Systems and Technologies

MATHEMATICAL MODELING OF FUZZY COMMUNICATION IN MULTI-ROBOT SYSTEMS BASED ON PSEUDOANALOG SIGNALS

2026-03-17T15:52:23+02:00

The article presents the results of a study devoted to modeling communication in multi-robot systems using fuzzy (pseudoanalog) signals. In modern conditions, when groups of autonomous robots operate in complex and dynamically changing environments, the tasks of ensuring effective, stable and energy-saving information exchange between agents are extremely relevant. Traditional approaches to inter-robot communication – mostly discrete and based on rigid binary protocols – often do not allow achieving sufficient adaptability in noisy, unstructured or resource-limited environments. They tend to rely on constant signal retransmission and error-correction procedures, which increases energy costs and reduces real-time responsiveness. In response to these challenges, the author proposed a hybrid model that imitates the principles of communication inherent in biological systems, in particular the variability and context dependence of analog signals used in flocks of birds, schools of fish or insect colonies. Such natural communication is not strictly digital: it combines amplitude, frequency and temporal modulation, enabling organisms to convey uncertainty, urgency or intent even under external disturbances. The developed mathematical model applies the concept of fuzzy logic, in which each transmitted signal is represented by three parameters – amplitude, frequency and duration – interpreted in linguistic categories such as high, medium, low, alert or coordination. Membership functions determine the degree to which a received signal corresponds to a specific linguistic value, enabling each agent to form a flexible response depending not only on the signal itself but also on the situational context. This reduces the need for exact matching of discrete messages and allows communication to remain functional even when signals are partially lost or distorted. A series of experiments was conducted in a simulation environment representing predator–prey interactions, in which “Lions” acted as pursuing agents while “Antelope” could exchange messages about detected danger or decreasing energy reserves. Three categories of environments were modeled: a basic scenario with ideal discrete communication, a scenario with partial signal loss, and one with distortion introduced during pseudoanalog transmission. The results demonstrated that the fuzzy model enables maintaining the same level of agent survival and task completion efficiency as in ideal discrete communication, while reducing overall energy consumption by approximately 18 %. Furthermore, the swarm demonstrated significantly higher robustness under conditions of interference or incomplete data, as fuzzy interpretation prevented critical communication breakdowns. The use of analog-like communication with linguistic interpretation decreases unnecessary agent activation, smooths collective decision-making and increases the overall efficiency of group behavior. The proposed model can be applied to the development of distributed technical systems for search-and-rescue missions, reconnaissance, environmental monitoring, agricultural robotics and low-cost swarm systems, where adaptability and resilience are more critical than precision. The article contributes to the advancement of flexible decentralized communication protocols capable of maintaining functionality under uncertainty and without centralized control.

VISUALIZATION OF LINEAR ALGEBRA AS A TOOL OF APPLIED MATHEMATICS: EXPERIENCE OF IMPLEMENTING GEOGEBRA IN THE EDUCATION OF FUTURE SOFTWARE ENGINEERS

2026-03-17T15:52:21+02:00

In modern technical education, dynamic computer environments play a key role in combining theoretical mathematical models with their practical application. The article shows the experience of systematic use of the GeoGebra environment in teaching the section “Linear Algebra and Analytical Geometry” of the discipline “Higher and Applied Mathematics” to future software engineers. Based on a review of scientific sources and an experiment, it is proven that visualization of abstract concepts of linear algebra and analytical geometry significantly enhances the understanding of the material by higher education students of the specified specialty. The approach used in our institution – the University of Economics and Entrepreneurship – where GeoGebra is integrated for conducting practical classes and independent work of higher and pre-higher education students is described. An educational experiment was conducted with two groups: experimental (group of 8 students) – worked with GeoGebra applets, control (group of 12 students) – worked according to the classical method of presenting the material of the discipline. A comparative analysis of the results of testing and questionnaires proves that higher education students from the experimental group not only improved their results and knowledge, but also demonstrated a deeper understanding of the geometric interpretation of vector operations, matrix transformations, and solving systems of linear algebraic equations. Visual interpretation and examples contributed to the formation of an instinctive vision of the structure of linear models used in software engineering. The obtained experimental results confirm the high efficiency of the dynamic computer environment GeoGebra as a tool for quick and visual demonstration, which significantly increases the level of motivation of students of technical specialties. Using GeoGebra allows higher education students not only to see the result, but also to explore it interactively by changing the parameters of the model. The article also presents practical recommendations for further scaling the approach to using GeoGebra – from creating author’s applets to implementing project-oriented tasks.

MATHEMATICAL MODELS AND ALGORITHMS OF CRYPTOGRAPHIC DATA PROTECTION IN DISTRIBUTED INFORMATION SYSTEMS

2026-03-17T15:52:19+02:00

In modern conditions, information networks and data processing and transmission centers are a determining factor in the effective functioning of management systems of state institutions, financial and industrial organizations, energy and communications enterprises, transport and logistics infrastructure. Accordingly, the increase in the number of such centers and the number of different communication lines between them significantly increases the risks of unauthorized access to confidential information, ensuring data security and integrity. Therefore, the study of the problem of information security is of great importance for preventing and countering phishing attacks, which are constantly exposed to both individual enterprise networks and national networks. The paper analyzes mathematical models that describe the process of information protection using cryptographic data encryption algorithms. The practical application of the algorithms of the given substitution, the Vigenère cipher, and selfsynchronizing stream ciphers is considered. To increase the stability of the ciphertext, a modification of the Vigenère algorithm is proposed using separate unified alphabet tables for the text and the encryption/decryption key. Recommendations are developed for the use of the considered crypto algorithms to ensure effective protection of information systems from intentional or accidental interference. To increase the efficiency of the use of crypto algorithms, the feasibility of the integrated use of symmetric and asymmetric encryption methods to use their positive qualities, such as the speed of processing secret keys and the security of their transmission, is indicated. Promising directions for the development of information security systems using cryptographic algorithms are identified.

MODELING OF TURBULENT FLOWS AROUND GROUND TRANSPORT VEHICLES USING HYBRID APPROACHES

2026-03-17T15:52:18+02:00

The practical application of Direct Numerical Simulation (DNS) and Large Eddy Simulation (LES) is associated with a number of problems. These include insufficient computing power and a number of methodological issues. Over the past decades, models of turbulent flows have appeared that involve the simultaneous use of the LES method and Reynolds-averaged Navier-Stokes equations (RANS). These are the so-called hybrid RANS-LES models. The use of hybrid approaches simplifies the solution of certain aerodynamic problems. Today, there are several dozen RANS-LES models in use. A huge number of works are devoted to their further improvement and development. The most common method has become the detached eddy simulation (DES) method. This demonstrates the positive aspects of the idea underlying hybrid models. How- ever, even today, there is no complete satisfaction with the existing models. The construction of a mathematical model of turbulent flow requires further study of the physical characteristics of the for- mation of local unsteady characteristics of turbulent flows. When solving aerodynamic problems numerically using Reynolds-averaged Navier-Stokes equations, it is not the differ- ential equations themselves that are solved, but their finite difference analogues. It is necessary to correctly apply the physical laws of conservation of mass, momentum, and energy. This is a difficult task for mesh methods. In this work, for the numerical solution of the problem of calculating the characteristics of turbulent flow around a ground vehicle, a model of viscous compressible gas flow is chosen, which is described by Reynolds-averaged Navier-Stokes equations. A multi-block approach and a curvilinear coordinate system are used. To close the Reynolds-centered Navier-Stokes equations, the DES method was chosen, which is a further development of the Spalart-Allmaras turbulence model in the implementation of isolated vortices. To conduct the research, an appropriate methodol- ogy was developed, algorithms were constructed, and a software package was written in the Fortran-95 programming language. Calculations were performed for turbulent flow around a high-speed transport vehicle with a lifting body near a road structure. The distribution of isobars in the XOY plane of the calculation area, isochores in the XOY plane of the calculation area, isolines of the velocity vector component in the XOZ plane of the calculation area, and isolines of vorticity in the XOY plane of the calculation area were obtained.

IMPROVING DATA AVAILABILITY IN PEER-TO-PEER CONTENT-ADDRESSED SYSTEMS THROUGH PROACTIVE REPLICATION

2026-03-17T15:52:16+02:00

Modern decentralized systems, including IPFS, predominantly employ a “pull-on-demand” approach, in which replica- tion occurs only as a side effect of data retrieval. This leads to low availability in small networks where the number of active nodes is insufficient to naturally form an adequate number of copies. The paper examines an approach to improving data availability in peer-to-peer content-addressable systems through proactive replication and deterministic block placement. A mechanism is developed and analyzed that ensures predictable data availability without substantially increasing coor- dination among nodes and while preserving compatibility with DHTs. The requirements for a data-placement protocol in peer- to-peer networks are formulated, a proactive replication model, aligned with block distribution in the keyspace is proposed; and the capability of the approach to reduce load on individual nodes and achieve higher data availability is evaluated. Solution that integrates DHT-based routing with a placement mechanism driven by block identifiers that determine their position in the keyspace is proposed. Two-tier pinning model is introduced: hard pins guarantee persistent storage of blocks, whereas soft pins are created automatically during replication and have a limited lifetime that is extended upon repeated access. A garbage collector interprets combinations of pins to determine which blocks may be safely removed, retaining only relevant and frequently used data. Experimental results demonstrate that the proposed approach decreases the load on individual nodes and increases the system’s resilience to failures, making it a promising option for small private peer-to-peer networks with stringent availability requirements.

OPTIMIZATION OF COMPUTER GAME PERFORMANCE USING MACHINE LEARNING METHODS

2026-03-17T15:52:14+02:00

The article presents a comprehensive method for optimizing the performance of computer games based on machine learning techniques. The proposed approach combines GPU load prediction using deep neural networks (DNN) with adaptive real-time management of rendering parameters, enabling the automatic determination of optimal graphics settings to maintain stable frame rates (FPS), reduce GPU load fluctuations, and preserve high visual quality. A mathematical efficiency model is for- mulated, integrating frame rate, graphics quality, and GPU utilization into an optimization function with weighted coefficients reflecting user priorities. The approach includes adaptive adjustment of graphics parameters based on gradient descent and predictive modeling of computational load, allowing dynamic resource management without developer intervention. To evaluate the effectiveness of the proposed method, comparative experiments were conducted against traditional optimization strategies, including static configuration and linear GPU load prediction. The results demonstrate a 20–30 % increase in FPS, a 10–12 % reduction in average GPU load while maintaining high graphics quality (≈ 98 % of maximum), and a significant reduction in frame rate fluctuations in complex scenes. The prototype implementation, developed in Python, was successfully integrated into modern game engines such as Unity and Unreal Engine, confirming the practical applicability of the method in real-world projects. The scientific novelty of this research lies in the integration of multiple classes of machine learning models into a sin- gle adaptive rendering optimization system, capable of considering both hardware parameters and user behavior in real time. The proposed framework provides a foundation for further development of adaptive ML-based systems in game development, including VR/AR and cloud gaming platforms, as well as for enhancing the efficiency of game production pipelines with high performance and graphics quality requirements. Overall, this method demonstrates the potential to substantially improve real-time performance optimization in interactive entertainment applications while maintaining visual fidelity, providing valuable insights for both academic research and industry practice.

APPLICATION OF ADAPTIVE METHODS IN NUMERICAL MODELING OF MECHANICS PROBLEMS

2026-03-17T15:52:13+02:00

The article investigates the application of adaptive hp-methods within the finite element method for modelling the spa- tial contact interaction between a perfectly rigid punch and a homogeneous isotropic elastic half-space. The study considers the problem of indentation of a rigid punch into an elastic half-space with a doubly connected contact region in the form of a symmetrically deformed elliptical ring. For the numerical implementation, several variants of adaptive meshes with differ- ent approximation orders were constructed. Adaptation is performed through local h-refinement in zones with intensive stress gradients and p-enrichment to improve approximation accuracy. An integrated software package has been developed. Its architecture is implemented as a multimodule system combining the capabilities of Ansys for solving the mechanical problem, Gmsh and APDL for adaptive mesh generation, MATLAB for post-processing, and Python/batch scripts for automating the computational cycle. The control module ensures the sequential execution of iterations, monitoring of intermediate data, and logging. The weight-matrix module constructs an importance field that identifies local regions requiring mesh refinement. The adaptive meshing module generates meshes with variable local ele- ment size, while the conversion module ensures correct transfer of meshes into a format compatible with Ansys. The package supports result processing, generation of plots and tables, and formation of data structures for subsequent adaptation iterations. Despite limitations on the available approximation orders (p = 1 and p = 2), the implemented concept enables effective control of both h- and p-adaptation. The analysis shows that increasing the approximation order significantly reduces the error and improves the efficiency index of the a posteriori error estimate, while maintaining rational use of compu- tational resources. The results obtained confirm the effectiveness of the hp-adaptive approach for contact mechanics problems with complex geometry and demonstrate its potential for further use in engineering computations.

EVOLUTIONARY OPTIMIZATION OF CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES IN INTELLIGENT DATA ANALYSIS TASKS

2026-03-17T15:52:11+02:00

This paper presents an approach to the automated optimization of convolutional neural network architectures based on evolutionary computation. The main focus is placed on the application of genetic algorithms as one of the most effective classes of evolutionary algorithms capable of performing global search for optimal solutions in complex high-dimensional parameter spaces. The proposed approach is aimed at optimizing the structural parameters of a convolutional neural network, including the number of convolutional and fully connected layers, the number of neurons in each layer, as well as the number and size of convolutional filters, which directly affect the generalization properties of the model. The paper analyzes the fundamental principles of convolutional neural networks and their application to image classifi- cation tasks. The basic components of convolutional neural network architectures are examined, including convolutional layers, subsampling layers, and fully connected layers, and the influence of their parameters on classification accuracy is investigated. Based on this analysis, a baseline convolutional neural network with an architecture manually selected by the developer was implemented, and an experimental evaluation of its performance on both test and real-world datasets was conducted. In addition, a proprietary genetic algorithm was developed on the basis of the fundamental principles of genetic algo- rithms to optimize the architecture of the convolutional neural network. Classification accuracy was employed as the fitness function, allowing the evolutionary search process to be directly guided toward improving the model’s performance. The developed genetic algorithm performs an automated search for an optimal neural network architecture through evolutionary modification of its structural parameters. During the evolutionary process, a gradual improvement of network characteristics is achieved by selecting the most effective configurations and generating new architectures through crossover and mutation operations. Experimental results demonstrate that within ten generations of evolution, it is possible to obtain a convolutional neural network architecture that significantly outperforms the initial manually designed model.

METHOD FOR FORMING A/B TEST GROUPS WITH MINIMAL INTERFERENCE USING THE STOER–WAGNER ALGORITHM

2026-03-17T15:52:09+02:00

This article examines contemporary methods for constructing experimental groups in A/B testing for digital products, with a specific focus on minimizing mutual interference between participants and reducing spillover effects arising from social or behavioral interactions. The challenge of uncontrolled user-to-user influence has grown increasingly important in highly con- nected online environments, where actions, recommendations, and shared content can propagate across a network and distort the measured impact of an experimental treatment. Such interference undermines the validity of causal inference and often leads to biased or unstable conclusions about product performance. The paper provides an overview of established approaches, including cluster-based randomization, geographic exper- iments, and hierarchical assignment strategies, as well as practical implementations widely adopted by leading technology companies such as Meta and Google. These organizations regularly face large-scale network effects and have developed robust methodologies to ensure reliable experimentation even when user interactions cannot be ignored. A central contribution of the article is the introduction of a graph-based method for constructing experimental groups. In this approach, users are represented as nodes within an interaction graph, and the strength of their connections reflects the probability or intensity of potential interference. Group formation is achieved through identifying a minimum cut in this graph, computed using the Stoer–Wagner algorithm. This algorithm enables efficient and scalable partitioning of large networks while guaranteeing an optimally minimal sum of cross-group connections. As a result, the proposed method effectively reduces spillo- ver effects and significantly improves the internal validity of A/B tests. The advantages of minimum-cut partitioning include natural alignment with real-world interaction patterns, suitability for graphs containing millions of users, reduced risk of contamination between treatment and control groups, and consist- ency with experimentation frameworks observed in industry practice. These benefits underscore the relevance and applicability of graph-based experimental design for modern digital ecosystems.

OBJECT-ORIENTED APPROACH TO NEURAL NETWORK-BASED DETECTION OF CYBERBULLYING SUBJECTS FROM MESSAGES IN A MANAGED CLOUD ENVIRONMENT

2026-03-17T15:52:07+02:00

The aim of the work is to develop and substantiate an object-oriented approach to neural network detection of cyber- bullying subjects from messages with a combination of primary detection and subsequent syntactic-semantic interpretation in a managed cloud environment. A coherent architecture is proposed in which the neural network module filters messages at the “cyberbullying / non-cyberbullying” level, after which the results undergo dependency analysis with the reconstruction of role relationships of the “subject – action – object” type. An object model, which includes classes of messages, sentences, tokens, dependencies, predicates, role triples and summary structures, is defined as the basis for ensuring transparent traceability of decisions, while a managed cloud environment ensures the reproducibility of launches and scalability of experiments. The effectiveness of the initial detection was experimentally confirmed: the BERT-based module demonstrated a metric of F1 = 0.98 in a two-class setting (“cyberbullying” / “not cyberbullying”), which indicates a sufficient level of screening out irrel- evant messages before role analysis. Consistent indicators of the quality of role identification were established on the expert-ver- ified subset: for the subject, values of 0.88, 0.86, 0.87 were obtained for Precision, Recall and F1, respectively; for the object – 0.85, 0.83, 0.84; for the verb center – 0.91, 0.89, 0.90. The exact restoration of the role triple provided a value of F1 = 0.76. The inter-expert agreement was Cohen’s coefficient of 0.82 with 87 % complete agreement, which indicates the reliability of the reference labels and the correctness of the applied evaluation procedure. In controversial cases, a third auxiliary assessment by a language model with a fixed instruction was used; the final labels were determined by majority rule. The results obtained demonstrate that the proposed approach not only allows to determine the fact of aggressive commu- nication, but also provides a structured presentation of information about its addressability, remaining reproducible and audit- able in practical conditions. Conclusions are drawn that create a basis for further integration of the approach into moderation systems and possible expansion to corpora with more detailed role markup and multilingual support.

PREDICTION OF SEO EFFECTIVENESS USING SVM: DATA, MODELING, AND VALIDATION

2026-03-17T15:52:06+02:00

Developed a data-driven framework for forecasting the effectiveness of search engine optimization (SEO) interventions using supervised machine learning with support vector machines (SVM). Investigated a multi-source dataset comprising on-page signals (content quality, metadata compliance, structured data), off-page signals (backlink authority, anchor distribution), and technical performance (Core Web Vitals, crawlability), with standardized preprocessing, feature scaling, and class-balance control. Established a rigorous evaluation protocol based on stratified k-fold cross-validation, hold-out testing, and comparative baselines (regularized logistic regression, random forests), with model selection via grid search. Identified stable predictors of SEO uplift across heterogeneous sites and industries, revealing that the joint presence of high-quality content, semantically coherent keyword targeting, authoritative backlink profiles, and solid technical health produces the highest probability of measurable gains in rankings, organic traffic, and conversions. Revealed that combining on-page optimization with sustained, high-authority link acquisition outperforms single-channel strategies, while insufficient technical performance constrains returns even under strong content or backlink signals. Determined that SVM with RBF kernel achieves competitive accuracy and precision–recall characteristics relative to baselines, with superior robustness under class imbalance and distributional shift. Proposed a decision-support workflow that prioritizes high-leverage actions under uncertainty, quantifies expected gains with confidence intervals, and surfaces feature-level explanations to guide stakeholders. Outlined future research on causal identification (e.g., staggered rollouts and synthetic controls), standardized reporting of SEO interventions, and open effect-size repositories to improve reproducibility and external validity.

MONITORING THE STATUS OF A SMART HOME USING IOT TECHNOLOGY

2026-03-17T15:52:04+02:00

The article provides a detailed analysis of the process of developing a smart home monitoring system model based on Internet of Things (IoT) technologies and reveals the key aspects of building modern intelligent home automation systems. It examines the conceptual principles of smart home operation, which involve close interaction between sensors, microcontrollers, gateways, and actuators in real time. It is the combination of these components that enables the continuous collection, analysis and processing of data necessary to control environmental parameters and improve the safety, comfort and energy efficiency of residential premises. The paper conducts a thorough study of the architecture of the IoT system and describes the principles of its implementation in the Cisco Packet Tracer virtual environment, which serves as a tool for modelling the interaction of IoT devices without the need for physical equipment. Particular attention is paid to algorithms for automatic response to events. This approach demonstrates the importance of integrating intelligent control mechanisms that form the basis of the concept of a ‘smart’ living environment. The technical and functional capabilities of the developed model are considered separately. The characteristics of the types of sensors used, methods of data transmission, features of component interaction within a local network, as well as means of ensur- ing the stability and correct operation of the system are presented. The model was tested, and the results confirmed its reliability, the coordination of individual IoT components, and the system’s ability to perform basic security monitoring functions in automatic mode. The results obtained indicate the feasibility of using the created model as a training tool for acquiring practical skills in the field of IoT technologies, as well as a demonstration example for studying the principles of building integrated smart home systems. The article demonstrates an approach to creating IoT solutions focused on ensuring user safety and comfort. The devel- oped model can serve as a basis for further experiments and research in the field of residential automation and is a relevant example of the application of Internet of Things technologies in the current conditions of digital infrastructure development.

A CLASSIFICATION–STRUCTURAL APPROACH TO RANDOMNESS EVALUATION OF SHORT BINARY SEQUENCES IN CRYPTOGRAPHIC PROTECTION SYSTEMS AND IOT TELEMETRY

2026-03-17T15:52:03+02:00

The article addresses the problem of evaluating the randomness of short binary sequences used in cryptographic protec- tion systems and IoT telemetry as keys, authentication tokens, service markers, and identifiers. It is shown that traditional sta- tistical randomness test suites (NIST STS, DIEHARD, TestU01) are designed for long samples and lose reliability when applied to sequences of 8–128 bits, which are typical for lightweight protocols such as ZigBee, LoRaWAN, RFID, and embedded con- trollers. To overcome these limitations, a classification-driven structural approach to randomness assessment is proposed, which relies on the analysis of non-overlapping k-bit blocks, the construction of empirical frequency vectors, and the use of a maximum deviation statistic from the theoretical distribution. A theoretical estimate of the probability of large deviations is derived using Hoeffding’s inequality, allowing one to specify a threshold value of the criterion for a predefined significance level and formally control the error probability in decision-making. The proposed approach formalizes the problem of randomness evaluation as a classification task, which makes it possible to compare generators and filter out non-random sequences based on quality metrics. The experimental framework includes four classes of sequence sources (algorithmic PRNGs, the cryptographic AES-CTR generator, and hardware noise sensors). For each source, short fragments of 8–128 bits were generated and evaluated in compari- son with ENT and basic NIST SP 800-22 tests using metrics such as classification accuracy, Type II error, ROC characteristics, and computational performance. The results demonstrate that the proposed method provides higher classification accuracy, superior ROC indicators, and lower variability of results on short fragments, while maintaining acceptable computational complexity for deployment on microcontrollers. Practical application scenarios are outlined for IoT sensors, smart home systems, embedded controllers, and telemetry-based IDS/IPS solutions, where the proposed criterion can serve as a lightweight randomness quality module and enhance the overall resilience of cryptographic protocols against the exploitation of structural defects in generators.

COMPREHENSIVE DECISION ANALYSIS: INFORMATION SYSTEM BASED ON CLASSICAL CRITERIA

2026-03-17T15:52:01+02:00

The study presents the development and implementation of a comprehensive information system for decision-making support based on classical criteria applied under uncertainty – Laplace, Wald, Savage, and Hurwicz. The system is designed as a tool for automating calculations, visualizing results, storing problem parameters, and ensuring increased accuracy in the analysis of alter- natives. This makes it effective for use both in the educational process, where it serves to demonstrate theoretical principles, and in applied analytical scenarios where rapid risk assessment, strategy comparison, and justification of an optimal choice are required. The system’s interface adaptation includes the introduction of a modern design, interactive graphical components based on Plotly, an optimized navigation structure, as well as support for data import and export in Excel format. This ensures conven- ience when working with large volumes of information and allows the user to quickly adjust problem parameters. The updated database structure includes new entities for storing the calculation history, problem parameters, and criterion types. This approach increases the system’s flexibility and scalability, creating the foundation for further functional expansion, integration of new methods, and development of additional modules. Each criterion is implemented as an autonomous software module with clearly structured data-processing logic. The user enters a payoff or loss matrix, after which the system automatically performs the calculations, generates intermediate coeffi- cients, and presents the final solution in a form convenient for analysis. Mechanisms for reusing parameters, generating reports, and storing all performed operations for subsequent analysis or comparison are provided.The presented implementation combines mathematical rigor with modern technological solutions, offering a compre- hensive tool for educational, research, and managerial applications. The system improves the quality of analytics, minimizes the influence of human error, and supports the adoption of strategically justified decisions across various domains.

SMART CONTRACTS IN DECENTRALIZED ENERGY MARKETS: OPPORTUNITIES AND REGULATORY CHALLENGES

2026-03-17T15:52:00+02:00

The accelerating digitalization of the energy sector is redefining how electricity is generated, traded, and consumed. Among emerging innovations, smart contracts being self-executing programs embedded on blockchains have become pivotal to the development of decentralized energy markets. This article reviews the state of knowledge and practical progress in applying smart contracts to energy systems, with particular attention to their potential in Ukraine’s evolving energy and digital infrastructure. Through a systematic analysis of academic studies, pilot projects, and policy frameworks, the article identifies the main opportunities, challenges, and future trajectories of blockchain-based automation in energy markets. The starting sections introduce the conceptual foundations of smart contracts, highlighting their essential properties of transparency, immutability, and autonomy. These characteristics enable direct peer-to-peer transactions without intermediaries, potentially lowering transaction costs and improving market efficiency. The subsequent analysis focuses on how smart contracts can support decentralized energy trading, renewable integration, and dynamic pricing, using examples from Australia’s Power Ledger, Brooklyn Microgrid in the United States, and Europe’s Enerchain, WePower, and Sunchain initiatives. To complement international evidence, the article discusses Ukraine’s readiness for pilot adoption in microgrid environments, given its digital transformation agenda and renewable energy policies. The study further examines technological, regulatory, and security challenges hindering large- scale deployment. Issues such as interoperability, scalability of consensus algorithms, and the legal enforceability of smart contracts remain critical barriers. Nevertheless, emerging frameworks like regulatory sandboxes and advances in IoT and AI integration offer pathways to overcome them. MATLAB-based simulation examples illustrate the potential for dynamic pricing and automated market balancing. The article concludes with strategic recommendations for policymakers, engineers, and researchers by emphasizing the need for hybrid architectures combining blockchain, artificial intelligence, and energy optimization models. Overall, the article underscores that while smart contracts promise to democratize and decarbonize energy systems, their success ultimately depends on coordinated technical innovation and adaptive governance.

CORE ASPECTS OF DEVELOPING A MOBILE APPLICATION FOR SUPPLY CHAIN OPTIMIZATION AND ROLE-BASED MANAGEMENT OF INTERNAL TASKS

2026-03-17T15:51:58+02:00

The article examines approaches to the digitalization of internal business processes in retail and to the optimization of personnel management through the development of a mobile application with a multi-level access system. The aim of the study is to design and implement an architecture for a mobile application that provides differentiated access for employees, admin- istrators, and managers according to their roles and functional responsibilities. The paper analyzes modern task management solutions and defines requirements for developing a system intended to standardize and streamline internal operations within a retail network. The developed application enables centralized task creation, assignment, monitoring, and execution, as well as internal communication between personnel levels. Separate UI/UX interfaces have been designed for each user group to improve inter- action efficiency. The software implementation is based on C# and Blazor Server technologies, which made it possible to create a unified codebase for both mobile and web functionality. Data storage and processing are performed using Microsoft SQL Server Express, ensuring system scalability and information reliability. The scientific novelty lies in integrating hierarchical personnel subordination principles with access control mechanisms (ACL) in a mobile environment. The proposed solution enhances the operational efficiency of retail networks, optimizes internal workflows, and can be adapted for various organizational structures.

INTELLECTUAL DECISIONS TO COMPRESSIVE SENSING FOR EFFICIENT DATA ACQUISITION IN WIRELESS SENSOR NETWORKS

2026-03-17T15:51:57+02:00

This paper considers the application of linear algebra methods to Compressive Sensing technology for wireless sensor networks (WSNs) specialized in temperature monitoring in “smart” greenhouses. It is shown that the traditional approach, which involves the transmission of full data streams from each individual sensor, leads to high energy consumption and generates information redundancy, which is a problematic factor for autonomous battery-powered systems. As an alternative, a special architecture of compressive sensing is proposed, within which not the original data are transmitted, but their compressed linear combinations. This radically reduces traffic, but, thanks to mathematical transformations, retains the possibility of full and accurate restoration of the entire temperature field on the receiver side. The methodological basis is the use of the fundamental property of sparsity of temperature signals when they are presented in certain bases, in particular, the basis of the discrete cosine transform. Modern optimization and iterative linear-algebraic algorithms are used to reconstruct the original data from compressive sensing. The practical effectiveness is illustrated by a model example of a network with six sensors, where compressing sensing allowed to reduce the amount of transmitted information by 50 % and simultaneously detect anomalies in the operation of a faulty sensor, confirming the stability of the system. The proposed architecture provides comprehensive energy efficiency, reliability and scalability of the monitoring system, supports the dynamic addition of new sensors and can be successfully integrated into modern Internet of Things systems, industrial complexes and «smart» cities. Thus, the work clearly demonstrates the powerful synergy between mathematical rigor and practical efficiency of compressing sensing for creating intelligent agricultural systems of the next generation.

LINEAR RECURRENT RELATIONS IN THE SYSTEM OF SYMMETRIC ENCRYPTION ALGORITHMS

2026-03-17T15:51:55+02:00

The article proposes the principles of functioning of symmetric encryption based on a gamma cipher obtained by gen- erating linear recurrence relations. The influence of a linear recurrence relation on the cryptographic stability of algorithms is investigated, and examples of practical application of recurrent models in ensuring the encryption process are also given. The encrypted text obtained by the gamma method based on a linear recurrence relation is quite laborious to reveal if the gamma cipher does not contain repeated bit sequences and changes randomly for each encrypted word. If the gamma period exceeds the length of the entire encrypted text and no part of the original text is known, then the cipher can be revealed only by direct search of the set of probable key values, in this case, the cryptographic stability is determined by the size of the key. The extent to which the formed gamma meets the security requirements in most cases depends on the initial data on the basis of which the linear recurrence relation is generated. The maximum value of the sequence period depends entirely on the depth of the linear recurrence relation and the relation that sets it, and the specific values – on the initial state of the sequence. The cryptographic strength of the algorithm can be increased by combining several different linear recurrence relations in the context of LFSRs and/or by using nonlinear functions in the register feedback, nonlinear logic and filtering of the register contents. The symmet- ric encryption algorithm based on the linear recurrence relation allows for both software and hardware implementation. From the point of view of practicality, software implementation allows for greater flexibility in use. However, the implementation of encryption algorithms in the main program code provokes numerous security risks associated mainly with unauthorized access and message confidentiality issues. To solve such a problem, it is appropriate to apply the practice of removing encryption and/ or decryption functions to separate modules or libraries, as well as transmitting the protected key using only protected channels. The article describes how a linear recurrence relation functions based on establishing dependencies between members of an unknown sequence and an index. The current method has found wide application in algorithm analysis, digital signal processing, economics, and in the context of cryptography it is often used within the framework of symmetric encryption. The article defines that a modular approach involves dividing software into logically independent parts, each of which performs a separate function. When converting a module into a dynamic library, the main program will only call the function, when necessary, completely hiding access to the algorithm code. Thus, cryptographic operations will be implemented in isolation, which will reduce the risk of unauthorized access to the encryption algorithm.

LOGISTICS MANAGEMENT OF MOTOR VEHICLE TERMINAL OPERATIONS AS A QUEUING SYSTEM

2026-03-17T15:51:54+02:00

The article analyzes calculating the motor vehicle terminal operations as a queuing system and determining the effectiveness of proposed methods for optimizing terminal operations based on logistic approaches. The research assesses the feasibility of implementing a control system at a humanitarian aid warehouse that would ensure the collection and storage of data on vehicle locations at loading areas, the status of loading and unloading mechanisms, and decision-making regarding their use, as well as to transmit commands to drivers to enhance the efficiency of road transport operations during humanitarian cargo delivery. The core principle of the terminal’s operations is optimal logistics management of cargo flows. Goods are sorted by priority and destination, ensuring minimal delays and rapid delivery of humanitarian aid to those in need. Calculations have been made for the operation of the motor vehicle terminal as a queuing system, specifically focusing on vehicle downtime during unloading at the humanitarian warehouse. The effectiveness of the proposed terminal optimization methods based on logistic approaches was also evaluated. The vehicle idle time during cargo operations was determined for traditional technology and for the implementation of a regulation system for truck arrival at loading fronts. Furthermore, the annual cost savings achievable through the organization of an optimal regulation system for truck arrivals at terminal loading fronts were calculated. It is proposed to develop the integrated decision support within the terminal system’s functional cycle as an automated system. The main objective of this method is to eliminate the ‘human factor,’ as automation is intended to enhance data quality, streamline the processing of cargo owner information, and optimize technological processes. The primary challenges for humanitarian aid terminals include ensuring cargo security, preventing delays, and maintaining operational responsiveness to changing conditions. At the same time, investments in advanced technologies and infrastructure improvements can significantly increase the operational efficiency of such terminals.

PRINCIPLES OF INTEGRATING ECOLOGICAL STANDARDIZATION AND LIFE SAFETY STANDARDS INTO UKRAINE’S POST-CONFLICT RECOVERY STRATEGIES

2026-03-17T15:51:52+02:00

Issue of ensuring ecological security in Ukraine has acquired critical and unprecedented importance due to the full-scale military aggression, which has triggered a unique and dangerous synergy of military, chemical, and technogenic threats. The current, largely static system of environmental regulation, relying predominantly on fixed Maximum Permissible Concentration (MPC) and Maximum Permissible Discharge (MPD) standards, has proven fundamentally incapable of effectively responding to unpredictable, massive, and acute releases of highly toxic substances resulting from kinetic warfare. Furthermore, the existing normative framework lacks the flexibility and legal mechanisms required for seamless integration into Civil Protection (CP) and disaster management response protocols. This systemic inadequacy creates a vast, long-term “ecological debt” for the nation and a postponed, latent danger to public health and the sustainability of natural ecosystems. Addressing this vulnerability neces- sitates a complete paradigm shift in environmental governance. Goal. The primary objective of this research is to scientifically substantiate and formulate a set of crucial methodological principles for integrating adaptive ecological standardization with core life safety standards. These principles are intended to serve as the foundational scientific and regulatory basis for the development of a coherent, resilient national strategy for the post- conflict reconstruction and «green» recovery of Ukraine. The goal extends beyond simple cleanup to establishing a preventative, forward-looking environmental security system. Methodology. The methodological framework employs a robust, multi-faceted approach. It relies on the Systemic Approach to analyze the complex interdependencies between military-induced environmental damage, public health, and socio- economic restoration efforts. Crucial to this is the application of Risk Analysis Methods to quantify both the immediate and latent hazards posed by war-related contaminants (e.g., heavy metals, unexploded ordnance residues, asbestos). The research adopts the Socio-Ecosystem Concept to understand how damage to natural capital directly compromises human and societal resilience. A central pillar of the methodology is the development of Adaptive Regulation – a dynamic normative framework – leveraging Artificial Intelligence (AI) and Geographic Information Systems technologies for the rapid, real-time forecasting and management of multimedia pollutant effects across various environmental compartments. Results. The investigation critically identifies two fundamental, interrelated gaps within the current ecological standardization system that hinder effective post-conflict management: 1) Absence of Temporary Emergency Regulation (MPC-EMERGENCY): The lack of formalized, temporary, context-specific normative standards (referred to here as MPC- EMERGENCY) applicable immediately following catastrophic military or man-made events to guide emergency remediation and immediate safety measures. 2) Inability to Account for Cumulative, Multimedia Effects: The existing standards fail to adequately model and regulate the cumulative, synergistic effects of multiple pollutants simultaneously affecting different environmental media (air, soil, water), which is a characteristic feature of war damage.

MODERN APPROACHES TO RISK MANAGEMENT IN THE SAFETY SYSTEM AT INDUSTRIAL ENTERPRISES

2026-03-17T15:51:51+02:00

The article provides an in-depth analysis of the safety management system at Ukrainian oil and gas industry enterprises, which operates under conditions of an unprecedented convergence of risks, making outdated, reactive approaches to labor pro- tection not only ineffective but also unacceptable. It is substantiated that in addition to the traditional production hazards inher- ent in all stages of the cycle–from geological exploration to hydrocarbon processing–three key aggravating factors of modernity have been added. Firstly, a high degree of physical and moral deterioration of infrastructure, which significantly increases the likelihood of accidents due to equipment failure. Secondly, the intensification of production at depleted fields, which gener- ates new, insufficiently studied professional risks. Thirdly, direct military threats that have turned the industry’s facilities into strategic targets for missile strikes and UAV attacks. Under such conditions, safety management is transformed from a narrowly focused function into a fundamental element of national resilience. The key thesis of the work is the need for a fundamental paradigm shift–a transition to a proactive, risk-oriented manage- ment model that focuses on predicting and neutralizing threats before they are realized. The theoretical basis for this approach is, in particular, James Reason’s “Swiss cheese model”, which demonstrates that an accident is the result of a confluence of hidden deficiencies at several levels of defense, namely technological, organizational, competence, and supervisory. The goal of a pro- active system is the constant identification and elimination of these “holes” to prevent a catastrophic coincidence. The technological foundation for implementing a proactive approach is identified as digital transformation based on Industry 4.0 principles. The creation of a unified intellectual ecosystem is proposed, where: the Internet of Things IoT acts as a real-time data collection system through vibration and corrosion sensors on equipment, gas analyzers in work areas, and weara- ble “smart” helmets and biometric trackers for personnel; artificial intelligence AI processes this data for predictive equipment failure and intelligent video analytics; immersive VR/AR technologies and digital twins provide skills training in extreme situa- tions without risk to life and allow for the safe modeling of the most complex emergency scenarios. To objectify management decisions, a transition from qualitative assessments to quantitative risk analysis QRA is pro- posed. This approach is based on the formula Risk = Probability × Consequences and uses mathematical tools such as deductive “fault tree analysis” FTA and inductive “event tree analysis” ETA to calculate the probabilities of complex accidents. Such analysis allows for an economically justified “cost-benefit” assessment for investments in safety systems. The key conclusion of the work is that in modern conditions, the line between labor protection, technological safety, and civil defense is completely blurred. A conceptual model of a unified, integrated safety management system that combines these three components is proposed. The practical implementation of the model involves the arrangement of shelters, the creation of duplicate notification and communication systems, and the conduct of comprehensive training that combines first aid skills with elements of tactical medicine. The creation of such an adaptive system is the only way to ensure the protection of personnel and the resilience of the functioning of strategic enterprises in Ukraine.

ADAPTATION AND RESILIENCE OF SMALL-FORMAT RETAIL: STRATEGIC APPROACHES IN TIMES OF CRISIS TRANSFORMATIONS

2026-03-17T15:51:50+02:00

The article analyzes priority strategic approaches to the development of retail food trade companies in the context of full- scale military aggression in Ukraine, particularly against the backdrop of growing competition, declining purchasing power of the population, and the destruction of logistics chains. The dynamics of key macroeconomic indicators, including the number of economic entities, sales revenue, and net profit in the food retail sector for the period 2013–2023, are examined. The key fac- tors that influenced the development of the market were analyzed, among which the full-scale invasion in 2022, the loss of part of the market in the occupied territories, changes in consumer behavior, and the geographical reorientation of business played a special role. Based on the analysis, the high volatility of the sector and the need for structural transformation, in particular, the geographical reorientation of the business, were established. The strategic need to reorient retail towards small towns and rural areas is justified by the stable demand for basic goods and the limited presence of chain operators. In this context, a strategy for adapting food chains by expanding micro-retail formats (“neighborhood stores”) is proposed, which involves opening compact retail outlets with an optimized product range, value-oriented pricing, and minimal operating costs. A step-by-step organiza- tional and structural diagram of small-format retail outlets is provided, with an emphasis on local flexibility, digital support (analytics, chatbots), and the social function of the store in the community. In addition to economic feasibility, the proposed structure ensures the implementation of a corporate social function aimed at guaranteeing the population’s access to basic goods in socially vulnerable regions. The results obtained can serve as a practical guideline for economic entities seeking to adapt to the real conditions of the time.

FLEXITANKS IN BAG-IN-BOX TRANSPORTATION TECHNOLOGY: TERM REVISION BASED ON FUNCTIONAL ROLE

2026-03-17T15:51:48+02:00

This article is devoted to researching the place and functional role of flexitanks (FT) in bag-in-box (BIB) container trans- port technology. The relevance of this work is due to the growing share of containerization in the global transport market and the active search for more flexible and economical solutions for the delivery of liquid cargo. The development of FT as an alternative to traditional tank containers in the 1970s opened up new opportunities for the logistics of food products, chemicals, and other safe liquids, but at the same time raised a number of questions regarding the legal definition of their status. In particular, there is no clear interpretation in international practice as to whether FTs should be classified as packaging materials or considered as supplementary container equipment. The purpose of the study is to clarify the term “flexitank” based on its functional purpose, as well as to analyze the conse- quences of classifying this category under the terms ‘packaging’ or “container equipment” in insurance and customs clearance practices. The basis for the study was the definition of the elements of BIB technology from international standards and conven- tions, as well as practical case studies of legal cases. The article describes in detail the technological aspects of the BIB system and proves the functional dependence of FT on container equipment, which makes it impossible to use it as an independent transport tank. Through analysis of legal norms and court decisions, it has been proven that FTs cannot be considered packag- ing, since they require a container to perform their role and are therefore auxiliary equipment. The scientific novelty lies in the proposal of a clear terminological distinction: defining FT as an elastic tank that is installed in a universal cargo container and acts as additional equipment. This approach allows for the removal of a number of contradictions in the field of insurance and customs clearance, and also provides a basis for a clear distinction between the rights and obligations of participants in inter- modal transport. The results of the study are of practical importance for intermodal, freight forwarding, shipping, and insurance companies, carriers, and cargo owners, as they enable the optimization of contractual relationships, reduce the risk of litigation, and ensure transparency in intermodal bulk cargo transportation procedures. The practice recommendations in the conclusions are meant to make bulk cargo transportation safer and more efficient, as well as to standardize transportation terminology.

INSPECTION AND MODERNIZATION OF WORKING BODIES TOOL FOR SURFACE TREATMENT OF SOIL

2026-03-17T15:51:46+02:00

The article conducts theoretical and practical research on the existing working bodies of tools for surface tillage and proposes their modernization in order to improve the quality of tillage and reduce energy consumption for performing technological operations. As a result of the research we conducted, we found that there are different designs of working parts of machines and tools for surface tillage, such as arrow-shaped tines and discs, which are passive working parts and have a number of disadvantages, namely, soil sticking, incomplete cutting of weeds, etc. In addition, they do not fully meet the agrotechnical requirements for performing this type of work. The best performance is achieved by annular working bodies with passive and active drives. According to the results of the research, it was determined that for stable performance of surface tillage without soil clogging of the working elements and better destruction of weeds, their design should be in the form of rotary working elements with an active drive. A modernization of the working elements is proposed, which consists in their having the form of ring working elements with the division of the continuous cutting blade into separate parts and their rotation at an angle to the axis of rotation. Research has shown that the proposed working bodies of tools for surface tillage practically do not clog in this form and do not leave any defects. The advantage of this technical solution is that, in addition to all of the above, the proposed design of the working elements reduces their traction resistance and they completely cut the weeds. It has been determined that a promising direction in the development of soil cultivation equipment is the use of machines and tools equipped with rotary working bodies with an active drive to improve the quality of surface tillage, which meets the established agrotechnical requirements.

HOTEL SERVICE ON VEHICLES IN TOURIST SERVICES: GENERAL PRINCIPLES AND DIRECTIONS OF RESEARCH

2026-03-17T15:51:45+02:00

The article is devoted to systematizing information on the application of the basic principles and procedures of hotel ser- vice on vehicles, mainly during their movement within the framework of a tourist trip, with the aim, first of all, of further improv- ing the system of standardization and certification of tourist transportation and accommodation services and their combinations. The organization of overnight stays on vehicles during their movement in many cases requires the application of the principles, methods and technologies of the hotel industry and hotel service. The authors have identified various areas of combination and connection of hotel and transport services in the process of servicing tourists during travel – hotels at hubs, common booking procedures, transport service of classic hotel enterprises, stationary hotels in former mobile vehicles, and the presence of hotel service of various levels of manifestation and specific- ity on vehicles during long and long-distance transportation of tourists. The latter area determines the differentiation of the depth of application of hotel technologies on mobile vehicles depending on the duration of the trip, their design and technical characteristics. The above circumstances allowed the authors to identify five levels of hotel support for long-distance tourist transport – from providing minimal service to improve the comfort and convenience of overnight stays in seats in conditions of limited interior space (classic tourist buses) to a full-fledged diversified service-production mobile hotel complex with the widest range of basic, additional and related services (large multi-deck cruise liners). At the last level, all organizational, managerial, technical and technological components, rules and principles of classic hotel management are used particularly vividly and fully, including laundry, elevator, cleaning and other typical hotel services. The use of this five-level model will allow in further developments to include vehicles, during the movement of which hotel service is most clearly manifested when providing tourist travel, to important components in the system of hotel organization, to take them into account in the formation of national and other standards of hotel service, which is of great importance for improv- ing the quality of services and competitiveness of enterprises in the tourism, transport and hotel business.

INNOVATIVE ACTIVITIES IN THE HOTEL AND RESTAURANT BUSINESS

2026-03-17T15:51:43+02:00

The features of implementing innovations in the hotel and restaurant business are considered. The description of the “Khimik” hotel, which belongs to the category of two-star accommodation establishments, which provides a wide range of services of average comfort at affordable prices, is provided. It has been established that the organizational structure of the “Khimik” hotel is formed in accordance with the needs of a medium-sized enterprise and includes the main functional units that ensure effective management and guest service. Innovative activities in the “Khimik” hotel are implemented through the introduction of modern technologies aimed at improving service, effective management, saving resources and improving the quality of service. The hotel demonstrates a practical approach to innovation, focused on specific results. The innovative technologies implemented in the activities of the “Khimik” hotel are considered, which contribute to improving the quality of service, management efficiency and safety. It was observed that innovative activities at the “Khimik” hotel are an important component of the development strategy, allowing it to adapt to rapid market changes, increase the efficiency of internal processes and strengthen its position among competitors. Continuous improvement of the technological base and implementation of new innovative solutions opens up prospects for expanding the range of services, improving the level of service and meeting the growing needs of modern customers. A SWOT-analysis of the innovative activities of the “Khimik” hotel was conducted, identifying key internal advantages that ensure the effective implementation of modern technologies, in particular automated accounting systems, CRM and energy-saving equipment. At the same time, there are certain weaknesses, in particular, budget constraints and insufficient level of IT competence of the staff, which require attention for further development. External opportunities include the potential for expanding digital services, the introduction of new energy-saving technologies, and the growing demand for environmentally responsible service. At the same time, the hotel must consider threats in the form of increased competition, technical risks, changes in legislation, and economic instability. Based on the analysis, it is recommended to focus on improving staff skills, modernizing equipment, developing environmental initiatives, and strengthening cybersecurity. This will strengthen the hotel’s position in the market, improve the quality of service, and ensure the sustainable development of innovative activities.

PRACTICAL METHODS OF SERVICE QUALITY MANAGEMENT IN ESTABLISHMENTS OF RESTAURANT INDUSTRY

2026-03-17T15:51:41+02:00

The article systematises practical methods of managing service quality in restaurant industry establishments (RIEs). Tools for analysing situations and developing ideas for improving quality are identified. Recommendations are provided on selecting the most favourable ideas for this sector. It is established that improving and ensuring service quality are key tasks for restaurant management. Quality management as a direction of activity of RIEs is carried out to improve services continuously and the mechanisms of enterprise activity as well as bring products and services into line with standards. The essence of the concept of “service quality management” in the context of restaurant activity is shown, its components and specifics of assessment are outlined. It has been found that effective quality management requires a comprehensive approach that includes standardisation of service processes, continuous monitoring of customer satisfaction, staff training and the introduction of innovative technologies. Among the tools that determine practical quality management methods, the tools of control, analysis, design, and management are highlighted. The work is the quintessence of the results of research on the practice of functioning of RIEs. Attention is focused on practical methods and analytical tools that allow negative trends emerging in the activities of RIEs to be identified. In order to identify problem areas in the activity of establishments, it is recommended to conduct a self-assessment using various assessment methods and digital technologies to collect and analyse feedback. Based on the study of the experience of RIEs, the factors that form the quality of restaurant services are characterised. Goals for improving service quality are formulated. It has been established that if measures aimed at continuous and gradual improvement of service quality are insufficiently effective (or lose their effect), it is advisable to resort to a policy of breakthrough improvements. The practical significance lies in the formulation of recommendations for improving quality management efficiency for managers of RIEs in order to strengthen competitive positions and customer loyalty. The results can be used in the practical activity of RIEs seeking to achieve high standards of service.

OPTIMIZATION OF TAXATION OF RESTAURANT BUSINESS ESTABLISHMENTS IN THE CONTEXT OF SUSTAINABLE DEVELOPMENT

2026-03-17T15:51:40+02:00

The article analyzes the taxation systems of restaurant businesses in Ukraine, considering their organizational and legal status. The purpose of the study is to substantiate the theoretical and methodological foundations for the use of mechanisms of tax optimization in restaurant business establishments in the context of sustainable development. The peculiarities of applying the simplified and general taxation systems are examined, along with the specifics of tax regulation in this sector. It is substantiated that the modern restaurant business requires effective tax mechanisms that ensure not only a reduction in the tax burden but also the financial stability of enterprises, promote the formation of social responsibility, and create conditions for investment in ecological practices. A scenario-based approach to determining the level of tax burden is proposed, demonstrating significant differences among enterprises operating under various organizational and legal forms and applying different tax regimes. The practical significance of the research lies in utilizing the proposed approach to select optimal taxation models for restaurant establishments in the context of sustainable development. This contributes to reducing the tax burden, enhancing financial stability, and adapting enterprises to modern challenges caused by the martial law. Prospects for further research involve the application of modeling optimization strategies as a vital tool for analyzing and forecasting the results of implementing tax measures.

MODELING INTERRELATIONS IN TRANSPORT SYSTEMS USING THE METHOD OF CORRESPONDENCE ANALYSIS

2026-03-17T15:51:38+02:00

The article explores the modeling of interrelations within transport systems through the application of Correspondence Analysis (CA), a statistical method particularly effective for categorical and mixed discrete data. The relevance of this research is shaped by the profound transformation of Ukraine’s transport network under the dual pressures of military aggression and global digitalization. These challenges necessitate innovative approaches to strategic and tactical decision-making in logistics, especially in the domain of intermodal transportation, which is increasingly recognized as a cornerstone of sustainable development. Intermodal transport enables the integration of rail, road, maritime, and inland waterways, thereby reducing costs, saving time, and minimizing environmental impact. The study systematizes the diverse factors influencing intermodal transport perfor- mance, including state policy support, infrastructure maturity, terminal network development, pricing mechanisms, and the role of ICT-driven innovations. A comprehensive review of contemporary literature highlights the shift from fragmented analyses toward systemic approaches that incorporate scenario planning, multi-criteria evaluation, and intelligent decision support sys- tems capable of processing large-scale datasets in real time. The methodological contribution of the paper lies in the proposed algorithm for applying CA to the segmentation of the intermodal transport market. This algorithm involves the construction of contingency matrices, normalization and profile building, χ²-distance calculations, singular value decomposition, and visualization of results in low-dimensional Euclidean space. Such an approach allows researchers and practitioners to uncover hidden associations, identify clusters of clients with similar modal preferences, and design differentiated tariff and service strategies tailored to specific market segments. Unlike traditional numerical methods, CA provides a more nuanced representation of categorical relationships, enabling the detection of structural patterns that are often overlooked in complex transport systems. The practical significance of the study is twofold. First, it offers a robust analytical tool for private stakeholders such as logistics providers, terminal operators, and intermodal carriers, who require accurate market segmentation to optimize service delivery. Second, it provides state institutions and policymakers with evidence-based insights for infrastructure planning, regu- latory frameworks, and sustainable transport strategies. By bridging methodological rigor with applied relevance, the research contributes to the modernization of transport systems and supports Ukraine’s integration into global logistics networks.