Introduction. A digital twin dynamically reflects and simulates the behavior of its physical counterpart. To predict the behavior of a digital twin under certain conditions, it is necessary to create a large database and develop a software model. Artificial intelligence, smart sensors, 5G, cloud computing, VR/AR and blockchain are technologies that provide digital twins with intelligence, the ability to work constantly in real time and increased security. The purpose of the study is to demonstrate the possibilities of using digital twin technology to predict the remaining life of aircraft structures in order to ensure timely maintenance and optimize costs. Results. The paper substantiates the use of software and hardware for digital twins, which provides significant potential for optimizing processes and increasing efficiency in many industries The main hardware includes sensors and transducers: sensors for temperature, pressure, humidity and other physical parameters; cameras for visual monitoring. Computing devices: servers for real-time data processing, graphics processing units (GPUs) for processing complex models; data transmission devices: network devices for fast data transmission (5G, LoRaWAN); cloud services for data storage and synchronization (AWS, Azure). Further development of the technology is important to ensure greater accuracy and integration. Conclusions. Digital twins help improve productivity by enabling teams to collaborate in real time to accelerate and improve decision-making. Their wide range of possible applications is making them increasingly important in business and industry. The use of digital twins provides a comprehensive effect, which is manifested in reducing costs and waste, increasing productivity, improving product quality and storage conditions, as well as reducing production cycle times and lead times.
Introduction. Contemporary techno-informational systems are increasingly acquiring the characteristics of complex, multi-level human–machine formations with pronounced cognitive features, in which quasi-cognitive artificial intelligence is employed for information processing, adaptation, and decision-support functions. At the same time, the prevailing information–algorithmic approaches to their analysis fail to account for fundamental properties of the systems under consideration, namely thermodynamic constraints, as well as the role of cognitive and semantic processes that are critical for the stability of such systems. This necessitates the extension of classical entropy-based models and the integration of topological, cognitive, and semantic components into them. The subject of this study comprises methods of thermodynamic analysis based on entropy–topological models and the determination of conditions for their thermodynamic and semantic stability when analysing complex techno-informational systems while taking into account their incommensurable properties. The purpose. The aim of the work is to investigate complex techno-informational systems involving cognitive (human) and quasi-cognitive (artificial intelligence, AI) agencies, considering the incommensurable properties of these systems, by means of thermodynamic analysis using entropy-topological models, and to determine the conditions of their thermodynamic and semantic stability. Results. To achieve this aim, a hierarchical model of system organisation levels-from the physico-energetic to the socio-normative levels - proposed, along with its formalisation in the form of an entropy-weighted hypergraph with multidimensional adjacency matrices. The corresponding scientific novelty lies in the introduction of a set of fundamentally irreducible forms of entropy (physical, informational, cognitive, semantic, socio-normative, etc.), in the formulation of a generalised criterion of thermodynamic-semantic stability of the system, and in the theoretical proof of the impossibility of fully substituting a cognitive agent with artificial intelligence at a certain semantic level. Conclusions. It is demonstrated that AI effectively reduces informational entropy but is not capable of autonomously reducing semantic uncertainty, which establishes fundamental limits to its applicability. The applied relevance of the work consists in the possibility of using the proposed method for the analysis and design of hybrid human-machine systems, for assessing the role of AI in control, learning, and interface solutions, as well as for substantiating human-in-the-loop architectures from the standpoint of thermodynamic and semantic stability.
Introduction. The article addresses the relevant scientific and practical problem of developing and substantiating a subsystem for intelligent server monitoring and load forecasting in IP telephony networks. It highlights the critical importance of transitioning from traditional reactive observation of telecommunication nodes to proactive infrastructure management. This is particularly vital for digital educational ecosystems, where the time series of telephone traffic exhibit a complex structure with pronounced daily and weekly seasonality, directly influenced by academic schedules, consultations, and examination periods. The purpose of the paper is to develop a software module utilizing the Holt–Winters method (triple exponential smoothing) for short-term traffic forecasting. Results. A detailed comparative analysis of existing monitoring tools (Zabbix, Prometheus, Homer) was conducted, identifying a significant gap between hardware resource tracking (CPU, RAM) and specific SIP signaling parameters. It was established that most systems focus on historical data analysis, lacking tools for predicting future peak loads. The scientific novelty lies in the experimental determination of the optimal model parameters (α = 0.2; β = 0.05; γ = 0.1), ensuring high stability against anomalous spikes and noise typical of VoIP networks. It is demonstrated that using this method achieves a MAPE accuracy of < 15 % without the need for resource-intensive neural network architectures. The practical value of the results involves integrating the developed algorithm into the modern VictoriaMetrics and Grafana technology stack. This solution automates the detection of overload risks and ensures the stability of communication services during peak periods of educational activity. Conclusions. The findings establish a methodological foundation for the further implementation of decision support systems within the IT infrastructure of educational institutions.
Introduction. This article presents a brief overview of modern artificial intelligence (AI) architectures and models for use in digital platforms. This review identifies emerging trends, advantages of modern models, and promising areas of application, which are driving the development of both scientific and industrial technologies. The article provides information on the functional features and shortcomings of AI models and architectures and draws conclusions about the future direction of AI platform development. The purpose. The purpose of this article is to provide a brief overview of the main AI architectures and models, substantiating the methodological and theoretical shortcomings of existing approaches and predicting future development paths for AI platforms. Results. The main types of AI architectures used in digital platforms are briefly described. Dedicated, multi-tenant, and hybrid architectures are compared. Modern linguistic models of artificial intelligence, which are fundamental for the development of AI platforms, are listed and their main shortcomings are outlined. A list of alternative approaches to creating AI models is presented, and it is concluded that event-driven models are the most promising when used as foundational models for digital platforms. A list of companies and their approaches to digital intelligence based on event-driven models is provided, which enable not only event tracking but also event prediction. In conclusion, the article compares the key characteristics of the LLM, EDM, and SEM/RSM models that dominate modern digital platform architectures. Conclusions. The development of architectures and models for AI platforms requires addressing the existing methodological gap caused by the infatuation with large linguistic models among AI platform architects. To overcome this methodological gap, a new cognitive architecture is needed, based on the principles of "Platform Thinking," that is, a transition to a subject-event approach based on Situational-Event Models (SEM) and Role-Subject Models (SRM). Furthermore, multi-tenant configurations will likely remain an important area of development for the creation of universal AI services.
Introduction. The proliferation of fixed-wing Unmanned Aerial Vehicles (UAVs), such as loitering munitions, presents a significant challenge to airspace security. Detection relies heavily on Thermal Infrared (TIR) imaging for 24/7 passive monitoring. A critical challenge in developing these systems is the "Multiscale Approach" problem: tracking a target as it rapidly transitions from a distant sub-pixel dot to a close-range resolved object. This transition is a critical failure point for modern defense systems, known as the "handover" problem in SHORAD. Existing datasets fail to capture this continuous evolution due to the dangers and costs associated with filming air-to-air collision courses. This data vacuum hinders the development of robust Counter-UAS (C-UAS) algorithms, as traditional synthetic data often lacks the thermodynamic fidelity of real sensors. The purpose of the paper is to introduce Gen-Thermal-UAV, a novel synthetic dataset designed to fill this gap, and to propose a "Seed-Driven" methodology utilizing advanced video diffusion models (Gemini Veo 3). This approach aims to generate high-fidelity synthetic videos that maintain authentic sensor characteristics while simulating diverse flight trajectories, enabling the training of end-to-end trackers robust to extreme scale changes. Methodology. We employed a Seed-Driven Generative AI pipeline. Instead of generating data from scratch, we used Image-to-Video generation anchored by two real thermal images: one far-field (a blurry dot) and one near-field (a resolved plane). This approach ensures thermodynamic fidelity, as the diffusion model propagates the real sensor noise, blur, and heat signatures present in the seed images along realistic flight paths. The diffusion model predicts the motion of the pixel distribution, effectively "hallucinating" the preservation of physics rather than relying on low-fidelity rasterization. A structured prompt engineering taxonomy was developed to constrain the generative model to scientific consistency. The resulting videos were automatically annotated using the Segment Anything Model 2 (SAM 2), leveraging temporal consistency for zero-shot labeling, validated by an 85% confidence filter. Results. Gen-Thermal-UAV comprises 220 videos (1,760 seconds, approx. 42,000 frames) at 720p resolution, depicting air-to-air fixed-wing engagement scenarios. It is the first dataset to capture the continuous dot-to-object transition in the thermal domain. A comparative analysis confirms its unique position at the intersection of Thermal modality, Air-to-Air platform, and Extreme multiscale dynamics, distinguishing it from benchmarks like AOT, HIT-UAV, and Anti-UAV410. Conclusions. Gen-Thermal-UAV addresses a high-value gap in the computer vision landscape for C-UAS applications. The verified methodology demonstrates that Generative AI, when constrained by real-world seed data, can produce physics-compliant training data for dangerous or rare scenarios. This work not only provides a crucial benchmark but also establishes a reproducible protocol for generating and auto-labeling synthetic data, democratizing access to "edge case" training and facilitating rapid adaptation to emerging threats.
Introduction. In 1936, John Maynard Keynes published his work “The General Theory of Employment, Interest and Money”, which was a response to the challenges of the Great Depression. Classical and neoclassical theories, which advocated minimal government intervention, proved to be unable to explain the global crisis. Keynes' theory offered a new approach to the analysis of economic processes, causing widespread resonance and discussion among scientists. Modern scientists also discuss the relevance of Keynesian theory in the 21st century, analyzing its application in the context of globalization and financial crises. Keynesian theory gave impetus to the development of mathematical modeling in economics at the national and regional level, as the need for quantitative analysis and short-run forecasting of economic processes led to the emergence of models by V. Leontief and M. Mikhalevich. The purpose of the article is to analyze John Maynard Keynes' revolutionary economic theory, set forth in his work “The General Theory of Employment, Interest, and Money,” with a particular emphasis on its significance for understanding the causes of the Great Depression, the role of effective demand, the macroeconomic interdependence of markets, psychological factors in the economy, as well as the integration of Keynesian ideas with mathematical modeling of the economy – from Leontief's “input-output” models to M.V. Mikhalevich's optimization developments – and their influence on contemporary economic approaches and governmental economic policy. Results. The key concepts of J. M. Keynes' economic theory, set out in his work “The General Theory of Employment, Interest and Money,” are analyzed. The evolution of economic ideas from classical and neoclassical to Keynesianism is examined. The integration of Keynesian approaches with methods of mathematical modeling in economics based on the models of V. Leontief and M. Mikhalevich is considered.
The article analyzes the results of the forecasting and analytical study “Ukrainian Scientific and Technical Foresight” on the thematic area “Information and Communication and Digital Technologies, Artificial Intelligence, Robotics, Cybersecurity”, carried out in 2024 by employees of the State Institution “Institute for Research of Scientific and Technical Potential and History of Science named after G.M. Dobrov, NAS of Ukraine” with the support of the Foundation named after Friedrich Ebert in Ukraine, which is a continuation and development of the forecast-analytical study conducted in 2021. The purpose of the study is to assess the relevance of scientific directions that were obtained in 2021 and to propose new ones that have become of paramount importance after the full-scale invasion of the Russian Federation into Ukraine. Leading scientists from the institutions of the National Academy of Sciences of Ukraine were invited as experts, whose candidacies were submitted by the branches of the National Academy of Sciences of Ukraine. The experts were asked to assess the relevance of priority research identified in 2021 on a five-point scale, as well as the provision of personnel and equipment, the presence of significant results and international cooperation; to recommend new or clarify existing thematic sub-directions of research. According to the methodology for conducting the study, the survey was conducted in three rounds, for each round, Google questionnaires were developed and sent to experts. As a result, priority technological areas were identified that require priority solutions, resource provision problems were identified, and measures were proposed to ensure an effective scientific and technical policy of the state.
The impact of global challenges on international relations is steadily increasing. One of the most serious problems is ensuring the supply of safe drinking water for the world’s population. Control over water resources plays an important role in interstate relations and has become an increasingly significant factor in modern geopolitical interactions. Within this context, the issue of drinking water availability and safety represents one of the key components of contemporary political and socio-economic relations. The concept of water diplomacy has emerged in the international arena to describe situations where water resources may be used as an instrument of political influence. Similar to many other countries, the Republic of Azerbaijan also faces the problem of water scarcity. Among the most important issues in this area are maintaining the balance of water circulation, substance exchange processes, energy balance, and ecological safety. In this regard, the development of an automated intelligent information system for ensuring the ecological safety of drinking water in the republic is considered appropriate. The proposed system is implemented on a client–server platform. Based on the conceptual and functional models of the problem, the architecture of system objects has been developed. Reservoirs integrated into the network can be centrally controlled from the server–center node during emergency situations, while under normal operating conditions they retain autonomous functionality. The communication routes “object–server–center” and “center–server–object” remain continuously active. The paper provides information on measurement instruments, computational algorithms, and the developed software components. Experimental studies were conducted at the Jeyranbatan Reservoir during 2021–2024 based on 720 measurements obtained from 6–7 monitoring points. The main objective of the research is to ensure operational monitoring of drinking water quality and early detection of potential risks based on long-term measurements of parameters of different origins (physical, chemical, organoleptic, etc.). To verify the adequacy of the automated system, archival data from 2017–2020 were also used. The obtained results demonstrate that the proposed system detects deviations of parameters from normative values with high accuracy (approximately 86–92 %) and effectively supports the decision-making mechanism.
The article presents an approach to terrain verification based on the analysis of visual features in distributed information systems. The proposed approach is based on formalising the process of spatial matching of reference and current visual scenes, followed by a quantitative assessment of their correspondence. A step-by-step mathematical model has been developed that takes into account the presence of errors, incomplete data, and variable conditions for the formation and transmission of visual information. The paper introduces a terrain verification criterion that allows making an informed decision about the correspondence of a scene to a given area of space. The effectiveness of the proposed approach is confirmed by the results of simulation modelling, which demonstrate the dependence of verification accuracy on the noise level, the number of visual correspondences, and the threshold parameters of the model. The purpose develop a method for verifying terrain based on the analysis of visual features and a corresponding mathematical model that provides a quantitative assessment of the scene's correspondence to a given terrain area. Results. To evaluate the effectiveness of the proposed terrain verification method, a series of simulation experiments were conducted in the Python environment. The experiments investigated the influence of the main parameters of the mathematical model on the accuracy of the decision regarding the correspondence of the current visual scene to a given terrain area. In particular, the influence of the noise level in the input data, the number of visual matches, and the choice of the verification criterion threshold value were analysed. Conclusions. As a result of the study, a method for verifying terrain based on the analysis of visual features was developed and experimentally tested, which provides a formalised decision-making process regarding the correspondence of a visual scene to a given area of space. Simulation modelling confirmed the stability of the proposed mathematical model to noise, incomplete data and changes in basic parameters, which indicates the feasibility of applying the method as part of information technologies for processing visual data in distributed systems.
In [1], a mathematical model for minimizing the power moment in an electrical network was developed, taking into account transmission line capacities, balance relations at the main network nodes, and load levels of energy nodes. The model is formulated as a mixed-integer linear programming problem, where the integer variables are binary and determine one of the possible directions of power transmission along the network lines. This paper presents a mathematical model of the same problem in the form of a linear programming (LP) problem and shows that the optimal power flows along the network edges are transmitted in only one direction, as required for power transmission in electrical energy systems. An algorithm for verifying the uniqueness of the solution to the resulting LP problem is proposed and tested on a sample case. It is based on comparing the solutions of two quadratic programming problems with separable strictly convex quadratic objective functions, each of which has a unique solution. The paper is organized as follows. In Section 1, the LP problem for flow distribution with respect to the minimum power moment in the electrical network is formulated. Section 2 investigates the properties of the LP problem related to power flow transmission in the network. Section 3 describes a quadratic programming problem and, based on it, justifies a criterion for determining the uniqueness of the LP solution. Section 4 presents the results of computational experiments for a model electrical network performed using the Gurobi solver. The developed mathematical model can be used for balancing regional power systems under conditions of de-energization of main nodes by setting the corresponding node power values to zero. The proposed approach can also be applied to modeling a wide class of network systems, such as flow control problems in energy, transportation, and logistics networks.
Introduction. The rapid development of artificial intelligence (AI) technologies and the associated transformation of organizational processes are radically changing traditional approaches to human resource management (HRM). Classical optimization methods often prove insufficient to solve new dynamic problems faced by modern organizations and do not take into account the growing complexity of effective application of AI. The emergence of hybrid human + AI teams, automation of routine TR processes, the emergence of career trajectory recommendation systems, predictive analytics of fluidity and affective computing create a fundamentally new field of interaction in which the traditional dichotomy "manager – subordinate" gives way to the synergistic interaction of multiple agents (human and artificial) with different cognitive architectures, goals and constraints. The article proposes concepts and methodologies for synergistic optimization of human and artificial agents in the field of labor resource management of hybrid organizations, allowing to improve the work of the organization. The purpose of the work is to study modern approaches to optimizing labor resources based on AI agents, critically analyze existing models, formulate new mathematical statements of tasks and determine directions for further research. The development of algorithms for optimizing "human-AI" interaction allows you to reduce the costs of personnel management, while maintaining or increasing productivity. Thus, the work clearly outlines the problem of the lack of comprehensive models of "Human + AI" interaction, taking into account the degradation of both parties. Based on this, an own integrated model is proposed, aimed at optimizing computational and human resources. The results of the study show that the integration of autonomous and cognitive AI agents into labor resource management systems forms a new paradigm of organizational efficiency, which is significantly different from traditional TR approaches. The focus of change is not the replacement of human labor, but the construction of hybrid socio-technical systems, where human abilities are supplemented by the computational capabilities of AI. The mathematical models presented in the article show that the combination of discrete optimization methods, game theory, queueing models, multi-agent systems and machine learning algorithms creates a toolkit capable of providing balanced load management, preventing personnel fatigue and preventing technical degradation of AI agents. Conclusions. The proposed formal statements of optimization problems, in particular task assignment models, two-level hierarchical management structures and behavioral models in the form of Markov processes, make it possible to unify the processes of interaction between people and agents. Analysis of the influence of the orchestrator showed that the introduction of a super-level coordinator transforms the system into a controlled dynamic architecture, ensuring global consistency and stability even under conditions of stochasticity and high variability of tasks. At the same time, the study of behavior trees proves that the formalization of local behavior of agents significantly increases the predictability and reliability of the system, but requires careful synchronization with the orchestrator policies to avoid loss of flexibility.
Coverage criteria play a central role in software testing by providing objective measures of test suite adequacy and serving as a foundation for systematic test generation. Structural criteria – such as statement, branch, and dataflow coverage – enable verification of control and information dependencies within a program. At the same time, classical value-based techniques, including equivalence partitioning and boundary value analysis, are typically applied only to external inputs and do not account for the variability of values propagated within the program’s internal state. In many contemporary software systems, behavior is determined not only by control-flow structure or by the mere fact that variable uses are reached, but also by specific ranges of values propagated along execution paths. Traditional dataflow coverage criteria, in particular the all-uses criterion, consider a definition–use pair to be covered if it has been executed at least once, without distinguishing the values with which the variable reaches the use site. As a result, semantically significant yet narrow or domain-specific value ranges may remain untested, even when full structural coverage has been achieved. This limitation is especially critical for input-sensitive programs whose correctness depends on threshold conditions, numeric constraints, or implicit assumptions about admissible value ranges. Modern approaches to automated test generation, such as symbolic execution and model checking, partially account for value constraints; however, their coverage objectives typically remain structurally oriented. This creates a gap between dataflow analysis and systematic value-based testing, motivating the development of coverage criteria that are more sensitive to semantic distinctions. Objective. The goal of this work is to develop and formally justify a dataflow coverage criterion that accounts for semantically distinct value ranges of variables at use sites, and to design model-based methods for test case generation aimed at achieving such coverage. Results. We propose an extension of classical dataflow coverage by incorporating value-range distinctions at variable use points. Two complementary approaches to achieving this coverage are described: one based on symbolic modeling, and another based on model checking with Linear Temporal Logic specifications. Illustrative examples demonstrate that the proposed method reveals defects that remain undetected under traditional structural coverage criteria. Conclusions. The proposed approach integrates structural dataflow analysis with an explicit treatment of value variability, thereby providing semantically enriched test coverage. The criterion is intended to complement, rather than replace, existing approaches and appears particularly promising for testing input validation logic, detecting potential vulnerabilities, and also verifying cyber-physical systems. Although achieving such coverage may incur significant computational cost, it is justified for critical use points associated with external inputs and constraint checking.
Introduction. Anodic oxide films of metals, both dense and nanostructured (nanoporous, nanotubular) are widely used in modern microelectronics. By liquid anodizing of multilayer thin-films structures it is possible to obtain not only sequential layered structures from oxides, but also periodic three-dimensional nanostructures, which is extremely difficult to make by other methods. The field of applications of anodic oxide films is expanding with the growth of the ability to control their parameters: thickness, structure, morphology. It is possible to vary the sizes of structural elements of anodic oxides by changing of anodizing parameters preseted by equipment: voltage, current density, electrolyte composition, anodic oxidation time. Along with this, there is the task of precise control over the process, which is carried out by registering of the dependences V(t), I(t), dV/dt(t), dV/dt(V) on the electrochemical cell in-situ. Information on dV/dt(V) can be used to study the characteristics of layers being anodized (thickness of thin metal films and transition layers, oxygen content in the films, surface roughness), especially in the case of multilayer thin film structures. In such a way, the manufacturing method can simultaneously be a research method. The creation of software and hardware complex that provides the necessary anodization modes and careful control of the output data in real time is relevant for our developments on nanostructured sensor substrates, and can also be useful for other applications. The purpose of the work. The work is devoted to the creation of an improved software and hardware complex for controlling the parameters of liquid anodising with extended ranges of operating currents and voltages, as well sampling rate. The complex is designed for the formation of anodic oxide films, both dense and nanostructured, and for studying the processes of formation of anodic oxides and thin-film structures during their anodization in-situ. Results. An advanced software and hardware complex for controlled anodizing has been created. Its development used a modular design principle with maximum separation of digital and analog circuits, optimized power supply circuits, adjustable signal sampling rate from the electrolytic cell, and a computer communication protocol. The improved device is designed to set and record current and voltage during the anodization in electrolytic cell where dense or nanostructured anodic oxide is formed in direct current mode or in constant voltage mode. When anodizing occurs in direct current mode, the range of currents set by the operator is 1 μA to 20 mA. When anodizing occurs in constant voltage mode, oxide formation is first carried out in constant current source mode, and when the preseted voltage value is reached, the transition from constant current mode to voltage source mode is automatically performed, while the current through the forming oxide film is recorded in-situ. The range of voltages that can be set by the operator: 0 – 200 V. The software provides the ability to visualize I(t), V(t), dV/dt(t), and dV/dt(V) on the computer in real time, the ability to average the output data, and further work with the data in various processing programs. Voltage and current recording is performed with a time resolution adjustable by the operator: 2,5 ms; 5 ms; 10 ms; 25 ms; 0,1 s; 0,5s. Conclusions. Owing to improvements in circuit design, the device's interference stability has been increased, stable operation of the measuring complex over a wide range of currents and voltages has been achieved, and the data reading interval has been reduced. The created software and hardware measuring complex makes it possible to perform controlled anodization on samples with different areas (from units to hundreds of mm2) on massive metals, thin films and multilayer thin-film structures. It can be used for research, development, and manufacturing of samples in areas related to the creation and study of dense and nanostructured anode oxides and nanomaterials based on them. The areas of application of the measuring complex include the development and manufacture of elements for optical and electrochemical sensors, electrochromic devices, solar cells, photocatalysis, new promising energy storage elements, and biomedicine (biosensors, implant coatings, drug delivery systems).
Introduction. Wireless Sensor Networks (WSN) have become one of the fundamental technologies of digital transformation, enabling real-time data collection, transmission, and processing. Their applications span a wide range of domains, from environmental monitoring and precision agriculture to industrial automation and healthcare. In the current context of the Internet of Things (IoT), WSN serve as the foundation of cyber-physical systems, integrating physical processes with information flows. The scientific novelty of this work lies in the systematic analysis of contemporary standards, architectural solutions, and commercial implementations of WSN, which makes it possible to formulate a generalized methodology for designing Wireless Sensor Distributed Intelligent Systems (WSDIS). WSDIS are understood as WSN that: – integrate sensor nodes with artificial intelligence algorithms; – provide distributed data processing at the node level (edge computing); – enable intelligent interaction between sensors, coordinators, and user systems; – are oriented toward real-time decision-making scenarios (ecology, agriculture, healthcare). Aim of the Article. The purpose of this study is to investigate the current state and development trends of WSN, to identify key challenges and principles for designing WSDIS architectures, and to analyze practical examples of commercial sensor networks across different domains. To achieve this aim, the following objectives were set: – to analyze the evolution of WSN standards and their integration into global IoT ecosystems; – to identify global development trends and key challenges of the current stage; – to examine the principles of WSDIS architecture design and architectural solutions; – to evaluate contemporary commercial implementations of WSN in ecology, agriculture, industry, and healthcare. Results. The article examines the current state and development trends of WSN. The evolution of standards (IEEE 802.15.4, ZigBee, BLE, LoRaWAN, NB-IoT) is analyzed, along with global tendencies toward scalability, energy efficiency, and node intelligence. The key challenges of the present stage are identified: energy constraints, cybersecurity, standardization, and interoperability. Particular attention is devoted to the principles of WSN architecture design (modularity, adaptability, scalability, interoperability, security) and to architectural solutions (centralized, decentralized, hybrid). An overview of contemporary commercial WSN implementations in ecology, agriculture, industry, and healthcare (Bosch, Libelium, John Deere, Siemens, Philips, among others) is provided. The focus is placed on the role of WSN as a foundation of digital transformation and their integration into global IoT ecosystems. Conclusions. The development of WSN has progressed from local energy-efficient protocols (IEEE 802.15.4, ZigBee, BLE) to integration into global digital ecosystems (LoRaWAN, NB-IoT, ISO/IEC 30141, IIRA). The evolution of standards demonstrates a gradual expansion of functionality, scalability, and interoperability, forming the basis for universal WSDIS architectures. Global development trends are defined by the implementation of energy-efficient technologies, integration with AI and edge computing, and increasing requirements for cybersecurity. The challenges of the current stage – energy constraints, cyber threats, standardization, and the need for flexible architectures – stimulate the search for new solutions that combine modularity, adaptability, and reliability. The principles of WSDIS architecture – modularity, scalability, adaptability, interoperability, and security – constitute the methodological foundation for the creation of universal sensor systems. Architectural solutions are evolving from centralized to hybrid models that integrate edge computing and cloud services, ensuring a balance between efficiency and resilience.
The purpose of the work is to analyze the existing definitions of the terms method and way and to resolve the problem with the existence of the term process (way) in the patent and scientific legislation of Ukraine. There is no term "sposib" in English technical text. In the future, instead of the Ukrainian term "sposib", we will use the English way. There are different opinions regarding the interpretation of the essence of the terms method and process (way). One point of view is recorded in the regulatory documents of Ukraine, which recognize that a way is a specific implementation of a certain method. In defining a process (way), its focus on achieving a certain technical result is emphasized. State and scientific institutions of Ukraine do not consider patents for a process (way) to be new methods and, accordingly, new scientific results. From another point of view, the concepts of method and way are identical and have the same meaning in science and technology. The authors propose to complete the harmonization of the standards of patent legislation of Ukraine with international norms, namely, instead of the term process (way), use the international term method, and instead of the term product – the term means. Accordingly, the type of invention method characterizes the scientific side of the invention, and is a scientific result in technical sciences, and the type of invention means – the technical novelty of the invention.
Introduction. A matrix method for constructing one-norm sine-cosine transforms of type II of order 4 has been developed, which has better efficiency compared to the known sine transform of type II. An integer one-norm sine-cosine transform of type II of order 4 has been proposed, and on its basis an integer one-norm simplified sine-cosine transform of order 8 of low computational complexity has been developed. Fast calculation algorithms for the proposed transforms have been considered. The computational complexity of the simplified sine-cosine transform of type II of order 8 is only 40 operations, which is three times less than the computational complexity of the known sine transform of type VII of order 8, and the compression ratio is 1.5-2.3% lower, as shown by the presented experimental results. The proposed integer one-norm simplified sine-cosine transform of order 8 can be used for image and signal analysis and coding tasks, in particular for separable adaptive transforms as an alternative to the sine transform of type VII for high-speed and extreme coding modes. Two simplified modes for adaptive separable transforms are proposed: mode A, which uses two combinations out of four total, namely 2D cosine and split cosine/sine; mode B, which uses three combinations out of four, excluding the sine/sine variation. As an alternative to the type VII sine transform in the separable transform scheme for adaptive application in high-speed modes, a modified type II sine transform is proposed, which consistently compresses 0.3 % better than the classic type II sine transform, and lags behind the type VII sine transform by 1.2–1.4 %.
The article presents an approach to constructing a mathematical model for predicting the throughput capacity of unmanned aerial vehicle communication channels based on the analysis of SITL telemetry logs. The proposed methodology takes into account the dynamic nature of the information flow, the unevenness of the intervals between packets, the probability of losses, and the fluctuations in channel parameters under the influence of external and internal factors. The paper identifies the key characteristics that affect channel stability: packet arrival rate, amount of useful information, loss rate, and throughput degradation rate. The structure of information flows in the UAV control system is considered and the relationships between channel parameters are formalised, which allows predicting its future state. The purpose develop a mathematical model for predicting the throughput capacity of the UAV communication channel using SITL telemetry logs to ensure early detection of channel degradation and improve data exchange reliability. Results. Based on the analysis of packet protocols and channel behaviour in different load modes, a mathematical model was developed to describe the temporal dynamics of throughput and the relationship between current parameters and predicted values. The model includes data normalisation, time series smoothing, estimation of the local rate of change in throughput, and determination of the expected effective throughput, taking into account packet loss. Modelling was performed in Mission Planner, which implemented various load scenarios: changes in telemetry frequency, artificial interference, SNR fluctuations, and increased traffic intensity. The results of the experiments showed that the forecast data corresponded to the actual values and confirmed that the model is capable of detecting the approach of the channel to a critical state in advance. The derivative throughput indicator, which decreases sharply before the onset of peak losses, proved to be particularly informative. Conclusions. The results obtained show that mathematical forecasting of the UAV communication channel bandwidth based on telemetry logs allows for early identification of critical channel conditions and increases the efficiency of information system resource utilisation. The proposed model can be integrated into real-time systems to adapt telemetry parameters, increase the stability of communication channels, and ensure the reliability of information transmission even in unstable environments and under variable loads.
This paper investigates the strategic behavior of validators in blockchain systems utilizing the Proof-of-Stake (PoS) consensus mechanism through the application of game theory. A mathematical model of a non-cooperative game with complete information is proposed, where validators act as rational agents aiming to maximize their expected payoff by choosing between honest validation and malicious actions, specifically a double-spending attack. The model incorporates key economic parameters of the system: block and attestation rewards, transaction fees, operational costs, slashing penalties, and the probability of detecting protocol violations. Utility functions for two primary strategies – honest and attacking – are formalized, and conditions for the existence of Nash equilibrium, the central solution concept in game theory, are analyzed. The analysis demonstrates that under effective punishment mechanisms, the "all-honest" equilibrium is stable: an individual validator has no incentive to deviate from protocol-compliant behavior, as potential losses from penalties significantly outweigh any gains from a failed attack. Conversely, the "all-attackers" equilibrium, while theoretically possible, is practically unattainable due to the prohibitively high cost of acquiring a majority stake, rendering such a strategy economically infeasible. A quantitative example based on a hypothetical network of 1000 validators confirms these findings and highlights the critical importance of balancing incentives for honest behavior with strong disincentives for malicious actions. The study emphasizes the crucial role of economic security in PoS systems, where stability is ensured not only by technical safeguards but also by carefully designed economic mechanisms. The developed model can be used by blockchain protocol designers to calibrate consensus parameters, thereby promoting decentralization, resilience, and long-term network reliability. Future research can extend the model by incorporating heterogeneous validators, repeated games, and the analysis of other attack vectors.
Introduction. Modern developments in the field of block-oriented algorithms are aimed at improving performance, reducing computational costs, and integrating such algorithms into quantum-resistant national standards. To enhance the security of the WBC1 algorithm, this paper proposes its modification – the WBC2 algorithm – which is a modern symmetric block cipher that extends the WBC1 model to a more robust, nonlinear, and flexible structure. WBC2 is an interesting example of a cryptographic model with a visual representation (the Rubik’s Cube), which enables the creation of unique key-dependent operations. The paper presents a detailed description of the encryption process, an analysis of the algorithm’s complexity and execution speed, and the results of its testing. The purpose. The aim of this work is to describe a new symmetric block cryptographic algorithm, WBC2, to investigate its computational complexity and execution speed, and to conduct its testing. Results. An improved symmetric block cryptographic algorithm, WBC2, has been developed. The complexity analysis and execution speed of the algorithm have been investigated. The applicability of the new algorithm is demonstrated through illustrative examples. Conclusions. The WBC2 algorithm represents a cryptographically secure encryption method that provides a high level of security through the use of complex dynamic permutations, cyclic shifts, round transformations, extended S-boxes, and diffusion procedures. The increase in processing time in WBC2 is the cost of additional cryptographic complexity and an enhanced avalanche effect. One of the directions for improving the efficiency of the algorithm is the use of parallel computations, which significantly reduces execution time without compromising security. Further research is aimed at developing a parallel modification, PWBC2, and a quantum version, WBCQ, which employs parameterized quantum mixing (PQM) to increase key dynamism and the nonlinearity of transformations.
The paper proposes a comprehensive concept of energy modeling for wireless sensor networks (WSNs). A structural model of the node operating cycle is introduced, distinguishing between full and partial cycles, which enables clear separation of background and active energy consumption and improves modeling accuracy. A new multifactor mathematical model of WSN energy consumption over the vegetation period is developed and formalized simultaneously in scalar and matrix forms. This provides a unified description of network nodes, ensures network scalability, and accounts for variable operating modes. Based on the multifactor model, a software application for predicting the energy consumption of a wireless sensor network, “Energy Consumption Analyzer”, has been developed. The Friis transmission equation is specialized, and a modified Weissberger model is calibrated to describe signal attenuation in a vegetation environment. On the basis of these models, a practically applicable signal attenuation model for vegetation environments of the orchard type is proposed, specifically adapted to the impact of this type of environment on radio signal propagation. Empirical modeling based on experimental data and the synthesis of theoretical and empirical models are performed, enabling the solution of the inverse problem of determining the minimum required transmission power to ensure a given quality of service (QoS) and yielding an analytical model of adaptive power control. A dynamic model of WSN energy consumption is developed, establishing the relationship between energy expenditure and communication quality. A daily optimization coefficient for hierarchical power control is proposed, which forms the basis for a method to improve the energy efficiency of wireless sensor networks.
