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For grating-based x-ray phase contrast imaging (GPCI), a multi-order moment analysis (MMA) has been recently developed to obtain multiple contrasts from the ultra-small-angle x-ray scattering distribution, as a novel information retrieval approach that is totally different from the conventional Fourier components analysis (FCA). In this paper, we present an analytic form of MMA in theory that can retrieve multiple contrasts directly from raw phase-stepping images, with no scattering distribution involved. For practical implementation, a truncated analytic analysis (called as TA-MMA) is adopted and it is hundreds of times faster in computation than the original deconvolution-based MMA (called as DB-MMA). More importantly, TA-MMA is proved to establish a quantitative connection between FCA and MMA, i.e., the first-order moment computed by TA-MMA is essentially the product of the phase contrast and the dark-field contrast retrieved by FCA, providing a new physical parameter for GPCI. The new physical parameter, in fact, can be treated as a ``hybrid-field'' contrast as it fuses the original phase contrast and dark-field contrast in a straightforward manner, which may be the first physical fusion contrast in GPCI to our knowledge and may have a potential to be directly used in practical applications., Comment: 9 pages, 10 figures

Two kinds of plate-like NaNbO 3 were separately prepared by the one- and two-step molten salt processes via topochemical micro-crystal conversion methods. Meanwhile, the composite photocatalysts were obtained via heating the mixture of corresponding NaNbO 3 powders and urea. Their photocatalytic activities were evaluated from the photodegradation of Rhodamine B under full arc and visible light irradiation of Xe lamp. The sample containing NaNbO 3 prepared by the one-step molten salt process and carbon nitride displays the highest activity. The enhancement of photocatalytic activity was attributed to the surface properties and the state of the carbon nitride.

Based on the combination of clustering algorithm and fuzzy control, a kind of clustering adaptive fuzzy controller is designed and applied to the nonlinear big-lagged superheat temperature system of drum boiler. The subjection parameters of the fuzzy controller are optimized by improved genetic algorithm. The problem of nonlinear and time-delay for superheat temperature system is solved effectively. Simulation results show that the proposed project has satisfying control performance and further prospects for its engineering applications.