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Earthquake-triggered landslides (EQTLs) are affected by both seismogenic faults and topography. Using landslide data associated with the 2008 MW7.9 Wenchuan earthquake, the distribution characteristics of EQTLs were identified. And the influence of topographical changes and fault dip angle changes on the distribution of EQTLs was analyzed. Fault dip angle had a controlling impact on EQTL spatial distribution. When the dip angle increased, EQTL distribution was concentrated in a certain range on both sides of the fault and decayed rapidly with distance from the fault. When dip angle decreased, the hanging wall effect became more obvious, and the number of landslides on the hanging wall gradually decreased with distance from the fault. In terms of topography, EQTLs were closely related to relative elevation differences, forming a single peak for a local relief range of 400–1600 m. For > 70% of statistical units, EQTL density increased with local relief; the landslide distribution conformed to an exponential relationship with relief. Peak local relief and dominant local relief were highly negatively correlated with fault dip angle. Fault geometry and motion characteristics control the propagation of seismic waves, directly affecting the distribution of EQTLs; moreover, as tectonic activity controls local relief, it also indirectly affects the distribution of EQTLs. Our findings provide empirical evidence for the rapid assessment of EQTL distributions. [ABSTRACT FROM AUTHOR]

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]