https://dspace.chmnu.edu.ua/jspui/handle/123456789/803 2026-09-13T13:24:13Z https://dspace.chmnu.edu.ua/jspui/handle/123456789/3372 Title: Why Magenta Is not a Real Color, and How it Is Related to Fuzzy Control and Quantum Computing Authors: Timchenko, V. L.; Kondratenko, Y. P.; Kosheleva, O.; Kreinovich, V. Abstract: It is well known that every color can be represented as a combination of three basic colors: red, green, and blue. In particular, we can get several colors by combining two of the basic colors. Interestingly, while a combination of two neighboring colors leads to a color that corresponds to a certain frequency, the combination of two non-neighboring colors—red and blue—leads to magenta, a color that does not correspond to any frequency. In this paper, we provide a simple explanation for this phenomenon, and we also show that a similar phenomenon happens in two other areas where we can find a natural analogy with colors: fuzzy control and quantum computing. Since the analogy with fuzzy control has already led to efficient applications, we hope that the newly discovered analogy with quantum computing will also lead to computational speedup. Description: Timchenko, V. L., Kondratenko, Y. P., Kosheleva, O., & Kreinovich, V. (2026). Why Magenta Is not a Real Color, and How it Is Related to Fuzzy Control and Quantum Computing. Studies in Systems, Decision and Control, 660, 229–237. DOI : 10.1007/978-3-032-16494-0_18 2026-01-01T00:00:00Z https://dspace.chmnu.edu.ua/jspui/handle/123456789/3371 Title: Digital Transformation of Insurance Industry: Implications of AI Tools Integration Authors: Fedorovych, I.; Rykhalskyy, O.; Poltavskyi, D. Abstract: The relevance of the issue under consideration is driven not only by the rapid development of technology but also by the growing need for automation and personalization of insurance products. The main purpose of this article is to analyze the prospects and problems of integrating AI into insurance services. The article aims to identify the main directions of implementing AI technologies in risk management, optimizing insurance products, as well as studying the legal, ethical and technical aspects of this transformation. During this study, the following general scientific methods of cognition were used: literature synthesis, statistical data analysis, expert assessment, systematization and generalization for segmentation, dynamics assessment, factor classification and formation of a comprehensive vision of the integration of artificial intelligence into insurance services. The results of the expert survey and analysis of the obtained ratings on the integration of artificial intelligence into insurance services showed that for Group 1 the most significant factors are improved underwriting (Total A2 = 1.35) and predictive analytics (Total A5 = 1.35), while Group 2 prefers automation of repetitive tasks (Total A1 = 1.35) and optimization of claims management (Total A3 = 1.0). Among the main problems for Group 1 are regulatory restrictions (Total D3 = 1.5) and data quality (Total D1 = 1.8), while Group 2 highlights privacy risks (Total D6 = 1.35) and high implementation costs (Total D8 = 1.0). The integration of artificial intelligence into the insurance sector opens up new opportunities for modernizing processes and improving the efficiency of insurance companies. The results of the study confirm that AI is the main tool for optimizing the management aspects of insurance activities. Therefore, the successful integration of artificial intelligence into insurance activities requires a systematic approach to current challenges, which will allow to maximize the potential of AI within the insurance industry. Description: Fedorovych, I., Rykhalskyy, O., & Poltavskyi, D. (2025). Digital Transformation of Insurance Industry: Implications of AI Tools Integration. International Journal of Organizational Leadership, 14 (First Special Issue), 508–522. DOI: 10.33844/ijol.2025.60497 2025-01-01T00:00:00Z https://dspace.chmnu.edu.ua/jspui/handle/123456789/3370 Title: Oil Spill Detection in UAV-based Environmental Monitoring Using Lightweight Deep Learning Architectures Authors: Zheng, Y.; Dong, C.; Gu, C.; Aleksieieva, A.; Kozlov, O.; Sova, I.; Demchyna, M.; Maksymov, M. Abstract: This paper presents the development and evaluation of a lightweight onboard deep learning module for real-time oil spill detection within a distributed UAV-based maritime environmental monitoring system. The study investigates four object detection architectures under controlled training conditions using both baseline and augmented dataset configurations: three convolutional one-stage detectors (YOLOv8s, YOLO11s, YOLO26s) and one transformer-based model (RF-DETR Nano). Performance was assessed using precision, recall, mAP@50, mAP@50–95, and inference latency on an NVIDIA Tesla T4 GPU. Experimental results show that RF-DETR Nano achieves the highest localization accuracy (mAP@50–95 = 0.863) while maintaining real-time throughput (≈ 50 FPS), whereas YOLO26s provides the most favourable efficiency–accuracy trade-off among CNN-based models (mAP@50–95 = 0.806 at ≈ 67 FPS). The findings demonstrate that accurate and computationally efficient oil spill detection can be performed directly onboard UAV platforms, enabling reliable autonomous maritime monitoring without dependence on continuous ground-based processing. Description: Zheng, Y., Dong, C., Gu, C., Aleksieieva, A., Kozlov, O., Sova, I., Demchyna, M., & Maksymov, M. (2026). Oil Spill Detection in UAV-based Environmental Monitoring Using Lightweight Deep Learning Architectures. Comptes Rendus de L'Academie Bulgare des Sciences, 79 (7), 904–912. DOI : 10.7546/CRABS.2026.07.07 2026-01-01T00:00:00Z https://dspace.chmnu.edu.ua/jspui/handle/123456789/3369 Title: The impact of digitalization and marketing spending volatility on the financial performance indicators of insurance companies in the global financial services market Authors: Dranus, V.; Dranus, L.; Lunkina, T.; Prokopyshyn, O.; Tsvihun, I. Abstract: Purposes. The purpose of this study is to examine how digitalization and volatility of marketing spending influence the financial performance of insurance companies in the global financial services market. The research aims to assess the link between investments in digital technologies and key profitability indicators, while identifying the modifying role of marketing expenditure dynamics. Methodology. The study is based on quantitative analysis using panel data of major international insurance companies over several years. Return on assets (ROA) and return on equity (ROE) are selected as dependent financial indicators. Independent variables include IT investment growth, the volatility of marketing expenses, and control variables reflecting company size and market features. Econometric modelling is used to identify both direct and lagged effects of digital investment, as well as interactive effects between digitalization and marketing volatility. Findings. The results indicate that the direct short-term effect of digital investment on profitability is weak or neutral, mainly due to delayed returns and high implementation costs. However, positive effects become significant over time, particularly for ROA. The study further confirms that volatility of marketing spending modifies these relationships: companies with stable and strategically aligned marketing expenditures demonstrate stronger financial gains from digitalization, while high volatility weakens long-term financial outcomes. Originality. The originality of the study lies in empirically conceptualizing marketing spending volatility as a mediating factor influencing the effectiveness of digital transformation. Unlike existing studies that evaluate digitalization in isolation, this research integrates financial dynamics with marketing‐driven behavioral effects, offering a new explanatory mechanism for divergent financial performance among insurers. Description: Dranus, V., Dranus, L., Lunkina, T., Prokopyshyn, O., & Tsvihun, I. (2026). The impact of digitalization and marketing spending volatility on the financial performance indicators of insurance companies in the global financial services market. Intellectual Economics, 20 (1), 63–89. DOI : 10.13165/IE-26-20-1-03 2026-01-01T00:00:00Z