The hemodynamic effects of in-tandem carotid artery stenosis: implications for carotid endarterectomy.

OBJECTIVES: It remains controversial whether patients with severe disease of the internal carotid artery and a coexisting stenotic lesion downstream would benefit from a carotid endarterectomy (CEA) of the proximal lesion. The aim of this study was to simulate the hemodynamic and wall shear effects of in-tandem internal carotid artery stenosis using a computational fluid dynamic (CFD) idealized model to give insight into the possible consequences of CEA on these lesions. METHODS: A CFD model of steady viscous flow in a rigid tube with two asymmetric stenoses was introduced to simulate blood flow in arteries with multiple constrictions. The effect of varying the distance between the two stenoses, and the severity of the upstream stenosis on the pressure and wall shear stress (WSS) distributions on the second plaque, was investigated. The influence of the relative positions of the two stenoses was also assessed. RESULTS: The distance between the plaques was found to have minimal influence on the overall hemodynamic effect except for the presence of a zone of low WSS (range -20 to 30 dyne/cm2) adjacent to both lesions when the two stenoses were sufficiently close (<4 times the arterial diameter). The upstream stenosis was protective if it was larger than the downstream stenosis. The relative positions of the stenoses were found to influence the WSS but not the pressure distribution. CONCLUSIONS: The geometry and positions of the lesions need to be considered when considering the hemodynamic effects of an in-tandem stenosis. Low WSS is thought to cause endothelial dysfunction and initiate atheroma formation. The fact that there was a flow recirculation zone with low WSS in between the two stenoses may demonstrate how two closely positioned plaques may merge into one larger lesion. Decision making for CEA may need to take into account the hemodynamic situation when an in-tandem stenosis is found. CFD may aid in the risk stratification of patients with this problem.

Make or buy decision in the context of manufacturing strategy

This paper describes an approach to structuring the make or buy decision process, basing it firmly in the context of an overall manufacturing strategy. The work has been carried out jointly by the University of Cambridge Manufacturing Engineering Group and Lucas Industries. A review of the current state of ideas surrounding the linked issues of vertical integration and make or buy decisions is presented. Important features of the approach include identification of core manufacturing capabilities, assessment of the role of technology in manufacturing, the development of a cost model to support make or buy decisions and a review of the strategic implications of varying degrees of vertical integration.

Bayesian learning of noisy Markov decision processes

This work addresses the problem of estimating the optimal value function in a Markov Decision Process from observed state-action pairs. We adopt a Bayesian approach to inference, which allows both the model to be estimated and predictions about actions to be made in a unified framework, providing a principled approach to mimicry of a controller on the basis of observed data. A new Markov chain Monte Carlo (MCMC) sampler is devised for simulation from theposterior distribution over the optimal value function. This step includes a parameter expansion step, which is shown to be essential for good convergence properties of the MCMC sampler. As an illustration, the method is applied to learning a human controller.

Sequential decision making in general state space models

Many problems in control and signal processing can be formulated as sequential decision problems for general state space models. However, except for some simple models one cannot obtain analytical solutions and has to resort to approximation. In this thesis, we have investigated problems where Sequential Monte Carlo (SMC) methods can be combined with a gradient based search to provide solutions to online optimisation problems. We summarise the main contributions of the thesis as follows. Chapter 4 focuses on solving the sensor scheduling problem when cast as a controlled Hidden Markov Model. We consider the case in which the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. In sensor scheduling, our aim is to minimise the variance of the estimation error of the hidden state with respect to the action sequence. We present a novel SMC method that uses a stochastic gradient algorithm to find optimal actions. This is in contrast to existing works in the literature that only solve approximations to the original problem. In Chapter 5 we presented how an SMC can be used to solve a risk sensitive control problem. We adopt the use of the Feynman-Kac representation of a controlled Markov chain flow and exploit the properties of the logarithmic Lyapunov exponent, which lead to a policy gradient solution for the parameterised problem. The resulting SMC algorithm follows a similar structure with the Recursive Maximum Likelihood(RML) algorithm for online parameter estimation. In Chapters 6, 7 and 8, dynamic Graphical models were combined with with state space models for the purpose of online decentralised inference. We have concentrated more on the distributed parameter estimation problem using two Maximum Likelihood techniques, namely Recursive Maximum Likelihood (RML) and Expectation Maximization (EM). The resulting algorithms can be interpreted as an extension of the Belief Propagation (BP) algorithm to compute likelihood gradients. In order to design an SMC algorithm, in Chapter 8 uses a nonparametric approximations for Belief Propagation. The algorithms were successfully applied to solve the sensor localisation problem for sensor networks of small and medium size.