The estimation of distances and angles is a routine part of an orthopedic surgical procedure. However, despite their prevalence, these steps are most often performed manually, heavily relying on the surgeon’s skill and experience. To address these issues, this study presents a sensor-equipped drill system which enables automatic estimation of the drilling angle and channel length. The angular accuracy and precision of the system were tested over a range of inclination angles and proved to be superior to the manual approach, with mean absolute errors ranging from 1.9 to 4.5 degrees for the manual approach, and from 0.6 to 1.3 degrees with the guided approach. When sensors were used for simultaneous estimation of both the inclination and anteversion angles, the obtained mean absolute errors were 0.35 ± 0.25 and 2 ± 1.33 degrees for the inclination and anteversion angles, respectively. Regarding channel length estimation, using measurements obtained with a Vernier caliper as a reference, the mean absolute error was 0.33 mm and the standard deviation of errors was 0.41 mm. The obtained results indicate a high potential of smart drill systems for improvement of accuracy and precision in orthopedic surgical procedures, enabling better patient clinical outcomes.

Abstract The paper provides an overview of all freshwater red algae species recorded to date in the territory of Bosnia and Herzegovina. Based on fieldwork and analysis of all available previously published data, it was determined that a total of 15 taxa from eight genera have been recorded to date: Bangia (1), Audoinella (3), Batrachospermum (2), Peludicola (1), Shaethia (1), Lemanea (4), Paralemanea (2) and Hildenbrandia (1). All taxa were found in clear, cold, well-oxygenated water. Bosnia and Herzegovina is very rich in different types of aquatic habitats. More than 100 sites were visited during the field research, but there are still many potential habitats where freshwater red algae can be found, which will be explored in the coming years. This work is the first step toward establishing long-term monitoring and listing of protected and threatened red algae in Bosnia and Herzegovina.

Abstract This paper investigates the potential application of neural networks for predicting electricity production in hybrid systems combining photovoltaic (PV) panels and wind turbines. The research focuses on identifying key factors affecting the efficiency and reliability of these systems, including weather variability, PV panel temperature control, solar irradiation, and panel contamination by dust and other pollutants. Artificial neural network (ANN) models are used to predict power output, incorporating robust data filtering and parameter optimization techniques. Through case studies from Germany, the significant role of stochastic weather patterns on energy production is demonstrated, highlighting the need for accurate modeling and strategic management. The findings emphasize that accurate modeling and prediction are crucial for optimizing the operation and reliability of hybrid systems, facilitating a reduced dependency on fossil fuels and promoting sustainable power accessibility in remote areas. By applying a Feed Forward Back Propagation Network (FFBPN), this research demonstrates improved prediction accuracy of power outputs, which is crucial for effective integration and management of renewable sources in the power grid. The study supports ongoing refinement of predictive models and system integration strategies to fully harness the potential of hybrid renewable energy systems.

Abstract This paper examines the potential for transforming the Faculty of Mechanical Engineering building in Mostar into a Net Zero Energy Building (NZEB). The study assesses the building’s current energy consumption and explores feasible upgrades to achieve complete energy independence and zero CO2 emissions. The hybrid system, composed of rooftop solar panels, wind turbines, and a heat pump, has been thoroughly analyzed in this research to identify opportunities for optimization and expansion. Using software tools such as HOMER Pro and SUNNY DESIGN, the study models renewable energy capacities and simulates system performance to determine the investment potential and economic feasibility of achieving net-zero energy status. The findings offer valuable insights into practical strategies for implementing NZEB principles in public buildings across Bosnia and Herzegovina, contributing to broader sustainability goals and energy security in the region.

Abstract In modern power systems, reliable electricity supply is crucial. This study analyzes the impact of small hydro power plants (SHPP) and photovoltaic (PV) systems on a low-voltage network using DigSILENT PowerFactory software tool. Results show that PV systems increase total harmonic distortion (THD), while sHPP reduces THD and significantly increases short-circuit currents during faults. These findings highlight the importance of strategic integration of distributed generation (DG) sources to maintain network quality and stability.

Background: Severe coagulation abnormalities are common in patients with COVID-19 infection. We aimed to investigate the relationship between pro-inflammatory cytokines and coagulation parameters concerning socio-demographic, clinical, and laboratory characteristics. Methods: Our study included patients hospitalized during the second wave of COVID-19 in the Republic of Serbia. We collected socio-demographic, clinical, and blood-sample data for all patients. Cytokine levels were measured using flow cytometry. Results: We analyzed data from 113 COVID-19 patients with an average age of 58.15 years, of whom 79 (69.9%) were male. Longer duration of COVID-19 symptoms before hospitalization (B = 69.672; p = 0.002) and use of meropenem (B = 1237.220; p = 0.014) were predictive of higher D-dimer values. Among cytokines, higher IL-5 values significantly predicted higher INR values (B = 0.152; p = 0.040) and longer prothrombin times (B = 0.412; p = 0.043), and higher IL-6 (B = 0.137; p = 0.003) predicted longer prothrombin times. Lower IL-17F concentrations at admission (B = 0.024; p = 0.050) were predictive of higher INR values, and lower IFN-γ values (B = −0.306; p = 0.017) were predictive of higher aPTT values. Conclusions: Our findings indicate a significant correlation between pro-inflammatory cytokines and coagulation-related parameters. Factors such as the patient’s level of education, gender, oxygen-therapy use, symptom duration before hospitalization, meropenem use, and serum concentrations of IL-5, IL-6, IL-17F, and IFN-γ were associated with worse coagulation-related parameters.

Over the past 30 years, forensic experts from Croatia and Bosnia and Herzegovina have embraced advanced technologies and innovations to enable great efficacy and proficiency in the identification of war victims. The wartime events in the countries of former Yugoslavia greatly influenced the application of the selected DNA analyses as routine tools for the identification of skeletal remains, especially those from mass graves. Initially, the work was challenging because of the magnitude of the events, technical aspects, and political aspects. Collaboration with reputable foreign forensic experts helped tremendously in the efforts to start applying DNA analysis routinely and with increasing success. In this article, we reviewed the most significant achievements related to the application of DNA analysis in identifying skeletal remains in situations where standard identification methods were insufficient.

Due to its turbulent demographic history, marked by extensive settlement and gene flow from diverse regions of Eurasia, Southeastern Europe (SEE) has consistently served as a genetic crossroads between East and West and a junction for the migrations that reshaped Europe’s population. SEE, including modern Croatian territory, was a crucial passage from the Near East and even more distant regions and human populations in this region, as almost any other European population represents a remarkable genetic mixture. Modern humans have continuously occupied this region since the Upper Paleolithic era, and different (pre)historical events have left a distinctive genetic signature on the historical narrative of this region. Our views of its history have been mostly renewed in the last few decades by extraordinary data obtained from Y-chromosome studies. In recent times, the international research community, bringing together geneticists and archaeologists, has steadily released a growing number of ancient genomes from this region, shedding more light on its complex past population dynamics and shaping the genetic pool in Croatia and this part of Europe.

Abstract This paper presents an artificial neural network (ANN) based method for overhead lines magnetic flux density estimation. The considered method enables magnetic flux density estimation for arbitrary configurations and load conditions for single-circuit, multi-circuit, and also overhead lines that share a common corridor. The presented method is based on the ANN model that has been developed using the training dataset that is produced by a specifically designed algorithm. This paper aims to demonstrate a systematic and comprehensive ANN-based method for simple and effective overhead lines magnetic flux density estimation. The presented method is extensively validated by utilizing experimental field measurements as well as the most commonly used calculation method (Biot - Savart law based method). In order to facilitate extensive validation of the considered method, numerous magnetic flux density measurements are conducted in the vicinity of different overhead line configurations. The validation results demonstrate that the used method provides satisfactory results. Thus, it could be reliably used for new overhead lines’ design optimization, as well as for legally prescribed magnetic flux density level evaluation for existing overhead lines.

BACKGROUND As artificial intelligence within digital processes continues to advance and replace conventional manual workflows, it is crucial that digital data are consistent with analog data. The aim was to evaluate the validity and time efficiency of digital cast analysis on digital models in comparison with the manual, gold standard, cast analysis on plaster models. METHODS Cast analysis was performed on 30 patients in three various methods: manually measured variables on plaster models (MP), manually measured variables on digital three-dimensional models (MD), and automatically measured variables on digital three-dimensional models (AD) on digital models. Digital cast analysis was performed in CS Model+. Analyses included metrical and categorical variables and the required work time. Measurements in MD and AD were validated to MP. Validity of the metrical variables was analyzed with Bland-Altman, Dahlberg's formula, and paired sample t test. Categorical variables were validated by Cohen's Kappa. Work time was analyzed with Wilcoxon signed-rank test. RESULTS Metrical variables had measurement errors ranging 0.4 to 1.4 mm between MP-MD, and 0.6 to 3.2 mm between MP-AD. Observations of categorical variables had a moderate to strong (0.65 to 0.9) level of agreement between MP-MD, and a weak to moderate (0.4 to 0.68) level of agreement between MP-AD. Data for dental stage, vertical, and transversal relation was not provided in AD. Cast analysis was performed quicker digitally, P ≤ 0.05. CONCLUSIONS Digital cast analysis is consistent with manual cast analysis for metrical variables. Analyses of categorical variables show a weak level of agreement with automatic digital analysis, such as space conditions and midline assessments. Digital cast analysis optimizes time compared with manual cast analysis, with automatic analysis being the fastest.

Aim: To assess Red blood cell Distribution Width (RDW) and platelet indices values in patients with type 2 diabetes mellitus (T2DM) and to verify its association with kidney dysfunction (KD). Patients and Methods: A cross-sectional study included 149 T2DM subjects divided into two groups with (T2DM – KD; n=52) and without (T2DM-nKD; n=97) presence of kidney dysfunction and 30 healthy subjects. White Blood Cells (WBC) count, C-reactive protein (CRP), fibrinogen, RDW, platelet indices, urea, and creatinine, were measured in all participants. Kidney function was evaluated by the estimated glomerular filtration rate (eGFR) calculated using the simplified Modification of Diet in Renal Disease (MDRD) formula. Results: T2DM-KD subjects showed statistically significantly higher values of the parameters RDW (p<0.01), Mean Platelet Volume - MPV (p<0.01), Platelet Distribution Width-PDW (p<0.01), Platelecrit-PCT (p<0.01), and Platelet Mass Index-PMI (p<0.01) compared to T2DM-nKD subjects, and statistically significantly lower values of the WBC count in T2DM-KD subjects compared to subjects suffering from T2DM without kidney dysfunction (p<0.01). ROC curve analysis revealed that RDW (sensitivity of 80.8%, specificity of 78.3%), MPV (sensitivity of 75%, specificity of 78.4 %), and PDW (sensitivity of 80.8%, specificity of 83.5%) could be used as markers in distinguishing between T2DM subjects with and without kidney dysfunction. Conclusion: This study confirms the reliability of the RDW,MPV, and PDW as simple, low cost and useful markers in distinguishing between T2DM subjects with and without kidney dysfunction.

Abstract This paper investigates the strategic placement of capacitor banks in the distribution network of Gračanica, with a specific focus on the medium-voltage feeder Grades. The primary objective is to optimize voltage profiles, minimize power losses, and enhance the overall performance of the distribution network. The significance of this research lies in its thorough examination of optimal capacitor placement within the medium-voltage (MV) branch of distribution networks, specifically considering the intricate interplay between capacitor banks and MV branch components, underlining the necessity for informed decisions in the context of distributed generators (DG) integration to enhance overall network performance. The study further investigates the impact of integrating DGs on these objectives on capacitor placement in the MV feeder. Employing the DIgSILENT PowerFactory software tool for modelling the MV feeder and utilizing a genetic algorithm for capacitor placement optimization, the study underscores the robustness of this approach in handling various conditions and seeking optimal solutions. Simulation results demonstrate that strategically placing capacitor banks and integrating DGs can significantly improve the voltage profile and reduce power losses within the distribution system. The findings of this research support 2MVA of concentrated DGs at the middle of the line as the most efficient and most economically beneficial situation on the medium-voltage feeder Grades study case and contribute valuable insights, serving as a reference for future studies on optimal capacitor placement.

Abstract Voltage unbalance and voltage variations are common issues in low voltage distribution networks caused by unbalanced connection of single-phase loads and electric vehicles (EVs) among phases. Part of the real low voltage grid in Bosnia and Herzegovina, in the vicinity of the town of Tešanj, was analysed. The impact on single-phase consumer and single-phase EVs penetration was analysed. The grid was modelled using DIgSILENT PowerFactory software. Three cases of the distribution of consumers by phases were analysed. Each of the three cases is expanded with four scenarios depending on the penetration of EV into the grid. Results showed that none of the twelve scenarios remained within the permissible limits of −10% voltage variation limit of EN 50160 for phase A. Seven of the twelve scenarios exceeded voltage unbalance limits +2% according to EN 50160.

Halogenated boroxine K2[B3O3F4OH] (HB), an inorganic derivative of cyclic anhydride of boronic acid, is patented as a boron-containing compound with potential for the treatment of both benign and malignant skin changes. HB has effectively inhibited the growth of several carcinoma cell lines. Because of the growing interest in autophagy induction as a therapeutic approach in bladder carcinoma (BC), we aimed to assess the effects of HB on metabolic phenotype and autophagy levels in 5637 human bladder carcinoma cells (BC). Cytotoxicity was evaluated using the alamar blue assay, and the degree of autophagy was determined microscopically. Mitochondrial respiration and glycolysis were measured simultaneously. The relative expression of autophagy-related genes BECN1, P62, BCL-2, and DRAM1 was determined by real-time PCR. HB affected cell growth, while starvation significantly increased the level of autophagy in the positive control compared to the basal level of autophagy in the untreated negative control. In HB-treated cultures, the degree of autophagy was higher compared to the basal level, and metabolic phenotypes were altered; both glycolysis and oxidative phosphorylation (OXPHOS) were decreased by HB at 0.2 and 0.4 mg/mL. Gene expression was deregulated towards autophagy induction and expansion. In conclusion, HB disrupted the bioenergetic metabolism and reduced the intracellular survival potential of BC cells. Further molecular studies are needed to confirm these findings and investigate their applicative potential.