J. A. Eccleston

Strategic maintenance management

The approach to maintenance management has changed over the last one hundred years. Over the last few years, the Reliability Engineering and Risk Management Group (RERMG) at the University of Queensland has developed an approach called the strategic maintenance management (SMM) approach. The paper outlines the approach and contrasts it with the current approaches. It then discusses the industry‐university partnership in the implementation of this approach and the current activities at the University of Queensland to assist industry in the implementation of the SMM approach.

Designs for generalized linear models with several variables and model uncertainty

Standard factorial designs sometimes may be inadequate for experiments that aim to estimate a generalized linear model, for example, for describing a binary response in terms of several variables. A method is proposed for finding exact designs for such experiments that uses a criterion allowing for uncertainty in the link function, the linear predictor, or the model parameters, together with a design search. Designs are assessed and compared by simulation of the distribution of efficiencies relative to locally optimal designs over a space of possible models. Exact designs are investigated for two applications, and their advantages over factorial and central composite designs are demonstrated.

Weibull model selection for reliability modelling

A large number of models have been derived from the two-parameter Weibull distribution and are referred to as Weibull models. They exhibit a wide range of shapes for the density and hazard functions, which makes them suitable for modelling complex failure data sets. The WPP and IWPP plot allows one to determine in a systematic manner if one or more of these models are suitable for modelling a given data set. This paper deals with this topic.

Some Considerations on the Design of Population Pharmacokinetic Studies

The goal of this manuscript is to introduce a framework for consideration of designs for population pharmacokinetic orpharmacokinetic–pharmacodynamic studies. A standard one compartment pharmacokinetic model with first-order input and elimination is considered. A series of theoretical designs are considered that explore the influence of optimizing the allocation of sampling times, allocating patients to elementary designs, consideration of sparse sampling and unbalanced designs and also the influence of single vs. multiple dose designs. It was found that what appears to be relatively sparse sampling (less blood samples per patient than the number of fixed effects parameters to estimate) can also be highly informative. Overall, it is evident that exploring the population design space can yield many parsimonious designs that are efficient for parameter estimation and that may not otherwise have been considered without the aid of optimal design theory

Beneficial effects of air inclusions on the performance of ethylene vinyl acetate (EVA) mouthguard material

Objective: To investigate the impact characteristics of an ethylene vinyl acetate (EVA) mouthguard material with regulated air inclusions, which included various air cell volumes and wall thickness between air cells. In particular, the aim was to identify the magnitude and direction of forces within the impacts. Method: EVA mouthguard material, 4 mm thick and with and without air inclusions, was impacted with a constant force impact pendulum with an energy of 4.4 J and a velocity of 3 m/s. Transmitted forces through the EVA material were measured using an accelerometer, which also allowed the determination of force direction and magnitude within the impacts. Results: Statistically significant reductions in the transmitted forces were observed with all the air inclusion materials when compared with EVA without air inclusions. Maximum transmitted force through one air inclusion material was reduced by 32%. Force rebound was eliminated in one material, and reduced second force impulses were observed in all the air inclusion materials. Conclusion: The regulated air inclusions improved the impact characteristics of the EVA mouthguard material, the material most commonly used in mouthguards world wide.

Optimal design for model discrimination and parameter estimation for itraconazole population pharmacokinetics in cystic fibrosis patients

Optimal sampling times are found for a study in which one of the primary purposes is to develop a model of the pharmacokinetics of itraconazole in patients with cystic fibrosis for both capsule and solution doses. The optimal design is expected to produce reliable estimates of population parameters for two different structural PK models. Data collected at these sampling times are also expected to provide the researchers with sufficient information to reasonably discriminate between the two competing structural models

Effect of ethylene vinyl acetate (EVA) closed cell foam on transmitted forces in mouthguard material

Objectives: To compare transmitted forces through ethylene vinyl acetate (EVA) mouthguard material and the same EVA material with gas inclusions in the form of a closed cell foam. Method: EVA mouthguard materials with and without foam gas inclusions and 4 mm thick were impacted with a constant force from an impact pendulum. Various porosity levels in the foam materials were produced by 1%, 5%, and 10% by weight foaming agent. The forces transmitted through the EVA after energy absorption by the test materials were measured with a force sensor and compared. Results: Only minor non-significant differences in transmitted forces through the EVA with and without foam were shown. Conclusions: The inclusion of gas in the form of a closed cell foam in 4 mm thick EVA mouthguard materials did not improve the impact performance of the EVA mouthguard material.

Optimal Design Criteria for Discrimination and Estimation in Nonlinear Models

Nonlinear models are common in pharmacokinetics and pharmacodynamics. To date, most work in design in this area has concentrated on parameter estimation. Here, we introduce the idea of optimization of both estimation and model selection. However, experimental designs that provide powerful discrimination between a pair of competing model structures are rarely efficient in terms of estimating the parameters under each model. Conversely, designs which are efficient for parameter estimation may not provide suitable power to discriminate between the models. Several different methods of addressing both of these objectives simultaneously are introduced in this paper and are compared to an existing optimality criterion.

Estimation in a multiplicative mixed model involving a genetic relationship matrix

Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments.

Compound Optimal Design Criteria for Nonlinear Models

Three approaches for combining parameter estimation with opposing design criteria are proposed for nonlinear models. The first method discussed is the technique found in the literature and as such is the reference method for this paper. The compound crtierion is formed by maximizing a weighted product of efficiencies. The second criterion involves maximizing an opposing criterion while minimizing a defined loss function. The third method simultaneously maximizes both efficiencies with respect to parameter estimation and an opposing criterion with a multiple objective simulated annealing algorithm. The examples presented are based on a PK-model and a generalized linear model found in the literature.