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Compared with traditional computational fluid dynamics methods, the lattice Boltzmann method (LBM) has the advantages of simple program structure, adaptability to complex boundaries, and easy parallel computation. However, since LBM is an explicit algorithm, there are many iterations in the computation process, which leads to an increase in computation time. In this paper, we improve LBM based on deep learning by combining a convolutional neural network (CNN) and a gated recurrent unit neural network (GRU). Based on previous test data, the CNN module extracts spatial features during the computation, while the GRU processes the corresponding temporal features. Compared with the conventional LBM, this method can significantly reduce the computation time and improve the computational efficiency with guaranteed low Reynolds numbers of 1000 and 2000. At the high Reynolds number of 4000, the prediction error of the proposed method is increasing but still has a better performance. In order to verify the effectiveness and accuracy of the proposed algorithm, an eddying model widely used in the computational fluid field is developed. The proposed method not only has impressive results but also deals with non-stationary processes and steady-state problems. [ABSTRACT FROM AUTHOR]

Shoot branching is fundamentally important in determining soybean yield. Here, through genome-wide association study, we identify one predominant association locus on chromosome 18 that confers soybean branch number in the natural population. Further analyses determine that Dt2 is the corresponding gene and the natural variations in Dt2 result in significant differential transcriptional levels between the two major haplotypes. Functional characterization reveals that Dt2 interacts with GmAgl22 and GmSoc1a to physically bind to the promoters of GmAp1a and GmAp1d and to activate their transcription. Population genetic investigation show that the genetic differentiation of Dt2 display significant geographic structure. Our study provides a predominant gene for soybean branch number and may facilitate the breeding of high-yield soybean varieties. Shoot branching is critical in determining soybean yield. Here, the authors report natural variation of Dt2 in controlling soybean branching number and the interaction of Dt2 with GmAgl22 and GmSoc1a to activate transcription of GmAp1a and GmAp1d. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing imagery provides highly precise spectral information. Thus, it is suitable for the land use classification of urban areas that are composed of complicated structures. In this study, a new spectral angle and vector mapping (SAVM) classification method, which adds a factor based on "the differences in the spectral vector lengths" among image pixels to the spectral angle mapping (SAM) classification method, is proposed. The SAM and SAVM methods were applied to classify the aerial hyperspectral digital imagery collection experiment imagery acquired from the business district of Washington, DC, USA. The results demonstrated that the overall classification accuracy of the SAM was 64.29%, with a Kappa coefficient of 0.57, while the overall classification accuracy of the SAVM was 81.06%, with a Kappa coefficient of 0.76. The overall classification accuracy was improved by 16.77% by the SAVM, indicating that the use of a SAVM classification method that considers both the spectral angle between the reference spectrum and the test spectrum and the differences in the spectral vector lengths among image pixels can improve the classification accuracy of urban area with hyperspectral remote sensing imagery. [ABSTRACT FROM AUTHOR]