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Hard carbon anode demonstrates exceptional potential in sodium‐ion batteries due to their cost‐effectivenss and superior plateau capacity. However, the proximity of the plateau capacity to the cut‐off voltage of battery operation and the premature cut‐off voltage response caused by polarization at high rates greatly limit the exploitation of plateau capacities, raising big concerns about inferior rate performance of high‐plateau‐capacity hard carbon. In this work, a facile pre‐oxidation strategy is proposed for fabricating lignin‐derived hard carbon. Both high‐plateau capacity and sodiation kinetics are significantly enhanced due to the introduction of expanded pseudo‐graphitic domains and high‐speed closed pores. Impressively, the optimized hard carbon exhibits an increased reversible capacity from 252.1 to 302.0 mAh g−1, alongside superior rate performance (174.7 mAh g−1 at 5 C) and stable cyclability over 500 cycles. This study paves a low‐cost and effective pathway to modulate the microstructure of biomass‐derived hard carbon materials for facilitating plateau sodium storage kinetics. [ABSTRACT FROM AUTHOR]

Ship detection and identification play pivotal roles in ensuring navigation safety and facilitating efficient maritime traffic management. Aiming at ship detection in complex environments, which often faces problems such as the dense occlusion of ship targets, low detection accuracy, and variable environmental conditions, in this paper, we propose a ship detection algorithm CSD-YOLO (Context guided block module, Slim-neck, Deformable large kernel attention-You Only Look Once) based on the deformable large kernel attention (D-LKA) mechanism, which was improved based on YOLOv8 to enhance its performance. This approach integrates several innovations to bolster its performance. Initially, the utilization of the Context Guided Block module (CG block) enhanced the c2f module of the backbone network, thereby augmenting the feature extraction capabilities and enabling a more precise capture of the key image information. Subsequently, the introduction of a novel neck architecture and the incorporation of the slim-neck module facilitated more effective feature fusion, thereby enhancing both the accuracy and efficiency of detection. Furthermore, the algorithm incorporates a D-LKA mechanism to dynamically adjust the convolution kernel shape and size, thereby enhancing the model's adaptability to varying ship target shapes and sizes. To address data scarcity in complex marine environments, the experiments utilized a fused dataset comprising the SeaShips dataset and a proprietary dataset. The experimental results demonstrate that the CSD-YOLO algorithm outperformed the YOLOv8n algorithm across all model evaluation metrics. Specifically, the precision rate (precision) was 91.5%, the recall rate (recall) was 89.5%, and the mean accuracy (mAP) was 91.5%. Compared to the benchmark algorithm, the Recall was improved by 0.7% and the mAP was improved by 0.4%. These results indicate that the CSD-YOLO algorithm can effectively meet the requirements for ship target recognition and tracking in complex marine environments. [ABSTRACT FROM AUTHOR]

The pedagogical development (PD) for educators in higher engineering education is indispensable to the promotion of educational changes and high-quality teaching and learning necessary to train twenty-first century engineering talents. With the expansion of various PD activities in engineering education in recent decades, there is a growing need to understand the characteristics and impact of current PD activities and to develop strategies for their further development. This study reviews 116 articles to provide an overview of current PD activity designs and the challenges reported in research on engineering education. The selected papers' frameworks, duration and organizational formats, evaluation methods, teachers' learning outcomes, research methods, and reported challenges are summarized. The findings of this study call for additional research on theory-driven designs for future PD activities as well as additional empirical PD studies using multiple data sources, a variety of research methods, and various analytical tools. [ABSTRACT FROM AUTHOR]