This study investigates and compare the students’ entrepreneurial mindset dimensions and intentions from EU member countries Italy, Austria, Sweden, and Greece, and an EU candidate country Bosnia and Herzegovina, which are important for fostering start-ups, economic development, and job creation. By analyzing students’ entrepreneurial mindset dimensions, demographic and academic characteristics, and availability of resources, the research aims to identify factors that impact students’ entrepreneurial intentions. Findings provide valuable insights into how these factors vary across different educational, economic, and social contexts with guidance for enhancing education to better support students’ entrepreneurial aspirations.Machine learning Random Forest was used to analyze the impact of entrepreneurial mindset dimensions, resources, and demographic and academic characteristics on students’ entrepreneurial intentions of students from EU member countries and Bosnia and Herzegovina. SHapley Additive exPlanations (SHAP) values were utilized to analyze feature importances and contributions to the model’s predictions. Statistical hypothesis tests were also conducted to compare differences of students’ entrepreneurial mindset dimensions, intentions and availability of resources between the EU member countries and Bosnia and Herzegovina.High values of entrepreneurial mindset dimensions have positive impact on entrepreneurial intentions in both EU member countries and Bosnia and Herzegovina. The availability of resources and orientation to innovations were the most impactful features for students in EU and Bosnia and Herzegovina, respectively. Gender and academic characteristics showed minimal influence. There are no significant differences in all dimensions between EU member countries and Bosnia and Herzegovina, except for confidence dimension and entrepreneurial intentions, which are significantly greater in Bosnia and Herzegovina.Findings suggest that tailored educational interventions focusing on key entrepreneurial mindset dimensions and resource access could significantly enhance entrepreneurial intentions among students. For policymakers and educators, this study provides a foundation for developing targeted strategies that align with the specific contexts of both EU member countries and Bosnia and Herzegovina. In this way higher education institutions can better support students’ entrepreneurial aspirations, contributing to broader economic development and job creation. This research offers recommendations for improving entrepreneurship education across diverse educational, economic, and social contexts and more balanced and inclusive economic development in Europe.

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 PM10 and meteorological parameters with and without temperature inversion. The main aim of this paper was to develop forecasting models of the hourly average of PM10 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 PM10 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 PM10 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 (λ<1). A total of 21 models were developed. The performance of the models were evaluated based on the coefficient of determination R2 and the standard error SE. The backward elimination models were shown to have high performances with the highest value of R2= 97.19 and the lowest value of SE=1.32. The study showed that some principal components with eigenvalues λ<1 were significantly related to the independent variable PM10 and thus were retained in the PCR models. In the study, it was shown that backward elimination PCR was an adequate tool to develop PM10 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 PM10 with and without temperature inversion.

Background: During the process of the treatment of COVID-19 hospitalized patients, physicians still face a lot of unknowns and problems. Despite the application of the treatment protocol, it is still unknown why the medical status of a certain number of patients worsens and ends with death. Many factors were analyzed for the prediction of the clinical outcome of the patients using different methods. The aim of this paper was to develop a prediction model based on initial laboratory blood test results, accompanying comorbidities, and demographics to help physicians to better understand the medical state of patients with respect to possible clinical outcomes using neural networks, hypothesis testing, and confidence intervals. Methods: The research had retrospective-prospective, descriptive, and analytical character. As inputs for this research, 12 components of laboratory blood test results, six accompanying comorbidities, and demographics (age and gender) data were collected from hospital information system in Sarajevo for each patient from a sample of 634 hospitalized patients. Clinical outcome of the hospitalized patients, survival or death, was recorded 30 days after admission to the hospital. The prediction model was designed using a neural network. In addition, formal hypothesis tests were performed to investigate whether there were significant differences in laboratory blood test results and age between patients who died and those who survived, including the construction of 95% confidence intervals. Results: In this paper, 11 neural networks were developed with different threshold values to determine the optimal neural network with the highest prediction performance. The performances of the neural networks were evaluated by accuracy, precision, sensitivity, and specificity. Optimal neural network model evaluation metrics are: accuracy = 87.78%, precision = 96.37%, sensitivity = 90.07%, and specificity = 62.16%. Significantly higher values (P < 0.05) of blood laboratory result components and age were detected in patients who died. Conclusion: Optimal neural network model, results of hypothesis tests, and confidence intervals could help to predict, analyze, and better understand the medical state of COVID-19 hospitalized patients and thus reduce the mortality rate.

This paper presents the use of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to determine the optimum process parameters in plasma arc cutting of stainless steel. Two input process parameters, cutting speed and plasma gas pressure are considered and experiments are conducted based on Taguchi L9 orthogonal array. After performing the experiments, the surface roughness, cut perpendicularity and kerf width are measured. The analysis of variance (ANOVA) are performed in order to identify the effect of each input process parameters on the output responses. The results indicate that TOPSIS method is appropriate for solving multi-criteria optimization of process parameters. Results also showed that cutting speed of 2500 mm/min and plasma gas pressure of 6 bar are the optimum combination of process parameters.

This paper presents the use of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to determine the optimum process parameters in plasma arc cutting of stainless steel. Two input process parameters, cutting speed and plasma gas pressure are considered and experiments are conducted based on Taguchi L9 orthogonal array. After performing the experiments, the surface roughness, cut perpendicularity and kerf width are measured. The analysis of variance (ANOVA) are performed in order to identify the effect of each input process parameters on the output responses. The results indicate that TOPSIS method is appropriate for solving multi-criteria optimization of process parameters. Results also showed that cutting speed of 2500 mm/min and plasma gas pressure of 6 bar are the optimum combination of process parameters.