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Drug prediction and treatment using bioinformatics and large-scale modeling have emerged as pivotal research areas. This study proposes a novel multi-level collaboration framework named Syn-COM for feature extraction and data integration of diseases and drugs. The framework aims to explore optimal drug combinations and interactions by integrating molecular virtuality, similarity clustering, overlap area, and network distance. It uniquely combines the characteristics of Chinese herbal medicine with clinical experience and innovatively assesses drug interaction and correlation through a synergy matrix. Gouty arthritis (GA) was used as a case study to validate the framework's reliability, leading to the identification of an effective drug combination for GA treatment, comprising Tamaricis Cacumen (Si = 0.73), Cuscutae Semen (Si = 0.68), Artemisiae Annuae Herba (Si = 0.62), Schizonepetae Herba (Si = 0.73), Gleditsiae Spina (Si = 0.89), Prunellae Spica (Si = 0.75), and Achyranthis Bidentatae Radix (Si = 0.62). The efficacy of the identified drug combination was confirmed through animal experiments and traditional Chinese medicine (TCM) component analysis. Results demonstrated significant reductions in the blood inflammatory factors IL1A, IL6, and uric acid, as well as downregulation of TGFB1, PTGS2, and MMP3 expression (p < 0.05), along with improvements in ankle joint swelling in GA mice. This drug combination notably enhances therapeutic outcomes in GA by targeting key genes, underscoring the potential of integrating traditional medicine with modern bioinformatics for effective disease treatment. [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]

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]

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]