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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]

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 the water supply network, leakage of pipes will cause water loss and increase the risk of environmental pollution. For water supply systems, identifying the leak point can improve the efficiency of pipeline leak repair. Most existing leak location methods can only locate the leak point approximately at the node or pipe section of the pipe network but cannot locate the specific location of the pipe section. This paper presents a framework for accurate water supply network leakage location based on Residual Network (ResNet). This study proposes a leak localization idea with a parallel classification and regression process that enables the framework to pinpoint the exact position of leak points in the pipeline. Furthermore, a multi-supervision mechanism is designed in the regression process to speed up the model's convergence. For a pipe network containing 40 pipes, the positioning accuracy of the pipe section is 0.94, and the MSE of the specific location of the leakage point is 0.000435. For the pipe network containing 117 pipes, the positioning accuracy of the pipe section is 0.91. The MSE of the specific location of the leakage point is 0.0009177. Experiments confirm the robustness and applicability of the framework. [ABSTRACT FROM AUTHOR]

With the development of cities, the water resources loss and environmental pollution caused by pipeline leakage need to be solved urgently. In this paper, a probabilistic method of model-based Bayesian analysis is designed to solve the multi-leakage detection problem of reservoir pipeline valve system. Bayesian inference selects the model best suited to the measured data. This process estimates the number of leaks and then extracts the leak locations from a model that measures data preferences. In this paper, according to the characteristics of water head in pipeline, the Likelihood function of water head for Bayesian evidence calculation is given. It solves the problem that the location ability of recent research methods is limited by leakage location. The number and locations of leakages can be determined simultaneously. Different experimental Settings and scenarios are given to verify the effectiveness of the proposed method. For three leaks that do not contain tight leaks, the RMSE of each leak is 2.3068 m, and in the case of tight leaks, the average RMSE of each leak is 3.5011 m. The results demonstrating that this model-based Bayesian analysis is an accurate tool for leakage enumeration and location estimation. [ABSTRACT FROM AUTHOR]