We introduce the concept of Diophantine spherical vague set approach to multiple-attribute decision-making. The Spherical vague set is a novel expansion of the vague set and interval valued spherical fuzzy set. Three new concepts have been introduce such as Diophantine spherical vague weighted averaging operator, Diophantine spherical vague weighted geometric operator, generalized Diophantine spherical vague weighted averaging operator and generalized Diophantine spherical vague weighted geometric operator. We provide a numerical example to show how Euclidean distance and Hamming distance interact. Applications of the Diophantine spherical vague number include idempotency, boundedness, commutativity and monotonicity in algebraic operations. They can determine the optimal option and are more well-known and reasonable. Our goal was to identify the optimal choice by comparing expert opinions with the criteria. As a result, the model’s output was more accurate as well as in the range of the natural number . The weighted averaging distance and weighted geometric distance operators are distance measure that is based on aggregating model. By comparing the models under discussion with those suggested in the literature, we hoped to show their worth and reliability. It is possible to find a better solution more quickly, simply, and practically. Our objective was to compare the expert evaluations with the criteria and determine which option was the most suitable. Because they yield more precise solutions, these models are more accurate and more related to models with . To show the superiority and the validity of the proposed aggregation operations, we compared it with the existing method and concluded from the comparison and sensitivity analysis that our proposed technique is more effective and reliable. This investigation yielded some intriguing results.

BACKGROUND Non-ST segment elevation myocardial infarction (NSTEMI) poses significant challenges in clinical management due to its diverse outcomes. Understanding the prognostic role of hematological parameters and derived ratios in NSTEMI patients could aid in risk stratification and improve patient care. AIM To evaluate the predictive value of hemogram-derived ratios for major adverse cardiovascular events (MACE) in NSTEMI patients, potentially improving clinical outcomes. METHODS A prospective, observational cohort study was conducted in 2021 at the Internal Medicine Clinic of the University Hospital in Tuzla, Bosnia and Herzegovina. The study included 170 patients with NSTEMI, who were divided into a group with MACE and a control group without MACE. Furthermore, the MACE group was subdivided into lethal and non-lethal groups for prognostic analysis. Alongside hematological parameters, an additional 13 hematological-derived ratios (HDRs) were monitored, and their prognostic role was investigated. RESULTS Hematological parameters did not significantly differ between non-ST segment elevation myocardial infarction (NSTEMI) patients with MACE and a control group at T1 and T2. However, significant disparities emerged in HDRs among NSTEMI patients with lethal and non-lethal outcomes post-MACE. Notably, neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were elevated in lethal outcomes. Furthermore, C-reactive protein-to-lymphocyte ratio (CRP/Ly) at T1 (> 4.737) demonstrated predictive value [odds ratio (OR): 3.690, P = 0.024]. Both NLR at T1 (> 4.076) and T2 (> 4.667) emerged as significant predictors, with NLR at T2 exhibiting the highest diagnostic performance, as indicated by an area under the curve of 0.811 (95%CI: 0.727-0.859) and OR of 4.915 (95%CI: 1.917-12.602, P = 0.001), emphasizing its important role as a prognostic marker. CONCLUSION This study highlights the significant prognostic value of hemogram-derived indexes in predicting MACE among NSTEMI patients. During follow-up, NLR, PLR, and CRP/Ly offer important insights into the inflammatory processes underlying cardiovascular events.

In the Federation of Bosnia and Herzegovina, through the Law on Internal Trade, it is planned to restrict work on Sundays for most sales facilities. This would apply to the territory of the entity concerned, i.e., 51% of Bosnia and Herzegovina’s territory. Previously, unlike other countries that introduced the same or similar practices, there have been no significant social and political discussions or surveys of the views of workers, employers, and citizens. This paper aims to research the citizens' views of the Federation of Bosnia and Herzegovina on non-working Sundays and thus offer a basis for a better discussion of this issue. A convenience sample of 406 respondents from the Federation of Bosnia and Herzegovina (FB&H) entity was used. Despite the expressed bias on the topic of banning trade on Sundays, the analysis of respondents’ answers regarding the willingness to work on Sundays (with the condition of a second day off some other day and 50% higher wages for working on Sundays) showed that more than half of the respondents support working on Sundays under this condition. The survey results showed that citizens are less inclined to restrict the operation of smaller shops and that the ban on working on Sundays is mainly supported by those who do not work on Sundays.

With the advancement of Artificial Intelligence (AI), clinical engineering has witnessed transformative opportunities, enabling predictive maintenance of medical devices, optimization of healthcare workflows, and personalized patient care. Respiratory equipment plays a vital role in modern healthcare, supporting patients with compromised or impaired respiratory capacities. However, ensuring the reliability and safety of these devices is crucial to prevent adverse events and ensure patient well-being. This study aims to explore machine learning techniques to enhance predictive maintenance for mechanical ventilators. The dataset used for this study contains information about 1350 entries of mechanical ventilators, made by 15 different manufacturers and available in 30 distinct models. Different machine learning algorithms, including Logistic Regression, Decision Trees, Random Forest, K-nearest Neighbors, Support Vector Machines, Naive Bayes, and XG Boost are developed and tested in terms of their performance in predicting mechanical ventilator failures. The ensemble methods, particularly Random Forest and XGBoost, have proven to be more adept at handling the complexities of the dataset. The Decision Tree and Random Forest models both showed remarkable accuracies of approximately 0.993, while K-Nearest Neighbors (KNN) performed exceptionally with near perfect accuracy. Adoption of automated systems based on artificial intelligence will help in overcoming challenges of ensuring quality of MDs that are already being used in healthcare institutions. Implementing machine learning-based predictive maintenance can significantly enhance the reliability of mechanical ventilators in healthcare settings.

Accurate ear counting is essential for determining wheat yield, but traditional manual methods are labour-intensive and time-consuming. This study introduces an innovative approach by developing an automatic ear-counting system that leverages machine learning techniques applied to high-resolution images captured by unmanned aerial vehicles (UAVs). Drone-based images were captured during the late growth stage of wheat across 15 fields in Bosnia and Herzegovina. The images, processed to a resolution of 1024 × 1024 pixels, were manually annotated with regions of interest (ROIs) containing wheat ears. A dataset consisting of 556 high-resolution images was compiled, and advanced models including Faster R-CNN, YOLOv8, and RT-DETR were utilised for ear detection. The study found that although lower-quality images had a minor effect on detection accuracy, they did not significantly hinder the overall performance of the models. This research demonstrates the potential of digital technologies, particularly machine learning and UAVs, in transforming traditional agricultural practices. The novel application of automated ear counting via machine learning provides a scalable, efficient solution for yield prediction, enhancing sustainability and competitiveness in agriculture.

Background and Objectives: Burden of cervical cancer in Central and Eastern Europe is higher than in other parts of Europe. We analyzed cervical cancer epidemiology in Serbia and Bosnia and Herzegovina (the Federation of Bosnia and Herzegovina and the Republic of Srpska) from January 2016 to December 2020, exploring the role of available sociodemographic factors and healthcare service parameters on incidence and mortality rates, using an ecological approach based on aggregated data. Materials and Methods: Incidence and mortality rates are standardized using the method of direct standardization with the World-ASR-W. Administrative units are grouped by tertiles of incidence and mortality to explore sociodemographic factors and healthcare parameters across these groups. Results: Average age-standardized incidence rates of cervical cancer per 100,000 females were 19.28 in Serbia, 12.48 in the Federation of Bosnia and Herzegovina, and 22.44 in the Republic of Srpska. Mortality rates per 100,000 females were 6.67, 5.22, and 4.56 in Serbia, the Federation of Bosnia and Herzegovina, and the Republic of Srpska, respectively. Several parameters of sociodemographics and health service usage differed significantly across units grouped by tertiles based on incidence level, i.e., female population ≥ 15 years old (p = 0.028), population density (p = 0.046), percent of gynecologists in the primary healthcare (p = 0.041), number of gynecologists per 10,000 females ≥ 15 years (p = 0.007), and the area-to-gynecologist ratio (p = 0.010). A moderate negative correlation was found between incidence and population density (rho = −0.465, p = 0.017), and a moderate positive correlation between incidence and area-to-gynecologist ratio (rho = 0.534, p = 0.005). Conclusions: Cervical cancer remains a leading cause of cancer among women in developing countries. Implementing tailored activities, such as educational programs, preventive services, and investments in healthcare infrastructure, particularly at the administrative units’ level, can help in reducing health disparities and improving health outcomes.

Mental health is deteriorating quickly and significantly globally post-COVID. Though there were already over 1 billion people living with mental disorders pre-pandemic, in the first year of COVID-19 alone, the prevalence of anxiety and depression soared by 25% worldwide. In light of the chronic shortages of mental health provider and resources, along with disruptions of available health services caused by the pandemic and COVID-related restrictions, technology is widely believed to hold the key to addressing rising mental health crises. However, hurdles such as fragmented and often suboptimal patient protection measures substantially undermine technology's potential to address the global mental health crises effectively, reliably, and at scale. To shed light on these issues, this paper aims to discuss the post-pandemic challenges and opportunities the global community could leverage to improve society's mental health en masse.

Hydropower is the world's most exploited renewable energy source. It provides a substantial, flexible, and reliable source of renewable energy, complementing other renewables like solar and wind power. Besides conventional hydropower potentials and technologies, the development of technologies for the exploitation of hidden hydropower potentials is an ongoing process. This paper presents the current state of hidden hydropower technologies and links them with possible applications in different hydropower potentials. Technologies and potential applications are structured within three main groups (pressurized systems, hydro storage, unpressurized systems), with their mutual interconnections analysed and displayed throughout the paper. The opportunity for the application of hidden hydropower technologies in different roles within the energy system is recognized through the concepts of off- and on-grid roles, the prosumer concept, and on-site measurement powering. This paper shows that hidden hydropower technologies could emerge as significant contributors to a smoother energy transition, especially with the prosumer and off-grid concepts.

Proton decay has been studied for decades now as one of the consequences of grand unified theories. Among those theories exists SU(5) theory, firstly postulated by H. Georgi and S. Glashow [1]. However, there were some problems with this theory such as mass degeneration and coupling unification [1-3]. This created a need for an extension of an original SU(5) model – a specific minimal SU(5) [4-5]. In this minimal SU(5) there is a viable parameter space with achievable gauge coupling unification. In this article, we present the process of gauge coupling unification for three mass scales of new physics states in this model, namely for 1 TeV, 10 TeV, and 100 TeV.

In this study, we compared the effectiveness of AR-based homework, traditional homework, and mixed-approach homework in learning about circular motion. To that end, we conducted a pretest-posttest quasi-experiment involving 135 first-year students enrolled in an introductory physics course at the University of Zagreb, Faculty of Chemical Engineering and Technology, Croatia. The students in the experimental group completed augmented reality (AR)-based homework assignments. In these assignments, their learning about circular motion was supported by a meticulously designed worksheet that included four AR-supported activities. In the mixed-approach group, students were given a homework assignment that included three AR-supported activities and one quantitative textbook problem, whereas the traditional group’s homework consisted of four quantitative textbook problems covering the same content. Findings from our study suggest that the post-treatment scores for all groups were significantly higher than the pretreatment scores, with the largest pre-post gains observed in the mixed-approach group. We conclude that combining carefully selected quantitative problems with key AR activities is the most promising approach.