Forward Symplectic Integrators and the Long Time Phase Error in Periodic Motions

We show that when time-reversible symplectic algorithms are used to solve periodic motions, the energy error after one period is generally two orders higher than that of the algorithm. By use of correctable algorithms, we show that the phase error can also be eliminated two orders higher than that of the integrator. The use of fourth order forward time step integrators can result in sixth order accuracy for the phase error and eighth accuracy in the periodic energy. We study the 1-D harmonic oscillator and the 2-D Kepler problem in great details, and compare the effectiveness of some recent fourth order algorithms.Comment: Submitted to Phys. Rev. E, 29 Page

Who needs what from a national health research system: Lessons from reforms to the English Department of Health's R&D system

This article has been made available through the Brunel Open Access Publishing Fund.Health research systems consist of diverse groups who have some role in health research, but the boundaries around such a system are not clear-cut. To explore what various stakeholders need we reviewed the literature including that on the history of English health R&D reforms, and we also applied some relevant conceptual frameworks. We first describe the needs and capabilities of the main groups of stakeholders in health research systems, and explain key features of policymaking systems within which these stakeholders operate in the UK. The five groups are policymakers (and health care managers), health professionals, patients and the general public, industry, and researchers. As individuals and as organisations they have a range of needs from the health research system, but should also develop specific capabilities in order to contribute effectively to the system and benefit from it. Second, we discuss key phases of reform in the development of the English health research system over four decades - especially that of the English Department of Health's R&D system - and identify how far legitimate demands of key stakeholder interests were addressed. Third, in drawing lessons we highlight points emerging from contemporary reports, but also attempt to identify issues through application of relevant conceptual frameworks. The main lessons are: the importance of comprehensively addressing the diverse needs of various interacting institutions and stakeholders; the desirability of developing facilitating mechanisms at interfaces between the health research system and its various stakeholders; and the importance of additional money in being able to expand the scope of the health research system whilst maintaining support for basic science. We conclude that the latest health R&D strategy in England builds on recent progress and tackles acknowledged weaknesses. The strategy goes a considerable way to identifying and more effectively meeting the needs of key groups such as medical academics, patients and industry, and has been remarkably successful in increasing the funding for health research. There are still areas that might benefit from further recognition and resourcing, but the lessons identified, and progress made by the reforms are relevant for the design and coordination of national health research systems beyond England.This article is available through the Brunel Open Access Publishing Fund

MRI based diffusion and perfusion predictive model to estimate stroke evolution

In this study we present a novel automated strategy for predicting infarct evolution, based on MR diffusion and perfusion images acquired in the acute stage of stroke. The validity of this methodology was tested on novel patient data including data acquired from an independent stroke clinic. Regions-of-interest (ROIs) defining the initial diffusion lesion and tissue with abnormal hemodynamic function as defined by the mean transit time (MTT) abnormality were automatically extracted from DWI/PI maps. Quantitative measures of cerebral blood flow (CBF) and volume (CBV) along with ratio measures defined relative to the contralateral hemisphere (r(a)CBF and r(a)CBV) were calculated for the MTT ROIs. A parametric normal classifier algorithm incorporating these measures was used to predict infarct growth. The mean r(a)CBF and r(a)CBV values for eventually infarcted MTT tissue were 0.70 +/-0.19 and 1.20 +/-0.36. For recovered tissue the mean values were 0.99 +/-0.25 and 1.87 +/-0.71, respectively. There was a significant difference between these two regions for both measures (