Biomedical subjects

Stephen J Payne

Publications and source records attributed to Stephen J Payne.

3 recordsLinked to original sources

PubMed2006

Modeling the cycles of growth and detachment of bubbles in carbonated beverages.

In this paper, a model for the formation of bubbles in carbonated beverages is presented. It has previously been shown that bubbles form from cellulose fibers within such beverages and the passage of such bubbles from the fibers to the liquid surface has been modeled. A model is thus presented here that considers the process of formation, which is governed by diffusion through the fiber and bubble surfaces. The model comprises two stages, growth and detachment, and it is shown here that both play an important role. The latter process is found to occur over a much shorter time scale than the former, enabling the models to be partially decoupled. The total number of bubbles released from individual fibers over time is found to be approximated well by an exponential relationship, and the parameters in this relationship are presented for a range of different detachment angles and fiber sizes. It is found that bubble formation is promoted in narrow, long tubes, but that the time constant is solely determined by the rate of diffusion across the liquid surface. The surface tension is found to have minimal influence on the number of bubbles produced.

Journal Article↗

PubMed2006

Memory for the process of constructing an integrated mental model.

We report two experiments in which people read descriptions of integrated spatial configurations, together with comparable descriptions that did not describe integrated spatial configurations. The integrated spatial descriptions, but not the comparable descriptions, thus supported the construction of a coherent mental model. In Experiment 1, each sentence of the comparable descriptions described the spatial relation between two objects that were not mentioned elsewhere in the description. In Experiment 2, the comparable descriptions were nonspatial, having been constructed by replacing the spatial relations with nonspatial relations. In both experiments, participants were given a surprise recognition test in which they had to identify each of the original descriptions--of both integrated spatial configurations and nonspatial configurations--among a set of distractors. When the sentences in the original description were reordered (and participants were instructed to ignore sentence order), recognition memory was reliably depressed, but only for the integrated spatial descriptions. Reordering descriptions does not change their propositional content, nor does it change the described situation; however, it does change the process of constructing a mental model of that situation. These findings thus suggest that memory for the descriptions retains a trace of the process of constructing an integrated mental model and that reordering the sentences disrupts this memory because the reordering reduces the similarity of the processing of the descriptions at recognition.

Cognition↗

PubMed2005

Automated determination of bubble grades from Doppler ultrasound recordings.

INTRODUCTION: One of the difficulties in the development of automated algorithms for the detection of bubbles in Doppler ultrasound recordings is that expert labels are only available on an aggregate basis, i.e., the expert provides a single label for a recording which may contain many bubbles. It is thus very difficult to determine whether an algorithm is correctly identifying the actual bubbles or simply identifying the correct number of events, but mislabeling some events that are not due to bubbles. METHODS: The analysis presented here shows that the classification probabilities for the detection of bubble events and other artifacts can be determined if a large number of recordings are available. DISCUSSION: Using a half-integer scoring system from 0-4 gives a bias of approximately 1-2% and a standard deviation that varies with the number of event sequences available, dropping from approximately 7.5% for 100 60-s recordings to 3% for 1000 60-s recordings. These values are larger if an integer scoring system is used, but using a scale finer than half-integers confers no extra benefit due to the fact that the expert labels the whole recording rather than individual bubbles. CONCLUSIONS: It is thus possible to estimate the classification probabilities with a reasonably high degree of accuracy, but difficult to show that one bubble detection algorithm is superior to another to any degree of statistical significance. Expert labels can be used to validate, but not to compare the performance of bubble detection algorithms.

Algorithms↗