Managing multi-granular probabilistic linguistic information in large-scale group decision making: A personalized individual semantics-based consensus model

The multi-granular probabilistic linguistic modeling allows decision makers to express cognitive information using multiple linguistic term sets based on their preferences. However, personalized individual semantics (PIS) can lead to different meanings of the same word within the linguistic context. To address this issue and manage consensus in large-scale group decision making, this study proposes a decision framework that employs multi-granular probabilistic linguistic preference relations (MGPLPRs). First, a transformation method is presented to unify different granularity levels of MGPLPRs, thus ensuring the consistency of granularity. Moreover, a consistency-driven optimization model is constructed to generate the numerical scales with PIS for different experts. Thereafter, a two-stage consensus reaching process (CRP) is developed, including both within-cluster and across-cluster CRP, to achieve group consensus. The experts' original weights are derived from a social network, taking into account the trust relationships among them. A dynamic weighting mechanism is used to update the experts' weights based on their contributions to group consensus, which better reflects the actual situation than fixed weights. The proposed method is exemplified through a case study of assessing and selecting campus surveillance measures for COVID-19. Finally, the effectiveness and robustness of the proposed framework are verified through comparative analysis and sensitivity analysis.

A spatiotemporal decay model of human mobility when facing large-scale crises.

A common feature of large-scale extreme events, such as pandemics, wildfires, and major storms is that, despite their differences in etiology and duration, they significantly change routine human movement patterns. Such changes, which can be major or minor in size and duration and which differ across contexts, affect both the consequences of the events and the ability of governments to mount effective responses. Based on naturally tracked, anonymized mobility behavior from over 90 million people in the United States, we document these mobility differences in space and over time in six large-scale crises, including wildfires, major tropical storms, winter freeze and pandemics. We introduce a model that effectively captures the high-dimensional heterogeneity in human mobility changes following large-scale extreme events. Across five different metrics and regardless of spatial resolution, the changes in human mobility behavior exhibit a consistent hyperbolic decline, a pattern we characterize as "spatiotemporal decay." When applied to the case of COVID-19, our model also uncovers significant disparities in mobility changes-individuals from wealthy areas not only reduce their mobility at higher rates at the start of the pandemic but also maintain the change longer. Residents from lower-income regions show a faster and greater hyperbolic decay, which we suggest may help account for different COVID-19 rates. Our model represents a powerful tool to understand and forecast mobility patterns post emergency, and thus to help produce more effective responses.

Formation mechanism and governance strategies of stigma in public health emergencies: Based on event system theory.

Introduction: With the new coronavirus (COVID-19) pandemic across the world, it is critical to propose effective strategies for stigma governance in public health emergencies in order to reduce negative effects caused by stigma. However, no known research has focused on the essential role of events in understanding stigma phenomenon from the perspective of external dynamic changes. Methods: Based on the event system theory, this paper analyzes the evolution mode and characteristics of specific events in the process of stigmatization from strength, space and time aspects, and taking COVID-19 event as an example, 1202 questionnaires and empirical analysis were conducted. Results and discussion: Our results reveal that event strength directly affects the results of stigmatization, and such impact appears to be more prominent with a novel, disruptive and critical event. In addition, spatial and temporal attributes represent the dynamic development of an event, and they can interact with event strength to regulate the relationship between event strength and outcomes. Finally, stigma governance strategies under public health emergencies from three aspects of event strength, space, and time were put forward.

A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors.

In recent years, vital signals monitoring in sports and health have been considered the research focus in the field of wearable sensing technologies. Typical signals include bioelectrical signals, biophysical signals, and biochemical signals, which have applications in the fields of athletic training, medical diagnosis and prevention, and rehabilitation. In particular, since the COVID-19 pandemic, there has been a dramatic increase in real-time interest in personal health. This has created an urgent need for flexible, wearable, portable, and real-time monitoring sensors to remotely monitor these signals in response to health management. To this end, the paper reviews recent advances in flexible wearable sensors for monitoring vital signals in sports and health. More precisely, emerging wearable devices and systems for health and exercise-related vital signals (e.g., ECG, EEG, EMG, inertia, body movements, heart rate, blood, sweat, and interstitial fluid) are reviewed first. Then, the paper creatively presents multidimensional and multimodal wearable sensors and systems. The paper also summarizes the current challenges and limitations and future directions of wearable sensors for vital typical signal detection. Through the review, the paper finds that these signals can be effectively monitored and used for health management (e.g., disease prediction) thanks to advanced manufacturing, flexible electronics, IoT, and artificial intelligence algorithms; however, wearable sensors and systems with multidimensional and multimodal are more compliant.

Telemedicine management of type 2 diabetes mellitus in obese and overweight young and middle-aged patients during COVID-19 outbreak: A single-center, prospective, randomized control study.

OBJECTIVE: The coronavirus disease-2019 (COVID-19) pandemic severely affected the disease management of patients with chronic illnesses such as type 2 diabetes mellitus (T2DM). This study aimed to assess the effect of telemedicine management of diabetes in obese and overweight young and middle-aged patients with T2DM during the COVID-19 pandemic. METHODS: A single-center randomized control study was conducted in 120 obese or overweight (body mass index [BMI] ≥ 24 kg/m2) young and middle-aged patients (aged 18-55 years) with T2DM. Patients were randomly assigned to the intervention (telemedicine) or control (conventional outpatient clinic appointment) group. After baseline assessment, they were home isolated for 21 days, received diet and exercise guidance, underwent glucose monitoring, and followed up for 6 months. Glucose monitoring and Self-Rating Depression Scale (SDS) scores were evaluated at 22 days and at the end of 3 and 6 months. RESULTS: Ninety-nine patients completed the 6-month follow-up (intervention group: n = 52; control group: n = 47). On day 22, the fasting blood glucose (FBG) level of the intervention group was lower than that of the control group (p < 0.05), and the control group's SDS increased significantly compared with the baseline value (p < 0.05). At the end of 3 months, glycated hemoglobin (HbA1c) and FBG levels in the intervention group decreased significantly compared with those in the control group (p < 0.01). At the end of 6 months, the intervention group showed a significant decrease in postprandial blood glucose, triglyceride, and low-density lipoprotein cholesterol levels as well as waist-to-hip ratio compared with the control group (p < 0.05); moreover, the intervention group showed lower SDS scores than the baseline value (p < 0.05). Further, the intervention group showed a significant reduction in BMI compared with the control group at the end of 3 and 6 months (p < 0.01). CONCLUSION: Telemedicine is a beneficial strategy for achieving remotely supervised blood glucose regulation, weight loss, and depression relief in patients with T2DM. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04723550.

A probabilistic linguistic opinion dynamics method based on the DeGroot model for emergency decision-making in response to COVID-19.

Emergency decision-making entails a multi-criteria problem with a short period and urgent events, which creates difficulties for decision makers to undertake an optimal decision. To ensure the validity and rationality of decision results, the probabilistic linguistic term set is adopted to represent the evaluation information of experts because it can assign different probabilities or importance to different linguistic terms, which is closely related to human cognition. In addition, to portray the dynamic changes in the emergency decision-making process, this study develops a new dynamics method based on the DeGroot model with probabilistic linguistic information. First, to simulate the transition matrix of probabilistic linguistic opinions, the basic operational rules are defined based on the transformation function and expectation function. Next, three forms of influence matrices incorporating similarity, self-persistence, and authority degrees are constructed, and the consensus conditions of the models are discussed. Then, considering the social networks and incomplete trust relationships between experts, a fourth trust-based influence matrix is devised. A case study of emergency decision-making for assessing response plans to COVID-19 is performed to verify the feasibility and effectiveness of the dynamic method. Furthermore, a sensitivity analysis is conducted. Finally, comparisons with classical methods are performed to illustrate the superiorities of the proposed algorithms.

COVID-19 distributes socially in China: A Bayesian spatial analysis.

PURPOSE: The ongoing coronavirus disease 2019 (COVID-19) epidemic increasingly threatens the public health security worldwide. We aimed to identify high-risk areas of COVID-19 and understand how socioeconomic factors are associated with the spatial distribution of COVID-19 in China, which may help other countries control the epidemic. METHODS: We analyzed the data of COVID-19 cases from 30 provinces in mainland China (outside of Hubei) from 16 January 2020 to 31 March 2020, considering the data of demographic, economic, health, and transportation factors. Global autocorrelation analysis and Bayesian spatial models were used to present the spatial pattern of COVID-19 and explore the relationship between COVID-19 risk and various factors. RESULTS: Global Moran's I statistics of COVID-19 incidences was 0.31 (P<0.05). The areas with a high risk of COVID-19 were mainly located in the provinces around Hubei and the provinces with a high level of economic development. The relative risk of two socioeconomic factors, the per capita consumption expenditure of households and the proportion of the migrating population from Hubei, were 1.887 [95% confidence interval (CI): 1.469~2.399] and 1.099 (95% CI: 1.053~1.148), respectively. The two factors explained up to 78.2% out of 99.7% of structured spatial variations. CONCLUSION: Our results suggested that COVID-19 risk was positively associated with the level of economic development and population movements. Blocking population movement and reducing local exposures are effective in preventing the local transmission of COVID-19.

The 'Myth of Zero-COVID' Nation: A Digital Ethnography of Expats' Survival Amid Shanghai Lockdown during the Omicron Variant Outbreak.

This study presents a digital ethnography of expats' survival amid the Shanghai lockdown during the Omicron variant outbreak. This study drew insights from studies on resilience and secondary coping within the context of global migration to comprehend the diverse emotional challenges faced by expats in a series of lockdowns and persistent nucleic acid amplification tests. Thus, this study asks what the major emotional challenges expats faced and what sources of social support they could draw from citizens in their host country during the Shanghai lockdown. Accordingly, this study collected WeChat group conversations to draw empirical findings, promoted scholarly conversations about fundamental survival necessity, and traced the process for establishing intercultural collective resilience with citizens from their host country. Overall, this study emphasized the significance of host country members who can promote certain coping mechanisms for their visitors in the specific regional and geographical context of China.

Tax enforcement and corporate employment: Evidence from a quasi-natural experiment in China

This study investigates how tax enforcement affects corporate employment in China. We utilize the merger of the State Tax Bureau and Local Tax Bureaus as a quasi-natural experiment and adopt a difference-in-differences framework to identify causality. The results show that tougher tax enforcement has a significant and negative effect on corporate employment and that this effect is more pronounced for firms with higher labor intensity, greater financial constraints, more severe labor market frictions, a lower initial tax rate, lower tax transfer ability, and greater credit market imperfections. Further, the mechanism tests demonstrate that tougher tax enforcement leads to increases in the effective income tax rate, cash holdings, and the cash flow sensitivity of real investment but decreases in accounts receivable and dividend payments. These results are consistent with the liquidity constraints channel. In addition, we exclude several alternative explanations and conduct a series of robustness checks. Overall, our findings indicate that corporate tax enforcement has large effects on the local labor demand, which provides some useful insights for local governments to stabilize employment during the COVID-19 pandemic.

What are the risk factors of hospital length of stay in the novel coronavirus pneumonia (COVID-19) patients? A survival analysis in southwest China.

BACKGROUND: The global epidemic of novel coronavirus pneumonia (COVID-19) has resulted in substantial healthcare resource consumption. Since patients' hospital length of stay (LoS) is at stake in the process, an investigation of COVID-19 patients' LoS and its risk factors becomes urgent for a better understanding of regional capabilities to cope with COVID-19 outbreaks. METHODS: First, we obtained retrospective data of confirmed COVID-19 patients in Sichuan province via National Notifiable Diseases Reporting System (NNDRS) and field surveys, including their demographic, epidemiological, clinical characteristics and LoS. Then we estimated the relationship between LoS and the possibly determinant factors, including demographic characteristics of confirmed patients, individual treatment behavior, local medical resources and hospital grade. The Kaplan-Meier method and the Cox Proportional Hazards Model were applied for single factor and multi-factor survival analysis. RESULTS: From January 16, 2020 to March 4, 2020, 538 human cases of COVID-19 infection were laboratory-confirmed, and were hospitalized for treatment, including 271 (50%) patients aged ≥ 45, 285 (53%) males, and 450 patients (84%) with mild symptoms. The median LoS was 19 (interquartile range (IQR): 14-23, range: 3-41) days. Univariate analysis showed that age and clinical grade were strongly related to LoS (P<0.01). Adjusted multivariate analysis showed that the longer LoS was associated with those aged ≥ 45 (Hazard ratio (HR): 0.74, 95% confidence interval (CI): 0.60-0.91), admission to provincial hospital (HR: 0.73, 95% CI: 0.54-0.99), and severe illness (HR: 0.66, 95% CI: 0.48-0.90). By contrast, the shorter LoS was linked with residential areas with more than 5.5 healthcare workers per 1,000 population (HR: 1.32, 95% CI: 1.05-1.65). Neither gender factor nor time interval from illness onset to diagnosis showed significant impact on LoS. CONCLUSIONS: Understanding COVID-19 patients' hospital LoS and its risk factors is critical for governments' efficient allocation of resources in respective regions. In areas with older and more vulnerable population and in want of primary medical resources, early reserving and strengthening of the construction of multi-level medical institutions are strongly suggested to cope with COVID-19 outbreaks.

A Cross-Sectional Survey on COVID-19 Vaccine Hesitancy Among Parents From Shandong vs. Zhejiang.

Introduction: With the approval of COVID-19 vaccinations for children and adolescents in China, parental vaccine hesitancy will emerge as a new challenge with regard to the administration of these vaccines. However, little is known regarding this hesitancy as well as regional differences that may exist between parents from Shandong vs. Zhejiang. Methods: To assess these issues, an online survey was conducted via a Wenjuanxing platform over the period from July 22 to August 14, 2021. Parents from Shandong and Zhejiang were recruited from Wechat groups and results from a total of 917 subjects were analyzed. Factors evaluated in this survey included socio-demographic variables, parental vaccine hesitancy, Parental Attitudes toward Childhood Vaccines (PACV) domains (behavior, safety and efficacy, general attitudes) and social support. Results: Compared with those from Shandong (N = 443), parents from Zhejiang (N = 474) showed significantly higher prevalence rates of COVID-19 vaccine hesitancy (19.4 vs. 11.7%, p = 0.001). Multivariate logistic regression showed that yearly household incomes of ≥120,000 RMB (p = 0.041), medical workers (p = 0.022) and general attitudes of PACV (p = 0.004) were risk factors for vaccine hesitancy among parents from Shandong, while behavior (p = 0.004), safety and efficacy (p < 0.001) and general attitudes of PACV (p = 0.002) were risk factors for parents from Zhejiang. Among parents with vaccine hesitancy (N = 144), concerns over side effects (91.0%) and unknown effects (84.0%) of the COVID-19 vaccine were the most prevalent reasons for hesitancy. Evidence providing proof of vaccine safety (67.4%) and assurance of a low risk of being infected by COVID-19 (60.4%) were the two most effective persuasive factors. Conclusion: Parents from Zhejiang showed a higher prevalence of COVID-19 vaccine hesitancy as compared with those from Shandong. Behavior, safety and efficacy, and general attitudes of PACV were the risk factors associated with this hesitancy in these parents from Zhejiang. Given the identification of the various reasons for parental vaccine hesitancy, different strategies as well as regional adjustments in these strategies will be required for an effective and convincing protocol for childhood vaccinations.

Collateral menus and corporate employment: Evidence from China's Property Law

We investigate whether introducing the collateral menus of liquid assets (e.g., inventory and accounts receivable) influences corporate hiring decisions. Our identification scheme treats the enactment of China's Property Law in 2007 as a quasi-natural experiment and then conducts a difference-in-differences estimation. The results show that firms with less net fixed assets hire more workers, particularly for firms with tighter financial constraints, higher labor intensity and those located in cities with stricter law enforcement, greater fiscal pressure and lower bank density. Further analysis shows that liquid assets help firms obtain more debt, save less cash, decrease the cash flow sensitivity of cash and investment and invest more in fixed assets, consistent with the collateral channel. In addition, a significant convergence of labor productivity across firms illustrates that the Property Law also has a labor reallocation effect. Our findings shed light on the importance of expanding collateral menus in maintaining employment during the COVID-19 pandemic.

The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China.

Based on the high-frequency heterogeneous autoregressive (HAR) model, this paper investigates whether coronavirus news (in China and globally) contains incremental information to predict the volatility of China's crude oil, and studies which types of coronavirus news can better forecast China's crude oil volatility. Considering the information overlap among various coronavirus news items and making full use of the information in various coronavirus news items, this paper uses two prevailing shrinkage methods, lasso and elastic nets, to select coronavirus news items and then uses the HAR model to predict China's crude oil volatility. The results show that (i) coronavirus news can be utilized to significantly predict China's crude oil volatility for both in-sample and out-of-sample analyses; (ii) the Panic Index (PI) and the Country Sentiment Index (CSI) have a greater impact on China's crude oil volatility. Additionally, China's Fake News Index (FNI) have a significant impact on China's crude oil volatility forecast; and (iii) global coronavirus news provides more incremental information than China's coronavirus news for predicting the volatility of China's crude oil market, which indicates that global coronavirus news is also a key factor to consider when predicting the market volatility of China's crude oil.

Psychological impact of COVID-19 pandemic on healthcare workers in China Xi'an central hospital.

OBJECTIVE: COVID-19 significantly altered our routine, lifestyle, and stress level across the globe. This study investigated the psychological impact of COVID-19 on healthcare workers in China Xi'an Center hospital. METHODS: A modified online questionnaire of Psychological Status and the General Health Questionnaire (GHQ-12) was provided to 1,967 healthcare workers during the COVID-19 pandemic. Participation was voluntary, and the responses were anonymous. The survey lasted for 2 weeks, and the GHQ-12 was completed every other day. The data were collected automatically and electronically and then statistically analyzed. RESULTS: The 431 (21.9%) responders included 214 nurses (49.7%), 146 clinicians (33.9%), 29 pharmacists (6.7%), 15 medical technicians (3.5%), 17 administrative staff (3.9%), and 10 other departments (2.3%). Of these, 46.2% had 10 years of work experiences or more and 78.2% were married. Work experience increased emotional stress as 23% of participants with 10 years or more of experience exhibited higher stress compared to those with fewer than 3 years of work experience (7.5%). Moreover, 33.3% of participants who worked in or were exposed to the affected areas of the pandemic experienced psychological stress. Overall, this study identified four factors that were significantly associated with psychological stress: (a) work experience (OR 2.99; 95% CI: 1.06 to 8.41); (b) change in job position (OR 1.99; 95% CI: 1.10 to 3.59); (c) change in lifestyle (OR 4.06; 95% CI: 1.81 to 9.10); and (d) need for psychological counseling (OR 3.07; 95% CI: 1.62 to 5.82). CONCLUSIONS: The COVID-19 pandemic has increased psychological stress among healthcare workers with 10 years or more work experiences and who recently experienced a career position change.

Effect of the Novel Coronavirus Pneumonia Pandemic on Medical Students' Psychological Stress and Its Influencing Factors.

In December 2019, an outbreak of the novel coronavirus pneumonia infection occurred in Wuhan City, Hubei Province, China, and it has received substantial attention globally. Few studies have investigated the psychological stress of students in Health University during the COVID-19 outbreak, and almost no work has attended to the influencing factors that may cause their psychological stress risk. This cross-sectional, survey-based, region-stratified study collected demographic data and mental measurement from 2,498 medical students and 1,177 non-medical students in 31 provinces from March 5, 2020, to March 10, 2020, in China. The psychological stress was measured using the Chinese Perceived Stress Scales (CPSS) under a self-design questionnaire. Sociodemographic, major characteristics, and knowledge of the novel coronavirus pneumonia were also identified as potential influencing factors of stress. The study revealed that medical students are suffering from more stress than non-medical students almost in all provinces of China. Four influencing factors including level of familiarity with the novel coronavirus, family income, major of students, and status of the intern student can be significantly related to students' stress in the medical group by using the univariate and multivariate analysis. Further analysis showed that students with low stress had a greater number of positive psychological emotions and a lower number of negative psychological emotions than with medical students with high stress. In addition, high stress caused low enthusiasm for learning in these medical students and lead to little/no willingness to do professional medical work in the future. In conclusion, we need to increase the level of our knowledge related to the novel coronavirus pneumonia to reduce stress and strongly focus on the special populations in medical students with certain features, such as intern students, clinical nursing students, and low-income families, to improve their learning attitudes and establish positive professional mental outlooks.

Association Between Clinical Manifestations and Prognosis in Patients with COVID-19.

PURPOSE: The purpose of this study was to determine the risk factors associated with pneumonia, acute respiratory distress syndrome (ARDS), and clinical outcome among patients with novel coronavirus disease 2019 (COVID-19). METHODS: This was a cross-sectional multicenter clinical study. A total of 95 patients infected with COVID-19 were enrolled. The COVID-19 diagnostic standard was polymerase chain reaction detection of target genes of 2019 novel coronavirus (2019-nCoV). Clinical, laboratory, and radiologic results, as well as treatment outcome data, were obtained. ARDS was defined as an oxygenation index (arterial partial pressure of oxygen/fraction of inspired oxygen) ≤300 mm Hg. FINDINGS: Multivariate analysis showed that older age (odds ratio [OR], 1.078; p = 0.008) and high body mass index (OR, 1.327; p = 0.024) were independent risk factors associated with patients with pneumonia. For patients with ARDS, multivariate analysis showed that only high systolic blood pressure (OR, 1.046; p = 0.025) and high lactate dehydrogenase level (OR, 1.010; p = 0.021) were independent risk factors associated with ARDS. A total of 70 patients underwent CT imaging repeatedly after treatment. Patients were divided in a disease exacerbation group (n = 19) and a disease relief group (n = 51). High body mass index (OR, 1.285; p = 0.017) and tobacco smoking (OR, 16.13; p = 0.032) were independent risk factors associated with disease exacerbation after treatment. IMPLICATIONS: These study results help in the risk stratification of patients with 2019-nCoV infection. Patients with risk factors should be given timely intervention to avoid disease progression.