ICAN sensitivity analysis

A computer program called Integrated Composite Analyzer (ICAN) was used to predict the properties of high-temperature polymer matrix composites. ICAN is a collection of NASA Lewis Research Center-developed computer codes designed to carry out analysis of multilayered fiber composites. The material properties used as input to the program were those of the thermoset polyimide resin PMR-15 and the carbon fiber Celion 6000. The sensitivity of the predicted composite properties to variations in the resin and fiber properties was examined. In addition, the predicted results were compared with experimental data. In most cases, the effect of changes in resin and fiber properties on composite properties was reasonable. However, the variations in the composite strengths with the moisture content of the PMR-15 resin were inconsistent. The ICAN-predicted composite moduli agreed fairly well with experimental values, but the predicted composite strengths were generally lower than experimental values

Epizootiology of the Fungal Pathogen, \u3ci\u3eZoophthora Phytonomi\u3c/i\u3e (Zygomycetes: Entomophthorales) in Field Populations of Alfalfa Weevil (Coleoptera: Curculionidae) Larvae in Illinois

The influence of the fungal pathogen, Zoophthora phytonomi, on larvae of the alfalfa weevil, Hypera postica, was studied in three alfalfa fields in Illinois. Disease epizootics occurred in all three fields and disease onset was ob- served within a fairly narrow range of degree day accumulations. At the height of each epizootic, percentages of infected larvae were between 80 and 100%, and the fungus contributed to the collapse of the weevil population in each field. Percent parasitism by the larval parasitoids, Bathyplectes cur­culionis and B. anurus, was lower in our fields than is common in mid-season alfalfa weevil populations and was sometimes correlated negatively with Zoophthora phytonomi infection levels, strongly implying negative interfer- ence between the parasitoids and the pathogen. Control potential of Zooph­thora phytonomi disease in alfalfa weevil larval populations is addressed

Efficient Bayesian Nonparametric Modelling of Structured Point Processes

This paper presents a Bayesian generative model for dependent Cox point processes, alongside an efficient inference scheme which scales as if the point processes were modelled independently. We can handle missing data naturally, infer latent structure, and cope with large numbers of observed processes. A further novel contribution enables the model to work effectively in higher dimensional spaces. Using this method, we achieve vastly improved predictive performance on both 2D and 1D real data, validating our structured approach.Comment: Presented at UAI 2014. Bibtex: @inproceedings{structcoxpp14_UAI, Author = {Tom Gunter and Chris Lloyd and Michael A. Osborne and Stephen J. Roberts}, Title = {Efficient Bayesian Nonparametric Modelling of Structured Point Processes}, Booktitle = {Uncertainty in Artificial Intelligence (UAI)}, Year = {2014}