The classical Gumbel probability distribution is modified in order to study the failure times of a given system. Bayesian estimates of the reliability function under five different parametric priors and the square error loss are studied. The Bayesian reliability estimate under the non-parametric kernel density prior is compared with those under the parametric priors and numerical computations a...

We describe a framework for robust kinematic design based on tolerance analysis and parametric design via configuration space manipulation. The input is a parametric model of a planar mechanical system comprised of lower and higher kinematic pairs in open or closed loops. The robust synthesis task is to find a nominal design that functions correctly despite small parameter variations. This task...

The application of ergonomics in product design is essential to its accessibility and usability. The development of manual devices should be based on ergonomic principles. Effort perception analysis is an essential approach to understand the physical and subjective aspects of the interface. The objective of the present study was to analyze the effort perception during a simulated task with diff...

DesignScript, as the name suggests, is positioned at the intersection of design and programming. DesignScript can be viewed as part of the continuing tradition of the development of parametric and associative modeling tools for advanced architectural design and building engineering. Much of the thought processes that contribute to the effective use of DesignScript builds on the tradition of par...

BACKGROUND To establish whether a previously validated scoring system (Habib) for the prediction of risk or likelihood of posterior capsule rupture during phacoemulsification surgery could be used to: 1. Predict the difficulty of a phacoemulsification case, and 2. Select appropriate phacoemulsification cases for trainees. METHODS The study sample was consecutive phacoemulsification cases unde...

We consider independent sampling from a two-component mixture distribution, where one component (called the parametric component) is from a known distributional family and the other component (called the non-parametric component) is unknown. This is a semi-parametric mixture distribution. We discretize the non-parametric component and estimate the parameters of this mixture model, namely the mi...