Influence of strain on the magneto-exciton in single and coupled InP/GaInP quantum disks

We investigated theoretically the influence of strain on the exciton in both single and three vertically coupled self-assembled quantum dot systems in the presence of a perpendicular magnetic field. For the single disk, we find that the heavy-hole exciton is the ground state, while for the system of three stacked disks, the light hole state was found to be lower in energy. Results for the diamagnetic shift were compared with experimental results.     

Speciation Analysis of Inorganic Tin (Sn(II)/Sn(IV)) by Graphite Furnace Atomic Absorption Spectrometry Following Ion-Exchange Separation

A new method based on anion exchange resin separation and graphite furnace atomic absorption spectrometry (GFAAS) detection is proposed for the determination of inorganic tin species. The result showed that Sn(IV) was quantitatively retained on the resin when [HCl] = 9.0 mol · L⁻¹, but Sn(II) could not be adsorbed on the resin under the same condition. Thus, a separation of Sn(II) and Sn(IV) has been realized. When the concentration of NaOH solution was between 2.0–7.0 mol · L⁻¹, Sn(IV) that adsorbed on the resin could be eluated from the resin completely. Meanwhile, under the atmosphere and the nitrogen states, the translation between Sn(II) and Sn(IV) was investigated. Under the optimal conditions, the detection limit of Sn(IV) is 0.40 μg · L⁻¹ with RSD of 2.3% (n = 5, c = 2.0 μg · L⁻¹). The proposed method was applied to the speciation analysis of tin in different water samples and the recovery of total Sn was in the range of 98.7–101.7%. In order to verify the accuracy of the method, a certified reference water sample was analyzed and the results obtained were in good agreement with the certified value.     

Asymptotic properties of computationally efficient alternative estimators for a class of multivariate normal models

Parameters of Gaussian multivariate models are often estimated using the maximum likelihood approach. In spite of its merits, this methodology is not practical when the sample size is very large, as, for example, in the case of massive georeferenced data sets. In this paper, we study the asymptotic properties of the estimators that minimize three alternatives to the likelihood function, designed to increase the computational efficiency. This is achieved by applying the information sandwich technique to expansions of the pseudo-likelihood functions as quadratic forms of independent normal random variables. Theoretical calculations are given for a first-order autoregressive time series and then extended to a two-dimensional autoregressive process on a lattice. We compare the efficiency of the three estimators to that of the maximum likelihood estimator as well as among themselves, using numerical calculations of the theoretical results and simulations.     

Arithmetic expressions of Selberg's zeta functions for congruence subgroups

In [P. Sarnak, Class numbers of indefinite binary quadratic forms, J. Number Theory 15 (1982) 229-247], it was proved that the Selberg zeta function for SL₂(Z) is expressed in terms of the fundamental units and the class numbers of the primitive indefinite binary quadratic forms. The aim of this paper is to obtain similar arithmetic expressions of the logarithmic derivatives of the Selberg zeta functions for congruence subgroups of SL₂(Z). As applications, we study the Brun-Titchmarsh type prime geodesic theorem and the asymptotic formula of the sum of the class number.     

A second-order accurate numerical method for a fractional wave equation

We study a generalized Crank–Nicolson scheme for the time discretization of a fractional wave equation, in combination with a space discretization by linear finite elements. The scheme uses a non-uniform grid in time to compensate for the singular behaviour of the exact solution at t = 0. With appropriate assumptions on the data and assuming that the spatial domain is convex or smooth, we show that the error is of order k ² + h ², where k and h are the parameters for the time and space meshes, respectively.     

A preconditioned domain decomposition algorithm for the solution of the elliptic Neumann problem

A new preconditioned conjugate gradient (PCG)-based domain decomposition method is given for the solution of linear equations arising in the finite element method applied to the elliptic Neumann problem. The novelty of the proposed method is in the recommended preconditioner which is constructed by using cyclic matrix. The resulting preconditioned algorithms are well suited to parallel computation.     

An almost fourth order uniformly convergent difference scheme for a semilinear singularly perturbed reaction-diffusion problem

Summary. This paper is concerned with a high order convergent discretization for the semilinear reaction-diffusion problem: , for , subject to , where . We assume that on , which guarantees uniqueness of a solution to the problem. Asymptotic properties of this solution are discussed. We consider a polynomial-based three-point difference scheme on a simple piecewise equidistant mesh of Shishkin type. Existence and local uniqueness of a solution to the scheme are analysed. We prove that the scheme is almost fourth order accurate in the discrete maximum norm, uniformly in the perturbation parameter . We present numerical results in support of this result. Received February 25, 1994     

A robust and efficient adaptive reweighted estimator of multivariate location and scatter

This article proposes a reweighted estimator of multivariate location and scatter, with weights adaptively computed from the data. Its breakdown point and asymptotic behavior under elliptical distributions are established. This adaptive estimator is able to attain simultaneously the maximum possible breakdown point for affine equivariant estimators and full asymptotic efficiency at the multivariate normal distribution. For the special case of hard-rejection weights and the MCD as initial estimator, it is shown to be more efficient than its non-adaptive counterpart for a broad range of heavy-tailed elliptical distributions. A Monte Carlo study shows that the adaptive estimator is as robust as its non-adaptive relative for several types of bias-inducing contaminations, while it is remarkably more efficient under normality for sample sizes as small as 200.