Distinguishing the sources of Asian dust based on electron spin resonance signal intensity and crystallinity of quartz

IEECAS SKLLQG

Summer monsoon intensity controls C-4/C-3 plant abundance during the last 35 ka in the Chinese Loess Plateau: Carbon isotope evidence from bulk organic matter and individual leaf waxes

IEECAS SKLLQG

The coulomb potential energy effect on the intensity of the characteristic lines at highly charged ion incendence on Al surface

The Al atomic characteristic spectral lines were induced by the impact of Ar-40(q+) ions (8 <= q <= 16; kinetic energy 150 keV) on Al surface. The result shows that by Penning impinging and resonant capture, the ion energy is deposited on the Al surface to excite the target atom, which is different from light excitation. Not only are the transitions betweem electronic configurations of the atomic complex excited, but the enhancing tendency of the characteristic spectral line intensity is consistent with the enhancing tendency of the coulomb potential energy of the incident ions with increasing charged states.

Control of chemical reaction in high-intensity laser field

The prospects of control chemical reaction in high-intensity laser field are talked about here, and some experimental and theoretical designs are reviewed and discussed also.

Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction

The Gaussian process latent variable model (GP-LVM) has been identified to be an effective probabilistic approach for dimensionality reduction because it can obtain a low-dimensional manifold of a data set in an unsupervised fashion. Consequently, the GP-LVM is insufficient for supervised learning tasks (e. g., classification and regression) because it ignores the class label information for dimensionality reduction. In this paper, a supervised GP-LVM is developed for supervised learning tasks, and the maximum a posteriori algorithm is introduced to estimate positions of all samples in the latent variable space. We present experimental evidences suggesting that the supervised GP-LVM is able to use the class label information effectively, and thus, it outperforms the GP-LVM and the discriminative extension of the GP-LVM consistently. The comparison with some supervised classification methods, such as Gaussian process classification and support vector machines, is also given to illustrate the advantage of the proposed method.

Optical intensity gradient by colloidal photonic crystals with a graded thickness distribution

Gradient colloidal crystals with a thickness gradient were prepared by the vertical deposition technique with vertically graded concentration suspensions. The thickness of the gradient colloidal crystal gradually changes at different positions along the specific gradient direction of the crystal. The thickness gradient was determined by the concentration gradient, depending on the initial colloidal concentration and the settling time. The optical transmission intensity at the dip wavelength can be tuned by changing the thickness of the colloidal crystals. The gradient colloidal crystals lead to a gradient of optical intensity at the dip in transmission light. The gradient of optical intensity at the dip increases as the thickness gradient of the colloidal crystal increases.

Comparison of different methods for variable selection

In chemistry for chemical analysis of a multi-component sample or quantitative structure-activity/property relationship (QSAR/QSPR) studies, variable selection is a key step. In this study, comparisons between different methods were performed. These methods include three classical methods such as forward selection, backward elimination and stepwise regression; orthogonal descriptors; leaps-and-bounds regression and genetic algorithm. Thirty-five nitrobenzenes were taken as the data set. From these structures quantum chemical parameters, topological indices and indicator variable were extracted as the descriptors for the comparisons of variable selections. The interesting results have been obtained. (C) 2001 Elsevier Science B.V. All rights reserved.

Variable selection by orthogonal descriptors and prediction of R-f value of phenol and aniline derivatives

Orthogonal descriptors is a viable method for variable selection, but this method strongly depend on the orthogonalisation ordering of the descriptors. In this paper, we compared the different methods used for order the descriptors. It showed that better results could be achieved with the use of backward elimination ordering. We predicted R-f value of phenol and aniline derivatives by this method, and compared it with classical algorithms such as forward selection, backward elimination, and stepwise procedure. Some interesting hints were obtained.

Intensity exaggeration of endohedral metallofullerenes in laser-desorption-ionization time-of-flight mass spectrometry

The laser-desorption-ionization time-of-flight mass spectrometry and desorption-electron-ionization mass spectrometry were employed for the characterization of metallofullerenes extract. it was found that the relative intensities of metallofullerenes in this positive-ion, negative-ion LD-TOF MS and DEI MS were much different. This phenomenon should have relationship with the peculiar ionization energies and electron affinities of metallofullerenes.