Lithium metal batteries (LMBs) are promising candidates for next‐generation energy storage due to their high energy densities on both weight and volume bases. However, LMBs usually undergo uncontrollable lithium deposition, unstable solid electrolyte interphase, and volume expansion, which easily lead to low Coulombic efficiency, poor cycling performance, and even safety hazards, hindering their practical applications for more than forty years. These issues can be further exacerbated if operated at high current densities. Here a stable lithium metal battery enabled by 3D porous poly‐melamine‐formaldehyde (PMF)/Li composite anode is reported. PMF with a large number of polar groups (amine and triazine) can effectively homogenize Li‐ion concentration when these ions approach to the anode surface and thus achieve uniform Li deposition. Moreover, the 3D structured anode can serve as a Li host to mitigate the volume change during Li stripping and plating process. Galvanostatic measurements demonstrate that the 3D composite electrode can achieve high‐lithium Coulombic efficiency of 94.7% at an ultrahigh current density of 10 mA cm−2 after 50 cycles with low hysteresis and smooth voltage plateaus. When coupled with Li4Ti5O12, half‐cells show enhanced rate capabilities and Coulombic efficiencies, opening great opportunities for high‐energy batteries.

Slope stability assessment is a critical concern in construction projects. This study explores the use of multivariate adaptive regression splines (MARS) to capture the intrinsic nonlinear and multidimensional relationship among the parameters that are associated with the evaluation of slope stability. A comparative study of machine learning solutions for slope stability assessment that relied on backpropagation neural network (BPNN) and MARS was conducted. One data set with actual slope collapse events was utilized for model development and to compare the performance of BPNN and MARS. Research results suggest that BPNN and MARS models can model the relationship between the safety factor and the slope parameters. Also, the MARS model has the advantages of computational efficiency and easy interpretation.

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The rapid development of deep learning has accelerated the progress of related technologies in the computer vision field and it has broad application prospects. Due to flower inter-class similarity and intra-class differences, flower image classification has essential research value. To achieve flower image classification, this paper proposes a deep learning method using the current powerful object detection algorithm YOLOv5 to achieve fine-grained image classification of flowers. Overlap and occluded objects often appear in the images of the flowers, so the DIoU_NMS algorithm is used to select the target box to enhance the detection of the blocked objects. The experimental dataset comes from the Kaggle platform, and experimental results show that the proposed model in this paper can effectively identify five types of flowers contained in the dataset, Precision reaching 0.942, Recall reaching 0.933, and mAP reaching 0.959. Compared with YOLOv3 and Faster-RCNN, this model has high recognition accuracy, real-time performance, and good robustness. The mAP of this model is 0.051 higher than the mAP of YOLOv3 and 0.102 higher than the mAP of Raster-RCNN.

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