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The Environmental Cooperation System (ECS) is a new exploration of the government's spatial environmental policy to meet the requirements of green and sustainable development, so it is very important to scientifically evaluate its green innovation effect. Based on China's A-share listed companies from 2006 to 2021, from the perspective of corporate ESG performance, we apply the multi-dimensional fixed-effects difference-in-differences (DID) model, and empirically test the impact, mechanism, and heterogeneity of the Environmental Cooperation System of Shenzhen-Dongguan-Huizhou Metropolis (ECS-SDHM) on corporate green innovation. It found that ECS-SDHM can significantly improve corporate green innovation, and the policy effect is more significant in the private enterprise group. Secondly, we use ESG rating score and decomposition indicators to deeply analyze the green innovation effect mechanism of ECS-SDHM from the perspective of ESG performance. The results show that ECS-SDHM can enhance corporate green innovation by significantly improving corporate ESG performance, environmental governance, and social governance. Further research found that both corporate environmental social responsibility and executives' overseas backgrounds can positively moderate the green innovation effect of ECS-SDHM by positively moderating the ESG performance mechanism., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Qiang, Gang and Dawei.)

Objective: . To compare the signals of pulse diagnosis of fatty liver disease (FLD) patients and cirrhosis patients., Methods: After collecting the pulse waves of patients with fatty liver disease, cirrhosis patients, and healthy volunteers, we do pretreatment and parameters extracting based on harmonic fitting, modeling, and identification by unsupervised learning Principal Component Analysis (PCA) and supervised learning Least squares Regression (LS) and Least Absolute Shrinkage and Selection Operator (LASSO) with cross-validation step by step for analysis., Results: There is significant difference between the pulse diagnosis signals of healthy volunteers and patients with FLD and cirrhosis, and the result was confirmed by 3 analysis methods. The identification accuracy of the 1st principal component is about 75% without any classification formation by PCA, and supervised learning's accuracy (LS and LASSO) was even more than 93% when 7 parameters were used and was 84% when only 2 parameters were used., Conclusion: The method we built in this study based on the combination of unsupervised learning PCA and supervised learning LS and LASSO might offer some confidence for the realization of computer-aided diagnosis by pulse diagnosis in TCM. In addition, this study might offer some important evidence for the science of pulse diagnosis in TCM clinical diagnosis.