Fatigue-Crack Growth Behavior in the Superelastic and Shape-Memory Alloy Nitinol

This article presents a study of fatigue-crack propagation behavior in Nitinol, a 50Ni-50Ti (at. pct) superelastic/shape-memory alloy, with particular emphasis on the effect of the stress-induced martensitic transformation on crack-growth resistance. Specifically, fatigue-crack growth was characterized in stable austenite (at 120 8C), superelastic austenite (at 37 8C), and martensite (at 265 8C and 2196 8C). In general, fatigue-crack growth resistance was found to increase with decreasing temperature, such that fatigue thresholds were higher and crack-growth rates slower in martensite compared to stable austenite and superelastic austenite. Of note was the observation that the stress-induced transformation of the superelastic austenite structure, which occurs readily at 37 8C during uniaxial tensile testing, could be suppressed during fatigue-crack propagation by the tensile hydrostatic stress state ahead of a crack tip in plane strain; this effect, however, was not seen in thinner specimens, where the constraint was relaxed due to prevailing plane-stress condition

Dementia risk prediction in the population: are screening models accurate?

Early identification of individuals at risk of dementia will become crucial when effective preventative strategies for this condition are developed. Various dementia prediction models have been proposed, including clinic-based criteria for mild cognitive impairment, and more-broadly constructed algorithms, which synthesize information from known dementia risk factors, such as poor cognition and health. Knowledge of the predictive accuracy of such models will be important if they are to be used in daily clinical practice or to screen the entire older population (individuals aged 65 years). This article presents an overview of recent progress in the development of dementia prediction models for use in population screening. In total, 25 articles relating to dementia risk screening met our inclusion criteria for review. Our evaluation of the predictive accuracy of each model shows that most are poor at discriminating at-risk individuals from not-at-risk cases. The best models incorporate diverse sources of information across multiple risk factors. Typically, poor accuracy is associated with single-factor models, long follow-up intervals and the outcome measure of all-cause dementia. A parsimonious and cost-effective consensus model needs to be developed that accurately identifies individuals with a high risk of future dementia. © 2010 Macmillan Publishers Limited. All rights reserved