Impact of Social Media on Travel Behaviors during the COVID-19 Pandemic: Evidence from New York City

During the outbreak of COVID-19, people's reliance on social media for pandemic-related information exchange, daily communications, and online professional interactions increased because of self-isolation and lockdown implementation. Most of the published research addresses the performance of nonpharmaceutical interventions (NPIs) and measures on the issues impacted by COVID-19, such as health, education, and public safety;however, not much is known about the interplay between social media use and travel behaviors. This study aims to determine the effect of social media on human mobility before and after the COVID-19 outbreak, and its impact on personal vehicle and public transit use in New York City (NYC). Apple mobility trends and Twitter data are used as two data sources. The results indicate that Twitter volume and mobility trend correlations are negative for both driving and transit categories in general, especially at the beginning of the COVID-19 outbreak in NYC. A significant time lag (13 days) between the online communication rise and mobility drop can be observed, thereby providing evidence of social networks taking quicker reactions to the pandemic than the transportation system. In addition, social media and government policies had different impacts on vehicular traffic and public transit ridership during the pandemic with varied performance. This study provides insights on the complex influence of both anti-pandemic measures and user-generated content, namely social media, on people's travel decisions during pandemics. The empirical evidence can help decision-makers formulate timely emergency responses, prepare targeted traffic intervention policies, and conduct risk management in similar outbreaks in the future.

Impact of Social Media on Travel Behaviors during the COVID-19 Pandemic: Evidence from New York City.

During the outbreak of COVID-19, people's reliance on social media for pandemic-related information exchange, daily communications, and online professional interactions increased because of self-isolation and lockdown implementation. Most of the published research addresses the performance of nonpharmaceutical interventions (NPIs) and measures on the issues impacted by COVID-19, such as health, education, and public safety; however, not much is known about the interplay between social media use and travel behaviors. This study aims to determine the effect of social media on human mobility before and after the COVID-19 outbreak, and its impact on personal vehicle and public transit use in New York City (NYC). Apple mobility trends and Twitter data are used as two data sources. The results indicate that Twitter volume and mobility trend correlations are negative for both driving and transit categories in general, especially at the beginning of the COVID-19 outbreak in NYC. A significant time lag (13 days) between the online communication rise and mobility drop can be observed, thereby providing evidence of social networks taking quicker reactions to the pandemic than the transportation system. In addition, social media and government policies had different impacts on vehicular traffic and public transit ridership during the pandemic with varied performance. This study provides insights on the complex influence of both anti-pandemic measures and user-generated content, namely social media, on people's travel decisions during pandemics. The empirical evidence can help decision-makers formulate timely emergency responses, prepare targeted traffic intervention policies, and conduct risk management in similar outbreaks in the future.

Comparative research on the development of college students' physical fitness based on online physical education during the COVID-19 pandemic.

BACKGROUND: There are few studies that focus on the impact of online physical education teaching on college students during the coronavirus disease 2019 (COVID-19) pandemic. This research focuses on the impact of online physical education among medical school students in China by comparing physical fitness test results for three consecutive years from 2019 to 2021. METHOD: This study is a longitudinal survey. The subjects of the experiments were students enrolled in a medical school who completed a physical fitness test for three consecutive years from 2019 to 2021. The student subjects were divided into two groups, namely, male and female. The test indices included body mass index (BMI), vital capacity (VC), 50-metre run, sit-and-reach, standing long jump, pull-up (male), 1000-metre run (male), sit-ups (female) and 800-metre run (female). Repeated measures ANOVA method was used in physical fitness test indices at three consecutive time points ranging from 2019 to 2021. The Greenhouse-Geisser correction was applied when Mauchly's hypothesis test did not meet the assumption of sphericity, and the Bonferroni method was used for pairwise comparisons. RESULTS: A total of 3360 students (1490 males and 1870 females) completed physical fitness tests in three consecutive years from 2019 to 2021. The proportion of overweight and obesity in male students was significantly higher than that in female students (28.0% vs. 12.7%). For all subjects, in 2020, the BMI and VC indexes improved, while the 800-/1000-metre running indexes declined. In 2021, all indexes except sit-and-reach increased. CONCLUSION: The pairwise comparisons of physical fitness test results from 2019 to 2021 show that online physical education is effective in improving all items except long-distance running. Future research needs to involve a larger and geographically more dispersed sample to further analyse the effectiveness of online physical education.

Federal learning edge network based sentiment analysis combating global COVID-19.

As one of the important research topics in the field of natural language processing, sentiment analysis aims to analyze web data related to COVID-19, e.g., supporting China government agencies combating COVID-19. There are popular sentiment analysis models based on deep learning techniques, but their performance is limited by the size and distribution of the dataset. In this study, we propose a model based on a federal learning framework with Bert and multi-scale convolutional neural network (Fed_BERT_MSCNN), which contains a Bidirectional Encoder Representations from Transformer modules and a multi-scale convolution layer. The federal learning framework contains a central server and local deep learning machines that train local datasets. Parameter communications were processed through edge networks. The weighted average of each participant's model parameters was communicated in the edge network for final utilization. The proposed federal network not only solves the problem of insufficient data, but also ensures the data privacy of the social platform during the training process and improve the communication efficiency. In the experiment, we used datasets of six social platforms, and used accuracy and F1-score as evaluation criteria to conduct comparative studies. The performance of the proposed Fed_BERT_MSCNN model was generally superior than the existing models in the literature.

Discovery of Novel and Highly Potent Inhibitors of SARS CoV-2 Papain-Like Protease Through Structure-Based Pharmacophore Modeling, Virtual Screening, Molecular Docking, Molecular Dynamics Simulations, and Biological Evaluation.

Background and Objective: COVID-19 has struck our society as a great calamity, and the need for effective anti-viral drugs is more urgent than ever. Papain-like protease (PLpro) of SARS CoV-2 plays important roles in virus maturation, dysregulation of host inflammation, and antiviral immune responses, which is being regarded as a promising druggable target for the treatment of COVID-19. Here, we carried out a combined screening approach to identify novel and highly potent PLpro inhibitors for the treatment of COVID-19. Methods: We used a combined screening approach of structure-based pharmacophore modeling and molecular docking to screen an in-house database containing 35,000 compounds. SARS CoV-2 PLpro inhibition assay was used to carry out the biological evaluation of hit compounds. Molecular dynamics (MD) simulations were conducted to check the stability of the PLpro-hit complexes predicted by molecular docking. Results: We found that four hit compounds showed excellent inhibitory activities against PLpro with IC50 values ranging from 0.6 to 2.4 µM. Among them, the most promising compound, hit 2 is the best PLpro inhibitor and its inhibitory activity was about 4 times higher than that of the positive control (GRL0617). The study of MD simulations indicated that four hits could bind stably to the active site of PLpro. Further study of interaction analysis indicated that hit 2 could form hydrogen-bond interactions with the key amino acids such as Gln269 and Asp164 in the PLpro-active site. Conclusion: Hit 2 is a novel and highly potent PLpro inhibitor, which will open the way for the development of clinical PLpro inhibitors for the treatment of COVID-19.

Research Status, Hotspots, and Evolutionary Trends of Global Digital Education via Knowledge Graph Analysis

With the rapid development of emerging technologies such as big data, artificial intelligence, and blockchain and their wide application in education, digital education has received widespread attention in the international education field. The outbreak of COVID-19 in December 2019 further catalyzed the digitalization process in various industries, including education, and forced the education system to carry out digital reform and innovation. Digital education transformation has become a new hotspot of great interest in countries around the world and a major direction for education reform practices. Therefore, to better understand the status of global digital education research, this study uses CiteSpace (6.1.R2) visual analysis software to visualize and quantitatively analyze the literature on digital education research in the social science citation index (SSCI). First, the basic information of digital education was analyzed in terms of annual publication volume, authors, countries, and research institutions. Secondly, the main fields, basic contents, and research hotspots of digital education research were analyzed by keyword co-occurrence analysis mapping and keyword time zone mapping. Finally, the research frontiers and development trends of digital education between 2000 and 6 September 2022 were analyzed by cocitation clustering and citations. The results show that, based on the changes in annual publication volume, we can divide the development pulse of the digital education research field into three stages: the budding stage (2000-2006), the slow development stage (2007-2017), and the rapid development stage (6 September 2018-2022);there are 26 core authors in this field of research, among which Selwyn N has the highest number of publications;the USA, England, Spain, Australia, and Germany have the highest number of publications;Open Univ is the institution with the most publications;digital education's research hotspots are mainly focused on interdisciplinary field practice research and adaptive education research based on big data support. The research frontiers are mainly related to five areas: interdisciplinary development, educational equity, digital education practice, digital education evaluation, and digital education governance. This paper systematically analyzes the latest developments in global digital education research, and objectively predicts that human-computer interdisciplinary teaching models and smart education may become a future development trend of digital education. The findings of this study are useful to readers for understanding the full picture of digital education research so that researchers can conduct more in-depth and targeted research to promote better development of digital education.

A hesitation-feedback recommendation approach and its application in large-scale group emergency decision making

Group Decision Making (GDM) has been well studied in the last two decades. Yet, two challenges exist: (a) how to resolve large-scale groups in GDM and achieve the consensus of preferences and (b) how to conduct GDM under risk and emergency conditions. In this paper, we develop a complete problem-solving approach for GDM that orients twofold settings of the complex large-scale group and the time-sensitive emergency decision scenarios. The crux of the matter is to design a feasible mechanism of group consensus strategies in the environment of time pressure and natural language preferences. To solve this problem, we propose a closed-loop mechanism of feedback recommendation strategies accompanied with a new subgroup identification method. This mechanism is underlain by a fourfold decomposition of complex large-scale groups, which entails multiple thresholds of group consensus, group hesitation, and time-related iteration of loops. Our mechanism and the whole GDM approach thoroughly orient the most intuitive representation of preferences - human natural language, which can be elicited and quantitatively formulated in probability linguistic preference systems. We illustrate the proposed approach through a real case study of China's fight against the COVID-19 epidemic. We verify that our mechanism can perfectly tradeoff between the effectiveness and the efficiency of complex large-scale GDM under risk and emergency. The results of this research provide proposals for mechanisms on large-scale GDM and are expected to contribute to emergency management such as epidemic controls, anti-terrorism, and other man-made or natural hazards.

Reliability-based Multi-attribute Large Group Decision Making under Probabilistic Linguistic Environment

This study proposes a more rational and effective multi-attribute large group decision making (MALGDM) method with probabilistic linguistic term set (PLTS) from reliability perspective. A reliability measure method is first proposed to compute the reliability degree of the PLTS, and then a normalization method is presented to normalize the ignorant PLTSs with respect to maximizing their reliability degrees. An efficient clustering method combining the opinion similarity of experts and the reliability degrees of the clusters formed is introduced. Moreover, an objective method of determining the similarity and reliability thresholds is presented. After classifying the large-scale experts, the consensus levels of clusters and the global consensus level are measured and the cluster that need to adjust information is identified based on its consensus level and reliability degree. Then, an optimization model to maximize the global consensus level and the global reliability degree is then built to obtain the evaluation values for improving the consensus levels and reliability degrees. The deviation between the expectation values of the evaluation values before and after adjustment is constrained by the parameter provided by the experts within the cluster that need adjustment. Finally, an application example of the selection of the hotel for isolating the entry personnel during the Covid-19 pandemic and some comparative analyses are provided to validate the proposed method.

Discovery of Novel and Highly Potent Inhibitors of SARS CoV-2 Papain-Like Protease Through Structure-Based Pharmacophore Modeling, Virtual Screening, Molecular Docking, Molecular Dynamics Simulations, and Biological Evaluation

Background and Objective: COVID-19 has struck our society as a great calamity, and the need for effective anti-viral drugs is more urgent than ever. Papain-like protease (PLpro) of SARS CoV-2 plays important roles in virus maturation, dysregulation of host inflammation, and antiviral immune responses, which is being regarded as a promising druggable target for the treatment of COVID-19. Here, we carried out a combined screening approach to identify novel and highly potent PLpro inhibitors for the treatment of COVID-19. Methods: We used a combined screening approach of structure-based pharmacophore modeling and molecular docking to screen an in-house database containing 35,000 compounds. SARS CoV-2 PLpro inhibition assay was used to carry out the biological evaluation of hit compounds. Molecular dynamics (MD) simulations were conducted to check the stability of the PLpro-hit complexes predicted by molecular docking. Results: We found that four hit compounds showed excellent inhibitory activities against PLpro with IC50 values ranging from 0.6 to 2.4 μM. Among them, the most promising compound, hit 2 is the best PLpro inhibitor and its inhibitory activity was about 4 times higher than that of the positive control (GRL0617). The study of MD simulations indicated that four hits could bind stably to the active site of PLpro. Further study of interaction analysis indicated that hit 2 could form hydrogen-bond interactions with the key amino acids such as Gln269 and Asp164 in the PLpro-active site. Conclusion: Hit 2 is a novel and highly potent PLpro inhibitor, which will open the way for the development of clinical PLpro inhibitors for the treatment of COVID-19.

Unidirectional Elimination of Hydrogen by a Giant Local Field Saves First- and Last-Mile Performances of Semiconductor Devices.

Hydrogen, the smallest element, easily forms bonds to host/dopant atoms in semiconductors, which strongly passivates the original electronic characteristics and deteriorates the final reliability. Here, we demonstrate a concept of unidirectional elimination of hydrogen from semiconductor wafers as well as electronic chips through a giant local electric field induced by compact chloridions. We reveal an interactive behavior of chloridions, which can rapidly approach and take hydrogen atoms away from the device surface. A universal and simple technique based on a solution-mediated three-electrode system achieves efficient hydrogen elimination from various semiconductor wafers (p-GaN, p-AlGaN, SiC, and AlInP) and also complete light emitting diodes (LEDs). The p-type conductivity and light output efficiency of H-eliminated UVC LEDs have been significantly enhanced, and the lifetime is almost doubled. Moreover, we confirm that under a one-second irradiation of UVC LEDs, bacteria and COVID-19 coronavirus can be completely killed (>99.93%). This technology will accelerate the further development of the semiconductor-based electronic industry.

Driving factors of virtual water in international grain trade: A study for belt and road countries

Water resources are distributed in the form of virtual water through international trade, which influences the water supply and consumption of each country. Therefore, it is of significance to study the driving factors of grain virtual water trade to alleviate water stress and guarantee food security. In this paper, the virtual water volume of grain crops traded between China and countries along the Belt and Road (B&R) from 2000 to 2019 was calculated, and a gravity model using panel data was applied to explore the effect of natural and socioeconomic factors on virtual water trade. The virtual water export from B&R countries to China obviously increased in the twenty years and the contributions of various crops to virtual water were more balanced. The regression results indicate that GDP and exchange rate were positively correlated with virtual water inflow, while per capital water resources, arable land, geographic distance, and population were negative factors that hindered virtual water import. The most powerful driving force for grain virtual water trade is water endowment. GDP is an important driver on importing virtual water for countries without water shortage, and a large number of local water resources will not obviously inhibit the driving force of economic strength. By comparing the contribution of factors to virtual water in the past ten years, it can be found that the contribution rate of distance decreased due to the development of transportation industry which reduced the transportation cost of exporting products. The contribution rate of GDP and exchange rate increased, because economic globalization has promoted the effect of economic factors on grain trade. Therefore, the trade structure of agricultural products should be modified based on the characteristics of virtual water flow. For countries without high economic level but water shortage, export crops with high water consumption be reasonably controlled. [Display omitted] • A gravity model was applied to explore the effect of natural and socioeconomic factors on virtual water trade. • The most powerful driving force is water endowment, which were negative factor that hindered virtual water inflow. • Economic strength is an important driver on importing virtual water for countries without water shortage. • The contribution rate of distance decreased due to the development of transportation industry and economic globalization. [ FROM AUTHOR] Copyright of Agricultural Water Management is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Mobility in China, 2020: a tale of four phases.

2020 was an unprecedented year, with rapid and drastic changes in human mobility due to the COVID-19 pandemic. To understand the variation in commuting patterns among the Chinese population across stable and unstable periods, we used nationwide mobility data from 318 million mobile phone users in China to examine the extreme fluctuations of population movements in 2020, ranging from the Lunar New Year travel season (chunyun), to the exceptional calm of COVID-19 lockdown, and then to the recovery period. We observed that cross-city movements, which increased substantially in chunyun and then dropped sharply during the lockdown, are primarily dependent on travel distance and the socio-economic development of cities. Following the Lunar New Year holiday, national mobility remained low until mid-February, and COVID-19 interventions delayed more than 72.89 million people returning to large cities. Mobility network analysis revealed clusters of highly connected cities, conforming to the social-economic division of urban agglomerations in China. While the mass migration back to large cities was delayed, smaller cities connected more densely to form new clusters. During the recovery period after travel restrictions were lifted, the netflows of over 55% city pairs reversed in direction compared to before the lockdown. These findings offer the most comprehensive picture of Chinese mobility at fine resolution across various scenarios in China and are of critical importance for decision making regarding future public-health-emergency response, transportation planning and regional economic development, among others.

Reform and exploration of online and offline mixed teaching mode of fermentation engineering under empathy normalization

Under the normalization of the prevention and control of the novel coronavirus pneumonia epidemic, online and offline hybrid teaching has become a new teaching model adopted by many colleges and universities. This course team is student-centered, combined with the advantages of traditional teaching methods (Lecture-Based Learning, LBL) and MOOC teaching, and reconstructed the teaching process of the "fermentation engineering" course, with regard to the teaching objectives, teaching requirements, and teaching chapters of the course A series of reforms and explorations have been carried out on teaching assessment, teaching methods, etc., and the teaching mode of online flipped classrooms and curriculum ideology and politics integrated into the classroom has been tried. Students' enthusiasm for independent learning has been improved, which promotes and enhances the teaching and learning effects of the curriculum.

Impact of Social Media Use on Travel Behavior during COVID19 Outbreak: Evidence from New York City

Understanding the impact of COVID-19 on human mobility has now become one of the most important research questions after the nation-wide impacts of the pandemic have become prominent. Some recent studies address the effect of governmental policies or orders on issues impacted by COVID-19 including health, education, public safety, and, mobility. However, not much is known about the public concerns on social media platforms related to COVID-19 and its impact on people’s travel behaviors. This study investigates social media use and impact on traveling before and after the outbreak of the pandemic. New York City is selected as the case study site for studying the interplay between mobility trends and Twitter data. Results show that social media use is negatively correlated with mobility trends for both driving and transit use, and a significant time lag between tweets and mobility trends were found. Moreover, it is found that different influence mechanisms are resulting from user-generated content and governments/healthcare organizations' actions, both affecting people’s mode choice preferences for vehicle and transit use. This study provides insights into the impacts of COVID-19 by improving the authors' understanding of the complexities of travel behavior in the new information ecosystem due to both traditional sources of information and user-generated content. The findings of this paper can be used to support effective decision-making in response to any similar major disruptive events in the future.

Exploring essential travel during COVID-19 quarantine: Evidence from China.

The COVID-19 has created significant impacts on the economy and individual life around the world. Various countries and cities have adopted corresponding control measures to reduce transport activities and maintain social distance to combat the spread of COVID-19. In the circumstances, residents only maintained essential travel to ensure a normal and fundamental life. In order to explore the impacts of the epidemic and control measures on individually essential travel, we have collected 513 questionnaires between February and March 2020 in China to investigate the various characteristics of essential travel. Using a multivariate logistic regression model, we examine the major factors that potentially impact the mode choices of essential travel. Results show that various socioeconomic, transport supply, health concern and travel purpose have significantly influenced travel mode choices of essential travel. The concept of essential travel will, in the era of port-pandemic, have profound implications on transportation policy making, especially on how to improve the fundamental welfare of the disadvantaged population.

Improving Google Flu Trends for COVID-19 Estimates Using Weibo Posts

While incomplete non-medical data has been integrated into prediction models for epidemics, the accuracy and the generalizability of the data are difficult to guarantee. To comprehensively evaluate the ability and applicability of using social media data to predict the development of COVID-19, a new confirmed case prediction algorithm improving the Google Flu Trends algorithm is established, called Weibo COVID-19 Trends (WCT), based on the post dataset generated by all users in Wuhan on Sina Weibo. A genetic algorithm is designed to select the keyword set for filtering COVID-19 related posts. WCT can constantly outperform the highest average test score in the training set between daily new confirmed case counts and the prediction results. It remains to produce the best prediction results among other algorithms when the number of forecast days increases from one to eight days with the highest correlation score from 0.98 (p < 0.01) to 0.86 (p < 0.01) during all analysis period. Additionally, WCT effectively improves the Google Flu Trends algorithm's shortcoming of overestimating the epidemic peak value. This study offers a highly adaptive approach for feature engineering of third-party data in epidemic prediction, providing useful insights for the prediction of newly emerging infectious diseases at an early stage.

Will Healthcare Workers Accept a COVID-19 Vaccine When It Becomes Available? A Cross-Sectional Study in China.

Objective: The Coronavirus disease 2019 (COVID-19) vaccine is currently available. This timely survey was conducted to provide insight into on the willingness of healthcare workers (HCWs)to receive the vaccine and determine the influencing factors. Methods: This was a cross-sectional online survey. An online questionnaire was provided to all participants and they were asked if they would accept a free vaccine. The questionnaire gathered general demographic information, and included the General Health Questionnaire (GHQ-12); Myers-Briggs Type Indicator questionnaire (MBTI); Depression, Anxiety, and Stress Scales (DASS-21); and the 12-item Short Form Health Survey (SF-12). The data were collected automatically and electronically. Univariate analysis was done between all the variables and our dependent variable. Multivariable logistic regression models were employed to examine and identify the associations between the acceptance of the COVID-19 vaccine with the associated variables. Results: We collected 505 complete answers. The participants included 269 nurses (53.27%), 206 clinicians (40.79%), 15 administrative staff (2.97%), and 15 other staff (2.97%). Of these, 76.63% declared they would accept the vaccine. The major barriers were concerns about safety, effectiveness, and the rapid mutation in the virus. Moreover, four factors were significantly associated with the willingness to receive the vaccine: (a) "understanding of the vaccine" (odds ratio (OR):2.322; 95% confidence interval [CI]: 1.355 to 3.979); (b) "worried about experiencing COVID-19" (OR 1.987; 95% CI: 1.197-3.298); (c) "flu vaccination in 2020" (OR 4.730; 95% CI: 2.285 to 9.794); and (d) "living with elderly individuals" (OR 1.928; 95% CI: 1.074-3.462). Conclusions: During the vaccination period, there was still hesitation in receiving the vaccine. The results will provide a rationale for the design of future vaccination campaigns and education efforts concerning the vaccine.

Chicken Egg Yolk Antibodies (IgYs) block the binding of multiple SARS-CoV-2 spike protein variants to human ACE2.

The SARS-CoV-2 virus is still spreading worldwide, and there is an urgent need to effectively prevent and control this pandemic. This study evaluated the potential efficacy of Egg Yolk Antibodies (IgY) as a neutralizing agent against the SARS-CoV-2. We investigated the neutralizing effect of anti-spike-S1 IgYs on the SARS-CoV-2 pseudovirus, as well as its inhibitory effect on the binding of the coronavirus spike protein mutants to human ACE2. Our results show that the anti-Spike-S1 IgYs showed significant neutralizing potency against SARS-CoV-2 pseudovirus, various spike protein mutants, and even SARS-CoV in vitro. It might be a feasible tool for the prevention and control of ongoing COVID-19.