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This paper expects to analyze the connection between occupational stigma and job meaningfulness among Chinese takeaway riders, the mediating role of occupational identity and relative deprivation, and the moderating effect of job stress based on resource conservation theory.

The sample was derived from 371 takeaway riders across China. PLS-SEM was mainly utilized for the data analysis.

The findings of the study indicated a significant negative correlation between occupational stigma and job meaningfulness. Furthermore, it is worth noting that relative deprivation and occupational identity served as mediators and masks, respectively, in the relationship between occupational stigma and job meaningfulness. Furthermore, job stress amplifies the association between occupational stigma and occupational identity. Additionally, job stress diminishes the connection between occupational stigma and relative deprivation.

This study proposes a positive correlation between occupational stigma and occupational identity in the Chinese context. It also enriches the empirical research based on resource conservation theory. Furthermore, it holds practical implications for takeaway riders in China, offering insights to bolster their job meaningfulness.

The purpose of this paper is to summarize the existing point cloud target detection algorithms based on deep learning, and provide reference for researchers in related fields. In recent years, with its outstanding performance in target detection of 2D images, deep learning technology has been applied in light detection and ranging (LiDAR) point cloud data to improve the automation and intelligence level of target detection. However, there are still some difficulties and room for improvement in target detection from the 3D point cloud. In this paper, the vehicle LiDAR target detection method is chosen as the research subject.

Firstly, the challenges of applying deep learning to point cloud target detection are described; secondly, solutions in relevant research are combed in response to the above challenges. The currently popular target detection methods are classified, among which some are compared with illustrate advantages and disadvantages. Moreover, approaches to improve the accuracy of network target detection are introduced.

Finally, this paper also summarizes the shortcomings of existing methods and signals the prospective development trend.

This paper introduces some existing point cloud target detection methods based on deep learning, which can be applied to a driverless, digital map, traffic monitoring and other fields, and provides a reference for researchers in related fields.