Ranking binder creep performance using the arrb elastometer

This paper discusses a laboratory study used to characterize bituminous binders based on their dynamic creep resistance. Laboratory testing using four different loading regimes on asphalt mixes with six different bituminous binders was undertaken. Creep cycles to 2% accumulated strain were used to define the creep resistance of the asphalt mixes with the various binders. Underlying viscosities of the bitumens were derived using the Australian Road Research Board (ARRB) Elastometer. Marshall stability was measured on the specimens that were prepared using gyratory compaction. Regression plots were prepared that link creep resistance, underlying viscosity, and Marshall stability. It was found that the ARRB Elastometer is able to measure underlying viscosity, which is a reasonable predictor of dynamic creep resistance. Marshall stability was also shown to be a good indicator of dynamic creep resistance. Therefore, simpler tests such as Marshall stability and Elastometer can be used to rank bituminous materials for asphalt mix design purposes in the laboratory. © 2010 ASCE.

Gene function prediction from synthetic lethality networks via ranking on demand.

MOTIVATION: Synthetic lethal interactions represent pairs of genes whose individual mutations are not lethal, while the double mutation of both genes does incur lethality. Several studies have shown a correlation between functional similarity of genes and their distances in networks based on synthetic lethal interactions. However, there is a lack of algorithms for predicting gene function from synthetic lethality interaction networks. RESULTS: In this article, we present a novel technique called kernelROD for gene function prediction from synthetic lethal interaction networks based on kernel machines. We apply our novel algorithm to Gene Ontology functional annotation prediction in yeast. Our experiments show that our method leads to improved gene function prediction compared with state-of-the-art competitors and that combining genetic and congruence networks leads to a further improvement in prediction accuracy.

Application of neural networks to the ranking of perinatal variables influencing birthweight.

In this paper we compare Multi-Layer Perceptrons (a neural network type) with Multivariate Linear Regression in predicting birthweight from nine perinatal variables which are thought to be related. Results show, that seven of the nine variables, i.e., gestational age, mother's body-mass index (BMI), sex of the baby, mother's height, smoking, parity and gravidity, are related to birthweight. We found no significant relationship between birthweight and each of the two variables, i.e., maternal age and social class.