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Previous research has revealed higher career adaptivity leads to higher career adaptability from between‐person perspective. However, the construction of career adaptability is dynamic rather than static, so how career adaptivity influences episode‐level career adaptability from a within‐person perspective is critical but unknown. Drawing on career construction theory, we examined how future orientation, considered part of career adaptivity, influences weekly career adaptability. One wave of between‐person data (N = 97) and four waves of within‐person data (repeated measures) were collected from undergraduates in a Chinese university. The results found that future orientation positively predicted weekly career adaptability, and weekly future work self‐mediated the relationship between them. Loneliness negatively moderated the relationship between future orientation and weekly future work self and further negatively moderated the indirect effect of weekly future work self between future orientation and weekly career adaptability. The theoretical and practical implications are discussed. [ABSTRACT FROM AUTHOR]

Robust power line detection from aerial images is an essential step for intelligent unmanned aerial vehicle (UAV) inspection. However, compared to regular object detection tasks, the generalization ability and accuracy of current methods are seriously insufficient due to the variability of power line scenes and the low proportion of power lines in the image. In this letter, we propose an object-based Markov random field with a discrete multineighborhood system (OMRF-DMNS) model, which can provide a solution for tackling power line detection challenges. First, the proposed method constructs a discrete multineighborhood system (DMNS) for the noncontact nodes. This not only greatly improves the antinoise ability of the model, but also lays the foundation for the transfer of context information among nodes under different receptive fields. Second, a multilevel logical model with multineighborhood joint reasoning (MLL-MNJR) is defined on the DMNS, which makes full use of the line features and location information to achieve semantic inference of nodes. Therefore, the OMRF-DMNS model can fully mine the multidimensional feature associations and context information among nodes, enabling it to effectively distinguish between power line segments and interference noises. Experimental results demonstrate that the proposed method significantly outperforms previous methods in different power line scenes. [ABSTRACT FROM AUTHOR]