Negative elements of cryptocurrencies: Exploring the drivers of Bitcoin carbon footprints

This paper adopts an interactive network approach to investigate the factors driving the carbon footprint of Bitcoin, a negative aspect of cryptocurrencies. Our findings demonstrate that the dynamics of Bitcoin prices, including both returns and volatility, have a significant impact on the system comprising carbon emissions, energy prices, carbon prices, and financial indicators. Particularly during the first two years of the COVID-19 pandemic period, the spillover effects are observed to be particularly strong. Furthermore, we find that the dynamics of Bitcoin prices play a crucial role in driving its associated carbon emissions.

Predicting the Level of Background Current Noise in Graphene Biosensor through a Non-Covalent Functionalization Process

The rapid worldwide spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created a series of problems. Detection platforms based on graphene field-effect transistors (GFETs) have been proposed to achieve a rapid diagnosis of SARS-CoV-2 antigen or antibody. For GFET-based biosensors, the graphene surface usually needs to be functionalized to immobilize the bioreceptor and the non-covalent approach is preferred for functionalization because it is believed not to significantly alter the electronic properties of graphene. However, in this work, the non-covalent functionalization introduced by 1-pyrenebutyric acid N-hydroxysuccinimide ester (PBASE) was determined to lead to different changes in electrical properties in graphene samples with different defect densities. The fabricated graphene biosensor can successfully detect SARS-CoV-2 antigen with a concentration as low as 0.91 pg/mL. Further, by careful comparison, we determined that, for GFET fabricated on graphene with a higher defect density, the current variation caused by PBASE modification is greater and the background current noise in the subsequent antigen detection is also larger. Based on this relationship, we can predict the background current noise of the biosensors by evaluating the current change induced by the modification and screen the devices at an early stage of graphene biosensor fabrication for process optimization.

Intra-day co-movements of crude oil futures: China and the international benchmarks.

Investigating the co-movements between crude oil futures helps to understand the integration of the global markets. This paper focuses on Shanghai crude oil futures (INE) and study its co-movements with the international benchmarks of WTI and Brent crude oil futures in intra-day day and night trading sessions. A complex network model framework is proposed to analyse the intra-day co-movement patterns labelled by a functional data clustering approach on intra-day return curves. Our findings indicate INE is more integrated with the global market during the night session, but it shows a regional fractional effect during the day session. Based on the revealed dynamics of co-movement patterns, we further design a pairs trading strategy between INE crude oil futures and the international benchmarks. The simulation results show that the pairs trading strategy can be promisingly profitable, even during market turmoil phases.

Determining dependence, centrality, and dynamic networks between green bonds and financial markets.

We adopted a network approach to examine the dependence between green bonds and financial markets. We first created a static dependency network for a given set of variables using partial correlations. Secondly, to evaluate the centrality of the variables, we illustrated with-in system connections in a minimum spanning tree (MST). Afterward, rolling-window estimations are applied in both dependency and centrality networks for indicating time variations. Using the data spanning January 3, 2011 to October 30, 2020, we found that green bonds and commodity index had positive dependence on other financial markets and are system-wide net contributors before and after COVID-19. Time-varying dynamics illustrated heightened system integration, particularly during the crisis periods. The centrality networks reiterated the leading role of green bonds and commodity index pre- and post-COVID. Finally, rolling window analysis ascertained system dependence, centrality, and dynamic networks between green bonds and financial markets where green bond sustained their positive dependence all over the sample period. Green bonds' persistent dependence and centrality enticed several implications for policymakers, regulators, investors, and financial market participants.

Extreme risk spillover between chinese and global crude oil futures.

This paper investigates the risk spillover between China's crude oil futures and international crude oil futures by constructing upside and downside VaR connectedness networks. The findings show that China's crude oil futures behave as a net risk receiver in the global crude oil system, in which Brent and WTI play the leading roles in risk transmission in the system. The dynamic results indicate that the risk spillover between Chinese and international crude oil futures presents obvious time-varying characteristics and has risen sharply since the beginning of 2020, induced by the COVID-19 pandemic.

Financial markets under the global pandemic of COVID-19.

The rapid spread of coronavirus (COVID-19) has dramatic impacts on financial markets all over the world. It has created an unprecedented level of risk, causing investors to suffer significant loses in a very short period of time. This paper aims to map the general patterns of country-specific risks and systemic risks in the global financial markets. It also analyses the potential consequence of policy interventions, such as the US' decision to implement a zero-percent interest rate and unlimited quantitative easing (QE), and to what extent these policies may introduce further uncertainties into global financial markets.

Chinese expert brief consensus on newborn screening of inherited metabolic disorders during the novel coronavirus infection epidemic.

Novel coronavirus (2019-nCov) infection (COVID-19) rapidly spread across China and 25 countries in the worldwide, which infected not only adults but also children, even neonates. Each year, about 15 million newborns are delivered in China. Newborn screening (NBS) helps effectively prevent some mental retardation, premature death, and adverse outcomes in the early stage of baby, which could detect some inherited metabolic disorders (IMDs). During this COVID-19 epidemic, how to balance the risk of infected 2019-nCov and the risk of disability and teratogenesis of IMDs. Expert members of NBS extra quality assessment in National Clinical Center of Laboratory (NCCL) give a brief consensus for NBS of IMDs in the COVID-2019 epidemic, hoping that the brief consensus could be reference for NBS of IMDs in the other epidemic areas or periods all over the world.