Separation of particles from liquids by the solid core cyclone

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Analysis of bulk and thin film model samples intended for investigating the strain sensitivity of niobium-tin.

Bulk samples and thin films were fabricated and characterized to determine their suitability for studying the effect of composition and morphology on strain sensitivity. Heat capacity and resistivity data are used to determine the critical temperature distribution. It is found that all bulk samples contain stoichiometric Nb{sub 3}Sn regardless of their nominal Nb to Sn ratio. Furthermore, in bulk samples with Cu additions, a bi-modal distribution of stoichiometric and off-stoichiometric Nb-Sn is found. Thus the nominally off-stoichiometric bulk samples require additional homogenization steps to yield homogeneous off-stoichiometric samples. A binary magnetron-sputtered thin film has the intended off-stoichiometric Nb-Sn phase with a mid-point critical temperature of 16.3 K. This type of sample is a suitable candidate for investigating the strain sensitivity of A15 Nb{sub 1-{beta}}Sn{sub {beta}}, with 0.18 < {beta} < 0.25. The strain sensitivity of Nb-Sn as a function of composition and morphology is important for an in-depth understanding of the strain sensitivity of composite Nb{sub 3}Sn wires

Identifying nuclear phenotypes using semi-supervised metric learning

In systems-based approaches for studying processes such as cancer and development, identifying and characterizing individual cells within a tissue is the first step towards understanding the large-scale effects that emerge from the interactions between cells. To this end, nuclear morphology is an important phenotype to characterize the physiological and differentiated state of a cell. This study focuses on using nuclear morphology to identify cellular phenotypes in thick tissue sections imaged using 3D fluorescence microscopy. The limited label information, heterogeneous feature set describing a nucleus, and existence of sub-populations within cell-types makes this a difficult learning problem. To address these issues, a technique is presented to learn a distance metric from labeled data which is locally adaptive to account for heterogeneity in the data. Additionally, a label propagation technique is used to improve the quality of the learned metric by expanding the training set using unlabeled data. Results are presented on images of tumor stroma in breast cancer, where the framework is used to identify fibroblasts, macrophages and endothelial cells - three major stromal cells involved in carcinogenesis. © 2011 Springer-Verlag