Modelling Dependency Structures of Carbon Trading Markets between China and European Union: From Carbon Pilot to COVID-19 Pandemic

The exploration of the dependency structure of the Chinese and EU carbon trading markets is crucial to the construction of a globally harmonized carbon market. In this paper, we studied the characteristics of structural interdependency between China's major carbon markets and the European Union (EU) carbon market before and after the launch of the national carbon emissions trading scheme (ETS) and the occurrence of the new coronavirus (COVID-19) by applying the C-vine copula method, with the carbon trading prices of the EU, Beijing, Shanghai, Guangdong, Shenzhen and Hubei as the research objects. The study shows that there exists a statistically significant dependence between the EU and the major carbon markets in China and their extremal dependences and dependence structures are different at different stages. After the launch of the national carbon ETS, China has become more independent in terms of interdependency with the EU carbon market, and is more relevant between domestic carbon markets. Most importantly, we found that the dependence between the EU and Chinese carbon markets has increased following the outbreak of COVID-19, and tail dependency structures existed before the launch of the national carbon ETS and during the outbreak of the COVID-19. The results of this study provide a basis for the understanding of the linkage characteristics of carbon trading prices between China and the EU at different stages, which in turn can help market regulators and investors to formulate investment decisions and policies.

Scalable, effective, and rapid decontamination of SARS-CoV-2 contaminated N95 respirators using germicidal ultraviolet C (UVC) irradiation device.

Particulate respirators such as N95s are an essential component of personal protective equipment (PPE) for front-line workers. This study describes a rapid and effective UVC irradiation system that would facilitate the safe re-use of N95 respirators and provides supporting information for deploying UVC for decontamination of SARS-CoV-2 during the COVID-19 pandemic. To assess the inactivation potential of the proposed UVC germicidal device as a function of time by using 3 M 8211-N95 particulate respirators inoculated with SARS-CoV-2. A germicidal UVC device to deliver tailored UVC dose was developed and test coupons (2.5 cm2) of the 3 M-N95 respirator were inoculated with 106 plaque-forming units (PFU) of SARS-CoV-2 and were UV irradiated. Different exposure times were tested (0-164 s) by fixing the distance between the lamp and the test coupon to 15.2 cm while providing an exposure of at least 5.43 mWcm-2. Primary measure of outcome was titration of infectious virus recovered from virus-inoculated respirator test coupons after UVC exposure. Other measures included the method validation of the irradiation protocol, using lentiviruses (biosafety level-2 agent) and establishment of the germicidal UVC exposure protocol. An average of 4.38 × 103 PFU ml-1 (SD 772.68) was recovered from untreated test coupons while 4.44 × 102 PFU ml-1 (SD 203.67), 4.00 × 102 PFU ml-1 (SD 115.47), 1.56 × 102 PFU ml-1 (SD 76.98) and 4.44 × 101 PFU ml-1 (SD 76.98) was recovered in exposures 2, 6, 18 and 54 s per side respectively. The germicidal device output and positioning was monitored and a minimum output of 5.43 mW cm-2 was maintained. Infectious SARS-CoV-2 was not detected by plaque assays (minimal level of detection is 67 PFU ml-1) on N95 respirator test coupons when irradiated for 120 s per side or longer suggesting 3.5 log reduction in 240 s of irradiation, 1.3 J cm-2. A scalable germicidal UVC device to deliver tailored UVC dose for rapid decontamination of SARS-CoV-2 was developed. UVC germicidal irradiation of N95 test coupons inoculated with SARS-CoV-2 for 120 s per side resulted in 3.5 log reduction of virus. These data support the reuse of N95 particle-filtrate apparatus upon irradiation with UVC and supports use of UVC-based decontamination of SARS-CoV-2 during the COVID-19 pandemic.

Evaluation and management of COVID-19-related severity in people with type 2 diabetes.

INTRODUCTION: People with type 2 diabetes (T2D) have an increased rate of hospitalization and mortality related to COVID-19. To identify ahead of time those who are at risk of developing severe diseases and potentially in need of intensive care, we investigated the independent associations between longitudinal glycated hemoglobin (HbA1c), the impact of common medications (metformin, insulin, ACE inhibitors (ACEIs), angiotensin receptor blockers (ARBs), and corticosteroids) and COVID-19 severity in people with T2D. RESEARCH DESIGN AND METHODS: Retrospective cohort study was conducted using deidentified claims and electronic health record data from the OptumLabs Data Warehouse across the USA between January 2017 and November 2020, including 16 504 individuals with T2D and COVID-19. A univariate model and a multivariate model were applied to evaluate the association between 2 and 3-year HbA1c average, medication use between COVID-19 diagnosis and intensive care unit admission (if applicable), and risk of intensive care related to COVID-19. RESULTS: With covariates adjusted, the HR of longitudinal HbA1c for risk of intensive care was 1.12 (per 1% increase, p<0.001) and 1.48 (comparing group with poor (HbA1c ≥9%) and adequate glycemic control (HbA1c 6%-9%), p<0.001). The use of corticosteroids and the combined use of insulin and metformin were associated with significant reduction of intensive care risk, while ACEIs and ARBs were not associated with reduced risk of intensive care. CONCLUSIONS: Two to three-year longitudinal glycemic level is independently associated with COVID-19-related severity in people with T2D. Here, we present a potential method to use HbA1c history, which presented a stronger association with COVID-19 severity than single-point HbA1c, to identify in advance those more at risk of intensive care due to COVID-19 in the T2D population. The combined use of metformin and insulin and the use of corticosteroids might be significant to prevent patients with T2D from becoming critically ill from COVID-19.

Risk-Aware Identification of Highly Suspected COVID-19 Cases in Social IoT: A Joint Graph Theory and Reinforcement Learning Approach.

The recent outbreak of the coronavirus disease 2019 (COVID-19) has rapidly become a pandemic, which calls for prompt action in identifying suspected cases at an early stage through risk prediction. To suppress its further spread, we exploit the social relationships between mobile devices in the Social Internet of Things (SIoT) to help control its propagation by allocating the limited protective resources to the influential so-called high-degree individuals to stem the tide of precipitated spreading. By exploiting the so-called differential contact intensity and the infectious rate in susceptible-exposed-infected-removed (SEIR) epidemic model, the resultant optimization problem can be transformed into the minimum weight vertex cover (MWVC) problem of graph theory. To solve this problem in a high-dynamic random network topology, we propose an adaptive scheme by relying on the graph embedding technique during the state representation and reinforcement learning in the training phase. By relying on a pair of real-life datasets, the results demonstrate that our scheme can beneficially reduce the epidemiological reproduction rate of the infection. This technique has the potential of assisting in the early identification of COVID-19 cases.

Analysis of clinical characteristics and laboratory findings of 95 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a retrospective analysis.

BACKGROUND: Since December 2019, 2019 novel coronavirus pneumonia emerged in Wuhan city and rapidly spread throughout China and even the world. We sought to analyse the clinical characteristics and laboratory findings of some cases with 2019 novel coronavirus pneumonia . METHODS: In this retrospective study, we extracted the data on 95 patients with laboratory-confirmed 2019 novel coronavirus pneumonia in Wuhan Xinzhou District People's Hospital from January 16th to February 25th, 2020. Cases were confirmed by real-time RT-PCR and abnormal radiologic findings. Outcomes were followed up until March 2th, 2020. RESULTS: Higher temperature, blood leukocyte count, neutrophil count, neutrophil percentage, C-reactive protein level, D-dimer level, alanine aminotransferase activity, aspartate aminotransferase activity, α - hydroxybutyrate dehydrogenase activity, lactate dehydrogenase activity and creatine kinase activity were related to severe 2019 novel coronavirus pneumonia and composite endpoint, and so were lower lymphocyte count, lymphocyte percentage and total protein level. Age below 40 or above 60 years old, male, higher Creatinine level, and lower platelet count also seemed related to severe 2019 novel coronavirus pneumonia and composite endpoint, however the P values were greater than 0.05, which mean under the same condition studies of larger samples are needed in the future. CONCLUSION: Multiple factors were related to severe 2019 novel coronavirus pneumonia and composite endpoint, and more related studies are needed in the future.