Miracle-dz/UniFAR-FTUs huggingface dataset:
Collision-Free Velocity Tracking of a Moving Ground Target by Multiple Unmanned Aerial Vehicles

In this paper, we present a controller for collision-free velocity tracking of a moving ground target by multiple unmanned aerial vehicles (UAVs). The controller combines a feedforward proportional-derivative (PD) control term and a term that is based on the gradient of an artiﬁcial potential function. We use Lasalle’s invariance principle to analytically prove the convergence of the UAVs to a ﬁxed formation above the target that tracks the target’s velocity and provide mathematical guarantees on the UAVs’ collision avoidance. As a result, the Euclidean distance between each pair of UAVs approaches a constant value at equilibrium. In the event of UAV failure, the remaining UAVs reconﬁgure to a new ﬁxed formation and maintain collision-free tracking of the target’s velocity, demonstrating the robustness of our control approach to failure. We validate this control approach on diﬀerent simulated scenarios in MATLAB and the Gazebo robot simulator. We also experimentally test the performance of the control approach on physical robots, using Crazyﬂie quadrotors as the UAVs and a Turtlebot3 Burger robot as the moving ground target. The simulation and experimental results demonstrate the eﬀectiveness of our control approach at collision-free tracking and its robustness to UAV failure.
Soil Respiration Characteristics in Three Shrublands along an Elevation Gradient in a Semiarid Loess Plateau Mountain Area

ABSTRACT Shrub is one of the major vegetation types distributed mostly in the mountainous area in China, and its vegetation carbon storage is approximately one-third of both forests and grasslands. It is essential to investigate how soil temperature (Ts) and soil water content (Ws) affect soil respiration (Rs) in this ecosystem. The purpose of this study was to understand the correlations of Rs with Ts, Ws, and other factors in the shrubs. In the current study, Rs was characterized in three shrublands (hereafter, shrub 1, shrub 2, and shrub 3, respectively) located in different elevations over a 4-year period at a biweekly interval in the eastern Loess Plateau (Shanxi province) of China. Our results showed that the trend of seasonal change of Rs was controlled mainly by Ts and Ws. The measured mean Rs over 4 years was 3.64 ± 2.83 (mean ± S.D.), 2.69 ± 2.05, and 4.41 ± 3.28 μmol carbon dioxide (CO2) m−2 s−1 for shrubs 1, 2, and 3, respectively, exhibiting an increase trend with elevation increment. Over the season, Rs illustrated a significant change depending on the variation of Ts and Ws, with larger values appearing in summer when both Ts and Ws were high, and smaller values in winter or in summer whenever Ws was low. An exponential model (Rs = a e bTs) fitted well the relation between Rs and Ts for shrub 3, whereas linear (Rs = a Ws + b) and power (Rs = a Ws b) models of Rs to Ws fitted well for shrub 1. This indicated that at a lower elevation, Ws had a greater effect on Rs than that at a higher elevation. The reverse trend was true between Rs and Ts, i.e., at a higher elevation Ts had a greater effect on Rs than that at a lower elevation. The calculated Q10 values of 1.61, 3.03, and 3.73 for shrubs 1, 2, and 3 increased to 2.25, 3.63, and 4.07, respectively (when the data in low Ws conditions were excluded from the analysis), showing that Q10 increased with elevation increment. Furthermore, three two-variable models, one linear (Rs = a (Ts Ws) + b), and two nonlinear (Rs = a Ts b Ws c and Rs = a ebTs Ws c), were also well developed to predict the dependency of Rs on both Ts and Ws. Our research results might have important implications for the estimation of soil carbon emissions of the shrublands in this region.
Sparse matrix products for neural network compression

Over-parameterization of neural networks is a well known issue that comes along with their great performance. Among the many approaches proposed to tackle this problem, low-rank tensor decompositions are largely investigated to compress deep neural networks. Such techniques rely on a low-rank assumption of the layer weight tensors that does not always hold in practice. Following this observation, this paper studies sparsity inducing techniques to build new sparse matrix product layer for high-rate neural networks compression. Speciﬁcally, we explore recent advances in sparse optimization to replace each layer’s weight matrix, either convolutional or fully connected, by a product of sparse matrices. Our experiments validate that our approach provides a better compression-accuracy trade-oﬀ than most popular low-rank-based compression techniques
SARS-CoV-2-specific humoral and cell-mediated immune responses after immunization with inactivated COVID-19 vaccine in kidney transplant recipients (CVIM 1 study)

Immunogenicity following inactivated SARS‐CoV‐2 vaccination among solid organ transplant recipients has not been assessed. Seventy‐five patients (37 kidney transplant [KT] recipients and 38 healthy controls) received two doses, at 4‐week intervals, of an inactivated whole‐virus SARS‐CoV‐2 vaccine. SARS‐CoV‐2‐specific humoral (HMI) and cell‐mediated immunity (CMI) were measured before, 4 weeks post‐first dose, and 2 weeks post‐second dose. The median (IQR) age of KT recipients was 50 (42–54) years and 89% were receiving calcineurin inhibitors/mycophenolate/corticosteroid regimens. The median (IQR) time since transplant was 4.5 (2–9.5) years. Among 35 KT patients, the median (IQR) of anti‐RBD IgG level measured by CLIA after vaccination was not different from baseline, but was significantly lower than in controls (2.4 [1.1–3.7] vs. 1742.0 [747.7–3783.0] AU/ml, p < .01) as well as percentages of neutralizing antibody inhibition measured by surrogate viral neutralization test (0 [0–0] vs. 71.2 [56.8–92.2]%, p < .01). However, the median (IQR) of SARS‐CoV‐2 mixed peptides‐specific T cell responses measured by ELISpot was significantly increased compared with baseline (30 [4–120] vs. 12 [0–56] T cells/106 PBMCs, p = .02) and not different from the controls. Our findings revealed weak HMI but comparable CMI responses in fully vaccinated KT recipients receiving inactivated SARS‐CoV‐2 vaccination compared to immunocompetent individuals (Thai Clinical Trials Registry, TCTR20210226002).
Disability Training for Health Care Workers: A Global Narrative Systematic Review

Objective: To conduct a systematic review of the literature on disability training to improve knowledge, confidence, self-efficacy and competence among health care workers around the world. Methods: We searched five databases for relevant peer-reviewed articles published between January 2012 and January 2021. Studies that focused on training health care workers to improve knowledge, confidence, self-efficacy, and competence to support people with physical, sensory, or intellectual impairments were included. Data about the details of the intervention (setting, participants, format, impact assessments, etc.) and its effects were extracted. Findings: There is an array of highly local tools to train health workers across stages of their training and careers (pre-service, in-service, and continuing professional development). Studies involving people with disabilities in the training, community placements, simulations, or interactive sessions were found to be most effective in improving knowledge, confidence, competency, and self-efficacy. Conclusion: As part of initiatives to build inclusive health systems and improve health outcomes for people with disabilities, health workers around the world need to receive appropriate and evidence-based training that combine multiple methods and involve people with disabilities.
Azithromycin in patients with Covid-19; a systematic review and metanalysis

Background: Azithromycin (AZM) has been widely used in the management of Covid-19. However, the evidence on its actual effects remains disperse and difficult to apply in clinical settings. This systematic review and metanalysis summarizes the studies on the beneficial and adverse effect of AZM in patients with Covid-19. Methods: The PRISMA 2020 statement criteria were followed. Randomized controlled trials (RCTs) and observational studies comparing clinical outcomes of patients treated, and not treated, with AZM, indexed until the 5th of July 2021, were searched in PubMed, Embase, The Web of Science, Scopus, The Cochrane Central Register of Controlled Trials, and MedRXivs. We used Random-effects models to estimate pooled effect size from aggregate data. Results: The initial search produced 4950 results. Finally, 16 studies, five RCTs and 11 with an observational design, with a total of 22984 patients, were included. The metanalysis showed no difference in mortality for those treated, or not, with AZM, OR: 0.95 (0.79-1.13). There was also no significant difference for those treated, and not, with AZM in need for hospital admission or time to admission from ambulatory settings, clinical severity, need for intensive care, or adverse effects. Conclusions: These results presented in this review do not support the use of AZM in the management of Covid-19. They also show that any harm caused to the patient who received it is unlikely. Future research on treatment for patients with Covid-19 may need to focus on other drugs
Utility Valuation of Credit Derivatives and Application to CDOs

We study the impact of risk-aversion on the valuation of credit derivatives. Using the technology of utility-indifference pricing in intensity-based models of default risk, we analyze resulting yield spreads in both simple single-name credit derivatives, and complex multi-name securities, particularly CDOs. We introduce the diversity coefficient that characterizes the effects of defaultable investment opportunities. The impact of risk-averse valuation on CDO tranche spreads is also expressed in terms of implied correlations.
An “occlusive thrombosis-on-a-chip” microfluidic device for investigating the effect of anti-thrombotic drugs

Cardiovascular disease remains one of the world's leading causes of death. Myocardial infarction (heart attack) is triggered by occlusion of coronary arteries by platelet-rich thrombi (clots). The development of new anti-platelet drugs to prevent myocardial infarction continues to be an active area of research and is dependent on accurately modelling the process of clot formation. Occlusive thrombi can be generated in vivo in a range of species, but these models are limited by variability and lack of relevance to human disease. Although in vitro models using human blood can overcome species-specific differences and improve translatability, many models do not generate occlusive thrombi. In those models that do achieve occlusion, time to occlusion is difficult to measure in an unbiased and objective manner. In this study we developed a simple and robust approach to determine occlusion time of a novel in vitro microfluidic assay. This highlighted the potential for occlusion to occur in thrombosis microfluidic devices through off-site coagulation, obscuring the effect of anti-platelet drugs. We therefore designed a novel occlusive thrombosis-on-a-chip microfluidic device that reliably generates occlusive thrombi at arterial shear rates by quenching downstream coagulation. We further validated our device and methods by using the approved anti-platelet drug, eptifibatide, recording a significant difference in the “time to occlude” in treated devices compared to control conditions. These results demonstrate that this device can be used to monitor the effect of antithrombotic drugs on time to occlude, and, for the first time, delivers this essential data in an unbiased and objective manner.
Formulation and Evaluation of Cox-2 Inhibitor (Etoricoxib) Loaded Ethyl Cellulose Nanoparticles for Topical Drug Delivery

The aim of this investigation was to formulate, characterize and evaluate etoricoxib (ET) loaded polymeric nanoparticles for topical delivery. For nanoprecipitation method, ethyl cellulose (EC) was used as polymers. All the formulations were prepared by varying the drug and polymer concentrations. The obtained nanoparticles were evaluated for yield, drug content, entrapment efficiency, loading capacity and in-vitro drug release. Comparative study was performed among the formulations of ethyl cellulose. For the formulation of the gel, carbopol 934 was used as a gelling base. By comparison, F3 formulation of ethyl cellulose was found to be the best with the highest entrapment efficiency of 79.1%, the smallest mean particle diameter (538 nm), a higher stability (–43.8 mV) and the ability to control the release for 12 h with 87.1% drug release. F3 formulation was incorporated into gel F3G. Based on the results, it could be concluded that F3G formulation of etoricoxib topical gel prepared with ethyl cellulose was found to be more efficient with the highest spreadability of 41.22 g.cm/sec and was able to sustain the drug release for about 12 h with a cumulative release of 79.1%.
#ME TOO MOVEMENT: INFLUENCE OF SOCIAL MEDIA ENGAGEMENT ON INTENTION TO CONTROL SEXUAL HARASSMENT AGAINST WOMEN

Social media provide a common platform to the public to speak about social injustice and put grievances about the policies. They also unite and act against crimes. Social Media has been instrumental in the propagation of gender inequality protests around the world. This purpose of study is to assess the influence of the “#Me Too movement” on intention to control sexual harassment against women and further to suggest a framework using mass communication as a tool to create awareness and control sexual harassment against women. Multivariate data analysis was conducted through Partial Least Squares Path Modeling (PLS-SEM) to analyze the data. The questionnaire was circulated through WhatsApp and emails to 800 Indian participants. The results suggested that social media movement like “#Me Too movement” has a positive influence on the intention to control sexual harassment against women. This research showed that awareness about sex-based crimes could be increased with participation in these kinds of social movements which in turn leads to the formation of an intention to control sexual harassment against women.
AN OVERVIEW ON SOLID LIPID NANOPARTICLES AS A NOVEL DRUG DELIVERY SYSTEM IN SKIN SUN PROTECTION

Lipid based carriers have attracted increasing scientific and commercial attention during the last few years as an alternative material for novel drug delivery of various drug molecules such as peptides and proteins concerned with stability issues such delivery systems are discussed and exemplified regarding both more traditional lipid based delivery systems such as liposomes and lipid emulsions as well as more novel structures, e.g., lipid microtubules, microbubbles, and solid lipid particulate systems. [1] Several local or systemic therapeutic applications may be foreseen, such as immunization with protein antigens, infectious diseases treatment, chronic diseases and cancer therapy. [2] In this study, we focused on SLN applications as sunscreen topical products and its efficacy (its skin protection strength against UV radiation).
A Novel Approach for Network Intrusion Detection Using Multistage Deep Learning Image Recognition

The current rise in hacking and computer network attacks throughout the world has heightened the demand for improved intrusion detection and prevention solutions. The intrusion detection system (IDS) is critical in identifying abnormalities and assaults on the network, which have grown in size and pervasiveness. The paper proposes a novel approach for network intrusion detection using multistage deep learning image recognition. The network features are transformed into four-channel (Red, Green, Blue, and Alpha) images. The images then are used for classification to train and test the pre-trained deep learning model ResNet50. The proposed approach is evaluated using two publicly available benchmark datasets, UNSW-NB15 and BOUN Ddos. On the UNSW-NB15 dataset, the proposed approach achieves 99.8% accuracy in the detection of the generic attack. On the BOUN DDos dataset, the suggested approach achieves 99.7% accuracy in the detection of the DDos attack and 99.7% accuracy in the detection of the normal traffic.
Transformers for Ischemic Stroke Infarct Core Segmentation from Spatio-temporal CT Perfusion Scans

The infarct core size is a crucial biomarker for treatment selection for ischemic stroke patients. For this purpose, we present a novel approach to perform infarct core segmentation using CT perfusion (CTP) source data, without ordinary deconvolution analysis. We propose the use of transformers to encode sequential CTP scans in spatial attention maps, which we subsequently use for infarct core segmentation. We report new top results on the ISLES 2018 challenge test data set for infarct core segmentation. This work presents a primary benchmark for infarct core segmentation from CTP source data using transformers.
PRACTICES FOR THE RETENTION OF SCARCE AND CRITICAL TECHNICAL SKILLS: THE CASE OF THE KWAZULU-NATAL DEPARTMENT OF TRANSPORT

The KwaZulu-Natal Department of Transport has experienced a continuous staff turnover, especially regarding scarce and critical technical skills, is not efficient. Preliminary research findings discovered that appointed employees only remain in the Department of Transport for periods not exceeding five years on average and then resign or move to the private sector, municipalities, or non-governmental organizations. The study aims to explore the employers’ retention practices for managing critical skills, using the KwaZulu-Natal Department of Transport as a case study. The study seeks to explore the practices employed in order to retain scarce and critical technical skills. The study employed an exploratory qualitative research approach and used focus groups and interviews as data collection methods. The purposive sample that was used in this study involved ten senior managers and 30 skilled technical employees including engineers, production engineering technicians, and surveyors. The study revealed that the main reason for the exits of technical and scarce skilled employees in the Department is linked to remuneration. The Department is unable to offer competitive salaries. The Department must develop different drivers of retention to make them competitive beyond remuneration. The study also revealed that the Department has struggled to retain skilled technical employees for many years due to retention strategies that are not competitive. The study recommends that the Department of Public Service and Administration (DPSA) should open discussions on the review of the Occupation Specific Dispensation.
Testing the Differential Impact of COVID-19 on Self-Employed Women and Men in the United Kingdom

This paper investigates whether the female self-employed are more affected by the COVID-19 crisis than the male self-employed using longitudinal data four months following the first 'lockdown' in the UK. We specifically test the role of family/social, economic and psychological factors on gendered differential impact. We find that self-employment exits are not gendered but women are more likely to experience reductions in hours worked and earnings. This greater adverse impact on women's working hours and earnings is despite family responsibilities and home-schooling, industrial gender segregation and women's greater propensity to run a non-employing business and to work part-time. However, lower attitude to risk in women is associated with lower risk of reduction in earnings. Policy needs to look beyond business exits when considering crisis support for the self-employed.
Fine-Grained Facial Expression Recognition using Machine Learning

: A face emotion detection system that can analyses basic human facial expressions is being developed in this project. The proposed method analyses a person's face to determine his or her mood, and then plays an audio file that is related to that person's emotion based on the information obtained from the face. The system will first recognize a human face and then proceed to the next step in the process, and so on. The process of face detection is carried out. Following that, the technique of feature extraction is used to recognize the human face. This method aids in the recognition of the human's emotion by utilizing features of the face image. Those feature points are discovered through the feature extraction of the lip, mouth, and eyes, as well as the brow. If the input face matches exactly to the face in the emotions base dataset, we can determine the exact emotion of the human and play the audio file that corresponds to the exact emotion of the human. In addition, we recommend music based on the mood that has been detected. Recognition under a variety of environmental conditions can be achieved through training on a small number of distinguishing characteristics of different faces. A straightforward, efficient, and accurate approach is proposed here. In the field of recognition and detection, systems play a critical role in achieving success.
INTERPRETATION OF SHARIA ACCOUNTING PRACTICES IN INDONESIA

The aim of this study is to disclose a theory and practice of accounting in Indonesia on the basis of the Qur'an and Hadith. A review of research in several journals and associated books was used in this investigation process. This study is designed to study Sharia Accounting theology, interpreted sharia accounting theory, and accounting practices in Indonesia. The study is linked to Al Qur’an and Hadith. The results of this survey showed that the theory of sharia accounting is all about the Qur'an and Hadith. Maslahah and Maqasid al-Shari'ah are scaled in the world and beyond when implementing sharia accounting theory. In Islamic theory, Islamic accounting requires prioritization of group interests, not personal interests. Islamist accounts can be split in concept into three concepts: rahmatan lil 'alamin, spirituality concept, ukhuwah islamiah.
Prediction of Student’s Performance Using Support Vector Machine Classifier

Analyzing students’ performances based on both subjective and quantitative components is fundamental because sometimes these performances and so many other factors have led students to quit their studies or be dropped out of the institutions. This dropout rate is much higher in undergrad students than other educational stages. The first-year result of a student is very vital since within the larger part of cases this drives them to be either inspired or demotivated. So, the second-year result of an eminent university in Bangladesh is examined in this paper. This paper is basically based on finding the factors for students’ distinctive sorts of performances and after that predicting students’ results based on those six noteworthy variables. For this reason, support vector machines (SVM) has been utilized for classifying students’ diverse levels of outcomes and anticipating students’ performances. The input dataset for both training and testing were taken by merging the values obtained from two surveys done on students and experts utilizing an adaptive neuro-fuzzy interference system (ANFIS). The application of the proposed model can moreover be enhanced in predicting course-wise performances of the students and its precision can too be improved by adding new factors, increasing survey participants