22,364 research outputs found

    A metal–organic framework/α-alumina composite with a novel geometry for enhanced adsorptive separation

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    The development of a metal–organic framework/α-alumina composite leads to a novel concept: efficient adsorption occurs within a plurality of radial micro-channels with no loss of the active adsorbents during the process. This composite can effectively remediate arsenic contaminated water producing potable water recovery, whereas the conventional fixed bed requires eight times the amount of active adsorbents to achieve a similar performance

    Magnetoelectric effects due to elastic coupling in ferroelectric/ferromagnetic multilayers

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    Author name used in this publication: C. H. Woo2007-2008 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

    Curie temperature and critical thickness of ferroelectric thin films

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    Author name used in this publication: C. H. Woo2004-2005 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

    Curie-Weiss law in thin-film ferroelectrics

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    Author name used in this publication: C. H. Woo2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

    Controlling dielectric and pyroelectric properties of compositionally graded ferroelectric rods by an applied pressure

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    Author name used in this publication: C. H. Woo2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

    Reply to "Comment on 'Fano resonance for Anderson Impurity Systems' "

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    In a recent Comment, Kolf et al. (cond-mat/0503669) state that our analysis of the Fano resonance for Anderson impurity systems [Luo et al., Phys. Rev. Lett 92, 256602 (2004)] is incorrect. Here we want to point out that their comments are not based on firm physical results and their criticisms are unjustified and invalid.Comment: 1 page, 1 figure, to appear in PR

    Theoretical prediction on the structural, electronic, and polarization properties of tetragonal Bi₂ZnTiO₆

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    Author name used in this publication: C. H. Woo2008-2009 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

    The effect of glutamine supplement on small intestinal morphology and xylose absorptive ability of weaned piglets

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    The purpose of this study is to demonstrate the effects of glutamine (Gln) supplement on small intestinal  morphology, xylose absorptive and growth performance of weaned piglets. Forty eight piglets weaned at 28 ± 2 days of age were randomly allotted to three treatment groups. A basal corn-soybean diet was formulated to contain 20.3% protein and 3450 kcal DE/kg diet. Glutamine was supplemented to the basal diet at 0% (control), 1% (Gln 1%) and 2% (Gln 2%). Pigs were fed experimental diets for three weeks. The results  showed that the villous height of the Gln groups tended higher than the control group in duodenum and jejunum (P < 0.1). Glutamine supplementation increased plasma net xylose absorptive concentration from 0.78 to 1.20 and 0.95 to 1.23 in Gln 1% and Gln 2% group, respectively, which were better than the control group (0.86 to 0.97) in day 7 to 14 after weaning. Growth performance was not significantly affected by Gln supplement;  however, average daily gain was approximately improved from 21 to 28% by Gln supplement compared to the control group during 21 days of experimental period. In summary, the results suggested that dietary  supplementation of Gln could be beneficial in small intestinal villous morphology and xylose absorptive  capacity, and could have a slight contribution to the average daily gain of weaned piglets.Key words: Glutamine, growth performance, intestinal morphology, weaned piglets

    Coupled clustering ensemble by exploring data interdependence

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    © 2018 ACM. Clustering ensembles combine multiple partitions of data into a single clustering solution. It is an effective technique for improving the quality of clustering results. Current clustering ensemble algorithms are usually built on the pairwise agreements between clusterings that focus on the similarity via consensus functions, between data objects that induce similarity measures from partitions and re-cluster objects, and between clusters that collapse groups of clusters into meta-clusters. In most of those models, there is a strong assumption on IIDness (i.e., independent and identical distribution), which states that base clusterings perform independently of one another and all objects are also independent. In the real world, however, objects are generally likely related to each other through features that are either explicit or even implicit. There is also latent but definite relationship among intermediate base clusterings because they are derived from the same set of data. All these demand a further investigation of clustering ensembles that explores the interdependence characteristics of data. To solve this problem, a new coupled clustering ensemble (CCE) framework that works on the interdependence nature of objects and intermediate base clusterings is proposed in this article. The main idea is to model the coupling relationship between objects by aggregating the similarity of base clusterings, and the interactive relationship among objects by addressing their neighborhood domains. Once these interdependence relationships are discovered, they will act as critical supplements to clustering ensembles. We verified our proposed framework by using three types of consensus function: clustering-based, object-based, and cluster-based. Substantial experiments on multiple synthetic and real-life benchmark datasets indicate that CCE can effectively capture the implicit interdependence relationships among base clusterings and among objects with higher clustering accuracy, stability, and robustness compared to 14 state-of-the-art techniques, supported by statistical analysis. In addition, we show that the final clustering quality is dependent on the data characteristics (e.g., quality and consistency) of base clusterings in terms of sensitivity analysis. Finally, the applications in document clustering, as well as on the datasets with much larger size and dimensionality, further demonstrate the effectiveness, efficiency, and scalability of our proposed models
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