https://sci.ldubgd.edu.ua/jspui/handle/123456789/17084 2026-07-16T04:30:53Z https://sci.ldubgd.edu.ua/jspui/handle/123456789/18686 Title: Predictive modelling of iron leaching from mining waste: Civil protection and environmental safety implications Authors: Smotr, O.; Karabyn, V.; Karabyn, O.; Kochmar, I.; Linchevskyi, Ye.; Loik, V.; Babadzhanova, O.; Lazaruk, Ya.; Rogulia, A.; Synelnikov, O. Abstract: Coal waste piles pose significant risks to water quality, ecosystem health, and community well-being. In Ukraine’s Lviv–Volyn coal basin, the Chervonohrad concentrating plant’s waste pile exemplifies these challenges: unchecked physical processes can mobilise iron, contaminating groundwater and surface waters. No prior study has systematically linked precipitation to iron leaching in this context. This research creates and tests a predictive model relating precipitation to annual iron leaching from coal-mining waste. Laboratory column experiments on argillite flushed with deionised water under controlled flow generated empirical data for calibration. Using similarity theory (π-theorem) and regression analysis, we derived a simple linear relation for a unit spoil volume (1 Mm³): each additional millimetre of year precipitation mobilises 0.01 kg Fe∙Mm⁻³∙y⁻¹. Under the mean annual precipitation of 607.9 mm, the model indicates 24.8 kg Fe∙Mm⁻³∙y⁻¹; scaled to the Chervonohrad waste volume (48.89 Mm³), this equates to 1212 kg∙y⁻¹. Environmental and civil safety specialists and regulators, based on local precipitation data and the model we developed, can calculate seasonal or annual iron loading without large-scale field sampling. Civil protection agencies can likewise use these predictions for early warning and readiness during wet years. The approach developed by the authors is accurate, easy to apply, and can be used in other mining regions with similar climatic and geological conditions. It provides a compact rule-of-thumb for monitoring design, optimisation of containment and treatment, and policy-relevant thresholds to protect aquatic resources-bridging laboratory science and on-the-ground decision-making under changing precipitation patterns. 2026-01-01T00:00:00Z https://sci.ldubgd.edu.ua/jspui/handle/123456789/18510 Title: Інтелектуальні системи підтримки прийняття рішень для стратегічного управління освітою у сфері цивільного захисту. Authors: Смотр, Ольга 2026-04-01T00:00:00Z https://sci.ldubgd.edu.ua/jspui/handle/123456789/17751 Title: Machine Learning Method for Predicting Smoke Blockage Time at Apartment Evacuation Exits Authors: Smotr, О.; Khlevnoi, O.; Zhezlo-Khlevna, N.; Malets, I.; Golovatyi, R. Abstract: The article explores the application of machine learning methods to study the time of smoke blockage of evacuation routes during the initial stage of a fire in residential premises. A dataset was formed through numerical experiments conducted in the PyroSim software, where 140 fire scenarios were modeled with varying values of the fire spread angle, distance to the exit, total area of opened doors and windows. Correlation analysis was performed to assess relationships between parameters, and polynomial regression of the second degree with variable scaling was employed for modeling, yielding interpretable coefficients. The results were validated using mean squared error (MSE) and coefficient of determination (R²), complemented by visualizations of dependencies. The study demonstrates the effectiveness of combining numerical modeling with machine learning for predicting smoke blockage time, offering practical implications for enhancing evacuation safety 2026-01-01T00:00:00Z https://sci.ldubgd.edu.ua/jspui/handle/123456789/17695 Title: Розробка системи інтерактивного навчання для підготовки медичних спеціалістів на базі UNITY Authors: Смотр, Ольга; Намазило, Віктор 2026-03-01T00:00:00Z