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Dynamic Shear Deformation of a Precipitation Hardened Al0.7CoCrFeNi Eutectic High-Entropy Alloy Using Hat-Shaped Specimen Geometry (open access)

Dynamic Shear Deformation of a Precipitation Hardened Al0.7CoCrFeNi Eutectic High-Entropy Alloy Using Hat-Shaped Specimen Geometry

The article reports on the shear deformation of a eutectic structured HEA and effect of precipitation on shear deformation, particularly Al₀.₇CoCrFeNi high-entropy alloy (HEA). A split-Hopkinson pressure bar (SHPB) was used to compress the hat-shaped specimens to study the local dynamic shear response of the alloy. The change in the width of shear bands with respect to precipitation and deformation rates was studied. The precipitation of L1₂ phase did not delay the formation of adiabatic shear bands (ASB) or affect the ASB width significantly, however, the deformed region around ASB, consisting of high density of twins in fcc phase, was reduced from 80 µm to 20 µm in the stronger precipitation strengthened condition.
Date: April 10, 2020
Creator: Gwalani, Bharat; Wang, Tianhao; Jagetia, Abhinav; Gangireddy, Sindhura; Muskeri, Saideep; Mukherjee, Sundeep et al.
System: The UNT Digital Library
Modeling and Characterization of Scaling Factor of Flexible Spiral Coils for Wirelessly Powered Wearable Sensors (open access)

Modeling and Characterization of Scaling Factor of Flexible Spiral Coils for Wirelessly Powered Wearable Sensors

This article presents the design, modeling, and experimental characterization of flexible square-shaped spiral coils with different scaling factors for WPT systems. The effects of coil scaling factor on inductance, capacitance, resistance, and the quality factor (Q-factor) are modeled, simulated, and experimentally validated for the case of flexible planar coils. This article also presents the effect of skin contact with the flexible coil in terms of the power transfer efficiency (PTE) to validate the suitability as a wearable sensor.
Date: April 17, 2020
Creator: Biswas, Dipon K.; Sinclair, Melissa; Le, Tien; Pullano, Salvatore Andrea; Fiorillo, Antonino S. & Mahbub, Ifana
System: The UNT Digital Library
BAM: A Block-Based Bayesian Method for Detecting Genome-Wide Associations with Multiple Diseases (open access)

BAM: A Block-Based Bayesian Method for Detecting Genome-Wide Associations with Multiple Diseases

Article proposes a novel Bayesian method, named BAM, for simultaneously partitioning Single Nucleotide Polymorphisms (SNPs) into Linkage Disequilibrium(LD)-blocks and detecting genome-wide multi-locus epistatic interactions that are associated with multiple diseases. Experimental results on the simulated datasets demonstrate that BAM is powerful and efficient.
Date: April 16, 2020
Creator: Guo, Xuan; Wu, Guanying & Xu, Baohua
System: The UNT Digital Library