This study was designed to probe into the improvement of rehabilitation training combined with Jiaji electroacupuncture intervention on patients with upper limb peripheral nerve injury. A total of 114 patients with peripheral nerve injury of upper limbs in our hospital from August 2017 to November 2019 were collected as the research participants. Among them, 59 in the control group (CG) received rehabilitation training alone, while 65 in the observation group (OG) received rehabilitation training combined with Jiaji electroacupuncture intervention. The therapeutic efficacy, Barthel index, and Fugl–Meyer assessment score, motor nerve conduction velocity, sensory nerve conduction velocity and amplitude, and quality of life (score SF-36) were compared between the two groups before and after treatment. The total effective rate of the OG was markedly higher than that of the CG. After treatment, the Barthel index, Fugl–Meyer assessment score, motor nerve conduction velocity, and sensory nerve conduction velocity and amplitude of the OG were obviously higher than those of the CG, and the SF-36 scores of the OG were higher than those of the CG in 8 dimensions. Rehabilitation training combined with Jiaji electroacupuncture intervention can dramatically promote the recovery of muscle group function and improve the quality of life of patients with upper limb peripheral nerve injury.

With the rapid development of re‐electrification, traditional load forecasting faces a significant increase of influencing factors. Existing literature focuses on examining the influencing factors related to load profiles in order to improve the prediction accuracy. However, a large number of redundant features may lead to the overfitting of the forecasting engine. To enhance the performance of extreme learning machine (ELM) under massive data scale, this paper presents a kernel extreme learning machine (KELM) based method which can be used for short‐term load prediction. First, a feature dimensionality reduction is performed using a kernelized principal component analysis, which aims to eliminate redundant input vectors. Then, the hyperparameters of KELM are optimized to improve the prediction accuracy and generalization. Case studies based on a province‐level power system in China demonstrate that the presented method can significantly improve the accuracy of load forecasting by 3.14% in contrast to traditional ELM.

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Driver distraction behavior causes a large number of traffic accidents every year, resulting in economic losses and injuries. Currently, the driver still plays an important role in the driving and control of the vehicle due to the low level of vehicle automation and the immature development of autonomous driving. Therefore, it is vital to research distraction detection for drivers. However, in realistic driving scenarios with uncertain information, they are still some challenges in efficient and accurate driver distraction detection. In this paper, an improved deep learning model based on attention mechanisms and bi-directional feature pyramid networks (BiFPN) is proposed to identify driver distractions. Firstly, an improved data augmentation strategy is introduced to increase the data diversity to enhance the generalization capability of the model. Secondly, the squeeze-and-excitation (SE) attention mechanism layer is used after the C3 module of the original backbone network to enhance the important feature information and suppress the minor feature information. Finally, the BiFPN module is introduced into the neck network to better achieve multi-scale feature fusion without increasing the calculation amount too much. The experimental results show that the method proposed in this paper has an average mean accuracy rate (mAP) of 0.956 on the test set. Compared to the original model the mAP has improved by 13.2%. The detection speed of the model is 71 frames per second, and the memory occupation is 15.9 MB. This method has the advantages of high recognition accuracy, fast detection speed, and small memory occupation of the model, which are important for achieving engineering deployment.

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