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.

Background and Objectives: Depression is a common mental problem in the older population and has a significant impact on recovery and general well-being. A comprehensive understanding of the prevalence of depressive symptoms and their effects on functional outcomes is essential for improving care strategies. The primary aim of this study was to determine the prevalence of depressive symptoms in older patients undergoing geriatric rehabilitation and to assess their specific impact on their functional abilities. Materials and Methods: A retrospective study was conducted at the Lucerne Cantonal Hospital in Wolhusen, Switzerland, spanning from 2015 to 2020 and including 1159 individuals aged 65 years and older. The presence of depressive symptoms was assessed using the Geriatric Depression Scale (GDS) Short Form, while functional abilities were evaluated using the Functional Independence Measure (FIM) and the Tinetti test. Data analysis was performed using TIBCO Statistica 13.3, with statistical significance set at p < 0.05. Results: Of the participants, 22.9% (N = 266) exhibited depressive symptoms, with no notable differences between genders. Although all patients showed functional improvements, the duration of rehabilitation was prolonged by two days (p = 0.012, d = 0.34) in those with depressive symptoms. Alarmingly, 76% of participants were classified as at risk of falling based on the Tinetti score. However, no significant correlation was found between the GDS and Tinetti scores at admission (p = 0.835, r = 0.211) or discharge (p = 0.336, r = 0.184). The results from the non-parametric Wilcoxon matched-pairs test provide compelling evidence of significant changes in FIM scores when comparing admission scores to those at discharge across all FIM categories. Conclusions: Depressive symptoms are particularly common in geriatric rehabilitation patients, leading to prolonged recovery time and increased healthcare costs. While depressive symptoms showed no correlation with mobility impairments, improvements in functional status were directly associated with reduced GDS scores. Considering mental health during admission and planning is critical in optimizing rehabilitation outcomes.

Background/Objectives: Congenital heart disease (CHD), affecting approximately 1% of live births, has transitioned to a chronic condition due to advances in diagnostics and surgery, resulting in an increasing adult congenital heart disease (ACHD) population. This study characterizes the clinical and demographic profiles of ACHD patients in Serbia, focusing on congenital anomalies, mortality rates, and key clinical factors to identify opportunities for improving care and outcomes. Methods: This observational single-center study was conducted at the Cardiovascular Institute “Dedinje” in Belgrade, Serbia, involving patients diagnosed or treated for CHD between 2006 and 2022. Results: A total of 1532 patients were included in the study, with common diagnoses including atrial septal defects (ASD) (47.65%) and ventricular septal defects (VSD) (13.19%). The mean patient age was 48.31 years, with a slight predominance of females (57.21%). The complexity of CHD was categorized as mild (54.6%), moderate (36.5%), and severe (6.3%). The mortality rate was 4.2%, with higher rates observed in conditions like Ebstein anomaly (17.78%) and congenital aortic stenosis (11.76%). Conclusions: This study provides a comprehensive overview of the current state of ACHD management in Serbia, highlighting the high prevalence of ASD and VSD among patients, the challenges associated with moderate and severe CHD, and the notable mortality rates for certain conditions. The findings underscore the importance of improving early detection, individualized treatment plans, and multidisciplinary care to enhance patient outcomes in this growing population.

The success and failure of any organization largely depend on talented and competent employees. Through human resource management (HRM) practices and policies, organizations strive to ensure committed employees. One of the fundamental practices they use is undoubtedly material and immaterial compensation. Adequate management of such compensation may contribute to greater employee engagement in achieving the set goals, realizing the mission, and fulfilling the vision of the organization. In such ways employees confirm their affiliation with the organization, which classifies them as committed employees. The paper assumes that the adequate application of material and immaterial compensation in organizations in Bosnia and Herzegovina (BiH) may improve employee organizational commitment. This ultimately has a positive impact on the effectiveness and efficiency of organizations. The research was conducted in 128 BiH organizations with more than 50 employees across four sectors. The hypotheses were tested applying the Principal Components Analysis (PCA) through the Kaiser-Meyer-Olkin (KMO) values and Bartlett's test of sphericity and the regression analysis. The results show a statistically significant positive impact of material and immaterial compensation on employee organizational commitment. Creating more agile policies and practices of human resource management, especially those related to material and immaterial compensation, can significantly improve employee commitment as well as the entire organizational effectiveness.