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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]

Vegetation cover in the Loess Plateau region is an important component of ecological protection in the Yellow River Basin, and this study provides a scientific reference for further vegetation restoration. Based on Landsat images and related data, we utilized the dimidiate pixel model and Geodetector method to study the vegetation cover in the Wuding River Basin from 2000 to 2022. The results indicated the spatial and temporal distribution of the vegetation cover and its changes over the study period. Additionally, the driving factors influencing its spatial changes were also uncovered. We also propose a land use shift vegetation cover contribution formula to quantify the effect of land type change on the FVC. The study showed that (1) the overall vegetation cover of the watershed increased significantly, and the FVC showed an increasing trend from 2000 to 2013 and a slow decline from 2013 to 2022, with the gradual transformation of low-graded FVC into a higher graded one. (2) The FVC increased spatially from northwest to southeast, and the trend of future changes is mainly decreasing. (3) The strongest explanatory power for the FVC change is the land use type and its interactive combination with rainfall. (4) The conversion of grassland to cropland contributes the most to the vegetation cover at 1.52%, and the increase in the cropland area is more conducive to the increase in the vegetation cover. [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]

Previous study has explored that SNHG16, a long non-coding RNA (lncRNA), mediated cell growth and proliferation. Yet, the role of SNHG16 in human colorectal cancer (CRC) still remains to be explored. Therefore, we conducted the present study to explore the functions of SNHG16 in CRC. In the present study, SNHG16 was significantly up-regulated in CRC tissues and cell lines. Gain- and loss-of-function of SNHG16 further presented that SNHG16 promoted the progression of CRC cells, including proliferation, migration, and invasion. Further, in vivo study also revealed that overexpression of SNHG16 could promote tumor growth. Bioinformatics analysis and luciferase reporter assay showed that SNHG16 was a direct target of miR-200a-3p. MiR-200a-3p was inversely correlated with SNHG16 expression in CRC tissues. In brief, the above results elucidate the important role of SNHG16 in CRC tumorigenesis, suggesting that SNHG16 might be quite vital for the diagnosis and development of CRC., (© 2019 The Author(s).)

Simulating physiological neuronal and hormonal conditions during digestive and interdigestive periods, the study identified the changes of the motility of biliary system including bile duct and sphincter of Oddi (SO) before and after cholecystojejunostomy. Thirty-five rabbits were divided into five groups randomly. The experimental groups received the venous injection of CCK 10 ng/kg, erythromycin 10 mg/kg, atropine 3 micrograms/kg and L-NAME 10 mg/kg respectively. Each rabbit underwent manometry through introducing a three-lumen catheter via the papilla retrogradely, using the low-compliance papillary infusion system. Then the gallbladder and the upper segment of the jejunum was anastomosed and the manometric procedures repeated after one week. SO basal pressure was increased, contraction amplitude decreased, contraction time shortened after cholecystojejunostomy. L-NAME, CCK and erythromycin could all excite SO. L-NAME could increase basal pressure and contraction amplitude, CCK increase basal pressure contraction amplitude and frequency, and erythromycin increase contraction amplitude, respectively. But comparing with that before cholecystojejunostomy, the increasing extent was decreased. The tensional and spontaneous contractions of the SO were under the control of the neural and hormonal mechanism. The anastomosis of gallbladder and jejunum and the drainage of bile made the tensional contraction stronger, but the spontaneous contraction weakened after the operation due to the decreases of the sensitivity of SO to hormonal factors. The clinical symptoms may not be relieved when the patients with SO dysfunction accepted cholecystojejunostomy.