A Pilot Study on Behavioural Responses of Shelter Dogs to Olfactory Enrichment

The influence of essential oils (EOs) on emotions has been widely described among humans and animals. Several studies have investigated the effects and the actions of EOs on behaviour, mood and perception. In this study, shelter dogs (n=23) were exposed to olfactory stimulation through diffusion of 9 anxiolytic essential oils in one blend (olfactory enrichment) for 8 weeks in order to check long-term effects on behaviour. First, dog’s postures have been evaluated in both groups before and after exposure. Secondly, in order to collect the preliminary results on the distance necessary to obtain an effect of EOs, dogs were divided in 2 groups according to the distance from the diffuser. Our results indicate that olfactory enrichment with this blend of EOs is related to less time spent by dogs in high posture. More research is needed to investigate a potential gradual effect of distance and concentration of EOs on dog’s welfare.

Analyzing ordinal data from a split-plot design in the presence of a random block effect

© 2017 Taylor & Francis. Many industrial experiments involve some factors that are hard to change. In this situation, experimenters often choose to perform an experiment with restricted randomization, such as a split-plot or a strip-plot experiment. In this article, we discuss the analysis of an experiment concerning the adhesion between steel tire cords and rubber. Besides an ordinal response, the experiment also involves one hard-to-change factor. Therefore, the experimenters performed a split-plot experiment. An additional complication of the experiment is that there is also a blocking factor. A proper analysis of the experiment requires the inclusion of random effects in the model to account for its split-plot nature and its blocked nature. The need for random effects and the ordinal response necessitate the use of a mixed cumulative logit model.status: publishe

Staggered-level designs for response surface modeling

In industrial experiments, there are often restrictions in randomization caused by equipment and resource constraints, as well as budget and time constraints. Next to the split-plot and the split-split-plot design, the staggered-level design is an interesting design option for experiments involving two hard-to-change factors. The staggered-level design allows both hard-to-change factors to be reset at di?erent points in time, resulting in a typical staggering pattern of factor-level resettings. It has been shown that, for twolevel designs, this staggering pattern leads to statistical benefits in comparison to the split-plot and the split-split-plot design. In this paper, we investigate whether the benefits of the staggered-level design carry over to situations where the objective is to optimize a response and where a second-order response surface model is in place. To this end, we study several examples of D- and I-optimal staggered-level response surface designs.status: publishe

Staggered-Level Designs for Response Surface Modeling

In industrial experiments, there are often restrictions in randomization caused by equipment and resource constraints, as well as budget and time constraints. Next to the split-plot and the split-split-plot design, the staggered-level design is an interesting design option for experiments involving two hard-to-change factors. The staggered-level design allows both hard-to-change factors to be reset at di?erent points in time, resulting in a typical staggering pattern of factor-level resettings. It has been shown that, for twolevel designs, this staggering pattern leads to statistical benefits in comparison to the split-plot and the split-split-plot design. In this paper, we investigate whether the benefits of the staggered-level design carry over to situations where the objective is to optimize a response and where a second-order response surface model is in place. To this end, we study several examples of D- and I-optimal staggered-level response surface designs.status: publishe

Analyzing ordinal data from a split-plot design in the presence of a random block effect

© 2017 Taylor & Francis. Many industrial experiments involve some factors that are hard to change. In this situation, experimenters often choose to perform an experiment with restricted randomization, such as a split-plot or a strip-plot experiment. In this article, we discuss the analysis of an experiment concerning the adhesion between steel tire cords and rubber. Besides an ordinal response, the experiment also involves one hard-to-change factor. Therefore, the experimenters performed a split-plot experiment. An additional complication of the experiment is that there is also a blocking factor. A proper analysis of the experiment requires the inclusion of random effects in the model to account for its split-plot nature and its blocked nature. The need for random effects and the ordinal response necessitate the use of a mixed cumulative logit model.status: publishe

Update formulas for split-plot and block designs

For the algorithmic construction of optimal experimental designs, it is important to be able to evaluate small modifications of given designs in terms of the optimality criteria at a low computational cost. This can be achieved by using powerful update formulas for the optimality criteria during the design construction. The derivation of such update formulas for evaluating the impact of changes to the levels of easy-to-change factors and hard-to-change factors in split-plot designs as well as the impact of a swap of points between blocks or whole plots in block designs or split-plot designs is described