Structure and reflective properties of Al-Cu-Fe quasicrystalline thin film prepared by laser induced arc method

Al-Cu-Fe thin films were prepared by laser induced arc (laser-arc) method from a single source—Al₆₃Cu₂₅Fe₁₂ alloy, which was proved to consist of quasicrystalline phase together with approximant phase. The composition of the deposited films meets the requirement for formation of icosahedral symmetry phase. Quasicrystalline phase was obtained after annealing the amorphous as-deposit film samples. The optical properties of the samples were investigated. Thin film samples of Al, Cu and Fe deposited under the same condition were employed for comparison. The results showed specific reflective properties of Al-Cu-Fe quasicrystal thin film in some wavelength range. The optical conductivity of the films exhibited a negative peak, centered about 440 nm in range of 190 to 800 nm. The Al-Cu-Fe quasicrystal thin films could absorb almost all the ray in the wavelength range from 420nm to 450 nm. The ratio of absorption was greater than 99%.     

Growth process of TiC clusters from Ti nanoparticles with evaporated carbon layer

Titanium carbide formation by the solid–solid reaction on the surface of Ti nanoparticles was studied in situ using a high-resolution transmission electron microscope with a heating stage. The cross-sectional image of the Ti surface was clearly observed. Vacuum-deposited carbon covered the whole the surface of Ti nanoparticles in spite of the partly evaporation on the nanoparticle surface. The diffusion of the carbon atoms inside the Ti nanoparticles depended on the size of the nanoparticles. When the Ti nanoparticle diameter was less than 30 nm, carbon atoms diffused into the Ti nanoparticle and formed TiC. The superstructure of the Ti nanoparticles was observed, which revealed the growth process of TiC to be the diffusion of carbon atoms. For Ti nanoparticles with diameter larger than 30 nm it was observed that diffusion of Ti atoms into the carbon layer was dominant, which resulted in formation of TiC in the carbon layer at the surface of Ti nanoparticles.     

<Emphasis Type="Italic">L</Emphasis><Subscript>1</Subscript> linear interpolator for missing values in time series

We propose a minimum mean absolute error linear interpolator (MMAELI), based on theL ₁ approach. A linear functional of the observed time series due to non-normal innovations is derived. The solution equation for the coefficients of this linear functional is established in terms of the innovation series. It is found that information implied in the innovation series is useful for the interpolation of missing values. The MMAELIs of the AR(1) model with innovations following mixed normal andt distributions are studied in detail. The MMAELI also approximates the minimum mean squared error linear interpolator (MMSELI) well in mean squared error but outperforms the MMSELI in mean absolute error. An application to a real series is presented. Extensions to the general ARMA model and other time series models are discussed. This research was supported by a CityU Research Grant and Natural Science Foundation of China.     

A comparison between optically active CdZnSe/ZnSe and CdZnSe/ZnBeSe self-assembled quantum dots: effect of beryllium

The composition and size of optically active Cd_xZn_1−xSe/ZnSe quantum dots are estimated with a previously developed method. The results are then compared with those obtained for Cd_xZn_1−xSe/Zn_0.97Be_0.03Se QDs. We show that introducing Be into the barrier material enhances both Cd composition and quantum size effect of optically active quantum dots.     

The components of the Wired Spanning Forest are recurrent

 We show that a.s. all of the connected components of the Wired Spanning Forest are recurrent, proving a conjecture of Benjamini, Lyons, Peres and Schramm. Our analysis relies on a simple martingale involving the effective conductance between the endpoints of an edge in a uniform spanning tree. We believe that this martingale is of independent interest and will find further applications in the study of uniform spanning trees and forests. Received: 20 April 2001 / Revised version: 23 July 2002 / Published online: 14 November     

Non-existence of phase transition of oriented percolation on Sierpinski carpet lattices

 A percolation problem on Sierpinski carpet lattices is considered. It is obtained that the critical probability of oriented percolation is equal to 1. In contrast it was already shown that the critical probability p _c of percolation is strictly less than 1 in Kumagai [9]. This result shows a difference between fractal-like lattice and ℤ ^d lattice. Received: 15 May 2002 / Revised version: 11 October 2002 / Published online: 21 February 2003 Mathematics Subject Classification (2000): Primary: 60K35, 82B43; Secondary: 82B26     

Lower bounds for densities of uniformly elliptic random variables on Wiener space

 In this article, we generalize the lower bound estimates for uniformly elliptic diffusion processes obtained by Kusuoka and Stroock. We define the concept of uniform elliptic random variable on Wiener space and show that with this definition one can prove a lower bound estimate of Gaussian type for its density. We apply our results to the case of the stochastic heat equation under the hypothesis of unifom ellipticity of the diffusion coefficient. Received: 6 November 2001 / Revised version: 27 February 2003 / Published online: 12 May 2003 Key words or phrases: Malliavin Calculus – Density estimates – Aronson estimates     

Feed forward neural networks modeling for K–P interactions

Artificial intelligence techniques involving neural networks became vital modeling tools where model dynamics are difficult to track with conventional techniques. The paper make use of the feed forward neural networks (FFNN) to model the charged multiplicity distribution of K–P interactions at high energies. The FFNN was trained using experimental data for the multiplicity distributions at different lab momenta. Results of the FFNN model were compared to that generated using the parton two fireball model and the experimental data. The proposed FFNN model results showed good fitting to the experimental data. The neural network model performance was also tested at non-trained space and was found to be in good agreement with the experimental data.