The electron ionization rate in multilayered semiconductor structures

We present theoretical results of the electron impact ionization rate in GaAs/AlGaAs multiquantum well structures as a function of applied electric field for various geometries, i.e., well and barrier widths. In addition, we present preliminary measurements of the current-voltage characteristics of MBE grown devices which demonstrate very low leakage current as well as sharp breakdwon behavior. It is found that the net ionization rate, determined by averaging over the constitutent GaAs and AlGaAs layers, approaches the weighted average of the constituent bulk rates at high electric field strengths; the potential discontinuity is relatively unimportant. The electron ionization rate within the well regions alone is still higher than that in bulk GaAs, but is insufficiently enhanced to compensate for the much lower rate in the AlGaAs layers. As the field is lowered to 250.0 kV/cm, the average ionization rate in the multiquantum well structure becomes larger than in the bulk.     

Learning Theory Estimates via Integral Operators and Their Approximations

The regression problem in learning theory is investigated with least square Tikhonov regularization schemes in reproducing kernel Hilbert spaces (RKHS). We follow our previous work and apply the sampling operator to the error analysis in both the RKHS norm and the L² norm. The tool for estimating the sample error is a Bennet inequality for random variables with values in Hilbert spaces. By taking the Hilbert space to be the one consisting of Hilbert-Schmidt operators in the RKHS, we improve the error bounds in the L² metric, motivated by an idea of Caponnetto and de Vito. The error bounds we derive in the RKHS norm, together with a Tsybakov function we discuss here, yield interesting applications to the error analysis of the (binary) classification problem, since the RKHS metric controls the one for the uniform convergence.     

OR and the challenge to improve the NHS: modelling for insight and improvement in in-patient flows

This paper considers efforts to improve in-patient flows, a particularly urgent issue in the National Health Service (NHS). The context is described and related to reasons why OR has been making relatively little contribution. The paper argues that large complex models may often be unnecessary and even get in the way of providing clear insight and guidance for problem owners. The importance of understanding the generic working of systems to lead to improvement, and the limitations of simply describing them, is stressed. It is demonstrated that some very simple models can be of significant practical value in understanding and managing complex systems, changing mindsets and driving collection and use of operationally valuable data. Recommendations for more effective engagement with the NHS are offered.     

Periodic solutions for a Lotka–Volterra mutualism system with several delays

In the present paper, a Lotka–Volterra type mutualism system with several delays is studied. Some new and interesting sufficient conditions are obtained for the global existence of positive periodic solutions of the mutualism system. Our method is based on Mawhin’s coincidence degree and novel estimation techniques for the a priori bounds of unknown solutions. Our results are different from the existing ones such as those in of Yang et al. [F. Yang, D. Jiang, A. Ying, Existence of positive solution of multidelays facultative mutualism system, J. Eng. Math. 3 (2002) 64–68] and Chen et al. [F. Chen, J. Shi, X. Chen, Periodicity in a Lotka–Volterra facultative mutualism system with several delays, J. Eng. Math. 21 (3) (2004) 403–409].     

Improved tabu search algorithm for the handling of route duration constraints in vehicle routing problems with time windows

This note introduces a refinement to a previously proposed tabu search algorithm for vehicle routing problems with time windows. This refinement yields new best known solutions on a set of benchmark instances of the multi-depot, the periodic and the site-dependent vehicle routing problems with time windows.     

Dispersion and distribution of organically modified montmorillonite in nylon‐66 matrix

Nylon‐66 nanocomposites were prepared by melt‐compounding nylon‐66 with an alkyl ammonium surfactant pretreated montmorillonite (MMT). The thermal stability of the organic MMT powders was measured by thermogravimetric analysis. The decomposition of the surfactant on the MMT occurred from 200 to 500 °C. The low onset decomposition temperature of the organic MMT is one shortcoming when it is used to prepare polymer nanocomposites at high melt‐compounding temperatures. To provide greater property enhancement and better thermal stability of the polymer/MMT nanocomposites, it is necessary to develop MMT modified with more thermally stable surfactants. The dispersion and spatial distribution of the organic MMT layers in the nylon‐66 matrix were characterized by X‐ray diffraction. The organic MMT layers were exfoliated but not randomly dispersed in the nylon‐66 matrix. A model was proposed to describe the spatial distribution of the organic MMT layers in an injection‐molded rectangular bar of nylon‐66/organic MMT nanocomposites. Most organic MMT layers were oriented in the injection‐molding direction. Layers near the four surfaces of the bar were parallel to their corresponding surfaces; whereas those in the bulk differed from the near‐surface layers and rotated themselves about the injection‐molding direction. The influence of the spatial distribution of the organic MMT on crystallization of nylon‐66 was also investigated. © 2003 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 41: 1234–1243, 2003