BeefSpecs fat calculator to assist decision making to increase compliance rates with beef carcass specifications: evaluation of inputs and outputs

This study evaluated the BeefSpecs fat calculator, a decision-support system developed to assist the beef industry to increase compliance rates with carcass specifications (weight and fat specifications). A challenge to the BeefSpecs calculator and a sensitivity analysis were used to evaluate the inputs and outputs of BeefSpecs. Five industry datasets (n = 80, 97, 68, 25, and 13 for Datasets 1–5, respectively) of Bos taurus, Bos indicus, and Bos taurus × Bos indicus breeds for steers and heifers were collated to challenge BeefSpecs, and a nine-way factorial matrix (n = 57 600) of input variables was created for the sensitivity analysis. There were no significant (P > 0.05) differences in the mean bias between observed and predicted values in any of the datasets but there were significant (P < 0.01) differences in the unity of slope for Datasets 2, 3, and 5. The root-mean-square error was 1.72, 2.61, 2.87, 2.68, and 2.00 mm for Datasets 1–5. The decomposition of the mean-square error of prediction indicated that most of the error contained in the predictions of all models was of a random nature (94%, 85%, 85%, 95% for Datasets 1–4), except in Dataset 5, which had a 47% proportion of error in the slope component. All datasets indicated little bias (0.13%, 12.19%, 12.69%, 0.60%, and 0.12% for Datasets 1–5) in the model predictions. An analysis of variance with the nine-way factorial matrix on the predicted output of final P8 fat was conducted for the sensitivity analysis. A significant (P < 0.01) four-way interaction of days on feed × frame score × initial liveweight × sex was detected. Final P8 fat was sensitive to measurement error in the inputs of frame score when animals had longer feeding periods (e.g. 180 days) and to initial P8 fat when animals had lower initial liveweights (e.g. 200 kg) and higher frame scores (e.g. 7). For each unit of error in estimating frame score, BeefSpecs predicts final P8 with an error of up to 2.3 mm in heifers and up to 1.7 mm in steers. Error in the estimation of initial P8 fat of 2 mm will result in an error of up to 3 mm in the prediction of final P8 fat. The sensitivity analysis of BeefSpecs input variables (frame score and initial P8 fat) on the prediction of final P8 fat indicates that increasing the accuracy of estimating frame score and P8 fat is an issue that needs addressing.

Effect of Design Variables on Voids and Thermal Performance of QFN Packages

The objective of this study is to empirically investigate the dependence of solder voids and related interconnect defects in QFN packages, on such design variables as stencil aperture pattern and size, as well as quantity and location of thermal vias. A non-traditional fractional-factorial matrix was used for this study. The QFN packages produced by implementing this experiment were analyzed using destructive and nondestructive failure analysis techniques. The effect of design variables on different types of defects such as voids, and taper were evaluated. This study showed that stencil aperture size influences void area percentage the most in comparison with other design variables. An optimal stencil configuration was proposed based on void area fraction calculations. A 2-D steady-state finite element analysis was then conducted to parametrically evaluate the effect of solder voids on the performance of thermal vias under QFN thermal pads. The model confirmed that the performance of thermal vias is not very sensitive to void area fraction.

In vitro folding and characterization of the p53 DNA binding domain

The transcription factor p53 acts as major tumor suppressor and is inactivated by mutation in more than 50% of all human tumors. We have established an efficient procedure for the in vitro folding and purification of the p53 DNA binding domain (p53DBD) using a modified factorial matrix approach that supplies large amounts of homogeneous (isotope-labeled) p53DBD for application in biochemical, crystallographic and NMR spectroscopic studies. We further show with biophysical methods that in vitro folded p53DBD is fully functional and that its conformation is identical to that obtained from the soluble fraction.

Statistical Modelling in Materials Research

ABSTRACTThe role of statistical modelling in materials research is examined. It is shown that considerable benefits can be derived by following an appropriate full factorial design, in which a number of factors (process parameters) are varied simultaneously while the responses (materials properties) are monitored. It is also shown that by using a fractional factorial matrix, essentially the same conclusions can be drawn with a significantly smaller number of experimental trials. Examples drawn from recent materials research on glasses and ceramics are presented to demonstrate the effectiveness of this approach.