Miracle-dz/UniFAR-FTUs huggingface dataset:
Leading Innovative Work-Behavior in Times of COVID-19: Relationship Between Leadership Style, Innovative Work-Behavior, Work-Related Flow, and IT-Enabled Presence Awareness During the First and Second Wave of the COVID-19 Pandemic

Aim: The present study contributes to the conversation on remote (home) working, leadership, and innovation in times of COVID-19 by examining the mediating role of work-related flow in the relationship between empowering and directive leadership, on the one hand, and innovative work-behavior, on the other, and the moderating role of IT-enabled presence awareness in two lockdown periods during the pandemic. Method: We employed PLS-SEM analysis to analyze the perceptions, experiences, and behaviors of a group of employees (N = 257) regarding the study’s core variables during two phases of the COVID-19 pandemic (summer 2020 and autumn 2020). Results: In line with expectations, in the earlier phase of the pandemic, empowering leadership had both a positive direct and indirect relationship with innovative work-behavior via work-related flow, whereas directive leadership only had a negative direct relationship with innovative work-behavior. In the second phase, however, empowering leadership only had a positive indirect relationship with innovative work-behavior, running via work-related flow. Moreover, directive leadership was both directly and indirectly negatively related to innovative work-behavior, via work-related flow. In contrast to our expectations, IT-enabled presence awareness did not play a moderating role in these relationships in any phase. Discussion: Our findings underline the importance of empowerment in sustaining innovative work-behavior, particularly in intense and enduring remote work contexts, as this can amplify employees’ ability, motivation and opportunity to generate, share and implement novel ideas. In remote work contexts, empowering leadership can particularly foster innovation indirectly via work-related flow, which was also shown to be an increasingly important underlying mechanism across time periods. Directive leadership, in contrast, can reduce work-related flow and, therefore, hinder innovation. Our study did not find evidence for the moderating role of employees’ perceptions of IT-enabled presence awareness. Conclusion: We conclude that regardless of the IT-quality, the leadership style chosen plays an important role in innovative work-behavior in remote work-contexts, particularly in view of the divergent effects of empowering and directive leadership on work-related flow in enduring and intense remote work contexts.
Social Comparison Information Influences Intentions to Reduce Single-Use Plastic Water Bottle Consumption

Single-use plastic consumption is at an all-time high and threatens environmental and human health. College campuses in particular serve as a hub for single-use plastics due to their convenience for students on the go. The present research tests whether social comparison information can influence self-perceptions of single-use plastic consumption and motivate behavior change within the college campus environment. In a controlled experiment, we measured college students' existing plastic water bottle usage and gave them false feedback about their behaviors and relative standing to their classmates: participants in comparison conditions learned they were either above or below average in their plastic water bottle sustainability behaviors. Results indicated that (relative to a no-comparison control), being above average at water bottle sustainability led students to be more satisfied with their sustainability efforts. However, either kind of comparison information (i.e., being above or below average) led to greater behavioral intentions to reduce single-use plastic water bottle consumption in the future. This study highlights how comparison information can be used to motivate sustainable behavior change with regards to single-use plastics.
The Relationship Between the COVID-19 Pandemic and Vaccine Hesitancy: A Scoping Review of Literature Until August 2021

Background: Vaccines have been contributing to eradicate or drastically reduce the incidence of common diseases. Simultaneously, vaccine hesitancy is considered among the top ten global health threats. The COVID-19 pandemic has caused a tremendous impact on health, economics, and society worldwide, while also reinforcing faulty beliefs about the necessity of vaccine programs as a whole. This study aims to synthesise evidence on the impact of the COVID-19 pandemic on vaccine hesitancy. Methods: A scoping review of literature between 1 January 2020 and 1 August 2021 was performed. Results: COVID-19 vaccine acceptance decreased from more than 70 to <50% in 8 months starting from January 2020. Healthcare professionals demonstrate higher rates of vaccine receptivity than the public, which was more influenced by (social) media. The circulation of misinformation was associated with increased fear of side effects related to COVID-19 vaccines. Regarding other vaccines coverage, parents' intentions to vaccinate their children against influenza increased 15.8% during the COVID-19 pandemic so far. Nonetheless, the number of vaccines administered decreased, influenced by factors like fear of being exposed to the virus at healthcare facilities and restrictions. Conclusions: Several efforts should be undertaken to improve vaccine acceptance and coverage now and beyond the pandemic to optimal population protection.
Accurate Prediction of Band Structure of FeS2: A Hard Quest of Advanced First-Principles Approaches

The pyrite and marcasite polymorphs of FeS2 have attracted considerable interests for their potential applications in optoelectronic devices because of their appropriate electronic and optical properties. Controversies regarding their fundamental band gaps remain in both experimental and theoretical materials research of FeS2. In this work, we present a systematic theoretical investigation into the electronic band structures of the two polymorphs by using many-body perturbation theory with the GW approximation implemented in the full-potential linearized augmented plane waves (FP-LAPW) framework. By comparing the quasi-particle (QP) band structures computed with the conventional LAPW basis and the one extended by high-energy local orbitals (HLOs), denoted as LAPW + HLOs, we find that one-shot or partially self-consistent GW (G 0 W 0 and GW 0, respectively) on top of the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation with a converged LAPW + HLOs basis is able to remedy the artifact reported in the previous GW calculations, and leads to overall good agreement with experiment for the fundamental band gaps of the two polymorphs. Density of states calculated from G 0 W 0@PBE with the converged LAPW + HLOs basis agrees well with the energy distribution curves from photo-electron spectroscopy for pyrite. We have also investigated the performances of several hybrid functionals, which were previously shown to be able to predict band gaps of many insulating systems with accuracy close or comparable to GW. It is shown that the hybrid functionals considered in general fail badly to describe the band structures of FeS2 polymorphs. This work indicates that accurate prediction of electronic band structure of FeS2 poses a stringent test on state-of-the-art first-principles approaches, and the G 0 W 0 method based on semi-local approximation performs well for this difficult system if it is practiced with well-converged numerical accuracy.
A High-Resolution Regional Climate Model Physics Ensemble for Northern Sub-Saharan Africa

While climate information from General Circulation Models (GCMs) are usually too coarse for climate impact modelers or decision makers from various disciplines (e.g., hydrology, agriculture), Regional Climate Models (RCMs) provide feasible solutions for downscaling GCM output to finer spatiotemporal scales. However, it is well known that the model performance depends largely on the choice of the physical parameterization schemes, but optimal configurations may vary e.g., from region to region. Besides land-surface processes, the most crucial processes to be parameterized in RCMs include radiation (RA), cumulus convection (CU), cloud microphysics (MP), and planetary boundary layer (PBL), partly with complex interactions. Before conducting long-term climate simulations, it is therefore indispensable to identify a suitable combination of physics parameterization schemes for these processes. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis product ERA-Interim as lateral boundary conditions, we derived an ensemble of 16 physics parameterization runs for a larger domain in Northern sub-Saharan Africa (NSSA), northwards of the equator, using two different CU-, MP-, PBL-, and RA schemes, respectively, using the Weather Research and Forecasting (WRF) model for the period 2006–2010 in a horizontal resolution of approximately 9 km. Based on different evaluation strategies including traditional (Taylor diagram, probability densities) and more innovative validation metrics (ensemble structure-amplitude-location (eSAL) analysis, Copula functions) and by means of different observation data for precipitation (P) and temperature (T), the impact of different physics combinations on the representation skill of P and T has been analyzed and discussed in the context of subsequent impact modeling. With the specific experimental setup, we found that the selection of the CU scheme has resulted in the highest impact with respect to the representation of P and T, followed by the RA parameterization scheme. Both, PBL and MP schemes showed much less impact. We conclude that a multi-facet evaluation can finally lead to better choices about good physics scheme combinations.
Prediction Impairment May Explain Communication Difficulties in Autism

A central research question on autism is how the communication difficulties of autistic individuals can be explained. In this opinion paper we put forward the hypothesis that autistic individuals have problems with language because of an underlying impairment in the ability to generate and update predictions about language. Our hypothesis combines well-established findings from the past decade indicating that linguistic predictions facilitate faster language processing with recent evidence suggesting that autistic individuals show abnormalities in predictions outside the field of language. Investigating linguistic predictions in autism can help clarify the mechanisms underlying the communication difficulties of individuals with ASD. Our hypothesis subsumes earlier mechanistic explanations involving theory of mind and executive functions.
COVID-19 Rumor Detection on Social Networks Based on Content Information and User Response

The outbreak of COVID-19 has caused a huge shock for human society. As people experience the attack of the COVID-19 virus, they also are experiencing an information epidemic at the same time. Rumors about COVID-19 have caused severe panic and anxiety. Misinformation has even undermined epidemic prevention to some extent and exacerbated the epidemic. Social networks have allowed COVID-19 rumors to spread unchecked. Removing rumors could protect people’s health by reducing people’s anxiety and wrong behavior caused by the misinformation. Therefore, it is necessary to research COVID-19 rumor detection on social networks. Due to the development of deep learning, existing studies have proposed rumor detection methods from different perspectives. However, not all of these approaches could address COVID-19 rumor detection. COVID-19 rumors are more severe and profoundly influenced, and there are stricter time constraints on COVID-19 rumor detection. Therefore, this study proposed and verified the rumor detection method based on the content and user responses in limited time CR-LSTM-BE. The experimental results show that the performance of our approach is significantly improved compared with the existing baseline methods. User response information can effectively enhance COVID-19 rumor detection.
Fast Rotating Neutron Stars: Oscillations and Instabilities

In this review article, we present the main results from our most recent research concerning the oscillations of fast rotating neutron stars. We derive a set of time evolution equations for the investigation of non-axisymmetric oscillations of rapidly rotating compact objects in full general relativity, taking into account the contribution of a dynamic spacetime. Using our code, which features high accuracy at comparably low computational expense, we are able to extract the frequencies of non-axisymmetric modes of compact objects with rotation rates up to the Kepler limit. We propose various universal relations combining bulk properties of isolated neutron stars as well as of binary systems before and after merger; these relations are independent of the true equation of state and may serve as a valuable tool for gravitational wave asteroseismology. We also present an introductory example using a Bayesian analysis.
Dysfunctional Immune Regulation in Autoimmune Hepatitis: From Pathogenesis to Novel Therapies

Autoimmune hepatitis (AIH) is a chronic inflammatory disorder characterized by hypergammaglobulinemia, presence of serum autoantibodies and histological features of interface hepatitis. AIH therapeutic management still relies on the administration of corticosteroids, azathioprine and other immunosuppressants like calcineurin inhibitors and mycophenolate mofetil. Withdrawal of immunosuppression often results in disease relapse, and, in some cases, therapy is ineffective or associated with serious side effects. Understanding the mechanisms underlying AIH pathogenesis is therefore of paramount importance to develop more effective and well tolerated agents capable of restoring immunotolerance to liver autoantigens. Imbalance between effector and regulatory cells permits liver damage perpetuation and progression in AIH. Impaired expression and regulation of CD39, an ectoenzyme key to immunotolerance maintenance, have been reported in Tregs and effector Th17-cells derived from AIH patients. Interference with these altered immunoregulatory pathways may open new therapeutic avenues that, in addition to limiting aberrant inflammatory responses, would also reconstitute immune homeostasis. In this review, we highlight the most recent findings in AIH immunopathogenesis and discuss how these could inform and direct the development of novel therapeutic tools.
Coffee With a Hint of Data: Towards Using Data-Driven Approaches in Personalised Long-Term Interactions

While earlier research in human-robot interaction pre-dominantly uses rule-based architectures for natural language interaction, these approaches are not flexible enough for long-term interactions in the real world due to the large variation in user utterances. In contrast, data-driven approaches map the user input to the agent output directly, hence, provide more flexibility with these variations without requiring any set of rules. However, data-driven approaches are generally applied to single dialogue exchanges with a user and do not build up a memory over long-term conversation with different users, whereas long-term interactions require remembering users and their preferences incrementally and continuously and recalling previous interactions with users to adapt and personalise the interactions, known as the lifelong learning problem. In addition, it is desirable to learn user preferences from a few samples of interactions (i.e., few-shot learning). These are known to be challenging problems in machine learning, while they are trivial for rule-based approaches, creating a trade-off between flexibility and robustness. Correspondingly, in this work, we present the text-based Barista Datasets generated to evaluate the potential of data-driven approaches in generic and personalised long-term human-robot interactions with simulated real-world problems, such as recognition errors, incorrect recalls and changes to the user preferences. Based on these datasets, we explore the performance and the underlying inaccuracies of the state-of-the-art data-driven dialogue models that are strong baselines in other domains of personalisation in single interactions, namely Supervised Embeddings, Sequence-to-Sequence, End-to-End Memory Network, Key-Value Memory Network, and Generative Profile Memory Network. The experiments show that while data-driven approaches are suitable for generic task-oriented dialogue and real-time interactions, no model performs sufficiently well to be deployed in personalised long-term interactions in the real world, because of their inability to learn and use new identities, and their poor performance in recalling user-related data.
Psychological Distress Among Cancer Patients During COVID-19 Pandemic in the World: A Systematic Review

Aim: Patients with malignancies, experience high rates of psychological distress. Fear of Corona-infection combined with the interruptions in some treatment programs might affect the psychological health of cancer patients. This review study was conducted to investigate the psychological distress among cancer patients during COVID-19 pandemic to offer system-adapted individual solutions. Materials and methods: To identify the psychological distress of cancer patients, a comprehensive search was carried out in PubMed, Web of Science, and Scopus. English language and original articles were included in this study. Articles that addressed any psychological distress among cancer patients during COVID-19 pandemic were included. Results: At first 1,410 articles, were included in the study. After removing duplicate articles and reviewing the title and abstract, 55 articles were selected for the review. The findings of this study revealed COVID-19 greatly affects psychological health of cancer patients. Fear of COVID-19, fear of disease progression, disruption of oncology services, cancer stage, and immunocompromised status were the most common causes of psychological distress in oncology patients which can influence patients' decisions about treatment. Conclusion: The COVID-19 related anxiety is an expected reaction to the current situation. Although psychological distress affects many people, it can confuse cancer patients to the point that they refuse to continue treatment for the fear of infection and worsening of their condition. Since the end of this pandemic is unknown, this action can endanger the health and prognosis of this group of patients, so it seems that using psychological interventions and intensive counseling in the current situation is one of the main priorities for cancer patients.
Explaining the Geographic Pattern of Plant Invasion in 67 Nature Reserves in China

Biological invasion is a serious threat to biodiversity and ecosystem function in nature reserves. However, the knowledge of the spatial patterns and underlying mechanisms of plant invasions in nature reserves is still limited. Based on a recent dataset on both invasive and native plants in 67 nature reserves of China, we used correlation, regression, and variation partitioning methods to statistically assess the relative roles of the “human activity,” “biotic acceptance,” and “environmental heterogeneity” hypotheses in explaining the geographic pattern of plant invasion. A total of 235 invasive plant species were compiled from 67 nature reserves. The high explanatory power of the human activity variables supported the human activity hypothesis. The biotic acceptance hypothesis was weakly supported since no significant correlations between climatic variables and invasion levels were found when the effects of the other factors were controlled. The environmental heterogeneity hypothesis was partially supported, since the number of native plants, representing environmental heterogeneity at fine-scale explained remarkable proportion of spatial variance of invasive plants but not that of the proportion of invasive plants. We predict that nature reserves with high plant diversity affected by rapid economic development and increasing temperature will face a serious threat of exotic plant invasion. In conclusion, our results provide crucial clues for understanding geographic variance of plant invasion in China’s nature reserves and spatial risk assessment.
Derivation of a Line-Tension Model for Dislocations from a Nonlinear Three-Dimensional Energy: The Case of Quadratic Growth

In this paper we derive a line tension model for dislocations in 3D starting from a geometrically nonlinear elastic energy with quadratic growth. In the asymptotic analysis, as the amplitude of the Burgers vectors (proportional to the lattice spacing) tends to zero, we show that the elastic energy linearizes and the line tension energy density, up to an overall constant rotation, is identified by the linearized cell problem formula given in [S. Conti, A. Garroni, and M. Ortiz, Arch. Ration. Mech. Anal. , 218 (2015), pp. 699–755].
Eosinophilic features in clear cell renal cell carcinoma correlate with outcomes of immune checkpoint and angiogenesis blockade

Background Clear cell renal cell carcinoma (ccRCC) displays heterogeneity in appearance—a distinctive pale clear to eosinophilic cytoplasm; however, little is known about the underlying mechanisms and clinical implications. We investigated the role of these eosinophilic features in ccRCC on oncological outcomes and response to tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs). Methods One-hundred and thirty-eight ccRCC cases undergoing radical surgery (cohort 1) and 54 metastatic ccRCC cases receiving either TKIs or ICIs (cohort 2) were included. After histological evaluation, all cases were divided into three phenotypes based on the eosinophilic features at the highest-grade area: clear, mixed, or eosinophilic type. Gene expression and immunohistochemical analyses were performed to explore the potential mechanisms of these phenotypes in cohort 1. Further, the association of the three phenotypes with the best objective response to TKI or ICI, clinical benefit (complete/partial response or stable disease), and overall survival (OS) was assessed in cohort 2. Results The clear type was significantly associated with increased hypoxia as well as angiogenesis gene signatures compared with the eosinophilic type. Gene signatures and protein expression related to effector T cell and immune checkpoint molecules were elevated to a greater extent in the eosinophilic type, followed by the mixed and clear types. The mixed and eosinophilic types exhibited greater PBRM1-negativity and increased prevalence of the epithelial-mesenchymal transition gene signature than the clear type. In the mixed/eosinophilic types of cohort 2, significant clinical benefit was observed in the ICI therapy group versus the TKI therapy group (p=0.035), and TKI therapy vs ICI therapy was an independent factor for worse prognosis of OS (HR 3.236; p=0.012). Conclusion The histological phenotype based on the eosinophilic features, which are linked to major immunological mechanisms of ccRCC, was significantly correlated with therapeutic efficacy.