This note gives an overview of the tools for the precision matching of ultraviolet theories to the Standard Model effective field theory (SMEFT) at the tree level and one loop. Several semi- and fully automated codes are presented, as well as some supplementary codes for the basis conversion and the subsequent running and matching at low energies. A suggestion to collect information for cross-validations of current and future codes is made.

Essential role in replication and transcription of coronavirus makes the main protease of SARS-CoV-2 a great traget for drug design. The aim of this study was to predict structural interactions of compounds isolated from the Bosnian-Herzegovinian endemic plant Knautia sarajevensis (G. Beck) Szabó against the 3CLpro of SARS-CoV-2 virus. The three-dimensional crystal structure of SARS-CoV-2 main protease was retrieved from the RCSB Protein Data Bank and the three-dimensional structures of isolated compounds were obtained from the PubChem database. Active site was predicted using PrankWeb, while the preparation of protease and compounds was performed using AutoDock Tools and OpenBabel. Molecular docking was carried out using AutoDock Vina. Structural interactions are visualised and analyzed using PyMOL, LigPlus and UCSF Chimera. Apigenin, kaempferol, myricetin and quercetin showed the highest binding affinity for SARS-CoV-2 main protease and formed significant hydrogen bonds with the given protein. Results obtained in this study are in accordance with previous studies and showed that these compounds could potentially have antiviral effects against SARS-CoV-2. These findings indicate that K. sarajevensis could be potentially utilized as an adjuvant in the treatment of coronavirus disease 2019, but further pharmacological studies are required in order to prove the potential medicinal use of the plant.

ABSTRAKT Akutna bubrežna insuficijencija nakon kontrastnih procedura ili contrast indukovana nefropatija (CIN-Contrast-induced nephropaty) se definiše kao povečanje vrijednosti serum kreatinina najmanje 0,5 % mg/dl ili 25% do 50 % u odnosu na predproceduralne vrijednosti u toku 48 do 72 sata nakon apliciranja kontrasta, u odsustvu drugih objašnjenja za nastanak oštećenja bubrežne funkcije. Ona je odgovorna za pojavu akutne bubrežne insuficijencije u 11 do 14,5 % slućajeva. CILJ STUDIJE: Cilj ove studije je određivanje rizićnih grupa pacijenata i faktora kao I profilaktičkih mjera za kontrastnu nefropatiju nakon koronarografije. U studiju je ukljućeno 200 pacijenata (dijabetičari i nedijabetičari) koji su uradili koronarografiju zbog indikacije na koronarnu bolest. ZAKLJUČAK: Rizićni faktori za nastanak kontrastne nefropatije kod ovih pacijenata su, šećerna bolest, starija životna dob, aplicirana veća kolićina kontrasta, prethodne bolesti bubrega i srćana slabost.

We present a new algorithm called policy iteration plus (PI+) for the optimal control of nonlinear deterministic discrete-time plants with general cost functions. PI+ builds upon classical policy iteration and has the distinctive feature to enforce recursive feasibility under mild conditions, in the sense that the minimization problems solved at each iteration are guaranteed to admit a solution. While recursive feasibility is a desired property, it appears that existing results on the policy iteration algorithm fail to ensure it in general, contrary to PI+. We also establish the recursive stability of PI+: the policies generated at each iteration ensure a stability property for the closed-loop system. We prove our results under more general conditions than those currently available for policy iteration, by notably covering set stability. Finally, we present characterizations of near-optimality bounds for PI+ and prove the uniform convergence of the value functions generated by PI+ to the optimal value function. We believe that these results would benefit the burgeoning literature on approximate dynamic programming and reinforcement learning, where recursive feasibility is typically assumed without a clear method for verifying it and where recursive stability is essential for safe operation of the system.

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.

This study is the first report on the mtDNA profile of human settlements of the Konjuh and Majevica mountains of northeastern Bosnia. The aims of this study were: a) determination of mitochondrial genetic structure of populations of the Konjuh and Majevica mountains of northeastern Bosnia; b) detection of trace of ancient of mtDNA variations; and c) assessment of genetic relations with other Bosnian and Herzegovina populations and neighboring populations from the Balkan region. The genetic structure of populations of Konjuh and Majevica is shaped by western Eurasian maternal signals, which may trace their ancestry to the Paleolithic, pre-Neolithic and Neolithic. Especially interesting is the feature of the Neolithic expansion in this area. This applies especially to the presence of the pre-Neolithic lineages HV*and N1a in northeastern Bosnia, which can indicate an early settlement of this region of Bosnia by pre-Neolithic populations from the Middle East. This region abounds with resources of salt sites, which might suggest in favor of the thesis that the early-Neolithic colonists needed a safe source of salts so as to settle in the Balkan area. The populations of mountains of northeastern Bosnia indicate elements of the local population history, but they do not show strict genetic closure in relation to the neighboring populations of the Balkans. This may be a consequence of the population size, degree of geographic isolation and events of migration.

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.

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.

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.

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.

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.