Biomedical subjects
Publications and source records attributed to Ron Smith.
An increasing number of patients are returning to normal activity after implantation of intracorporeal left ventricular assist devices. We describe the emergency replacement of the impeller portion of a HeartMate II left ventricular assist system that had stopped functioning after the 14-year-old recipient experienced a sudden fall from a skateboard.
Process-based forest models generally have many parameters, multiple outputs of interest and a small underlying empirical database. These characteristics hamper parameterization. Bayesian calibration offers a solution to the calibration problem because it applies to models of any type or size. It provides parameter estimates, with measures of uncertainty and correlation among the parameters. The procedure begins by quantifying the uncertainty about parameter values in the form of a prior probability distribution. Then data on the output variables are used to update the parameter distribution by means of Bayes' Theorem. This yields a posterior calibrated distribution for the parameters, which can be summarized in the form of a mean vector and variance matrix. The predictive uncertainty of the model can be quantified by running it with different parameter settings, sampled from the posterior distribution. In a further step, one may evaluate the posterior probability of the model itself (rather than that of the parameters) and compare that against the probability of other models, to aid in model selection or improvement. Bayesian calibration of process-based models cannot be performed analytically, so the posterior parameter distribution must be approximated in the form of a representative sample of parameter values. This can be achieved by means of Markov Chain Monte Carlo simulation, which is suitable for process-based models because of its simplicity and because it does not require advance knowledge of the shape of the posterior distribution. Despite the suitability of Bayesian calibration, the technique has rarely been used in forestry research. We introduce the method, using the example of a typical forest model. Further, we show that reductions in parameter uncertainty, and thus in output uncertainty, can be effected by increasing the variety of data, increasing the accuracy of measurements and increasing the length of time series.
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Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Stem injections of 8-9-year-old black spruce (Picea mariana (Mill.) B.S.P.) trees with gibberellin A(4/7) (GA(4/7)) alone and in combination with root pruning increased both seed and pollen cone production in both 1991 and 1992. Combining GA(4/7) and root pruning produced a larger increase in reproductive bud production than either treatment alone, but the complementary effects were less during the warm dry summer in 1991 than during the wet summer in 1992. Cytokinin applications reduced the stimulatory effects of GA(4/7) and root pruning, and the degree of reduction varied with the amount and timing of application. Reduced reproductive bud production was attributed to cytokinin countering the effects of root pruning and natural drought on the GA(4/7) response.