Xi'an Jiaotong University

About: Xi'an Jiaotong University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Heat transfer & Dielectric. The organization has 85440 authors who have published 99682 publications receiving 1579683 citations. The organization is also known as: '''Xi'an Jiaotong University''' & Xi'an Jiao Tong University.

Strong crystal size effect on deformation twinning

Abstract: Deformation twinning(1-6) in crystals is a highly coherent inelastic shearing process that controls the mechanical behaviour of many materials, but its origin and spatio-temporal features are shrouded in mystery. Using micro-compression and in situ nano-compression experiments, here we find that the stress required for deformation twinning increases drastically with decreasing sample size of a titanium alloy single crystal(7,8), until the sample size is reduced to one micrometre, below which the deformation twinning is entirely replaced by less correlated, ordinary dislocation plasticity. Accompanying the transition in deformation mechanism, the maximum flow stress of the submicrometre-sized pillars was observed to saturate at a value close to titanium's ideal strength(9,10). We develop a 'stimulated slip' model to explain the strong size dependence of deformation twinning. The sample size in transition is relatively large and easily accessible in experiments, making our understanding of size dependence(11-17) relevant for applications.

Accelerated search for materials with targeted properties by adaptive design.

Abstract: Finding new materials with targeted properties has traditionally been guided by intuition, and trial and error. With increasing chemical complexity, the combinatorial possibilities are too large for an Edisonian approach to be practical. Here we show how an adaptive design strategy, tightly coupled with experiments, can accelerate the discovery process by sequentially identifying the next experiments or calculations, to effectively navigate the complex search space. Our strategy uses inference and global optimization to balance the trade-off between exploitation and exploration of the search space. We demonstrate this by finding very low thermal hysteresis (ΔT) NiTi-based shape memory alloys, with Ti50.0Ni46.7Cu0.8Fe2.3Pd0.2 possessing the smallest ΔT (1.84 K). We synthesize and characterize 36 predicted compositions (9 feedback loops) from a potential space of ∼800,000 compositions. Of these, 14 had smaller ΔT than any of the 22 in the original data set.

Relationship of obesity with osteoporosis.

Abstract: Context: The relationship between obesity and osteoporosis has been widely studied, and epidemiological evidence shows that obesity is correlated with increased bone mass. Previous analyses, however, did not control for the mechanical loading effects of total body weight on bone mass and may have generated a confounded or even biased relationship between obesity and osteoporosis. Objective: The objective of this study was to reevaluate the relationship between obesity and osteoporosis by accounting for the mechanical loading effects of total body weight on bone mass. Methods: We measured whole body fat mass, lean mass, percentage fat mass, body mass index, and bone mass in two large samples of different ethnicity: 1988 unrelated Chinese subjects and 4489 Caucasian subjects from 512 pedigrees. We first evaluated the Pearson correlations among different phenotypes. We then dissected the phenotypic correlations into genetic and environmental components with bone mass unadjusted or adjusted for body weight. T...

Graph Convolutional Networks for Hyperspectral Image Classification

Abstract: Convolutional neural networks (CNNs) have been attracting increasing attention in hyperspectral (HS) image classification, owing to their ability to capture spatial-spectral feature representations. Nevertheless, their ability in modeling relations between samples remains limited. Beyond the limitations of grid sampling, graph convolutional networks (GCNs) have been recently proposed and successfully applied in irregular (or non-grid) data representation and analysis. In this paper, we thoroughly investigate CNNs and GCNs (qualitatively and quantitatively) in terms of HS image classification. Due to the construction of the adjacency matrix on all the data, traditional GCNs usually suffer from a huge computational cost, particularly in large-scale remote sensing (RS) problems. To this end, we develop a new mini-batch GCN (called miniGCN hereinafter) which allows to train large-scale GCNs in a mini-batch fashion. More significantly, our miniGCN is capable of inferring out-of-sample data without re-training networks and improving classification performance. Furthermore, as CNNs and GCNs can extract different types of HS features, an intuitive solution to break the performance bottleneck of a single model is to fuse them. Since miniGCNs can perform batch-wise network training (enabling the combination of CNNs and GCNs) we explore three fusion strategies: additive fusion, element-wise multiplicative fusion, and concatenation fusion to measure the obtained performance gain. Extensive experiments, conducted on three HS datasets, demonstrate the advantages of miniGCNs over GCNs and the superiority of the tested fusion strategies with regards to the single CNN or GCN models. The codes of this work will be available at this https URL for the sake of reproducibility.