175 research outputs found

    Electrochemical Studies of Passive Film Stability on Fe48Mo14Cr15Y2C15B Amorphous Metal in Seawater at 90oC and 5M CaCl2 at 105oC

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    Several Fe-based amorphous metal formulations have been identified that appear to have corrosion resistance comparable to, or better than that of Ni-based Alloy C-22 (UNS N06022), based on measurements of breakdown potential and corrosion rate in seawater. Both chromium (Cr) and molybdenum (Mo) provide corrosion resistance, boron (B) enables glass formation, and rare earths such as yttrium (Y) lower critical cooling rate (CCR). Amorphous Fe{sub 48.0}Cr{sub 15.0}Mo{sub 14.0}B{sub 6.0}C{sub 15.0}Y{sub 2.0} (SAM1651) has a low critical cooling rate (CCR) of less than 80 Kelvin per second, due to the addition of yttrium. The low CCR enables it to be rendered as a completely amorphous material in practical materials processes. While the yttrium enables a low CCR to be achieved, it makes the material relatively difficult to atomize, due to increases in melt viscosity. Consequently, the powders produced thus far have had irregular shape, which had made pneumatic conveyance during thermal spray deposition difficult

    A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs

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    A large fraction of the electronic health records (EHRs) consists of clinical measurements collected over time, such as lab tests and vital signs, which provide important information about a patient's health status. These sequences of clinical measurements are naturally represented as time series, characterized by multiple variables and large amounts of missing data, which complicate the analysis. In this work, we propose a novel kernel which is capable of exploiting both the information from the observed values as well the information hidden in the missing patterns in multivariate time series (MTS) originating e.g. from EHRs. The kernel, called TCKIM_{IM}, is designed using an ensemble learning strategy in which the base models are novel mixed mode Bayesian mixture models which can effectively exploit informative missingness without having to resort to imputation methods. Moreover, the ensemble approach ensures robustness to hyperparameters and therefore TCKIM_{IM} is particularly well suited if there is a lack of labels - a known challenge in medical applications. Experiments on three real-world clinical datasets demonstrate the effectiveness of the proposed kernel.Comment: 2020 International Workshop on Health Intelligence, AAAI-20. arXiv admin note: text overlap with arXiv:1907.0525
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