The specific geographic location of Sarajevo, which is located in a valley surrounded by mountains, provides the opportunity to analyze the relation between the concentration of $P{M}_{10}$ and meteorological parameters with and without temperature inversion. The main aim of this paper was to develop forecasting models of the hourly average of $P{M}_{10}$ values in the Sarajevo urban area based on meteorological parameters measured in Sarajevo and on the Bjelasnica mountain with and without temperature inversion by using principal component regression (PCR). Also, this research explored and analyzed the differences in the values of the meteorological parameters and $P{M}_{10}$ in Sarajevo with and without temperature inversion, and the difference in temperatures between Sarajevo and Bjelasnica with temperature inversion using statistical hypothesis testing with a total of 240 hypothesis tests performed. The measurements of meteorological parameters were taken from 2020 to 2022 for both Sarajevo (630 m) and the Bjelasnica mountain (2067 m), which allowed for the identification of time periods with and without temperature inversion, while measurements of $P{M}_{10}$ were taken only in Sarajevo. Data were collected during the heating season (November, December, January, February and March). Since analyses have shown that only January and November had time periods with and without temperature inversion during each hour of the day, a total of seven cases were identified: two cases with and five cases without temperature inversion. For each case, three PCR models were developed using all principal components, backward elimination and eigenvalue principal component elimination criteria ($\lambda <1$). A total of 21 models were developed. The performance of the models were evaluated based on the coefficient of determination $R^2$ and the standard error $SE$. The backward elimination models were shown to have high performances with the highest value of $R^2=$ 97.19 and the lowest value of $SE=1.32$. The study showed that some principal components with eigenvalues $\lambda <1$ were significantly related to the independent variable $P{M}_{10}$ and thus were retained in the PCR models. In the study, it was shown that backward elimination PCR was an adequate tool to develop $P{M}_{10}$ forecasting models with high performances and that it could be useful for authorities for early warnings or other action to protect citizens from very harmful pollution. Hypothesis tests showed different relations of meteorological parameters and $P{M}_{10}$ with and without temperature inversion.

Machine learning prediction and analysis of students’ academic performance

This study explores the entrepreneurial intentions of university students from different educational, economic, and social backgrounds by comparing four European Union (EU) countries (Italy, Austria, Sweden, Greece) to an EU-candidate country (Bosnia and Herzegovina). Data were collected through surveys on a convenience sample of 301 students. The hierarchical regression and formal statistical hypothesis testing assess and compare the role of individual factors and contextual activating factors. In doing so, the paper adopts and adapts the EPIC tool, making it suitable for cross-country comparison. The results indicate a lack of significance of the risk-taking dimension, and a striking similarity in the influence of resources as a contextual activating factor, despite the differences of the investigated countries. In addition, the results indicate the individual mindset dimensions that significantly contribute to the entrepreneurial intentions of EU students (innovation-oriented, persistence, and peculiarity), and the different predictors for students from Bosnia and Herzegovina (innovation-oriented and action-oriented). The paper contributes to the stream of research on entrepreneurial intentions in higher education by assessing the individual and contextual factors within a fine-grained cross-cultural comparison. Insights for institutions and policymakers to enhance support and resources for aspiring entrepreneurs can ultimately be derived.