Breaking the Ice with Group Flow: A Collaborative VR Serious Game for Relationship Enhancement

It is usually hard for unfamiliar partners to rapidly 'break the ice' in the early stage of relationship establishment, which hinders the development of relationship and even affects the team productivity. To solve this problem, we proposed a collaborative serious game for icebreaking by combining immersive virtual reality (VR) with brain-computer interface based on the team flow framework. We designed a multiplayer collaboration task with the theme of fighting COVID-19 and proposed an approach to improve empathy between team members by sharing their real-time mental state in VR;in addition, we propose an EEG-based method for dynamic evaluation and enhancement of group flow experience to achieve better team collaboration. Then, we developed a prototype system and performed a user study. Results show that our method has good ease of use and can significantly reduce the psychological distance among team members. Especially for unfamiliar partners, both functions of mental state sharing and group flow regulation enhancement can significantly reduce the psychological distance. © 2023 IEEE.

Urban Mobility in the Postpandemic Stage: A Comprehensive Investigation of a Variety of Cities in China

The global outbreak of coronavirus disease 2019 (COVID-19) has affected the urban mobility of nations around the world. The pandemic may even have a potentially lasting impact on travel behaviors during the post-pandemic stage. China has basically stopped the spread of COVID-19 and reopened the economy, providing an unprecedented environment for investigating post-pandemic travel behaviors. This study conducts multiple investigations to show the changes in travel behaviors in the post-pandemic stage, on the basis of empirical travel data in a variety of cities in China. Specifically, this study demonstrates the changes in road network travel speed in 57 case cities and the changes in subway ridership in 26 case cities. Comprehensive comparisons can indicate the potential modal share in the post-pandemic stage. Further, this study conducts a case analysis of Beijing, where the city has experienced two waves of COVID-19. The variations in travel speed in the road network of Beijing at different stages of the pandemic help reveal the public's responses towards the varying severity of the pandemic. Finally, a case study of the Yuhang district in Hangzhou is conducted to demonstrate the changes in traffic volume and vehicle travel distance amid the post-pandemic stage based on license plate recognition data. Results indicate a decline in subway trips in the post-pandemic stage among case cities. The vehicular traffic in cities with subways has recovered in peak hours on weekdays and has been even more congested than the pre-pandemic levels;whereas the vehicular traffic in cities without subways has not rebounded to pre-pandemic levels. This situation implies a potential modal shift from public transportation to private vehicular travel modes. Results also indicate that commuting traffic is sensitive to the severity of the pandemic. This may be because countermeasures, e.g., work-from-home and suspension of non-essential businesses, will be implemented if the pandemic restarts. The travel speed in non-peak hours and on non-workdays is higher than pre-pandemic levels, indicating that non-essential travel demand may be reduced and the public's vigilance towards the pandemic may continue to the post-pandemic stage. These findings can help improve policymaking strategies in the post-pandemic new normal. © 2023 American Society of Civil Engineers.

An ultrasensitive electrochemiluminescence immunosensor for SARS-CoV-2 nucleocapsid protein detection based on signal amplification strategy of DMSN@QDs

Based on signal amplification strategy of dendritic mesoporous silica nanospheres loaded with CdSe/ZnS quantum dots (DMSN@QDs), an ultrasensitive electrochemiluminescence (ECL) immunosensor with magnetic separation was constructed for the detection of SARS-CoV-2 nucleocapsid protein (NP). DMSN, a mesoporous material with abundant radial pores, large specific surface area and high porosity, can increase the loading capacity of QDs and hinder their aggregation as the nanocarrier. DMSN@QDs with good ECL efficiency were used as signal labels to construct a sandwich immunosensor. The designed ECL immunosensor displayed a good linear relationship for NP concentrations ranging from 0.005 ng mL(-1) to 50 ng mL(-1), with a limit of detection of 3.33 pg mL(-1). The ECL immunosensor was successfully applied to detect NP in human serum samples with satisfactory recovery. This strategy provided a new method for detecting NP and expanded the application field of DMSN.

[Clinical characteristics and short-term prognosis of 22 cases with SARS-CoV-2 infection associated acute encephalopathy].

Objective: To investigate the clinical features and short-term prognosis of patients with SARS-CoV-2 infection associated acute encephalopathy (AE). Methods: Retrospective cohort study. The clinical data, radiological features and short-term follow-up of 22 cases diagnosed with SARS-CoV-2 infection associated AE in the Department of Neurology, Beijing Children's Hospital from December 2022 to January 2023 were retrospectively analyzed. The patients were divided into cytokine storm group, excitotoxic brain damage group and unclassified encephalopathy group according to the the clinicopathological features and the imaging features. The clinical characteristics of each group were analyzed descriptively. Patients were divided into good prognosis group (≤2 scores) and poor prognosis group (>2 scores) based on the modified Rankin scale (mRS) score of the last follow-up. Fisher exact test or Mann-Whitney U test was used to compare the two groups. Results: A total of 22 cases (12 females, 10 males) were included. The age of onset was 3.3 (1.7, 8.6) years. There were 11 cases (50%) with abnormal medical history, and 4 cases with abnormal family history. All the enrolled patients had fever as the initial clinical symptom, and 21 cases (95%) developed neurological symptoms within 24 hours after fever. The onset of neurological symptoms included convulsions (17 cases) and disturbance of consciousness (5 cases). There were 22 cases of encephalopathy, 20 cases of convulsions, 14 cases of speech disorders, 8 cases of involuntary movements and 3 cases of ataxia during the course of the disease. Clinical classification included 3 cases in the cytokine storm group, all with acute necrotizing encephalopathy (ANE); 9 cases in the excitotoxicity group, 8 cases with acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) and 1 case with hemiconvulsion-hemiplegia syndrome; and 10 cases of unclassified encephalopathy. Laboratory studies revealed elevated glutathione transaminase in 9 cases, elevated glutamic alanine transaminase in 4 cases, elevated blood glucose in 3 cases, and elevated D-dimer in 3 cases. Serum ferritin was elevated in 3 of 5 cases, serum and cerebrospinal fluid (CSF) neurofilament light chain protein was elevated in 5 of 9 cases, serum cytokines were elevated in 7 of 18 cases, and CSF cytokines were elevated in 7 of 8 cases. Cranial imaging abnormalities were noted in 18 cases, including bilateral symmetric lesions in 3 ANE cases and "bright tree appearance" in 8 AESD cases. All 22 cases received symptomatic treatment and immunotherapy (intravenous immunoglobulin or glucocorticosteroids), and 1 ANE patient received tocilizumab. The follow-up time was 50 (43, 53) d, and 10 patients had a good prognosis and 12 patients had a poor prognosis. No statistically significant differences were found between the two groups in terms of epidemiology, clinical manifestations, biochemical indices, and duration of illness to initiate immunotherapy (all P>0.05). Conclusions: SARS-CoV-2 infection is also a major cause of AE. AESD and ANE are the common AE syndromes. Therefore, it is crucial to identify AE patients with fever, convulsions, and impaired consciousness, and apply aggressive therapy as early as possible.

Intelligent medicine and beyond

With the developments of medical artificial intelligence (AI), meta-data analysis, intelligence-aided drug design and discovery, surgical robots and image-navigated precision treatments, intelligent medicine (IM) as a new era evolved from ancient medicine and biomedical medicine, has become an emerging topic and important criteria for clinical applications. It is fully characterized by fundamental research-driven, new-generation technique-directed as well as state-of-the-art paradigms for advanced disease diagnosis and therapy leading to an even broader future of modern medicine. As a fundamental subject and also a practice-oriented field, intelligent medicine is highly trans-disciplinary and cross-developed, which has emerged the knowledge of modern medicine, basic sciences and engineering. Basically, intelligent medicine has three domains of intelligent biomaterials, intelligent devices and intelligent techniques. Intelligent biomaterials derive from traditional biomedical materials, and currently are endowed with multiple functionalities for medical uses. For example, micro-/nanorobots, smart responsive biomaterials and digital drugs are representative intelligent biomaterials which have been already commercialized and applied to clinical uses. Intelligent devices, such as surgical robots, rehabilitation robots and medical powered exoskeleton, are an important majority in the family of intelligent medicine. Intelligent biomaterials and intelligent devices are more and more closely integrated with each other especially on the occasions of intelligence acquisition, remote transmission, AI-aided analysis and management. In comparison, intelligent techniques are internalized in the former two domains and are playing a critical role in the development of intelligent medicine. Representative intelligent techniques of telemedicine, image-navigated surgery, virtual/augmented reality and AI-assisted image analysis for early-stage disease assessments have been employed in nowadays clinical operations which to a large extent relieved medical labors. In the past decades, China has been in the leading groups compared to international colleagues in the arena of intelligent medicine, and a series of eminent research has been clinically translated for practical uses in China. For instance, the first 5G-aided remote surgery has been realized in Fujian Province in January 2019, which for the first time validated their applicability for human uses. The surgical robots have found China as the most vigorous market, and more than 10 famous Chinese companies are developing versatile surgical robots for both Chinese people and people all over the world. China also applied AI techniques to new drug developments especially in early 2020 when COVID-19 epidemic roared, and several active molecules and drug motifs have been discovered for early-stage COVID-19 screening and treatments. Based on the significance of intelligent medicine and its rapid developments in both basic research and industrials, this review summarized the comprehensive viewpoints of the Y6 Xiangshan Science Conferences titled with Fundamental Principles and Key Technologies of Intelligent Medicine, and gave an in-depth discussion on main perspectives of future developments of the integration of biomaterial and devices, the integration of bioinformatics and medical hardware, and the synergy of biotechnology and intelligence information. It is expected that this featuring article will further promote intelligent medicine to an even broader community not only for scientists but also for industrials, and in the long run embrace a perspective future for its blooming and rich contributions in China in the coming 5 years.

Performance of in-duct bipolar ionization devices on pollutant removal and potential byproduct formation in indoor environments

The COVID-19 pandemic has highlighted the importance of indoor air quality (IAQ) since SARS-CoV-2 may be transmitted through virus-laden aerosols in poorly ventilated spaces. Multiple air cleaning technologies have been developed to mitigate airborne transmission risk and improve IAQ. In-duct bipolar ionization technology is an air cleaning technology that can generate ions for inactivating airborne pathogens and increasing particle deposition and removal while without significant byproducts generated. Many commercial in-duct ionization systems have been developed but their practical performance on pollutant removal and potential formation of byproducts have not been investigated comprehensively. The results in this study showed that the in-duct bipolar ionization technology can significantly improve the particle removal efficiency of the regular filter, while no significant ozone and ion were released to the indoor air. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

[Estimation of COVID-19 incidence in Shanghai under optimized epidemic prevention and control strategies].

Objective: To quantitatively estimate the incidence of COVID-19 in different backgrounds, including vaccination coverage, non-pharmacological interventions (NPIs) measures, home quarantine willingness and international arrivals, and the demands of healthcare resource in Shanghai in the context of optimized epidemic prevention and control strategies. Methods: Based on the natural history of 2019-nCoV, local vaccination coverage and NPI performance, an age-structured Susceptible-Exposed-Infections-Removed (SEIR) epidemic dynamic model was established for the estimation of the incidence of COVID-19 and demand of hospital beds in Shanghai by using the data on December 1, 2022 as the basis. Results: Based on current vaccination coverage, it is estimated that 180 184 COVID-19 cases would need treatment in hospitals in Shanghai within 100 days. When the booster vaccination coverage reaches an ideal level, the number of the cases needing hospitalization would decrease by 73.20%. School closure or school closure plus workplace closure could reduce the peak demand of regular beds by 24.04% or 37.73%, respectively, compared with the situation without NPI. Increased willingness of home quarantine could reduce the number of daily new cases and delay incidence peak of COVID-19. The number of international arrivals has little impact on the development of the epidemic. Conclusions: According to the epidemiological characteristics of COVID-19 and the actual situation of vaccination in Shanghai, the incidence of COVID-19 and health resource demand might be reduced by increasing vaccination coverage and early implementation of NPI.

A meta-analysis of dynamic change of the SARS-CoV-2 specific antibody IgG positive rate in convalescent patients with COVID-19 in China

Objective To investigate the dynamic changes of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) specific antibody IgG positive rate in coronavirus disease 2019 (COVID-19) survivors in China. Methods the relevant literatures about the positive rate of SARS-COV-2 specific antibody IgG in COVID-19 survivors in China were retrieved from PubMed, Embase, CNKI, Wanfang database and VIP database from December 2019 to February 24, 2022. The quality of the documents were assessed according the revised AHRQ (Agency for Healthcare Research and Quality) statement. Freeman-tukey double arsinusoidal conversion method was used to calculate the positive rate, and StataSE15.0 software was used for statistical analysis. Subgroup analysis was performed according to detection method and fragment, and publication bias was examined by Egger method. Results A total of 12 articles were included, IgG was detected from the first month to the twelfth month after SARS-COV-2 infection, and the total sample size ranged from 74 to 2 907 cases per month. The positive rate was the highest in the second month and the third month, 96.35% (95% CI: 93.98%-98.14%) and 97.23% (95% CI: 94.47%-99.05%) respectively. The positive rate decreased gradually with time, and reached 73.63% (95% CI: 50.31%-91.45%) in the twelfth month. The results of subgroup analysis showed that the heterogeneity between studies with the different detection method and the different detection fragment were significant differences (X2=5.02-39.57, all P < 0.05). Egger method test published bias, and the difference was not statistically significant (t=1.85, P=0.101). Conclusion Most people, one year after infection with SARSCOV- 2, could still detect SARS-COV-2 specific antibody IgG.

Intelligent medicine and beyond

With the developments of medical artificial intelligence (AI), meta-data analysis, intelligence-aided drug design and discovery, surgical robots and image-navigated precision treatments, intelligent medicine (IM) as a new era evolved from ancient medicine and biomedical medicine, has become an emerging topic and important criteria for clinical applications. It is fully characterized by fundamental research-driven, new-generation technique-directed as well as state-of-the-art paradigms for advanced disease diagnosis and therapy leading to an even broader future of modern medicine. As a fundamental subject and also a practice-oriented field, intelligent medicine is highly trans-disciplinary and cross-developed, which has emerged the knowledge of modern medicine, basic sciences and engineering. Basically, intelligent medicine has three domains of intelligent biomaterials, intelligent devices and intelligent techniques. Intelligent biomaterials derive from traditional biomedical materials, and currently are endowed with multiple functionalities for medical uses. For example, micro-/nanorobots, smart responsive biomaterials and digital drugs are representative intelligent biomaterials which have been already commercialized and applied to clinical uses. Intelligent devices, such as surgical robots, rehabilitation robots and medical powered exoskeleton, are an important majority in the family of intelligent medicine. Intelligent biomaterials and intelligent devices are more and more closely integrated with each other especially on the occasions of intelligence acquisition, remote transmission, AI-aided analysis and management. In comparison, intelligent techniques are internalized in the former two domains and are playing a critical role in the development of intelligent medicine. Representative intelligent techniques of telemedicine, image-navigated surgery, virtual/augmented reality and AI-assisted image analysis for early-stage disease assessments have been employed in nowadays clinical operations which to a large extent relieved medical labors. In the past decades, China has been in the leading groups compared to international colleagues in the arena of intelligent medicine, and a series of eminent research has been clinically translated for practical uses in China. For instance, the first 5G-aided remote surgery has been realized in Fujian Province in January 2019, which for the first time validated their applicability for human uses. The surgical robots have found China as the most vigorous market, and more than 10 famous Chinese companies are developing versatile surgical robots for both Chinese people and people all over the world. China also applied AI techniques to new drug developments especially in early 2020 when COVID-19 epidemic roared, and several active molecules and drug motifs have been discovered for early-stage COVID-19 screening and treatments. Based on the significance of intelligent medicine and its rapid developments in both basic research and industrials, this review summarized the comprehensive viewpoints of the Y6 Xiangshan Science Conferences titled with Fundamental Principles and Key Technologies of Intelligent Medicine, and gave an in-depth discussion on main perspectives of future developments of the integration of biomaterial and devices, the integration of bioinformatics and medical hardware, and the synergy of biotechnology and intelligence information. It is expected that this featuring article will further promote intelligent medicine to an even broader community not only for scientists but also for industrials, and in the long run embrace a perspective future for its blooming and rich contributions in China in the coming 5 years. © 2023 Chinese Academy of Sciences. All rights reserved.

Maternal and Perinatal Outcomes of SARS-CoV-2 and Variants in Pregnancy

Pregnancy is a physiological state that predisposes women to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, a disease that can cause adverse maternal and perinatal outcomes. The severity of coronavirus disease 2019 (COVID-19) disease is known to vary by viral strain;however, evidence for the effects of this virus in pregnant women has yet to be fully elucidated. In this review, we describe maternal and perinatal outcomes, vaccination, and vertical transmission, among pregnant women infected with the different SARS-CoV-2 variants identified to date. We also summarize existing evidence for maternal and perinatal outcomes in pregnant women with specific information relating to SARS-CoV-2 variants. Our analysis showed that Omicron infection was associated with fewer severe maternal and perinatal adverse outcomes while the Delta variant was associated with worse pregnancy outcomes. Maternal deaths arising from COVID-19 were found to be rare (<1.0%), irrespective of whether the virus was a wild-Type strain or a variant. Severe maternal morbidity was more frequent for the Delta variant (10.3%), followed by the Alpha (4.7%), wild-Type (4.5%), and Omicron (2.9%) variants. The rates of stillbirth were 0.8%, 4.1%, 3.1%, and 2.3%, respectively, in pregnancies infected with the wild-Type strain, Alpha, Delta, and Omicron variants, respectively. Preterm birth and admission to neonatal intensive care units were more common for cases with the Delta infection (19.0% and 18.62%, respectively), while risks were similar for those infected with the wild-Type (14.7% and 11.2%, respectively), Alpha (14.9% and 13.1%), and Omicron variants (13.2% and 13.8%, respectively). As COVID-19 remains a global pandemic, and new SARS-CoV-2 variants continue to emerge, research relating to the specific impact of new variants on pregnant women needs to be expanded.Copyright © Wolters Kluwer Health, Inc. All rights reserved.

Potential Mechanism of Colchicine against COVID-19 and Non-Small Cell Lung Cancer based on Network Pharmacology and Bioinformatics Analysis

Colchicine is an alkaloid with antitumor effect isolated from Colchicum autumnale plants, it has been reported in multiple studies as a potential treatment for coronavirus disease-19 and this study applied network pharmacology and bioinformatics analysis to explore the potential mechanism of colchicine against non-small cell lung cancer and coronavirus disease-19. The R software was used to determine the differentially expressed genes of non-small cell lung cancer/coronavirus disease-19, and carry out prognostic analysis and clinical analysis of the differentially expressed genes, the targets of colchicine were obtained from various databases, the protein-protein interaction network of intersection targets of colchicine and non-small cell lung cancer/coronavirus disease-19 was constructed, enrichment analysis of gene ontology and kyoto encyclopedia of genes and genomes pathways was performed by Metascape database and the molecular docking and molecular dynamics simulation were completed. We obtained a total of 716 differentially expressed genes and identified 13 potential prognostic genes associated with the clinical characterization of non-small cell lung cancer/coronavirus disease-19 patients. C-C motif chemokine ligand 2, toll like receptor 4, intercellular adhesion molecule 1, peroxisome proliferator activated receptor gamma, interleukin 17A, interferon gamma, angiotensin I converting enzyme, fos proto-oncogene, nuclear factor kappa B inhibitor alpha, TIMP metallopeptidase inhibitor 1 and secreted phosphoprotein 1 were core targets. The intersection targets of colchicine and non-small cell lung cancer/coronavirus disease-19 were mainly enriched in biological processes such as inflammatory response, response to cytokine and response to lipopolysaccharide, as well as signal pathways such as interleukin 17 signaling pathway, hypoxia inducible factor 1 signaling pathway and nucleotide binding oligomerization domain-like receptor signaling pathway. The results of molecular docking showed that the colchicine is better combined with the core targets. Analysis of molecular dynamics simulation showed that the protein and ligand form a stabilizing effect. Based on bioinformatics analysis and network pharmacology, we obtained biomarkers that may be used for the prognosis of non-small cell lung cancer/coronavirus disease-19 patients and revealed that colchicine may play a potential role in the treatment of non-small cell lung cancer/coronavirus disease-19 by regulating multiple targets and multiple signaling pathways and participating in multiple biological processes.

Long-COVID Inducement Mechanism Based on the Path Module Correlation Coefficient

As the number of COVID-19 cases increases, the long-COVID symptoms become the focus of clinical attention. Based on the statistical analysis of long-COVID symptoms in European and Chinese populations, this study proposes the path module correlation coefficient, which can estimate the correlation between two modules in a network, to evaluate the correlation between SARS-CoV-2 infection and long-COVID symptoms, providing a theoretical support for analyzing the frequency of long-COVID symptoms in European and Chinese populations. The path module correlation coefficients between specific COVID-19-related genes in the European and Chinese populations and genes that may induce long-COVID symptoms were calculated. The results showed that the path module correlation coefficients were completely consistent with the frequency of long-COVID symptoms in the Chinese population, but slightly different in the European population. Furthermore, the cathepsin C (CTSC) gene was found to be a potential COVID-19-related gene by a path module correlation coefficient correction rate. Our study can help to explore other long-COVID symptoms that have not yet been discovered and provide a new perspective to research this syndrome. Meanwhile, the path module correlation coefficient correction rate can help to find more species-specific genes related to COVID-19 in the future.

Near-real-time methodology for assessing global carbon emissions

Climate change is a major challenge for the sustainable development of mankind. Carbon emissions from human activities are the main driving force of global climate change, and the quantification of carbon emissions is the basis for coping with global changes and achieving carbon neutrality. Developing more spatially and temporally fine-grained carbon emission data to achieve more precise, accurate, and timely carbon emission monitoring is at the current forefront of the field and a major national demand. Here, a carbon emission quantitative method for near-real-time global carbon emissions is proposed, based on multi-source activity data such as statistics, satellite remote sensing, and observation. By parameterizing the extent of daily human activity, it can achieve a near-real-time quantitative estimation of global and regional carbon emissions according to the methodology of the IPCC 2006 guidelines, resolving preexisting challenges, including time lag of yearly emission inventories and how to spatialize the national inventories in high temporal resolution. This paves the way for more accurate, reliable, and verifiable carbon monitoring. Specifically, near-real-time estimates can reveal daily, weekly, monthly, and seasonal changes in global carbon emissions. Results show that emissions are highly related to human activity (e.g., the emissions from Monday to Friday are at a high level but return to a relatively low level during weekends). In addition, winter emissions are higher than those of summer, reflecting the greater demand for heating in the winter for populations in the northern hemisphere and cooling demands in summer. This phenomenon can indicate the variations of seasonal changes in each country, where temperatures at different latitudes reflect heating and cooling demands. Sectoral emissions demonstrate the seasonality of power, including that used by residential sectors. During the COVID-19 pandemic, emissions dropped unprecedentedly, with the emissions of the power sector decreasing rapidly. During the pandemic, domestic aviation emissions were similar to ground transport emissions, while international aviation emissions remained low due to the restrictions imposed on entering and leaving countries. Spatialized daily emissions reveal discrepancies of fine-grained sectoral emissions with a spatial resolution of 0.1 degrees x0.1 degrees. Global daily average emissions show that emissions are concentrated within eastern America, western Europe, southeastern China, etc., with the emerging hotspots being the megacities in each region. Sectoral emissions vary because the sources of emissions of each sector are diverse. Uncertainties are crucial for evaluating the performance of spatialization and fine-grained temporal discretization of activity data. This methodology considers per-sector uncertainties according to the IPCC 2006 guidelines. The uncertainties for power, industry, ground transport, residential, aviation, and international shipping sectors are 14%, 36%, 9.3%, 40%, 10.2%, and 13.0%, respectively. For spatialization, the uncertainties come from national emission data and the EDGAR and GID datasets. The baseline emissions, point-source emissions and scale, non-point-source distribution, and proxy data contribute to the uncertainties. In the future, additional high spatiotemporal resolution data will be used in extra cross-validation and corrections to achieve more precise carbon monitoring.

issues of autonomy in public health ethics: a discussion in the context of the covid-19 pandemic

In the field of public health ethics, the COVID-19 pandemic has highlighted the tension between autonomy and public health. Using CiteSpace 6.1 software and information visualization analysis, we performed a search of literature in the Web of Science core collection database using thematic words such as "public health", "ethics" and "autonomy", we found that from January 1, 2020 to May 14, 2022, discussions on the concept of "autonomy" within the field of bioethics/public health ethics were generally focused on the following topics: "informed consent", "health care policy", "health quality", "information technology", "ageism" and "elderly group". In this paper, we distill and analyze four controversial issues: how can we avoid excessive restrictions on autonomy in the name of public health/public interest? How can we protect autonomy when using digital technology? How can we protect the autonomy and rights of the elderly? How can we advance the goals of public health by promoting autonomy? The COVID-19 pandemic is a unique historical opportunity to reshape the concept of autonomy within the field of public health ethics. Although the virus has bound the fate of humanity together, a reinvented concept of autonomy based on care and community ethics holds the promise of bringing solidarity, comfort, and hope to the world in the midst of the pandemic.

Social Media Driven Big Data Analysis for Disaster Situation Awareness: A Tutorial

Situational awareness tries to grasp the important events and circumstances in the physical world through sensing, communication, and reasoning. Tracking the evolution of changing situations is an essential part of this awareness and is crucial for providing appropriate resources and help during disasters. Social media, particularly Twitter, is playing an increasing role in this process in recent years. However, extracting intelligence from the available data involves several challenges, including (a) filtering out large amounts of irrelevant data, (b) fusion of heterogeneous data generated by the social media and other sources, and (c) working with partially geo-tagged social media data in order to deduce the needs of the affected people. Spatio-temporal analysis of the data plays a key role in understanding the situation, but is available only sparsely because only a small fraction of people post relevant text and of those very few enable location tracking. In this paper, we provide a comprehensive survey on data analytics to assess situational awareness from social media big data.

The Financial Impact of COVID-19 from the Perspective of Media Coverage: Evidence from China

This paper merges three textual models to construct a series of indicators, which can yield more refined proxies for financial media coverage, to measure the impacts of COVID-19 on Chinese financial markets. Results show that the basic indicator Granger causes the volatilities of bond and stock markets and contributes more to the stock market after the outbreak of COVID-19. Next, four specific market-related indicators have significant effects on the corresponding financial market after the outbreak. Finally, the policy-related indicator has a significant effect on four financial markets after the outbreak, and it causes greater volatility in the stock market. This paper can help the government to stabilize the financial market by managing financial media attention. © 2023 Taylor & Francis Group, LLC.

Do financial asset holdings affect investor expectations under negative events? The shock of COVID-19 pandemic

Holding excessive financial assets will lead to corporate financialization, making investors underestimate its risks in front of extreme benefits and the "reservoir effect” in boom periods, especially in rapid-growing emerging economies. Few studies have explored the investors' real perceptions and attitudes towards such risks when dealing with unexpected shocks. The 2019 novel coronavirus disease (COVID-19) provides new insights into these questions. Using event study method, this study examines how investors react to corporate financialization in the risk-release condition. First, we find that firms with more financial asset holdings experience significant lower market return during the COVID-19 pandemic. Second, we find that the pandemic-induced drop in stock returns is milder when firms hold more low-liquidity or safe financial assets, have higher solvency, are less exposed to COVID-19 pandemic and have better information environment. These findings show that the investors' attitude is widely negative towards corporate financialization when the negative shock comes and strong financial flexibility and good corporate governance can alleviate the risk. It implicates that the hidden risks of corporate financialization can be perceived by investors and responded by "voting with their feet” and the managers should be alert to it rather than just seeking financial benefits. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Work-from-home intention during the COVID-19 pandemic: a perspective integrating inclusive leadership and protection motivation theory

Purpose: The present study integrates inclusive leadership and protection motivation theory to propose a new model predicting employees' intention to work from home during an emergency situation such as the COVID-19 pandemic. Design/methodology/approach: A questionnaire was developed to collect data from 939 Taiwanese and Vietnamese office employees using a non-probability convenience sampling method. A total of 887 valid questionnaires were used for further analysis. The data were analysed following a two-stage structural equation modelling using SPSS 22 and AMOS 20 software. The validity and reliability of the instrument were tested and ensured. Findings: The results revealed that inclusive leadership and factors related to protection motivation theory– including perceived severity and perceived vulnerability – have positive direct and indirect effects on employees' work-from-home intentions through the mediating role of employees' work-from-home-related attitudes. Protection motivation theory factors were found to have a stronger effect on employees' work-from-home intention than inclusive leadership. Differences in the relationship between perceived vulnerability, perceived severity and employees' intentions towards working from home were also discovered among participants from the two studied countries. Practical implications: The integration of inclusive leadership and protection motivation theory brings into light what will drive employees' intention to work from home during an emergency situation. The present study has several theoretical and practical implications for scholars, governments, managers and policymakers that can help them improve management policies for working from home in the future. Originality/value: Based on integrating inclusive leadership and protection motivation theory to explore employees' intention to work from home during an emergency situation, the present study demonstrated that inclusive leadership and protection motivation theory should be considered for studies on working from home in a pandemic setting. © 2023, Emerald Publishing Limited.

Data Model Study for Advancement of Japanese PSI Management Introducing the Concepts of S&OP

Under the impact of COVID-19, the global economy exhibits obvious slowdown. In such situation, the issues on how to keep balance between supply and demand in SCM (Supply Chain Management) operation have been more apparent than before. For SCM rebuilding, S&OP received extensive attention worldwide. However, there are few examples of successful implementation of S&OP in Japan because Japanese have not been growing accustomed to the phrase S&OP, although the challenges in SCM operations and PSI (Product, Sales, Inventory) management are recognized. Thus, no clear and exact solution is found to guide the operation. In order to further improve the current management level, a new design proposal of data model is rendered to advance the current PSI management that introduces the concepts of S&OP. Especially, we will address the motivation why we need use multidimensional database architecture to design the S&OP process instead of using RDB (Relational Database) which is often used in ERP (Enterprise Resource Planning). © 2022 IEEE.

Will Online MOOCs Improve the Efficiency of Chinese Higher Education Institutions? An Empirical Study Based on DEA

In recent years, the increasingly fierce competition among higher education institutions (HEIs), the finite resources, and the enormous influence of the COVID-19 epidemic on higher education have made it especially important to evaluate the performance of Chinese higher education institutions. This paper utilizes the DEA-BCC and Malmquist index to analyze the efficiency and productivity of 34 Chinese "985 Project” universities in the period 2017–2021. The indicator system includes three inputs and five outputs, contained in Model 1 and Model 2 for comparative analysis. The results demonstrate that the COVID-19 epidemic has had a considerable negative impact on Chinese higher education, and has induced the reduction of technical efficiency and productivity. Setting up online MOOCs is conducive to enhancing the efficiency and productivity of HEIs;in addition, the efficiency mentioned varies noticeably among different university levels, and there is no significant difference in different university types and geographical locations. © 2023 by the authors.