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.
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.
Copyright (c) 2026 Information technologies in economics and environmental sciences https://creativecommons.org/licenses/by-nc/4.0
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.
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.
AUTONOMOUS VEHICLES ESSE: UNSUPERVISED ONLINE LEARNING WITH SEMANTIC SEGMENTATION CONCEPT
The presented study explores continuous adaptation techniques for monocular depth estimation and semantic segmentation to improve real-time scene understanding capabilities for autonomous vehicles and driver assistance systems. The proposed methodologies enable models to dynamically adjust to new information in video sequences, sustaining high performance amidst ongoing changes in scene appearance, lighting, and other contextual factors. The first contribution is continuous online adaptation for monocular depth estimation, eliminating the need for isolated fine-tuning techniques and retaining information across video frames. The method addresses data drift by perpetually adapting to new frames, preventing overfitting due to limited data diversity. Experience replay is integrated to stabilize the learning process and introduce minimal computational overhead. Techniques like auto-masking and velocity supervision help differentiate between stationary and moving objects, mitigating errors related to inconsistent depth cues. The study validates the effectiveness of the proposed approach through intra-dataset and cross-dataset adaptation scenarios, showing substantial accuracy gains while maintaining real-time runtime.
DESIGN OF A CARTRIDGE TEMPERATURE MEASURING DEVICE
The work is devoted to the current topic of designing and manufacturing mobile, portable, ergonomic and accurate devices for measuring disturbing extraneous factors (in particular, the temperature of the cartridge), which undoubtedly and significantly affect the accuracy of the performance of a military task, namely sniper shooting. The purpose of the article is to create a cartridge temperature measuring device that meets certain schematic, design and technological parameters, manufacture a product prototype, test it in operating conditions and debug the assembly process. In the material of the article, the effect of cartridge temperature on the qualitative characteristics of the shooting process was investigated, in particular, when the temperature of the charge and air changed by the same amount, it was concluded that an increase in the temperature of the charge of the cartridge and air contribute to a slight increase in the duration of the bullet’s flight and its final speed, but a significant increase in range her flight. The geometric design of the device is presented, namely, two variants of the design of a portable cartridge temperature measuring device were created depending on the indicator variant used. The area of the products differs by more than two times, the linear dimensions are 103x55 mm х mm and 54x39 mm х mm with almost the same height of the finished product, which is due to the use of different types of indicators and, accordingly, the need to be powered by different voltage ratings of power cells. Proposed prototypes of the device for measuring the temperature of the cartridge, which satisfy certain schematic, design and technological parameters, small batches of products of two varieties were directly manufactured, submitted for approval under operating conditions, and initial debugging of the assembly process was carried out. Based on the results of the tests, the supply chains of the components, the design of the device and the assembly technology were adjusted. Prospects for further research should include the need for more detailed study of the issues of assessing the demand for finished products, the dynamics of the nomenclature of the open sale of components, the parameters of the technological process of assembly and finding out the costs of production and the market value of the device, since the specified activity is innovative, proactive and volunteer in nature
MODELING THE DYNAMICS OF TRAFFIC FLOWS BASED ON QUEUEING THEORY FOR INTEGRATION INTO INTELLIGENT TRANSPORTATION SYSTEMS
The article focuses on the study of traffic flow dynamics modeling in urban environments using queueing theory (QT). The research aims to develop a methodological approach to formalizing dynamic traffic flow processes, enabling their adaptation to modern intelligent transportation systems (ITS). Proposed mathematical models account for the stochastic nature of traffic flows and key performance indicators such as average waiting time, queue length, and throughput. Simulations of various transportation infrastructure scenarios integrating these models into ITS were conducted. The research findings confirm that applying QT under conditions of uneven traffic distribution significantly reduces delays, optimizes routing, and improves the efficiency of road infrastructure utilization. These results pave the way for further enhancements of urban transportation systems by integrating machine learning algorithms and big data analysis, allowing for consideration of the complex behavior of road users. The integration of QT models into ITS contributes to the improved efficiency of transport networks, fostering sustainable development of urban infrastructure. The proposed approaches are universal and can address pressing mobility challenges in contemporary urban agglomerations.
MATHEMATICAL MODELING AND ADAPTATION STRATEGIES IN THE CONFRONTATION BETWEEN CRYPTOCURRENCIES AND QUANTUM COMPUTERS
This article is dedicated to studying the resilience of cryptocurrency systems (CCS) under new threats associated with the development of quantum computing. A differential game model is introduced, allowing for the formalization of the interaction between CCS and quantum computers (QC), as well as the analysis of their mutual influence. The research methodology is based on the application of differential game theory to model the dynamics of resource allocation between the parties and to evaluate their strategies under conditions of uncertainty and competition. Various scenarios of confrontation between CCS and quantum computing were considered during the modeling process, which made it possible to identify key patterns and factors that significantly affect the effectiveness of cryptographic protection, as well as the computational capabilities of attackers utilizing QCs. Special attention is given to the analysis of different methods for protecting digital assets under potential quantum threats. The results of the study may serve as a foundation for developing new cryptographic security standards and adaptive protection strategies that will remain effective amid the rapid growth of quantum computing capabilities
DETECTION OF COMMUNITIES IN SOCIAL NETWORKS
One of the main threats is malicious programs (bots), fake accounts capable of imitating human behavior. At the moment, bots create a lot of problems, both for ordinary users and for those who use social networks to conduct a marketing campaign or conduct social research. Using bot profiles in social networks greatly distorts information about the real benefits and interests of portal users. Therefore, it is necessary to determine which users of the social network are programmed, and to be able to divide the flow of data into that generated by bots and by humans. The threatening scale of the use of social bots requires the creation of effective algorithms for their detection. Internet platforms and social services themselves are not too concerned about this problem. As a result, both ordinary users who organize various communities and companies that promote goods, brands, and services through social networks suffer. Thus, the task of recognizing malicious accounts in social networks and combating them remains relevant in the issue of cyber security. The solution to the problem will be the development of network analysis methods that are designed to identify and classify communities in social networks, assess their connectivity, degree of trust, as well as develop effective algorithms for detecting malicious accounts. The purpose of the work is to develop an improved algorithm for detecting malicious accounts in social networks, which is based on the study of modern methods.
CHARACTERISTICS OF SORTING ALGORITHMS OPERATION IN PYTHON
This article is dedicated to the analysis of various sorting algorithms using the Python programming language and sorting algorithms in Cython. The study compares classic sorting methods, such as bubble sort, insertion sort, and quicksort, with the aim of determining their effectiveness for large datasets. Special attention is given to localization issues when sorting strings in non-English languages, particularly, the application of a specialized dictionary for the correct processing of the Ukrainian alphabet. Techniques for measuring performance and visualizing results in the form of graphs are presented, allowing a deeper assessment of the scaling of each algorithm depending on the dataset size.
USE OF OLAP AND DATA MINING TECHNOLOGIES IN DECISION SUPPORT SYSTEMS IN GREENHOUSE FARMING
In the modern world, technologies play a key role in many areas of life, including agriculture. Growing vegetables and fruits in closed ground structures is becoming increasingly popular and requires control over conditions and resources to increase yields, however, with the increase in the amount of data collected in monitoring systems, it becomes more difficult to effectively analyze the collected information. This article considers the use of OLAP and Data Mining technologies to increase the efficiency of growing vegetables and fruits in closed ground structures. The purpose of the study is to determine the feasibility and effectiveness of using these technologies to analyze large amounts of data collected during the cultivation of vegetables and fruits. The authors of the study analyze how OLAP and Data Mining can provide useful information for making decisions on optimizing growing processes to increase yields.
ALGORITHMS FOR THE DEVELOPMENT OF A KNOWLEDGE BASE TO ENHANCE DECISION SUPPORT SYSTEMS IN ADDRESSING CYBERSECURITY CHALLENGES
This article presents the development of a modular decision support system (DSS) for cybersecurity, aimed at enhancing the protection of critical computer systems (CCS). The system is based on a fuzzy logic inference subsystem (FIS) model that utilizes data from sensors and SIEM systems to detect signs of threats, anomalies, and attacks through fuzzification of input values. A developed algorithm for forming a knowledge base of typical and emergency situations allows the system not only to effectively respond to known threats but also to analyze unforeseen situations. The application of the FIS module enables the creation of a multi-parameter image of CCS vulnerability, which ensures a more comprehensive and accurate assessment of their security.