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The cost-effectiveness, compact size, and inherent flexibility of UAV technology have garnered significant attention. Utilizing sensors, UAVs capture ground-based targets, offering a novel perspective for aerial target detection and data collection. However, traditional UAV aerial image recognition techniques suffer from various drawbacks, including limited payload capacity, resulting in insufficient computing power, low recognition accuracy due to small target sizes in images, and missed detections caused by dense target arrangements. To address these challenges, this study proposes a lightweight UAV image target detection method based on YOLOv8, named Aero-YOLO. The specific approach involves replacing the original Conv module with GSConv and substituting the C2f module with C3 to reduce model parameters, extend the receptive field, and enhance computational efficiency. Furthermore, the introduction of the CoordAtt and shuffle attention mechanisms enhances feature extraction, which is particularly beneficial for detecting small vehicles from a UAV perspective. Lastly, three new parameter specifications for YOLOv8 are proposed to meet the requirements of different application scenarios. Experimental evaluations were conducted on the UAV-ROD and VisDrone2019 datasets. The results demonstrate that the algorithm proposed in this study improves the accuracy and speed of vehicle and pedestrian detection, exhibiting robust performance across various angles, heights, and imaging conditions. [ABSTRACT FROM AUTHOR]

Aqueous zinc ion batteries coupling with conventional hydrogel electrolyte have the advantages of high safety, low cost, and simple manufacturing process while they are difficult for fast charging/discharging application scenarios due to the sluggish kinetics. Herein, a new strategy is developed for synthesizing a highly‐entangled polyacrylamide (HE‐PAM) hydrogel electrolyte to dramatically enhance the ion transportation and mechanical stability. The developed hydrogel electrolyte has lower ionic resistance and a strong elastic modulus. After being assembled into Zn/MnO2 batteries, the HE‐PAM hydrogel electrolyte exhibits excellent cycling stability and high‐rate capability under high current densities. Specifically, the Zn//HE‐PAM//MnO2 battery can resist the highest current of 35 A g−1, which outperforms previously reported works. Moreover, the HE‐PAM hydrogel electrolyte can also support the fast charging/discharging in proton ion batteries with a high capacity retention rate of 50% under 50 A g−1. This progress on hydrogel electrolytes can boost the development of quasi‐solid‐state batteries in the fast charging/discharging aspect. [ABSTRACT FROM AUTHOR]

Linear discriminant analysis (LDA) is one of commonly used supervised subspace learning methods. However, LDA will be powerless faced with the no-label situation. In this paper, the unsupervised LDA (Un-LDA) is proposed and first formulated as a seamlessly unified objective optimization which guarantees convergence during the iteratively alternative solving process. The objective optimization is in both the ratio trace and the trace ratio forms, forming a complete framework of a new approach to jointly clustering and unsupervised subspace learning. The extension of LDA into Un-LDA enables to not only complete unsupervised subspace learning via the explicitly presented subspace projection matrix but also simultaneously finish clustering and even clustering out-of-sample data via the explicitly presented transformation matrix. To overcome the difficulty in solving the non-convex objective optimization, we mathematically prove that the Un-LDA optimization in both forms can be transformed into the simple K-means clustering optimization when the subspace is determined. The Un-LDA optimization is eventually completed by alternatively optimizing the clusters using K-means and the subspace using the supervised LDA methods and iterating this whole process until convergence or stopping criterion. The experiments demonstrate that our proposed Un-LDA algorithms are comparable or even much superior to the counterparts. [ABSTRACT FROM AUTHOR]

The occurrence and damage of Xylotrechus quadripes Chevr were investigated in different time points and micro environments in Simao south island river coffee test demonstration field. The results showed that X. quadripes mainly damaged 5-year-old coffee trees. X. quadripes occurred one generation per year in Simao region, and the larval stage lasted 296-322 d. The adult could be found in four seasons because of different development progress and overlapping generations. In different micro environments, X. quadripes were mainly distributed in mixed forests and the coffee land in sunny slope. The adult enjoyed sunshine on the coffee trees at noon. The natural enemy Prislauacus nigripea kieff was found to parasitize in the larvae of X. quadripes, which could control the population of X. quadripes. [ABSTRACT FROM AUTHOR]