Collaborative Engineering Environments. Two Examples of Process Improvement

Companies are recognising that innovative processes are determining factors in competitiveness. Two examples from projects in aircraft development describe the introduction of collaborative engineering environments as a way to improve engineering processes. A multi-disciplinary simulation environment integrates models from all disciplines involved in a common functional structure. Quick configuration for specific design problems and powerful feedback / visualisation capabilities enable engineering teams to concentrate on the integrated behaviour of the design. An engineering process management system allows engineering teams to work concurrently in tasks, following a defined flow of activities, applying tools on a shared database. Automated management of workspaces including data consistency enables engineering teams to concentrate on the design activities. The huge amount of experience in companies must be transformed for effective application in engineering processes. Compatible concepts, notations and implementation platforms make tangible knowledge like models and algorithms accessible. Computer-based design management makes knowledge on engineering processes and methods explicit

Reuse of pervasive system architectures

Developers are often confronted with incompatible systems and lack a proper system abstraction that allows easy integration of various hardware and software components. To try solve these shortcomings, building blocks are identified at different levels of detail in today’s pervasive/communication systems and used in a conceptual reasoning framework allowing easy comparison and combination. The generality of the conceptual framework is validated by decomposing a selection of pervasive systems into models of these building blocks and integrating these models to create improved ones. Additionally, the required properties of pervasive systems on scalability, efficiency, degree of pervasiveness, and maintainability are analysed for a number of application areas. The pervasive systems are compared on these properties. Observations are made, and weak points in the analysed pervasive systems are identified. Furthermore, we provide a set of recommendations as a guideline towards flexible architectures that make pervasive systems usable in a variety of applications

Modeling Joint Exposures and Health Outcomes for Cumulative Risk Assessment: The Case of Radon and Smoking

Community-based cumulative risk assessment requires characterization of exposures to multiple chemical and non-chemical stressors, with consideration of how the non-chemical stressors may influence risks from chemical stressors. Residential radon provides an interesting case example, given its large attributable risk, effect modification due to smoking, and significant variability in radon concentrations and smoking patterns. In spite of this fact, no study to date has estimated geographic and sociodemographic patterns of both radon and smoking in a manner that would allow for inclusion of radon in community-based cumulative risk assessment. In this study, we apply multi-level regression models to explain variability in radon based on housing characteristics and geological variables, and construct a regression model predicting housing characteristics using U.S. Census data. Multi-level regression models of smoking based on predictors common to the housing model allow us to link the exposures. We estimate county-average lifetime lung cancer risks from radon ranging from 0.15 to 1.8 in 100, with high-risk clusters in areas and for subpopulations with high predicted radon and smoking rates. Our findings demonstrate the viability of screening-level assessment to characterize patterns of lung cancer risk from radon, with an approach that can be generalized to multiple chemical and non-chemical stressors

An IPW estimator for mediation effects in hazard models: with an application to schooling, cognitive ability and mortality

Large differences in mortality rates across those with different levels of education are a well-established fact. Cognitive ability may be affected by education so that it becomes a mediating factor in the causal chain. In this paper, we estimate the impact of education on mortality using inverse-probability-weighted (IPW) estimators. We develop an IPW estimator to analyse the mediating effect in the context of survival models. Our estimates are based on administrative data, on men born between 1944 and 1947 who were examined for military service in the Netherlands between 1961 and 1965, linked to national death records. For these men, we distinguish four education levels and we make pairwise comparisons. The results show that levels of education have hardly any impact on the mortality rate. Using the mediation method, we only find a significant effect of education on mortality running through cognitive ability, for the lowest education group that amounts to a 15% reduction in the mortality rate. For the highest education group, we find a significant effect of education on mortality through other pathways of 12%