The University of Priština (Serbian: Универзитет у Приштини, romanized: Univerzitet u Prištini) is a public university in Kosovo[a] with a temporary seat in North Mitrovica.It is the post-secondary institution that emerged after the disestablishment of the Serbian-language University of Pristina as a result of the Kosovo War. Despite its official name, it is also referred to as the University of Kosovska Mitrovica after its temporary relocation to North Mitrovica in 2001.
In this paper we performed flood frequency analysis by using L-moments and annual maximum series data from two hydrological stations on the Rasina River (Brus and Bivolje), for the 1961-2020 period. Homogeneity testing confirmed that the basin can be considered hydrologically homogeneous (V-i = 0.38 < 1). Five probability distributions - Normal, Log-Normal, Gumbel, Pearson Type III, and Log-Pearson Type III - were tested to identify the best-fit model. An L-moment ratio diagram and Z-statistics indicate that all distributions satisfy the test criteria, with Log-Pearson Type III showing the best overall fit for the region (Z = 0.09). Goodness-of-fit tests (Kolmogorov-Smirnov, Cramer-von Mises, and x(2)) confirmed Log-Pearson Type III as the best fit at the Brus station and Pearson Type III at the Bivolje station. Theoretical flood quantiles were calculated for various return periods (T-year floods). Mann-Kendall trend analysis indicated a significant decreasing discharge trend at Bivolje (-1.28 m(3)/s per year).
The global shift toward renewable energy is driven by the dual imperatives of rising energy demand and the need to reduce environmental harm caused by fossil fuels. However, renewables like wind and solar power pose unique challenges, particularly due to their intermittent generation and current limitations in energy storage technologies. Battery banks, commonly used to store surplus energy, degrade over time, making accurate forecasting of their remaining usable lifecycles critical for maintaining system reliability and efficiency. This study proposes a novel approach for forecasting battery health using an optimized long short-term memory (LSTM) network. To address the complexity of deep learning hyperparameter selection, a modified metaheuristic optimization algorithm is developed and integrated into a broader optimization framework aimed at improving model performance while minimizing overfitting. The method is benchmarked against several state-of-the-art optimizers, with results validated through comprehensive simulations and statistical analysis. This work contributes a scalable forecasting methodology, an effective optimization strategy, and interpretable results to support sustainable energy storage solutions.
This study investigates the hydrodistillation of lavender essential oil from lavender (Lavandula angustifolia L.) on a laboratory scale using a historical data design and response surface methodology. Distillation was conducted in flasks of various volumes (250 to 2000 mL) with different distillation rates and water-to-flower ratios (10:1, 15:1, and 20:1 mL/g). The aim was to optimize process conditions - specifically flask size, distillation rate, and water-to-flower ratio - and evaluate their influence on essential oil yield. A linear relationship between these variables and oil yield was established. ANOVA confirmed the statistical significance of the linear model and all three variables. Optimized conditions (2000 mL flask, 7.5 mL/min distillation rate, 10 mL/g water-to-flower ratio) predicted a maximum yield of 2.28%. The study also analyzed the essential oil's composition, physicochemical properties, and biological activities, identifying 47 compounds comprising 97.6% of the oil, notably linalool (32.19%) and linalyl acetate (18.29%). The essential oil demonstrated antioxidant activity (EC50 = 10.13 mg/mL) and moderate antimicrobial effects (MICs = 25.7 to 206.6 mg/mL).
This paper will present the process of digital transformation with an emphasis on artificial intelligence, as one of the fastest growing technologies in today's world, from the aspect of impact on business processes, operations and green economy. Based on a systematic review of the latest literature, the theoretical foundations of the process of digital transformation and the definition of the phenomenon of artificial intelligence were evolved. The results show that the responses of key users differ regarding the application of digital transformation and artificial intelligence in business from the aspect of business decision-making and application in the green economy.
The concepts of identification and strong identification among sigma-algebras are introduced in Florens et al. [Elements of Bayesian statistics, New York: Marcel Dekker; 1990. (Pure and applied mathematics: a series of monographs and textbooks)]. In this paper we propose generalization of these definitions for information represented by filtrations (i.e., by sigma-algebra families). Then, we prove that identification and strong identification are directly connected to the concept of statistical causality (based on Granger's definition of causality). Also, we apply some of this results on general reduced Bayesian experiment.
