Abstract. Vegetation management is important to the power transmission and distribution networks. The encompassed towering tree is always the key factor of the high impedance faults(HIFs).LiDAR is an efficient way to detect trees with 3D point cloud. The classical tree detection algorithm can handle the tree with high and distinct trunk,but limited to the tree with messy trunks. While the deeplearning based detection algorithms are also suffered from the terrain noise points. In this paper, we propose an efficient LiDAR reconstruction system which can efficiently reconstruct the point cloud of surrounding vegetation without the ground plane noise. We also use different weight strategies to improve the localization accuracy. We have conducted our system on the real power network environment and the height detection result shows that our algorithm has a better accuracy and robustness compared with the classical methods.

In the treatment of spinal cord injury (SCI), the complex process of secondary injury is mainly responsible for preventing SCI repair or even exacerbating the injury. In this experiment, we constructed the 8-gingerol (8G)-loaded mesoporous polydopamine (M-PDA), M@8G, as the in vivo targeting nano-delivery platform, and investigated the therapeutic effects of M@8G in secondary SCI and its related mechanisms. The results indicated that M@8G could penetrate the blood-spinal cord barrier to enrich the spinal cord injury site. Mechanism research has shown that all of the M-PDA,8G and M@8G displayed the anti-lipid peroxidation effect, and then M@8G can inhibit the secondary SCI by suppressing the ferroptosis and inflammation. In vivo assays showed that M@8G significantly diminished the local injury area, reduced axonal and myelin loss, thus improving the neurological and motor recovery in rats. Based on the analysis of cerebrospinal fluid samples from patients, ferroptosis occurred locally in SCI and continued to progress in patients during the acute phase of SCI as well as the stage after their clinical surgery. This study showcases effective treatment of SCI through the aggregation and synergistic effect of M@8G in focal areas, providing a safe and promising strategy for the clinical treatment of SCI.

Abstract. Natural disasters cause considerable losses to people’s lives and property. Satellite images can provide crucial information of the affected areas for the first time, conducive to relieving the people in disaster and reducing the economic loss. However, the traditional satellite image analysis method based on manual processing drains workforce and material resources, which slowed the government’s response to the disaster. Aiming at the natural disasters like floods and earthquakes that often happen in the south of China, we propose a dual-stage damage assessment method based on LEDNet and ResNet. Our method detects the changes between the satellite images captured before and after a disaster of the same area, segments the buildings, and evaluates the damage level of affected buildings. In addition, we calculate influence maps based on the damage scale to the building and estimate the damage situation for electrical facilities. We used images related to earthquakes and floods in the xBD dataset to train the network model. Moreover, qualitative and quantitative evaluations demonstrated that our method has higher accuracy than the xBD baseline.