Pandemic-induced fear and stock market returns: Evidence from China

We construct a pandemic-induced fear (PIF) index to measure fear of the COVID-19 pandemic using Internet search volumes of the Chinese local search engine and empirically investigate the impact of fear of the pandemic on Chinese stock market returns. A reduced-bias estimation approach for multivariate regression is employed to address the issue of small-sample bias. We find that the PIF index has a negative and significant impact on cumulative stock market returns. The impact of PIF is persistent, which can be explained by mispricing from investors' excessive pessimism. We further reveal that the PIF index directly predicts stock market returns through noise trading. Investors' Internet search behaviors enhance the fear of the pandemic, and pandemic-induced fear determines future stock market returns, rather than the number of cases and deaths caused by the COVID-19 pandemic.

SUPPLEMENTARY HEALTH SYSTEM IN BRAZIL: BUSINESS MODEL NOT THREATENED BY SOCIOECONOMIC CRISIS

Objective: The performance of the supplementary health system (SSS) in Brazil depends on the country's ability to generate employment and income. Yet, on the other hand, even in the face of the economic crises that have hit Brazil in recent years and, more sharply, after the Covid-19 pandemic, the SSS ended 2021 with continuous growth in the number of beneficiaries. This perspective suggests possible causes to explain this contradiction while questioning the role of the State in providing a constitutional right for Brazilians. Theoretical framework: Brazilian health system can be divided into three segments: a public segment financed by the State (Unified Health System, SUS), a private segment, and a supplementary health segment, the latter two with public and private funding. The Supplementary Health System (SSS) represents a highly regulated area. Design/methodology/approach: The methodology of this work followed the rigor and steps to develop a current perspective on supplementary health in Brazil. Insights from this perspective shed light on the subject of supplementary health by providing insight into existing issues, concepts and prevailing notions about health systems. Findings: The healthcare system in Brazil is complex and combines market elements of public and social interest in a single environment. In this way, the question remains whether business models geared to the base of the economic pyramid (BoP) community have focused exclusively on making a profit by "selling to the poor” or whether they have brought a valuable commitment to social development in the country. Research, Practical & Social implications: This question deserves attention due to the de-prioritization of health on the political agenda in an election year and the critical post-Covid-19 pandemic situation. Social policies in Brazil need to go beyond guaranteeing access to credit compensating for the lack of public provision, at the risk of mortgaging the SUS as a sign of modernity and progress. Originality/value: This perspective suggests possible causes to explain this contradiction while questioning the role of the State in providing a constitutional right for Brazilians. © 2022 AOS-Estratagia and Inovacao. All rights reserved.

How to avoid future "Covid-19 origins" questions?

Origins debates regarding Covid-19 are gaining momentum again. In light of the continued infections and deaths of Covid-19 seen in countries rich and poor, rather than focusing the approach with "whodunit", developing solutions that can help societies become better prepared for future pandemics might be a more meaningful way to move forward. In this paper, we propose a solution that could help society better predict and prevent future pandemics. A system could allow humans to anonymously report potential infectious disease outbreaks without fearing backlash or prejudice and could automatically surveil for potential disease transfers or virus leaks. The proposed autonomous and anonymous pandemic reporting and surveillance system has the potential to help health officials locate infectious disease outbreaks before they form into pandemics. And in turn, it better prevents future pandemics and avoids Covid-19 origins debates.

Multihop Clustering Routing Protocol Based on Improved Coronavirus Herd Immunity Optimizer and Q-Learning in WSNs

Wireless sensor networks (WSNs) are composed of a large number of spatially distributed sensor nodes to monitor and transmit information from the environment. However, the batteries used by these sensor nodes have limited energy and cannot be charged or replaced due to the harsh deployment environment. This energy limitation will seriously affect the lifetime of the network. Therefore, the purpose of this research is to reduce energy consumption and balance the load of sensor nodes by clustering routing protocols, so as to prolong the lifetime of the network. First, the coronavirus herd immune optimizer is improved and used to optimize the network clustering. Second, the cluster heads (CHs) are selected according to the energy and location factors in the clusters, and a reasonable CH replacement mechanism is designed to avoid the extra communication energy consumption caused by the frequent replacement of CHs. Finally, a multihop routing mechanism between the CHs and the base station is constructed by Q-learning. Simulation results show that the proposed work can improve the structure of clusters, enhance the load balance of nodes, reduce network energy consumption, and prolong the network lifetime. The appearance time of the first energy-depleted node is delayed by 25.8%, 85.9%, and 162.2% compared with IGWO, ACA-LEACH, and DEAL in the monitoring area of $300×300 m, respectively. In addition, the proposed protocol shows better adaptability in varying dynamic conditions. © 2001-2012 IEEE.

Synchronisation among short-term rental markets, co-movements and cycles in 39 European cities

This paper presents new evidence of the short-term rental market's prices and transactions from a daily time -series perspective in 39 European cities from 2015 to 2020. It uses Airbnb micro datasets to build time-series cycles by extracting the original observations containing total bookings (rent transactions), rental units sup-ply, and asking rent, with a daily periodicity. The cycles show the periods in which short-rental activity was more relevant for each city, and the level of rents across Europe. The paper provides empirical evidence of a long-term relationship among the city variables (tested via mean and variance). Causality supporting co-movements across cities was found by estimating a short-term naive market equilibrium model using the vector error correction model approach, supporting the hypothesis that the short-term rental market performs according to housing -market principles. Short-run elasticities among rents and contracts across the 39 cities show causal evidence of co-movements among rents and the supply and demand of properties. The market adjustment on the supply side estimates new units responding to changes in prices within 15 lags (days) and longer (350 lags) from the demand side, equivalent to eight to nine months. Evidence of the pandemic's limited effect on housing supply and prices' positive effect is also provided. A robust negative weekend impact on prices was found, suggesting stronger market relevance on weekdays.

Multi-hop Clustering Routing Protocol Based on Improved Coronavirus Herd Immunity Optimizer and Q-Learning in WSNs

Wireless sensor networks (WSNs) are composed of a large number of spatially distributed sensor nodes to monitor and transmit information from the environment. However, the batteries used by these sensor nodes have limited energy and can not be charged or replaced due to the harsh deployment environment. This energy limitation will seriously affect the lifetime of the network. Therefore, the purpose of this research is to reduce energy consumption and balance the load of sensor nodes by clustering routing protocols, so as to prolong the lifetime of the network. Firstly, the coronavirus herd immune optimizer is improved and used to optimize the network clustering. Secondly, the cluster heads are selected according to the energy and location factors in the clusters, and a reasonable cluster head replacement mechanism is designed to avoid the extra communication energy consumption caused by the frequent replacement of cluster heads. Finally, a multi-hop routing mechanism between the cluster heads and the base station is constructed by Q-learning. Simulation results show that the proposed work can improve the structure of clusters, enhance the load balance of nodes, reduce network energy consumption and prolong the network lifetime. The appearance time of the first energy-depleted node is delayed by 25.8%, 85.9% and 162.2% compared with IGWO, ACA-LEACH and DEAL in the monitoring area of 300m ×300m, respectively. In addition, the proposed protocol shows better adaptability in varying dynamic conditions. IEEE

A Short and Highly Efficient Method for Producing Meltblown Nano Yarn with a Helical Structure and High Conductivity

The COVID-19 outbreak has led to the overproduction of meltblown fabrics commonly used in personal protective equipment such as face mask. Moreover, the yield ofconventional fabrication methods for meltblown fabrics have poor mechanical properties and lack accessional value and functional applicability. In this study, a short and highly efficient process was employed to produce polypropylene/polypyrrole (PPy) meltblown nanoyarn (PPMNY). The mechanical properties were improved by utilizing a helical structure, and the conductivity was enabled using a combination of PPy nanoparticles. The breaking force of the proposed PPMNY was as high as 10.1cN/tex at 9T/10 cm, nearly 3.3 times more than PPMNY without the helical structure. The breaking force of the proposed PPMNY was unaffected by the washing process, and the frictional properties and snarling information were similarly maintained by the helical structure. Additionally, the optimal conductivity of the proposed PPMNY reached 0.044S·m-1. Therefore, the novel methods investigated in this study can improve the properties of meltblown fabrics to yield a highly efficient and low-cost technique to produce conductive PPMNY. This concept can be extended for solving the problems of the single two-dimensional structure with poor mechanical properties and application on Smart Wearable with preferable conductivity. © Textile Bioengineering and Informatics Symposium Proceedings 2022 - 15th Textile Bioengineering and Informatics Symposium, TBIS 2022.

Study on the construction workforce management based on lean construction in the context of COVID-19

Purpose: The construction industry is facing challenges not only for workers' mobility in the pandemic situation but also for Lean Construction (LC) practise in responding to the high-quality development during the post-pandemic. As such, this paper presents a construction workforce management framework based on LC to manage both the emergency goal in migrant worker management and the long-term goal in labour productivity improvement in China. Design/methodology/approach: The framework is created based on the integrated culture and technology strategies of LC. A combination of qualitative and quantitative methods is taken to explore factors influencing the mobility of construction workers and to measure labour productivity improvement. The case study approach is adopted to demonstrate the framework application. Findings: For method application, a time-and-motion study and Percent Plan Complete indicator are proposed to offer labour productivity measurements of “resources efficiency” and “flow efficiency”. Moreover, the case study provides an industry level solution for construction workforce management and Lean Construction culture shaping, as well as witnesses the LC culture and technology strategies alignment contributing to LC practise innovation. Originality/value: Compared with previous studies which emphasised solely LC techniques rather than socio-technical system thinking, the proposed integration framework as well as implementation of “Worker's Home” and “Lean Work Package” management models in the COVID-19 pandemic contribute to new extensions of both the fundamental of knowledge and practise in LC. © 2022, Emerald Publishing Limited.

Preparation and preliminary application of antiserum against S protein of SARS-CoV-2

Objective To prepare antiserum against S protein of SARS-CoV-2 and preliminarily develop a method for determination of antigen content in inactivated SARS-CoV-2 vaccine. Methods Goats and rabbits were immunized with recombinant S protein of SARS-CoV-2, and the obtained antisera were determined for titer by indirect ELISA and neutralization assay and for specificity by Western blot, then purified by protein G resin affinity chro-matography. A sandwich ELISA for determination of antigen content of inactivated SARS-CoV-2 vaccine was developed by using purified goat antibody as capture antibody and purified rabbit antibody as detection antibody. Results The titer of antiserum of goats after immunization for 4 times reached 220 000 by ELISA and 1 536 by neutralization assay, while that of rabbits after immunization for 3 times reached 220 000 by ELISA and 4 096 by neutralization assay. Both goat and rabbit antibodies showed specific binding to the S protein of SARS-CoV-2. A double antibody sandwich ELISA method for determination of antigen content was successfully developed by using the purified antibodies, which showed a good linearity with a R2 value of more than 0. 99. Conclusion High titer goat and rabbit antisera against S protein of SARS-CoV-2 were prepared successfully, and double antibody sandwich ELISA method for determination of antigen content of inactivated SARS-CoV-2 vaccine was preliminarily developed. © 2021 Changchun Institute of Biological Products. All rights reserved.

COVID-19 Social Distance Proximity Estimation Using Machine Learning Analyses of Smartphone Sensor Data

Airborne transmittable diseases such as COVID-19 spread from an infected to healthy person when they are in proximity to each other. Epidemiologists suggest that the risk of COVID-19 transmission increases when an infected person is within 6 feet from a healthy person and contact between them lasts longer than 15 minutes (also called Too Close For Too Long (TC4TL). In this paper, we systematically investigate Machine Learning (ML) methods to detect proximity by analyzing publicly available dataset gathered from smartphones' built-in Bluetooth, accelerometer, and gyroscope sensors. We extract 20 statistical features from accelerometer and gyroscope sensors signals and 28 statistical features of Bluetooth signal, which are classified to determine whether subjects are closer than 6 feet as well as the subjects' context. Using machine learning regression, we also estimate the range between the subjects. Among the 19 ML classification and regression methods that we explored, we found that ensemble (boosted and bagged trees) methods perform best with accelerometer and gyroscope data while regression trees ML algorithm performs best with the Bluetooth signal. We further explore sensor fusion methods and demonstrate that the combination of all three sensors achieves a higher accuracy of range estimation than when using each individual sensor. We show that proximity (< 6ft or not) can be classified with 72%-90% accuracy using the accelerometer, 78%-84% accuracy using gyroscope sensor, and with 76%-92% accuracy with the Bluetooth data. Our model outperforms the current state-of-the-art methods using neural networks and achieved a Normalized Decision Cost Function (nDCF) score of 0.34 with Bluetooth radio and 0.36 with sensor fusion.

A Retrospective Study of the Epidemiologic and Clinical Characteristics of COVID-19 among Hospitalized Patients in Quanzhou, China

Coronavirus disease 2019 (COVID-19) has spread throughout China. However, information about COVID-19 in cities and regions outside Wuhan is limited and the indicators that predict the length of hospital stay for patients with COVID-19 are unclear. Therefore, we collected clinical data from 47 patients with COVID-19 in Quanzhou City. The median age was 38 years [interquartile range (IQR): 31-50 years], and 24 (51%) were male. There were 8 mild, 36 moderate, and 3 severe/critical cases. The median interval from exposure to disease onset was 13 days (IQR: 8-18 days). The incidence of severe/critical cases was 33% (3/10) in patients with hypertension. Common symptoms included fever (83%), cough (77%), fatigue (40%), a sore, dry throat (28%), and diarrhea (21%). One patient (2%) developed respiratory distress syndrome on day 13 of inpatient treatment. Six patients had leukopenia, 17 had elevated C-reactive protein (CRP), and 8 had lymphocytopenia and elevated lactate dehydrogenase (LDH). The median length of hospitalization was 22 days (IQR: 16-30 days). Dynamic monitoring of LDH, CRP, and neutrophil-lymphocyte ratio predicted whether length of hospitalization would exceed 21 days. Most patients presented with mild and moderate disease. Patients with hypertension were more likely to become severe or critical. Dynamic monitoring of LDH, CRP, and neutrophil-lymphocyte ratio levels can help predict delayed discharge from the hospital. © 2022 Lippincott Williams and Wilkins. All rights reserved.

The path analysis of the influence of anxiety, job burnout and self-efficacy on well-being-take the sample of college english teachers under the novel coronavirus pneumonia

Objective: Novel Coronavirus Pneumonia is spreading all over the world. Through investigating the anxiety, job burnout, self-efficacy and general well-being of the college English teachers, it is to explore the social mentality of the people under the epidemic situation, and provide evidence for improving the mental health of occupational population and formulating psychological intervention measures. Methods: A cross-sectional study of 520 college English teachers from Jilin Province(n=340) and Hubei Province(n=180) were investigated by anxiety scale, job burnout scale, self-efficacy scale and general well-being scale. Results: The detection rate of anxiety in the occupational group was 48.65%, and there was a significant difference in the scores of anxiety among the subjects of different ages(P<0.05);the level of burnout were higher than the average, and the subjects of different ages were in a serious state of burnout was statistically significant (P< 0.05);the general well-being of the subjects was significantly higher than that of the national norm (P<0.01). Path analysis showed that anxiety, job burnout and self-efficacy were the negative and positive factors that affect general well-being, and self-efficacy played an intermediary role between anxiety, job burnout and general well-being separately. Conclusion: Under the Novel Coronavirus Pneumonia it can improve the self-efficacy of occupation population, further overcome panic and learned helplessness to reduce anxiety, job burnout, enhance resilience, improve general well-being. © 2021 ACM.

Investigating students' dynamic learning emotions and interactions in online discussion

Affected by COVID-19, college students had to study at home in China. Therefore, a great significance is to investigate learners' emotions and interactions in online discussion. Many researchers have studied the emotions and interactions among students. However, most of them have overlooked how students' emotions and interactions vary over time, which is a dynamic process in online asynchronous discussion. Using emotion analysis and temporal network analysis, this paper investigates learners' positive, negative and confused states during learning, and uses a methodological approach to build, visualize, and quantitatively analyze temporal network in each week of the course. Results revealed that the ratio of students' positive states was extremely large at the beginning of the semester, and then decreases while the confused or negative ratios increased. The interactions in each week varied with time and could be divided into three patterns according the characteristics of temporal networks. Finally, the relationships among emotions, interactions, and academic performances were analyzed based on temporal networks, and the data revealed that middle-achieving learners were the major contributors while learning emotional states have a slight effect on learning outcomes. This study might assist teachers to provide timely and effective assistance during early warning, so to achieve the ultimate goal of improving the effect of online learning.

Government Trust in a Time of Crisis: Survey Evidence at the Beginning of the Pandemic in China

China adopted rigorous lockdowns and restrictions to contain the spread of COVID-19 from 23 January to 8 April 2020. Although the quarantine severely limited people's freedom and caused multiple secondary disasters, most Chinese citizens tolerated it. Based on an online survey conducted at the beginning of the lockdown (from 31 January to 4 February 2020), we argue that local governments in many parts of the country gained more trust than usual, narrowing the trust gap with the central government. In the early stage of the pandemic, effective implementation of anti-COVID policies, official media propaganda, and public expectation all contributed to the public's increased confidence in local governments.

Traditional Chinese medicine as a tourism recovery drawcard to boost China's inbound tourism after COVID-19

Purpose: This paper explores the role of traditional Chinese medicine (TCM) as a tourism recovery drawcard to boost China's inbound tourism after COVID-19. Design/methodology/approach: This paper employed a mixed method involving a cross-disciplinary literature review along with reflections from experts in TCM and health communication to inform tourism management. Specifically, this paper examines TCM and its potential benefits as a medical tourism drawcard to combat COVID-19. The selected literature focusses on the image and merits of TCM to frame how this medical philosophy can be used to position China as a tourist destination. Reflections on the use of TCM as a tourism marketing tool can guide promotional strategies from the Chinese government and destination managers during and after COVID-19. Findings: The Chinese government, the tourism industry (e.g. destination managers), the media and tourists must focus on three aspects of the role of TCM: to provide medical benefits to travellers amid COVID-19 and beyond, elevate China as a destination for global medical tourists and be leveraged as a tool for economic recovery. Practical implications: The paper builds a tourism recovery framework for stakeholders to adopt tailored TCM communication strategies to boost its inbound tourism programme. Originality/value: This paper is the first academic paper to review TCM comprehensively and critically in relation to China tourism and post-COVID-19 recovery measures. © 2021, Emerald Publishing Limited.

Visualizing sustainable supply chain management: A systematic scientometric review

Sustainable supply chain management (SSCM) has been attracting extensive attention from both practitioners and scholars. The main objective of this paper is to visualize and conduct a systematic scientometric review on 9151 articles and reviews published from 2007 to 2021. Research techniques of co-author analysis, co-word analysis, and co-citation analysis are applied to reveal the social structure, conceptual structure, and intellectual structure of the SSCM field, identify main concepts and research hotspots, and illuminate major specialties and emerging trends. The results of this work show that: (1) the top five most productive scholars are Joseph Sarkis, Kannan Govindan, Minglang Tseng, Angappa Gunasekaran, and Charbel Jose Chiappetta Jabbour. The top five most productive institutions are Hong Kong Polytech University, Islamic Azad University, University of Southern Denmark, Dalian University of Technology, and University of Tehran. (2) The main concepts include sustainable supply chain management, green supply chain management, circular economy, corporate social responsibility, and reverse logistics. The research hotspots of the SSCM field, currently, are game theory and circular economy related topics. (3) The leading researchers and influential journals are also identified. The emerging trends include sustainable supplier selection, circular economy, cap-and-trade regulation, blockchain technology, big data analytics, the COVID-19 pandemic, and the best-worst method and logistics performance. Finally, limitations and future researches are discussed. We expect this paper will show a big picture of the SSCM field for researchers as well as practitioners. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.