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.

This study investigates the use of deep learning algorithms to predict the discharge coefficient (Cd) of contaminated multi-hole orifice flow meters with circular opening. Datasets (MHO1 and MHO2) were obtained from computational fluid dynamic simulations for two circular multi-hole orifice flow meters of different geometries. To evaluate the performance and generalization capabilities of different models, three distinct scenarios, each involving different dataset configurations and normalization techniques were designed. For each scenario, three deep learning models (feedforward neural networks, convolutional neural network, and recurrent neural network) were implemented and evaluated based on their performance metrics, including mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). For all three scenarios eight models for each neural network model were developed (FFNN – four models, CNN – two models, RNN – two models). The same structure of models was used across all scenarios to ensure consistency in the evaluation process. Key input parameters include geometrical and flow variables such as β – parameter, contamination thickness, radial distance, Reynolds number, and orifice diameters. Results demonstrate the effectiveness of deep learning in accurately predicting discharge coefficient for different contamination conditions and different geometries. This study showed that deep learning models can be used for prediction of discharge coefficients for multi-hole orifice flow meters of similar geometry, based on data obtained from one orifice flow meter for different contamination parameters.

As manufacturing technologies advance, the integration of artificial neural networks in machining high-hardness materials and optimization of multi-objective parameters is becoming increasingly prevalent. By employing modeling and optimization strategies during the machining of such materials, manufacturers can improve surface roughness and tool life while minimizing cutting time, tool vibrations, and cutting forces. In this paper, the aim was to analyze the impact of input parameters (cutting speed, feed rate, depth of cut, and insert radius) on surface roughness and cutting forces during the machining of 90MnCrV7 using feed-forward neural network models and SHAP analysis. Afterward, multi-criteria optimization was applied to determine the optimal parameter levels to achieve minimum surface roughness and cutting forces using the modified PSI-TOPSIS method. According to the SHAP analysis, the insert radius has the most significant impact on the surface roughness and passive force, while in the multi-criteria analysis, according to ANOVA results, the insert radius has the most significant impact on all considered outputs. The results show that an insert radius of 0.8 mm, a cutting speed of 260 m/min, a feed rate of 0.08 mm, and a depth of cut of 0.5 mm are the optimal combination of input parameters.

Background Determining human identity has always been important in forensic investigations. Forensic dentistry has developed significantly having a key role in determining gender and age. One of the methods that is important in forensic dentistry is the analysis of orthopantomograms, which are X-rays of the complete upper and lower jaw, including the surrounding anatomical structures. The uniqueness of the dental features recorded in orthopantomograms makes them useful for individual identification, more specifically for the assessment of gender and age. This study was conducted to evaluate the application of convolutional neural networks in automating the process of gender and age estimation based on orthopantomograms, to improve accuracy and efficiency in forensic dentistry. Methodology Convolutional neural networks are powerful tools in the field of artificial intelligence for image processing and analysis because their convolutional layers extract specific features that are characteristic of a certain class. A total of 3716 orthopantomograms collected from the database of the University of Sarajevo - Faculty of Dentistry with the Dental Clinical Center were used to create convolutional neural network models for predicting gender and age. The orthopantomograms were taken in the period from January to December 2022 for the needs of doctors and providing services to patients at four polyclinics: Clinic for Dental Diseases and Endodontics, Clinic for Oral Diseases and Periodontology, Clinic for Oral Surgery, and Clinic for Pediatric and Preventive Dentistry. Results The results derived from three developed models confirm that the developed convolutional neural networks have high accuracy. The first model estimated gender, while the second and the third models estimated age within certain age ranges, the second from 12 to 24 years, and the third from 20 to 70 years. After training on the training dataset, all models achieved high accuracy on the validation dataset. The models demonstrated high accuracy without signs of overfitting, with the first model achieving 95.98%, the second model achieving 97.90%, and the third model achieving 96.12% accuracy. Conclusion This research concluded that the developed convolutional neural networks for gender and age estimation from orthopantomograms showed high accuracy. Models' predictions of gender and two age groups exceeded 95% accuracy. Therefore, convolutional neural networks can be considered useful tools for gender and age determination in forensic dentistry and can facilitate and speed up the processes of assessment and determination of essential characteristics.

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.

Background: Angiotensin-converting enzyme 2 (ACE2) is not only an enzyme but also a functional receptor on cell surfaces through which Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2). The exact mechanism by which arterial hypertension (particularly regulated) could affect the presentation and outcome of Coronavirus disease-19 (COVID-19) has not been fully elucidated. Objective: The aim of this study was to analyze the parameters of patients with verified COVID-19 and existing arterial hypertension at the time of hospital admission and to develop neural network model. Methods: The research had a cross-sectional descriptive and analytical character, and included patients (n=634) who were hospitalized in the General Hospital “Prim. dr. Abdulah Nakas” in Sarajevo, Bosnia and Herzegovina, in the period from 01 Sep 2020 to 01 May 2021. From the hospital information system, which is used in everyday clinical work, laboratory parameters at admission were verified, along with demographic data, the comorbidities, while the outcome (recovery, death) was recorded thirty days after the admission. Results: Out of the total number, in 314 patients (200 males), arterial hypertension was verified, out of which, 56 (17.83%) patients died. Patients were divided into two groups, according to outcome, i.e., whether they survived COVID-19 infection or not. A significant difference in age (p = 0.00), erythrocyte count (p = 0.03), haemoglobin (p = 0.05), hematocrit (p = 0.03), platelets count (p = 0.00), leukocytes (p = 0.01), neutrophils (p = 0.00), lymphocytes (p = 0.00), monocytes (p = 0.00), basophils (p = 0.00), eosinophils (p = 0.00), C-reactive protein (p = 0.00) and D-dimer (p = 0.01) was noted. When patients who died and had hypertension were compared with those who died and did not have hypertension (n = 15), out of alll the analyzed parameters, the only significant difference was established in the patient’s age (p = 0.00). In case when patients with hypertension who died were compared to patients with hypertension and diabetes mellitus who died no significant differences were found between features. Conclusion: Patients with hypertension and COVID-19 who died were older, had higher values of erythrocytes, hemoglobin, hematocrit, leukocytes, neutrophils, CRP and D-dimer, and lower values of platelets, lymphocytes, monocytes, basophils and eosinophils count at admission. Compared to deaths without hypertension, the only difference that was established was that patients with hypertension were older.

In this paper five neural network models were developed using NARX-SP neural network type in order to predict air pollutants concentrations (SO2, PM10, NO2, O3 and CO ) for the 72nd hour ahead for Sarajevo. Hourly values of air pollutants concentrations and meteorological parameters (air temperature, pressure and humidity, wind speed and direction) for Sarajevo were used. Optimal model was selected based on the values of R2, MSE and the complexity of models. Optimal neural network model can predict air pollutants concentrations for the 72nd hour ahead with high accuracy, as well as for all hours up to 72nd hour.

Neural networks are important method of machine learning that can be used to predict air quality with high accuracy. Using NARX-SP neural network type, several neural network models are developed to predict concentration of air pollutants in Sarajevo for two prediction cases, for 24th and 48th hour ahead, with different combinations of inputs and outputs. The data used in this paper contain hourly values of meteorological parameters (air humidity, pressure and temperature, wind speed and direction) and concentrations of SO2, PM10, NO2, O3 and CO from 2016 to 2018. Optimal models are selected for both prediction cases. It is concluded that the optimal models have very good performances and can be used to predict concentration of pollutants in Sarajevo with great accuracy and contribute to improve quality of life. By adequate application of optimal models, concentration of air pollutants can be predicted for each hour over the next 48 hours.