High temperature compositions Patent

Method for producing refractory molybdenum disilicide

Survival ensembles by the sum of pairwise differences with application to lung cancer microarray studies

Lung cancer is among the most common cancers in the United States, in terms of incidence and mortality. In 2009, it is estimated that more than 150,000 deaths will result from lung cancer alone. Genetic information is an extremely valuable data source in characterizing the personal nature of cancer. Over the past several years, investigators have conducted numerous association studies where intensive genetic data is collected on relatively few patients compared to the numbers of gene predictors, with one scientific goal being to identify genetic features associated with cancer recurrence or survival. In this note, we propose high-dimensional survival analysis through a new application of boosting, a powerful tool in machine learning. Our approach is based on an accelerated lifetime model and minimizing the sum of pairwise differences in residuals. We apply our method to a recent microarray study of lung adenocarcinoma and find that our ensemble is composed of 19 genes, while a proportional hazards (PH) ensemble is composed of nine genes, a proper subset of the 19-gene panel. In one of our simulation scenarios, we demonstrate that PH boosting in a misspecified model tends to underfit and ignore moderately-sized covariate effects, on average. Diagnostic analyses suggest that the PH assumption is not satisfied in the microarray data and may explain, in part, the discrepancy in the sets of active coefficients. Our simulation studies and comparative data analyses demonstrate how statistical learning by PH models alone is insufficient.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS426 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Spectroscopic analysis of radiation-generated changes in tensile properties of a polyetherimide film

The effects of electron radiation on Ultem, a polyetherimide were studied for doses from 2 x 10 to the 9th power to 6 x 10 to the 9th power rad. Specimens were studied for tensile property testing and for electron paramagnetic resonance and infrared spectroscopic measurements of molecular structure. A Faraday cup design and a method for remote temperature measurement were developed. The spectroscopic data show that radiation caused dehydrogenation of methyl groups, rupture of main-chain ether linkage, and opening of imide rings, all to form radicals and indicate that the so-formed atomic hydrogen attached to phenyl radicals, but not to phenoxyl radicals, which would have formed hydroxyls. The observed decays of the radiation-generated phenoxyl, gem-dimethyl, and carbonyl radicals were interpreted as a combining of the radicals to form crosslinking. This crosslinking is the probable cause of the major reduction in the elongation of the tensile specimens after irradiation. Subsequent classical solubility tests indicate that the irradiation caused massive crosslinking

Stochastic simulation of the influence of cure kinetics uncertainty on composites cure

A stochastic cure simulation methodology is developed and implemented to investigate the influence of cure kinetics uncertainty due to different initial resin state on the process of cure. The simulation addresses heat transfer effects and allows quantification of uncertainty in temperature overshoot during the cure. Differential Scanning Calorimetry was used to characterise cure kinetics variability of a commercial epoxy resin used in aerospace applications. It was found that cure kinetics uncertainty is associated with variations in the initial degree of cure, activation energy and reaction order. A cure simulation model was coupled with conventional Monte Carlo and an implementation of the Probabilistic Collocation Method. Both simulation schemes are capable of capturing variability propagation, with the collocation method presenting benefits in terms of computational cost against the Monte Carlo scheme with comparable accuracy. Simulation of the cure of a carbon fibre–epoxy panel shows that cure kinetics uncertainty can cause considerable variability in the process outcome with a coefficient of variation in temperature overshoot of about 30%

Derivation of transformation formulas between geocentric and geodetic coordinates for nonzero altitudes

Four formulas, for the nonzero altitude transformation from geodetic coordinates (geodetic latitude and altitude) to geocentric coordinates (geocentric latitude and geocentric distance) and vice versa, are derived. The set of four formulas is expressed in each of the three useful forms: series expansion in powers of the earth's flattening; series expansion in powers of the earth's eccentricity; and Fourier series expansion in terms of the geodetic latitude or the geocentric latitude. The error incurred in these series expansions is of the order of one part in 3 x 10 to the 7th power

Uncertainty in the manufacturing of fibrous thermosetting composites: A review

Composites manufacturing involves many sources of uncertainty associated with material properties variation and boundary conditions variability. In this study, experimental and numerical results concerning the statistical characterization and the influence of inputs variability on the main steps of composites manufacturing including process-induced defects are presented and analysed. Each of the steps of composite manufacturing introduces variability to the subsequent processes, creating strong interdependencies between the process parameters and properties of the final part. The development and implementation of stochastic simulation tools is imperative to quantify process output variabilities and develop optimal process designs in composites manufacturing

Control of Arabidopsis apical-basal embryo polarity by antagonistic transcription factors.

Plants, similarly to animals, form polarized axes during embryogenesis on which cell differentiation and organ patterning programs are orchestrated. During Arabidopsis embryogenesis, establishment of the shoot and root stem cell populations occurs at opposite ends of an apical-basal axis. Recent work has identified the PLETHORA (PLT) genes as master regulators of basal/root fate, whereas the master regulators of apical/shoot fate have remained elusive. Here we show that the PLT1 and PLT2 genes are direct targets of the transcriptional co-repressor TOPLESS (TPL) and that PLT1/2 are necessary for the homeotic conversion of shoots to roots in tpl-1 mutants. Using tpl-1 as a genetic tool, we identify the CLASS III HOMEODOMAIN-LEUCINE ZIPPER (HD-ZIP III) transcription factors as master regulators of embryonic apical fate, and show they are sufficient to drive the conversion of the embryonic root pole into a second shoot pole. Furthermore, genetic and misexpression studies show an antagonistic relationship between the PLT and HD-ZIP III genes in specifying the root and shoot poles

Stochastic heat transfer simulation of the cure of advanced composites

A stochastic cure simulation approach is developed to investigate the variability of the cure process during resin infusion related to thermal effects. Boundary condition uncertainty is quantified experimentally and appropriate stochastic processes are developed to represent the variability in tool/air temperature and surface heat transfer coefficient. The heat transfer coefficient presents a variation across different experiments of 12.3%, whilst the tool/air temperatures present a standard deviation over 1℃. The boundary condition variability is combined with an existing model of cure kinetics uncertainty and the full stochastic problem is addressed by coupling a cure model with Monte Carlo and the Probabilistic Collocation Method and applied to the case of thin carbon epoxy laminates. The overall variability in cure time reaches a coefficient of variation of about 22%, which is dominated by uncertainty in surface heat transfer and tool temperature; with ambient temperature and kinetics contributing variability in the order of 1%