The moderate level of digital transformation: from the perspective of green total factor productivity

In the context of accelerated development of the digital economy, whether enterprises can drive green total factor productivity (GTFP) through digital technology has become the key to promoting high-quality development of the economy and achieving the goal of "dual-carbon", However, the relationship between digital transformation and GTFP is still controversial in existing studies. Based on the data of 150 listed companies in China's A-share energy industry from 2011 to 2021, this study empirically analyzes the impact of digital transformation on GTFP using a fixed-effect model. The study shows an inverted U-shaped nonlinear effect of digital transformation on enterprises' GTFP, and the conclusion still holds after a series of robustness tests. Mechanism analysis shows that enterprise investment efficiency and labour allocation efficiency play a significant mediating role in the above inverted U-shaped relationship, in which the inverted U-shaped relationship between digital transformation and GTFP mainly stems from the influence of enterprise investment efficiency. Heterogeneity analysis finds that the inverted U-shaped relationship between digital transformation and GTFP of enterprises is more significant in large-scale enterprises, new energy enterprises and enterprises in central and western regions. The study's findings provide important insights for enterprises to promote digital transformation and realize the green and high-quality development of the energy industry

Did the “double carbon” policy improve the green total factor productivity of iron and steel enterprises? a quasi-natural experiment based on carbon emission trading pilot

Based on the data of listed companies in China’s iron and steel industry from 2007 to 2020, the article investigates the impact mechanism and the path of action of China’s carbon emissions trading pilot on the green total factor productivity of iron and steel enterprises by constructing a multi-period difference-in-difference model difference-in-differences. The study finds that: 1) China’s iron and steel enterprises significantly improve their green total factor productivity driven by the carbon trading pilot, and the findings pass the corresponding robustness tests. 2) the mechanism analysis indicates that the carbon trading pilot promotes the green total factor productivity of iron and steel enterprises by forcing the technological progress of enterprises. 3) The heterogeneity analysis shows that the positive effect is more significant for large iron and steel enterprises with high social responsibility rating and high local government competition intensity, but not for small enterprises with low social responsibility rating and low local government competition intensity. 4) the dynamic effect shows that there is a certain lag in the promotion effect of the carbon emission trading pilot on the green total factor productivity of iron and steel enterprises, but its long-term effect is more obvious. This paper puts forward corresponding suggestions for accelerating the construction of a national unified green and low-carbon market system and actively promoting the deepening of the “dual-carbon” goal

Presentation1_Bank digital transformation, bank competitiveness and systemic risk.pdf

The aim of this paper is to analyze the impact of the digital transformation of banks on their systemic risks. We find that the digital transformation of commercial banks can significantly inhibit the systemic risk of banks, and this conclusion is still valid after considering the endogeneity of the model. The bank’s digital transformation reduces its systemic risk by increasing its own competitiveness. Further analysis shows that the reduction of banks’ marginal costs due to digital transformation is a key factor in promoting banks’ competitiveness as the mechanism by which digital transformation reduces banks’ systemic risk. The role of bank digital transformation in reducing systemic risk is heterogeneous, which is more obvious in large commercial banks, commercial banks that have not established financial technology subsidiaries, and systemically important banks.</p

Dissolution kinetics of malachite in ethylene diamine phosphate solutions

Ethylene diamine phosphate (EDP), as a synthetic organic reagent, was used for the first time to leach malachite, and a new method of using organic amine to leach copper oxide ore was developed. The effects of stirring speed, particle size, reagent concentration, and reaction temperature on EDP-dissolution malachite were investigated. Results showed that malachite rapidly dissolved in EDP solution. The malachite-dissolving rate also increased with increased reagent concentration, increased reaction temperature, and decreased particle size. Stirring speed exhibited nearly no effect on EDP-induced malachite dissolution. The leaching kinetics was found to follow the shrinking-core model, and dissolution was controlled by surface chemical reaction with an activation energy of 52.63kJ×mol−1. A semiempirical rate equation was obtained to describe the dissolution process expressed as 1-(1-XCu)1/3=0.0149(CEDP)0.7814 × (Pmalachite)−0.7982×exp(−6.3308/T) ×t

Attacks and design of image recognition CAPTCHAs

We systematically study the design of image recognition CAPTCHAs (IRCs) in this paper. We first review and examine all IRCs schemes known to us and evaluate each scheme against the practical requirements in CAPTCHA applications, particularly in large-scale real-life applications such as Gmail and Hotmail. Then we present a security analysis of the representative schemes we have identified. For the schemes that remain unbroken, we present our novel attacks. For the schemes for which known attacks are available, we propose a theoretical explanation why those schemes have failed. Next, we provide a simple but novel framework for guiding the design of robust IRCs. Then we propose an innovative IRC called Cortcha that is scalable to meet the requirements of large-scale applications. Cortcha relies on recognizing an object by exploiting its surrounding context, a task that humans can perform well but computers cannot. An infinite number of types of objects can be used to generate challenges, which can effectively disable the learning process in machine learning attacks. Cortcha does not require the images in its image database to be labeled. Image collection and CAPTCHA generation can be fully automated. Our usability studies indicate that, compared with Google’s text CAPTCHA, Cortcha yields a slightly higher human accuracy rate but on average takes more time to solve a challenge

Mitochondrial IRG1 traps MCL-1 to induce hepatocyte apoptosis and promote carcinogenesis

Abstract Hepatocarcinogenesis is initiated by repeated hepatocyte death and liver damage, and the underlying mechanisms mediating cell death and the subsequent carcinogenesis remain to be fully investigated. Immunoresponsive gene 1 (IRG1) and its enzymatic metabolite itaconate are known to suppress inflammation in myeloid cells, and its expression in liver parenchymal hepatocytes is currently determined. However, the potential roles of IRG1 in hepatocarcinogenesis are still unknown. Here, using the diethylnitrosamine (DEN)-induced hepatocarcinogenesis mouse model, we found that IRG1 expression in hepatocytes was markedly induced upon DEN administration. The DEN-induced IRG1 was then determined to promote the intrinsic mitochondrial apoptosis of hepatocytes and liver damage, thus enhancing the subsequent hepatocarcinogenesis. Mechanistically, the mitochondrial IRG1 could associate and trap anti-apoptotic MCL-1 to inhibit the interaction between MCL-1 and pro-apoptotic Bim, thus promoting Bim activation and downstream Bax mitochondrial translocation, and then releasing cytochrome c and initiating apoptosis. Thus, the inducible mitochondrial IRG1 promotes hepatocyte apoptosis and the following hepatocarcinogenesis, which provides mechanistic insight and a potential target for preventing liver injury and HCC

Early warning of hand, foot, and mouth disease transmission: A modeling study in mainland, China.

BackgroundHand, foot, and mouth disease (HFMD) is a global infectious disease; particularly, it has a high disease burden in China. This study was aimed to explore the temporal and spatial distribution of the disease by analyzing its epidemiological characteristics, and to calculate the early warning signals of HFMD by using a logistic differential equation (LDE) model.MethodsThis study included datasets of HFMD cases reported in seven regions in Mainland China. The early warning time (week) was calculated using the LDE model with the key parameters estimated by fitting with the data. Two key time points, "epidemic acceleration week (EAW)" and "recommended warning week (RWW)", were calculated to show the early warning time.ResultsThe mean annual incidence of HFMD cases per 100,000 per year was 218, 360, 223, 124, and 359 in Hunan Province, Shenzhen City, Xiamen City, Chuxiong Prefecture, Yunxiao County across the southern regions, respectively and 60 and 34 in Jilin Province and Longde County across the northern regions, respectively. The LDE model fitted well with the reported data (R2 > 0.65, P ConclusionsThe disease burden of HFMD in China is still high, with more cases occurring in the southern regions. The early warning of HFMD across the seven regions is heterogeneous. In the northern regions, it has a high incidence during summer and peaks in June every year; in the southern regions, it has two waves every year with the first wave during spring spreading faster than the second wave during autumn. Our findings can help predict and prepare for active periods of HFMD