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In recent years, researchers have made significant contributions to 3D face reconstruction with the rapid development of deep learning. However, learning-based methods often suffer from time and memory consumption. Simply removing network layers hardly solves the problem. In this study, we propose a solution that achieves fast and robust 3D face reconstruction from a single image without the need for accurate 3D data for training. In terms of increasing speed, we use a lightweight network as a facial feature extractor. As a result, our method reduces the reliance on graphics processing units, allowing fast inference on central processing units alone. To maintain robustness, we combine an attention mechanism and a graph convolutional network in parameter regression to concentrate on facial details. We experiment with different combinations of three loss functions to obtain the best results. In comparative experiments, we evaluate the performance of the proposed method and state-of-the-art methods on 3D face reconstruction and sparse face alignment, respectively. Experiments on a variety of datasets validate the effectiveness of our method.

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Ag-based catalysts have been used in many practical reactions, such as p-nitrophenol reduction, due to the advantages of low cost and excellent activity. In order to facilitate the development of Ag-based catalysts, it may be helpful to use automated equipment for experiments. In this study, a system for the high-throughput synthesis of Ag-based catalysts was developed based on a facile impregnation method. Notably, the system automates the batch synthesis of Ag-based catalysts by setting the catalyst formulation in a dedicated software. Moreover, the software used employs the ant colony algorithm to optimize the synthesis path and improve the synthesis efficiency. The catalysts obtained from the high-throughput system are found to be similar to the manually prepared samples based on comparison of characterization results. In addition, experiments also reveal that this high-throughput system is capable of achieving high-throughput synthesis of Ag-based catalysts at the gram level. The synthesis of Pt–Ag bimetallic catalysts shows that this high-throughput system can be effectively used for exploratory experiments. This work paves the way for a high-throughput technique to synthesize Ag-based catalysts in a short period of time, which could be extended to the preparation of other catalyst systems. Moreover, the high-throughput synthesis system of Ag-based catalysts provides a feasible prerequisite for subsequent high-throughput characterization, which is a significant advancement in the development of industrial catalysts.