The S′-convolution with singular kernels in the Euclidean case and the product domain case

We characterize those tempered distributions which are S′-convolvable with a given class of singular convolution kernels. We study both, the Euclidean case and the product domain case. In the Euclidean case, we consider a class of kernels that includes Riesz kernels, Calderón–Zygmund singular convolution kernels, finite part distributions defined by hypersingular convolution kernels, and Hörmander multipliers. In the product domain case, we consider a class of singular kernels introduced by Fefferman and Stein as a generalization of the n-dimensional Hilbert kernel.     

调和Bergman度量

We have constructed the positive definite metric matrixes for the bounded domains of Rn and proved an inequality which is about the Jacobi matrix of a harmonic mapping on a bounded domain of Rn and the metric matrix of the same bounded domain.     

Commutant lifting and factorization of reproducing kernels

A general version of the commutant lifting theorem for operators between different spaces is proved. It includes as special cases the lifting theorems of Ball-Trent-Vinnikov and Volberg-Treil. A multivariable variant of the Volberg-Treil theorem is obtained as a corollary. A certain factorization property of reproducing kernels is shown to be a sufficient condition for the lifting. Another factorization property is shown to be a necessary condition.     

Chernoff's Theorem and Discrete Time Approximations of Brownian Motion on Manifolds

Let be a one-parameter family of positive integral operators on a locally compact space . For a possibly non-uniform partition of define a finite measure on the path space by using a) for the transition between any two consecutive partition times of distance and b) a suitable continuous interpolation scheme (e.g. Brownian bridges or geodesics). If necessary normalize the result to get a probability measure. We prove a version of Chernoff's theorem of semigroup theory and tightness results which yield convergence in law of such measures as the partition gets finer. In particular let be a closed smooth submanifold of a manifold . We prove convergence of Brownian motion on , conditioned to visit at all partition times, to a process on whose law has a density with respect to Brownian motion on which contains scalar, mean and sectional curvatures terms. Various approximation schemes for Brownian motion on are also given.      

Mixed radial-angular integrability for rough maximal singular integrals and Marcinkiewicz integrals with mixed homogeneity

This paper is devoted to studying maximal singular integrals and Marcinkiewicz integrals with rough kernels in a mixed homogeneity setting. Assuming that the kernels satisfy certain rather weak size conditions, the boundedness of such operators on the mixed radial-angular spaces are established, respectively. Meanwhile, the corresponding vector-valued versions are also given.     

求解Helmholtz方程基于核重构思想的最小二乘配点法

基于核重构思想构造近似函数，将配点法和最小二乘原理相结合对微分方程进行离散， 建立了Helmholtz方程的最小二乘配点格式，并分别研究了Helmholtz方程的波传播问题和 边界层问题. 通过数值算例可以发现，给出的数值计算结果非常接近于精确解，计算精度明显高于SPH 法的数值结果，且随着节点数目的增加，其精确度越来越高，具有良好的收敛性.     

Tocochromanols composition in kernels recovered from different apricot varieties: RP-HPLC/FLD and RP-UPLC-ESI/MSn study

Composition of tocochromanols in kernels recovered from 16 different apricot varieties (Prunus armeniaca L.) was studied. Three tocopherol (T) homologues, namely α, γ and δ, were quantified in all tested samples by an RP-HPLC/FLD method. The γ-T was the main tocopherol homologue identified in apricot kernels and constituted approximately 93% of total detected tocopherols. The RP-UPLC-ESI/MSⁿ method detected trace amounts of two tocotrienol homologues α and γ in the apricot kernels. The concentration of individual tocopherol homologues in kernels of different apricots varieties, expressed in mg/100 g dwb, was in the following range: 1.38–4.41 (α-T), 42.48–73.27 (γ-T) and 0.77–2.09 (δ-T). Moreover, the ratio between individual tocopherol homologues α:γ:δ was nearly constant in all varieties and amounted to approximately 2:39:1.     

Development and validation of high-performance thin-layer chromatographic method for determination of amygdalin

ABSTRACT

A new method for the extraction and quantitative determination of amygdalin has been proposed. Accelerated solvent extraction was applied for the extraction, and reversed-phase high-performance thin-layer chromatography method was developed, validated, and applied for the determination of amygdalin in the extracts of apricot, plum, almond, and peach kernels. The chromatographic system used was RP-18 silica, as stationary phase and acetonitrile/water (50:50, v/v), as mobile phase. Densitometric scanning was performed at 210 nm. The method was validated with respect to specificity, linearity, precision, and accuracy. The results showed that the peak area responses were linear within the concentration range of 2.5–50.0 µg/spot (R² = 0.9984). The limit of quantification was 4.28 µg/spot, and the detection limit 1.28 µg/spot. The intra-day and inter-day reproducibility, in terms of %RSD, were in the range of 0.81–1.15 and 1.32–1.89, respectively. The accuracy data were in the range from 99.98 to 100.56%. The method is linear, quantitative and reproducible, and could be used as an efficient and economical green chromatographic procedure for the determination of amygdalin in the fruit kernel.     

h-ADAPTIVITY ANALYSIS BASED ON MULTIPLE SCALE REPRODUCING KERNEL PARTICLE METHOD

IntroductionAs one of the meshfree methods, multiple scale reproducing kernel particle method(RKPM)[1,2]bears, besides the common features of all meshfree methods, a uniqueinherent feature of multiresolution analysis by which the structural response can b…     

Constrained kernelized partial least squares

Nonlinear kernel methods have been widely used to deal with nonlinear problems in latent variable methods. However, in the presence of structured noise, these methods have reduced efficacy. We have previously introduced constrained latent variable methods that make use of any available additional knowledge about the structured noise. These methods improve performance by introducing additional constraints into the algorithm. In this paper, we build upon our previous work and introduce hard‐constrained and soft‐constrained nonlinear partial least squares methods using nonlinear kernels. The addition of nonlinear kernels reduces the effects of structured noise in nonlinear spaces and improves the regression performance between the input and response variables. Copyright © 2014 John Wiley & Sons, Ltd.