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.

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 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.

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.

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.

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.

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.

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.

Abstract Cathodic protection system is commonly employed approach for the protection of the metallic infrastructure placed in electrolyte against corrosion. Adequate design of the cathodic protection system requires the determination of electrical potential and current density distribution on the protected object surface that meets the defined criteria. In this paper, the application of the direct boundary element method in conjunction with Newton-Raphson method was considered for the calculation of the electric potential and current density distribution on the surface of the cathodically protected underground object. The considered method was applied on the sacrificial anode cathodic protection system of the underground pipeline. The non-linear boundary conditions of the electrode surfaces of the cathodic protection system are taken into account. The method was used to determine the current density and electric potential distribution on the external wall of the protected pipeline.

Wastewater from industrial process of uranium ore mining contains a large amount of this radioactive pollutant. Regarding the advantages of biosorption, it was found that varieties of biomasses such as agricultural waste, algae and fungi are effective for uranium removal. However, there is limited research on cyanobacteria, therefore, cyanobacteria, Anagnostidinema amphibium (CAA) was investigated by batch method for the first time for biosorption of uranium (VI). Optimization of biosorption parameters showed that maximum removal efficiency of 92.91% was reached at pH range of 9-11 with 50 mg of cyanobacteria to 100 mg/L U(VI) initial concentration, at 25 °C within 40 min. Used biosorbent exhibited very good selectivity for U(VI) ions and reusability in IV sorption/desorption cycles. Characterization of CAA surface was performed by FTIR, EDS, EDXRF and SEM analysis and it has shown various functional groups (CONH, COOH, OH, PO alkyl group) and that it is very rich in elements such as iron, potassium and calcium. In binary systems, contained of U(VI) and selected ions, CAA exhibits very good selectivity towards U(VI) ions. Kinetic data revealed the best accordance of experimental data with the pseudo-second-order model, and isotherms data agreed with Freundlich model. Thermodynamic data implied that U(VI) biosorption process by A. amphibium exhibited spontaneity and modelling of the investigated process showed that the adsorption of uranium ions occurs mainly via peptidoglycan carboxyl groups. Overall results show that these cyanobacteria with a maximum sorption capacity of 324.94 mg/g have great potential for the processing of wastewater polluted with uranium (VI).

Abstract The global Covid-19 pandemic led to the turn toward online learning and the need to adapt to new ways of English foreign language (EFL) teaching. This mixed-methods research aimed to examine teacher attitudes, well-being, and coping strategies related to online EFL teaching. The results indicated that Croatian EFL teachers had a positive attitude toward online teaching and acknowledged the importance of being digitally competent. EFL teachers were unsure about their ability to deal with online teaching and indicated their need to improve their IT skills. They revealed more negative than positive responses regarding their well-being and showed that they tried to actively cope during the pandemic. Results also showed better coping strategies among female teachers.