Evaluation and uncertainty analysis of Himawari-8 hourly aerosol product version 3.1 and its influence on surface solar radiation before and during the COVID-19 outbreak.

The hourly Himawari-8 version 3.1 (V31) aerosol product has been released and incorporates an updated Level 2 algorithm that uses forecast data as an a priori estimate. However, there has not been a thorough evaluation of V31 data across a full-disk scan, and V31 has yet to be applied in the analysis of its influence on surface solar radiation (SSR). This study firstly investigates the accuracy of V31 aerosol products, which includes three categories of aerosol optical depth (AOD) (AODMean, AODPure, and AODMerged) as well as the corresponding Ångström exponent (AE), using ground-based measurements from the AERONET and SKYNET. Results indicate that V31 AOD products are more consistent with ground-based measurements compared to previous products (V30). The highest correlation and lowest error were seen in the AODMerged, with a correlation coefficient of 0.8335 and minimal root mean square error of 0.1919. In contrast, the AEMerged shows a larger discrepancy with measurements unlike the AEMean and AEPure. Error analysis reveals that V31 AODMerged has generally stable accuracy across various ground types and geometrical observation angles, however, there are higher uncertainties in areas with high aerosol loading, particularly for fine aerosols. The temporal analysis shows that V31 AODMerged performs better compared to V30, particularly in the afternoon. Finally, the impacts of aerosols on SSR based on the V31 AODMerged are investigated through the development of a sophisticated SSR estimation algorithm in the clear sky. Results demonstrate that the estimated SSR is significant consistency with those of well-known CERES products, with preservation of 20 times higher spatial resolution. The spatial analysis reveals a significant reduction of AOD in the North China Plain before and during the COVID-19 outbreak, resulting in an average 24.57 W m-2 variation of the surface shortwave radiative forcing in clear sky daytime.

Clinical application of SARS-CoV-2 antibody detection and monoclonal antibody therapies against COVID-19.

The purpose of this study was to investigate the clinical application of severe acute respiratory distress syndrome coronavirus-2 (SARS-CoV-2) specific antibody detection and anti-SARS-CoV-2 specific monoclonal antibodies (mAbs) in the treatment of coronavirus infectious disease 2019 (COVID-19). The dynamic changes of SARS-CoV-2 specific antibodies during COVID-19 were studied. Immunoglobulin M (IgM) appeared earlier and lasted for a short time, while immunoglobulin G (IgG) appeared later and lasted longer. IgM tests can be used for early diagnosis of COVID-19, and IgG tests can be used for late diagnosis of COVID-19 and identification of asymptomatic infected persons. The combination of antibody testing and nucleic acid testing, which complement each other, can improve the diagnosis rate of COVID-19. Monoclonal anti-SARS-CoV-2 specific antibodies can be used to treat hospitalized severe and critically ill patients and non-hospitalized mild to moderate COVID-19 patients. COVID-19 convalescent plasma, highly concentrated immunoglobulin, and anti-SARS-CoV-2 specific mAbs are examples of anti-SARS-CoV-2 antibody products. Due to the continuous emergence of mutated strains of the novel coronavirus, especially omicron, its immune escape ability and infectivity are enhanced, making the effects of authorized products reduced or invalid. Therefore, the optimal application of anti-SARS-CoV-2 antibody products (especially anti-SARS-CoV-2 specific mAbs) is more effective in the treatment of COVID-19 and more conducive to patient recovery.

Dynamics of Microbial Community and Potential Microbial Pollutants in Shopping Malls.

Shopping malls offer various niches for microbial populations, potentially serving as sources and reservoirs for the spread of microorganisms of public health concern. However, knowledge about the microbiome and the distribution of human pathogens in malls is largely unknown. Here, we examine the microbial community dynamics and genotypes of potential pathogens from floor and escalator surfaces in shopping malls and adjacent road dusts and greenbelt soils. The distribution pattern of microbial communities is driven primarily by habitats and seasons. A significant enrichment of human-associated microbiota in the indoor environment indicates that human interactions with surfaces might be another strong driver for mall microbiomes. Neutral community models suggest that the microbial community assembly is strongly driven by stochastic processes. Distinct performances of microbial taxonomic signatures for environmental classifications indicate the consistent differences of microbial communities of different seasons/habitats and the strong anthropogenic effect on homogenizing microbial communities of shopping malls. Indoor environments harbored higher concentrations of human pathogens than outdoor samples, also carrying a high proportion of antimicrobial resistance-associated multidrug efflux genes and virulence genes. These findings enhanced the understanding of the microbiome in the built environment and the interactions between humans and the built environment, providing a basis for tracking biothreats and communicable diseases and developing sophisticated early warning systems. IMPORTANCE Shopping malls are distinct microbial environments which can facilitate a constant transmission of microorganisms of public health concern between humans and the built environment or between human and human. Despite extensive investigation of the natural environmental microbiome, no comprehensive profile of microbial ecology has been reported in malls. Characterizing microbial distribution, potential pathogens, and antimicrobial resistance will enhance our understanding of how these microbial communities are formed, maintained, and transferred and help establish a baseline for biosurveillance of potential public health threats in malls.

COVID-19, a blessing in disguise for the Tech sector: Evidence from stock price crash risk.

In this paper we document that although COVID-19 has brought uncertainties to the overall economy, the Technology (tech) sector is the systematic beneficiary of the pandemic. Using a quasi-natural setup, we find a significant notion that the Stock Price Crash Risk (SPCR) of firms within the Tech sector decreases during the COVID-19 pandemic compared to the recent past and firms belonging to other sectors. Our analyses further reveal that firms in the Tech sector with stronger external monitoring and better information environment receive an even greater advantage from the pandemic. Overall, our study suggests that the higher systemic dependency on the Tech sector during the COVID-19 outbreak results in an economic benefit for this sector.

An AHP-based regional COVID-19 vulnerability model and its application in China.

Since the COVID-19 outbreak, four cities-Wuhan, Beijing, Urumqi and Dalian-have experienced the process from outbreak to stabilization. According to the China Statistical Yearbook and China Center for Disease Control records, regional, pathological, medical and response attributes were selected as regional vulnerability factors of infectious diseases. Then the Analytic Hierarchy Process (AHP) method was used to build a regional vulnerability index model for the infectious disease. The influence of the COVID-19 outbreak at a certain place was assessed computationally in terms of the number of days of epidemic duration and cumulative number of infections, and then fitted to the city data. The resulting correlation coefficient was 0.999952. The range of the regional vulnerability index for COVID-19 virus was from 0.0513 to 0.9379. The vulnerability indexes of Wuhan, Urumqi, Beijing and Dalian were 0.8733, 0.1951, 0.1566 and 0.1119, respectively. The lack of understanding of the virus became the biggest breakthrough point for the rapid spread of the virus in Wuhan. Due to inadequate prevention and control measures, the city of Urumqi was unable to trace the source of infection and close contacts, resulting in a relatively large impact. Beijing has both high population density and migration rate, which imply that the disease outbreak in this city had a great impact. Dalian has perfect prevention and good regional attributes. In addition, the regional vulnerability index model was used to analyze other Chinese cities. Accordingly, the regional vulnerability index and the prevention and control suggestions for them were discussed. Supplementary Information: The online version contains supplementary material available at 10.1007/s40808-021-01244-y.

Social Media Overload and Anxiety Among University Students During the COVID-19 Omicron Wave Lockdown: A Cross-Sectional Study in Shanghai, China, 2022.

Objectives: The increase in the intensity of social media use during the COVID-19 lockdown has affected mental health. Therefore, it is of practical implications to explore the association between social media overload and anxiety and the underlying mechanisms. Methods: Using data from 644 university students during the COVID-19 blockade in Shanghai from March to April 2022, the chain mediation model of information strain and risk perception of omicron between social media overload and anxiety was then tested using the macro PROCESS4.0 tool. Results: The findings showed that social media overload (including information overload and social overload) was positively associated with anxiety. This relationship was mediated by information strain and risk perception of Omicron. A chain mediating role of information strain and risk perception of Omicron has also been proved in this study. Conclusion: Social media overload has a positive effect on anxiety by increasing information strain and risk perception of Omicron. This study provides some implications for future interventions on how to use social media properly for mental health during the pandemic and health management of urban governance.

Efficacy and safety of tofacitinib in Chinese patients with active psoriatic arthritis: a phase 3, randomised, double-blind, placebo-controlled study.

OBJECTIVES: Efficacy and safety of tofacitinib, an oral Janus kinase inhibitor, were evaluated in a 6-month, double-blind, phase 3 study in Chinese patients with active (polyarthritic) psoriatic arthritis (PsA) and inadequate response to ≥1 conventional synthetic disease-modifying antirheumatic drug. METHODS: Patients were randomised (2:1) to tofacitinib 5 mg twice daily (N=136) or placebo (N=68); switched to tofacitinib 5 mg twice daily after month (M)3 (blinded). PRIMARY ENDPOINT: American College of Rheumatology (ACR50) response at M3. Secondary endpoints (through M6) included: ACR20/50/70 response; change from baseline in Health Assessment Questionnaire-Disability Index (HAQ-DI); ≥75% improvement in Psoriasis Area and Severity Index (PASI75) response, and enthesitis and dactylitis resolution. Safety was assessed throughout. RESULTS: The primary endpoint was met (tofacitinib 5 mg twice daily, 38.2%; placebo, 5.9%; p<0.0001). M3 ACR20/ACR70/PASI75 responses, and enthesitis and dactylitis resolution rates, were higher and HAQ-DI reduction was greater for tofacitinib 5 mg twice daily versus placebo. Incidence of adverse events (AEs)/serious AEs (M0-3): 68.4%/0%, tofacitinib 5 mg twice daily; 75.0%/4.4%, placebo. One death was reported with placebo→tofacitinib 5 mg twice daily (due to accident). One serious infection, non-serious herpes zoster, and lung cancer case each were reported with tofacitinib 5 mg twice daily; four serious infections and one non-serious herpes zoster case were reported with placebo→tofacitinib 5 mg twice daily (M0-6). No non-melanoma skin cancer, major adverse cardiovascular or thromboembolism events were reported. CONCLUSION: In Chinese patients with PsA, tofacitinib efficacy was greater than placebo (primary and secondary endpoints). Tofacitinib was well tolerated; safety outcomes were consistent with the established safety profile in PsA and other indications. TRIAL REGISTRATION NUMBER: NCT03486457.

Social Media Overload and Anxiety Among University Students During the COVID-19 Omicron Wave Lockdown: A Cross-Sectional Study in Shanghai, China, 2022

Objectives: The increase in the intensity of social media use during the COVID-19 lockdown has affected mental health. Therefore, it is of practical implications to explore the association between social media overload and anxiety and the underlying mechanisms. Methods: Using data from 644 university students during the COVID-19 blockade in Shanghai from March to April 2022, the chain mediation model of information strain and risk perception of omicron between social media overload and anxiety was then tested using the macro PROCESS4.0 tool. Results: The findings showed that social media overload (including information overload and social overload) was positively associated with anxiety. This relationship was mediated by information strain and risk perception of Omicron. A chain mediating role of information strain and risk perception of Omicron has also been proved in this study. Conclusion: Social media overload has a positive effect on anxiety by increasing information strain and risk perception of Omicron. This study provides some implications for future interventions on how to use social media properly for mental health during the pandemic and health management of urban governance.

Explaining customer satisfaction via hotel reviews: A comparison between pre- and post-COVID-19 reviews

This study aims to examine whether and how COVID-19 has changed the effects of consumer evaluations of hotel attributes on customer satisfaction. We extracted positive and negative evaluations of hotel attributes from online reviews both pre- and post-COVID-19 and examined their effects on customer satisfaction. Using a sample of 1,947,391 reviews of 35,022 Chinese hotels collected from ctrip.com, we conducted a fine-grained sentiment analysis based on sentiment triples to identify important positive and negative evaluations of hotel attributes. Subsequently, we applied regression analyses to examine how these evaluations of hotel attributes influenced customer satisfaction. The results revealed that positive and negative evaluations of hotel attributes had differentiated effects on customer satisfaction. We classified these attributes into basic, excitement, and performance attributes, from which management implications can be derived.

Multiple-Inputs Convolutional Neural Network for COVID-19 Classification and Critical Region Screening From Chest X-ray Radiographs: Model Development and Performance Evaluation.

Background: The COVID-19 pandemic is becoming one of the largest, unprecedented health crises, and chest X-ray radiography (CXR) plays a vital role in diagnosing COVID-19. However, extracting and finding useful image features from CXRs demand a heavy workload for radiologists. Objective: The aim of this study was to design a novel multiple-inputs (MI) convolutional neural network (CNN) for the classification of COVID-19 and extraction of critical regions from CXRs. We also investigated the effect of the number of inputs on the performance of our new MI-CNN model. Methods: A total of 6205 CXR images (including 3021 COVID-19 CXRs and 3184 normal CXRs) were used to test our MI-CNN models. CXRs could be evenly segmented into different numbers (2, 4, and 16) of individual regions. Each region could individually serve as one of the MI-CNN inputs. The CNN features of these MI-CNN inputs would then be fused for COVID-19 classification. More importantly, the contributions of each CXR region could be evaluated through assessing the number of images that were accurately classified by their corresponding regions in the testing data sets. Results: In both the whole-image and left- and right-lung region of interest (LR-ROI) data sets, MI-CNNs demonstrated good efficiency for COVID-19 classification. In particular, MI-CNNs with more inputs (2-, 4-, and 16-input MI-CNNs) had better efficiency in recognizing COVID-19 CXRs than the 1-input CNN. Compared to the whole-image data sets, the efficiency of LR-ROI data sets showed approximately 4% lower accuracy, sensitivity, specificity, and precision (over 91%). In considering the contributions of each region, one of the possible reasons for this reduced performance was that nonlung regions (eg, region 16) provided false-positive contributions to COVID-19 classification. The MI-CNN with the LR-ROI data set could provide a more accurate evaluation of the contribution of each region and COVID-19 classification. Additionally, the right-lung regions had higher contributions to the classification of COVID-19 CXRs, whereas the left-lung regions had higher contributions to identifying normal CXRs. Conclusions: Overall, MI-CNNs could achieve higher accuracy with an increasing number of inputs (eg, 16-input MI-CNN). This approach could assist radiologists in identifying COVID-19 CXRs and in screening the critical regions related to COVID-19 classifications.

Multiple-Inputs Convolutional Neural Network for COVID-19 Classification and Critical Region Screening From Chest X-ray Radiographs: Model Development and Performance Evaluation

Background The COVID-19 pandemic is becoming one of the largest, unprecedented health crises, and chest X-ray radiography (CXR) plays a vital role in diagnosing COVID-19. However, extracting and finding useful image features from CXRs demand a heavy workload for radiologists. Objective The aim of this study was to design a novel multiple-inputs (MI) convolutional neural network (CNN) for the classification of COVID-19 and extraction of critical regions from CXRs. We also investigated the effect of the number of inputs on the performance of our new MI-CNN model. Methods A total of 6205 CXR images (including 3021 COVID-19 CXRs and 3184 normal CXRs) were used to test our MI-CNN models. CXRs could be evenly segmented into different numbers (2, 4, and 16) of individual regions. Each region could individually serve as one of the MI-CNN inputs. The CNN features of these MI-CNN inputs would then be fused for COVID-19 classification. More importantly, the contributions of each CXR region could be evaluated through assessing the number of images that were accurately classified by their corresponding regions in the testing data sets. Results In both the whole-image and left- and right-lung region of interest (LR-ROI) data sets, MI-CNNs demonstrated good efficiency for COVID-19 classification. In particular, MI-CNNs with more inputs (2-, 4-, and 16-input MI-CNNs) had better efficiency in recognizing COVID-19 CXRs than the 1-input CNN. Compared to the whole-image data sets, the efficiency of LR-ROI data sets showed approximately 4% lower accuracy, sensitivity, specificity, and precision (over 91%). In considering the contributions of each region, one of the possible reasons for this reduced performance was that nonlung regions (eg, region 16) provided false-positive contributions to COVID-19 classification. The MI-CNN with the LR-ROI data set could provide a more accurate evaluation of the contribution of each region and COVID-19 classification. Additionally, the right-lung regions had higher contributions to the classification of COVID-19 CXRs, whereas the left-lung regions had higher contributions to identifying normal CXRs. Conclusions Overall, MI-CNNs could achieve higher accuracy with an increasing number of inputs (eg, 16-input MI-CNN). This approach could assist radiologists in identifying COVID-19 CXRs and in screening the critical regions related to COVID-19 classifications.

Relationship between internet use intensity and quality of life in chronic patients during the COVID-19 pandemic: The role of physical exercise and health insurance

The internet use intensity of human has increased substantially during the COVID-19 Pandemic, and it is severely impacting the well-being of chronic patients. This study aimed to explore the underlying mechanism of the relationship between internet use intensity and quality of life in chronic patients, based on the cross-sectional data from China Family Panel Studies (CFPS) during the COVID-19 Pandemic in 2020. The results showed that the internet use intensity had significant positive association with quality of life among chronic patients, and such association has been found in both urban and rural samples. Among the relationship of internet use intensity and quality of life in chronic patients, the mediating effect of physical exercise reached 10.25%. Furthermore, health insurance positively moderated this relationship. There are new insights for policy recommendations and clinical guidance on the role of physical activity and health insurance aimed at improving chronic patients' quality of life. Meanwhile, in both rural and urban governance, public health agencies should promote the “Internet + Healthcare” program to improve health insurance and physical activity literacy, thus providing a higher level of quality of life for patients with chronic diseases during the COVID-19 Pandemic.

Intellectual Capital and Financial Performance of Chinese Manufacturing SMEs: An Analysis from the Perspective of Different Industry Types

In the knowledge era, intellectual capital (IC) has been recognized as the determinant of firm performance. The main goal of the current study is to analyze the relationship between IC and its elements and financial performance of Chinese manufacturing small and medium-sized enterprises (SMEs). We also examine whether industry type has an impact on this relationship. This study uses the data of 588 Chinese listed SMEs in the manufacturing industry between 2015 and 2020 and employs the modified value-added intellectual coefficient (MVAIC) model to assess IC. The results show that IC improves SMEs’ financial performance, and physical and human capitals are the main contributor. In addition, the impact of IC and its elements on the financial performance of Chinese manufacturing SMEs is different in different types of industries. Specifically, capital-intensive SMEs have a greater impact of IC on financial performance than labor- and technology-intensive SMEs;labor-intensive SMEs have a higher efficiency of physical capital, while technology-intensive SMEs have higher human capital efficiency. The findings could help SMEs’ managers improve corporate performance by the effective utilization of their IC.

Exploring the Impact of the COVID-19 Pandemic on Firms' Financial Performance and Cash Holding: New Evidence from China's Agri-food Sector

The objective of this paper is to investigate the impact of coronavirus disease 2019 (COVID-19) on the financial performance and cash holdings of Chinese agri-food companies. We also examine whether or not company ownership, the affected areas, and leverage level affect this relationship. The empirical results show that the COVID-19 outbreak has had no significant impact on financial performance and the cash-holding level of agri-food companies. In addition, the financial performance of state-owned companies is enhanced during such a crisis, whereas COVID-19 reduced the financial performance and cash-holding level of privately owned companies. In middle- and high-risk areas, the pandemic has had a negative impact on financial performance, while it has had a positive impact on financial performance in low-risk areas. The negative impact of COVID-19 on cash holding is greater in highly leveraged companies than it has been in low-leveraged companies. This paper may provide some new insights for managers to ensure smooth operation and improve firms' performance in order to overcome this crisis.

How Social Media Influences Public Attitudes to COVID-19 Governance Policy: An Analysis Based on Cognitive-Affective Model.

Introduction: Based on the cognitive-affective model, this paper examines how social media affects the public cognitive and affective factors, further influence their attitudes towards COVID-19 governance policy. Methods: Through an online survey, we measured individual COVID-19 policy attitude, social media use and other related factors of 1222 respondents from 12 countries, and based on this, we carried out regression and mediation analysis on the data to obtain the research results. Results: From the perspective of cognitive factors, the public perception of the severity of the COVID-19 itself does not significantly affect their attitudes towards governance policy. On the contrary, the evaluation on government governance performance, risks and governance anticipations have more significant impacts. Among the affective factors, personal anxiety and patriotism significantly affect the formation of public attitudes, personal anxiety is positively correlated, and patriotism is negatively correlated. It is important to note that nationalism has no significant influence on public attitudes to COVID-19 policy on a global scale. Conclusion: (1) Social media influences the public COVID-19 policy attitudes through their moderating effect on affective and cognitive factors. (2) The impact of social media on affective pathways is more significant than that on cognitive pathways. (3) The positive moderating effect of social media on patriotism obscures the tendency of strict governance of COVID-19 caused by aggravating people's anxiety.