Brown coal mining conducted at greater depths, characterised with severe mining-geological conditions often results in deviation from the applied mining methods. Experiences gained in mechanised longwall mining of roof plate in deeper parts of Raspotočje mine, using method of roof caving without safety pillars left behind the working panels indicates to shortcomings of the applying mining method. Frequent occurences of spontaneous mine fires, of hazardous gasses well above the limit values, and constant exposure to rock bursts of various in- tensity resulted in application of three longwall face ventilation regimes: conventional „U“ ventilation system, ventilation with gas channel and middle hallway, and separate ventilation of the upper part of the longwall face. This paper presents experience based information gained in a five years period of mining, analysed using one- factor and two factor regression analyses applied to a number of technical-technological parameters (ventilation, mining system, etc) influencing occurences of spontaneous mine fires and increased gas release in various ventilation regimes at longwall face aimed to give basic guidelines for minimisation and elimination of certain hazards. Keywords: ventilation regime, spontaneous fires, gas release, mechanised longwall.

InxGa1−x N is a strategically important material for electronic devices given its tunable bandgap, modulated by the In/Ga ratio. However, current applications are hindered by defects caused by strain relaxation and phase separation in the material. Here, we demonstrate growth of homogeneous InxGa1−x N films with 0.3 < x < 0.8 up to ∼30 nm using atomic layer deposition (ALD) with a supercycle approach, switching between InN and GaN deposition. The composition is uniform along and across the films, without signs of In segregation. The InxGa1−x N films show higher In-content than the value predicted by the supercycle model. A more pronounced reduction of GPCInN than GPCGaN during the growth processes of InN and GaN bilayers is concluded based on our analysis. The intermixing between InN and GaN bilayers is suggested to explain the enhanced overall In-content. Our results show the advantage of ALD to prepare high-quality InxGa1−x N films, particularly with high In-content, which is difficult to achieve with other growth methods.

Background: Restrictive measures implemented by governments around the world to control the coronavirus have an impact on communication, especially the communication of the elderly. Objective: The aim for the article is to determine the attitudes of the elderly towards the ways of communication during the COVID 19 pandemic, and to present the challenges faced by the elderly when using digital ways of communication. The goal is also to point out the importance of providing adequate social support to the elderly. Methods: The sample consisted of 71 respondents from Bosnia and Herzegovina, aged over 60 years, divided into two groups. The questionnaire was used to gather information from respondents on their demographic characteristics as well as to examine participants’ attitudes and confidence regarding the manner and risk of communication associated with COVID-19. Results and Discussion: The results of this research showed that the elderly are actively using digital technologies, but that they are largely dissatisfied with their skills in using digital technologies. Most of the information during the COVID 19 pandemic is given to elderly people via television and telephone (video) conversations with family members and friends. 57.7% of the elderly stated that they greatly lacked direct communication, and it turned out that people living in elderly homes were in a slightly more favorable position. As many as 4/5 of the respondents reported feelings of anxiety, fear, loneliness, and depression. Conclusion: The older part of the population (those over 60 years old), especially in developing countries, including Bosnia and Herzegovina, is facing the challenge of “digitization of communication“. In the context of the COVID-19 pandemic, the elderly are “calling“ for special support and the provision of training and access to technological resources in order to reduce negative psychological consequences and go one step further in creating an “elder-friendly” society.

Abstract The paper explores the possibility of using the novel Deep Operator Networks (DeepONet) for forward analysis of numerically intensive and challenging multiphysics designs and optimizations of advanced materials and processes. As an important step towards that goal, DeepONet networks were devised and trained on GPUs to solve the Poisson equation (heat-conduction equation) with the spatially variable heat source and highly nonlinear stress distributions under plastic deformation with variable loads and material properties. Since DeepONet can learn the parametric solution of various phenomena and processes in science and engineering, it was found that a properly trained DeepONet can instantly and accurately inference thermal and mechanical solutions for new parametric inputs without re-training and transfer learning and several orders of magnitude faster than classical numerical methods.

The development of material production technology has led to the application of new materials in construction. Because concrete is currently the most commonly used construction material, which in addition to numerous advantages (water resistance, low maintenance costs, easy workability, low cost, etc.) also has certain disadvan- tages (low tensile strength and brittle fracture behavior). Reinforcement of concrete using discrete fibers, ran- domly distributed, is an acceptable solution for improving the ductility of concrete. Carbon fiber reinforcement (CFRP) has been widely studied in the last two decades, as it represents a suitable alternative for the reinforce- ment of existing (endangered) RC structures. The advantages of this material are reflected in the relatively simple application, increased performance of the RC structure, low weight of the elements, etc. The paper also analyzes the existing cases of application of this material, as well as the presentation of previous research in the field of structural reinforcement using carbon fibers. Key words: Carbon fiber, Carbon fiber-reinforced polymer (CFRP), reinforcement concrete structures, reinfor- cement of existing structures, structural behavior of construCTIONS

Background: Human papillomaviruses (HPV) are one of the most prevalent causes of cervical, anal, and oral cancer, and it is believed that nearly all sexually active males and females have been infected with HPV at some point in their lives. Objective: The purpose of this study is to highlight epidemiological characteristics of the age and genotypical incidence of HPV infection among females in Canton Sarajevo during a 10-year period. Methods: This cross-sectional study was conducted in the period between January 2012 and December 2021 at the medical institutions “Zavod za Ginekologiju, Perinatologiju i Neplodnost “Mehmedbašić“ among females who were admitted for a routine gynaecological exam in Canton Sarajevo in Bosnia and Herzegovina. Results: In total, 1517 patients who reported typical sexarche at 18.35±1.92, having one 721 (47.5%) sexual partner, regular use of contraceptive methods 820 (54.5%) with male condom 513 (62.6%) being the dominant choice of contraception and having gynacological hereditary oncological positive history 141 (9.3%), were included in the study from which 653 (43.05%) patients had positive HPV test and 864 (56.95%) had negative HPV results. HPV positive patients, 386 (59.1%) were infected either with one type of virus only, while 267 (40.9%) patients were infected with multiple virus strains where we have identified 166 virus strain combinations. Analysis showed that the patients infected by only one virus strain were one of next specific HPV viruses: 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66 and 68. HPV-16 was the predominately strain 16 (22.5%) identified among all the patients. Mean age of HPV positive patients was 33.38±7.85, ranging from 18 to 61 years. Conclusion: This study highlights the significance of the most frequent HPV high-risk strains (HPV-16, -18, -31.-33,-45,-52, and -58) among the Bosnia and Herzegovina population as one of the most common cancer-causing strains worldwide.

The aim of this research was to determine the effects of programmed work on the development of basic motor skills in young soccer players. The sample of participants in this research were football players aged 11 and 12 and they were divided into two equal groups of 30 entities. All participants are involved in a systematic training process in their football clubs. The research problem was to determine whether there are positive effects of programmed work on the development of basic motor skills. A total of 16 variables from the area of basic motor skills were used in the research. The experimental work program was carried out over a period of three months and included 38 training units. After initial testing and successfully implemented programmed work and final testing, a significant impact of programmed work on the development of basic (fundamental) motor skills was determined. In general, looking at the total motor abilities that were evaluated from 16 variables in the experimental group, a statistically significant difference was achieved in 11 variables, in the control group 6 with a positive significant difference. Keywords: basic motor skills, football program effects, young soccer players

The aim of the article is to present, primarily to the medical world and also the general public, the personality and work of Maša Živanović (1890–1960), a pioneer in the health care of children and mothers in Bosnia and Herzegovina (BH), a health educator and one of the leaders of the Yugoslav Women’s Rights Movement in the period between the two world wars. She was born in Croatia (then part of the Austro-Hungarian Empire) as Maria Skopszyński, in a family of Polish-Czech origin. After studying at the Temporary Women’s Lyceum in Zagreb and passing the matriculation exam at the boy’s High School (1909), she obtained the title of Doctor of Medicine in Vienna (1916). Her activity in the Women’s Rights Movement has so far generally attracted more attention from researchers than her medical work. However, this work was very important because the general and health education of women, expectant mothers and mothers, after the two World Wars was very poor in BH, and the rates of child morbidity and mortality were high. Maša Živanović spent almost her entire working life in Sarajevo. For 30 years, she was the head of the Dispensary for Mothers and the Children, later the Institute for Maternal and Child Health Care, into which the previous institution grew in 1931. She was among the first followers of the new concept of “comprehensive paediatrics”, which included social care for children, disease prevention and treatment of the sick. She successfully connected the medical mission with the mission of a women’s rights activist, also trying to act as a health educator through articles published in the Women’s Movement magazine (Ženski pokret). For a time, she was the president of the Society for the Education of Woman and Protection of her Rights, i.e. the Women’s Movement, and a delegate at conferences of international feminist organizations. Conclusion. Maša Živanović was a physician, a pioneer in the health care of children and mothers in BH, a long-time director of the Institute for Health Care of Mothers and Children in Sarajevo, and one of the leaders of the Yugoslav Women’s Rights Movement.

Specific emitter identification (SEI) is a potential physical layer authentication technology, which is one of the most critical complements of upper layer authentication. Radio frequency fingerprint (RFF)-based SEI is to distinguish one emitter from each other by immutable RF characteristics from electronic components. Due to the powerful ability of deep learning (DL) to extract hidden features and perform classification, it can extract highly separative features from massive signal samples, thus enabling SEI. Considering the condition of limited training samples, we propose a novel few-shot SEI (FS-SEI) method based on hybrid data augmentation and deep metric learning (HDA-DML) which gets rid of the dependence on auxiliary datasets. Specifically, HDA consisting rotation and CutMix is designed to increase data diversity, and DML is used to extract high discriminative semantic features. The proposed HDA-DML-based FS-SEI method is evaluated on an open source large-scale real-world automatic-dependent surveillance-broadcast (ADS-B) dataset and a real-world WiFi dataset. The simulation results of two datasets show that the proposed method achieves better identification performance and higher feature discriminability than five latest FS-SEI methods.

Rogue emitter detection (RED) is a crucial technique to maintain secure internet of things applications. Existing deep learning-based RED methods have been proposed under friendly environments. However, these methods perform unstably under low signal-to-noise ratio (SNR) scenarios. To address this problem, we propose a robust RED method, which is a hybrid network of denoising autoencoder and deep metric learning (DML). Specifically, denoising autoencoder is adopted to mitigate noise interference and then improve its robustness under low SNR while DML plays an important role to improve the feature discrimination. Several typical experiments are conducted to evaluate the proposed RED method on an automatic dependent surveillance-Broadcast dataset and an IEEE 802.11 dataset and also to compare it with existing RED methods. Simulation results show that the proposed method achieves better RED performance and higher noise robustness with more discriminative semantic vectors than existing methods.

With the ubiquitous deployment and applications of Internet of Things (IoT), security issues pose a critical challenge to IoT devices. External attackers often utilize vulnerable IoT devices to invade the target’s internal network and then further cause a security threat to the whole network. To prevent such attacks, it is necessary to develop a security mechanism to control the access of suspicious IoT devices and manage the internal devices. In recent years, deep learning (DL) algorithm has been widely used in the field of edge device identification (EDI), and has made great achievements. However, these previous methods are essentially centralized learning-based EDI (CentEDI) that trains all data together, which can not guarantee data security and not conducive to deployment on edge devices. To address this problem, we introduce a federated learning-based EDI (FedeEDI) method via network traffic to automatically identify edge devices connected to the whole network. Experimental results show that the training efficiency of our proposed FedeEDI method is much higher than that of the CentEDI method, although its classification accuracy is slightly reduced. In contrast to the CentEDI method, the proposed FedeEDI method has two main advantages: faster training speed and safer training process.