Analyzing students’ academic performance is important for evaluating enrollment criteria which establish the standards required for pupils who finished secondary school to gain admission to a higher education institution. The aims of this research were to develop a machine learning prediction Decision Tree classifica-tion model and analyze the performance of engineering students based on their performances during second-ary school education. The performance of students was analyzed and measured as a binomial response whether students successfully finished the first and the second study years. The developed model examined general success, number of awards obtained at competitions, special awards, average grades in mathematics, physics, and one of the official state languages during secondary school as predictor variables. The number of courses transferred from the first into the second study year and students’ GPA obtained during the first study year were added as predictor variables in the analysis and development of a prediction model for the students’ performance during the second study year and their enrollment in the third study year. Developed machine learning prediction model showed that for the performance of enrolled students in the first study year general success of students during secondary school is the most important predictor variable, followed by mathematics and physics grades. However, for the performance of the students enrolled in the second study year the most important predictor variable was number of the courses transferred from the first into the second study year, followed by students’ GPA obtained during the first study year and general success. Machine learning Decision Tree classification modeling was shown to be an adequate tool for the prediction of the performance of engineering students during the first and second study years.

Context: Chronic obstructive pulmonary disease (COPD) is one of the most common chronic lung diseases and is an important cause of mortality and morbidity in the world. Chronic obstructive pulmonary disease is a heterogeneous and multisystemic disease. Aims of the study was to assess workability of patients with COPD in relation to the workplace. Settings and Design: Prospective study. Methods and Material: The study was conducted on 150 patients with COPD. Each patient was examined by an occupational medicine specialist and a standardized COPD Questionnaire was completed. A physical examination was performed with special emphasis on auscultatory findings on the lungs. Spirometric testing was performed. Using the statistical methods, the results of the parameters of temporary (number of lost working days) and permanent incapacity (exercised right to some kind of disability) of the examined group were analyzed. Statistical Analysis Used: Descriptive statistics. SPSS 10.0. Results of the study showed that out of 150 patients, 48.67% had preserved working capacity for jobs. There are mostly jobs without special working conditions. Out of 150 patients, 51.33% had reduced working capacity for jobs. Conclusions: These are mostly workplaces with special working conditions, where respondents are exposed to the harmfulness of the workplace. Reduced working capacity was found in 51.33% of examined simple.

Extracranial internal carotid artery aneurysms (EICAAs) can lead to serious medical conditions, such as stroke or compression over cranial nerves. In very few cases, there may be hemorrhagic complications due to the rupture. Although rare, they should be suspected cause in every patient with transitory ischemic attack or stroke, especially in the presence of pain, palpable mass or bruit in the neck.

The paper discusses figurative conceptualizations of nations, countries, and institutions as either a container, a person, a sinking ship, a fabric, or hell in media discourse on the European migrant crisis. Applying Steen et al.’s (2010) three-dimensional model of metaphor analysis, we analyze a specific set of metaphorical linguistic expressions, which are inextricably related in the segments of the real discourse on migration, to discuss their rhetorical power and communicative function. The aim of this paper is to describe and identify cases when these are used as perspective-changing devices to influence readers’ opinion on an important issue such as migration.

Although many women perform postural tasks while listening to music, no study has investigated whether preferred music has different effects than non-preferred music. Thus, this study aimed to explore the effects of listening to preferred versus non-preferred music on postural balance among middle-aged women. Twenty-four women aged between 50 and 55 years were recruited for this study. To assess their static balance, a stabilometric platform was used, recording the mean center of pressure velocity (CoPVm), whereas the timed up and go test (TUGT) was used to assess their dynamic balance. The results showed that listening to their preferred music significantly decreased their CoPVm values (in the firm-surface/eyes-open (EO) condition: (p < 0.05; 95% CI [−0.01, 2.17])). In contrast, when the women were listening to non-preferred music, their CoPVm values significantly (p < 0.05) increased compared to the no-music condition in all the postural conditions except for the firm-surface/EO condition. In conclusion, listening to music has unique effects on postural performance, and these effects depend on the genre of music. Listening to preferred music improved both static and dynamic balance in middle-aged women, whereas listening to non-preferred music negatively affected these performances, even in challenged postural conditions.

Abstract The paper aims to evaluate the role of language in a specific socio-political context. It offers a critical approach and evaluation of the political statements of the European Union representatives regarding the process of the accession of Bosnia and Hercegovina to the European Union. The focus of the linguistic investigation is on the identification of language structures that participate in the development of communicative models that enable the establishment of power relations between participating entities. The linguistic data is obtained through systemic functional grammar and evaluated using critical discourse analysis.

This paper highlights the growing importance of edge computing and the need for AI techniques to enable intelligent processing at the edge. Edge computing has emerged as a paradigm shift that brings data processing and storage closer to the source, minimizing the need for transmitting large volumes of data to remote locations. The integration of AI capabilities at the edge enables intelligent and real-time decisionmaking on resource-constrained devices. This paper discusses the significance of Edge AI across various domains, including automotive applications, smart homes, industrial IoT, and healthcare. By leveraging AI algorithms on edge devices, efficient implementation and deployment become possible, leading to improved latency, privacy, and security.The various AI techniques used in edge computing are presented, including machine learning, deep learning, reinforcement learning and transfer learning. As AI continues to play a pivotal role in driving edge computing, the integration of hardware accelerators and software platforms is gaining utmost significance to efficiently run inference models. A variety of popular options have emerged to accelerate AI at the edge, and notable among them are NVIDIA Jetson, Intel Movidius Myriad X, and Google Coral Edge TPU. The importance of specialized System-on-a-Chip (SoC) solutions for Edge AI, capable of supporting high-performance video, voice, and vision processing alongside integrated AI accelerators is presented as well. By examining the transformative potential of Edge AI, this paper aims to inspire researchers, practitioners, and industry professionals to explore the vast possibilities of integrating AI at the edge. With Edge AI reshaping the future of edge computing, intelligent decision-making becomes seamlessly integrated into our daily lives, driving advancements across various sectors.

With modern technological progress and scientific achievements, companies are actively adjusting their business strategies in order to take advantage of new business opportunities and achieve maximum profit for their companies. One of the current changes in the field of marketing is the increasing emphasis on digital marketing. Digital channels, such as social networks, mobile applications and online advertising, are becoming essential tools for communication and engagement with target audiences. Mobile applications, m-commerce and other innovations allow consumers to easily buy products and access information about brands anytime, anywhere. This trend requires companies to be active on digital platforms and adapt to the mobile experience in order to achieve success in the market. In the decision-making process, it is important to have the best possible analysis so that the strategy is as good as possible. SWOT analysis of digital and traditional marketing through strengths, weaknesses, opportunities and threats gives clear facts about how marketing is developing in the modern market, especially in Bosnia and Herzegovina. This paper provides an analysis and comparison of digital and traditional marketing with the aim of understanding their strengths and weaknesses in the market of Bosnia and Herzegovina.

: Modern technologies are essential parts of Industry 4.0. From automation, robotics, digitalization and additive manufacturing (3D printing) up to 3D scanning and reverse engineering. 3D scanning has a wide range of usage in today product development and design processes. This paper will present several real case studies of 3D scanning in reverse engineering andnew product development and design processes. Paper explores importance of 3D scanning technology, as integral part of Industry 4.0. Seven case studies are explored in more detail. Five of these case studies are realized in Laboratory for Product development and design at University of Sarajevo – Faculty of mechanical engineering as a part of the projects realized in cooperation with several companies from Bosnia and Herzegovina, while two of them are realized in Protodevs company in Sarajevo. Artec Eva 3D scanner and Artec Studio software were used for most of the presented case studies.

Vaginal inflammation represents a heterogeneous group of disorders caused by infection, inflammation, or disruption of vaginal microflora. The most common causes of vaginal infection are Staphylococcus aureus, Enterococcus faecalis, Streptococcus agalactiae, Escherichia coliand Candida albicans. Antibiotic resistance is a major global problem, which can be mitigated by using natural antimicrobial substances such as essential oils. Each essential oil has an extremely complex composition (some essential oilshave over 200 components), which prevents microorganisms from developing resistance. Therefore, essential oils retain their effects.The aim of our study was to investigate antibacterial activity Melaleuca alternifolia, Achillea millefoliumand Cinnamomumcamphoravaginal suppositories, and see which essential oil has the strongest potential to be used as active ingredient for vaginal infections.The antimicrobial activity of the vaginal suppositories was examined using the disk diffusion method. Standard bacterial strains were used for the ATCC collection: Staphylococcus aureus (S. aureus) ATCC 25923, Enterococcus faecalis (E. faecalis)ATCC 51299, Escherichia coli (E. coli)ATCC 25922, Candida albicans (C. albicans)ATCC 10231.The results showed that Melaleuca alternifolia essential oil has an antimicrobial effect on all tested strains, with the strongest effect on Candida albicans(ZI 22.7 mm). Achillea millefoliumessential oil had no effect on Enterococcus faecalis, whereas Cinnamomum camphoraessential oil did not show zones of inhibition of Candida albicans.KEYWORDS:vaginal suppository, Melaleuca alternifolia, Achillea millefolium, Cinnamomum camphora,antimicrobial activity