TESTING FOR BIOTECHNOLOGY-ENHANCED GRAINS AND OILSEEDS

Crop Production/Industries, Research and Development/Tech Change/Emerging Technologies,

Statistical classification techniques for engineering and climatic data samples

Fisher's sample linear discriminant function is modified through an appropriate alteration of the common sample variance-covariance matrix. The alteration consists of adding nonnegative values to the eigenvalues of the sample variance covariance matrix. The desired results of this modification is to increase the number of correct classifications by the new linear discriminant function over Fisher's function. This study is limited to the two-group discriminant problem

Statistical analysis of SSME system data

A statistical methodology to enhance the Space Shuttle Main Engine (SSME) performance prediction accuracy is proposed. This methodology was to be used in conjunction with existing SSME performance prediction computer codes to improve parameter prediction accuracy and to quantify that accuracy. However, after a review of related literature, researchers concluded that the proposed problem required a coverage of areas such as linear and nonlinear system theory, measurement theory, statistics, and stochastic estimation. Since state space theory is the foundation for a more complete study of each of the before mentioned areas, these researchers chose to refocus emphasis to cover the more specialized topic of state vector estimation procedures. State vector estimation was also selected because of current and future concerns by NASA for SSME performance evaluation; i.e., there is a current interest in an improved evaluation procedure for actual SSME post flight performance as well as for post static test performance of a single SSME. A current investigation of analytical methods may be used to improve test stand failure detection. This paper considers the issue of post flight/test state variable reconstruction through the application of observations made on the output of the Space Shuttle propulsion system. Rogers used the Kalman filtering procedure to reconstruct the state variables of the Space Shuttle propulsion system. An objective of this paper is to give the general setup of the Kalman filter and its connection to linear regression. A second objective is to examine the reconstruction methodology for application to the reconstruction of the state vector of a single Space Shuttle Main Engine (SSME) by using static test firing data

Payload Operations Control Center (POCC)

The Spacelab payload operations control center (POCC) timeline analysis program which is used to provide POCC activity and resource information as a function of mission time is described. This program is fully automated and interactive, and is equipped with tutorial displays. The tutorial displays are sufficiently detailed for use by a program analyst having no computer experience. The POCC timeline analysis program is designed to operate on the VAX/VMS version V2.1 computer system

The Data Processing Pipeline for the Herschel-HIFI Instrument

The HIFI data processing pipeline was developed to systematically process diagnostic, calibration and astronomical observations taken with the HIFI science instrumentas part of the Herschel mission. The HIFI pipeline processed data from all HIFI observing modes within the Herschel automated processing environment, as well as, within an interactive environment. A common software framework was developed to best support the use cases required by the instrument teams and by the general astronomers. The HIFI pipeline was built on top of that and was designed with a high degree of modularity. This modular design provided the necessary flexibility and extensibility to deal with the complexity of batch-processing eighteen different observing modes, to support the astronomers in the interactive analysis and to cope with adjustments necessary to improve the pipeline and the quality of the end-products. This approach to the software development and data processing effort was arrived at by coalescing the lessons learned from similar research based projects with the understanding that a degree of foresight was required given the overall length of the project. In this article, both the successes and challenges of the HIFI software development process are presented. To support future similar projects and retain experience gained lessons learned are extracted.Comment: 18 pages, 5 figure

Smooth operator? Understanding and visualising mutation bias

The potential for mutation operators to adversely affect the behaviour of evolutionary algorithms is demonstrated for both real-valued and discrete-valued genotypes. Attention is drawn to the utility of effective visualisation techniques and explanatory concepts in identifying and understanding these biases. The skewness of a mutation distribution is identified as a crucial determinant of its bias. For redundant discrete genotype-phenotype mappings intended to exploit neutrality in genotype space, it is demonstrated that in addition to the mere extent of phenotypic connectivity achieved by these schemes, the distribution of phenotypic connectivity may be critical in determining whether neutral networks improve the ability of an evolutionary algorithm overall. Mutation operators lie at the heart of evolutionary algorithms. They corrupt the reproduction of genotypes, introducing the variety that fuels natural selection. However, until recently, the process of mutation has taken..

Spatially Extended Dislocations Produced by the Dispersive Swift-Hohenberg Equation

Motivated by previous results showing that the addition of a linear dispersive term to the two-dimensional Kuramoto-Sivashinsky equation has a dramatic effect on the pattern formation, we study the Swift-Hohenberg equation with an added linear dispersive term, the dispersive Swift-Hohenberg equation (DSHE). The DSHE produces stripe patterns with spatially extended dislocations that we call seam defects. In contrast to the dispersive Kuramoto-Sivashinsky equation, the DSHE has a narrow band of unstable wavelengths close to an instability threshold. This allows for analytical progress to be made. We show that the amplitude equation for the DSHE close to threshold is a special case of the anisotropic complex Ginzburg-Landau equation (ACGLE) and that seams in the DSHE correspond to spiral waves in the ACGLE. Seam defects and the corresponding spiral waves tend to organize themselves into chains, and we obtain formulas for the velocity of the spiral wave cores and for the spacing between them. In the limit of strong dispersion, a perturbative analysis yields a relationship between the amplitude and wavelength of a stripe pattern and its propagation velocity. Numerical integrations of the ACGLE and the DSHE confirm these analytical results