DSpace Collection:http://elibrary.kdpu.edu.ua/xmlui/handle/0564/562026-06-07T02:45:27Z2026-06-07T02:45:27ZAdvanced Permutation Entropy Metrics for Bitcoin: Towards Robust Early Warning Indicators of Market InstabilitySoloviev, VladimirMatviychuk, AndriyBielinskyi, AndriiMyronenko, TimurСоловйов, Володимир МиколайовичМатвійчук, Андрій ВікторовичБєлінський, Андрій ОлександровичМироненко, Тимур Ігоровичhttp://elibrary.kdpu.edu.ua/xmlui/handle/123456789/120852025-07-28T06:44:39Z2025-07-09T00:00:00ZTitle: Advanced Permutation Entropy Metrics for Bitcoin: Towards Robust Early Warning Indicators of Market Instability
Authors: Soloviev, Vladimir; Matviychuk, Andriy; Bielinskyi, Andrii; Myronenko, Timur; Соловйов, Володимир Миколайович; Матвійчук, Андрій Вікторович; Бєлінський, Андрій Олександрович; Мироненко, Тимур Ігорович
Abstract: The article explores the potential of advanced permutation entropy (PEn) techniques as early-warning indicators for detecting instability in cryptocurrency markets, specifically focusing on Bitcoin. While classical permutation entropy is a popular method for assessing time series complexity due to its simplicity and computational efficiency, it has limitations - especially in its inability to account for amplitude variations and identical values.
To address these shortcomings, the authors present a comparative analysis of the classical PEn and three of its extended versions: Weighted Permutation Entropy (WPEn); Amplitude-Aware Permutation Entropy (AAPEn); Uniform Quantization-Based Permutation Entropy (UPEn).
These methods are applied to the 2017–2018 Bitcoin market crash. The study reveals that advanced metrics, particularly AAPEn, are more sensitive to subtle changes in market dynamics that precede price collapses. AAPEn is highlighted for its ability to incorporate both the order and amplitude of data points, allowing it to detect significant fluctuations that may signal panic or uncertainty in the market.
The results suggest that variations in advanced entropy metrics can serve as valuable indicators of market efficiency shifts and irregular patterns, making them promising tools for forecasting financial turbulence. The article concludes that incorporating such methods into financial risk management systems could significantly enhance the predictive capabilities of early-warning mechanisms in the volatile digital asset ecosystem.
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2019(1). https://doi.org/10.1155/2019/14038292025-07-09T00:00:00ZАдгезія кристалічного CO2 до алюмінію: розрахунки з перших принципівСтепанюк, Олександр МиколайовичБалабай, Руслана Михайлівнаhttp://elibrary.kdpu.edu.ua/xmlui/handle/123456789/120232025-06-24T11:21:41Z2025-01-01T00:00:00ZTitle: Адгезія кристалічного CO2 до алюмінію: розрахунки з перших принципів
Authors: Степанюк, Олександр Миколайович; Балабай, Руслана Михайлівна
Abstract: У цій роботі досліджено адгезію кристалічного CO2 до алюмінієвої плівки з перших принципів, що є важливим для майбутніх марсіанських місій. Автори розрахували енергетичні рельєфи відриву, електричні заряди та сили, що діють на атоми, які адгезійно взаємодіють, використовуючи методи функціоналу електронної густини та псевдопотенціалу.
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http://dx.doi.org/10.1016/j.icarus.2017.03.0122025-01-01T00:00:00ZDesigning and evaluating an affordable Arduino-based lie detector prototypeПравицький, Станіслав ВячеславовичМерзликін, Павло ВолодимировичСтепанюк, Олександр Миколайовичhttp://elibrary.kdpu.edu.ua/xmlui/handle/123456789/120222025-06-25T07:23:23Z2025-01-01T00:00:00ZTitle: Designing and evaluating an affordable Arduino-based lie detector prototype
Authors: Правицький, Станіслав Вячеславович; Мерзликін, Павло Володимирович; Степанюк, Олександр Миколайович
Abstract: Lie detection is an important issue in various contexts ranging from criminal investigations to hiring processes.
The paper covers the prototyping and evaluation of an affordable Arduino-based lie detector that integrates
physiological sensors and machine learning to detect deception. Testing on 20 questions showed the detector
achieved 55% accuracy in identifying truth and 45% accuracy in identifying lies, with an overall accuracy of 50%.
While further refinements are needed, this prototype demonstrates the challenges of developing an accessible
lie-detection system.
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Authors: Шокалюк, Світлана Вікторівна; Кавецький, Андрій Олександрович
Abstract: This paper presents the design, development, and evaluation of an adaptive mathematics assessment tool for grade
6 students. The tool uses Python and CustomTkinter to create an engaging and personalized user experience. It
generates adaptive questions, offers immediate feedback, and tracks student progress in a real-time. A quasiexperimental study was conducted, comparing the tool’s effectiveness with traditional assessment methods.
Results indicate that students using the tool demonstrated more positive attitudes compared to the control group.
System performance was also evaluated, showing an efficient and smooth user experience, with an average
response time of 1.2 seconds. Future work will focus on expanding the tool’s content coverage and integrating
machine learning techniques to further enhance adaptability and personalized feedback.
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