Introduction: Aneurysms of brain vessels are life-threatening conditions with various adverse outcomes, some stemming from microsurgical intervention, particularly when major vessel perforators are inadequately protected. The use of endoscopes enhances the approach to aneurysms by providing closer visualization (180–360 degrees) of the local anatomy, potentially reducing accidental damage. To improve visualization and efficiency, a microscope-integrated 45-degree angled microinspection endoscopic tool (QEVO®, Carl Zeiss, OberkochenTM) has been developed and employed in various neurosurgical procedures. Methods: Between 2021 and 2025, 27 brain aneurysms were treated with QEVO® assistance at the Department of Neurosurgery, Clinical Center of the University of Sarajevo. The choice of the videos corresponds to the best image quality in videos and on the microscopic determination of adjacent vessel perforators, which were not adequately seen purely by the surgical microscope in specific cases. Exclusion criteria included cases without a need for QEVO® assistance in perforator visualization, severe brain edema, intraoperative aneurysm rupture, posterior circulation, or low video quality. Results: Case 1 demonstrates an anterior choroidal artery (AchA) aneurysm; Case 2 presents an anterior communicating artery (AcommA) aneurysm; and Case 3 features contralateral middle cerebral artery (MCA) microsurgical clipping with QEVO® assistance. Conclusions: The QEVO® tool significantly improves the visualization of aneurysm–perforator relationships, increasing the likelihood of preserving perforators during standard microsurgical clipping. This innovative approach may reduce surgical complications and enhance patient outcomes, highlighting the tool’s potential as an adjunct in aneurysm microsurgery.

Background/Objectives: Syncope is a common clinical occurrence, with neurally mediated and orthostatic types accounting for about 75% of cases. The exact pathophysiological mechanisms remain unclear, with recent evidence suggesting autonomic nervous system damage and a potential infectious etiology. This study aimed to examine the role of infection in the development of syncope and orthostatic hypotension (OH). Methods: The cross-sectional study included 806 patients from the Neurocardiological Laboratory of the Institute for Cardiovascular Diseases “Dedinje”. Patients were divided into three groups: unexplained recurrent syncope (n = 506), syncope with OH during the head-up tilt test (HUTT) (n = 235), and OH without a history of syncope (n = 62). All participants underwent the HUTT, and 495 underwent serological testing for various microorganisms. Data were analyzed using chi-squared tests and binary and multinomial logistic regression. Results: The HUTT was positive in 90.6% of patients with syncope and OH, compared with 61.6% with syncope alone (p < 0.001). Serological testing revealed that 57.85% of syncope patients, 62.9% of syncope with OH patients, and 78% of OH patients had positive IgM antibodies to at least one microorganism. Multivariate analysis indicated that IgM antibodies to Coxsackievirus and Epstein–Barr virus were significant predictors of OH. Conclusions: This study demonstrated a potential association between infections and syncope/OH. Further investigation into the role of infectious agents in autonomic dysfunction is warranted to clarify the underlying mechanisms of syncope and OH.

The paper presents the application of the universal motion controller to a 6-DOF robotic manipulator in a realistic simulation environment within the Robot Operating System (ROS). This approach addresses the limitations of earlier simulation methods. ROS allows for the measurement of interaction forces, a feature significant for validating the universal motion controller. This evaluation represents a crucial step toward bridging the gap between simulation and real-world implementation on robotic systems, highlighting the controller's robustness and adaptability under realistic conditions. Two control laws are implemented: one featuring exponential convergence and the other finite-time convergence. Furthermore, a method for managing both redundant and non-redundant tasks is utilized. The control code is written in discrete time using C++, which is particularly significant due to its ease of implementation on hardware systems.

Plant oils have attracted interest for centuries as natural remedies in treatment of various diseases. The Inula verbascifolia (Willd.) Hausskn.isgrowing wild plant in Bosnia and Herzegovina. Aromatic natural oils are one of the most significant sources of natural organic components. The natural vegetable oil of the selected plant (Inula verb.) was obtained by the hydrodistillation method. A comparison was made between the fragrant vegetable oil from the aerial parts of the plant in the flowering period (summer) and after the flowering period (autumn). In this study, chemical compounds were tested, comparing the content and composition of natural oils from the plant Inula verbascifolia. The aerial parts of the plant contained a fragrant and yellow essential oil. The identified 125 constituents accounted for 86.87% and 88.38% of the oil. The dominant compounds of both EOs were tridecanal, (3Z)- hexenyl benzoate, α-murolol, hexadecanoic acid, linalool and undecanal. Since essential aromatic oils possess a number of antimicrobial properties, an analysis of antimicrobial activity was also performed in this work. The antimicrobial activity of a mixture of EOs was determined on selected ATCC strains of microorganisms. Results of antimicrobial activity indicated that all used the microorganisms were sensitive to the EO. No data about antimicrobial activity of Inula verbascifolia has been published yet.

This is the first study of microelement variability in needles of seven natural populations of Picea omorika (Panč.) Purkyně from Serbia. Seven essential, three usefull, and six toxic microelements were analysed by ICP-Spectrometer. Their range was as follows: Mn > Fe > Zn > Al > Na > B > Cu > Ni > Cr > Cd > Co. The microelements Mo, As, Hg, Pb and Se were detected in traces. The amounts of Al, B, Co, Cr and Na were recorded in P. omorika needles for the first time. Among usefull microelements the most abundant was Al, while among toxic it was Cr. The most southern population, Mileševka canyon, had the highest amounts of Zn, Cu, Ni, Co and Cd and the lowest values of B. Discriminant and cluster analyses visualized that this population also showed the greatest separation from all other populations. Further research could usefully explore factors that affect the endangerment of P. omorika in its natural habitats or prevent its natural regeneration.

Accurate identification of ships is vital for global trade and maritime security. More specifically, reliable recognition of the vessel registration number helps in orderly navigation and port use for maritime transportation activities. The purpose of this research was to develop and analyze different methods for detecting vessel registration numbers with a monocular camera. We used existing OCR tools available online and modified them to improve their out-of-the-box performance. Three neural network-based text detection methods were developed and tested. All three methods use different text region detection modules for real scenes, while available OCR software is used in all of them for specific character recognition. The methods are compared using standard optical character recognition metrics. The best method that was developed uses a deep neural network model to detect a vessel, then another fine-tuned neural network model to detect a text region, coupled with deterministic image processing methods to improve picture quality. In conclusion, with an accuracy of 72.69% recognized characters of vessel registrations using a limited image dataset, we achieve satisfactory results. Further effort focusing on collecting and annotating diverse maritime environment datasets featuring footage of vessels with visible registration numbers is needed to have a more reliable and robust system.

Smart irrigation systems play a crucial role in water management, particularly in urban greening applications aimed at mitigating urban heat islands and enhancing environmental sustainability. These systems rely on soil moisture sensors to optimize water usage, ensuring that irrigation is precisely tailored to plant needs. This study evaluates the performance of four commercially available capacitive soil moisture sensors—TEROS 10, SMT50, Scanntronik, and DFROBOT—across three different substrates under controlled laboratory conditions. A total of 380 measurements were conducted to assess sensor accuracy, reliability, and the influence of insertion technique on measurement variability. Results indicate that while all sensors adequately cover the moisture ranges critical for plant health, their accuracy varies significantly, highlighting the necessity of substrate-specific calibration. TEROS 10 exhibited the lowest relative deviation and highest measurement consistency, making it the most reliable among the tested sensors. DFROBOT, despite being the least expensive, performed comparably to SMT50 and Scanntronik in certain conditions. The findings provide valuable insights for selecting and calibrating soil moisture sensors in smart irrigation applications, ultimately contributing to improved water efficiency, plant vitality, and sustainable building-integrated greenery.

This research explores the thermal performance of biomass-based composite panels enhanced with Phase Change Materials (PCM) for energy-efficient building applications. The experimental method under lab-controlled conditions was used to investigate thermal performances. Four samples of PCM-biomass-based composite were fabricated using encapsulated PCM, straw, and mortar as a binding material, and four sample without PCM as a reference. The effective thermal conductivity of each sample was determined using the guarded hot plate method under steady-state conditions. Results indicate that the incorporation of PCM does not significantly alter the thermal conductivity of the composite, but it enhances the material’s ability to store thermal energy. Compared to conventional construction materials, both the reference and PCM-enhanced samples exhibit superior thermal insulation properties, making them promising candidates for sustainable building applications. The findings suggest that optimizing PCM concentration and distribution could further enhance thermal performance, contributing to the development of passive energy-saving solutions.

This research examines the relationship between leadership styles, communicator’s competence, and job satisfaction of employees in Bosnia and Herzegovina. Results will show the importance of communication in leadership as an essential element in the leadership process. Questionnaires for primary data collection were adopted from previous literature on leadership, competence, and job satisfaction. There are 201 valid responses collected using convenience sampling method. Data testing was done using regression analysis and the results showed negative and no significant relationship between leadership styles, communicator’s competence, and job satisfaction. However, a positive and significant relationship existed between leadership styles and communicator’s competence. The findings guide managers into the effects of their behavior at work towards their subordinates, where communicator’s competence shows positive and significant relationship between both task oriented and relationship-oriented leadership styles.

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS), characterized by neurodegeneration, axonal damage, demyelination, and inflammation. Recently, gut dysbiosis has been linked to MS and other autoimmune conditions. Namely, gut microbiota has a vital role in regulating immune function by influencing immune cell development, cytokine production, and intestinal barrier integrity. While balanced microbiota fosters immune tolerance, dysbiosis disrupts immune regulation, damages intestinal permeability, and heightens the risk of autoimmune diseases. The critical factor in shaping the gut microbiota and modulating immune response is diet. Research shows that high-fat diets rich in saturated fats are associated with disease progression. Conversely, diets rich in fruits, yogurt, and legumes may lower the risk of MS onset and progression. Specific dietary interventions, such as the Mediterranean diet (MD) and ketogenic diet, have shown potential to reduce inflammation, support neuroprotection, and promote CNS repair. Probiotics, by restoring microbial balance, may also help mitigate immune dysfunction noted in MS. Personalized dietary strategies targeting the gut microbiota hold promise for managing MS by modulating immune responses and slowing disease progression. Optimizing nutrient intake and adopting anti-inflammatory diets could improve disease control and quality of life. Understanding gut-immune interactions is essential for developing tailored nutritional therapies for MS patients.

The persistent use of physical money, despite the rise of digital payment methods, poses security challenges for vaults storing banknotes and coins. Traditional vault security measures, including physical barriers, time locks, dual control systems, and surveillance, are susceptible to sophisticated attacks and insider threats. This paper introduces a novel approach to enhance vault security by incorporating smart Internet of Things (IoT) devices and machine learning algorithms to monitor the weight of banknotes on vault shelves. By tracking and analysing weight variations, this system aims to detect discrepancies and potential theft. The system employs various machine learning models, including Linear Regression, Lasso Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forest, to predict the number of banknotes based on weight and denomination. The evaluation demonstrates that Linear Regression and Lasso Regression achieve the highest accuracy, making them the most effective models for this application. Challenges such as limited data, computational resource constraints, and the need for more refined features are discussed, alongside potential improvements like data augmentation and enhanced interpretability. This approach offers a significant advancement in vault security by integrating modern technology to safeguard physical money against theft and unauthorized access.

Modern electric power systems have an increasingly complex structure due to rise in power demand and integration of diverse energy sources. Monitoring these large-scale systems, which relies on efficient state estimation, represents a challenging computational task and requires efficient simulation tools for power system steady-state analyses. Motivated by this observation, we propose JuliaGrid, an open-source framework written in the Julia programming language, designed for high performance execution across multiple platforms. The framework implements observability analysis, weighted least-squares and least-absolute value estimators, bad data analysis, and various algorithms related to phasor measurements. To complete power system analysis, the framework includes power flow and optimal power flow, enabling measurement generation for the state estimation routines. Leveraging computationally efficient algorithms, JuliaGrid solves large-scale systems across all methods, offering competitive performance compared to other open-source tools. It is specifically designed for quasi-steady-state analysis, with automatic detection and reuse of computed data to boost performance. These capabilities are validated on systems with 10000, 20000 and 70000 buses.