The complex relationship between environmental regulation and green technology progress has always been a hot topic of research, especially in developing countries, where the impact of environmental regulation is important. Current research is mainly concerned with the impact of the single environmental regulation on technological progress and lacks study on the diversity of environmental regulations. The main purpose of this paper is to examine the heterogeneity of the effects of different types of environmental regulation on industrial green technology progress. As China’s scale of economy and pollution emissions are both large, and the government has also made great efforts in environmental regulation, this paper takes China as the example for analyses. We first use the EBM-GML method to measure the industrial green technology progress of 30 provinces in China from 2000 to 2018, and then apply the panel econometric model and threshold model to empirically investigate the influence of 3 types of environmental regulation. The results show that, first, the impacts of environmental regulation on industrial green technology progress are significantly different; specifically, command-based regulation has no direct significant impact, and autonomous regulation has played a positive role, and market-based regulation’s quadratic curve effect is significant, in which the cost-based and investment-based tool presents an inverted U-sharped and U-sharped, respectively. Second, there may be a weak alternative interaction among different types of environmental regulation. Third, a market-based regulatory tool has a threshold effect; with the upgrading of environmental regulation compliance, the effect of a cost-based tool is characterized by “promotion inhibition”, and that of an investment-based tool is “inhibition promotion”. Finally, the results of regional analysis are basically consistent with those of the national analysis. Based on the study, policy enlightenment is put forward to improve regional industrial green technology progress from the perspective of environmental regulation. This paper can provide a useful analytical framework for studying the relationship between environmental regulation and technological progress in a country, especially in developing countries.

Under the pressure of low-carbon development at county level in China, this paper takes Jiangsu province as an example to analyze the relationship between economic growth and carbon emissions, aiming to provide a reference for the low-carbon development in Jiangsu and other regions in China. Based on the county-level panel data from 2000 to 2017, this paper uses the Tapio elasticity model and environmental Kuznets curve model, and focuses on the differences in regional economic development and the impacts of the 2008 global economic crisis. The results show that, in general, the decoupling effect of carbon emissions in Jiangsu counties has gradually increased during the study period. Since 2011, all counties achieved the speed decoupling, with more than half of them showing strong decoupling. The environmental Kuznets curves of carbon emissions in different income groups are established, and changed before and after the 2008 global economic crisis. In 2017, only 10 of the 53 counties were on the right side of the curve, realizing the quantity decoupling between the two. Therefore, to achieve a win–win situation between carbon emission reduction and economic growth, efforts should be made from the aspects of industrial structure and energy efficiency, and measures should be taken according to local conditions.

At present, there is much literature on economic growth and energy consumption, but there is little literature combined with the industry perspective. This paper aims to clarify whether the development of energy-intensive industries is an indirect way for economic growth to affect energy consumption, which can provide a reference for the coordination of economic growth goals, industry development and reducing energy consumption. Based on China’s provincial panel data from 2000 to 2019, this paper measures the scale of provincial energy-intensive industries by entropy method and uses the panel regression model to test its transmission effect on energy consumption. The results show that 23.96% of the effects of economic growth on energy consumption are indirectly generated through the transmission of energy-intensive industries. Moreover, the transmission effects are only established in the eastern and western regions but are not significant in the central region. Therefore, controlling the rapid development of energy-intensive industries is an effective way to curb the expansion of China’s energy consumption scale. Green technology innovation, new-type urbanization construction and other supportive measures should be taken in accordance with local conditions. This research contributes to the coordinated and sustainable development of the economy, industry, and energy.

This paper aims to solve the asymmetric problem of sample classification recognition in extreme class imbalance. Inspired by Krawczyk (2016)’s improvement direction of extreme sample imbalance classification, this paper adopts the AdaBoost model framework to optimize the sample weight update function in each iteration. This weight update not only takes into account the sampling weights of misclassified samples, but also pays more attention to the classification effect of misclassified minority sample classes. Thus, it makes the model more adaptable to imbalanced sample class distribution and the situation of extreme imbalance and make the weight adjustment in hard classification samples more adaptive as well as to generate a symmetry between the minority and majority samples in the imbalanced datasets by adjusting class distribution of the datasets. Based on this, the imbalance boosting model, the Imbalance AdaBoost (ImAdaBoost) model is constructed. In the experimental design stage, ImAdaBoost model is compared with the original model and the mainstream imbalance classification model based on imbalanced datasets with different ratio, including extreme imbalanced dataset. The results show that the ImAdaBoost model has good minority class recognition recall ability in the weakly extreme and general class imbalance sets. In addition, the average recall rate of minority class of the mainstream imbalance classification models is 7% lower than that of ImAdaBoost model in the weakly extreme imbalance set. The ImAdaBoost model ensures that the recall rate of the minority class is at the middle level of the comparison model, and the F1-score comprehensive index performs well, demonstrating the strong stability of the minority class classification in extreme imbalanced dataset.

Single-cell RNA sequencing (scRNA-seq) is a high-throughput experimental technique for studying gene expression at the single-cell level. As a key component of single-cell data analysis, differential expression analysis (DEA) serves as the foundation for all subsequent secondary studies. Despite the fact that biological replicates are of vital importance in DEA process, small biological replication is still common in sequencing experiment now, which may impose problems to current DEA methods. Therefore, it is necessary to conduct a thorough comparison of various DEA approaches under small biological replications. Here, we compare 6 performance metrics on both simulated and real scRNA-seq datasets to assess the adaptability of 8 DEA approaches, with a particular emphasis on how well they function under small biological replications. Our findings suggest that DEA algorithms extended from bulk RNA-seq are still competitive under small biological replicate conditions, whereas the newly developed method DEF-scRNA-seq which is based on information entropy offers significant advantages. Our research not only provides appropriate suggestions for selecting DEA methods under different conditions, but also emphasizes the application value of machine learning algorithms in this field.

Smart manufacturing is an important development mode in the transition of China’s industry from weak to strong, and the realization of comprehensive smart manufacturing demands the coordinated efforts of all regions in China. Based on the panel data of 30 provincial administrative regions in China from 2014 to 2019, this paper constructs an index system for the development environment, infrastructure facilities, and industrial development. This paper uses methods of entropy weight TOPSIS and the dynamic comprehensive evaluation based on the time ordered weighted averaging (TOWA) operator to evaluate the smart manufacturing capability in China and analyze its characteristics of spatial difference for exploring the appropriate paths for development. The result shows that there are only two provinces, Guangdong and Jiangsu, with the values of dynamic comprehensive evaluation greater than 0.5, seven provinces with values between 0.25 and 0.5, and 21 provinces with values less than 0.25. This reflects the fact that the gradient difference in provincial smart manufacturing capability in China is obvious and most provinces are not good. The decline in the Theil index from 0.17 to 0.15 indicates that the difference in capability between provinces is narrowing, which is a good phenomenon. The increase in the Global Moran’s index from 0.1156 to 0.1478 shows that the capability in each province has a significant positive spatial correlation, and the correlation is strengthening. Moreover, during the six years, the spatial aggregation models of most provinces have not changed. The smart manufacturing capability of the Yangtze River Delta constitutes a stable high-high aggregation region. Guangdong and Chongqing have been in high-low aggregation regions for a long time, while most of the low-low aggregation regions are in the west.