Measurement of shrinkage of composite resin by laser speckle contrast analysis

The aim of this study was to evaluate the shrinkage of a microhybrid dental composite resin photo-activated by one LED with different power densities by means of speckle technique. The dental composite resin Filtek (TM) Z-250 (3M/ESPE) at color A(2) was used for the samples preparation. Uncured composite was packed in a metallic mold and irradiated during 20 s from 100 to 1000 mW cm(-2). For the photo-activation of the samples, it was used a LED prototype (Light Emission Diode) with wavelength centered at 470 nm and adjustable power density until 1 W cm(-2). The speckle patterns obtained from the bottom composite surfaces were monitored using a CCD camera without lens. The speckle field is recorded in a digital picture and stored by CCD camera as the carrier of information on the displacement of the tested surface. The calculated values were obtained for each pair of adjacent patterns and the changes in speckle contrast as a function of time were obtained from six repeated measurements. The speckle contrasts obtained from the bottom surface with 100 mW cm(-1) were smaller than those than the other power densities. The higher power densities provided the higher shrinkage.

Bayesian Analysis for Nonlinear Regression Model Under Skewed Errors, with Application in Growth Curves

We have considered a Bayesian approach for the nonlinear regression model by replacing the normal distribution on the error term by some skewed distributions, which account for both skewness and heavy tails or skewness alone. The type of data considered in this paper concerns repeated measurements taken in time on a set of individuals. Such multiple observations on the same individual generally produce serially correlated outcomes. Thus, additionally, our model does allow for a correlation between observations made from the same individual. We have illustrated the procedure using a data set to study the growth curves of a clinic measurement of a group of pregnant women from an obstetrics clinic in Santiago, Chile. Parameter estimation and prediction were carried out using appropriate posterior simulation schemes based in Markov Chain Monte Carlo methods. Besides the deviance information criterion (DIC) and the conditional predictive ordinate (CPO), we suggest the use of proper scoring rules based on the posterior predictive distribution for comparing models. For our data set, all these criteria chose the skew-t model as the best model for the errors. These DIC and CPO criteria are also validated, for the model proposed here, through a simulation study. As a conclusion of this study, the DIC criterion is not trustful for this kind of complex model.