The effect of hole size upon the strength of metallic and polymeric foams

Tensile and compressive tests have been performed on centre-hole panels, made from three types of metallic foams and two polymeric foams. In compression, the foams fail in a ductile, notch-insensitive manner, in support of a "net section strength" criterion. In tension, a ductile-brittle transition is observed for some of the foams at sufficiently large specimen sizes: for a small hole diameter the net section strength criterion is obeyed, whereas for a large hole a local stress criterion applies and the net section strength is reduced. For a number of the foams, the panel size was not sufficiently large to observe this ductile-brittle switch in behaviour. The predictions of a cohesive zone model are compared with the measured strengths and are found to be in good agreement. © 2001 Elsevier Science Ltd. All rights reserved.

Mechanical and magnetic properties of metal fibre networks, with and without a polymeric matrix

Bonded networks of metal fibres are highly porous, permeable materials, which often exhibit relatively high strength. Material of this type has been produced, using melt-extracted ferritic stainless steel fibres, and characterised in terms of fibre volume fraction, fibre segment (joint-to-joint) length and fibre orientation distribution. Young's moduli and yield stresses have been measured. The behaviour when subjected to a magnetic field has also been investigated. This causes macroscopic straining, as the individual fibres become magnetised and tend to align with the applied field. The modeling approach of Markaki and Clyne, recently developed for prediction of the mechanical and magneto-mechanical properties of such materials, is briefly summarised and comparisons are made with experimental data. The effects of filling the inter-fibre void with compliant (polymeric) matrices have also been explored. In general the modeling approach gives reliable predictions, particularly when the network architecture has been characterised using X-ray tomography. © 2005 Published by Elsevier Ltd.

Bayesian model selection for time series using Markov chain Monte Carlo

We present a stochastic simulation technique for subset selection in time series models, based on the use of indicator variables with the Gibbs sampler within a hierarchical Bayesian framework. As an example, the method is applied to the selection of subset linear AR models, in which only significant lags are included. Joint sampling of the indicators and parameters is found to speed convergence. We discuss the possibility of model mixing where the model is not well determined by the data, and the extension of the approach to include non-linear model terms.

The design of an agent for modelling supply chain network dynamics

A useful insight into managerial decision making can be found from simulation of business systems, but existing work on simulation of supply chain behaviour has largely considered non-competitive chains. Where competitive agents have been examined, they have generally had a simple structure and been used for fundamental examination of stability and equilibria rather than providing practical guidance to managers. In this paper, a new agent for the study of competitive supply chain network dynamics is proposed. The novel features of the agent include the ability to select between competing vendors, distribute orders preferentially among many customers, manage production and inventory, and determine price based on competitive behaviour. The structure of the agent is related to existing business models and sufficient details are provided to allow implementation. The agent is tested to demonstrate that it recreates the main results of the existing modelling and management literature on supply chain dynamics. A brief exploration of competitive dynamics is given to confirm that the proposed agent can respond to competition. The results demonstrate that overall profitability for a supply chain network is maximised when businesses operate collectively. It is possible for an individual business to achieve higher profits by adopting a more competitive stance, but the consequence of this is that the overall profitability of the network is reduced. The agent will be of use for a broad range of studies on the long-run effect of management decisions on their network of suppliers and customers.

Perturbation of the stability of amyloid fibrils through alteration of electrostatic interactions.

The self-assembly of proteins and peptides into polymeric amyloid fibrils is a process that has important implications ranging from the understanding of protein misfolding disorders to the discovery of novel nanobiomaterials. In this study, we probe the stability of fibrils prepared at pH 2.0 and composed of the protein insulin by manipulating electrostatic interactions within the fibril architecture. We demonstrate that strong electrostatic repulsion is sufficient to disrupt the hydrogen-bonded, cross-β network that links insulin molecules and ultimately results in fibril dissociation. The extent of this dissociation correlates well with predictions for colloidal models considering the net global charge of the polypeptide chain, although the kinetics of the process is regulated by the charge state of a single amino acid. We found the fibrils to be maximally stable under their formation conditions. Partial disruption of the cross-β network under conditions where the fibrils remain intact leads to a reduction in their stability. Together, these results support the contention that a major determinant of amyloid stability stems from the interactions in the structured core, and show how the control of electrostatic interactions can be used to characterize the factors that modulate fibril stability.