Background/Objectives: The aim of this study was to explore the differences in physical characteristics, leg strength, and jumping performance between 3 × 3 and 5 × 5 male basketball players. Methods: Twelve elite-level 5 × 5 basketball players (26.0 ± 13.0 years; 201.4 ± 6.6 cm; 95.50 ± 11.50 kg) and twelve elite-level 3 × 3 basketball players (26.7 ± 7.3 years; 193.0 ± 5.1 cm; 98.03 ± 9.77 kg), all male, were enrolled in the study. After anthropometric measurements and standardized warm ups, countermovement jump (CMJ), drop jump (DJ) and isokinetic strength testing were conducted, respectively. Results: An independent two-sample t-test revealed that 5 × 5 athletes were notably (p < 0.005) taller, with a lower body fat percentage (11.9 ± 3.6% vs. 18.6 ± 10.9%) and higher quadricep strength (317.21 ± 36.54 N·m vs. 284.76 ± 29.77 N·m and 313.32 ± 24.08 N·m vs. 285.87 ± 31.2 N·m for left and right leg, respectively). Conversely, 3 × 3 players displayed superior CMJ performance in concentric and eccentric peak forces, peak power, and reactive strength index. In the DJ, 3 × 3 players also excelled in eccentric peak force, reactive strength index, and jump height. Conclusions: The findings indicate that while 5 × 5 basketball players excel in body physique and in the strength of their lower body, 3 × 3 basketball players outperform them in power-related metrics.

Abstract The paper presents the results of Cr, Co, Cu, Fe, Ni, Mn, Pb, Zn, and four radionuclides (226Ra,232Th, 137Cs, and 40K) determination in transplanted lichens after two, four, and six months of exposure. Lichens were sampled from the area of Mountain Igman in Bosnia and Herzegovina (BiH) and transplanted to two locations (Pofalići and Bjelave) in Sarajevo, the capital city of BiH. The total metals content was determined by flame atomic absorption spectrometry (FAAS). Gamma spectrometry (GS) was used for radionuclide activity determination. Content of Cr, Co, Cu, Fe, Mn, Ni, Pb an Zn in lichen after two, four, and six months of exposure ranged as follows: <LOD-0.61, <LOD-1.55, 3.85–8.08, 332.5–497.9, 19.68–31.65, 2.29–4.24, <LOD-10.30, 32.76–58.58 µg/g, respectively. Cr was not detected in exposed lichen samples. A very strong positive correlation for Cu-Cu, Cu-Fe, Cu-Pb, Cu-Zn, Ni-Ni and Fe-Fe was obtained in lichens, while a strong correlation was between Co-Zn, Co-Cu, Pb-Pb and Mn-Mn. The specific activity of 137Cs ranged from 19.95 to 56.66 Bq/kg, while for 40K ranged from 49.65 to 330.61 Bq/kg. The specific activity of 226Ra and 232Th was below the GS limit of detection.

INTRODUCTION Antimicrobial resistance (AMR) poses a significant threat, particularly in low- and middle-income countries (LMICs), exacerbated by inappropriate antibiotic use, access to quality antibiotics and weak antimicrobial stewardship (AMS). There is a need to review current evidence on antibiotic use, access, and AMR, in primary care across key countries.Areas covered: This narrative review analyses publications from 2018 to 2024 regarding access, availability and use of appropriate antibiotics. EXPERT OPINION There were very few studies focussing on a lack of access to antibiotics in primary care. However, there was considerable evidence of high rates of inappropriate antibiotic use, including Watch antibiotics, typically for minor infections, across studied countries exacerbated by patient demand. The high costs of antibiotics in a number of LMICs impacts on their use resulting in short courses and sharing of antibiotics. This can contribute to AMR alongside the use of substandard and falsified antibiotics. Overall, limited implementation of national action plans, insufficient resources, and knowledge gaps, affects sustainable development goals to provide routine access to safe, effective and appropriate antibiotics. CONCLUSIONS There is a clear need to focus health policy on the optimal use of essential AWaRe antibiotics in primary care settings to reduce AMR in LMICs.

Background and Objectives There is uncertainty about whether patients with an anterior circulation large vessel occlusion (LVO) and a low NIH Stroke Scale (NIHSS) score (≤5) benefit from endovascular therapy (EVT) in the late time window (6–24 hours). We compared the clinical outcomes of these patients receiving EVT with those receiving medical management (MM). Methods The CT for Late Endovascular Reperfusion multinational cohort study was conducted at 66 sites across 10 countries from January 2014 to May 2022. This subanalysis included consecutive patients with late-window stroke due to an anterior circulation LVO, defined as occlusion of the internal carotid artery or proximal middle cerebral artery (M1/M2 segments), and a baseline NIHSS score ≤5 who received EVT or MM alone. The primary end point was a 90-day ordinal shift in the modified Rankin Scale (mRS) score. Secondary outcomes were 90-day excellent outcome (defined as mRS scores 0–1 or return to baseline mRS score in patients with a prestroke mRS score >1) and favorable outcome (defined as mRS scores 0–2 or return to baseline mRS score in patients with prestroke mRS score >2). Safety outcomes were symptomatic intracranial hemorrhage and 90-day mortality. We used ordinal and binary logistic regression models to test for outcome differences. Results Among 5,098 patients, 318 patients were included (median [interquartile range] age 67 [56–76] years; 149 [46.9%] were female; baseline NIHSS score was 4 [2–5]). A total of 202 patients (63.5%) received EVT and 116 MM (36.5%). There was no difference in favorable 90-day ordinal mRS score shift (adjusted common odds ratio [OR] 0.77, 95% CI 0.45–1.32), excellent outcome (adjusted OR 0.86, 95% CI 0.49–1.50), or favorable outcome (adjusted OR 0.72, 95% CI 0.35–1.50) in the EVT group compared with MM. Symptomatic intracranial hemorrhage risk (adjusted OR 3.40, 95% CI 0.84–13.73) and mortality at 90 days (adjusted OR 2.44, 95% CI 0.60–10.02) were not statistically different between treatment groups. Discussion In patients with an anterior LVO and low NIHSS score in the 6–24-hour time window, there was no statistical difference in disability outcomes or intracranial bleeding risk between patients treated with EVT compared with MM. The retrospective and observational design limits our findings. Ongoing randomized controlled trials will provide further insight. Classification of Evidence This study provides Class III evidence that in adult patients with anterior circulation LVO and low NIHSS score (≤5) presenting in the late time window (6–24 hours), EVT does not improve clinical outcome vs MM. Trial Registration This study was registered at clinicaltrials.gov under NCT04096248.

The water distribution system is a critical infrastructure aiming to deliver safe and clean drinking water, with pipeline materials significantly influencing water quality and efficiency. One critical factor in selecting pipeline materials is the potential for biofilm formation on the inner surfaces of pipes. This study investigates the effects of three iron salts—iron (II) sulfate heptahydrate, iron (III) nitrate nonahydrate, and iron (III) chloride on biofilm formation by Escherichia coli and Enterococcus faecalis in pipeline environments, focusing on water distribution systems. While previous research has examined the effects of iron on various bacterial species, there are limited data on E. coli and E. faecalis biofilm formation in the context of water distribution systems. Results reveal that iron (III) chloride significantly inhibited E. coli biofilm formation by up to 80%, while E. faecalis biofilm growth was promoted by iron (II) sulfate heptahydrate, with an increase of approximately 45%. These findings underscore the critical role of managing iron concentrations to mitigate biofilm-related issues, which influence water quality, infrastructure durability, and microbial resistance. The study highlights the importance of integrating these insights into sustainable water management practices and advancing pipeline material innovations to enhance public health and environmental resilience.

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.

Today, as technology and games have become integral to everyday life, challenges arise in maintaining and enhancing the focus on learning. Although digital games offer excellent opportunities for innovative ways of acquiring knowledge, distraction and how best to balance the entertainment side with the education objectives also create problems. In such an environment, the main goal is to utilize games as motivational tools that engage students while contributing to the development of critical thinking, problem-solving, and creativity. Research is based on a survey among students and teachers from three partner countries, targeting their perception of Serious Games and AI as an educational means in which barriers and obstacles may arise when including it in the teaching and learning process. These data will help formulate recommendations on how to optimize the use of Serious Games within educational systems across three diverse cultures and educational practices.

The architecture of a Deep Neural Network (DNN) plays a major role in determining its performance, yet the traditional methods for optimizing these architectures often depend on iterative trial-and-error processes requiring substantial expertise and manual effort. Neural Architecture Search (NAS) has emerged as a rapidly advancing field focused on automating the optimization of hyperparameters and network architectures. This study presents a comparative analysis of three heuristic approaches for NAS: Evolutionary Genetic Algorithms, Reinforcement Learning, and Random Forest Optimization. The efficacy of these methods is evaluated on two widely recognized benchmark classification datasets—MNIST and CREDIT CARD FRAUD—as well as a synthetically generated dataset. A comprehensive evaluation of performance metrics provides insight into the strengths, limitations, and relative effectiveness of each NAS methodology in optimizing neural network architectures for diverse data distributions.

The neural network training process produces black-box models with low explainability. In addition, the process itself is numerical, with parameters (such as learning rate, momentum, and early stopping trigger) being chosen ad hoc. During the training with chosen parameters, after each calculated update of the weights, the observed total change of weights indicates which training stage the network is currently in. At the same time, neural networks are limited in the data they can model due to various reasons, such as architecture, activation functions, data itself, and the training approach. This limitation is expressed in the phenomenon of the efficient computational frontier, which, it seems, cannot be crossed, no matter the hyperparameters of the network. This paper tackles the efficient usage of information regarding the total change of weights and the efficient computational frontier to determine when the training should be stopped. The results demonstrate the efficiency of training of simpler models compared to more complex models and prove that the general weight structure of models is formed very quickly in the training, while the forming of finer details takes up much more time.

Efficient decision-making in fruit production involves evaluating multiple criteria, such as yield, fruit quality, and resistance, to rank available alternatives. Multi-Criteria Decision-Making (MCDM) methods provide a structured and objective framework for such tasks. This paper presents a web-based application named FRUITrank, designed to implement the MARCOS MCDM method for ranking and selection of plum varieties. The application uses a predefined set of criteria, whose weights were determined externally by using other MCDM methods. By leveraging a simple and intuitive interface, the application aims to overcome barriers to the practical adoption of MCDM methods among researchers and fruit producers, such as mathematical complexity and lack of accessible tools. The application was tested using a set of 11 criteria relevant to plum production, demonstrating its capability to deliver reliable and transparent rankings. This paper builds upon prior research in MCDM applications for agriculture, offering a practical solution for producers and researchers to enhance decision-making processes. Future improvements to the developed tool may include automated criteria weight calculation and broader applicability across various agricultural contexts.

In modern medical circumstances, effective assessment of patients' conditions is recognized as crucial for quick decision-making, especially in critical situations. In these circumstances, the application of automated triage systems and their role in improving health care are considered key elements, with special focus being placed on the integration of technologies that enable a more accurate and faster assessment of the patient's condition. Based on the above, the paper analyzed various traditional methods of patient triage, as well as the potential for e-triage. Special importance is attached to reducing subjectivity in decision-making and improving the efficiency of emergency services. Challenges and advantages of implementing automated triage systems in real conditions were also discussed, with the aim of achieving optimal results. Considering the factors, a one of possible framework was proposed for the future development of advanced triage systems, which contribute to the improvement of the quality of health care provision.

The fluent API, also known as Internal DSL, is one of the concepts introduced primarily for the purpose of increasing readability and maintainability in the process of software development. It is most commonly used when there is a need to perform operations according to precisely defined rules that determine their possible orders. However, implementing a Fluent API by manual coding can divert focus from defining these rules toward technical implementation, increasing the risk of errors, unexpected behavior, and higher development costs. To address these challenges, a model-driven engineering (MDE) approach can be applied, enabling the visual design of the Fluent API model and its transformation into a code skeleton.This paper aims to present how the previously created graphical development tool, implemented as a Microsoft Visual Studio extension for modeling Fluent API, can be enhanced with model-to-text transformation in order to provide code generation of a fluent API structure. That objective is achieved by using the template-based code generation technique, implemented by enabling the execution of the appropriate T4 text templates. The proposed approach is validated by demonstrating a real-life fluent API example's code generation from its model, resulting in C# source files that contain classes, interfaces, and their corresponding methods.

The continuous growth of the world’s population and extending life expectancy, as well as frequent natural disasters and emergencies, increase the demand for health services every day. One of the crucial elements for addressing this problem is triage – a critical process that enables healthcare providers to efficiently identify patients’ needs in terms of medical treatments and resources. However, the complexity of implementing an appropriate triage process has led to the development of various systems, each offering a unique approach to address this challenge.This paper presents a comparative analysis of different patient triage systems, focusing on their key characteristics, identifying their common and unique elements, and providing an understanding of their advantages and limitations. Relying on the findings of the conducted analysis, the paper proposes a generic model of the triage process, designed around universal components that provide a foundation for standardizing the process while maintaining the flexibility to adapt to specific requirements. The proposed generic model further can be employed as a basis for exploring opportunities to enhance the patient triage process through the application of model-driven engineering concepts and techniques.