The Western Balkans (WB) region is highly prone to water erosion processes, and therefore, the estimation of rainfall erosivity (R-factor) is essential for understanding the complex relationships between hydro-meteorological factors and soil erosion processes. The main objectives of this study are to (1) estimate the spatial-temporal distribution R-factor across the WB region by applying the RUSLE and RUSLE2 methodology with data for the period between 1991 and 2020 and (2) apply cluster analysis to identify places of high erosion risk, and thereby offer a means of targeting suitable mitigation measures. To assess R-factor variability, the ERA5 reanalysis hourly data (0.25° × 0.25° spatial resolution) comprised 390 grid points were used. The calculations were made on a decadal resolution (i.e., for the 1990s, the 2000s, and the 2010s), as well as for the whole study period (1991–2020). In order to reveal spatial patterns of rainfall erosivity, a k-means clustering algorithm was applied. Visualization and mapping were performed in python using the Matplotlib, Seaborn, and Cartopy libraries. Hourly precipitation intensity and monthly precipitation totals exhibited pronounced variability over the study area. High precipitation values were observed in the SW with a >0.3 mm h⁻¹ average, while the least precipitation was seen in the Pannonian Basin and far south (Albanian coast), where the mean intensity was less than an average of 0.1 mm h⁻¹. R-factor variability was very high for both the RUSLE and RUSLE2 methods. The mean R-factor calculated by RUSLE2 was 790 MJ mm ha⁻¹·h⁻¹·yr⁻¹, which is 58% higher than the mean R-factor obtained from RUSLE (330 MJ mm ha⁻1·h⁻¹·yr⁻¹). The analysis of the R-factor at decadal timescales suggested a rise of 14% in the 2010s. The k-means algorithm for both the RUSLE and RUSLE2 methods implies better spatial distribution in the case of five clusters (K = 5) regarding the R-factor values. The rainfall erosivity maps presented in this research can be seen as useful tools for the assessment of soil erosion intensity and erosion control works, especially for agriculture and land use planning. Since the R-factor is an important part of soil erosion models (RUSLE and RUSLE2), the results of this study can be used as a guide for soil control works, landscape modeling, and suitable mitigation measures on a regional scale.

The manuscript examines the cause-and-effect relationships of fires in the Republic of Serbia over a fifteen-year period, primarily from the aspect of human safety. For this purpose, numerical variables describing the number of injuries and deaths in fires were introduced, on which various analysis and modeling techniques were implemented, which can be viewed in the context of data mining (DM). First, for both observed variables, stochastic modeling of their temporal dynamics was analyzed, and subsequently, cluster analysis of the values of these variables was performed using two different methods. Finally, by interpreting these variables as outputs (objectives) for the classification problem, several decision trees were formed that describe the influence and relationship of different fire causes on situations in which injuries or human casualties occur or not. In that way, several different types of fires have been identified, including rare but deadly incidents that require urgent preventive measures. Key risk factors such as fire cause, location, season, etc., have been found to significantly influence human casualties. These findings provide practical insights for improving fire protection policies and emergency response. Through such a comprehensive analysis, it is believed that some important results have been obtained that precisely describe the specific relationships between the causes and consequences of fires occurring in the Republic of Serbia.

This manuscript is a continuation of the research published in Fire 2025, 8(8), 302, i.e., it deals with the examination of the cause-and-effect relationships of fires in the Republic of Serbia from the aspect of human safety. Among others, variables related to gender, age, and severity of injuries caused by fires are introduced, on which various methods of statistical analysis and stochastic modeling are first applied. Continuous age variables are modelled using the flexible Generalized Additive Models for Location, Scale, and Shape (GAMLSS) framework, where the Generalized Normal Distribution (GND) is identified as the optimal generative model for injuries, while a Reflected Log-Normal Distribution with positive support (RefLOGND+) provides the best fit for fatalities. The quality of such modeling is formally verified, and the probabilities of injury and death of individuals in certain age categories are predicted, revealing a pronounced concentration of injuries in the working-age population and a markedly higher relative risk of fatal outcomes among elderly individuals. Thereafter, by applying certain Data Mining (DM) techniques, primarily the Apriori algorithm, the most frequently occurring association rules are found, which indicate typical patterns and demographic structure of injuries and deaths in fires in Serbia. Finally, using the CART (Classification and Regression Trees) algorithm, several decision trees are formed that describe the impact and relationship of different causes of fires on injury and death in fires. In this way, some important and frequent patterns are observed that indicate key fire risk factors that significantly affect the demographic structure of human casualties. The results thus obtained provide a basis for developing targeted strategies for fire prevention and improving emergency response planning.

The development of latent fingermarks is very important for forensic investigations because they serve as key pieces of evidence. Our previous research has shown that this technique can be applied to the development of latent fingermarks on conductive substrates. This study aimed to find the optimal nickel electrodeposition process conditions for the visualization of latent fingermarks on brass cartridge cases. The optimal range of thicknesses of nickel films was determined and an excellent image contrast and development accuracy was achieved, which allowed the characteristics of fingermarks to be visualized.

For analysis of adhesion performance, the composite structures consisting of electrodeposited (ED) nickel coatings on copper and molybdenum substrates were prepared. Optimal electrodeposition process parameters, significant for adhesion testing, were determined. With the experimentally determined deposition rate for a current density of $35 ~\text{mA} / \text{cm}^{2}, 0.7 \mu ~\mathrm{m} / \text{min}$, the deposition time was calculated for coating thicknesses of $\mathbf{5, 1 0}$ and $20 \mu ~\mathrm{m}$. The adhesion performance of the “hard coating on a soft substrate” composite systems was investigated using three types of adhesion testing: a bidirectional bending test, a crosshatching method, and a Vickers microindentation method. The morphology of the surfaces of substrates and coatings before and after the adhesion testing was examined using a stereomicroscope and a metallurgical microscope. The adhesion strength of nickel coatings on molybdenum substrate was poor and decreased with increasing coating thickness, but on copper substrates, adhesion strength was excellent for all the coating thicknesses. Different adhesion tests gave consistent results.

Ремедијација земљишта загађеног опасним материјама представља сложен процес који захтева научни приступ и детаљно истраживање како би се обновила животна средина и земљиште вратило у безбедно стање. Форензички аспект овог процеса обухвата низ корака који се примењују у циљу идентификације, анализе и решавања проблема загађења. У овом научном раду, истражујемо главне изворе загађења, поступке (технологије) ремедијације земљишта, анализирамо форензичке аспекте ремедијације земљишта загађеног опасним материјама и истичемо важност интегрисаног приступа и научне методологије у успешном обнови загађених земљишта.