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To improve fault identification accuracy in power distribution systems, a model named gated recurrent attention network is proposed. First, a higher weight is put on the key cycles of fault phase based on the attention mechanism, making the model focus more on these key messages by weight assignment. Then, the gated recurrent network is adopted, which controls the memory transmission with gate signal and constructs the relationship between input waveform and probability of events at different stages to process the waveform sequence, thereby improving recognition accuracy. Experiments based on both simulation and field data show that the proposed method, under the small-sample-learning condition, is much better than other commonly-used classification models, such as support vector machine, gradient boosting decision tree, and convolutional neural network, providing new insights into fault identification technology in power distribution systems.

Mandibular angle osteotomy is a popular craniofacial plastic surgery in recent years. Usually, preoperative planning of mandibular angle osteotomy is completed by an experienced doctor, which is cumbersome and time-consuming. In order to improve the efficiency of osteotomy planning, a design method of mandibular angle osteotomy plane based on point cloud semantic segmentation network is proposed. After three-dimensional reconstruction of the skull computer tomography (CT) scan data, the three-dimensional model of the mandible is converted into point cloud data through uniform sampling. The resection area of the mandible is predicted by the proposed algorithm, which is used to calculate the mandibular angle osteotomy plane. The proposed semantic segmentation network mainly includes 2 parts: a local feature extraction layer based on the attention mechanism, which is used to extract fine-grained local structure information, and a non-local feature extraction layer based on Transformer, which is used to extract the global context information of the point cloud. On the constructed mandible semantic segmentation data set, the proposed algorithm is compared with other point cloud semantic segmentation algorithms. The results show that the proposed algorithm can achieve the best prediction of the mandibular angle resection area, which is better than current common point cloud semantic segmentation algorithms.

Climate warming, often accompanied by extreme drought events, could have profound effects on both plant community structure and ecosystem functioning. However, how warming interacts with extreme drought to affect community‐ and ecosystem‐level stability remains a largely open question. Using data from a manipulative experiment with three warming treatments in an alpine meadow that experienced one extreme drought event, we investigated how warming modulates resistance and recovery of community structural and ecosystem functional stability in facing with extreme drought. We found warming decreased resistance and recovery of aboveground net primary productivity (ANPP) and structural resistance but increased resistance and recovery of belowground net primary productivity (BNPP), overall net primary productivity (NPP), and structural recovery. The findings highlight the importance of jointly considering above‐ and belowground processes when evaluating ecosystem stability under global warming and extreme climate events. The stability of dominant species, rather than species richness and species asynchrony, was identified as a key predictor of ecosystem functional resistance and recovery, except for BNPP recovery. In addition, structural resistance of common species contributed strongly to the resistance changes in BNPP and NPP. Importantly, community structural resistance and recovery dominated the resistance and recovery of BNPP and NPP, but not for ANPP, suggesting the different mechanisms underlie the maintenance of stability of above‐ versus belowground productivity. This study is among the first to explain that warming modulates ecosystem stability in the face of extreme drought and lay stress on the need to investigate ecological stability at the community level for a more mechanistic understanding of ecosystem stability in response to climate extremes. [ABSTRACT FROM AUTHOR]