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The conventional numerical solvers for partial differential equations encounter a formidable challenge, as their computational efficiency and accuracy are heavily contingent on grid size. Recently, machine learning (ML) has exhibited substantial promise in addressing partial differential equations. Nevertheless, substantial hurdles persist in practical applications. In this work, we endeavor to establish a deep learning framework founded on the Fourier neural operator (FNO) for resolving the intricacies of simulating real landslide dynamic processes. Our findings demonstrate that the current FNO approach adeptly replicates landslide dynamic processes and boasts exceptional computational efficiency. Additionally, it is noteworthy that this data‐driven ML methodology can seamlessly incorporate data from other experimental sources or numerical simulation techniques. Consequently, this work underscores the significant potential of utilizing ML methodologies to supplant conventional numerical simulation methods. Plain Language Summary: There are great challenges involved in leveraging machine learning methods to learn realistic physical dynamic processes. When it comes to the real landslide movement across intricate terrains, it is meaningful to validate the capacities of machine learning in tackling the complicated problem. This study aims to propose an innovative solution of modeling of landslide dynamic processes from a machine learning perspective. Here, we introduce a data‐driven framework based on Fourier neural operator to predict the dynamic behavior of actual landslides. Following an exhaustive assessment, the superior performance of our suggested model in real landslide situations and its versatility in adapting to landslides across various geographical regions have been confirmed. This study explores a new approach to modeling landslide dynamic processes and highlights the great potential of data‐driven approaches to address dynamic process challenges present in real physical world. Key Points: The data‐driven deep learning method based on FNO can achieve fast prediction of real landslide accumulation processThe proposed data‐driven method for predicting landslide dynamic processes can be extended to new areas after transfer learningWe provide numerical datasets of landslide dynamics, which can serve as the foundational resources for ML‐based landslide forecasting tasks [ABSTRACT FROM AUTHOR]

Influences of high in-situ stress generally need to be considered when excavating deep underground caverns. The dynamic fracture behaviors of rocks under blast loads were investigated by using the rock-anchored beam excavation in underground powerhouses of Shuangjiangkou Hydropower Station in Sichuan Province, China as the engineering background. To solve the problems of the poor blasting breakage effect of rocks and the difficulty in protecting surrounding rocks during excavation, mechanical properties of granite under static and dynamic loads were investigated and the sequential controlled fracture blasting (SCFB) method was adopted during in-situ tests. Based on the Riedel-Hiermaier-Thoma constitutive model and the strength criterion, software LS-DYNA was employed to simulate the dynamic propagation of blasting-induced cracks. The contour shaping effect obtained via numerical simulation is generally consistent with the test results. The results show that SCFB can to some extent control the direction of crack initiation and rock fracture behavior of the blasthole wall cracks and the spacing of successive bursting holes is about 10 times the diameter of the blastholes when the cracks between the blastholes are shaped the best effect. Moreover, the magnitude and direction of principal in-situ stress can both affect the propagation path and length of blasting-induced cracks. The results of the research on the excavation and construction of deeply buried underground caverns have a certain reference value. [ABSTRACT FROM AUTHOR]

The Yan River Basin of the Loess Plateau is a key region for ensuring the environmental protection and sustainable development of the Yellow River Basin. Therefore, it is essential to identify how vegetation cover has changed and determine the factors that have driven these changes. In this study, we applied a three-dimensional vegetation cover model to examine the spatiotemporal variation characteristics of vegetation cover at the watershed scale in the Yan River Basin from 2001 to 2020 and forecast future trends. Subsequently, the driving forces of fractional vegetation cover (FVC) change were quantified based on meteorological, surface, and anthropogenic factors to explore the common driving relationships among these factors. (1) The accuracy of 3DFVC is better than that of FVC in the Yanhe River Basin, where the terrain is complex. (2) The temporal change trends indicated that the vegetation cover in the Yan River Basin significantly recovered and the basin FVC increased rapidly from 2001 to 2013 (S = 0.0152/a, p < 0.01) and increased gradually from 2013 to 2020 (S = 0.0015/a). The main reason for the increase in vegetation cover was the enhanced growth of medium FVC. (3) The vegetation spatial distribution showed that the FVC values varied substantially from north to south, indicating spatial heterogeneity, and 83.9% of the area presented a trend of increasing vegetation. Furthermore, vegetation cover was predicted to improve in the future. (4) The spatial heterogeneity of FVC was mainly influenced by relative humidity and rainfall, and the spatial variations in FVC were mainly determined by climate factors. Land use and cover change variations, which are influenced by human activities, represent major factors underlying the observed spatial heterogeneity. Most interactions between driving factors showed two-way enhancement or non-linear enhancement, with relative humidity and land use patterns presenting the strongest explanatory power. This study provides a scientific basis for vegetation conservation in the Yan River Basin and contributes theoretical support for decision-making regarding ecological environmental protection in the Loess Plateau and sustainable development in the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Abstract: Oriented ZnO nanowire (NW) arrays were fabricated via hydrothermal method using TiO x films as the seeding layer. Structural and morphological analysis indicates that the TiO x seeding layer can greatly improve the crystallinity and alignments of ZnO NWs. The photoluminescence (PL) spectra of ZnO NWs grown on the 1nm TiO x seed layer exhibit enhanced UV emission. Improved growth of ZnO NWs on TiO x seed layer may facilitate the fabrication of high performance dye sensitized solar cell devices. [Copyright &y& Elsevier]