Can green assets hedge against economic policy uncertainty? Evidence from China with portfolio implications

This paper examines whether green assets can hedge against economic policy uncertainty (EPU) via asymmetric time-varying connectedness and EGARCH models. Using daily data in China spanning from March 2014 to June 2022, we find that (1) an evident asymmetric connectedness exists between green assets and EPU. (2) Green bond, carbon emission allowances and some green stocks can act as hedging or safety-haven assets against EPU, and the conclusion remains robust to an alternative proxy of EPU. (3) The minimum variance and connectedness portfolios provide superior performance during pre- and post-COVID-19 periods, respectively, thereby carrying substantial portfolio implications. © 2023 Elsevier Inc.

Research on the time-varying effects among green finance markets in China: A fresh evidence from multi-frequency scale perspective

Green finance is an essential instrument for achieving sustainable development. Objectively addressing correlations among different green finance markets is conducive to the risk management of investors and regulators. This paper presents evidence on the time-varying correlation effects and causality among the green bond market, green stock market, carbon market, and clean energy market in China at multi-frequency scales by combining the methods of Ensemble Empirical Mode Decomposition Method (EEMD), Dynamic Conditional Correlation (DCC) GARCH model, Time-Varying Parameter Vector Autoregression with Stochastic Volatility Model (TVP-VAR-SV), and Time-varying Causality Test. In general, the significant negative time-varying correlations among most green finance markets indicate a prominent benefit of risk hedging and portfolio diversification among green financial assets. In specific, for different time points and lag periods, the green finance market shock has obvious time-varying, positive and negative alternating effects in the short-term scales, while its time delay and persistence are more pronounced in the medium-term and long-term scales. Interestingly, a positive event shock will generate positive connectivity among most green finance markets, whereas a negative event including the China/U.S. trade friction and the COVID-19 pandemic may exacerbate the reverse linkage among green finance markets. Furthermore, the unidirectional causality of "green bond market - carbon market - green stock and clean energy markets” was established during 2018–2019. © 2023

Enhanced electrochemiluminescence immunoassay: 2. Enabling signal detection at an early stage of incubation for rapid point-of-care testing

Signal detection in a label-based immunoassay is performed normally when the antigen/antibody binding reaction reaches the equilibrium state during the incubation period of an assay process. Shortening the incubation period in an assay helps reduce the turnaround time and is particularly valuable for point-of-care testing, but the cost is the reduction of signal level and, possibly, measurement precision as well. This work demonstrates that the signal loss could be offset by the stronger emission of an electronically neutral ruthenium(II) complex label, Ru(2, 2′-bipyridine) (bathophenanthroline disulfonate)[4-(2, 2′-bipyridin-4-yl)butanoic acid], used in the electrochemiluminescence (ECL) immunoassay. Combined with the uniquely well-established flow-through washing process in the automated ECL analyzers and the precise control over liquid handling, the assays performed with a 5-minute incubation period showed the same signal level and measurement precision as those of conventional ECL assays. Additionally, the absence of biotin and streptavidin components in the reagent formulation avoids the biotin-streptavidin interaction during assay incubation and fundamentally eliminates the interference of biotin, especially when used in some high-dose therapies. The results obtained from the procalcitonin prototype kit and the supporting evidence from other preliminary reagents (for SARS-CoV-2 N protein and troponin T) are general. The nonequilibrium detection, along with the downsized instrument design, makes the enhanced ECL (EECL) technology a fast high-performance POCT platform that provides the same high-quality data as those generated from the widely deployed [Ru(bpy)3]2+ based laboratorial ECL systems. The anticipated regulatory approval and follow-up clinical implementation will be a significant stride in the decade-long pursuit of novel ECL labels. © 2023 The Author(s)

Impact of ICT-Based Tools on Team Effectiveness of Virtual Software Teams Working From Home Due to the COVID-19 Lockdown: An Empirical Study

The research examines the usage of ICT tools by software engineering teams, especially the virtual teams during COVID-19 and how it impacts the effectiveness of the team. This research has adapted the framework proposed by Salas et al. and Hackman et al. to measure team effectiveness. Team effectiveness was measured using 10 constructs. The research instrument proposed by Nagy and Habok has been adapted to measure the usage of ICT tools. The moderating role of gender and age has also been examined in this study. The sample size is 136 software professionals. Quantitative approach has been adapted. The study is descriptive in nature, and cluster sampling is adapted. The data is gathered through a closed-ended questionnaire, and analysis is done through SPSS software. The results reveal that usage of ICT tools enhances the team effectiveness in virtual software teams. Copyright © 2022, IGI Global.

Risk factors for fatal outcome in patients with severe COVID-19: an analysis of 107 cases in Wuhan.

Objective: To analyze the risk factors of fatal outcome in patients with severe COVID-19. Method(s): The clinical characteristics of 107 patients with severe COVID-19 admitted in Renmin Hospital of Wuhan University from February 12 to March 12, 2020 were retrospectively analyzed. During the hospitalization 49 patients died (fatal group) and 58 patients survived (survival group). The clinical characteristics, baseline laboratory findings were analyzed using R and Python statistical software. The risk factors of fatal outcome in patients with severe COVID-19 were analyzed with multivariate logistic regression. Result(s): Univariate analysis showed that the two groups had statistically significant differences in age, clinical classification, dry cough, dyspnea and laboratory test indicators (P<0.05 or <0.01). The random forest model was used to rank the significance of the statistically significant variables in the univariate analysis, and the selected variables were included in the binary logistic regression model. After stepwise regression analysis, the patient's clinical type, age, neutrophil count, and the proportion of CD3 cells are independent risk factors for death in severe COVID-19 patients. Dry cough is an independent protective factor for the death of severe COVID-19 patients. Conclusion(s): COVID-19 patients with fatal outcome are more likely to have suppressed immune function, secondary infection and inflammatory factor storm. These factors may work together in severe patients, leading to intractable hypoxemia and multiple organ dysfunction and resulting in fatal outcome of patients. The study indicates that timely intervention and treatment measures against above factors may be effective to save the lives of patients with severe COVID-19.Copyright © 2020 by the Chinese Medical Association.

Risk factors for fatal outcome in patients with severe COVID-19: an analysis of 107 cases in Wuhan.

Objective: To analyze the risk factors of fatal outcome in patients with severe COVID-19. Method(s): The clinical characteristics of 107 patients with severe COVID-19 admitted in Renmin Hospital of Wuhan University from February 12 to March 12, 2020 were retrospectively analyzed. During the hospitalization 49 patients died (fatal group) and 58 patients survived (survival group). The clinical characteristics, baseline laboratory findings were analyzed using R and Python statistical software. The risk factors of fatal outcome in patients with severe COVID-19 were analyzed with multivariate logistic regression. Result(s): Univariate analysis showed that the two groups had statistically significant differences in age, clinical classification, dry cough, dyspnea and laboratory test indicators (P<0.05 or <0.01). The random forest model was used to rank the significance of the statistically significant variables in the univariate analysis, and the selected variables were included in the binary logistic regression model. After stepwise regression analysis, the patient's clinical type, age, neutrophil count, and the proportion of CD3 cells are independent risk factors for death in severe COVID-19 patients. Dry cough is an independent protective factor for the death of severe COVID-19 patients. Conclusion(s): COVID-19 patients with fatal outcome are more likely to have suppressed immune function, secondary infection and inflammatory factor storm. These factors may work together in severe patients, leading to intractable hypoxemia and multiple organ dysfunction and resulting in fatal outcome of patients. The study indicates that timely intervention and treatment measures against above factors may be effective to save the lives of patients with severe COVID-19.Copyright © 2020 by the Chinese Medical Association.

Risk factors for fatal outcome in patients with severe COVID-19: an analysis of 107 cases in Wuhan.

Objective: To analyze the risk factors of fatal outcome in patients with severe COVID-19. Method(s): The clinical characteristics of 107 patients with severe COVID-19 admitted in Renmin Hospital of Wuhan University from February 12 to March 12, 2020 were retrospectively analyzed. During the hospitalization 49 patients died (fatal group) and 58 patients survived (survival group). The clinical characteristics, baseline laboratory findings were analyzed using R and Python statistical software. The risk factors of fatal outcome in patients with severe COVID-19 were analyzed with multivariate logistic regression. Result(s): Univariate analysis showed that the two groups had statistically significant differences in age, clinical classification, dry cough, dyspnea and laboratory test indicators (P<0.05 or <0.01). The random forest model was used to rank the significance of the statistically significant variables in the univariate analysis, and the selected variables were included in the binary logistic regression model. After stepwise regression analysis, the patient's clinical type, age, neutrophil count, and the proportion of CD3 cells are independent risk factors for death in severe COVID-19 patients. Dry cough is an independent protective factor for the death of severe COVID-19 patients. Conclusion(s): COVID-19 patients with fatal outcome are more likely to have suppressed immune function, secondary infection and inflammatory factor storm. These factors may work together in severe patients, leading to intractable hypoxemia and multiple organ dysfunction and resulting in fatal outcome of patients. The study indicates that timely intervention and treatment measures against above factors may be effective to save the lives of patients with severe COVID-19.Copyright © 2020 by the Chinese Medical Association.

Deregulation and green innovation: Does cultural reform pilot project matter

Green innovation is an important driving force for sustainable development. However China often imposes a wide variety of government regulations on green innovation One important reason behind these government regulations is the confinement of the cultural market. However, does this confinement actually affect the green innovation in China? By employing a 278 Chinese cities' dataset, we examine the effect of cultural reform pilot project on green innovation. Through the spatial difference-in-difference approach with the time trend, our results show that cultural reform pilot project (CRPP) is a significant determinist affecting the green innovation in China. Specifically, implementing CRPP promote green innovation in pilot cities which resulting from labour productivity exaltation, marketization rate increasing. The CRPP also have a spatial ripple effect which resulting from economic density promotion. Furthermore, the green innovation promotion is greater in cities which participating into World Technopolis Association, being included in the National Historical and Cultural Cities List and having high political hierarchy. Our conclusions still robustness after adopting a series of tests and alternative analyses. This paper not only provide evidence for the further implementation of cultural reform pilot project nationwide, but also provide policy implications on sustainable development in the post Covid-19 era. © 2023 Economic Society of Australia, Queensland

Infection control status of novel coronavirus pneumonia and radiation safety at CT workplace.

CT is an important imaging tool for the diagnosis of novel coronavirus pneumonia (COVID-19), therefore, it's necessary to strictly control the disinfection of CT workplace and equipment and biosafety to avoid the place from becoming a potential infection source and to reduce the risk of infection of patients and radiological staff. It is also necessary to reduce the CT scan dose to minimize the radiation hazards on patients under the premise of ensuring the CT image quality and diagnostic efficiency. Based on the survey that novel coronavirus residues after disinfection at some CT workplace in domestic and overseas and the application of low-dose CT scan in diagnosis of COVID-19, as well as the current situation of radiological protection management in emergency hospital, this paper summarizes and proposes suggestions on infection control and radiological protection for CT workplace to strengthen the defense line of COVID-19 prevention and control.Copyright © 2021 by the Chinese Medical Association.

What drives the volatility of non-fungible tokens (NFTs): macroeconomic fundamentals or investor attention?

Non-fungible tokens (NFTs) have experienced wild market fluctuation during the past years, which leads to the high volatility of NFT's daily price. This paper examines two potential volatility drivers of NFTs: macroeconomic fundamentals and investor attention. We employ the global and local economic policy uncertainty (EPU) indices as the economic fundamentals' proxies. The investor attention is represented by the Google search volumes (GSV) or NFTs attention index. Based on the empirical results of a modified generalized autoregressive conditional heteroskedasticity –mixed-data sampling (G-M) model, we find that either economic fundamentals or investor attention can increase the volatility of NFTs significantly. The monthly global EPU index adjusted by the current GDP and weekly GSV contain complementary information. Macroeconomic fundamentals and investor attention can jointly model the volatility of NFTs better than considering only one explanatory variable, as suggested by the G-M model with two explanatory variables. The results remain robust to alternative Twitter-based EPU indices and the ongoing COVID-19 pandemic period. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Transmission risk of asymptomatic SARS-CoV-2 infection: a systematic review and meta-analysis

Background: Global evidence on the transmission of asymptomatic SARS-CoV-2 infection needs to be synthesized. Methods: A search of 4 electronic databases (PubMed, EMBASE, Cochrane Library, and Web of Science databases) as of January 24, 2021 was performed. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies which reported the transmission rate among close contacts with asymptomatic SARS-CoV-2 cases were included, and transmission activities occurred were considered. The transmission rates were pooled by zero-inflated beta distribution. The risk ratios (RRs) were calculated using random-effects models. Results: Of 4923 records retrieved and reviewed, 15 studies including 3917 close contacts with asymptomatic indexes were eligible. The pooled transmission rates were 1.79 per 100 person-days (or 1.79%, 95% confidence interval [CI] 0.41%–3.16%) by asymptomatic index, which is significantly lower than by presymptomatic (5.02%, 95% CI 2.37%–7.66%;p<0.001), and by symptomatic (5.27%, 95% CI 2.40%–8.15%;p<0.001). Subgroup analyses showed that the household transmission rate of asymptomatic index was (4.22%, 95% CI 0.91%–7.52%), four times significantly higher than non-household transmission (1.03%, 95% CI 0.73%–1.33%;p=0.03), and the asymptomatic transmission rate in China (1.82%, 95% CI 0.11%–3.53%) was lower than in other countries (2.22%, 95% CI 0.67%–3.77%;p=0.01). Conclusions: People with asymptomatic SARS-CoV-2 infection are at risk of transmitting the virus to their close contacts, particularly in household settings. The transmission potential of asymptomatic infection is lower than symptomatic and presymptomatic infections. This meta-analysis provides evidence for predicting the epidemic trend and promulgating vaccination and other control measures. Registered with PROSPERO International Prospective Register of Systematic Reviews, CRD42021269446;https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=269446. © 2022 The Author(s)

Mortality Prediction with Bidirectional Coupled and Gumbel Subset Network on Irregularly Multivariate Time Series

Mortality prediction is a crucial challenge because of multivariate time series (MTS) complexity, which are sparse, irregularly, asynchronous and hold missing values for various reasons in a single acquisition. Various methods are proposed to deal with missing values for the final mortality prediction. However, existing models only capture the temporal dependencies within a time series and are inefficient to capture the dependencies between time series to rebuild missing values for mortality prediction. To address these challenges, in this paper, we present an end-to-end imputation and mortality prediction model, named bidirectional coupled and Gumbel subset network (BiCGSN), for mortality prediction with such irregularly multivariate time series. Our proposed model (BiCGSN) uses a recurrent network to learn the temporal dependencies (intra-time series couplings) and uses a Gumbel selector on multi-head attention to obtain the relationship between the variables (inter-time series couplings) in the forward and backward directions. Then the learned bidirectional inter-and intra-time series couplings are fused to impute missing values for further mortality prediction. We evaluate our model on PhysioNet2012 and COVID-19 datasets to imputation and predict mortality. Experiments show that BiCGSN obtains the AUC 0.869 and 0.911 on two real-world datasets respectively and outperforms all the baselines. © 2022 IEEE.

Blurred Boundaries: What It Means To Be an Early Career Faculty Member During a Pandemic

Early career faculty are undergoing a stressful transition period and actively negotiating their professional identity. The COVID-19 pandemic has changed the daily activities in early career faculty's personal and professional lives and thus complicated the negotiation process. This study explores how engineering faculty members redefine and reconceptualize what it means to be in their early career during the COVID-19 pandemic. Through an emergent qualitative coding technique, we identified two themes: 1) the blurring of personal and professional boundaries, and 2) the renegotiation of different identities. The findings offer insights into how to better support early career faculty and allow them to balance these different dimensions of their academic identities. © 2022 IEEE.

Research on Matching Mechanism and Route Planning of Intercity Carpool

Under the influence of COVID-19, intercity ride-sharing has become more and more popular due to its relatively little contact and low price and has gradually become one of the important ways of intercity transportation. The ride-sharing platform provides functions of information interaction among passengers and drivers, allocating the transportation tasks and recommending the optimal route planning. Existing ride-sharing platforms fail to take user's personalized needs into account when assigning tasks, and users have low satisfaction with the planned routes. This paper designs an allocation algorithm (Allocation Algorithm 4 Inter-city Carpool) for intercity carpool and proposes a pricing function related to the detour distance and user's satisfaction, so as to ensure the optimal benefits for ride-sharing platforms and drivers, as well as the optimal passenger satisfaction. The AA4IC algorithm is proved to be incentive compatible and budget balanced theoretically, and the effectiveness of allocation scheme generation and path planning is verified by experiments. When the algorithm is iterated 1000 times, the time is less than 200 s, and the task assignment under the optimal user satisfaction can be achieved. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Visualization of Parallel Coordinates Based on Comprehensive Index Evaluation of Media Platforms and Economic Psychology

After experiencing problems such as shutdown and limited offline activities caused by the COVID-19 pandemic, various industries have taken the initiative to take self-help measures such as Internetization, which makes the industry economy change significantly in the post-pandemic era and makes it necessary to re-evaluate the industry economic pattern. However, online public opinion is diverse and platform content is complex. Therefore, it is essential to observe economic activities from the network information platform and complete the screening and purification of information. In order to improve the degree of quantification, and deepen the understanding of data, this paper solves the above problems through parallel coordinate visualization. At the same time, the lines in the parallel axis are used to indicate the amount and specific trend of industry information, which not only reflects the role of parallel coordinate visualization methods in industry dynamic analysis and real-time display but also makes industry development forecasts more feasible. © 2022 The authors and IOS Press.

Immune inhibitory receptor LILRB2 enhances SARS-CoV-2 spike protein-mediated immune inflammation

Objective·To explore the possible roles of immune inhibitory receptor leukocyte immunoglobulin-like receptor subfamily B member 2 (LILRB2) in the immune inflammation after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, and provide a potential therapeutic way for the coronavirus disease 2019 (COVID-19). Methods·The supernatants containing the extracellular domain of spike protein (S-ECD) were collected, and the detection of the protein expression and activity in the conditional medium by Western blotting and flow cytometric analysis was followed by. The binding of S-ECD with LILRB2 was measured by co-immunoprecipitation and flow cytometric analysis. The mRNA expression levels of several inflammation genes in a human mononuclear cell line (THP1) or peripheral blood mononuclear cells (PBMC) were measured after spike protein stimulation for 24 h by quantitative RT-PCR. The protein levels of interleukin-6 (IL-6) and interleukin-1β (IL-1β) in the conditional medium were examined by enzyme-linked immunosorbent assay (ELISA). The siLILRB2 was transferred into CD33+ myeloid cells purified from human peripheral blood with Lipofectamine 3000 reagents. The knockdown efficiency was detected 24 h after transfection by flow cytometric analysis. The difference in the protein levels of IL-6 between the control cells and LILRB2-knocked-down cells after spike protein treatment was evaluated by ELISA. Results·The study established a transfection system with 293T cells by which the SARSCoV-2 S-ECD could be secreted to supernatants with normal biological activities. The interaction and the binding of spike protein with LILRB2 were evaluated by a co-immunoprecipitation assay and flow cytometric analysis, respectively. The mRNA expression levels of IL-6, IL-8, arginase 1 and IL-2 in THP1 cells were significantly up-regulated 24 h after spike protein treatment compared to the control cells (all P<0.05). Consistently, the mRNA levels of IL-6, transforming growth factor-β (TGF-β), IL-8, IL-10 and IL-β in PBMC were notably increased after spike protein stimulation (all P<0.05). In addition, spike protein could also induce the release of IL-6 and IL-1β in PBMC as measured by ELISA (all P<0.05). More importantly, spike protein was able to increase the secretion of IL-1β and IL-6 by CD33+ myeloid cells 24 h after treatment (both P<0.05). LILRB2-overexpressing THP1 cells produced more IL-6 24 h after treatment with spike protein than the control cells (P<0.05). Two siRNAs could efficiently down-regulate the expression of LILRB2 in CD33+ cells as evaluated by flow cytometric analysis. Consistently, spike protein had no effect on the IL-6 secretion to supernatant from LILRB2-knockdown CD33+ myeloid cells. Conclusion·SARS-CoV-2 can induce cytokine release syndrome by inflammatory factors, such as IL-6 and IL-1β, released by myeloid cells through spike protein binding to LILRB2. © 2022 Editorial Department of Journal of Shanghai Second Medical University. All rights reserved.

Effects of Microblog Comments on Chinese User's Sentiment with COVID-19 Epidemic Topics

Social media is one of the most significant sources of information in modern people’s life. Due to the large quantity of user base and public opinions, when people read a blog post, the different tendencies of comments may affect their views on the event to a certain extent. This paper, taking the COVID-19 epidemic as an example, investigated the impact of Weibo (a popular social software in China) comments on readers’ sentiments. In this paper, text mining technology was adopted to collect data including the blogs and the comments under each blog, and the NLPIR-Parser platform was used to analyze the sentiment of the comments. Finally, the conclusion that the sentiments of other comments tend to follow the sentiments of the first comments was drawn. Based on the research results, this paper also gave some enlightenment on social media management and suggestions of public opinions oversight. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.