Search

Purpose: Virtual collaboration provides students with an opportunity to develop cultural intelligence while fitting into the team where the members are from diverse cultures. The purpose of this study is to explore whether global virtual team (GVT) projects raise students' understanding of cultural differences. In addition, it is interesting to know how internationally disruptive events such as the coronavirus disease 2019 (COVID-19) pandemic influence GVT projects. Design/methodology/approach: The research involved two parts--In the first part, a two-wave longitudinal study was conducted to investigate how intercultural sensitivity and intercultural communication competence coevolve within a group of international students enrolled in a virtual business professional project. In the second part, using word clouds and topic modelling on the participants' perceptions, the study investigated whether the sudden disruption caused by the pandemic show similar results in performance, focussing primarily on the resilience of virtual teams. Further, the study explored participants' perceptions towards online learning in higher education institutions as well as the attitude of corporate organizations towards remote working in the post-pandemic years. Findings: The results confirmed that GVT projects, in fact, do raise students' understanding of cultural differences and the need to adjust their behaviour accordingly in order to engage with their culturally different counterparts effectively. Participants reported an increase in their cognitive, behavioural and affective attributes. Research limitations/implications: Among the limitations of this study is the relatively small number of student participants. Furthermore, the number of respondents from India dominated the sample. Since the Indian students were disproportionately affected by the shutdown, causing them to return often to rural areas with poor Internet connectivity, responses concerning the disruption caused by the pandemic may be overriding negative. The same could be said of responses from US-American students, who often rely heavily on-campus employment or whose parents became unemployed during the pandemic, and thus were faced with disproportionate economic insecurity. Practical implications: This paper provides insights to the educators and international organizations on how such projects provide the skills essential for reducing costs, accessing knowledge, skills and abilities (KSAs) across borders, maintaining flexible work schedules and arrangements, and taking advantage of multiple time zones to increase productivity. Originality/value: While highlighting the significance of cultural intelligence, this paper investigated how the sudden disruption caused by a crisis such as the COVID-19 pandemic impacts performance, focussing primarily on the resilience of virtual teams.

The global rise of populism and concomitant polarizations across disenfranchised and marginalized groups has been magnified by so-called echo chambers, and a major public health crisis like the COVID-19 pandemic has only served to fuel these intergroup tensions. Media institutions disseminating information on ways to prevent the propagation of the virus have reactivated a specific discursive phenomenon previously observed in many epidemics: the construction of a defiled 'Other'. With anthropological lenses, discourse on defilement is an interesting path to understand the continuous emergence of pseudo-scientific forms of racism. In this paper, the authors focus on 'borderline racism', that is the use of an institutionally 'impartial' discourse to reaffirm the inferiority of another race. The authors employed inductive thematic analysis of 1200 social media comments reacting to articles and videos published by six media in three different countries (France, United States and India). Results delineate four major themes structuring defilement discourses: food (and the relationship to animals), religion, nationalism and gender. Media articles and videos portrayed Western and Eastern countries through contrasting images and elicited a range of reaction in readers and viewers. The discussion reflects on how borderline racism can be an appropriate concept to understand the appearance of hygienic othering of specific subgroups on social media. Theoretical implications and recommendations on a more culturally sensitive approach of media coverage of epidemics and pandemics are discussed. [ABSTRACT FROM AUTHOR]

The first Covid-19 patient in India was reported on January 30, 2020 at the state of Kerala. The patient number rose to three by February 3, 2020. In the month of March 2020, the transmissions started to increase when the people started to return back to India from the Covid-19 affected countries. On March 12, a 76-year-old man having a travel history to Saudi Arabia was the first reported fatality in India due to Covid 19. Then for the prevention of the propagation of Covid, the Indian government declared a state of health emergency and strict counter measures were taken, including locking down of cities, prohibiting almost all avoidable activities and restricting population's mobility. From March 24, 2020 due to the complete lockdown in the country, human activities were heavily restricted in the whole geographical regions of India. This pandemic lockdown eventually serves as an opportunity to observe the background concentrations of pollutants in the atmosphere. The PM 2.5 distribution can affect human health and to overcome this problem, setting up of regulation for PM is necessary. In the present study Probability density functions (PDF) method have been utilised for the investigation of PM 2.5 pollutant data distribution of five countries namely, India, China, France, Brazil and United States of America (USA) for their respective lockdown period of 2020 and corresponding same period of 2019. A detailed study has been done for India, and for that purpose India has been divided into three regions (Central India, Coastal India and Indo-Gangetic Plain (IGP)) on the basis of different meteorological conditions. PM 2.5 concentration for hourly basis has been analysed for the lockdown period 24th March to 15th June 2020 and compared with the PM 2.5 concentration of previous year 2019 for the same time period. To understand the effect of lockdown in PM 2.5 emission in India, which will give us an idea about the background concentration, PDFs (probability density functions) have also been generated for the whole year from 2015 to 2019. The "goodness-of-fit" of the probability density functions, to the data, was assessed, using various statistical indices (Chi-square test). Results show that the PM 2.5 reduction during the lockdown period of 2020 as compared to the same period of 2019 is sufficiently large. This study will give a certain degree of idea to the regulatory bodies on planning and implementation of strict air quality control plans., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Within the recent pandemic, scientists and clinicians are engaged in seeking new technology to stop or slow down the COVID-19 pandemic. The benefit of machine learning, as an essential aspect of artificial intelligence, on past epidemics offers a new line to tackle the novel Coronavirus outbreak. Accurate short-term forecasting of COVID-19 spread plays an essential role in improving the management of the overcrowding problem in hospitals and enables appropriate optimization of the available resources (i.e., materials and staff).This paper presents a comparative study of machine learning methods for COVID-19 transmission forecasting. We investigated the performances of deep learning methods, including the hybrid convolutional neural networks-Long short-term memory (LSTM-CNN), the hybrid gated recurrent unit-convolutional neural networks (GAN-GRU), GAN, CNN, LSTM, and Restricted Boltzmann Machine (RBM), as well as baseline machine learning methods, namely logistic regression (LR) and support vector regression (SVR). The employment of hybrid models (i.e., LSTM-CNN and GAN-GRU) is expected to eventually improve the forecasting accuracy of COVID-19 future trends. The performance of the investigated deep learning and machine learning models was tested using confirmed and recovered COVID-19 cases time-series data from seven impacted countries: Brazil, France, India, Mexico, Russia, Saudi Arabia, and the US. The results reveal that hybrid deep learning models can efficiently forecast COVID-19 cases. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models. Furthermore, results showed that LSTM-CNN achieved improved performances with an averaged mean absolute percentage error of 3.718%, among others., (Copyright © 2021 Elsevier Inc. All rights reserved.)

This paper studies the interplay between social distancing and the spread of the COVID-19 disease-a global pandemic that has affected most of the world's population. Our goals are to (1) to observe the correlation between the strictness of social distancing policies and the spread of disease and (2) to determine the optimal adoption level of social distancing policies. The earliest instances of the virus were found in China, and the virus has reached the United States with devastating consequences. Other countries severely affected by the pandemic are Brazil, Russia, the United Kingdom, Spain, India, Italy, and France. Although it is impossible to stop it, it is possible to slow down its spread to reduce its impact on the society and economy. Governments around the world have deployed various policies to reduce the virus spread in response to the pandemic. To assess the effectiveness of these policies, the system's dynamics of the society needs to be analyzed, which is generally not possible with mathematical linear equations or Monte Carlo methods because human society is a complex adaptive system with continuous feedback loops. Because of the challenges with the other methods, we chose agent-based methods to conduct our study. Moreover, recent agent-based modeling studies for the COVID-19 pandemic show significant promise in assisting decision-makers in managing the crisis by applying policies such as social distancing, disease testing, contact tracing, home isolation, emergency hospitalization, and travel prevention to reduce infection rates. Based on modeling studies conducted in Imperial College, increasing levels of interventions could slow the spread of disease and infection. We ran the model with six different percentages of social distancing while keeping the other parameters constant. The results show that social distancing affects the spread of COVID-19 significantly, in turn decreasing the spread of disease and infection rates when implemented at higher levels. We also validated these results by using the behavior space tool with ten experiments with varying social distancing levels. We conclude that applying and increasing social distancing policy levels leads to a significant reduction in infection spread and the number of deaths. Both experiments show that infection rates are reduced drastically when social distancing intervention is implemented between 80% to 100%.

We investigate the climatic influence on COVID-19 transmission risks in 228 cities globally across three climatic zones. The results, based on the application of a Boosted Regression Tree algorithm method, show that average temperature and average relative humidity explain significant variations in COVID-19 transmission across temperate and subtropical regions, whereas in the tropical region, the average diurnal temperature range and temperature seasonality significantly predict the infection outbreak. The number of positive cases showed a decrease sharply above an average temperature of 10°C in the cities of France, Turkey, the US, the UK, and Germany. Among the tropical countries, COVID-19 in Indian cities is most affected by mean diurnal temperature, and those in Brazil by temperature seasonality. The findings have implications on public health interventions, and contribute to the ongoing scientific and policy discourse on the complex interplay of climatic factors determining the risks of COVID-19 transmission. [ABSTRACT FROM AUTHOR]