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Detecting taxi passengers is crucial for assessing taxi driver behavior, which plays a significant role in regulating the taxi industry. Despite the advancements in deep learning, object detection algorithms have not been extensively applied to this domain. In this article, an innovative taxi passenger detection algorithm is introduced based on YOLOv8, a lightweight and highly accurate method designed to automatically monitor driver behavior and regulate the taxi industry. To address the challenge of deploying complex object detection models on mobile devices, the ghost module is incorporated in place of standard convolutions within the C2f module, thereby making the model more lightweight. Furthermore, the model's performance is enhanced by integrating an improved version of Frequency Channel Attention (FCA), termed Parallel Frequency Channel Attention (PFCA), which boosts detection accuracy with minimal additional parameters and computational overhead. Experimental results on a specific taxi passenger dataset demonstrate that the proposed method significantly outperforms the baseline YOLOv8n model. Specifically, the model reduces the number of parameters and floating point operations by 12.96% and 8.18%, respectively, while achieving increases in mAP50 and mAP50‐95 by 0.27 and 0.73 percentage points, respectively. [ABSTRACT FROM AUTHOR]

Utilizing data from the China Family Panel Studies (CFPS), this research focuses on the differences and mechanisms of intergenerational educational transmission between left-behind and non-left-behind children using structural equation modeling and multi-group path analysis. The findings indicate that significant intergenerational educational transmission effects exist in both groups, with parental educational attainment significantly impacting children's academic performance. Further mediation analysis demonstrates that parental educational attainment influences children's academic performance through the chain mediation effects of parental educational expectations and self-educational expectations. Multi-group path analysis reveals distinct mechanisms affecting academic performance: among non-left-behind children, parental educational attainment exerts a stronger direct influence on academic performance, while self-educational expectations are more influential for left-behind children. Additionally, the path coefficients for the effects of parental educational expectations and self-educational expectations on academic performance are higher for left-behind children than for non-left-behind children. Consequently, educational interventions should focus on enabling parents of left-behind children to effectively convey educational expectations through indirect methods (e.g., phone or online communication) and on enhancing left-behind children's self-educational expectations through school and community support to facilitate academic achievement in the absence of direct family support. [ABSTRACT FROM AUTHOR]

Landslide susceptibility maps (LSMs) are valuable tools typically used by local authorities for land use management and planning activities, supporting decision-makers in urban and infrastructure planning. To address this, we proposed a refined method for landslide susceptibility assessment, which comprehensively considered both static and dynamic factors. Neural network methods were used for susceptibility analysis. Land use and land cover (LULC) change and InSAR deformation were then integrated into the traditional susceptibility zoning to obtain a refined susceptibility map with higher accuracy. Validation was conducted on the improved landslide susceptibility map using site landslide data. The results showed that the LULC were proven to be the core driving factors for landslide occurrence in the study area. The GRU model achieved the highest model performance (AUC = 0.886). The introduction of InSAR surface deformation and land use and land cover change data could rationalize the inappropriateness of traditional landslide susceptibility zoning, correcting the false positive and false negative areas in the traditional landslide susceptibility map caused by human activities. Ultimately, 12.25% of the study area was in high-susceptibility zones, with 3.10% of false positive and 0.74% of false negative areas being corrected. The proposed method enabled refined analysis of landslide susceptibility over large areas, providing technical support and disaster prevention and mitigation references for geological hazard susceptibility assessment and land management planning. [ABSTRACT FROM AUTHOR]