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This study investigates the potential association between corporate digitization and disclosure quality, and how this relationship is moderated by non-state ownership and institutional environment.

Drawing on signaling theory and factors that affect disclosure quality, the authors developed a framework to study how corporate digitization is associated with disclosure quality. The proposed framework was empirically tested using a comprehensive analysis that integrated corporate-level data on digitalization, disclosure quality, and ownership structure, with regional-level data on the institutional environment. The authors employed linear panel regression models with fixed effects.

The authors found that corporate digitization is significantly and positively associated with higher disclosure quality. This positive association is particularly pronounced for non-state-owned enterprises compared to state-owned enterprises. Additionally, an improvement in the institutional environment strengthens the positive relationship between digitization and disclosure quality.

This work contributes to the literature on corporate digitization by empirically investigating its impact on disclosure quality. The study also extends previous research by considering the moderating roles of ownership structure and institutional environment on the digitization-disclosure quality relationship.

This study aims to apply deep convolutional neural network Mask-R-CNN algorithm based on transfer learning to realize the segmentation of online wear fragments.

Wear debris analysis is considered to be one of the most effective methods to maintain the condition of mechanical equipment. In this paper, the friction and wear testing machine was used to design pin-disk rotation, pin-disk reciprocation and four-ball test to produce cutting, sliding, laminar and fatigue debris. A semi-online sampling system was designed to collect ferrographic images containing various fragments. The images were rotated and flipped to augment the data and enhance the generalization ability of the model. The data set required for data analysis is established. Using COCO pre-trained Mask R-CNN data set as a benchmark, the region proposal network (RPN) is trained with labeled wear debris images to enhance the ability of RPN to recognize background and wear debris. Two transfer learning scenarios are tested in the network head of the Mask R-CNN.

The results show that the deep convolutional neural network is suitable for the automatic classification and detection of wear fragments. Through transfer learning and proper training configuration, the ferrographic image recognition based on Mask R-CNN achieves high accuracy.

The results show that the deep convolutional neural network is suitable for the automatic classification and detection of wear fragments. Through transfer learning and proper training configuration, the ferrographic image recognition based on Mask R-CNN achieves high accuracy.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0182/