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In this paper, a series of novel Sr3-xGa2Ge4O14:xDy3+ (x = 0 - 0. 40) (mole fraction) and Sr2.68Ga2Ge4O14 :0. 16Dy3+, 0. 16M+ (M = Li, Na, K) phosphors were prepared by high-temperature solid-phase method. EDS analysis confirms the presence of Sr, Ga, Ge, O and Dy elements in the phosphors. The series Sr3-xGa2Ge4O14 :xDy3+ produce strong yellow light emission with 568 nm as the dominant wavelength under 350 nm optical excitation, corresponding to the 4F9/2→6H13/2 transition. The emission spectra of the phosphor show that the emission intensity varies with the increase of Dy3+ concentration and reaches its maximum at x = 0. 16. The co-doping of alkali metal M(M = Li, Na, K) as a charge compensation ion, among which Li+ has the most obvious effect on increasing the emission intensity of Dy3+, led to the increase of the emission intensity of the phosphor to twice that of the phosphor in the absence of the charge compensation ion. In addition, the fluorescence lifetime of the phosphor decrease continuously with the increase of Dy3+ doping concentration. Finally, the CIE chromaticity coordinates and thermal stability of the phosphor Sr2.68Ga2Ge4O14 :0.16Dy3+, 0.16Li+ are explored, and its CIE chromaticity coordinates are (0. 371 9, 0.404 6), which are located in the yellow region, and its luminescence intensity at 453 K is about 95. 5% of its room temperature luminescence intensity. Therefore, Dy3+, Li+ co-doped Sr3-xGa2Ge4O14 phosphor is a potential candidate material for display devices and white light LED devices. [ABSTRACT FROM AUTHOR]

The spatial-numerical association of response codes (SNARC) and Simon effects are attributed to the same type of conflict according to dimensional overlap (DO) theory: the congruency of task-irrelevant spatial information and the selected response (e.g., left or right). However, previous studies have yielded inconsistent results regarding the relationship between the two effects, with some studies reporting an interaction while others did not. This discrepancy may be attributed to the use of different types of Simon effects (visuomotor and cognitive Simon effects) in these studies, as the spatial codes associated with these two types of Simon effects are distinct (exogenous and endogenous, respectively). The aim of this study was to address these inconsistencies and gain a better understanding of the similarities and differences in spatial representations generated by spatial location, semantic information, and numerical information. We attempted to classify the relationships among the SNARC and Simon effects. Specifically, the visuomotor Simon, cognitive Simon, and SNARC effects were compared from three perspectives: the response time (RT) distribution, hand-stimulus proximity, and temporal dynamics (with the drift diffusion model; DDM). Regarding RTs, the results showed that the visuomotor Simon effect decreased with increased values of RT bins, while the cognitive Simon and SNARC effects increased. Additionally, the visuomotor Simon effect was the only effect influenced by hand-stimulus proximity, with a stronger effect observed in the hand-proximal condition than in the hand-distal condition. Regarding the DDM results, only the visuomotor Simon effect exhibited a higher drift rate and longer non-decision time in the incompatible condition than in the compatible condition. Conversely, both the SNARC and cognitive Simon effects exhibited an inverse pattern regarding the drift rate and no significant difference in non-decision time between the two conditions. These findings suggest that the SNARC effect is more similar to the cognitive Simon effect than the visuomotor Simon effect, indicating that the endogenous spatial-numerical representation of the SNARC effect might share an underlying processing mechanism with the endogenous spatial–semantic representation of the cognitive Simon effect but not with the exogenous location representation of the visuomotor Simon effect. Our results further demonstrate that the origin of spatial information could impact the classification of conflicts and supplement DO theory. [ABSTRACT FROM AUTHOR]

• The solidification microstructures of Fe-68wt.%Sn alloy powders commonly present "irregular" core/shell structure. • The average velocity of airflow on the windward side of powder is faster than that on the leeward side. • The core-deviated structure fundamentally originates from the asymmetric coagulation of minority phase inside the powder. • The observed structures of powder are strongly dependent upon the position and orientation of cross-sectional plane. Immiscible alloy powders with core/shell structure have recently garnered considerable interest due to their excellent industrial performance. However, a frequently observed yet often overlooked feature is the presence of core-deviated structure in various cross-sectional planes of the powder. To date, limited research has been conducted on this structure and its origin, leaving the formation mechanism unclear. Gaining a comprehensive understanding of this mechanism is crucial for advancements in powder materials research. In this study, we first analyzed the solidification microstructures of Fe-68 wt.%Sn alloy powders, followed by a numerical analysis of airflow and internal temperature fields within a powder during the free-falling process. Our findings revealed that the average airflow velocity on the windward side is faster than that on the leeward side, causing the highest temperature point within the powder to shift towards the leeward side over time. This phenomenon is fundamentally linked to the formation of the core-deviated structure. Furthermore, we explored the relationship between cross-sectional planes and potential microstructures, demonstrating that the final observed structures are strongly influenced by the position and orientation of the cross-sectional plane. Additionally, the core deviation distance was theoretically predicted and found to be consistent with experimental results in Fe-68 wt.%Sn powders. This research provides valuable insights into the actual solidification process of immiscible alloy powders and contributes to the development of specialized structural materials. [ABSTRACT FROM AUTHOR]

Building the forest fire prevention road in mountainous areas is an effective means to prevent forest fire from spreading wantonly. Nevertheless, adverse influences are that the construction of these roads has destroyed the inherent stability of the original slope, resulting a large number of unstable slopes appear in the catchment, which could trigger human-generated landslides and debris flows. On June 17, 2022, a landslide-generated debris flow occurred in the Longjiagou catchment, Mianning County, Sichuan Province. The initial landslide that induced this debris flow is one of the unstable slopes on the forest fire prevention road. After the debris flow event, we conducted a detailed on-site investigation of the Longjiagou catchment. Many unstable slopes have been found near the road, and the width of cracks at the rear edge of some unstable slopes has exceeded 10 cm. This is already a very dangerous precursor signal for landslides or debris flow. And several soil samples were collected in the catchment to test the hydraulic conductivity and particle size distribution of soil at different locations. Combined with multi-source data, we comprehensively analyzed the cause and mechanism of the disaster. The dynamic process modeling framework of earth-surfaced flow—Massflow—was adopted to simulate the dynamic process of the debris flow to support the quantitative analysis of this disaster. The results show that the simulation results are basically consistent with the actual situation. Based on field reconnaissance and numerical simulation, the mechanism of debris flow is comprehensively analyzed, which can provide important insights and guidance for the prediction and prevention of such debris flow disasters. [ABSTRACT FROM AUTHOR]

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]