Application of neural networks to the ranking of perinatal variables influencing birthweight.

In this paper we compare Multi-Layer Perceptrons (a neural network type) with Multivariate Linear Regression in predicting birthweight from nine perinatal variables which are thought to be related. Results show, that seven of the nine variables, i.e., gestational age, mother's body-mass index (BMI), sex of the baby, mother's height, smoking, parity and gravidity, are related to birthweight. We found no significant relationship between birthweight and each of the two variables, i.e., maternal age and social class.

Inserting innovations in-service

Military platforms have exceptionally long lifecycles and given the state of defense budgets there is a significant trend in sustaining the operational capability of legacy platforms for much greater periods than originally designed. In the context of through-life management, one of the key questions is how to manage the flow of technology for platform modernization during the in-service phase of the lifecycle? Inserting technological innovations in-service is achieved through technology insertion processes. Technology insertion is the pre-eminent activity for both maintaining and enhancing the functional capability of a platform especially given the likely changes in future military operations, the pace of change in technology and with the increasing focus on lifecycle cost reduction. This chapter provides an introduction to technology insertion together with an overview of the key issues that practitioners are faced with. As an aid to planning technology insertion projects, a decision-support framework is presented. © 2010 Springer-Verlag Berlin Heidelberg.