ABSTRACT
The sustainable development of human society and economy needs the support of senior talents. Postgraduate teaching is one of the crucial components of higher education, and the priority method to cultivate senior talents. The 7th United Nations STI Forum in 2022 will focus on open science and postgraduate teaching;the theme of the forum is "While comprehensively implementing the 2030 Agenda for Sustainable Development, strengthen science, technology, and innovation, and promote the world's recovery from the COVID-19 pandemic". Therefore, the analysis of the field of postgraduate teaching is of great theoretical and practical significance to the cultivation of postgraduate students, the research of researchers, and the management of postgraduate teaching by the education sector. This research has carried out a bibliometric analysis to better obtain the knowledge structure in the field of postgraduate teaching and research, and help other researchers obtain the characteristics of the field of postgraduate teaching and research. VOSviewer and CiteSpace are used to analyze 4816 scientific core collection articles related to postgraduate teaching. These publications are from the Web of Science database. The dates of the articles range from 1995-2022. This research intuitively introduces a systematic overview of postgraduate teaching literature research, covering a number of publications, major categories, the most significant nations, groups, publications, writers, significant literature, and academic trends. The goal of this article is to create a map of the postgraduate teaching knowledge structure, while also examining the research collaboration across organizations, authors, nations, and areas. For scholars and practitioners in the field of graduate education, objective advice and helpful ideas are given through the analysis and discussion of the data acquired.
ABSTRACT
Intense scientific interest in the mechanisms of aerosol transport has been aroused due to the global COVID-19 pandemic. In this study, a new droplet evaporation model considering solid components such as salt, has proposed to simulate the diffusion and evaporative flow behaviors of saliva-forming aerosols droplets, caused by human breathing, coughing and sneezing. The model considers the evaporation process on the surface of aerosols and couples the droplet kinetic equations, including the incorporation of influencing factors such as flow resistance, gravity, droplet-like size and initial velocity. Different ambient temperatures, relative humidity and wind speed have been simulated and the mechanisms of aerosols migration behaviors have been analyzed. For individual droplet, the results not only show that the larger the droplet size, the longer it remains suspended in airborne, but also the lower the humidity and the higher the temperature, the faster the evaporation rate. © 2022, Science Press. All right reserved.
ABSTRACT
This research paper investigated the adoption behavior of college students toward the e-learning system amidst the current COVID-19. The model was developed and validated based on the Technology Acceptance Model (TAM) and the partial least squares-structural equation modeling (PLS-SEM) was used to analyze the data. The data was generated from 316 Chinese college students based on the convenient sampling technique. The research outcomes indicate that the perceived ease of use is a significant predictor of the intention to use and perceived usefulness of an e-learning system. To our surprise, perceived usefulness does not determine the intention to use an e-learning system. Computer self-efficacy and technical support respectively were significant determinants of the perceived usefulness and the perceived ease of use of an e-learning system. Interestingly, the study showed that internet experience does not influence students’ sensitivity to the usefulness and ease of use associated with an e-learning system. The theoretical and managerial implications of these results findings are thoroughly interrogated. © The Author(s) 2022.
ABSTRACT
Under the pandemic of COVID-19, people experiencing COVID19-related symptoms have a pressing need to consult doctors. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialog system that can provide COVID19-related consultations. We collected two dialog datasets - CovidDialog - (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19. While the largest of their kind, these two datasets are still relatively small compared with generaldomain dialog datasets. Training complex dialog generation models on small datasets bears high risk of overfitting. To alleviate overfitting, we develop a multi-task learning approach, which regularizes the data-deficient dialog generation task with a masked token prediction task. Experiments on the CovidDialog datasets demonstrate the effectiveness of our approach. We perform both human evaluation and automatic evaluation of dialogs generated by our method. Results show that the generated responses are promising in being doctorlike, relevant to conversation history, clinically informative and correct. © 2021 Association for Computational Linguistics.