An overview is given of research activities in the field of fluid components or systems built with microfabrication technologies. This review focuses on the fluidic behaviour of the various devices, such as valves, pumps and flow sensors as well as the possibilities and pitfalls related to the modelling of these devices using simple flow theory. Finally, a number of microfluidic systems are described and comments on future trends are given.

No abstract

Dielectric elastomer actuators, based on the field-induced deformation of elastomeric polymers with compliant electrodes, can produce a large strain response, combined with a fast response time and high electromechanical efficiency. This unique performance, combined with other factors such as low cost, suggests many potential applications, a wide range of which are under investigation. Applications that effectively exploit the properties of dielectric elastomers include artificial muscle actuators for robots; low-cost, lightweight linear actuators; solid-state optical devices; diaphragm actuators for pumps and smart skins; acoustic actuators; and rotary motors. Issues that may ultimately determine the success or failure ofthe actuation technology for specific applications include the durability of the actuator, the performance of the actuator under load, operating voltage and power requirements, and electronic driving circuitry, to name a few.

No abstract

The Greenland ice sheet is reacting to climate change. Yet, mass-budget estimates differ considerably, partly due to climatic variability and partly to uncertainties in the techniques of assessing mass change (IPCC 2007). Nevertheless, all recent estimates agree that the ice sheet is losing mass (e.g. 286 Gt/yr; Velicogna 2009) at an accelerating rate (Rignot et al. 2011). On top of this, the area with a negative mass budget is expanding rapidly (Khan et al. 2010). The mass loss is attributed equally to increases in both iceberg production and melting of the ice sheet (Van den Broeke et al. 2009).

No abstract

No abstract

A linear regression model has been developed for the prediction of indoor (222)Rn in Danish houses. The model provides proxy radon concentrations for about 21,000 houses in a Danish case-control study on the possible association between residential radon and childhood cancer (primarily leukaemia). The model was calibrated against radon measurements in 3116 houses. An independent dataset with 788 house measurements was used for model performance assessment. The model includes nine explanatory variables, of which the most important ones are house type and geology. All explanatory variables are available from central databases. The model was fitted to log-transformed radon concentrations and it has an R(2) of 40%. The uncertainty associated with individual predictions of (untransformed) radon concentrations is about a factor of 2.0 (one standard deviation). The comparison with the independent test data shows that the model makes sound predictions and that errors of radon predictions are only weakly correlated with the estimates themselves (R(2) = 10%).