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  • 1
    Electronic Resource
    Electronic Resource
    s.l. ; Stafa-Zurich, Switzerland
    Materials science forum Vol. 546-549 (May 2007), p. 101-104 
    ISSN: 1662-9752
    Source: Scientific.Net: Materials Science & Technology / Trans Tech Publications Archiv 1984-2008
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: The microstructure, mechanical properties, creep and corrosion resistance ofMg-Gd-Y-Zr(-Ca) alloys were studied. Small additions of 0.4-0.6 wt% Ca toMg-(9-10)Gd-3Y-0.4Zr(wt.%) alloys led to a slight improvement in creep resistance and aremarkable increase in corrosion resistance, but an obvious decrease in elongation to fracture. UTSand TYS of the Mg-Gd-Y-Zr(-Ca) alloys are obviously higher than those of WE54, especially in thetemperature range from room temperature to 200 oC. TEM images and corresponding energydispersive x-ray spectra showed that the Ca element primarily segregated to the grain boundaries andexisted in the cuboid-shaped particles with a trace concentration, and the small addition of Ca had noobvious effect on the orientation, morphology, and distribution of β′ phase, which is responsible forthe peak hardness in Mg-Gd-Y-Zr alloys
    Type of Medium: Electronic Resource
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  • 2
    Publication Date: 2019
    Description: Computational graphs (CGs) have been widely utilized in numerical analysis and deep learning to represent directed forward networks of data flows between operations. This paper aims to develop an explainable learning framework that can fully integrate three major steps of decision support: Synthesis of diverse traffic data, multilayered traffic demand estimation, and marginal effect analyses for transport policies. Following the big data-driven transportation computational graph (BTCG) framework, which is an emerging framework for explainable neural networks, we map different external traffic measurements collected from household survey data, mobile phone data, floating car data, and sensor networks to multilayered demand variables in a CG. Furthermore, we extend the CG-based framework by mapping different congestion mitigation strategies to CG layers individually or in combination, allowing the marginal effects and potential migration magnitudes of the strategies to be reliably quantified. Using the TensorFlow architecture, we evaluate our framework on the Sioux Falls network and present a large-scale case study based on a subnetwork of Beijing using a data set from the metropolitan planning organization.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by MDPI
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