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Sand porosity is an important compactness parameter that influences the mechanical properties of sand. In order to evaluate the temporal variation in sand porosity, a new method of sand porosity evaluation based on the statistics of target sand particles (which refers to particles within a specific particle size range) is presented. The relationship between sand porosity and the number of target sand particles at the soil surface considering observation depth is derived theoretically, and it is concluded that there is an inverse relationship between the two. Digital image processing and the k-means clustering method were used to distinguish particles in digital images where particles may mask each other, and a criterion for determining the number of particles was proposed, that is, the criterion of min(Dao). The execution process was implemented by self-written codes using Python (2021.3). An experiment on a simple case of Go pieces and sand samples of different porosities was conducted. The results show that the sum of the squared error (SSE) in the k-means method can converge with a small number of iterations. Furthermore, there is a minimum value between the parameter Dao and the set value of a single-particle pixel, and the pixel corresponding to this value is a reasonable value of a single-particle pixel, that is, the min(Dao) criterion is proposed. The k-means method combined with the min(Dao) criterion can analyze the number of particles in different particle size ranges with occlusion between particles. The test results of sand samples with different densities show that the method is reasonable. [ABSTRACT FROM AUTHOR]

Amidst growing environmental challenges linked to coal dependence, fostering green innovation in new energy enterprises is vital for sustainable development in China. Although there have been studies on green innovation of new energy enterprises in recent years, few studies have been conducted from the perspective of ESG, whether informal environmental regulation represented by ESG can stimulate the green innovation of new energy enterprises is of great significance to China's construction of a low-carbon and secure energy system. In this paper, from the perspective of informal environmental regulation, based on the ESG ratings of SynTaoGreen Finance's first public newenergy listed companies as an exogenous shock, and taking A-share new energy listed companies as a sample from 2010 to 2021, we empirically verified the effect and mechanism of ESG ratings on the green innovation of new energy companies in terms of the quantity and quality of green innovations by utilizing the Staggered Difference-indifference (DID) model. The findings demonstrate that new energy enterprises' green patent numbers and quality are greatly enhanced by ESG grading. However, there is clear heterogeneity in this green innovation effect, which is particularly visible in new energy firms with state-owned enterprise and greater enterprise scales and a higher level of digitization. The Mechanistic findings suggest that ESG ratings drive green innovation by alleviating financial constraints, reducing agency risk, and boosting R&D, thus providing empirical evidence for the development of a green innovation ecosystem in the new energy industry. [ABSTRACT FROM AUTHOR]