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In recent years, the rare earth doped upconversion luminescent materials have been widely used in many fields. In practical applications, we observed that various dopants have distinct effects on different substances to some extent. In this context, we have successfully synthesized a dual-mode-excited Ca3TeO6 double perovskite. After introduce the Yb3+/Er3+ ions pair, the designed phosphor can emit green and red luminescence under 980 nm and 1550 nm excitation, respectively, making it suitable for diverse scientific and technological applications. The potential up-conversion mechanisms driven by different excitation sources have been investigated. Moreover, it was discovered that adjusting the molar ratio of Er3+ and Yb3+ ions enhances the performance of the studied materials, rendering them well-suited for application as a high-quality optical thermometer based on the FIR technique. Our results clearly indicate that Er3+/Yb3+ co-doped Ca3TeO6 phosphors allows for multiple optimizations and control of material properties, showcasing great potential for applications in various field. [ABSTRACT FROM AUTHOR]

Font classification of oracle bone inscriptions serves as a crucial basis for determining the historical period to which they belong and holds significant importance in reconstructing significant historical events. However, conventional methods for font classification in oracle bone inscriptions heavily rely on expert knowledge, resulting in low efficiency and time-consuming procedures. In this paper, we proposed a novel recurrent graph neural network (R-GNN) for the automatic recognition of oracle bone inscription fonts. The proposed method used convolutional neural networks (CNNs) to perform local feature extraction and downsampling on oracle bone inscriptions. Furthermore, it employed graph neural networks (GNNs) to model the complex topologiure and global contextual information of oracle bone inscriptions. Finally, we used recurrent neural networks (RNNs) to effectively combine the extracted local features and global contextual information, thereby enhancing the discriminative power of the R-GNN. Extensive experiments on our benchmark dataset demonstrate that the proposed method achieves a Top-1 accuracy of 88.2%, significantly outperforming the competing approaches. The method presented in this paper further advances the integration of oracle bone inscriptions research and artificial intelligence. The code is publicly available at: https://github.com/yj3214/oracle-font-classification. [ABSTRACT FROM AUTHOR]