Miracle-dz/UniFAR-FTUs huggingface dataset:
Using a cultural and social identity lens to understand pandemic responses in the US and India

The world over, countries have been racing to control the spread of the Covid-19 pandemic. Central to the mitigation of the virus spread is the ability of nations to ensure behavior of its people adheres to the constraints imposed in the wake of the pandemic. However, there has been much variation in how individuals and collectives have responded in conformance to expected behavioral changes necessitated by the pandemic. The paper offers a cross-cultural and social identity perspective based on group categorizations to understand the variation in pandemic responses in the context of two different countries, that of India and the United States. Relevant cultural dimensions of difference shaping behavior such as individualism-collectivism, power distance, and other cultural norms shaping divergent behavioral responses in the US and India are examined. Differing group categorizations relevant for each country are also explored to understand the dynamics of behavioral response, be it adherence to mask wearing and following norms of social distancing, or the migrant labor exodus in India from urban to rural areas amidst the first wave of the pandemic. Implications for managing behavioral responses considering cross-cultural differences and group categorization processes are also discussed.
Generative inter-class transformations for imbalanced data weather classification

This paper presents an evaluation of how data augmentation and inter-class transformations can be used to synthesize training data in low-data scenarios for single-image weather classification. In such scenarios, augmentations is a critical component, but there is a limit to how much improvements can be gained using classical augmentation strategies. Generative adversarial networks (GAN) have been demonstrated to generate impressive results, and have also been successful as a tool for data augmentation, but mostly for images of limited diversity, such as in medical applications. We investigate the possibilities in using generative augmentations for balancing a small weather classification dataset, where one class has a reduced number of images. We compare intra-class augmentations by means of classical transformations as well as noise-to-image GANs, to interclass augmentations where images from another class are transformed to the underrepresented class. The results show that it is possible to take advantage of GANs for inter-class augmentations to balance a small dataset for weather classification. This opens up for future work on GAN-based augmentations in scenarios where data is both diverse and scarce.
A review on Privacy-Preserving Data Preprocessing

Health care information has great potential for improving the health care system and also providing fast and accurate outcomes for patients, predicting disease outbreaks, gaining valuable information for prediction in future, preventing such diseases, reducing healthcare costs, and improving overall health. In any case, deciding the genuine utilization of information while saving the patient's identity protection is an overwhelming task. Regardless of the amount of medical data it can help advance clinical science and it is essential to the accomplishment of all medicinal services associations, at the end information security is vital. To guarantee safe and solid information security and cloud-based conditions, It is critical to consider the constraints of existing arrangements and systems for the social insurance of information security and assurance. Here we talk about the security and privacy challenges of high-quality important data as it is used mainly by the healthcare structure and similar industry to examine how privacy and security issues occur when there is a large amount of healthcare information to protect from all possible threats. We will discuss ways that these can be addressed. The main focus will be on recently analyzed and optimized methods based on anonymity and encryption, and we will compare their strengths and limitations, and this chapter closes at last the privacy and security recommendations for best practices for privacy of preprocessing healthcare data.
Family Communication and Psychological Distress in the Era of COVID-19 Pandemic: Mediating Role of Coping

This study aims to examine the association between family communication and psychological distress with coping as a potential mediator. The study also developed and validated the Family Communication Scale (FCS) in the context of COVID-19 pandemic. Participants (n = 658; 74.9% female) were general public ranged in age between 18 and 58 years (mean age = 26.38, SD = 10.01). The results showed that family communication directly influenced psychological distress and indirectly influenced through approach coping. However, avoidant coping was not directly associated with psychological distress, nor did it mediate the association between family communication and psychological distress. The findings suggest that people, who have better family communication, highly engage in approach coping which in turn leads to better psychological health in face of adversity. The findings have important empirical and theoretical implications.
Critical Updates on Chronic Hepatitis B Virus Infection in 2021

Chronic hepatitis B virus (HBV) infection is a global healthcare burden in the form of chronic liver disease, cirrhosis, liver failure and liver cancer. There is no definite cure for the virus and even though extensive vaccination programs have reduced the burden of liver disease in the future population, treatment options to eradicate the virus from the host are still lacking. In this review, we discuss in detail current updates on the structure and applied biology of the virus in the host, examine updates to current treatment and explore novel and state-of-the-art therapeutics in the pipeline for management of chronic HBV. Furthermore, we also specifically review clinical updates on HBV-related acute on chronic liver failure (ACLF). Current treatments for chronic HBV infection have seen important updates in the form of considerations for treating patients in the immune tolerant phase and some clarity on end points for treatment and decisions on finite therapy with nucleos(t)ide inhibitors. Ongoing cutting-edge research on HBV biology has helped us identify novel target areas in the life cycle of the virus for application of new therapeutics. Due to improvements in the area of genomics, the hope for therapeutic vaccines, vector-based treatments and focused management aimed at targeting host integration of the virus and thereby a total cure could become a reality in the near future. Newer clinical prognostic tools have improved our understanding of timing of specific treatment options for the catastrophic syndrome of ACLF secondary to reactivation of HBV. In this review, we discuss in detail pertinent updates regarding virus biology and novel therapeutic targets with special focus on the appraisal of prognostic scores and treatment options in HBV-related ACLF.
Functional impairment in bronchiectasis: Spirometry parameters versus St. George's Respiratory Questionnaire scores: Any co-relation?

Objectives: Bronchiectasis is a common respiratory disease which has significant morbidity and mortality. Health-related quality of life scores are not routinely used for the assessment of bronchiectasis. The present study was undertaken with an aim to assess the clinical profile and functional impairment using spirometry in patients with bronchiectasis and to co-relate functional impairment with their St. George's Respiratory Questionnaire (SGRQ) score. Methodology: This was a cross-sectional study carried out on 102 patients of bronchiectasis. All patients were assessed for clinical profile, spirometry, and SGRQ scores. Forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and FEV1/FVC were measured and compared with SGRQ scores. Data analysis was done using SPSS version 20.0 and MS-Excel. Results: Obstruction was found in 62.7% and significant bronchodilator reversibility was observed in 30.4%. All spirometry parameters individually and combined showed a negative co-relation which was stastically significant (P < 0.001). Best co-relation was with FEV1 r = −0.809; symptom score, r = −0.821; activity score, r= −0.849; impact score and r = −0.873 total score. FVC% versus symptoms score r = −0.735; activity score r = −0.729, impacts score r = −0.778; total score r = −0.792. FEV1/FVC versus symptoms score r = −0.227, activity score r = −0.278, impacts score r = −0.263, total score r = −0.274. Conclusion: SGRQ scores have shown good correlation with functional impairment. It can be used as a modality to evaluate health status of patient in resource constraint settings.
DNN-Based Low-Musical-Noise Single-Channel Speech Enhancement Based on Higher-Order-Moments Matching

SUMMARY We propose deep neural network (DNN)-based speech enhancement that reduces musical noise and achieves better auditory impressions. The musical noise is an artifact generated by nonlinear signal processing and negatively a ﬀ ects the auditory impressions. We aim to develop musical-noise-free speech enhancement methods that suppress the musical noise generation and produce perceptually-comfortable enhanced speech. DNN-based speech enhancement using a soft mask achieves high noise reduction but generates musical noise in non-speech regions. Therefore, ﬁrst, we deﬁne kurtosis matching for DNN-based low-musical-noise speech enhancement. Kurtosis is the fourth-order moment and is known to correlate with the amount of musical noise. The kurtosis matching is a penalty term of the DNN training and works to reduce the amount of musical noise. We further extend this scheme to standardized-moment matching. The extended scheme involves using moments whose orders are higher than kurtosis and generalizes the conventional musical-noise-free method based on kurtosis matching. We formulate standardized-moment matching and explore how e ﬀ ectively the higher-order moments reduce the amount of musical noise. Experimental evaluation results 1) demonstrate that kurtosis matching can reduce musical noise without negatively a ﬀ ecting noise suppression and 2) newly reveal that the sixth-moment matching also achieves low-musical-noise speech enhancement as well as kurtosis matching.
Phish Catch: Machine Learning Way of Detecting Phishing Websites

: With the advent of 4G technology, the internet became available to masses. Everyone started to use internet services in different spheres of their life, making them vulnerable to diverse threats. One of the primary risks for internet users is Phishing Websites. Instead of breaching the security of systems phishing websites try to fool the users and make them give away the credentials which they are not supposed to share with anyone. In this study, we took 21 features and tried to predict their class i.e legitimate or phish using a supervised learning algorithm
A Cross-cultural Investigation on Attitudes Towards Social Robots: Iranian and Chinese University Students

Recent years have witnessed a growing interest in the study of robotics in academic contexts; however, little attention has been given to the investigation of the effects of social and cultural backgrounds in people’s receptivity of robots, especially across nations. To this end, the present study is a cross–cultural exploration on the Iranian and Chinese attitude towards social robots. Utilizing the adapted version of the questionnaire Negative Attitude towards Robots Scale (NARS), this study explores the effects of cultural background (Chinese vs. Iranian), gender, and previous robot familiarity on robot acceptance. To reach this goal, 320 participants including 150 Iranians (equal males and females) and 170 Chinese (equal males and females), filled in the adapted NARS questionnaire which consists of 27 questions in three clusters: attitude towards interaction with robots, attitude towards the social influence of robots, and attitude towards emotions in interaction with robots. The data were analyzed by employing a three-way ANOVA to investigate the effects of cultural background (Chinese vs. Iranian), gender, and previous robot familiarity on the robot acceptance. The findings indicated that there was a significant difference between Chinese and Iranian respondents’ robot acceptance due to their cultural background, not to their gender neither to their previous familiarity. Therefore, an interaction between cultural factors and robot acceptance was seen between the two cultures. These findings can be useful for educational technologists, robot designers and operators to be more attentive to cultural differences and manufacture more adaptive robots . Minoo Alemi *
Effect of Sizing Agents on Surface Properties of T800 Grade CF and Thermal Aging Time on Mechanical Properties of T800 Grade CF/Epoxy Composites

This paper investigates the effect of sizing agent molecular weight on carbon fiber (CF) surface properties and the effect of thermal aging time on mechanical properties of CF/epoxy composites. The surface properties of three CCF800 CF samples with varying sizing agent molecular weight were characterized by surface morphology, surface roughness, chemical functional groups, and element composition. The results showed that the sample with low molecular weight exhibited low roughness and high proportion of activated carbon atoms. The flexural strength, flexural modulus, and interlaminar shear strength of CCF800/5228 composites were measured at 25°C and 150°C by thermal ageing time 0, 100 h, 250 h, 500 h, and 1000 h. The results showed that the thermal aging time up to 1000 h had little effect on the flexural modulus, and the interlaminar shear performance at 150°C showed a trend of increasing at the beginning and then decreasing.
Modeling image aesthetics through aesthetics-related attributes

Automatic assessment of image aesthetics is a challenging task for the computer vision community that has a wide range of applications. The most promising state-of-the-art approaches are based on deep learning methods that jointly predict aesthetics-related attributes and aesthetics score. In this article, we propose a method that learns the aesthetics score on the basis of the prediction of aesthetics-related attributes. To this end, we extract a multi-level spatially pooled (MLSP) features set from a pretrained ImageNet network and then these features are used to train a Multi Layer Perceptron (MLP) to predict image aesthetics-related attributes. A Support Vector Regression machine (SVR) is finally used to estimate the image aesthetics score starting from the aesthetics-related attributes. Experimental results on the ”Aesthetics with Attributes Database” (AADB) demonstrate the effectiveness of our approach that outperforms the state of the art of about 5.5% in terms of Spearman’s Rankorder Correlation Coefficient (SROCC).
Mathematical Multiobjective Optimization for Metal Hip Arthroplasty with Medical Physics Applications

Total hip metal arthroplasty (THA) model-parameters for a group of commonly used ones is optimized and numerically studied. Based on previous ceramic THA optimization software contributions, an improved multiobjective programming method/algorithm is implemented in wear modeling for THA. This computational nonlinear multifunctional optimization is performed with a number of THA metals with different hardnesses and erosion in vitro experimental rates. The new software was created/designed with two types of Sytems, Matlab and GNU Octave. Numerical results show be improved/acceptable for in vitro simulations. These findings are verified with 2D Graphical Optimization and 3D Interior Optimization methods, giving low residual-norms. The solutions for the model match mostly the literature in vitro standards for experimental simulations. Numerical figures for multifunctional optimization give acceptable model-parameter values with low residual-norms. Useful mathematical consequences/calculations are obtained for wear predictions, model advancements and simulation methodology. The wear magnitude for in vitro determinations with these model parameter data constitutes the advance of the method. In consequence, the erosion prediction for laboratory experimental testing in THA add up to the literature an efficacious usage-improvement. Results, additionally, are extrapolated to efficient Medical Physics applications and metal-THA Bioengineering designs.
Trajectory tracking optimization and control of a three link robotic manipulator for application in casting

In this study, forward kinematics, inverse kinematics, dynamic simulation and control of a three-link robotic manipulator for the pouring of molten metal using a small crucible are described. A 3D Computer Added Design (CAD) model of the robotic manipulator is designed in SolidWorks software. Simulation and control of this robotic manipulator were conducted in MATLAB 2016 b. The robotic manipulator SolidWorks CAD file is first converted into an Extensible Markup Language (XML) file after and then imported into MATLAB/Sim-Mechanics environment. A study on forward and inverse dynamics with a sinusoidal wave and cycloid trajectory as input signal was conducted and the joint actuator is controlled by Proportional-Integral-Derivative (PID) controller having a derivative filter. Trajectory tracking is optimized by Integral of Time multiplied by Absolute Error (ITAE) criteria using a pattern search algorithm. Variation in joint torque with a vertical load on the end-effector is described. It was found that the optimized controller reduced the ITAE error by 93.05%, 94.44%, and 97.6% in join1, joint2, and joint3 respectively.
Implantação de Melhorias de Processos do Registro Acadêmico de uma Universidade Federal através da Técnica BPMN (Business Process Model And Notation)

Toda organização, seja pública ou privada, tem uma razão de ser (missão), além de objetivos e metas de curto, médio e longo prazos (visão). No atual contexto de modernização e inovação, as empresas têm cada vez mais recorrido a melhorias de processos e novas Tecnologias da Informação e Comunicação (TIC’s) para alcançarem maior eficiência operacional, tática e estratégica. No âmbito público, os órgãos governamentais, empresas e instituições públicas têm buscado aplicar novas formas de gestão, novas tecnologias, sistemas e métodos de trabalho, visando maior efetividade dos recursos públicos aplicados em programas governamentais, rotinas administrativas e serviços oferecidos aos cidadãos. Nas Instituições Federais de Ensino Superior (IFES), o ensino, a pesquisa e extensão são consideradas atividades-fim, enquanto que rotinas administrativas por vezes são vistas como atividades-meio. A capacitação dos servidores públicos, a implantação de iniciativas como o Programa Nacional de Gestão Pública e Desburocratização (GesPública), bem como a definição de indicadores de desempenho na Administração Pública têm contribuído para mostrar a grande importância das atividades de gestão em instituições de ensino como as universidades públicas. Embora seja uma das áreas de maior relevância nas universidades por ser responsável por apontamentos primordiais como a matrícula de ingresso e a Colação de Grau, a área de Registro Acadêmico ainda é pouco explorada como objeto de pesquisa em gestão. O objetivo deste trabalho é, a partir da intervenção em alguns processos do Registro Acadêmico de uma Universidade Federal, analisar a interação e possíveis contribuições da ferramenta de mapeamento de processos BPMN (Business Process Model and Notation) para a gestão administrativa. Como uma pesquisa aplicada, o trabalho foi conduzido pelo método da pesquisa-ação, tendo em vista a intervenção no objeto de estudo de forma cooperativa, para resolução de problemas e contribuição para a base do conhecimento. Os resultados parciais entregues foram os mapas dos 03 processos, conforme diagnosticados antes das melhorias implementadas, bem como a análise dos pontos passíveis de melhoria.
Tönnis Grade 1 dysplastic hips have improved patient-reported outcome scores when intraarticular pathology is treated during periacetabular osteotomy

ABSTRACT It is unclear whether treatment of intraarticular pathology should be performed during periacetabular osteotomy (PAO) to improve outcomes. Therefore, we asked: (i) What are the clinical results of PAO in patients with and without intraarticular intervention? (ii) Is there a difference in reoperations with and without intraarticular intervention? and (iii) Is there a difference in clinical results and reoperations depending on preoperative Tönnis Grade if intraarticular intervention is performed? Prospective evaluation of 161 PAO in 146 patients was performed. The cohort was 84.5% female, mean age was 26.7 ± 7.9 years and mean follow-up was 2.4 years; 112 hips had Grade 0 changes and 49 hips had Grade 1 changes. Patients were classified into three groups based on treatments during PAO: major (labral repair, femoral head–neck osteochondroplasty), minor (labral debridement, femoral/acetabular chondroplasty) or no intervention. A subset of eight patient-reported outcome measures (PROMs) was analyzed to determine whether the minimal clinically important difference (MCID) was achieved. Major, minor and no intervention groups exceeded the MCID in 5, 8 and 8, of 8 PROMs (P ≥ 0.20), respectively; intraarticular interventions did not influence reoperation-free survival (P ≥ 0.35). By Tönnis Grade, PROMs exceeding MCID decreased in Grade 1 versus 0 receiving no intervention (P < 0.001) but did not decrease for either intervention (P ≥ 0.14); intraarticular interventions did not influence reoperation-free survival (P ≥ 0.38). Overall, intraarticular intervention was associated with excellent PROMs and reoperation-free survival. Although Grade 1 patients had fewer PROM which achieved MCID, intraarticular interventions attenuated this decrease, suggesting a therapeutic advantage of intraarticular procedures for more advanced pathology.
Different responses of soil organic carbon fractions to additions of nitrogen

Carbon–nitrogen (C–N) interactions in terrestrial ecosystems regulate climate–C cycle feedbacks. How additions of N affect soil C sequestration and then regulate climate change, however, are not fully understood. Previous studies have assessed effects of N on bulk soil organic C (SOC) but have not yet carefully examined its effects on different SOC fractions, which determine how fast the N‐regulated C cycle feeds back to climate. Here we synthesized data from 36 studies with 296 observations by a meta‐analysis to evaluate the responses of SOC fractions to N additions. We hypothesized that additions of N might increase both labile and non‐labile C fractions. The SOC that was separated by density fractionation increased by 18.3% for free light fractions and by 3.0% for heavy density fractions without change in the occluded light fraction under N addition compared with the control. The SOC that was separated by aggregate fractionation increased by 4.4% for macroaggregate‐associated fractions and by 6.5% for aggregate mineral‐associated fractions without change in the microaggregate‐associated fraction under N additions. When bulk SOC was separated by chemical permanganate oxidation, the oxidizable fraction increased by 10.4%, whereas the unoxidizable fraction increased by 4.5%. The changes in different soil C fractions were related to the mechanisms of SOC stabilization. Because of the significant increases in non‐labile SOC fractions, our findings suggest that on a global scale, increased additions of N might promote SOC accumulation and slow down climate change in the long term. Our results will be useful in model development for better prediction of SOC sequestration under additions of N.