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.

Abstract Aware of the danger that the risk of money laundering and terroist financing has on the whole society, and with the aim of increasing awareness of the effective fight against the same risk in Bosnia and Herzegovina (BiH), the paper deals with the research of mechanisms for managing the risk of money laundering and terrorist financing activities. The research of this work will be focused on determining the degree of application of the requirements of the legislative and institutional framework for the fight against money laundering and terrorist financing by those obliged to apply law. Also, the paper points to the cooperation of state institutions with entities of the BiH, and examines the degree of compliance of the BiH system with international standards related to the fight against money laundering and terrorist financing. In order to assess the management of this risk as efficiently as possible, the paper will cover financial and non-financial money transfer systems, and indicate the level of measures taken in this fight by the competent institutions. For the purposes of research in the paper, the data published by the competent institutions through their reports/publications will be used, with a special focus on the data published by the Banking Agency of the Federation of Bosnia and Herzegovina as the legal entity that reported the largest number of suspicious transactions in the analyzed period. The results of the research indicate that there is no concern regarding the management of the risks of money laundering and terrorist financing in BiH, and that the risk can be quantified in the category of moderate risk.

Man consists of different abilities and traits. The degree of development and the level of achievement, as well as their mutual relationship, are different from person to person. Morphological characteristics are the characteristics responsible for the dynamics of growth and development, as well as for the characteristics of the body structure. Motor abilities are features that participate in solving motor tasks and that can be developed to a greater extent. We will find out the difference in these abilities between students active in football and physically inactive students through the results of this master's thesis. The goal of the research is to determine the differences between students who are actively engaged in football and those students who are not active in sports. The research was conducted on 52 subjects, students of an elementary school. Keywords: Motor abilities, elementary school, students