Design, construction and evaluation of systems to predict risk in obstetrics.

We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy.

Sustainability Education

This response does not only include greening the campus but also transforming curricula and teaching and learning. This book explains why this is necessary and--crucially--how to do it.

Anchors, scales and the relative coding of value in the brain.

People are alarmingly susceptible to manipulations that change both their expectations and experience of the value of goods. Recent studies in behavioral economics suggest such variability reflects more than mere caprice. People commonly judge options and prices in relative terms, rather than absolutely, and display strong sensitivity to exemplar and price anchors. We propose that these findings elucidate important principles about reward processing in the brain. In particular, relative valuation may be a natural consequence of adaptive coding of neuronal firing to optimise sensitivity across large ranges of value. Furthermore, the initial apparent arbitrariness of value may reflect the brains' attempts to optimally integrate diverse sources of value-relevant information in the face of perceived uncertainty. Recent findings in neuroscience support both accounts, and implicate regions in the orbitofrontal cortex, striatum, and ventromedial prefrontal cortex in the construction of value.

A numerical study into the effects of Bio-synthetic Paraffinic Kerosine blends with Jet-A fuel for civil aircraft engine

Growing concerns regarding fluctuating fuel costs and pollution targets for gas emissions, have led the aviation industry to seek alternative technologies to reduce its dependency on crude oil, and its net emissions. Recently blends of bio-fuel with kerosine, have become an alternative solution as they offer "greener" aircraft and reduce demand on crude oil. Interestingly, this technique is able to be implemented in current aircraft as it does not require any modification to the engine. Therefore, the present study investigates the effect of blends of bio-synthetic paraffinic kerosine with Jet-A in a civil aircraft engine, focusing on its performance and exhaust emissions. Two bio-fuels are considered: Jatropha Bio-synthetic Paraffinic Kerosine (JSPK) and Camelina Bio-synthetic Paraffinic Kerosine (CSPK); there are evaluated as pure fuels, and as 10% and 50% blend with Jet-A. Results obtained show improvement in thrust, fuel flow and SFC as composition of bio-fuel in the blend increases. At design point condition, results on engine emissions show reduction in NO x, and CO, but increases of CO is observed at fixed fuel condition, as the composition of bio-fuel in the mixture increases. Copyright © 2012 by ASME.