Universities are increasingly expected to engage with external stakeholders beyond traditional research and education. This “third mission” is driven by factors like policy pressures, funding needs, and graduate employability. University-Business Collaboration (UBC) has emerged as a key form of engagement, fostering innovation, regional development, and job creation. Prior studies focus on a narrow vision for engagement activities, primarily on research and commercialisation, which excludes many more academics than it includes, to the detriment of academia. Moreover, some of the studies control for knowledge area, however, limited research focuses specifically on these knowledge areas, which have a large impact on the way in which academics create impact across a broad range of UBC activities in education, research and management. Using a large European dataset (3153 academics, 33 countries), this research explores academic engagement through UBC across medical sciences, technology & engineering, and social sciences & humanities. Specifically, the study deepen how individual factors such as academics’ beliefs and capabilities, as well as the university context, influence UBC engagement. Statistical analyses reveal potential variations in how disciplines engage and create impact. The findings contribute to a more nuanced understanding of UBC, potentially moving the conversation beyond traditional commercialization models towards a more holistic and transformative approach, which involves a much larger segment of the academic population. This research also lays the groundwork for future studies to explore a broader and more inclusive model of academic impact through engagement with industry and through UBC partnerships.

Evidence and reporting on the resolution of administrative matters represent an important instrument for monitoring the efficiency, legality, transparency, and accountability of administrative authorities. This paper analyzes the normative framework and the practice of maintaining evidence and preparing annual reports on the resolution of administrative matters in the institutions of Bosnia and Herzegovina, with a particular focus on the period 2019–2024. Based on data from the Consolidated Reports on the Resolution of Administrative Matters, the paper presents trends regarding the number of institutions submitting reports, the number of administrative fields, the volume of first-instance and second-instance proceedings, as well as the structure of deadlines and decision-making outcomes. The analysis shows that during the observed period there was an increase in the number of institutions submitting reports, as well as an increase in the number of administrative fields, indicating a gradual strengthening of reporting practice. At the same time, statistical indicators reveal a relatively high level of timeliness in resolving administrative cases, with an average of more than 89% of cases resolved within the legally prescribed deadlines. However, certain shortcomings have also been identified, particularly the presentation of data in a summary form, which prevents detailed analysis by administrative fields and limits the possibility of timely identification of bottlenecks in administrative decision-making. The paper emphasizes the need to improve the existing system of evidence and reporting through the standardization of evidence forms, more frequent reporting, and the introduction of modern analytical mechanisms. It concludes that the transition from traditional, annual, and predominantly paper-based reporting to a system enabling continuous monitoring and analytical processing of data represents an important prerequisite for improving administrative decision-making, strengthening institutional accountability, and aligning with the standards of the European Administrative Space.

The photoacid 8-hydroxypyrene-1,3,6-trisulfonate (HPTS) is one of the most widely used fluorescent probes for studying proton transfer and local pH in systems from advanced materials to plants, environmental sensors to medicine. HPTS exists as two different species: the acid and its conjugate base, which lead to unique protonation-state-dependent translocation of the molecule when it is nanoconfined within anionic AOT reverse micelles. Using steady-state and time-resolved optical spectroscopy, molecular simulations, and IR solvation shell spectroscopy, we report that the protonated HPTS species associates strongly with the micelle interface via hydrogen bonding. In contrast, its deprotonated species resides in the micelle's aqueous interior. Our results show that photoexcitation of the acid species and its subsequent deprotonation leads the conjugate base to rapidly move away from the interface into the water pool. This light-induced translocation, an effect observed for a range of micelle sizes, challenges the prevailing view where molecular probes are assumed to be static reporters of their environments, remaining in a fixed location for the duration of an experiment. This is especially relevant for interpreting results in the numerous studies enlisting optical spectroscopy of HPTS to report on complex systems. Our findings reveal the potential for molecular probes as dynamic explorers capable of mapping environmental heterogeneity on the timescale of the very processes they are designed to measure.

AIM To evaluate factors associated with perioperative anxiety in patients undergoing major abdominal surgery under general anaesthesia (GA) using the Hamilton Anxiety Rating Scale (HAM-A). METHODS This prospective observational study included 107 adult patients scheduled for major abdominal surgery under GA. Anxiety was assessed preoperatively and postoperatively using the HAM-A. Demographic characteristics, medical history, lifestyle habits, and perioperative variables were analysed. Multivariable analysis was conducted to identify factors independently associated with pre- and postoperative anxiety. RESULTS Preoperative anxiety was observed in 54 patients (50.5%), while postoperative anxiety occurred in 34 patients (31.8%). Multivariable analysis identified alcohol consumption (β = 8.10, 95%CI: 0.46-14.07; p = 0.037), hyperlipoproteinemia (β = 1.81, 95%CI: 1.42-2.19; p < 0.001), preoperative fasting duration (β = 0.03, 95%CI: 0.02-0.04; p = 0.005), surgery duration (β = -0.45, 95%CI: -0.74- -0.13; p = 0.006), and anaesthesia duration (β = 0.43, 95%CI: 0.07-0.70; p = 0.015) as factors independently associated with preoperative anxiety. The type of intravenous anaesthetic showed a trend toward significance (β = -5.45, 95%CI: -10.20-0.08; p = 0.054). Factors independently associated with postoperative anxiety included age (β ='0.08, 95%CI: 0.01-0.17; p = 0.018), previous hospitalisations (β = 6.43, 95%CI: 3.69-11.86; p < 0.001), previous surgeries (β = 8.13, 95%CI: 6.25-14.44; p < 0.001), and preoperative fasting duration (β = 2.87, 95%CI: 1.90-4.79; p < 0.001). CONCLUSION Routine assessment using the HAM-A scale may help identify high-risk patients and guide targeted perioperative strategies, including preoperative counselling and optimization of fasting protocols.

AIM To analyse patient admission patterns, clinical outcomes, and organisational workload in a medical intensive care unit (ICU), with emphasis on early mortality and post-pandemic changes in healthcare demand. METHODS This retrospective, observational, single-centre cohort study included all adult patients admitted to the medical ICU of the Clinic for Internal Medicine at the University Clinical Centre Tuzla between January 1, 2018, and December 31, 2025. Aggregated data were obtained from the hospital information system and internal ICU records. Analysed variables included annual admission volume, admission sources, discharge outcomes, in-hospital and early mortality (within 24-72 hours after ICU admission), estimated length of stay, invasive procedures, and patient age. Temporal trends were assessed across pre-pandemic (2018-2019), pandemic (2020-2021), and post-pandemic (2022-2025) periods. RESULTS A total of 9,342 ICU hospitalisations were analysed. Admissions remained relatively stable through 2020, declined in 2021, reached their lowest level in 2022, and then increased markedly from 2023 onward. Admissions per bed rose from 67.5 in 2022 to 108.6 in 2025, while the estimated mean ICU length of stay decreased from 5.4 to 3.4 days. Overall, in-hospital mortality was approximately 22%, with 75-80% of deaths occurring between 24 and 72 hours from admission. The patient population was predominantly elderly, with a mean age of approximately 70 years. CONCLUSION Medical ICU services operated under increasing organisational strain, reflected by rising admission volume, higher admissions per bed, and reduced estimated length of stay despite fixed bed capacity. Persistently high early mortality remained a prominent feature of this population.

Healthcare organizations operate in environments characterized by high job demands, resource constraints, and increasing service expectations. Under such conditions, sustaining employee work engagement becomes essential for maintaining service quality, employee well-being, and organizational performance. Drawing on the Job Demands–Resources (JD-R) framework, this study examines the role of managerial competencies as organizational resources that may stimulate employee work engagement in healthcare institutions. The primary objective of this research is to investigate whether managerial competencies significantly predict employee work engagement and to determine the relative contribution of specific competency dimensions. The study focuses on five managerial competency domains: leadership, communication, strategic, operational, and emotional intelligence competencies. Data were collected through a survey conducted among 201 employees working in public and private healthcare institutions in Tuzla Canton, Bosnia and Herzegovina. Work engagement was measured using the Utrecht Work Engagement Scale (UWES), while managerial competencies were assessed using a competency-based evaluation instrument. Data were analyzed using descriptive statistics, correlation analysis, and regression modeling.

Large language models (LLMs) show great potential for clinical decision-making, yet most applications remain narrow, task-specific chat tools rather than systems integrated into clinical workflows1,2. However, building physician copilots will require models that operate within the electronic health record (EHR), with governed access to patient data and the ability to initiate permitted EHR actions within defined safety constraints. Yet it remains unproven whether such a system can manage patient cases with physician-level performance. Here we show that MIRA (Medical Intelligence for Reasoning and Action), an autonomous artificial intelligence agent operating in a sandboxed EHR environment, can navigate a large clinical action space to obtain patient histories; order and interpret laboratory, imaging and microbiology tests; generate differential diagnoses; and formulate treatment plans such as prescribing medications, scheduling surgical procedures and planning admissions. In simulations on real patient cases spanning multiple diagnoses, MIRA outperformed physicians in diagnostic accuracy and made guideline-concordant, medication-safe and appropriate admission decisions. Compared with previous LLM applications that addressed isolated subtasks or provided free-text advice, these results suggest that an EHR-integrated artificial intelligence agent can turn clinical intent into structured, actionable EHR operations, possibly making it a more effective decision-support partner for physicians. Further work is needed to establish generalization, safety and governance through prospective, real-world studies. A large language model artificial intelligence agent operating in a sandboxed electronic health record system can autonomously take patient histories, order tests, interpret findings, diagnose conditions and propose treatments, outperforming experienced clinicians while adhering to safety standards and clinical guidelines.

We investigate the relationship between environmental protection public expenditure (EPE) and green total factor productivity (GTFP) across 27 EU countries from 2013 to 2022. Using the global Malmquist Luenberger index and Two-Step System GMM estimation, we test contemporaneous and lagged effects of EPE scale and structure on GTFP. The findings reveal that absolute EPE (EPEA) has a positive and significant effect on GTFP through pollution-emission reduction and clean-technology investment channels. However, expenditure intensity (EPEI) shows no significant effect. This points to a threshold issue: current spending (averaging 0.76% of GDP) likely falls below levels needed for measurable productivity gains. Neither one-period nor two-period lagged variables demonstrate significant relationships with GTFP, indicating that environmental spending impacts may require longer evaluation horizons than the observation period allows. Disaggregated analysis of individual EPE components reveals no significant effects for any single category, suggesting that integrated environmental strategies may be more effective than targeted categorical spending. Several limitations affect our findings. We cannot establish definitive causality. The observation period is relatively short. Our focus on public expenditure excludes private environmental investment. Our empirical findings suggest four policy priorities: prioritising absolute investment over intensity targets, integrating spending across categories, extending evaluation beyond 2-year horizons, and strengthening public-private coordination.  

This case study proposes a multi-stage deep learning-based system for an automated inventory analysis of stent boxes cabinets in angiography rooms. The proposed pipeline integrates cabinet image segmentation, vendor classification, detection of region-of-interest (ROI) with numerical features, and character recognition enabling extraction of stent boxes’ attribute triples (vendor, diameter, length). Experimental results show that the system achieves high performance across individual stages: mAP@0.5 of 0.995 in box segmentation task, top-1 accuracy of 99.3% in the stent vendor classification and mAP@0.5 of 0.991 in ROI detection. The overall F1 score at the system level for stent box attribute triples is 0.811. The error analysis indicates that system performance is strongly influenced by camera-to-cabinet distance determining the scale of segmented ROI, and can be improved to F1=0.984 by partial standardization of image acquisition step.

The provisions of the Constitution of Bosnia and Herzegovina (hereinafter: the Constitution of BiH), inter alia, regulate the catalogue of human rights and freedoms, as well as the right to access the exercise of rights through institutions whose primary task is to protect human rights in the country. The basic issue concerning the effective protection of human rights in Bosnia and Herzegovina is reflected in the legal nature of the Constitution of BiH; however, it is also reflected in the relationship between the Constitution of BiH and ratified international instruments for the protection of human rights—primarily the European Convention for the Protection of Human Rights and Fundamental Freedoms (hereinafter: the European Convention). In other words, the provisions of the Constitution of BiH address the effective institutional protection of human rights and freedoms in the material sense of the phrase. In addition to the judicial authorities, which certainly represent the most important institutions in resolving disputes, the institution of the Ombudsman for Human Rights of Bosnia and Herzegovina, the Constitutional Court of BiH and the Ministry of Human Rights and Refugees of BiH play a key role in the process of protecting human rights and fundamental freedoms. One particularly important issue is the trust of citizens in state institutions whose primary task is to protect human rights.

Climate change affects all sectors, with a particularly significant impact on agricultural production. Therefore, agricultural production must adapt to these changes, and adaptive strategies for managing agricultural production should be applied. This research evaluates which adaptive strategies yield the best results in agricultural production in Bosnia and Herzegovina and Serbia through expert decision-making. In doing so, the interval type-2 fuzzy set (IF2S) is applied, using symmetric fuzzy numbers through the membership function. The results obtained by applying IF2S M-SiWeC (Modified Simple Weight Calculation) show that the criteria of the greatest importance are yield stability and climate risk reduction. The ranking of the six selected adaptive strategies is carried out using the IF2S MABAC (Multi-Attributive Border Approximation area Comparison) method, which indicates that agricultural production diversification and adaptive water management strategies provide the best results according to expert assessments. These results are confirmed by additional analyses, including comparative analysis and sensitivity analysis. The contribution of this research is reflected in proposing guidelines on the adaptive strategies that should be applied in practice in agricultural production in order to reduce the negative effects of climate change.