Bayesian Modeling of Measurement Errors and Pesticide Concentration in Dietary Risk Assessments

We propose new models for dealing with various sources of variability and uncertainty that influence risk assessments for dietary exposure. The uncertain or random variables involved can interact in complex ways, and the focus is on methodology for integrating their effects and on assessing the relative importance of including different uncertainty model components in the calculation of dietary exposures to contaminants, such as pesticide residues. The combined effect is reflected in the final inferences about the population of residues and subsequent exposure assessments. In particular, we show how measurement uncertainty can have a significant impact on results and discuss novel statistical options for modeling this uncertainty. The effect of measurement error is often ignored, perhaps due to the laboratory process conforming to the relevant international standards, for example, or is treated in an  ad hoc  way. These issues are common to many dietary risk analysis problems, and the methods could be applied to any food and chemical of interest. An example is presented using data on carbendazim in apples and consumption surveys of toddlers.     

A new fuzzy-c-means and assignment technique-based cell formation algorithm to perform part- type clusters and machine-type clusters separately

The incorporation of fuzziness in the cell formation problem by Chu and Hayya (1991 International Journal of Production Research, 29, 1475-1487) is a notable contribution, in which non-binary classification logic is used. However, despite this development, numerical illustrations performed in this research demonstrate that the Chu and Hayya approach can result in solutions with empty party-type cluster(s) and/or empty machine-type cluster(s). Further, it is noted that solutions based on the Chu and Hayya approach can contain non-empty part-type cluster(s) being assigned to empty machine-type cluster(s) and vice versa. Three strategies are offered in this research to overcome these inadequacies: the separate formation of part-type and machine-type clusters; modification of the stopping criterion; and the adoption of the assignment technique in the formation of the final manufacturing cell solutions. A new algorithm has resulted from these modifications and has been rigorously compared to the performance of the Chu and Hayya approach. In general, the new algorithm demonstrates superiorities in global efficiency and in generating feasible party-type clusters, machine-type clusters and manufacturing cells.     

Balancing Scientific and Community Interests in Community-Based Participatory Research

Community-based participatory research is an approach to studying human populations that emphasizes extensive partnerships between researchers and community members. While there are many advantages of this approach, it also faces a number of conceptual and practical challenges, one of which is managing the conflict that sometimes arises between promoting scientific and community interests. This essay explores the potential conflict between scientific and community interests in several different stages of community-based participatory research, including research design, data interpretation, and publication, and makes some suggestions for practice and policy. To manage potential conflicts between scientific and community interests, investigators and community partners should enter into written agreements at the beginning of the study. In some cases, it may be necessary for a third party, such as a review committee from a supporting institution, the community, or a funding agency, to help investigators and community partners resolve disagreements. It may also be useful, in some situations, to publish a dissenting opinion when investigators and community partners cannot agree on how to interpret findings resulting from a study. These strategies may help address some of the challenges of implementing community-based participatory research.     

Financial Planning for Retirement Village Living: A Qualitative Exploration

This Australian exploratory study investigates the financial considerations retirees experienced before moving into a retirement village. A qualitative content analysis was undertaken on the responses of 52 retirement village residents, who were part of a much larger study on retirees’ accommodation choices in 2007. The respondents were grouped according to income, and the results were compared across the following groups (self-funded retirees, part-pension retirees, and full-pension retirees). Differences between the groups extended beyond affordability. Both pension groups were more anxious about the increasing costs of maintenance fees than self-funded retirees, and the importance of pensions and superannuation was also discussed.     

Designing field experiments which are subject to representation bias

The term 'representation bias' is used to describe the disparities that exist between treatment effects estimated from field experiments, and those effects that would be seen if treatments were used in the field. In this paper we are specifically concerned with representation bias caused by disease inoculum travelling between plots, or out of the experimental area altogether. The scope for such bias is maximized in the case of airborne spread diseases. This paper extends the work of Deardon et al. (2004), using simulation methods to explore the relationship between design and representation bias. In doing so, we illustrate the importance of plot size and spacing, as well as treatment-to-plot allocation. We examine a novel class of designs, incomplete column designs, to develop an understanding of the mechanisms behind representation bias. We also introduce general methods of designing field trials, which can be used to limit representation bias by carefully controlling treatment to block allocation in both incomplete column and incomplete randomized block designs. Finally, we show how the commonly used practice of sampling from the centres of plots, rather than entire plots, can also help to control representation bias.