No abstract

In the boundaryless career era, employability is valued by both employees and employers. In this study, we investigated the effects of employability on employees’ strain and turnover intention in the Chinese setting, focusing on the moderating role of perceived career opportunity (PCO). We collected two-wave data from a sample of 214 employees over a three-month period. The results showed that employability was negatively related to emotional exhaustion and nonsignificantly related to turnover intention after controlling for baseline levels. Further, we found that PCO moderated the relationships between employability, emotional exhaustion, and turnover intention. Specifically, the negative relationship between employability and emotional exhaustion was strong (weak) when PCO within the organization was high (low). Employability was negatively related to turnover intention only when PCO was high. Our study highlights that PCO should be given more attention in the employability research.

No abstract

No abstract

With the development of computer vision, attention mechanisms have been widely studied. Although the introduction of an attention module into a network model can help to improve e classification performance on remote sensing scene images, the direct introduction of an attention module can increase the number of model parameters and amount of calculation, resulting in slower model operations. To solve this problem, we carried out the following work. First, a channel attention module and spatial attention module were constructed. The input features were enhanced through channel attention and spatial attention separately, and the features recalibrated by the attention modules were fused to obtain the features with hybrid attention. Then, to reduce the increase in parameters caused by the attention module, a group-wise hybrid attention module was constructed. The group-wise hybrid attention module divided the input features into four groups along the channel dimension, then used the hybrid attention mechanism to enhance the features in the channel and spatial dimensions for each group, then fused the features of the four groups along the channel dimension. Through the use of the group-wise hybrid attention module, the number of parameters and computational burden of the network were greatly reduced, and the running time of the network was shortened. Finally, a lightweight convolutional neural network was constructed based on the group-wise hybrid attention (LCNN-GWHA) for remote sensing scene image classification. Experiments on four open and challenging remote sensing scene datasets demonstrated that the proposed method has great advantages, in terms of classification accuracy, even with a very low number of parameters.