Diffuse optical tomography (DOT) is a promising noninvasive imaging modality and is capable of providing functional characteristics of biological tissue by quantifying optical parameters. The DOT image reconstruction is ill-posed and ill-conditioned, due to the highly diffusive nature of light propagation in biological tissues and limited boundary measurements. The widely used regularization technique for DOT image reconstruction is Tikhonov regularization, which tends to yield oversmoothed and low-quality images containing severe artifacts. It is necessary to accurately choose a regularization parameter for Tikhonov regularization. To overcome these limitations, we develop a noniterative reconstruction method, whereby optical properties are recovered based on a back-propagation neural network (BPNN). We train the parameters of BPNN before DOT image reconstruction based on a set of training data. DOT image reconstruction is achieved by implementing a single evaluation of the trained network. To demonstrate the performance of the proposed algorithm, we compare with the conventional Tikhonov regularization-based reconstruction method. The experimental results demonstrate that image quality and quantitative accuracy of reconstructed optical properties are significantly improved with the proposed algorithm.
Background: The prediction of protein-protein binding site can provide structural annotation to the protein interaction data from proteomics studies. This is very important for the biological application of the protein interaction data that is increasing rapidly. Moreover, methods for predicting protein interaction sites can also provide crucial information for improving the speed and accuracy of protein docking methods.
A total power injection up to 0.3 GJ has been achieved in EAST long pulse H-mode operation of 101.2 s with an ITER-like water-cooled tungsten (W) mono-block divertor, which has steady-state power exhaust capability of 10 MWm−2. The peak temperature of W target saturated at 12 s to the value T ~ 500 °C with a heat flux ~3.3 MW m−2 being maintained during the discharge. By tailoring the 3D divertor plasma footprint through edge magnetic topology change, the heat load was broadly dispersed and thus peak heat flux and W sputtering were well controlled. Active feedback control of H-mode detachment with D2 fuelling or divertor impurity seeding has been achieved successfully, with excellent compatibility with the core plasma performance. Active feedback control of radiative power utilizing neon seeding was achieved with f
rad = 18%–41% in H-mode operation, exhibiting potential for heat flux reduction with divertor and edge radiation. This has been further demonstrated in DIII-D high β
P H-mode scenario within the joint DIII-D/EAST experiment using impurity seeding from the divertor volume. Steady-state particle control and impurity exhaust has been achieved for long pulse H-mode operation over 100 s with the W divertor by leveraging the effect of drifts and optimized divertor configuration, coupled with strong pumping and extensive wall conditioning. Approaches toward the reduction of divertor W sourcing, which is of crucial importance for a metal-wall tokamak, are also explored. These advances provide important experimental information on favourable core-edge integration for high power, long-pulse H-mode operation in EAST, ITER and CFETR.
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