COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION

Automation of the process of settlement in dormitories using the unified modeling language UML

L. Samchuk — 2025-03-26

This paper considers the computerization of the lodging check-in prepare, especially room reservation. The research's fundamental objective is to create an successful framework that permits robotization and optimizing the forms of settling understudies in a inn, decreasing time and asset costs, and expanding the comfort and fulfillment of clients. The work analyzes the existing frameworks and strategies of booking rooms and uncovers their inadequacies and focal points. Based on this investigation, a modern computerized framework was created, counting a helpful interface for clients and the residence organization, usefulness for booking and saving rooms, bookkeeping for complimentary places, and integration with the understudy database. For creating the framework UML graphs were utilized, counting case charts, action graphs, and grouping graphs, which made a difference to arrange the structure and behavior of the framework in detail. The most comes about of the investigate are the creation of a framework model and testing of its effectiveness in genuine conditions. The test comes about appeared a significant reduction within the time required to prepare settlement applications, a diminishment within the number of blunders, and an advancement within the in general organization of the method. The work is of viable significance for the organizations of instructive teach looking for to progress the effectiveness of quarters administration, as well as for computer program engineers who specialize in making computerization frameworks for instructive teach.

Integration of Artificial Intelligence into Industrial Process Automation Systems

S. Prymyska — 2025-03-26

The article investigates the introduction of artificial intelligence into the automation of industrial processes, which is a key area of digital transformation of production. It has been established that the use of machine learning algorithms, neural networks, computer vision, and cognitive computing helps increase productivity, optimise resource management, and minimise human intervention in technological operations. The article aims to analyse the role of artificial intelligence in the automation of industrial processes and develop approaches to its effective integration to increase the productivity and adaptability of production systems. To achieve this goal, the article uses system analysis to determine the impact of artificial intelligence (AI) on industrial automation, comparative analysis methods to assess existing approaches to the implementation of intelligent technologies, predictive modelling to assess the efficiency of autonomous systems and a structural-functional approach to developing an integration model. The main problems of introducing artificial intelligence into industrial automation include the high integration cost, the complexity of adapting AI algorithms to changing production conditions, the need to modernise technological infrastructure, and cybersecurity risks. It is proved that the effectiveness of integrating artificial intelligence into production processes largely depends on the choice of an appropriate approach: centralised, decentralised or hybrid. The article proposes a conceptual model of such integration, which includes a sensor level of data collection, an analytical level of processing, and a managerial level of decision-making, which allows for the management of production systems to be optimised in real time. The article formulates recommendations for the effective implementation of artificial intelligence in industrial processes, including analyzing an enterprise's technological capabilities, adapting algorithms to the specifics of production, ensuring cybersecurity, and training personnel to work with intelligent technologies. Prospects for further research include improving adaptive machine-learning methods, creating self-learning AI systems, optimising the interaction of autonomous AI modules with centralised management platforms, and developing methods for assessing the effectiveness of artificial intelligence in automated production environments.

Development of an automated system to support the activities of a youth centre

Ya. Savka — 2025-03-26

The article considers the design and development of an automated system for supporting the activities of a youth centre. The relevance of developing the system is substantiated. An analysis of existing similar systems is carried out, and their advantages and disadvantages are considered. A conceptual model of an automated system for supporting the activities of a youth centre is presented. Use-case diagrams for the users of the system are constructed. Block diagrams of the system's operation algorithms are developed. A functional model of the system is constructed with different levels of its detailing. A class diagram and the system database are developed. The development of the system components, database and user interface carried out, and their testing were conducted. The developed automated system for supporting the activities of a youth centre can be successfully applied to the needs of youth centres of various directions and other public organizations of Ukraine and worldwide

Assessment and Failure Prediction of Submersible Pumps Using Advanced Modeling Techniques

M. Bautina — 2025-03-26

The article examines modern approaches to predictive maintenance of submersible pumps using artificial intelligence algorithms and IoT sensors. The study's relevance is determined by the need to improve the reliability of pump equipment and optimize maintenance costs. It has been established that the main challenges include high infrastructure modernization costs, the complexity of adapting algorithms to variable data streams, and the need for qualified personnel to operate the monitoring system. The study's purpose is to develop recommendations for integrating predictive maintenance systems to reduce unplanned downtime and optimize maintenance costs. The article employs methods of comparative analysis of predictive model efficiency and investigates the impact of key parameters, such as pressure, temperature, and vibration, on real-time equipment condition prediction. The results demonstrated that the proposed model based on recurrent neural networks outperforms traditional approaches across all key metrics, particularly regarding precision and recall. The study concludes that a phased implementation of pilot projects is necessary to adapt the system to industrial conditions and ensure continuous monitoring. The prospects for further research include the development of adaptive models capable of working with incomplete data and enhancing the autonomy of maintenance systems through self-learning algorithms, which will contribute to the stable operation of pump systems under complex operating conditions

Optimising Machine Learning Integration in Real-Time Text Analytics Platforms: Technical Approaches and Performance Criteria

O. Korostin — 2025-03-26

The article investigates the integration of machine learning into real-time platforms for analysing text streams. The relevance of the topic is driven by the growing volume of unstructured textual data and the need for its prompt and accurate processing to support decision-making in such fields as media monitoring, cybersecurity, finance, and healthcare. The effectiveness of such platforms is shown to depend on the adaptability of algorithms, analysis accuracy, scalability, and transparency of results. Special attention is paid to the technical aspects of implementation, including distributed architecture, streaming data processing, optimisation of computing resources, and integration of explainable models. The purpose of the article is to study the possibilities of integrating machine learning algorithms into real-time platforms for analysing text streams, in particular, to develop approaches to improving the efficiency of data processing, ensuring their transparency and adaptability in a changing information environment. To achieve this goal, the study applies a combination of literature analysis, comparative evaluation of existing algorithms, and an experimental assessment of technical solutions. The findings indicate that the main challenges of integration include the computational complexity of deep models, scalability constraints, and delays in data stream processing. It has been shown that the use of distributed computing technologies, hardware accelerators (GPU/TPU), and online learning mechanisms significantly improves the performance of such platforms. The application of adaptive algorithms capable of real-time parameter updates increases analysis accuracy under unstable data conditions. The study concludes that integrating machine learning into real-time systems enhances the speed, reliability, and scalability of text analytics. Further research should focus on developing universal multilingual platforms that combine energy efficiency, modularity, and high analytical performance.

Ethical Implications of AI Applications in Nonprofit and Charity Sectors

Y. Kravchuk — 2025-03-26

The article analyzes the modern ethical aspects of the use of artificial intelligence (AI) in the activities of non-profit and charitable organizations. The relevance of the study is due to the need to ensure transparency, social responsibility and equal access to digital services in the context of the growing use of AI to automate processes and decision-making in humanitarian initiatives. It was established that the key ethical challenges are the opacity of algorithms, the risks of algorithmic bias, non-compliance with data confidentiality and digital inequality among beneficiaries. The aim of the work is to develop recommendations for the introduction of ethical standards for the use of AI in the non-profit sector, taking into account international practices and ensuring social responsibility. The study used methods of content analysis, systematic comparison and a review of international experience to identify key factors for ensuring the ethics and inclusiveness of digital solutions. The results of the analysis indicate the need to implement the principle of "explained AI", which ensures the transparency of algorithms and their accountability. It has been proven that the use of multilingual interfaces adapted for socially vulnerable groups, as well as the integration of multi-layered data protection policies, increase the level of trust in digital solutions. It is concluded that the effective implementation of AI in humanitarian programs is possible under conditions of constant monitoring and independent audit of algorithms to reduce the risks of "black boxes". Prospects for further research include the development of methods for adapting algorithms to local socio-cultural needs, the study of innovations in the field of personal data protection and the creation of training programs to increase the digital literacy of beneficiaries.

Analysis of the Possibilities of Using Digital Libraries in the Educational Process of Higher Military Educational Institutions

O. Buialo — 2025-03-26

The article presents an analysis of the potential use of digital automated library information systems (DALIIS) with open source code in higher military educational institutions (HMEIs). The evolution of such systems is examined, from their inception to modern solutions. Special attention is given to the analysis of popular software platforms, including Koha, Evergreen, OpenBiblio, PMB, and NewGenLib. Their functional capabilities, selection criteria, and comparative advantages for military educational institutions are described. Specifically, it is noted that the Koha system stands out as the most promising due to its advantages: open-source code, the ability to deploy in local networks, support for multiple interface languages, and the continuous updating of electronic content. It is established that the implementation of DALIIS is a key step in creating a network environment for accessing electronic resources, especially in wartime conditions. This contributes to enhancing the effectiveness of the educational process, reducing material costs, and accelerating the implementation of scientific research outcomes. The article proposes criteria for evaluating DALIIS, including the operating system, accessibility of settings, and the ability to operate under restrictions. The conclusion is drawn that the use of Koha in HMEIs is advisable, considering its versatility and functionality. Further practical testing and the formulation of recommendations for optimizing the system's performance in military educational institutions are planned.

Classification and coding of software defects resulting from cyberattacks

Yu. Dobryshyn — 2025-03-26

Based on the analysis of the impact of cyberattacks on the state of operability of automated systems and complexes, the article proposes a methodology for classifying and coding defects of damaged software. The methodology for classifying and coding software defects was developed based on certain characteristics and classification features of the most common cyberattacks and software defects resulting from their effects. The classification is based on existing relationships between damaged software and possible defects resulting from cyberattacks, as well as between software defects and methods for their detection and recovery. Based on the analysis of classification features, the structure of the technological code of a damaged software defect was developed, the elements of which are certain classification features. Using the example of one of the types of cyberattacks, SQL-injection, a practical example of classification and assignment of codes for defects of damaged software is given, as well as options for possible combinations of defect codes that may arise during the action of a cyberattack of the type SQL-injection. In order to further formalize the repair of damaged software defects, a hierarchical relationship equal to the theoretical state tree of the SQL injection attack type is given, which corresponds to the order of classification and coding. The studies that have been conducted allow applying the methodology for classifying and coding damaged software defects due to cyberattacks for automated design of technological processes for diagnosing damaged software defects, as well as determining the feasibility of restoring software modules and solving other technological issues related to information protection.

Modeling and Simulation Tools for Studying Computer Networks: Overview and Practical Experience

O. Kozak — 2025-03-26

Simulation and modelling can play an essential role in studying computer networks. These tools help students study the behaviour of the network and compare its performance indicators during different scenarios of operation without requiring special equipment. However, the process of integrating modelling and simulation tools into network courses usually depends on the topic of the training course or the personal preferences of the teacher, which may not meet the requirements of the training of a future specialist. Combining theory and practice in one course of studying computer networks is extremely difficult due to the constant increase in the complexity and scale of modern networks. This was one of the main reasons for writing the current scientific work, during which the functionality of the most popular network simulators was considered, and the advantages and disadvantages of each of the considered tools were also mentioned, which helped to analyse the possibility of using each of them in the process of studying networks. Our recommendations for effectively implementing these tools in the learning process are provided. These recommendations can be used both at the level of schools and lyceums and in higher education institutions (HEIs), making them universal. The issue of changing approaches in domestic education to the process of studying computer networks is determined to be relevant for future research since, due to the technological revolution and the rapid development of information technologies, which has been going on for the past several decades, educational material loses its relevance every year, and networks, on the contrary, are becoming more and more complex.

Development of an application for interaction with a computer using gestures using Computer Vision technologies

N. Mironov — 2025-03-26

The work investigates methods for contactless computer control using hand gestures based on computer vision and machine learning technologies. Examples of the application of technologies in various areas of life are given, where they show superior efficiency and convenience over traditional interfaces for interacting with the system. The tools and instruments used in the development of the software application are described. A modular architecture of the system is proposed that provides real-time gesture recognition, including image segmentation, tracking of key hand points and classification of movements. Functionality for moving the cursor, performing left and right clicks, double clicks, scrolling pages and adjusting the volume is implemented. The result of the work is a software product that supports moving the mouse cursor, left and right clicks, double clicks, dragging objects, scrolling and adjusting the volume. Both the functioning of the main application commands and the operation on different hardware configurations in real time were tested. Solutions for further improvement and expansion of the software functionality were proposed.

Classification of errors in geospatial databases based on the example data of the state land cadastre

B. Moroz — 2025-03-26

The article investigates the issue of error classification in geospatial databases using data of the State Land Cadastre of Ukraine. An extended classification of errors is proposed, including attributive, geometric, topological, and contextual errors. Specifically, it examines positioning errors, topological violations, incorrect attributive data, duplication, and data inconsistency. The article emphasizes the importance of automated error detection to ensure the quality of geospatial data. It concludes with the need for further research using statistical methods for anomaly detection. The proposed approaches enhance analysis accuracy and support decision-making in geographic information systems

Methods of modeling and classification of electrocardiograms

L. Mosiy — 2025-03-26

The article thoroughly analyzes methods for modeling, processing, and classifying electrocardio signals based on the study of publications in the scientometric database Web of Science Core Collection for 2014-2024. 513 scientific papers reflecting the dynamics of the development of approaches to interpreting cardiac data were analyzed. Bibliometric analysis revealed an increase in publication activity with maximum indicators in 2019 (58 publications), 2022 (51 publications), and 2024 (54 publications), which confirms the relevance of the topic under study. Based on the keywords of the publications, a term cloud has been created, which demonstrates the prevalence of such concepts as electrocardio signal, deep learning, classification, signal processing, and heart rate variability. Methodological analysis has shown the evolution of approaches from traditional methods of digital signal processing to the introduction of innovative artificial intelligence technologies. In particular, the effectiveness of the use of convolutional neural networks for the classification of electrocardio signals that demonstrate an accuracy of more than 95% in diagnosing various types of arrhythmias has been investigated. The geographical distribution of publications showed the leadership of scientific institutions in the United States (19% of publications), India (12%), Germany (10%), and China (9%). The thematic direction is dominated by research in the field of engineering (51% of publications) and computer science (29%), which emphasizes the interdisciplinarity of the problem. The priority directions for the development of methods for analyzing ESN have been identified: the development of adaptive algorithms for filtering interference, the improvement of methods for extraction of diagnostically significant features, and the creation of intelligent classification systems based on hybrid architectures. Particular attention is paid to the synergy of machine learning methods with classical approaches to the analysis of ECS to increase the reliability of diagnosing cardiovascular pathologies.

Threat model for the information security of the moodle distance learning system

V. Nazaruk — 2025-03-26

This paper examines the information security threats faced by Moodle, the most widely used distance learning system in Ukrainian higher education institutions. The study outlines the primary categories of threats that may arise during the implementation and operational use of this system in academic settings. Key areas of analysis include unauthorized physical access to data processing and storage equipment, breaches in access control and role-based management policies, software errors and malfunctions, introduction of computer viruses and malware, as well as both intentional and unintentional actions by personnel, and emergency situations. Each threat's impact on critical security attributes – confidentiality, integrity, availability, and observability – is detailed and summarized in tabular form. To prevent information security incidents within the system, the authors offer targeted recommendations to counteract each identified threat. The developed threat model for Moodle-based distance learning systems can serve as a foundation for creating a comprehensive information security system (CISS) or conducting security audits in line with international standards. Implementing a CISS or conducting an audit enhances the security and reliability of this educational platform in academic environments, ensuring compliance with Ukrainian legislation.

Understanding and Building Operational Excellence for Large Language Models

N. Polyakovska — 2025-03-26

Effective life cycle management of large language models (LLMs) ensures their reliability and adaptability in production environments. The study's relevance is due to the rapid growth in the use of large language models in various industries, accompanied by challenges such as high computational requirements, risks of generating false results (“hallucinations”), and algorithmic bias. It is established that traditional MLOps methods do not provide adequate quality control and scalability, which requires the development of specialized operational approaches for LLM. The study aims to form an operational framework for LLMOps that covers all stages of the life cycle of large language models, from data processing to real-time monitoring and support. The research methods are based on an interdisciplinary analysis of current practices of implementing large-scale language models, identifying problematic aspects of their use, and developing practical recommendations for effective system management. The main stages of the operational framework are identified, including data preparation, model development and deployment, and control of results at the monitoring and support stages. The most critical aspects are integrating multi-level monitoring, compliance with ethical standards, and introducing automated algorithms to reduce the frequency of false positives. The study's results confirm that the proposed operational framework increases the reliability of language models in high-load environments and ensures their adaptation to dynamic changes in the query structure. The use of distributed computing methods, resource optimization, and post-processing verification helps minimize the risks associated with the performance and accuracy of model responses. Simulation stress tests are recommended to check the system's stability during peak load periods. Conclusions. The paper emphasizes the importance of continuously auditing models' operations to ensure their transparency and compliance with regulatory requirements. Prospects for further research include optimizing retraining processes and implementing energy-efficient computing resource management methods.

Development of AI assistant for local user in Python programming language

O. Pronina — 2025-03-26

The article considers a system for developing a multifunctional AI assistant capable of performing a wide range of tasks. An AI assistant model developed based on neural networks is presented, which provides high accuracy in executing commands and is capable of working in real time. The proposed model has achieved high accuracy in voice recognition, working effectively even when used without an Internet connection. The model integrates the Vosk and Speech Recognition speech recognition systems, which allow for accurate and efficient processing of voice commands. Voice commands are recognized in real time, regardless of whether they are simple commands for individual actions or scenarios that include obtaining information. Text input processing for executing commands is also implemented. Particular attention in the work was paid to the development of an effective mathematical model that minimizes delays in executing requests and ensures stable operation.

Prospects for the Use of Machine Learning to Ensure Compliance of Software Products with State Regulatory Requirements

S. Shykerynets — 2025-03-26

Ensuring compliance of software products with various government regulatory requirements, industry standards and best practices is one of the key tasks for modern organizations and companies that develop or implement these products. In this article, the prospects for using machine learning to ensure compliance of software products with government regulatory requirements are discussed. Modern machine learning methods that can be applied to automate the process of verifying software products for compliance with governmental requirements have been analyzed. An assessment of the potential of implementing machine learning has discovered significant benefits, including increased efficiency, accuracy, and scalability of compliance verification processes. The potential benefits and challenges of implementing these technologies in the field of ensuring compliance of software products with state standards are described. Based on the obtained results practical recommendations for the effective implementation of machine learning are proposed.

Intelligent automated systems for processing and analysis of vehicle diagnostic data

D. Nikitin — 2025-03-26

This article presents an approach to automating the process of vehicle technical condition diagnostics using intelligent automaton-based systems. The primary focus is on the application of automaton models, which enhance the accuracy of diagnostic parameter analysis in vehicles and reduce the likelihood of erroneous conclusions. The proposed system is based on spectral analysis methods, specifically orthogonal Fourier polynomials, which significantly reduce the root-mean-square error of diagnostic calculations. The study covers vehicle diagnostics via OBD-2, methods for retrieving and processing Diagnostic Trouble Codes (DTC), and the use of automaton models to assess the technical condition of vehicles. The main challenges in interpreting OBD data are discussed, and an approach combining automaton algorithms with spectral analysis methods is proposed to improve diagnostic accuracy. The proposed system has adaptive capabilities based on historical data, allowing it to autonomously adjust its diagnostic parameters depending on accumulated experience. The learning process is based on trend analysis, anomaly identification, and parameter adjustments in the automaton model to enhance diagnostic accuracy. Numerical modeling conducted as part of the study confirmed the effectiveness of the applied methodology. The developed intelligent automaton-based vehicle diagnostic system demonstrates high potential in the field of automated technical control. The use of automaton models improves the efficiency of diagnostic parameter analysis, reduces the probability of false conclusions, and enhances vehicle reliability. Future research prospects include expanding the application of probabilistic methods for handling large-scale diagnostic datasets.

Software comparison of scientific article annotations using statistical natural language processing methods

S. Prykhodchenko — 2025-03-26

This paper presents an advanced approach to the automated comparison of scientific article abstracts using statistical natural language processing (NLP) techniques. The authors analyze state-of-the-art methodologies, including Cosine Similarity, Jaccard Similarity, and TF-IDF, alongside clustering methods and machine learning models such as SciBERT and BioBERT. A research-based software model is proposed to enhance text similarity assessment, facilitating efficient scientific literature analysis, reducing research duplication, and improving bibliographic accuracy. The study highlights the practical application of NLP techniques in academic publishing, plagiarism detection, and automated literature review systems. The proposed system integrates various computational approaches to refine text analysis and classification, making it a valuable tool for researchers and journal editors. Future research directions include optimizing NLP algorithms, incorporating deep learning methods, and integrating the system with major scientific databases to enhance further its applicability and performance in academic and industrial contexts.

Integrating API into Web Application for Collecting Music Data from Open Sources

O. Piatykop — 2025-03-26

Popularization of Ukrainian music in the world is an important tool for preserving the cultural identity and positive image of the country. This paper considers the issue of automated collection and analysis of musical data of Ukrainian performers using the Spotify API. A web application based on the Remix framework and the TypeScript language has been developed, which allows for authorization, obtaining information about compositions, performers and albums, and storing them in a convenient format for further processing. As part of the study, a sample featuring 1,106 composition records and 568 unique performers was formed, containing 12 musical characteristics and genre labels. Analysis of the collected data showed that the genre classification of the sample needs to be improved, since Spotify did not classify all compositions correctly. The obtained results can be used for further analysis of the genre classification of Ukrainian music, optimization of recommendation systems and popularization of Ukrainian-language content in the international music space.

Programming A Game Application Using Artificial Intelligence

O. Pronina — 2025-03-26

The article is devoted to the importance of the development of video games in modern society and the role of artificial intelligence in this area. The contribution of video games to culture, economy and technological progress is considered, as well as their impact on the popularization of national heritage. The application of artificial intelligence in the creation of game mechanics, texture generation, animation and development of NPCs (non-player characters) is described, which provides a more realistic gaming experience. The use of Reinforcement Learning and the Proximal Policy Optimization algorithm for NPC training is highlighted, which optimizes the development process and improves the behavior of characters. The article presents the advantages of implementing artificial intelligence in video games, which make them more complex, dynamic and more interactive.

Patient Health Monitoring Using IoT Information Technology

Y. Fedoniuk — 2025-03-26

The paper examines the features of IoT devices that collect real-time medical parameters from patients and their integration with machine learning methods to improve data quality. The article describes common approaches to correcting these artifacts to ensure the accuracy and reliability of medical diagnostic systems. The authors also focus on the practical application of filters and machine learning methods to optimize signal processing, which enables high-precision patient monitoring in real-time. Key technical and methodological challenges associated with integrating IoT into medical systems are highlighted, and solutions are proposed to improve monitoring efficiency and accuracy.

Scientific and Analytical Study on the Application of Artificial Intelligence in Medical Imaging

A. Horkunenko — 2025-03-26

This scientific paper presents an analytical study of modern approaches to the application of artificial intelligence (AI) in medical imaging. This study analyzes scientific publications from 2014 to 2024 using the Scopus database. Key trends, challenges, and prospects for AI development in radiology, diagnostics, and related fields were identified. The study revealed an exponential increase in the number of scientific papers since the mid-2010s, which is attributed to the advancement of deep learning, particularly convolutional neural networks (CNN) and transformers. The leading research centers are institutions in the United States and China, with the primary sources of funding being the National Institutes of Health (NIH) and the National Natural Science Foundation of China (NSFC). Despite significant achievements, issues of algorithm reliability, interpretability, and ethical considerations remain relevant. Future research is aimed at improving model accuracy and adapting them to clinical practice

Method for fast exponential on Galose Fields GF(2n) calculation for cryptographic applications

O. Markovskyi — 2025-03-26

The accelerated calculation method of the exponentiation one Galoise Fields GF (2ⁿ), which sells group processing k exponent code bits using precomputations for reducing the number of the square and multiplication operations and, thus, accelerate the calculation process. In the theoretical terms, the proposed method is based on the properties of the operation of exponentiation on Galoise Fields in case the code of exponent is the degree of number two. The procedures for constructing two precomputations tables, as well as the exponentiation on the Galoise Fields procedure with their use, are described in detail. The presentation is illustrated with numerical examples. It is theoretically shown and experimentally proven that the developed method provides acceleration of the calculation of the exponential on Galoise Fields practically on k times.

Chaos Engineering methodology and the resilience of distributed IT teams

R. Vaskiv — 2025-03-26

The paper explores the application of Chaos Engineering methodology to enhance the resilience of distributed IT teams through improved risk management and project process optimization. A mathematical model has been developed to quantitatively assess key parameters, including latency, communication barriers, information system stability, functional distribution, proxy role efficiency, and team variability. The model employs discrete interval analysis, enabling the evaluation of dynamic changes and the impact of risks on the performance of distributed teams. Special attention is given to failure modeling methodology, which incorporates the simulation of critical scenarios to test system resilience under high uncertainty conditions. The proposed theoretical and practical approaches contribute to advancing risk management methods in contemporary dynamic IT environments.

Creation of Smart Contracts in the Information System for Arms Sales Accounting

N. Kunanets — 2025-03-26

The article analyzes the capabilities of the Ethereum platform for developing and implementing smart contracts in an information system for arms sales accounting. The architecture of the platform, the features of the Solidity programming language, and mechanisms for ensuring transparency, security, and data integrity in purchase and sale processes are examined. The potential of smart contracts for automating key accounting stages, including permit verification, transaction registration, and compliance with legal regulations, is explored. The advantages of using blockchain technologies are identified, including immutability of records, real-time transaction auditing, and minimization of human error. Challenges associated with system implementation are outlined, particularly issues of scalability, data confidentiality, and legal compliance. The study demonstrates the prospects of integrating Ethereum to enhance the efficiency and transparency of processes in arms control systems.

Corporate Governance in Starlink Projects

O. Khilukha — 2025-03-26

The article focuses on analyzing of corporate governance in the context of the Starlink project, which is part of SpaceX’s global strategy to create a worldwide satellite internet network. Specifically, it examines the potential implementation of Starlink technology in Ukraine to develop the infrastructure for space missions and scientific research. The Starlink project provides reliable, high-speed internet connectivity that can be used for satellite management, space mission monitoring, as well as for connecting in remote and hard-to-reach areas, which is especially important for Ukrainian scientists and the space agency. The authors propose the integration of Starlink into Ukraine's national space mission infrastructure, particularly through the establishment of stable communication between mission, as well as ensuring international coordination and cooperation with other space agencies and private companies. Furthermore, the article analyzes the importance of using such digital technologies to enhance governance efficiency and support national interests in the field of space technology.