Improving the battery lifespan is really important. Improving the battery lifespan can lower the cost reduce the waste and support the future of Electric Vehicles (EVs) and clean energy. The study focuses on existing chemical methods and new system methods that can help solve battery ageing. The study focuses on world use, in China. I think the study uses a literature review as the research method. I focus on studying system-level methods which consist of cell balancing and Battery Management Systems (BMS). I also consider chemistry-based methods such as additives, surface coatings, doping, high-entropy cathodes, single-crystal cathodes, anode pre-litigation, ionic liquids and fluorinated electrolytes. Then, I start to argue two ideas. The main claim says that the current infrastructure and the chemistry knowledge can help battery lifespan development. The counterarguments point out gaps, in the technology the cost, the data sharing, the safety and the uneven access. The findings show that electrolyte additives and smart charging control is the most practical and logical. The future of battery life appears to be within reach through the development of advanced coatings and electrolytes and new cathode designs. The development of advanced coatings together with solid electrolytes and new cathode designs faces challenges because they remain expensive to produce and their manufacturing processes are slow. The conclusion matters in life. In my point of view, rebuilding new factories is unnecessary. The extended lifespan of EV batteries enables car owners to reduce their expenses. The extended lifespan of EV batteries which last longer serves to minimize damage. The better EV battery lifespan supports EV use.

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Wind turbine blades are continuously exposed to environmental factors such as wind-blown sand, humidity, heat, and ultraviolet radiation. As service time increases, surface defects—including cracks, stains, and material peeling—gradually emerge. If not detected at an early stage, these defects may worsen over time and compromise the safety and operational stability of the turbine. In current operation and maintenance practices, blade inspection still relies heavily on manual methods, which not only suffer from limited efficiency but also involve risks associated with high-altitude work. Moreover, inspection results are often influenced by the experience of individual inspectors. In response to these practical challenges, this study focuses on the identification of surface defects on wind turbine blades and introduces targeted improvements within a deep learning–based detection framework. A single-stage object detection approach is adopted for model construction, with two key enhancements. First, a DySample dynamic upsampling module is incorporated into the feature fusion process to improve information transfer across features of different scales. Second, the FocalUIoU loss function is introduced in the bounding box regression stage to enhance the model's localization capability for small-scale defects and to improve optimization during training. Experimental results show that the improved model achieves a Precision of 0.874, a Recall of 0.793, and mAP@50 and mAP@50–95 of 0.856 and 0.544, respectively, on the test set. Compared with the baseline YOLO11 model, Recall is increased by approximately 15.6%, while mAP@50 and mAP@50–95 are improved by 6.5% and 7.9%, respectively. The model contains only 2.60 million parameters, indicating minimal change in overall scale while retaining the characteristics of a lightweight architecture. Under the premise of acceptable detection efficiency, the proposed method improves the recognition performance for multiple categories of blade defects and provides technical support for wind power equipment inspection and subsequent maintenance.

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This study investigates the Total Ionizing Dose (TID) effects in commercial 1200 V SiC MOSFET power devices with planar, trench, and double-trench structures. Radiation test results indicate that, with the accumulation of radiation dose, the transfer characteristic curves of all three device structures exhibit a negative shift. However, differences are observed in their breakdown characteristics. The breakdown curve of the planar-structure device shows no degradation with increasing radiation dose, whereas the breakdown characteristic curves of the trench and double-trench structures deteriorate as the radiation dose accumulates.

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In the field of computer vision, the existing real-time multi-view 3D human pose estimation method Faster VoxelPose suffers from redundant detections and fixed plane-fusion weights in densely occluded scenarios. To address these issues, this study proposes an improved method based on geometric constraints, termed FVP-GC. During the human detection stage, a Redundancy Suppression Mechanism (RSM) is introduced. This mechanism applies geometric constraint filtering to predicted bounding boxes during training using dual thresholds of distance and overlap ratio, thereby reducing redundant detections caused by projection overlaps. During the keypoint localization stage, a Geometric Consistency Adaptive Weighting (GCAW) strategy is proposed. By evaluating the geometric consistency among orthogonal planes, the method dynamically adjusts fusion weights, improving the fusion quality of multi-plane predictions. Experimental results on the Shelf and CMU Panoptic datasets demonstrate that, while maintaining real-time inference speed, the proposed method improves pose estimation accuracy compared with Faster VoxelPose, achieving average PCP3D values of 97.7% and 97.1%, and reducing MPJPE to 17.45 mm.

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