Pseudospin symmetry: Recent progress with supersymmetric quantum mechanics

It is an interesting and open problem to trace the origin of the pseudospin symmetry in nuclear single-particle spectra and its symmetry breaking mechanism in actual nuclei. In this report, we mainly focus on our recent progress on this topic by combining the similarity renormalization group technique, supersymmetric quantum mechanics, and perturbation theory. We found that it is a promising direction to understand the pseudospin symmetry in a quantitative way.Comment: 4 pages, 1 figure, Proceedings of the XX International School on Nuclear Physics, Neutron Physics and Applications, Varna, Bulgaria, 16-22 September, 201

Processing, microstructure and mechanical properties of bimodal size SiCp reinforced AZ31B magnesium matrix composites

AbstractThe bimodal size SiC particulates (SiCp) reinforced magnesium matrix composites with different ratios of micron SiCp and nano SiCp (M-SiCp:N-SiCp = 14.5:0.5, 14:1, and 13.5:1.5) were prepared by semisolid stirring assisted ultrasonic vibration method. The AZ31B alloy and all as-cast SiCp/AZ31B composites were extruded at 350 °C with the ratio of 12:1. Microstructural characterization of the extruded M14 + N1 (M-SiCp:N-SiCp = 14:1) composite revealed the uniform distribution of bimodal size SiCp and significant grain refinement. Optical Microscopy(OM) observation showed that, compared with the M14.5 + N0.5 (M-SiCp:N-SiCp = 14.5:0.5) composite, there are more recrystallized grains in M14 + N1 (M-SiCp:N-SiCp = 14:1) and M13.5 + N1.5 (M-SiCp:N-SiCp = 13.5:1.5) composites, but in comparison to the M13.5 + N1.5 composite, the average grain size of the M14 + N1 composite is slightly decreased. The evaluation of mechanical properties indicated that the yield strength and ultimate tensile strength of the M14 + N1 composite were obviously increased compared with other composites

Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network

Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral–spatial features within such images. In this paper, a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral–spatial-combined features effectively. In addition, it requires fewer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation, we test the proposed method along with three other deep learning-based HSI classification methods—namely, stacked autoencoder (SAE), deep brief network (DBN), and 2D-CNN-based methods—on three real-world HSI datasets captured by different sensors. Experimental results demonstrate that our 3D-CNN-based method outperforms these state-of-the-art methods and sets a new record