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.

The seislet transform is a waveletlike transform that analyzes seismic data by following varying slopes of seismic events across different scales and provides a multiscale orthogonal basis for seismic data. It generalizes the discrete wavelet transform (DWT) in the sense that the DWT in the lateral direction is simply the seislet transform with a zero slope. Our earlier work used plane-wave destruction (PWD) to estimate smoothly varying slopes. However, the PWD operator can be sensitive to strong noise interference, which makes the seislet transform based on PWD (PWD-seislet transform) occasionally fail in providing a sparse multiscale representation for seismic field data. We adopted a new velocity-dependent (VD) formulation of the seislet transform, in which the normal moveout equation served as a bridge between local slope patterns and conventional moveout parameters in the common-midpoint domain. The VD slope has better resistance to strong random noise, which indicated the potential of VD seislets for random noise attenuation under 1D earth assumption. Different slope patterns for primaries and multiples further enabled a VD-seislet frame to separate primaries from multiples when the velocity models of primaries and multiples were well disjoint. We evaluated the results by applying the method to synthetic and field-data examples in which the VD-seislet transform helped in eliminating strong random noise. We performed synthetic and field-data tests that showed the effectiveness of the VD-seislet frame for separation of primaries and peg-leg multiples of different orders.

Background: Migrant workers are a susceptible population to the hepatitis b virus (HBV) and a vulnerable spot in China's immunization procedures. There is no free HBV immunization program for migrant workers in China, so understanding migrant workers' motivation to receive the HBV vaccine is the first step in designing effective immunization policies. Methods: A fully specified protection motivation theory (PMT) model of HBV vaccination intention among migrant workers was specified. Data were collected through a cross-sectional survey of 406 migrant workers in three migrant-dense industries in Tianjin, China. Principal component factor analysis was used to produce PMT factors and nested binary logistic regression modeling was applied to assess the associations between protection motivation and HBV vaccination intention of migrant workers. Results: The nested binary logistic regression model suggested that the severity factor and self-efficacy factor were positively related to HBV vaccination intention (OR = 2.15, 95% CI: 1.25-3.71; OR = 2.75, 95% CI: 1.62-4.66) while the response costs was negatively related to the HBV vaccination motivation (OR = 0.50, 95% CI: 0.29-0.83). The sociodemographic variables showed that younger, married and good self-rated health status participants were statistically associated with the intention of taking the HBV vaccine. Sex, education level and income group were not significantly associated with vaccination intention. The migrant-industry variables showed that migrant location had a strong effect on migrant workers' vaccination intention.

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