The first three anisotropy constants of nickel

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.     

紧致完备正规T2空间上概率Rado n测度的Loeb测度表示

证明了紧致完备正规Ｔ２空间上的概率Ｒａｄｏｎ测度空间可用其Ｌｏｅｂ空间进行表示。     

MRI of polysplenia syndrome

The polysplenia syndrome is the association of multiple spleens, situs inversus, congenital heart disease, and azygous continuation of the inferior vena cava. Magnetic resonance (MR) is a noninvasive imaging modality which can easily confirm the multiplicity of spleens, situs inversus, and identify complex congenital cardiovascular malformations. The anomalies of the polysplenia syndrome as imaged by MR are presented.     

Effects of NO2, CO,O2, and SO2 on oxidation kinetics of NO over Pt‐WO3/TiO2 catalyst for fast selective catalytic reduction process

The selective catalytic reduction rate of NO with N‐containing reducing agents can be enhanced considerably by converting a part of NO into NO₂. The enhanced reaction rate is more pronounced at lower temperatures by using an equimolar mixture of NO and NO₂. The kinetics of NO oxidation over Pt‐WO₃/TiO₂ catalyst has been determined in a fixed‐bed reactor with different concentrations of oxygen, nitric oxide, and nitrogen dioxide in the presence of 8% water. It has been found that the reaction is second order with respect to nitric oxide, first order for oxygen with a third‐order rate constant. Also, it is found that there is no effect on the reaction order with an addition of NO₂, CO, or SO₂. It follows the same second order but the reaction rate is found to be changed. It is observed that in the case of NO₂ and SO₂, the reaction rate tends to decrease, but it increases with the addition of CO into the feed. © 2006 Wiley Periodicals, Inc. Int J Chem Kinet 38: 613–620, 2006     

Manifold preprocessing for laser‐induced breakdown spectroscopy under Mars conditions

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.