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The transmission from offline activities to online activities due to the social disorder evolved from COVID-19 pandemic lockdown has led to increase in the online economic and social activities. In this regard, the Automatic Keyword Extraction (AKE) from textual data has become even more interesting due to its application over different domains of Natural Language Processing (NLP). It is observed that the Graphical Keyword Extraction Techniques (GKET) use Graph of Words (GoW) in literature for analysis in different dimensions. In this article, efforts have been made to study these different dimensions for GKET, namely, the GoW representation, the statistical properties of GoW, the stability of the structure of GoW, the diversity in approaches over GoW for GKET, and the ranking of nodes in GoW. To elucidate these different dimensions, a comprehensive survey of GKET is carried in different domains to make some inferences out of the existing literature. These inferences are used to lay down possible research directions for interdisciplinary studies of network science and NLP. In addition, the experimental results are analysed to compare and contrast the existing GKET over 21 different dataset, to analyse the Word Co-occurrence Networks (WCN) for 15 different languages, and to study the structure of WCN for different genres. In this article, some strong correspondences in different disciplinary approaches are identified for different dimensions, namely, GoW representation 'Line Graphs' and 'Bigram Words Graphs'; Feature extraction and selection using eigenvalues 'Random Walk' and 'Spectral Clustering'. Different observations over the need to integrate multiple dimensions has open new research directions in the inter-disciplinary field of network science and NLP, applicable to handle streaming data and language-independent NLP.In an overwhelming demand scenario, such as the SARS-CoV-2 pandemic, pressure over health systems may outburst their predicted capacity to deal with such extreme situations. Therefore, in order to successfully face a health emergency, scientific evidence and validated models are needed to provide real-time information that could be applied by any health center, especially for high-risk populations, such as transplant recipients. We have developed a hybrid prediction model whose accuracy relative to several alternative configurations has been validated through a battery of clustering techniques. Using hospital admission data from a cohort of hospitalized transplant patients, our hybrid Data Envelopment Analysis (DEA)-Artificial Neural Network (ANN) model extrapolates the progression towards severe COVID-19 disease with an accuracy of 96.3%, outperforming any competing model, such as logistic regression (65.5%) and random forest (44.8%). In this regard, DEA-ANN allows us to categorize the evolution of patients through the values of the analyses performed at hospital admission. Our prediction model may help guiding COVID-19 management through the identification of key predictors that permit a sustainable management of resources in a patient-centered model.

The online version contains supplementary material available at 10.1007/s10462-021-10008-0.

The online version contains supplementary material available at 10.1007/s10462-021-10008-0.Research suggests that mindfulness is associated with psychological health including a healthier response to stressors.

This research tested associations between trait mindfulness and mental health factors related to the novel coronavirus (COVID-19).

Two studies (Study 1

=248 college students; Study 2

=300U.S adults) assessed trait mindfulness, perceived stress and anxiety, worry about the coronavirus, and anticipated negative affect of a coronavirus diagnosis. Additionally, Study 2 assessed depressive symptoms and coping with the coronavirus.

In both studies, findings indicated that individuals higher in trait mindfulness reported less stress and anxiety. Higher mindfulness in both studies was also associated with less worry about the virus and anticipating less negative affect if one gets the virus. In Study 2, trait mindfulness was negatively related to depression, and numerous associations between mindfulness and coping emerged, showing higher trait mindfulness was associated with healthier strategies in coping with coronavirus.

These data are consistent with research that has revealed that those who think and act more mindfully are less stressed and anxious. By revealing these associations with mindfulness in the context of a real-world, novel stressor, this research makes an important contribution to the literature.

These data are consistent with research that has revealed that those who think and act more mindfully are less stressed and anxious. By revealing these associations with mindfulness in the context of a real-world, novel stressor, this research makes an important contribution to the literature.Human recognition systems based on biometrics are much in demand due to increasing concerns of security and privacy. selleck chemicals The human ear is unique and useful for recognition. It offers numerous advantages over popular biometrics traits face, iris, and fingerprints. A lot of work has been attributed to ear biometric, and the existing methods have achieved remarkable success over constrained databases. However, in unconstrained environment, a significant level of difficulty is observed as the images experience various challenges. In this paper, we first have provided a comprehensive survey on ear biometric using a novel taxonomy. The survey includes in-depth details of databases, performance evaluation parameters, and existing approaches. We have introduced a new database, NITJEW, for evaluation of unconstrained ear detection and recognition. A modified deep learning models Faster-RCNN and VGG-19 are used for ear detection and ear recognition tasks, respectively. The benchmark comparative assessment of our database is performed with six existing popular databases. Lastly, we have provided insight into open-ended research problems worth examining in the near future. We hope that our work will be a stepping stone for new researchers in ear biometrics and helpful for further development.