FOREST CANOPY – ATMOSPHERE INTERACTIONS OVER COMPLEX TERRAIN

Results from numerical simulations of neutral, turbulent flow over small forested hills using a first-order turbulence closure model are presented. The presence of the hill drives a strong mean flow into and out of the forest canopy. Such a flow would not be predicted by the more common roughness length parametrization of a forest. This can lead to an increase in the predicted drag caused by the hill, increased flow separation and increased transport of gases and other tracers between the forest canopy and the atmosphere. Preliminary results for large-eddy simulations (LES) show very similar features. The use of LES however allows a more detailed study of the turbulence within and above the canopy and are useful in validating the first-order closure scheme

FOREST CANOPY – ATMOSPHERE INTERACTIONS OVER COMPLEX TERRAIN

Results from numerical simulations of neutral, turbulent flow over small forested hills using a first-order turbulence closure model are presented. The presence of the hill drives a strong mean flow into and out of the forest canopy. Such a flow would not be predicted by the more common roughness length parametrization of a forest. This can lead to an increase in the predicted drag caused by the hill, increased flow separation and increased transport of gases and other tracers between the forest canopy and the atmosphere. Preliminary results for large-eddy simulations (LES) show very similar features. The use of LES however allows a more detailed study of the turbulence within and above the canopy and are useful in validating the first-order closure scheme

Inference of epidemiological parameters from household stratified data

We consider a continuous-time Markov chain model of SIR disease dynamics with two levels of mixing. For this so-called stochastic households model, we provide two methods for inferring the model parameters---governing within-household transmission, recovery, and between-household transmission---from data of the day upon which each individual became infectious and the household in which each infection occurred, as would be available from first few hundred studies. Each method is a form of Bayesian Markov Chain Monte Carlo that allows us to calculate a joint posterior distribution for all parameters and hence the household reproduction number and the early growth rate of the epidemic. The first method performs exact Bayesian inference using a standard data-augmentation approach; the second performs approximate Bayesian inference based on a likelihood approximation derived from branching processes. These methods are compared for computational efficiency and posteriors from each are compared. The branching process is shown to be an excellent approximation and remains computationally efficient as the amount of data is increased

Embedding, quoting, or paraphrasing? Investigating the effects of political leaders' tweets in online news articles:The case of Donald Trump

Journalists frequently turn to Twitter for quotes from elite and non-elite sources to include within their online news articles. While recent research has found that including posts from ordinary people can influence news consumers' issue perceptions, there is limited research on the impact of including politicians' posts. We conduct two similar survey experiments, with Republican and Democrat respondents, to test the relative impact of including Donald Trump's tweets in a news article either in embedded format, quoted in plain text, or quoted in paraphrased format. Among Republicans, embedded tweets were unique in eliciting positive emotions which mediated higher ratings of Donald Trump's warmth and competence. Among Democrats, no significant differences were elicited by tweet format on perceptions of Trump. However, Democrats rated articles containing verbatim Trump tweets as significantly lower in journalistic quality. Results are discussed in relevance to journalist-politician power relations and perceptions of journalistic quality

The six minute walk test accurately estimates mean peak oxygen uptake

<p>Abstract</p> <p>Background</p> <p>Both Peak Oxygen Uptake (peak VO2), from cardiopulmonary exercise testing (CPET) and the distance walked during a Six-Minute Walk Test (6 MWD) are used for following the natural history of various diseases, timing of procedures such as transplantation and for assessing the response to therapeutic interventions. However, their relationship has not been clearly defined.</p> <p>Methods</p> <p>We determined the ability of 6 MWD to predict peak VO2 using data points from 1,083 patients with diverse cardiopulmonary disorders. The patient data came from a study we performed and 10 separate studies where we were able to electronically convert published scattergrams to bivariate points. Using Linear Mixed Model analysis (LMM), we determined what effect factors such as disease entity and different inter-site testing protocols contributed to the magnitude of the standard error of estimate (SEE).</p> <p>Results</p> <p>The LMM analysis found that only 0.16 ml/kg/min or about 4% of the SEE was due to all of the inter-site testing differences. The major source of error is the inherent variability related to the two tests. Therefore, we were able to create a generalized equation that can be used to predict peak VO2 among patients with different diseases, who have undergone various exercise protocols, with minimal loss of accuracy. Although 6 MWD and peak VO2 are significantly correlated, the SEE is unacceptably large for clinical usefulness in an individual patient. For the data as a whole it is 3.82 ml/kg/min or 26.7% of mean peak VO2. Conversely, the SEE for predicting the mean peak VO2 from mean 6 MWD for the 11 study groups is only 1.1 ml/kg/min.</p> <p>Conclusions</p> <p>A generalized equation can be used to predict peak VO2 from 6 MWD. Unfortunately, like other prediction equations, it is of limited usefulness for individual patients. However, the generalized equation can be used to accurately estimate mean peak VO2 from mean 6 MWD, among groups of patients with diverse diseases without the need for cardiopulmonary exercise testing. The equation is:</p> <p><display-formula><graphic file="1471-2466-10-31-i1.gif"/></display-formula></p