The use of anticoagulant therapy is a part of the daily work of clinicians and a reason for fear, primarily due to the risk of bleeding. The use of anticoagulant drugs in rheumatology remains a challenge. first, a large number of clinicians consider rheumatic conditions as a hypercoagulable state, which often leads to wrong decisions. second, the use of drugs in the treatment of rheumatic diseases may be associated with an increased risk of venous thromboembolism (vte), and they can have effect on dose of anticoagulant agent. The aim of this paper is to present the properties of anticoagulant therapy through the prism of rheumatological pathology.

Beauvoir’s existentialist ethics relates to and informs eminently contemporary accounts of feminist ethics in the Western continental feminist canon. To date only a few scholars have emphasized this connection. In this work, I show the centrality of Beauvoirian philosophy to contemporary philosophical discussions by elucidating the influence of Beauvoir’s existentialist ethics on Judith Butler’s feminist philosophy. While I acknowledge other possible influences, especially by French philosophers, on Butler’s work, I find it important to emphasize Beauvoir’s contributions as they have not received the attention they deserve. My paper shows how Beauvoir’s account of agency as an ambiguous becoming reverberates in Butler’s theory of gender performativity developed in her early writings. I consider Butler’s theory of gender performativity to have existentialist roots based on the existentialist perception of the subject as a becoming that never coincides with itself. I also discuss how Butler takes on some basic ethical questions which Beauvoir already accentuates in her writings. I focus on three main points of intersection between the two philosophers, which are vulnerability and interconnectedness, violence and inevitability of ethical failure, and finally the ambiguity and opaqueness that come with situated ethics.

The flexibility introduced with the Open Radio Access Network (O-RAN) architecture allows us to think beyond static configurations in all parts of the network. This paper addresses the issue related to predicting the power consumption of different radio schedulers, and the potential offered by O-RAN to collect data, train models, and deploy policies to control the power consumption. We propose a black-box (Neural Network) model to learn the power consumption function. We compare our approach with a known hand-crafted solution based on domain knowledge. Our solution reaches similar performance without any previous knowledge of the application and provides more flexibility in scenarios where the system behavior is not well understood or the domain knowledge is not available.

Radio frequency fingerprint (RFF) identification is an emerging physical layer security technique, which provokes many promising applications in the internet of things (IoT). However, traditional machine learning-based RFF identification methods rely on complex manual feature extraction, while it is difficult for methods based on deep learning to deal with RFF identification under different channel environments. To solve these problems, we propose three different transfer learning-based RFF identification methods based on ConvMixer network, which is a mixture of different convolutional layers, using pre-trained model in the previous channel environment to assist in training under the new channel environment. Experimental results show that, compared with the previous retraining method, our proposed method reduces the number of training parameters and improves the identification performance at low SNR. Moreover, the proposed method can still have a certain performance guarantee with less training data.

With the explosive growth of advanced wireless technologies and computing device platforms, mobile sensing has gained huge attention. Indoor localization is actually considered as one of most valuable techniques in the field of contactless sensing. In this paper, we propose a novel graph convolutional network (GCN) empowered indoor localization method, which aggregates channel state information (CSI) features extracted from multiple multiple-input multiple-output (MIMO) links. CSI features from multiple antennas are basically converted into graph nodes in order to adopt GCN classification model. At the same time, graph attention mechanism is introduced to study and transfer spatial and frequency of CSI features. Eventually, output of graph is mapped with multiple measurement points through prediction network to provide final estimate position. 5GHz commercial Wi-Fi equipment is respectively utilized for data collection and experimental evaluation in two representative indoor scenarios. Experimental result shows that the proposed method has better performance in robust localization compared to other state-of-the-art deep learning methods.

Radio frequency-based device-free passive perception (RF-DFPP) is considered as one of the most promising techniques for ubiquitous smart applications in the WiFi field due to its extremely low deployment cost. Existing RF-DFPP methods typically employ received signal strength indicator (RSSI), ignoring the potential benefits of fine-grained sensing accuracy of channel state information (CSI). In addition, the robustness of such sensing methods is not good at present. To solve the problem, in this paper, we propose a robust CSI-based RF-DFPP method using a combination network of convolutional neural networks (CNN) and attention-based bi-directional long short term memory (LSTM). The combined network can extract the signal features of the collected CSI through CNN, and then realize RF-DFPP recognition through the training of LSTM and attention layers. Simulation results show that the proposed method significantly improves the recognition accuracy compared with the existing methods. Moreover, it performs robustly even if the model training is done under the different datasets.

Pelotherapy is the application of thermal muds (peloids) for therapeutic purposes. Artificial peloids were prepared usingpyrophilite shale maturated in three different types of thermal water in terms of their pH values. The samples after 30and 60 days of maturation were examined by X-ray diffraction. No significant variations in the mineralogical compositionand diffractograms of pyrophillite peloids were detected after maturation. Only the influence of the maturation processof pyrophillite on the pH value of mineral water with high and low pH value is noticed.

This book provides a solution to the control and motion planning design for an octocopter system. It includes a particular choice of control and motion planning algorithms which is based on the authors' previous research work, so it can be used as a reference design guidance for students, researchers as well as autonomous vehicles hobbyists. The control is constructed based on a fault tolerant approach aiming to increase the chances of the system to detect and isolate a potential failure in order to produce feasible control signals to the remaining active motors. The used motion planning algorithm is risk-aware by means that it takes into account the constraints related to the fault-dependant and mission-related maneuverability analysis of the octocopter system during the planning stage. Such a planner generates only those reference trajectories along which the octocopter system would be safe and capable of good tracking in case of a single motor fault and of majority of double motor fault scenarios. The control and motion planning algorithms presented in the book aim to increase the overall reliability of the system for completing the mission.

Polygenic risk scores (PRSs) are a promising approach to accurately predict an individual’s risk of developing disease. The area under the receiver operating characteristic curve (AUC) of PRSs in their population are often only reported for models that are adjusted for age and sex, which are known risk factors for the disease of interest and confound the association between the PRS and the disease. This makes comparison of PRS between studies difficult because the genetic effects cannot be disentangled from effects of age and sex (which have a high AUC without the PRS). In this study, we used data from the UK Biobank and applied the stacked clumping and thresholding method and a variation called maximum clumping and thresholding method to develop PRSs to predict coronary artery disease, hypertension, atrial fibrillation, stroke and type 2 diabetes. We created case-control training datasets in which age and sex were controlled by design. We also excluded prevalent cases to prevent biased estimation of disease risks. The maximum clumping and thresholding PRSs required many fewer single-nucleotide polymorphisms to achieve almost the same discriminatory ability as the stacked clumping and thresholding PRSs. Using the testing datasets, the AUCs for the maximum clumping and thresholding PRSs were 0.599 (95% confidence interval [CI]: 0.585, 0.613) for atrial fibrillation, 0.572 (95% CI: 0.560, 0.584) for coronary artery disease, 0.585 (95% CI: 0.564, 0.605) for type 2 diabetes, 0.559 (95% CI: 0.550, 0.569) for hypertension and 0.514 (95% CI: 0.494, 0.535) for stroke. By developing a PRS using a dataset in which age and sex are controlled by design, we have obtained true estimates of the discriminatory ability of the PRSs alone rather than estimates that include the effects of age and sex.

Congenital anomalies (CA) are any abnormality present at birth, either structural or functional, that may potentially affect an infant’s health, development, and/or survival. There is a paucity of studies on clinical characteristics and outcomes of CA in Bosnia and Herzegovina, mainly due to the lack of a nationwide congenital malformations monitoring system. A 5-year hospital-based study was conducted to determine the prevalence at birth and clinical characteristics of selected major CA in Sarajevo Canton, Bosnia and Herzegovina. Ninety-one CA were observed from 2012 to 2016 (the overall prevalence was 39.6 cases/10,000 live births). The mean age of neonates at diagnosis was 3 days. The gastrointestinal tract was the most commonly affected system (76.9%), with esophageal atresia (EA) being the most frequent (17.6% of all CA). Major CA were more prevalent among preterm infants than term infants (P = .001), particularly in males (61.5% vs. 38.5%; P = .028; M:F ratio was 1.59). Multiple CA were seen in 37.4% of neonates. The overall mortality rate of neonates was 11%, and the median length of hospital stay was 19.8 days. Our study revealed the distribution and clinical patterns of common major CA in the largest tertiary care facility in Bosnia and Herzegovina. It also confirmed a relatively high mortality rate, which requires further efforts to improve the quality of neonatal care in the country.

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.

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.