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The purpose is to identify and evaluate the safety risk factors in the coal mine construction process.

The text mining technique was applied in the stage of safety risk factor identification. The association rules method was used to obtain associations with safety risk factors. Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) were utilized to evaluate safety risk factors.

The results show that 18 safety risk factors are divided into 6 levels. There are 12 risk transmission paths in total. Meanwhile, unsafe behavior and equipment malfunction failure are the direct causes of accidents, and inadequate management system is the basic factor that determines the safety risk status.

Due to the limitation of the computational matrix workload, this article only categorizes numerous lexical items into 18 factors. Then, the workshop relied on a limited number of experts; thus, the findings may be potentially biased. Next, the accident report lacks a universal standard for compilation, and the use of text mining technique may be further optimized. Finally, since the data are all from China, subsequent cross-country studies should be considered.

The results can help China coal mine project managers to have a clear understanding of safety risks, efficiently carry out risk hazard identification work and take timely measures to cut off the path of transmission with risks identified in this study. This helps reduce the economic losses of coal mining enterprises, thus improving the safety standards of the entire coal mining industry and the national standards for coal mine safety policy formulation.

Coal mine construction projects are characterized by complexity and difficulties in construction. Current research on the identification and assessment of safety risk factors in coal mine construction is insufficient. This study combines objective and systematic research approaches. The findings contribute to the safety risk management of China coal mine construction projects by providing a basis for the development of safety measures.

Shareholder heterogeneity reflects the interactive relationship between shareholder groups of different industries and ownership types. This paper aims to discuss the impact of shareholder heterogeneity on ambidextrous corporate innovation.

Combining questionnaire and database data, this study empirically analyzes the internal mechanisms of the impact of shareholder heterogeneity on ambidextrous corporate innovation.

The authors find that shareholder heterogeneity can promote ambidextrous corporate innovation and that board’s decision-making processes play an intermediary role. Specifically, shareholder industry-type heterogeneity promotes ambidextrous corporate innovation by improving procedural rationality in board’s decision-making process, and shareholder ownership-type heterogeneity promotes ambidextrous corporate innovation by improving political behavior in board’s decision-making process. The analysis of the impact degree shows that shareholder industry-type heterogeneity has a greater impact on exploitation innovation, while shareholder ownership-type heterogeneity has a greater impact on exploratory innovation. In addition, the research also shows that shareholder groups dominated by industry-type heterogeneity have an impact on corporate innovation by shaping an engaged board with higher procedural rationality and lower political behavior. Shareholder groups dominated by ownership-type heterogeneity have an impact on corporate innovation by shaping a contested board with higher political behavior and lower procedural rationality.

This study not only enriches the research on shareholder heterogeneity and corporate innovation in the context of transformation but also provides an analytical framework for research on board’s decision-making process.

Image edge detection is an essential issue in image processing and computer vision. The purpose of this paper is to provide a novel and effective algorithm for image edge detection.

Because GM (1,1) model is a typical model for tendency analysis, GM (1,1) model can be used for detecting edge. Prediction image data are close to the original image data by reason of the data being smooth in the non‐edge zone of image. The principle of edge detection by GM (1,1) model is that the predicted value at an edge point will be an overestimate or underestimate owing to the data changing drastically in the edge zone of the image. First, the edge image information is obtained by a preprocessed image subtracting from prediction image via GM (1,1). Second, median filter is used to eliminate isolated point noise in edge information images, and discrete wavelet transform is used to extract the image edge. Finally, this paper verifies the proposed algorithm by experiment.

Experimental results show that the proposed algorithm has advantages such as precisely locating, abundant weak edge, and better anti‐noise performance.

The algorithm proposed in the paper can precisely detect the information of edge image, and get a clear image detail.

Grey system theory developed vigorously lays the foundation for image processing. Wavelet analysis in image processing has its characteristics. This paper combines grey prediction model with discrete wavelet transform (DWT) successfully and obtains a novel and effective algorithm for image edge detection.