lays the foundation for the development of highly effective tactical-level ICSs that take into account real combat conditions and the need for rapid response.

Microservice Architecture of an Adaptive Authentication System for Users of Financial Institutions

2025-09-23T17:06:09+00:00

The article addresses the problem of developing a microservice architecture for an adaptive authentication system of financial institutions’ users as a key direction for enhancing the security, scalability, and flexibility of modern information systems. The authors analyze current authentication methods, including single-factor, two-factor, and multi-factor approaches, and emphasize the particular importance of adaptive authentication, which provides dynamic adjustment of identity verification parameters depending on the assessed risk level of an operation. The study explores methods successfully applied in the financial sector, such as behavioral biometrics, contextual authentication, anomaly detection, and the use of machine learning algorithms. The proposed conceptual model implements a division of functionality into independent microservices, which facilitates flexibility, modularity, and easy integration with national and international identification standards (including BankID and Diia services). Special attention is given to the visualization of architectural dependencies, UML diagrams, and the use of modern client- and server-side software development tools such as Vue.js, Nest.js, and Spring Boot. The system provides multi-channel authentication (email, SMS, QR code), supports load balancing, and incorporates fault tolerance checking, which are critically significant for financial organizations. The results of the study may serve as a foundation for implementing scalable and reliable cybersecurity solutions, as well as a basis for further research in the field of adaptive authentication.

Data mining methodology for finding ergonomic reserves to increase management efficiency. Method for preventing errors by ERP system operators

2025-09-23T17:06:07+00:00

The article considers the task of increasing the efficiency of automated information processing for enterprise resource planning (ERP) systems. It is shown that in the era of digital transformation, ERPs are becoming critically important information structures that support the automation and coordination of business processes. It is demonstrated that most approaches to ERP reliability analysis, which focused, as a rule, on technical aspects such as data security, software stability or infrastructure stability, do not provide effective means of finding ways to improve ERP efficiency. It is shown that the human factor remains the main source of operational risks. Poor interface ergonomics, excessive navigation depth, lack of contextual guidance or cognitive overload often lead to errors, reduced efficiency and disruption of data integrity. In connection with the growing threats of neglecting the human factor, the article considers the task of identifying hidden patterns between user interaction, interface characteristics and the probability of errors. The methodology of data mining is used, in particular the principle of associative rules. It is shown that, unlike traditional statistical approaches, association rules allow to detect non-obvious dependencies in user behavior, forming the basis for adaptive, proactive decision support. An experimental ERP environment is proposed for collecting real interaction logs, user profiles, and error reports. The generation of over 500 association rules is demonstrated, of which 64 provided high stability and repeatability, which allowed the development of a decision support module integrated into the ERP interface. Empirical results show significant improvements: a 24% reduction in critical errors, a 12% reduction in task execution time, and a 23% reduction in cognitive load (NASA-TLX). The results confirm the potential of association rule analysis as a methodological basis for designing adaptive ERP systems, with an emphasis not only on technical but also on ergonomic and cognitive aspects of human-machine interaction. The results have been used in ergonomic ERP design in industry, agricultural production, and e-learning. It is proposed to use the results in the international scientific project "International Center for the Support of Ergonomic Design and Research" within the framework of the International Ergonomics Association.

Analysis of the current state of functioning of server clusters of information systems and load management methods

2025-09-23T17:06:06+00:00

The article analyzes the current state of functioning of the server infrastructure of information systems and methods of managing the load on server clusters. The main factors affecting the efficiency of work are identified: the growth in the number and complexity of client requests, limited hardware resources, the impossibility of scaling the infrastructure, as well as the insufficient performance of existing load balancing methods. It was found that most modern algorithms have high computational complexity, low resistance to failures and significant dependence on the quality of training data. The study considered classical and intellectual approaches to the optimal use of cluster system resources. Adaptive methods based on game theory, in particular Nash equilibrium, were recognized as the most effective. This approach allows you to formalize the interaction of servers with requests without the need for training, ensuring resistance to deviations. The scientific novelty lies in using Nash equilibrium for optimal load distribution taking into account the current state of servers and the possibility of task decomposition. The proposed method is the basis for creating new adaptive load balancing systems.

GIS tools for analysis and modeling of emergency situations (on the example of fires in Volyn region)

2025-09-23T17:06:04+00:00

The analysis of remote sensing materials using geographic information systems and technologies is important for research and preventing adverse natural phenomena, such as fires. In our article, we gave examples of fire research using GIS. The simplest method is fire analysis using the FIRMS web service. This method does not require any prior skills and abilities, allows you to record the existence of a fire and generally estimate its scale. The Copernicus Browser service allows you to work with multispectral satellite images from Copernicus missions for fire detection and analysis. The most promising is the study of bands obtained mainly in the near infrared and shortwave infrared bands. The greatest opportunities for analysis and visualization are opened during the analysis of satellite images in GIS. Thus, the calculation of the normalized burn ratio allows estimating the long-term consequences of fires. Satellite data and maps of fire intensity are of great practical importance. They can help develop emergency rehabilitation and revegetation restoration plans after a fire. They can also be used to assess the likelihood of future impacts on burned areas from floods, landslides, and soil erosion.