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The technique of ferromagnetic resonance at 23 GHz has been used to determine the first three anisotropy constants of pure Ni down to 4.2K. A temperature and orientation dependent linewidth has also been observed. 相似文献
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Yeow YL Pepperell CJ Sabturani FM Leong YK 《Langmuir : the ACS journal of surfaces and colloids》2008,24(19):10942-10949
A database linking the dimensionless volume of pendant droplets Vpen and the dimensionless volume of the spherical caps at the apex of the droplets Vcap has been constructed from the governing equations of pendant droplet tensiometry. The Bond number Bo that relates surface tension to gravitational body force appears as an independent parameter in this database. Computing Vpen and Vcap from the measured profile of a droplet and making use of the database allow the prevailing Bo to be determined and surface tension to be calculated. This new way of converting measured profiles into surface tension has a number of advantages, such as reliability and simplicity, compared to existing methods. These are demonstrated by applying the new method to a number of measured profile data taken from the literature. 相似文献
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Thomas Boucher CJ Carey Melinda Darby Dyar Sridhar Mahadevan Samuel Clegg Roger Wiens 《Journal of Chemometrics》2015,29(9):484-491
Laser‐induced breakdown spectroscopy (LIBS) is currently being used onboard the Mars Science Laboratory rover Curiosity to predict elemental abundances in dust, rocks, and soils using a partial least squares regression model developed by the ChemCam team. Accuracy of that model is constrained by the number of samples needed in the calibration, which grows exponentially with the dimensionality of the data, a phenomenon known as the curse of dimensionality. LIBS data are very high dimensional, and the number of ground‐truth samples (i.e., standards) recorded with the ChemCam before departing for Mars was small compared with the dimensionality, so strategies to optimize prediction accuracy are needed. In this study, we first use an existing machine learning algorithm, locally linear embedding (LLE), to combat the curse of dimensionality by embedding the data into a low‐dimensional manifold subspace before regressing. LLE constructs its embedding by maintaining local neighborhood distances and discarding large global geodesic distances between samples, in an attempt to preserve the underlying geometric structure of the data. We also introduce a novel supervised version, LLE for regression (LLER), which takes into account the known chemical composition of the training data when embedding. LLER is shown to outperform traditional LLE when predicting most major elements. We show the effectiveness of both algorithms using three different LIBS datasets recorded under Mars‐like conditions. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
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