Thanks to the climatic and geographical conditions, the area of the Northwestern part of Bosnia and Herzegovina has a long tradition of producing honey and other bee products. However, there is little or no literature data on the physico-chemical properties and biological activity of different types of honey and other bee products from Bosnia and Herzegovina. Five different types of honey were analyzed: monofloral honey (acacia, chestnut, linden), meadow honey and forest honey. Physico-chemical parameters, sensory analysis, color of honey, antioxidant activity, and content of total phenols were analyzed in five types off collected honey samples. The analyzes performed showed that chestnut honey contains the highest and acacia honey has the lowest content oftotal phenolic compounds. The forest honey showed the best antioxidant activity. The color of the honey was measured according to the CIELab system and the estimated L, a, bparameters show that all types of honey from this area can be characterized asdark types of honey (L50) with the presence of a yellow color. The obtained results show that the analyzed samples of five different types of honey are rich in polyphenolic components and represent a good source of antioxidants in the human diet.KEYWORDS:honey,physico-chemical parameters, color, antioxidant activity, total phenols

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.

Highlights • Changes over time from 3 MRIs are similar to those obtained from 5 MRIs.• Some differences are seen in the RR-MS dataset, but not in the healthy dataset.• The associations with the clinical outcomes are affected by the study design.• The effect of the design strategy is bundle- and MRI measure-dependent.• The optimal design will depend on the dynamics of change in the target population.

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.

: 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

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.

OBJECTIVES Infection is still the leading cause of morbidity and mortality among burn patients worldwide. Isolation and identification of pediatric burn wound bacterial colonizers can prevent infection and improve burn trauma treatment. In this study, we explored early microbial colonizers within the burn wounds and the susceptibility of those isolates to antibiotics among hospitalized pediatric patients with minor and moderate burns, clinically significant infections and outcomes. METHODS A retrospective analysis of pediatric patients admitted to the inpatient pediatric surgical ward and treated for minor and moderate burns from 2009 to 2018 was performed. RESULTS One hundred six patients met the inclusion criteria. The mean age was 3.6 ± three years (0.2-14.1 years). The most common type of burn was scald burns (82.1%). The mean TBSA of the hospitalized pediatric burn cases was 8.5% (IQR, 6-12%). Seventy-nine (74.5%) patients had positive wound cultures at admission, regardless of the hospital admission day. Fifty-eight (73.4%) had one bacterial growth (mono isolate), while 21 (26.6%) had mixed growth or poly isolates. Among patients with mixed growth or poly isolate, 16 had two bacteria, three had three bacteria, and one had four bacteria isolated, totaling 105 isolated microorganisms (14 different species, 70.5% Gram-positive bacteria and 29.5% Gram-negative bacteria). Twelve patients (11%) developed clinically significant infections (eleven got burn wound infection, and one had septicemia). All patients received prophylactic systemic antibiotics. Only 35.2% of the isolated bacteria from the wounds were sensitive to the prophylactic antibiotics, and only ∼17% in case of clinically significant infections. We found a statistically significant difference in the length of hospital stay between patients with initially colonized samples of burn wounds compared with patients with initial negative samples (p = 0.008). All patients in the cohort survived hospital discharge. CONCLUSION Despite common bacterial colonization of acute burn wounds, only ∼10% of the patients developed clinically significant infections, a minority of which were sensitive to prophylactic antibiotics. Our findings indicate the need to refine the antibiotic approach in pediatric patients with minor/moderate burns in our local setting.

INTRODUCTION Early diagnosis and treatment of primary vesicoureteral reflux (VUR) are essential for preserving renal function. OBJECTIVES The study explored whether preoperative cystoscopic grading of refluxing ureteric orifices (UO) correlated with their shape in an institution with non-performance of hydrodistention of the UO in the diagnosis and grading of VUR. We also assessed the relationship between the UO shape and VUR grade with the effectiveness of endoscopic correction of primary VUR in children. METHODS This retrospective study included consecutive patients ≤15 years treated for primary VUR. The reflux grade was based on the results of preoperative voiding cystourethrography as mild, moderate, or severe. RESULTS Fifty-one patients with 77 renal refluxing units (RRU) underwent endoscopic treatment with Deflux®. VUR was bilateral in 51 % of patients. VUR was mild in 13 %, moderate in 53 %, and severe in 34 % of cases. The patients with mild and moderate VUR had stadium-shaped UOs in 60 % and 54 % RRUs, respectively. Horseshoe-shaped UOs constituted 42 % of UOs in patients with severe VUR, followed by 31 % of golf-hole UOs. The reflux resolution rate after the first endoscopic injection was 84 %. The preoperative VUR grade correlated with UOs shape (p < 0.001). No significant correlation between UOs configuration and the outcome of endoscopic treatment was seen (p = 0.452). The preoperative VUR grade negatively correlated with a favorable endoscopic treatment (p = 0.043). DISCUSSION AND CONCLUSION Our data indicate ureteral orifice shapes are closely related to preoperative VUR grade. There was no correlation between the UO configuration and the success rate of endoscopic treatment of VUR, in contrast to the significant negative correlation between the VUR grade and the success rate of endoscopic treatment.

Wi-Fi-based passive sensing is considered as one of the promising sensing techniques in advanced wireless communication systems due to its wide applications and low deployment cost. However, existing methods are faced with the challenges of low sensing accuracy, high computational complexity, and weak model robustness. To solve these problems, we first propose a robust channel state information (CSI)-based Wi-Fi passive sensing method using attention mechanism deep learning (DL). The proposed method is called as convolutional neural network (CNN)-ABLSTM, a combination of CNNs and attention-based bi-directional long short-term memory (LSTM). Specifically, CSI-based Wi-Fi passive sensing is devised to achieve the high precision of human activity recognition (HAR) due to the fine-grained characteristics of CSI. Second, CNN is adopted to solve the problems of computational redundancy and high algorithm complexity which are often occurred by machine learning (ML) algorithms. Third, we introduce an attention mechanism to deal with the weak robustness of CNN models. Finally, simulation results are provided to confirm the proposed method in three aspects, high recognition performance, computational complexity, and robustness. Compared with CNN, LSTM, and other networks, the proposed CNN-ABLSTM method improves the recognition accuracy by up to 4%, and significantly reduces the calculation rate. Moreover, it still retains 97% accuracy under the different scenes, reflecting a certain robustness.

Radio-frequency fingerprint (RFF), which comes from the imperfect hardware, is a potential feature to ensure the security of communication. With the development of deep learning (DL), DL-based RFF identification methods have made excellent and promising achievements. However, on one hand, existing DL-based methods require a large amount of samples for model training. On the other hand, the RFF identification method is generally less effective with limited amount of samples, while the auxiliary data set and the target data set often needs to have similar data distribution. To address the data-hungry problems in the absence of auxiliary data sets, in this article, we propose a supervised contrastive learning (SCL)-based RFF identification method using data augmentation and virtual adversarial training (VAT), which is called “SCACNN.” First, we analyze the causes of RFF, and model the RFF identification problem with augmented data set. A nonauxiliary data augmentation method is proposed to acquire an extended data set, which consists of rotation, flipping, adding Gaussian noise, and shifting. Second, a novel similarity radio-frequency fingerprinting encoder (SimRFE) is used to map the RFF signal to the feature coding space, which is based on the convolution, long short-term-memory, and a fully connected deep neural network (CLDNN). Finally, several secondary classifiers are employed to identify the RFF feature coding. The simulation results show that the proposed SCACNN has a greater identification ratio than the other classical RFF identification methods. Moreover, the identification ratio of the proposed SCACNN achieves an accuracy of 92.68% with only 5% samples.

The communications between vehicle-to-vehicle (V2V) with high frequency, group sending, group receiving and periodic lead to serious collision of wireless resources and limited system capacity, and the rapid channel changes in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. For the Internet of Vehicles (IoV), it is a fundamental challenge to achieve low latency and high reliability communication for real-time data interaction over short distances in a complex wireless propagation environment, as well as to attenuate and avoid inter-vehicle interference in the region through a reasonable spectrum allocation. To solve the above problems, this paper proposes a resource allocation (RA) method using dueling double deep Q-network reinforcement learning (RL) with low-dimensional fingerprints and soft-update architecture (D3QN-LS) while constructing a multi-agent model based on a Manhattan grid layout urban virtual environment, with communication links between V2V links acting as agents to reuse vehicle-to-infrastructure (V2I) spectrum resources. In addition, we extend the amount of transmitted data in our work, while adding scenarios where spectrum resources are relatively scarce, i.e. the number of V2V links is significantly larger than the amount of spectrum, to compensate for some of the shortcomings in existing literature studies. We demonstrate that the proposed D3QN-LS algorithm leads to a further improvement in the total capacity of V2I links and the success rate of periodic secure message transmission in V2V links.

Alpine skiing is a sport and recreational physical activity which requires fine postural control to maintain balance in challenging conditions. Theoretically, balance dominates in alpine skiing, but coordinated action of the whole body of the skiers is equally important. The aim of this research was to determine the effects of experimental short-term program of intensive training of alpine skiing techniques to postural stability (on Biodex Balance System) of students. The sample is divided into an experimental (31 students, age 21.4±1.0 and body height 180.7±6.3 cm) and control group (34 students, age 20.6±0.8 and body height 180.3±6.8 cm). The results of ANCOVA within variables for the evaluation of postural stability show statistically significant effects of the applying experimental program in all applied variables at the level of significance p=.000. From the mean value results (M) it is obvious that the experimental group achieved better results compared to the identical tests applied to the control group. The results of this research show that learning to ski can improve the ability to maintain balance, especially if it is conducted under the expert supervision of a ski instructor, which can have the effect of reducing the risk of injury.