This paper explores the contribution of custom-trained Large Language Models (LLMs) to developing Open Education Resources (OERs) in higher education. Our empirical analysis is based on the case of a custom LLM specialized for teaching business management in higher education. This custom LLM has been conceptualized as a virtual teaching companion, aimed to serve as an OER, and trained using the authors’ licensed educational materials. It has been designed without coding or specialized machine learning tools using the commercially available ChatGPT Plus tool and a third-party Artificial Intelligence (AI) chatbot delivery service. This new breed of AI tools has the potential for wide implementation, as they can be designed by faculty using only conventional LLM prompting techniques in plain English. This paper focuses on the opportunities for custom-trained LLMs to create Open Educational Resources (OERs) and democratize academic teaching and learning. Our approach to AI chatbot evaluation is based on a mixed-mode approach, combining a qualitative analysis of expert opinions with a subsequent (quantitative) student survey. We have collected and analyzed responses from four subject experts and 204 business students at the Faculty of Economics, Business and Tourism Split (Croatia) and Faculty of Economics Mostar (Bosnia and Herzegovina). We used thematic analysis in the qualitative segment of our research. In the quantitative segment of empirical research, we used statistical methods and the SPSS 25 software package to analyze student responses to the modified BUS-15 questionnaire. Research results show that students positively evaluate the business management learning chatbot and consider it useful and responsive. However, interviewed experts raised concerns about the adequacy of chatbot answers to complex queries. They suggested that the custom-trained LLM lags behind the generic LLMs (such as ChatGPT, Gemini, and others). These findings suggest that custom LLMs might be useful tools for developing OERs in higher education. However, their training data, conversational capabilities, technical execution, and response speed must be monitored and improved. Since this research presents a novelty in the extant literature on AI in education, it requires further research on custom GPTs in education, including their use in multiple academic disciplines and contexts.

The aim of this work is to investigate the influence of gender on recovery after cerebral stroke.It is believed that functional outcome of cerebral stroke (CS) depends on gender. Female gender is mildly negative prognostic factor in after stroke results. Two hundred and two patients who had first ischemic cerebral stroke were questioned with help of, HADS and WHOQOL-Bref questionnaires, looking for differences in recovery depending on gender. Average patients' age was 72+/-13 (ME+/-IR) years. The youngest patient had 40 years, and the oldest 92 years, and medium range was 52 years. There were 112 males and 90 females. Quality of life was equally graded by both male and female after CS (p=0.208). Male patients had significantly better results in physical (p=0.035) and psychological (p=0.020) domain of life quality. After CS, male patients had better results only in memory dimension (p=0.003). Anxiety was statistically more frequent among female patients (p=0.009). Gender did not influence frequency of metabolic syndrome in patients with CS. Quality of life after CS was better in male patients, and statistically significant difference has been shown in physical, psychological domain and memory dimension. Female patients were more anxious then male after CS.

The aim of this study was to determine the association of cardiovascular disease (CVD) of selected human subjects with the hardness of water they consume. Laboratory testing of physical and chemical parameters of water were made using standardized methods: Standard Methods 19th edn, 1995 (APHA, AWWA & WEF, Washington, DC), and ISO 7888:1985, ISO 10523:1998. Pearson's chi-squared test was used for the statistical analysis of data, with the significance level of 0.05. The obtained data were analysed using the statistical program SPSS 16.0. The study sample consisted of 1,021 individuals divided into two groups: soft water consumers, 603 individuals, and, hard water consumers, 618 individuals. Results indicate that a statistically significant (χ2 = 5.315; df = 1; p = 0.021) number of individuals with CVD drink soft water. The prevalence of CVD in the age group 45–60 years in the study area where soft water is consumed was 21.3% and in the study area where hard water is consumed the prevalence of CVD was 13.7%. The summary results indicate significant correlation between the prevalence of CVD in the population group who drink soft water. The value of the relative risk is 1.127.

This aim of this study was to investigate surface gloss changes of different composite dental materials after chemical degradation or polishing. Five different composites were used (Evetric, GrandioSO, Admira Fusion, Filtek Z550, Dynamic Plus). The gloss of the tested material was measured with a glossmeter before and after chemical degradation in different acidic beverages. Statistical analysis was performed using a t-test for dependent samples, ANOVA, and a post hoc test. For comparison between groups, a level of significance was set at 0.05. Initial gloss values ranged from 51 to 93 at baseline to 32 to 81 after chemical degradation. The highest values were obtained for Dynamic Plus (93.5 GU) and GrandioSO (77.8 GU), followed by Admira Fusion (82 GU) and Filtek Z550 (70.5 GU). Evetric showed the lowest initial gloss values. After acidic exposures, the gloss measurements revealed different patterns of surface degradation. The results showed that the gloss of the samples decreased with time regardless of the treatment. The interaction between chemical-erosive beverages and the composite could lead to a decrease in the surface gloss of the composite restoration. The nanohybrid composite showed less gloss changes under acidic conditions, suggesting that it is more suitable for anterior restorations.

Digital transformation (DT) has become one of the most significant trends in higher education institutions (HEIs) in both EU and non-EU countries. Using Information and Communication Technologies (ICTs) to reinvent higher education is contingent upon several factors, including an institution’s development stage regarding the application and strategic integration of ICTs across its key activities and processes. In the extant literature, multiple frameworks of ICT development (maturity) paths have been developed. However, there is a lack of empirical studies on how well those models predict the DT success, and which of their dimensions are most relevant. In this paper, we use a research instrument, adapted from the HigherDecision research project, to capture the subjective assessments of academics and students at three public higher education institutions in Bosnia and Herzegovina and Croatia. Using seven dimensions of the DT construct, prescribed by the HigherDecision framework, we examine their contribution to the subjectively evaluated success of each HEI’s DT initiative and identify the most impactful dimension(s). Our results show that the digital infrastructure and academic teaching and learning are perceived as critical drivers of DT in the academic sector. Provided that the University of Mostar, as a mid-sized public university located in Bosnia and Herzegovina, currently represents one of the DT leaders in the Western Balkans (WB) region, we discuss implications for scaling its good practices in smaller HEIs across the region.

Background: Painful and damaged nipples are frequently associated with breastfeeding cessation in the early postpartum period. The results of researchers’ studies utilizing different treatments have been inconclusive. Research Aim: To compare the intensity of nipple pain and the healing of damaged nipples during the first 10 days postpartum using either lanolin or human milk treatments. Methods: This single-blind randomized controlled trial included participants ( N = 206) who were primiparous with painful and damaged nipples. Participants were recruited from the tertiary teaching hospital within the first 72 hr after delivery and randomized to the intervention group with lanolin ( n = 103) and a human milk control group ( n = 103). Data were collected in the maternity ward, 3 and 7 days after randomization. The primary outcome was nipple pain intensity and quality measured 3 and 7 days after randomization by the McGill Pain Questionnaire – short form. The nipple damage self-assessment questionnaire was used for the assessment of nipple healing. Breastfeeding self-efficacy, breastfeeding duration, and exclusivity were assessed as secondary outcomes. Results: Participants in both groups reported a statistically nonsignificant reduction in pain (quality and intensity of pain) as well as improved nipple healing 7 days after randomization. Participants in the lanolin group exclusively breastfed their infants 3 days after randomization—significantly more often than participants in the control group ( p = .026). The study did not reveal any statistically significant differences for other secondary outcomes. Conclusion: Both lanolin and human milk are equally effective in treating painful and damaged nipples. Registered with Clinicaltrials.gov (NCT04153513)