Strategies for product recovery decisions

Product recovery is beset by uncertainty regarding the quality of end-of-life (EOL) products, and in order to ascertain the reusability of these products, they have to undergo expensive tests. This undermines the profitability of the recovery process. The key to improve the effectiveness of product recovery is to improve the quality of information available before testing. Emerging data capture technologies can significantly improve the availability of information. However, in order to maximise the potential of these technologies, appropriate decision-making algorithms that exploit such information must be developed. We model the recovery process using a decision-theoretic approach, and derive strategies to ascertain the reusability of EOL products, and also to decide when tests are beneficial. We show that improving the quality of information leads to increase in effectiveness of the recovery process by reducing the need for tests. Copyright © 2009 Inderscience Enterprises Ltd.

Surface attachment of protein fibrils via covalent modification strategies.

Chemical control of surface functionality and topography is an essential requirement for many technological purposes. In particular, the covalent attachment of monomeric proteins to surfaces has been the object of intense studies in recent years, for applications as varied as electrochemistry, immuno-sensing, and the production of biocompatible coatings. Little is known, however, about the characteristics and requirements underlying surface attachment of supramolecular protein nanostructures. Amyloid fibrils formed by the self-assembly of peptide and protein molecules represent one important class of such structures. These highly organized beta-sheet-rich assemblies are a hallmark of a range of neurodegenerative disorders, including Alzheimer's disease and type II diabetes, but recent findings suggest that they have much broader significance, potentially representing the global free energy minima of the energy landscapes of proteins and having potential applications in material science. In this paper, we describe strategies for attaching amyloid fibrils formed from different proteins to gold surfaces under different solution conditions. Our methods involve the reaction of sulfur containing small molecules (cystamine and 2-iminothiolane) with the amyloid fibrils, enabling their covalent linkage to gold surfaces. We demonstrate that irreversible attachment using these approaches makes possible quantitative analysis of experiments using biosensor techniques, such as quartz crystal microbalance (QCM) assays that are revolutionizing our understanding of the mechanisms of amyloid growth and the factors that determine its kinetic behavior. Moreover, our results shed light on the nature and relative importance of covalent versus noncovalent forces acting on protein superstructures at metal surfaces.

Innovation processes within the healthcare industry: Determining critical success factors aligned to product strategies

The impact of differing product strategies on product innovation processes pursued by healthcare firms is discussed. The critical success factors aligned to product strategies are presented. A definite split between pioneering product strategies and late entrant product strategies is also recognised.

Bayesian separation and recovery of convolutively mixed autoregressive sources

In this paper we address the problem of the separation and recovery of convolutively mixed autoregressive processes in a Bayesian framework. Solving this problem requires the ability to solve integration and/or optimization problems of complicated posterior distributions. We thus propose efficient stochastic algorithms based on Markov chain Monte Carlo (MCMC) methods. We present three algorithms. The first one is a classical Gibbs sampler that generates samples from the posterior distribution. The two other algorithms are stochastic optimization algorithms that allow to optimize either the marginal distribution of the sources, or the marginal distribution of the parameters of the sources and mixing filters, conditional upon the observation. Simulations are presented.

Image-based localization and pose recovery using scale invariant features

In this paper, we propose a vision based mobile robot localization strategy. Local scale-invariant features are used as natural landmarks in unstructured and unmodified environment. The local characteristics of the features we use prove to be robust to occlusion and outliers. In addition, the invariance of the features to viewpoint change makes them suitable landmarks for mobile robot localization. Scale-invariant features detected in the first exploration are indexed into a location database. Indexing and voting allow efficient recognition of global localization. The localization result is verified by epipolar geometry between the representative view in database and the view to be localized, thus the probability of false localization will be decreased. The localization system can recover the pose of the camera mounted on the robot by essential matrix decomposition. Then the position of the robot can be computed easily. Both calibrated and un-calibrated cases are discussed and relative position estimation based on calibrated camera turns out to be the better choice. Experimental results show that our approach is effective and reliable in the case of illumination changes, similarity transformations and extraneous features. © 2004 IEEE.