A high spatial and temporal resolution soft x-ray (SXR) imaging diagnostic has been installed in EAST for the study of magnetohydrodynamics activities and core high-Z impurity transport. Up to 122 lines of sight view the poloidal plasma from three directions (two up-down symmetrical horizontal arrays and one vertical array), which renders the diagnostic able to provide detailed tomographic reconstructions under various conditions. Fourier-Bessel method based on flux coordinates was employed for 2-D SXR tomographic reconstruction. Examples of several events measured by SXR diagnostic in EAST are shown, namely the crash patterns of sawtooth, periodical burst of edge localized modes, and the transport of high-Z intrinsic impurities.
Abstract-An efficient higher order MLFMA is developed by using an "extended-tree". With this extended-tree, the size of the box at the finest level is reduced, and the cost associated with the aggregation and disaggregation operations is significantly decreased. The sparse approximate inverse (SAI) preconditioner is utilized to accelerate the convergence of iterative solutions. The Cholesky factorization, instead of the often used QR factorization, is employed to construct the SAI preconditioner for cavity scattering analysis, by taking advantage of the symmetry of the matrix arising from electric field integral equation. Numerical experiments show that the higher order MLFMA is more efficient than its low-order counterpart.
E-learning is achieved by the deep integration of modern education and information technology, and plays an important role in promoting educational equity. With the continuous expansion of user groups and application areas, it has become increasingly important to effectively ensure the quality of e-learning. Currently, one of the methods to ensure the quality of e-learning is to use mutually independent e-learning behaviour data to build a learning performance predictor to achieve real-time supervision and feedback during the learning process. However, this method ignores the inherent correlation between e-learning behaviours. Therefore, we propose the behaviour classification-based e-learning performance (BCEP) prediction framework, which selects the features of e-learning behaviours, uses feature fusion with behaviour data according to the behaviour classification model to obtain the category feature values of each type of behaviour, and finally builds a learning performance predictor based on machine learning. In addition, because existing e-learning behaviour classification methods do not fully consider the process of learning, we also propose an online behaviour classification model based on the e-learning process called the process-behaviour classification (PBC) model. Experimental results with the Open University Learning Analytics Dataset (OULAD) show that the learning performance predictor based on the BCEP prediction framework has a good prediction effect, and the performance of the PBC model in learning performance prediction is better than traditional classification methods. We construct an e-learning performance predictor from a new perspective and provide a new solution for the quantitative evaluation of e-learning classification methods.
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