Compositional analysis of thin film amorphous semiconductors and insulators using LIMA

LIMA (Laser-induced Ion Mass Analysis) is a new technique capable of compositional analysis of thin films and surface regions. Under UHV conditions a focused laser beam evaporates and ionizes a microvolume of specimen material from which a mass spectrum is obtained. LIMA has been used to examine a range of thin film materials with applications in electronic devices. The neutral photon probe avoids charging problems, and low conductivity materials are examined without prior metallization. Analyses of insulating silicon oxides, nitrides, and oxynitrides confirm estimates of composition from infrared measurements. However, the hydrogen content of hydrogenated amorphous silicon (a-Si : H) found by LIMA shows no correlation with values given by infrared absorption analysis. Explanations are proposed and discussed. © 1985.

Solving problems in production scheduling

Traditionally, production scheduling has been viewed as a problem-solving task that involves a single problem - generation of a suitable schedule. This paper presents an alternative model in which individual difficulties are viewed as problems, and the task is to maintain a suitable schedule by resolving as many of these problems as possible. Decision support software is described that has facilities for defining policies to handle numerous minor problems and complete problem-solving strategies to deal with major problems. The paper then discusses the potential for this style of decision support to improve the performance of human schedulers. © 1995.

Optimal control with stochastic PDE constraints and uncertain controls

The optimal control of problems that are constrained by partial differential equations with uncertainties and with uncertain controls is addressed. The Lagrangian that defines the problem is postulated in terms of stochastic functions, with the control function possibly decomposed into an unknown deterministic component and a known zero-mean stochastic component. The extra freedom provided by the stochastic dimension in defining cost functionals is explored, demonstrating the scope for controlling statistical aspects of the system response. One-shot stochastic finite element methods are used to find approximate solutions to control problems. It is shown that applying the stochastic collocation finite element method to the formulated problem leads to a coupling between stochastic collocation points when a deterministic optimal control is considered or when moments are included in the cost functional, thereby forgoing the primary advantage of the collocation method over the stochastic Galerkin method for the considered problem. The application of the presented methods is demonstrated through a number of numerical examples. The presented framework is sufficiently general to also consider a class of inverse problems, and numerical examples of this type are also presented. © 2011 Elsevier B.V.

Entrepreneurial innovation problems associated with the dynamic growth of university spin-outs in China: A capabilities perspective

This research aims to develop a capabilities-based conceptual framework in order to study the stage-specific innovation problems associated with the dynamic growth process of university spin-outs (hereafter referred to as USOs) in China. Based on the existing literature, pilot cases and five critical cases, this study attempts to explore the interconnections between the entrepreneurial innovation problems and the configuration of innovative capabilities (that acquire, mobilise and re-configure the key resources) throughout the lifecycle of a firm in four growth phases. This paper aims to contribute to the literature in a holistic manner by providing a theoretical discussion of USOs' development through adding evidence from a rapid growth emerging economy. To date, studies that have investigated the development of USOs in China recognised the heterogeneity of USOs in terms of capabilities still remain sparse. Addressing this research gap will be of great interest to entrepreneurs, policy makers and venture investors. © Copyright 2010 Inderscience Enterprises Ltd.

π-Conjugated molecules with fused rings for organic field-effect transistors: design, synthesis and applications

π-Conjugated molecular materials with fused rings are the focus of considerable interest in the emerging area of organic electronics, since the combination of excellent charge carrier mobility and high stability may lead to their practical applications. This tutorial review discusses the synthesis, properties and applications of π-conjugated organic semiconducting materials, especially those with fused rings. The achievements to date, the remaining problems and challenges, and the key research that needs to be done in the near future are all discussed.

Differential geometry and design coupling in MDO

The notion of coupling within a design, particularly within the context of Multidisciplinary Design Optimization (MDO), is much used but ill-defined. There are many different ways of measuring design coupling, but these measures vary in both their conceptions of what design coupling is and how such coupling may be calculated. Within the differential geometry framework which we have previously developed for MDO systems, we put forth our own design coupling metric for consideration. Our metric is not commensurate with similar types of coupling metrics, but we show that it both provides a helpful geo- metric interpretation of coupling (and uncoupledness in particular) and exhibits greater generality and potential for analysis than those similar metrics. Furthermore, we discuss how the metric might be profitably extended to time-varying problems and show how the metric's measure of coupling can be applied to multi-objective optimization problems (in unconstrained optimization and in MDO). © 2013 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

Optimization on manifolds: Methods and applications

This paper provides an introduction to the topic of optimization on manifolds. The approach taken uses the language of differential geometry, however,we choose to emphasise the intuition of the concepts and the structures that are important in generating practical numerical algorithms rather than the technical details of the formulation. There are a number of algorithms that can be applied to solve such problems and we discuss the steepest descent and Newton's method in some detail as well as referencing the more important of the other approaches.There are a wide range of potential applications that we are aware of, and we briefly discuss these applications, as well as explaining one or two in more detail. © 2010 Springer -Verlag Berlin Heidelberg.

Fixed-rank matrix factorizations and Riemannian low-rank optimization

Motivated by the problem of learning a linear regression model whose parameter is a large fixed-rank non-symmetric matrix, we consider the optimization of a smooth cost function defined on the set of fixed-rank matrices. We adopt the geometric framework of optimization on Riemannian quotient manifolds. We study the underlying geometries of several well-known fixed-rank matrix factorizations and then exploit the Riemannian quotient geometry of the search space in the design of a class of gradient descent and trust-region algorithms. The proposed algorithms generalize our previous results on fixed-rank symmetric positive semidefinite matrices, apply to a broad range of applications, scale to high-dimensional problems, and confer a geometric basis to recent contributions on the learning of fixed-rank non-symmetric matrices. We make connections with existing algorithms in the context of low-rank matrix completion and discuss the usefulness of the proposed framework. Numerical experiments suggest that the proposed algorithms compete with state-of-the-art algorithms and that manifold optimization offers an effective and versatile framework for the design of machine learning algorithms that learn a fixed-rank matrix. © 2013 Springer-Verlag Berlin Heidelberg.