Information technologies in economics and environmental sciences
Information Technologies in Economics and Environmental Sciences is an open-access, peer-reviewed academic journal established in 2017 by the National University of Life and Environmental Sciences of Ukraine (NUBIP). The journal publishes the results of fundamental and applied research in computer science, focusing on the digitalization and optimization of processes across various sectors of economics and business. It places a special emphasis on the agricultural sector, ecology, environmental management, education, and life sciences. The publication encompasses the following key areas: information technologies, control and management systems, computer systems and networks, information security technologies in environmental management, economic cybernetics, artificial intelligence, and machine learning. ISSN 3083-7502 | UDC: [33+502]:004.9 Frequency: The journal is published twice a year. Languages: Ukrainian, English. Website: https://journals.nubip.edu.ua/index.php/Inf/en. Registration: Media identifier R40-06595. Decision of the National Council of Ukraine on Television and Radio Broadcasting No. 2543, Protocol No. 25 dated 20 November 2025.
Національний університет біоресурсів і природокористування України
Усі матеріали розповсюджуються згідно з умовами міжнародної публічної ліцензії Creative Commons Attribution 4.0 International Public License, що дозволяє іншим поширювати статтю з визнанням авторства та першої публікації в цьому журналі.
ARCHITECTURE OF AN INTELLIGENT SUBSYSTEM FOR DETECTING WARM-BLOODED PESTS IN MONITORING PRODUCTION PROCESSES IN AGRONOMY
The paper considers the construction of an intelligent subsystem for detecting warm-blooded pests as a component of an automated system for monitoring production processes in agronomy. The relevance of the study is determined by the need for timely detection of biological threats in crop fields, which lead to significant economic losses and a reduction in the quality of the final product, a reduction in dependence on manual field inspection, and an improvement in the promptness of decision-making in precision agriculture. Based on the analysis of current approaches to computer vision, thermal imaging, drone monitoring, and IoT infrastructure, the requirements for a subsystem architecture are formulated. The subsystem should provide multimodal data acquisition, preprocessing, localization of potentially dangerous objects, threat assessment, and transmission of results to the decision-support loop. The purpose of the study is to substantiate the structure of such a subsystem, identify its main functional modules, and determine suitable classes of object detection models for field conditions. The study uses methods of system analysis, functional decomposition, comparative analysis of object detector architectures, and generalization of recent publications in precision agriculture. As a result, a multi-level architecture is proposed that combines the sensing layer, the analytical processing layer, the spatial verification layer, and the decision-support layer. The expediency of combining RGB and thermal data, as well as the use of YOLO-type one-stage detectors for real-time field detection, is substantiated. The practical value of the proposed approach lies in the possibility of its further integration into automated agricultural systems and the development of specialized early-warning services. Received 2026-04-01 Accepted 2026-04-20
Automation, computer-integrated technologies and robotics
Copyright (c) 2026 Information technologies in economics and environmental sciences https://creativecommons.org/licenses/by-nc/4.0
PROGRAMMING LANGUAGE SELECTION FOR INTELLIGENT FOREST MONITORING AND FIRE-FIGHTING SYSTEMS USING THE ANALYTIC HIERARCHY PROCESS
This paper addresses the critical issue of selecting the optimal programming language for developing intelligent environmental monitoring systems operating in real-time. The study emphasizes the transition from passive observation to the active use of artificial intelligence algorithms (Computer Vision, Deep Learning) for the early detection of forest fires. The scientific novelty lies in the application of the mathematical framework of the Analytic Hierarchy Process (AHP) to minimize subjectivity in the selection of the technology stack. The authors constructed a hierarchical evaluation model for four alternatives: Python, C++, Rust, and Lisp.For the comparative analysis, five key criteria were selected: processing speed, recognition accuracy, autonomy (energy efficiency), scalability, and development cost. The calculation of the criteria weight coefficients demonstrated the priority of detection accuracy (43%) and real-time system performance (28%). Based on the integral indicators, the Rust language (8.54) showed clear superiority over C++ (7.82) and Python (7.16). It was established that Rust is the most balanced solution for implementing the Edge Computing concept, as it ensures memory safety and high processing speeds directly on autonomous devices. Consequently, an optimal hybrid architecture strategy is proposed: utilizing Rust for low-level detection modules and Python for cloud data analytics and visualization. This approach allows for a shift from subjective tool selection to objective mathematical justification based on critical safety requirements.
MODERN DESIGN PARADIGMS FOR DECISION SUPPORT SYSTEMS: FROM INTEGRATING DATA WAREHOUSES AND DATA LAKES TO THE DATA LAKEHOUSE ARCHITECTURE
The article examines modern approaches to the design of decision support systems in the conditions of rapid growth of information volumes. Data warehousing information technology was born in the bowels of IBM and was finally formulated by B. Inmona and R. Kimball in the 90s of the last century as a method of solving information and analytical tasks in the field of decision-making and support. Arising at the intersection of database technology, decision support systems (DSS) and computer data analysis, the concept of data storage later evolved as it proved suitable for a wide range of applications in business, science and technology. Systems built on the basis of data warehouses have a number of characteristic features that distinguish them as a special class of information systems. Such features include the subject orientation of the system, the integration of data stored in it, collected from various sources, the invariance of this data over time, the relatively high stability of the data, the need to find a compromise in the redundancy of the data. The article examines the evolutionary transition from the use of isolated Data Warehouses and Data Lakes to a conceptually new hybrid Data Lakehouse architecture. The technological foundation of open table formats (Delta Lake, Apache Iceberg, Apache Hudi) and their role in ensuring the transactional reliability (ACID) of file systems are analyzed. The feasibility of applying the "medallion architecture" pattern (bronze, silver, and gold layers) for effective data quality management, collaborative work of machine learning algorithms, and management BI reporting preparation is substantiated. Received 2026-02-27 Accepted 2026-04-08
MODELING EVALUATION FRAMEWORK FOR ROBOTICS AND DIGITAL TWIN SIMULATION PLATFORMS WITH AN UNREAL ENGINE 5 WAREHOUSE CASE STUDY
Digital twins and high-fidelity simulators are becoming central to the design, testing, and operation of modern robotic systems in industry, logistics, and human–robot interaction. However, existing comparative studies typically focus on a single robot type, task, or simulator, and rarely consider digital–twin–specific aspects, such as telemetry pipelines, operator interfaces, or cloud connectivity. This article proposes a unified evaluation framework for robotics-oriented digital twin platforms. It applies it across five representative use cases: (i) small UAV inspection, (ii) an industrial hand-robot cell, (iii) a warehouse logistics cell with autonomous mobile robots (AMRs), (iv) HCI/VR-based teleoperation of a mobile manipulator, and (v) a general indoor patrol and docking scenario. The framework standardises inputs (robot and environment profiles, task definitions, digital-twin data contracts) and outputs (physics/task metrics, performance metrics, and DT metrics) and explicitly encodes task complexity via structured phases and control steps per episode. It is implemented on several widely used robotics simulators (Gazebo/gz-sim, Webots, NVIDIA Isaac Sim, CoppeliaSim) and on an Unreal Engine 5 (UE5)–based stack that combines Datasmith asset import, Chaos Physics, Blueprints/Control Rig, and UMG/VR interfaces. As a detailed case study, we instantiate the warehouse logistics use case as a UE5-based digital twin and report quantitative results on order throughput, path lengths, real-time factor, frame times, telemetry bandwidth, and UI-to-actuation latency, along with observed bottlenecks such as Blueprint CPU overhead and visual-load–induced slowdowns. Across all platforms and use cases, the results highlight trade-offs between physical fidelity, scalability, interaction richness, and DT responsiveness, and lead to practical guidelines for selecting and composing simulation and digital-twin stacks for UAV, manipulation, logistics, and HCI-centric applications. The paper concludes with a discussion of limitations, including the simplification of physics and hardware dependence, and outlines future steps toward sim-to-real correlation and automated benchmarking.
Copyright (c) 2026 Information technologies in economics and environmental sciences https://creativecommons.org/licenses/by-sa/4.0
SPECIALIZED MODULE BASED ON ARDUINO FOR A CAR SECURITY SYSTEM
The high rate of car theft has led to an increased demand for efficient and reliable security systems capable of providing continuous monitoring of a vehicle's condition and promptly responding to attempts of unauthorized access. A significant portion of existing commercial solutions is characterized by high cost, closed architecture, and insufficient adaptability to the specific requirements of car owners. The use of open hardware platforms such as Arduino makes it possible to develop cost-effective, flexible, and specialized security modules that can be integrated into any vehicle model while maintaining a high level of safety. The Arduino platform, due to its open hardware architecture and wide selection of compatible modules, is one of the most common foundations for building automotive security systems. Among the main advantages of Arduino are its low cost, support for standard interfaces (UART, I2C, SPI), compatibility with numerous digital and analog sensors, availability of libraries, and ease of debugging. These factors make Arduino a convenient tool for implementing fully functional security modules.
A FUZZY-LOGICAL MODEL FOR ASSESSING THE STATUS OF AN INFORMATION SYSTEM DURING DDOS ATTACKS
A fuzzy-logical model for assessing the state of an information system (IS) during DDoS attacks is proposed. The model is based on the use of a system of linguistic variables that describe critical parameters of network traffic and the operational characteristics of the IS. To form an assessment of the current risk, Mamdani-type fuzzy inference was applied, followed by defuzzification using the centre-of-gravity method. A simulation experiment was conducted, implemented in the Python environment using the scikit-fuzzy library. The results obtained confirmed that the proposed model adequately reflects the dependence of the integral risk on changes in load parameters, ensures continuity of assessment and sensitivity to critical combinations of traffic factors. The results obtained provide grounds for considering the developed model as a basis for the synthesis of an IS status monitoring module.
DIGITAL TWINS TECHNOLOGIES IN AGRICULTURAL PROJECT MANAGEMENT SYSTEMS
In the last few decades, global market and life processes have shown that many different aspects of human activity are closely tied. This leads to the Sustainable Development Strategy targeted at the balanced interconnection of the environment (lands, atmosphere, ecosystems), society (cultural, educational and health) and economics, of coarse. Such complex approach needs a new tools for development and evaluation projects in the all activity sphears. The current work is devoted to the potential directions for development such tools. The possible principles for developing project development systems in agriculture are considered and analysed in the research, which are based on modern technologies, including Digital Twins, Ontologies, and network services, in the context of increasing requirements for Sustainable Development and general trends towards rational and economical use of the planet's resources. The analysis demonstrates the potentially high efficiency and versatility of the proposed approach not only for solving issues of project creation, but also for their further management and monitoring of compliance with target functions. By examining the conditional example of a sowing field, the features and general approaches to forming the information model structure of the domain area and its connected environment are considered, based on determined and tied ontologies. Two main directions of further research are also identified: creating a base for the unified implementation of digital twins and automated analysis systems of ontologies presented in OWL formats, and generating new, complex framework models based on them. The paper consists of a preliminary justification for the concept of new system development for the life cycle of economic and business project management in the agricultural sector.
A SYSTEM FOR COMPREHENSIVE ANALYSIS OF COMMUNICATIVE BEHAVIOR IN PUBLIC DISCUSSIONS BASED ON SPEAKER DIARIZATION
The relevance of this study is driven by the increasing volume of online meetings and public discussions in digital formats, creating a demand for automated tools to analyze group communication. Traditional manual coding and transcription methods are highly labor-intensive and subjective, limiting large-scale research on communication patterns. The aim of this research is to develop and validate a comprehensive system for automated analysis of communicative behavior that integrates modern speaker diarization technologies, automatic speech recognition, and statistical analysis to provide a detailed picture of group dynamics in public discussions. Methods. The system is implemented using a microservice architecture with Python 3.10+, FastAPI, and React. Speaker diarization is performed using the pyannote.audio algorithm, which combines convolutional encoders with pre-trained WavLM models. Automatic speech recognition is carried out using transformer architectures (Whisper, AssemblyAI, Conformer). Communicative behavior analysis includes calculation of activity statistics, network analysis of interactions, and assessment of communication style. Results. The developed system successfully integrates speaker diarization with 0.5-second precision, automatic transcription, and multidimensional analysis of communication patterns. The modular architecture ensures flexibility for adaptation to various application domains. The system generates detailed timestamps of participant activity, visualizes speaking time distribution, and provides comprehensive analytics to improve decision-making processes. Prospects. Further development of the system includes integration of multimodal analysis considering non-verbal communication, improvement of stability in noisy conditions, domain adaptation for specific sectors, and implementation of real-time analysis of live discussions. The system opens new opportunities for studying group dynamics in corporate, educational, and governmental sectors. Received 2026-02-20 Accepted 2026-03-27
FROM MERKLE–DAMGÅRD TO SPONGE: ARCHITECTURAL IMPACT ON HASH FUNCTION SECURITY
The paper investigates the influence of cryptographic hash function architecture on their cryptographic strength. The main focus is on a comparative analysis of the classical Merkle–Damgård architecture used in the SHA-2 family and the Sponge architecture implemented in the SHA-3 standard. It is shown how the design features of the Sponge architecture, in particular the division of the internal state into speed (rate) and capacity parts, provide an increased margin of cryptographic strength and ensure low vulnerability to the inherent Merkle–Damgård constructions, including the message extension attack. The possibility of estimating the dispersion index for attributing a hash function to a cryptographic type has been confirmed. At the same time, the question remains about the unambiguity of the correspondence between theoretical statistical indicators of the quality of hash functions. The only known indicator of the quality of hash functions is based on the variance indicator and unambiguously shows only whether a particular hash function belongs to cryptographic or non-cryptographic. At the same time, it has been confirmed that the χ² test, as a “bias detector” can prove that the hash function is hack-resistant with high probability. But the question remains about the unambiguity of the correspondence between theoretical statistical indicators of the hash functions quality. Received 2026-03-19 Accepted 2026-04-13
DEVELOPMENT OF AN AUTOMATIC MICROCLIMATE CONTROL SYSTEM FOR THE STORAGE AREA OF A CUSTOMS TERMINAL
The article addresses a relevant scientific and technical problem of improving the efficiency of customs terminal operations in Ukraine under post-pandemic challenges, in particular ensuring proper microclimate parameters in warehouse facilities. The necessity of implementing automatic control systems to maintain temperature, humidity, and air quality in order to preserve goods and ensure safe working conditions for personnel is substantiated. An analysis of modern approaches to the automation of ventilation and air conditioning systems is carried out, and their limitations in the specific conditions of customs facilities are identified. A functional automation scheme for a supply and exhaust ventilation system with heat recovery has been developed, and a mathematical model of the temperature regime of a customs terminal building based on heat balance equations has been created. Based on simulation modeling in the MATLAB/Simulink environment, a digital control system with a PI controller has been synthesized and its parameters have been determined. The study shows that the proposed system provides stable and accurate control without overshoot and steady-state error, as well as effective maintenance of air quality. The asymptotic stability of the system has been confirmed using the Jury criterion, and its practical feasibility has been demonstrated. A comprehensive technical solution based on a modern programmable logic controller (PLC110) is proposed, ensuring high signal processing resolution and reliability under industrial conditions. The practical significance of the work is emphasized by the development of a human–machine interface (HMI) for remote monitoring, which ensures transparency of logistics processes and enables prompt intervention by personnel. The simulation results and techno-economic calculations demonstrate that the implementation of the proposed system reduces energy consumption for microclimate maintenance by 15–20% while simultaneously improving the accuracy of parameter stabilization. The obtained results can be used for the modernization of customs infrastructure and the implementation of digital systems for monitoring and controlling the microclimate in warehouse facilities. Received 2026-03-18 Accepted 2026-04-16
SOFTWARE AND HARDWARE OF THE SUBSYSTEM FOR MEASURING TECHNOLOGICAL PARAMETERS OF THE BIOGAS PRODUCTION PROCESS USING THE INTERNET OF THINGS
The article considers the issue of building a model of a subsystem for measuring technological parameters of the biogas production process. The specified measurement subsystem will collect data on the fermentation process of biomaterials (organic raw materials) for an intelligent control system for the technological process of biogas production. In this case, the type of organic raw materials used will be indicated in the data collection process. This is necessary for forming a control action in the algorithm of the functioning of the automated biogas production control system. The features of anaerobic biomass fermentation are analyzed and the main technological parameters are indicated from the analysis of literary sources. This affects the efficiency of the biogas production process. A model of the measurement subsystem is proposed, which is considered as a technical tool for measuring parameters of the fermentation process of organic raw materials, while the specified approach is implemented using Internet of Things technologies. The structure of the hardware of the measurement subsystem is developed and calculations of measurement errors of the information and measurement channel are given. The implementation of the Internet of Things technology is presented on the basis of the created web server, which operates on the principle of a client-server system using HTTP requests. The developed model of the subsystem for measuring technological parameters provides for measurements in the real-time system, and all measurement data will be stored in a separate file to non-volatile memory, namely to a micro-SD memory card. The measured data file will be stored in CSV format, which will allow data to be processed via cloud services or the Microsoft Office Excel application package. The specified approach will also allow data systematization and the possibility of prompt adjustment, if necessary, of the technological process. The functionality of the proposed model of the subsystem for measuring technological parameters can be increased by improving software and hardware. Received 2026-03-17 Accepted 2026-04-14
VISUALIZATION OF THE ALGORITHM FOR FINDING THE OPTIMAL PATH BETWEEN TWO POINTS IN A GRID MAZE WITH DYNAMICALLY CHANGING OBSTACLES
This paper investigates the current problem of finding the optimal path in dynamic environments modeled as cellular labyrinths with obstacles. The problem of finding the optimal path between two points of a cellular labyrinth is relevant due to its versatility, applied value and fundamental importance for the development of modern technologies. The main attention is paid to the development and software implementation of a system for visualizing algorithms on graphs, which allows real-time observation of the decision-making process of an autonomous agent. The paper provides a detailed comparative analysis of classical methods, such as Dijkstra's algorithm and A*, as well as specialized incremental approaches, in particular D* Lite. The advantages of using graph theory for solving problems in environmental management, logistics and robotics, where the environment can change unpredictably, are substantiated. The purpose of the presented work is to develop a program for visualizing the algorithm for finding the optimal path on a field, which can be represented as a labyrinth with surmountable and insurmountable obstacles. The task is to find the optimal path between two points on the field and display it. The practical part of the research includes the development of software in C#, which demonstrates the process of rerouting the route when new obstacles arise without the need for a complete recalculation of the entire network. This is critically important for minimizing computational costs in complex information and analytical systems. A special emphasis is placed on the educational aspect: the developed visualization is integrated into the educational process for teaching the disciplines "Algorithms and Data Structures" and "Object-Oriented Programming", which significantly improves the assimilation of complex mathematical concepts by students. The results of the work confirm that the combination of theoretical methods of pathfinding with interactive visualization provides high reliability and transparency of the functioning of modern navigation and environmental monitoring systems. The program for visualizing the algorithm for finding the optimal path in a maze has great practical importance and can be used in robotics and autonomous systems for robot navigation in a real environment, in networks and telecommunications it searches for the optimal data transmission route. Visualizing the algorithm for finding the optimal path in a cellular maze with dynamic obstacles allows for a deeper understanding of the principles of operation of algorithms, to assess their effectiveness in real time, and to experiment with different strategies for rerouting the route. Received 2026-02-23 Accepted 2026-04-01
A UE5 DEVELOPMENT ADOPTION FRAMEWORK FOR MULTIPLAYER DEVELOPMENT: REPLICATION, SERVICES, OBSERVABILITY, AND LIVEOPS
This paper proposes a practical, UE5-centered adoption framework that translates modern software development methodologies from Web/App, enterprise/heavy systems, AI systems, and contemporary game development into measurable production practices for multiplayer Unreal Engine 5 (UE5) teams. While Agile, MVP, DevOps, microservices, data-driven development, and architectural patterns are widely discussed, UE5 multiplayer imposes hard constraints that are often absent from generic methodology guidance: replication cost and relevance/dormancy management, server-authoritative state, dedicated server build and deployment operations, online session flows, and continuous delivery/LiveOps expectations. To address this “methodology translation” gap, we define a software-focused research design that operationalizes methodology adoption through implementable UE5 artifacts: a suite of test projects, multiplayer archetypes, and an instrumentation plugin stack for deterministic scenario execution, controlled network stress injection, replication observability, and standardized telemetry outputs. We present pilot-format result templates and case-study tables that connect methodology choices to objective UE5 indicators, including replication budget per client, temporal consistency of server simulation steps (server tick rate stability), session join reliability, build-to-playable time, crash-free server hours, and change lead time. A Lyra-style GameFeature plugin slice is introduced to demonstrate clean architectural boundaries and auditable iteration throughput via feature lifecycle telemetry tied to CI build identifiers. The paper synthesizes these results into a staged adoption map that prioritizes replication-first KISS discipline, multiplayer MVP defined as an operations-capable vertical slice, minimal services before microservice decomposition, data-driven tuning with safe rollbacks, and modular feature slices for scalable iteration. This framework is intended as a practical guide for UE5 multiplayer teams and as a foundation for larger empirical evaluations. Recieved 2026-02-09 Accepted 2026-03-16
ASPECTS OF DETECTING CYBER THREATS IN UNIVERSITY NETWORK TRAFFIC
Modern cyber threats to telecommunications systems and networks are characterized by a high degree of concealment, adaptability, and diversity. This complicates their rapid detection in network traffic, particularly at universities. Given the changing nature of cyberattacks, traditional methods based on signature analysis and fixed rules are proving insufficiently effective for identifying new or modified threats. In this regard, the development of intelligent hybrid approaches is becoming increasingly important. Such methods are capable of analyzing the behavioral characteristics of university traffic and adapting to its changes. The article presents a method for detecting cyber threats based on a combination of ensemble clustering and Bayesian probabilistic modeling methods. At the first stage, machine learning is used to identify hidden behavioral features of network connections in the university network based on various clustering algorithms. The resulting behavior embeddings are then used as input data for constructing a Bayesian network that describes the probabilistic dependencies between behavior parameters and anomaly features. The proposed approach not only allows detecting deviations from normal traffic behavior, but also ensures the interpretability of decisions in the field of information security. The practical value of the method lies in its potential for use in network traffic monitoring systems in corporate networks.
Copyright (c) 2025 Information technologies in economics and environmental sciences https://creativecommons.org/licenses/by-sa/4.0
CYBER RESILIENCE OF UKRAINIAN HIGHER EDUCATIONAL INSTITUTIONS IN A WARFARE CONDITION
The article examines the problem of ensuring cyber resilience of higher education institutions (HEIs) by developing and implementing a comprehensive cyber defense architecture. It is shown that the effectiveness of such a system is determined by the ability to integrate educational, administrative, and resource subsystems, taking into account their interdependence and specifics of functioning. The main risks and consequences of cyber attacks are considered. The principles of building a secure information infrastructure and the criteria that a reliable and effective HEI cybersecurity system must meet are outlined. A model for managing HEI information flows and resources using neural network technologies and intelligent decision support systems is proposed. The results of the study demonstrate the feasibility of using modeling tools to predict threats, optimize resource allocation, and increase the resilience of the educational environment to cyber risks.
SECURITY CONVERGENCE IN INDUSTRY 5.0: LESSONS FROM GAME ANTI-CHEAT SYSTEMS FOR DIGITAL TWIN PROTECTION IN COMPUTER SYSTEMS
Digital Twin (DT) systems are critical to the advancement of Industry 5.0, enabling synchronized, intelligent modeling of physical assets for simulation, monitoring, and predictive control. However, these platforms' growing integration of AI, telemetry data, and autonomous decision-making exposes them to escalating cybersecurity threats. This study explores how established security practices from the video game industry—specifically anti-cheat technologies—can be repurposed to address the evolving security demands of DTs. We conducted a comparative literature review and architecture mapping between video game environments and DT infrastructures, focusing on behavioral spoofing, telemetry injection, and runtime tampering. Additionally, we performed simulations using statistical and machine learning models (Z-score filters, SVM, LSTM) to assess the adaptability of game-based detection mechanisms. Results show that AI-assisted behavioral modeling significantly enhances threat detection accuracy while maintaining low latency. We propose a layered, privacy-conscious cybersecurity framework for digital twins based on these findings. This research demonstrates that the convergence of anti-cheat systems and computer engineering offers a viable strategy for building resilient and ethically aligned digital infrastructure in the Industry 5.0 era.
CREATION OF A CLOUD IT ENVIRONMENT IN ORGANIZATIONS
The rapid development of information technologies is driving the widespread adoption of cloud computing across various areas of organizational activity. The diversity of cloud services, their providers, and service models necessitates a well-grounded selection of optimal solutions that best meet the needs of a particular organization, taking into account economic, technical, functional, and security criteria [1]. Selecting an appropriate configuration of cloud services is a complex task, as it requires consideration of numerous variable parameters and potential risks. Traditional approaches based on expert assessments prove to be insufficiently effective under the complex and dynamic conditions of the market, which highlights the relevance of automated decision support systems in this domain. One of the key stages in creating a cloud IT environment within organizations is the development of a decision support system that enables the structured analysis of available alternatives through mathematical models of multi-criteria analysis. The application of such methods makes it possible to formalize the process of comparing options, account for numerous parameters, and make well-founded management decisions. At the same time, the development of such systems is associated with a number of technical challenges, in particular ensuring the correct processing of input data, the optimal selection of evaluation methods, and the design of a flexible architecture capable of adapting to specific user requirements.
RESEARCH ON THE IMPACT OF LARGE LANGUAGE MODELS ON WEBSITE DEVELOPMENT USING THE VUE FRAMEWORK
This paper investigates the transformative impact of large language models (LLMs) on modern component-based web development, using the Vue.js framework as a representative case study. By synthesizing the results of a broad empirical study on software development with the assistance of LLMs, we analyze a paradigmatic shift from native framework-driven workflows to workflows augmented by LLMs. The analysis spans the entire project lifecycle, revealing significant productivity gains during the implementation and deployment phases, particularly in component creation, test automation, and infrastructure configuration. However, these advantages are counterbalanced by critical challenges, including concerns about code reliability, the perpetuation of version conflicts due to outdated training data, and the risk of cognitive offloading among developers. We argue that the integration of LLMs redefines the role of the senior developer, transforming it from a primary code generator into an expert validator and architectural overseer. The paper concludes by outlining key risks and proposing directions for future research, emphasizing the need to develop framework-specific benchmarks for evaluating the quality of AI-generated code and to conduct longitudinal studies on the maintainability of Vue.js applications developed with the assistance of LLMs.
INFORMATION TECHNOLOGIES FOR AUTOMATING THE PROCESSING OF CANDIDATE CV TO INCREASE THE EFFICIENCY OF IT TEAM FORMATION
The article is devoted to the development of an information technology for automated processing of candidate CV in PDF format using the Python programming language. The approach to extracting, structuring, and further analyzing data with the use of the pdfplumber, spaCy, and pandas libraries is presented. The proposed module enables the identification of key resume elements, including education, skills, contact information, and work experience, followed by the formation of structured data in JSON format. Special attention is given to ensuring the universality of the algorithm for CV with arbitrary structure and Ukrainian-language content. The paper describes the main stages of implementing the software solution, including data flow diagrams, PDF processing schemes, and examples of unit testing of system functions. The developed technology can be used to automate the initial stage of recruitment and integrate with HR analytics systems, thereby improving the accuracy and speed of candidate data processing in the IT team formation process.
SOLID AS A SYSTEM OF CONSTRUCTIVE CONSTRAINTS IN SOFTWARE ARCHITECTURE DESIGN
The article presents SOLID as a system of constructive constraints that disciplines the degrees of freedom in design and transforms the evolution of software systems into a controlled process. It explains the relationship of the principles with the foundations of object-oriented programming, design patterns, and architectural styles. Non-obvious effects are examined, including premature abstractions under OCP, hidden configuration coupling under DIP/DI, class explosion and responsibility fragmentation under SRP/ISP, and semantic violations of LSP that are not captured by type signatures. An operational approach to decision validation is proposed through metrics, contract-based tests, and threshold controls for introducing abstractions, along with practical decision-making protocols.
HYBRID CLUSTERING OPTIMIZATION MODEL FOR INTELLIGENT DECISION SUPPORT SYSTEMS
The article presents a comprehensive approach to optimizing clustering algorithms within decision support systems (DSS) in big data environments. It analyzes issues of scalability, computational complexity, and result stability that are typical of classical methods such as K-Means, DBSCAN, and Agglomerative Clustering. An improved hybrid algorithm, K-Means++ Hybrid, is proposed, combining parallel computing mechanisms, adaptive parameter tuning, and dynamic control of the iterative search process. The methodological foundation of the research is based on systems analysis, mathematical modeling, and experimental testing using datasets from the UCI Repository and GPU acceleration technologies (CUDA). Experimental results confirm that the proposed approach reduces clustering execution time by approximately 43% compared to baseline algorithms, while increasing the silhouette coefficient to 0.73 and reducing CPU energy consumption by 20–25%. The resulting model demonstrates high robustness when processing heterogeneous datasets and can be integrated into systems for traffic flow analysis, financial risk assessment, and environmental monitoring. The developed approach provides a foundation for building adaptive intelligent data analysis modules that support scalability, result interpretability, and real-time operation in streaming analytics systems. Future research should focus on integrating hybrid clustering with deep learning models and evolutionary optimization algorithms.
SEMANTIC SEGMENTATION OF FETAL BRAIN ULTRASOUND IMAGES BASED ON A GENERAL FULLY CONVOLUTIONAL DISCRIMINATOR
The fetal central nervous system (CNS) is one of the most common fetal congenital diseases in the world. It is of great significance to use deep learning methods to provide doctors with reliable auxiliary diagnosis methods. The area outside the skull halo in fetal brain ultrasound images contains a lot of irrelevant information and has fuzzy boundaries, which is not conducive to the classification or recognition tasks of ultrasound images. This paper aims to study the effect of generative adversarial networks based on universal fully convolutional discriminators on the automatic segmentation results of skull halo in fetal brain ultrasound images. This paper proposes a method for automatic segmentation of skull halo in fetal brain ultrasound images using a generative adversarial network based on a universal fully convolutional discriminator. Inspired by the concept of generative adversarial networks, a new semantic segmentation network based on a universal discriminator is constructed. In order to verify the universality of the discriminator, the semantic segmentation network uses both Deeplab_v2 and Attention U-net networks as generators to generate probability maps of segmentation results. A universal fully convolutional discriminator is also designed to let it learn to distinguish whether the probability map input to the discriminator network is real data or segmentation results. Experimental results on the dataset of automatic measurement of fetal head circumference in ultrasound imaging demonstrate the effectiveness of the algorithm. Compared with the baseline of Deeplab_v2, the segmentation accuracy is significantly improved, and the Attention U-net also has a similar improvement. Generative adversarial network based on universal fully convolutional discriminator can effectively improve the accuracy of automatic segmentation of skull halo in fetal brain ultrasound images.
ENHANCING HUMAN-COMPUTER INTERACTION: THE ROLE OF INTELLIGENT USER INTERFACES IN EMBEDDED AND IOT SYSTEMS
The rapid evolution of innovative technologies has significantly transformed digital applications and human activities. Advances in hardware, digital software, and interactive user interfaces have made user experience (UX) design a critical factor in various industries, including healthcare, aerospace, construction, and military sectors. Human-Computer Interaction (HCI) is vital in enhancing system usability by ensuring intelligent interaction systems are user-centric and accessible. Integrating intelligent systems, the Internet of Things (IoT), and embedded technologies presents new challenges and opportunities in interface design. Additionally, as industries adopt intelligent automation and digital transformation strategies, the demand for adaptive, user-friendly interfaces grows. Understanding how users interact with these technologies is essential to optimizing efficiency and improving overall user satisfaction. This study explores the principles of HCI and interaction design, analyzing how digital and analog interfaces contribute to practical system usability. A qualitative approach was adopted, reviewing contemporary research on user interface (UI) design, interactive systems, and intelligent applications. The study also considers case studies from various industries where UX and interface optimization have significantly impacted system performance and user satisfaction. Data collection included literature analysis, expert interviews, and usability testing reports, ensuring a comprehensive understanding of the challenges and advancements in interaction design. The research methodology also incorporates a comparative analysis of traditional and emerging interface models to identify best practices in designing adaptive and intelligent systems. Findings indicate that well-designed interfaces enhance user engagement, efficiency, and system functionality. Factors influencing usability include adaptive design, accessibility considerations, and intuitive user experiences. Case studies reveal that industries incorporating advanced UX principles and innovative interaction technologies demonstrate improved operational efficiency and user satisfaction. Moreover, findings suggest that integrating AI-driven assistance, real-time feedback mechanisms, and multimodal interaction significantly improves user adaptability and system effectiveness. The study also highlights the importance of cognitive load reduction in interface design, emphasizing strategies such as predictive analytics and context-aware computing to enhance user interactions. The study highlights the Importance of HCI in designing user-friendly and efficient systems. It emphasizes the need for continuous innovation in UX and interface design, particularly in emerging fields such as IoT and embedded systems. The discussion underscores the role of AI, machine learning, and augmented reality in shaping the future of user interactions. Future research should focus on further integrating AI-driven personalization and adaptive interfaces to improve user experience. The findings suggest that interdisciplinary collaboration between engineers, designers, and cognitive scientists is essential to developing more effective and human-centered interaction systems. Addressing ethical considerations, such as data privacy and accessibility, will ensure equitable and inclusive design in future technological advancements
TECHNOLOGY FOR ANALYZING THE QUALITY OF HASH FUNCTION CHARACTERISTICS
The technology for analyzing the quality of a hash function is proposed. To test it, a specially developed hash function, which is a simplified analog of MD5, is used. It is shown that the traditional quality assessment of the obtained hash values based on the number of collisions has a significant drawback - high computational complexity. Therefore, the technology of quality assessment is reduced to the approximation of the analytical expression of the function, which is amenable to mathematical analysis by traditional methods applicable to polynomials. A better quality hash function is a hash function whose histogram is as close as possible to the graph of the function y=x. This will correspond to the Gaussian distribution of values. When using the proposed technology, it is necessary to develop criteria for classifying functions according to deviations from the ideal distribution of values by one or more of the following indicators: variance; mathematical expectation; arithmetic mean; geometric mean, etc.
DETERMINING THE STAGES OF STRAWBERRY MATURITY USING CONVOLUTIONAL NEURAL NETWORK MODELS
Throughout the history of agriculture, crop cultivation and harvesting technologies have been constantly improving, easing the work in the field and increasing the efficiency of fruit harvesting. Today, modern technologies, including artificial intelligence, are also penetrating the agro-industrial sector. Its use is becoming increasingly widespread every year. This technology allows agricultural producers to obtain large amounts of data in real-time, analyze them, and make decisions regarding fertilizer application, pesticide use, irrigation, and determining the ripeness of fruits or plants. An equally important aspect of artificial intelligence solutions is the tracking of carbon footprints, which gives an advantage when entering European markets. The combination of the latest developments in unmanned vehicles and robots allows for an increase in productivity and production volumes in agriculture. An important part of modern agro-industry is computer vision — one of the fields of artificial intelligence, focused on creating intelligent systems capable of processing and analyzing visual information in a way similar to the human sensory system. Neural networks are often used in this technology for both image recognition and classification. This article presents a comparative analysis of various convolutional neural network models for classifying the ripeness stage of strawberries, namely MobileNetV2, MobileNetV3Small, and EfficientNetB0. The network was evaluated based on parameters such as training and validation accuracy, training and validation losses, and training time. The best results were found with the MobileNetV3Small network. The results and methodology of this research can be useful for both scientists and entrepreneurs working in the agro-industrial sector and implementing artificial intelligence in the production process.
MACHINE VISION MODULE FOR OBJECT DETECTION IN IMAGES AND VIDEO STREAMS
This work addresses the topical issue of developing and applying intelligent computer vision methods for the automated detection and segmentation of protective equipment objects in images and video streams. The aim of the work is to identify objects of a specified type in images captured by a video camera, as well as to develop a detection model capable of effectively identifying and localising these objects under various lighting conditions, scales and perspectives. The article examines the data preparation process, in particular the application of augmentation methods to improve the representativeness of the sample, and performs a comparative analysis of the parameters, performance and results of the SAM and YOLO models. The results of experimental studies are presented, demonstrating the positive impact of increasing the volume and diversity of the dataset on data balance and the generalisation ability of computer vision models. The proposed approaches to training separate models for segmentation and classification tasks have proven their effectiveness in the context of automated image processing. In this work, a specialised dataset of protective equipment items was created to address the image segmentation task. During the model pre-training phase, data augmentation techniques—including mirroring, rotation, scaling and brightness adjustment—were applied to this dataset, which significantly increased the diversity of the training examples. Increasing the size of the dataset ensured a more balanced representation of the data and improved the model’s generalisation ability. The results obtained in this work confirm the feasibility and effectiveness of the authors’ proposed approach to this problem, namely the separate training of models for image segmentation and classification. Prospects for further research include expanding the dataset, optimising the computational complexity of the models, and investigating their application in real-time for video analytics.
TRENDS IN INCREASING THE CRYPTO-STRENGTH OF SYMMETRIC BLOCK CIVERS IN THE CONTEXT OF THE MODERN CRYPTOSYSTEMS DEVELOPMENT
The article examines the combination of analytical and applied approaches to improving symmetric block ciphers in light of current trends in the development of cryptographic systems. The study analyzes the current SOG-IS standards and recommendations that define the basic directions of symmetric algorithm evolution, such as the transition from outdated Feistel-based schemes to optimized structures (AES, lightweight block ciphers for IoT). Using a modified version of the Simple-DES (S-DES) cipher, the paper proposes a method for enhancing cryptographic strength without changing the key or block length. The main improvements involve an enhanced design of S-boxes and their dynamic selection based on a key graph. The results of the comparative analysis demonstrate a significant increase in resistance to brute-force and differential cryptanalysis compared to the standard S-DES, while maintaining low computational complexity. The conducted research confirms the relevance of using educational and experimental models such as S-DES to explore modern methods for improving the security of symmetric ciphers, which is of practical importance for prototyping and lightweight implementations in Internet of Things systems.
INTERACTION AND FEEDBACK LOOPS IN USER INTERFACE FOR DIGITAL ENGINEERING DESIGN
The article aims to review existing issues and look for novel ways of human-computer digital systems design and interaction. Such interaction takes place, as a rule, through an appropriate interface, which can be mechanical, digital, analog, with a touch screen, etc. For the end user of the relevant application or system, it is important how this interaction process will be implemented, presented, and programmed. That is, what controls, actions for interaction, and feedback mechanisms from the system to the user will be available. The research methods are based on the main methodologies of design and analysis of digital products with an emphasis on user experience, research of user requirements, the context of application use, and the presentation of the menu of interaction with a digital service. The article presents the results of a detailed description and modeling of the main process of user interaction with the feedback system (cyclic process). The context of using the application, the main desires and goals of the user when interacting, as well as the types of available options for interacting with the interactive system are taken into account. The seven-step process of user interaction presented in the paper includes functions, data, software systems and modules, appropriate software and hardware architecture, and methods of internal communication between cycle functions. A process of interactive interaction has been developed, which describes the full cycle from the beginning to the end of the user's work with any digital, analog, computing device, application, or service. This process can be used by designers, software developers, and scientists/researchers at the stage of planning a new interactive system or to improve an existing one.
Copyright (c) 2024 Інформаційні технології в економіці та природокористуванні https://creativecommons.org/licenses/by-sa/4.0
INFORMATION SYSTEM TO SUPPORT ASSESSMENT OF MASTER'S THESIS: AN EXAMPLE OF IMPLEMENTATION IN THE DIGITAL EDUCATIONAL ENVIRONMENT OF A HIGHER EDUCATION INSTITUTION
The digitization of education increasingly impacts fundamental business processes within modern higher education institutions. This study exemplifies the digitalization of specific processes involved in the preparation and presentation of master's thesis results. We have developed an information system designed to support the evaluation of master's theses through expert assessment and automated plagiarism detection, adhering to institutional regulations and general guidelines on academic integrity. Key business processes include user management, master's thesis evaluation management, and dissemination of research outcomes. These processes were decomposed following standards of structural and functional analysis and design, incorporating role assignment for potential system users. We propose implementing this system using the LMS Moodle platform, which facilitates resources for thesis text submission, document downloads, peer reviews, student counseling, and communication among educational stakeholders based on defined roles. Integration of the Unicheck plagiarism detection module within LMS Moodle enables efficient text similarity checks. Leveraging Learning Tools Interoperability (LTI) technology supported by LMS Moodle, electronic educational resources from the university's educational and informational portal can be seamlessly integrated with the master's theses repository. The study emphasizes that establishing an information and educational environment within a higher education institution and consolidating a unified user base will streamline the implementation of the proposed information system both organizationally and technologically, minimizing costs.
Copyright (c) 2025 Інформаційні технології в економіці та природокористуванні https://creativecommons.org/licenses/by-sa/4.0
ANALYSIS OF THE EDUCATIONAL OFFER FOR ADMISSION TO HIGHER EDUCATION INSTITUTIONS IN 2024 USING POWER BI
The article presents an analysis of the 2024 admission campaign to higher education institutions (HEIs) in Ukraine, investigating the relationship between the number of applications submitted by applicants and indicators characterizing educational offerings. The analysis was conducted using data from the Unified State Electronic Database on Education (USEDE) for 2024, applying the correlation-regression method. It is shown that there is a strong positive relationship between the volume of educational offerings and the number of applications to HEIs, while linear regression allows for a quantitative representation of this dependence, explaining nearly 50% of the sample. The article examines the impact of external factors, such as regional location, proximity to active combat zones, and HEI rankings, on the number of applications. The structure of the distribution of recommended places for admission under specific admission conditions is also presented. The conducted research reflects the peculiarities of demand formation for HEI services based on the results of the 2024 admission campaign and allows for optimizing approaches to planning educational programs, improving the efficiency of higher education institutions in Ukraine.