https://repository.hneu.edu.ua/handle/123456789/187 2026-06-04T23:39:22Z 2026-06-04T23:39:22Z Klimenko O. Lytvynenko A. Pyvavar I. Stepanenko N. Cherkashyna T. https://repository.hneu.edu.ua/handle/123456789/40315 2026-06-04T12:48:10Z 2025-01-01T00:00:00Z

Назва: Macro- and Microeconomics Автори: Klimenko O.; Lytvynenko A.; Pyvavar I.; Stepanenko N.; Cherkashyna T. Короткий огляд (реферат): The course of macro- and microeconomics is presented that considers economic relations between economic entities (households, enterprises, the state, the foreign economic sector) which are formed at the micro and macro levels. For students, postgraduate students, lecturers as well as managers who are interested in actual economic problems.

2025-01-01T00:00:00Z Маммедов А. https://repository.hneu.edu.ua/handle/123456789/40266 2026-06-02T10:23:24Z 2024-01-01T00:00:00Z

Назва: Напрямки розвитку сектору відновлювальних джерел енергії в Україні в умовах військового стану Автори: Маммедов А. Короткий огляд (реферат): У тезах розглянуто актуальні напрями розвитку сектору відновлюваних джерел енергії в Україні в умовах воєнного стану. Обґрунтовано стратегічне значення відновлюваної енергетики для забезпечення енергетичної безпеки держави, підвищення стійкості енергетичної системи та зменшення залежності від традиційних джерел енергії. Проаналізовано основні виклики, пов’язані з руйнуванням енергетичної інфраструктури, скороченням інвестиційної активності та необхідністю модернізації енергетичного сектору. Визначено перспективні напрями розвитку сонячної, вітрової та біоенергетики, а також роль міжнародної підтримки у відновленні та трансформації енергетичної системи України. Зроблено висновок, що розвиток відновлюваної енергетики є важливою складовою післявоєнного відновлення економіки та досягнення цілей сталого розвитку.

2024-01-01T00:00:00Z Holdobin S. Baranova V. Tiutiunyk V. Pyvavar I. Pecherytsia D. Zhyhalov M. https://repository.hneu.edu.ua/handle/123456789/40251 2026-05-29T10:51:07Z 2025-01-01T00:00:00Z

Назва: Neural Network Learning of Decision-Making Management Algorithms in Non-Invasive Smart Devices for Cardiovascular System Diagnostics Автори: Holdobin S.; Baranova V.; Tiutiunyk V.; Pyvavar I.; Pecherytsia D.; Zhyhalov M. Короткий огляд (реферат): Cardiovascular diseases (CVDs) are the leading cause of death globally, necessitating the development of efficient and interpretable diagnostic tools for real-time and out-of-hospital monitoring. This paper presents a hybrid neural network model that integrates clinical diagnostic logic directly into its architecture to enhance explainability and accuracy. A formalized algorithm based on biosignals - such as electrocardiography (ECG), photoplethysmography (PPG), and heart rate variability (HRV) - was developed to emulate expert decision-making. The algorithm was embedded into a Rule Injection Layer (RIL), enabling the network to combine expert knowledge with data-driven learning. Experiments using synthetic and real datasets demonstrate high diagnostic performance (up to 97.1% accuracy) and robustness under varying signal conditions. The model is optimized for deployment in low-power embedded systems, providing a reliable solution for non-invasive CVD monitoring with interpretable outputs. Explainability is further supported using the LIME framework, which highlights feature contributions for clinical validation.

2025-01-01T00:00:00Z Babenko M. V. Pyvavar I. V. Morozova N. L. Lytvynenko A. V. https://repository.hneu.edu.ua/handle/123456789/40250 2026-05-29T10:50:01Z 2026-01-01T00:00:00Z

Назва: Managerial Decision-Making in the Context of Digital Transformation Автори: Babenko M. V.; Pyvavar I. V.; Morozova N. L.; Lytvynenko A. V. Короткий огляд (реферат): In many organizations, managerial decisions are based on fragmented and unsystematic information and limited analytical capabilities of managers This reduces the soundness of decisions, increases the likelihood of errors, and complicates the achievement of strategic goals, especially in an unstable external environment. Digital transformation, in turn, leads to rapid growth in the volume and variety of data from internal and external sources, opening up opportunities for evidence-based decision-making, forecasting, and scenario modeling. At the same time, it complicates the collection, processing, and interpretation of data. Information overload, the lack of unified data standards, and insufficient digital competencies among managers can negate the advantages of digitalization and create additional barriers. Under such conditions, there is a growing need for a systemic approach to the application of digital technologies in managerial decision-making processes, which involves the integration of analytical platforms, automated systems, and intelligent data analysis tools into a single managerial space. The aim of this article is to analyze the specifics of managerial decision-making in the process of digital transformation and to clarify the importance of modern digital technologies for improving the effectiveness of managerial activities. To achieve this aim, the article analyzes the essence and content of the managerial decision-making process; characterizes the impact of digital transformation on managerial processes; identifies the main digital tools and technologies used in managerial decision-making; and summarizes the advantages and risks of using digital solutions in the management system. Particular emphasis is placed on identifying key areas for improving the managerial decision-making process in the context of digitalization, which involves the integration of modern digital technologies into management activities, the use of analytical platforms and decision support systems, and the introduction of intelligent data analysis and predictive analytics tools. The need to develop the digital competencies of management staff, unify data processing standards, automate information flows, and ensure a unified information space to improve the soundness, efficiency, and strategic focus of managerial decisions is identified.

2026-01-01T00:00:00Z