Vibrational characterization of a series of aromatic quinones

The vibrational characterization of a series of molecules: 1,4-anthracenedione, 5,14-pentacenedione, 6,15-hexacenedione, 8,13-benzo[a]naphthacenedione, 7-methyl-8,13-benzo[a]naphthacenedione has been carried out. The synthesis of these materials was carried out in the research laboratory of Dr. P. Dibble of the Department of Chemistry, University of Lethbridge, Lethbridge, Alberta, who provided to us a purified sample of each compound. The inelastic scattering was measured at 1064nm (FT-Raman) and the infrared absorption spectra were measured in the mid-infrared (300--3600 cm-1). The vibrational assignments of normal modes was assisted by semi-empirical quantum calculations with the AM1 and PM3 Hamiltonian. A high level ab initio computation for 5,14-pentacenedione is also reported. The thesis also contributes to the modern field of surface-enhanced vibrational spectroscopy (SEVS). Surface-enhanced Raman scattering (SERS) of vacuum evaporated quinone films and of single LB monolayers were obtained for some members of the series. For the first time, it has been observed that tin island films used in surface-enhanced infrared (SEIR) experiments give rise to an electromagnetic enhancement of the infrared absorption spectrum. The surface-enhancement of the infrared absorption by tin island films is shown for evaporated nanometric films of 5,14-pentacenedione (Q2). The SEIR enhancement was tuned by fabricating tin island films of varying mass thickness to achieve maximum enhancement. The film morphology was determined by transmission electron microscopy. Comparisons were made between the SEIR spectra on rough tin and on rough silver surfaces and the corresponding reflection-absorption infrared spectra (RAIRS) obtained for the same organic molecules on smooth reflecting surfaces of tin and silver.Dept. of Chemistry and Biochemistry. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1998 .P75. Source: Masters Abstracts International, Volume: 43-05, page: 1733. Adviser: Ricardo Aroca. Thesis (M.Sc.)--University of Windsor (Canada), 1999

STATISTICAL ANALYSIS OF GENOTYPE-BY-ENVIRONMENT INTERACTION USING THE AMMI MODEL AND STABILITY ESTIMATES

Understanding the implication of genotype-by-environment (GE) interaction structure is an important consideration in plant breeding programs. A significant GE interaction for a quantitative trait such as yield can seriously limit efforts in selecting superior genotypes for both new crop introduction and improved cultivar development. Traditional statistical analyses of yield trials provide little or no insight into the particular pattern or structure of the GE interaction. The Additive Main Effects and Multiplicative Interaction (AMMI) statistical model incorporates both additive and multiplicative components of the two-way data structure which can account more effectively for the underlying interaction patterns. Integrating results obtained from biplot graphic displays with those of the genotypic stability analysis enables clustering of genotypes based on similarity of response and the degree of stability in performance across diverse environments. The AMMI model is presented, and its usage in diagnosing the GE interaction structure is discussed. Tai\u27s stability statistics are employed to determine the stability of genotypes tested. Empirical applications are demonstrated using data from a national winter rapeseed variety trial

BAYESIAN NONPARAMETRIC BIOASSAY ESTIMATION

Estimation of unknown pesticide levels in experimental samples is an important aspect of many agricultural and environmental studies. Such measurements are often made utilizing a “standard” dose response curve. This methodology compares the biological response of a target organism at known dosages to the response of the same organism exposed to an unknown sample. These “bioassays” are typically more efficient in time and resources than direct chemical assessment of the unknown sample. The form and choice of the standard curve, however, is subjective and can influence the estimation of the unknown dose. Problems may also arise when incomplete or preliminary information is available for determining the standard curve. One means of reducing the effects of these problems is to use a more generalized nonparametric estimation technique. This work will outline an alternative bioassay method based on a Bayesian nonparametric standard curve estimation framework. Empirical results will be demonstrated using data from a trichorpyr herbicide dose-response trial on lettuce germination

ALTERNATIVE PROCEDURES FOR ESTIMATION OF NONLINEAR REGRESSION PARAMETERS

Biological research data are often represented using nonlinear model specifications that lend themselves to the testing of relevant hypotheses concerning the model parameters. This is typically achieved with classical nonlinear least squares techniques such as Gauss-Newton or Levenberg-Marquardt which allow for both the estimation and inference phases of the analysis. Under some circumstances, however, sensitivity to data or model specifications may lead these methods to fail convergence tests or exhibit nonlinearity in the parameter estimates, which will in turn limit the usefulness of inferential results. In such cases, other estimation methods may present a means of avoiding these problems while providing analogous results. The genetic algorithm combined with bootstrapping and Bayesian estimation are two such alternatives. Genetic algorithms represent a nonparametric approach which, when augmented with bootstrap methods, result in both parameter estimation and approximation of the distribution(s). Bayesian estimation, on the other hand, leads directly to parameter distribution and achieves the required moments. These methods and classical nonlinear least squares are demonstrated utilizing a four- parameter cumulative Wei bull function fitted to onion seed germination data

Beyond NETmundial: The Roadmap for Institutional Improvements to the Global Internet Governance Ecosystem

Beyond NETmundial: The Roadmap for Institutional Improvements to the Global Internet Governance Ecosystem explores options for the implementation of a key section of the “NETmundial Multistakeholder Statement” that was adopted at the Global Meeting on the Future of Internet Governance (NETmundial) held on April 23rd and 24th 2014 in São Paulo, Brazil. The Roadmap section of the statement concisely sets out a series of proposed enhancements to existing mechanisms for global internet governance, as well as suggestions of possible new initiatives that the global community may wish to consider. The sixteen chapters by leading practitioners and scholars are grouped into six sections: The NETmundial Meeting; Strengthening the Internet Governance Forum; Filling the Gaps; Improving ICANN; Broader Analytical Perspectives; and Moving Forward

USING LANDSCAPE CHARACTERISTICS AS PRIOR INFORMATION FOR BAYESIAN CLASSIFICATION OF REMOTELY SENSED IMAGERY

Yellow starthistle is a dominant weed of north-central Idaho canyon grasslands. The distribution of yellow starthistle can be affected by general landscape characteristics, such as land use, as well as specific terrain related features such as elevation, slope, and aspect. Slope and aspect can be considered as indicators of plant community composition and distribution. Hence, these variables may be incorporated into prediction models to estimate the likelihood of yellow starthistle occurrence. An empirically derived nonlinear model based on landscape characteristics was developed to predict the likelihood of yellow starthistle occurrence in north central Idaho (Shafii, et al. 1999). While the model was employed to predict the invasion potential of yellow starthistle into new areas, it could also be used as auxiliary data for classifying this weed species in remotely sensed imagery. To accomplish this, the predicted values of the model are regarded as prior information on the presence of yellow starthistle. A Bayesian image classification algorithm using this prior information is then applied to a corresponding set of remotely sensed data. The end result is a map indicating the posterior probabilities of yellow starthistle occurrence given the landscape characteristics. This technique is demonstrated considering the presence and absence of prior information and is shown to result in lower omissional and commissional error rates when the landscape characteristics are utilized

BAYESIAN ANALYSIS OF DOSE-RESPONSE CALIBRATION CURVES

The statistical analysis of dose-response experiments typically models observed responses as a function of an applied dosage series. The estimated dose-response curve is used in predicting future responses, however, it is also commonly rewritten in an inverted form where dose is expressed as a function of the response. This modified calibration curve is useful in cases where observed responses are available, but their associated dosages are unknown. Traditional statistical techniques for the estimation of unknown doses from the dose-response curve are problematic, involving approximate solutions and methods. Alternatively, this type of inverse calibration problem naturally falls into the framework of Bayesian analysis. That is, one wishes to estimate the probability of an unknown dose value at an observed value of the response given the underlying relationship between the dose and response. This paper examines some potential Bayesian solutions to the calibration problem under various assumptive conditions. The required methodology in each case will be outlined for a dichotomous response variable and a logistic dose-response function. Empirical results will be demonstrated using data from an organic pesticide dose-response trial

COMPARING BINOMIAL BOOTSTRAP AND BAYESIAN ESTIMATION METHODS IN ASSESSING THE AGREEMENT BETWEEN CLASSIFIED IMAGES AND GROUND TRUTH DATA.

The degree of agreement between classification and ground truth in remotely sensed data is often quantified with an error matrix and summarized using agreement measures such as Cohen\u27s kappa. In the case of ground truth however, the kappa statistic can be shown to be a transformation of the marginal proportions commonly referred to as omissional and commissional error rates. A more meaningful statistical interpretation of remote sensing results and less ambiguous conclusions can be obtained via direct utilization of these measures. Several estimation techniques have been suggested for these marginal proportions. In this study, we will develop the exact binomial, bootstrap and Bayesian estimation methods for omissional and commissional errors. Emphasis will be placed on comparing the various estimation methods and their corresponding empirical distributions. Results are demonstrated with reference to a study designed to evaluate the detectability of yellow hawkweed and oxeye daisy using multispectral digital imagery in Northern Idaho

Examining the Effect of Pore Size Distribution and Shape on Flow through Unsaturated Peat using Computer Tomography

The hydraulic conductivity of unsaturated peat soil is controlled by the air-filled porosity, pore size and geometric distribution as well as other physical properties of peat materials. This study investigates how the size and shape of pores affects the flow of water through peat soils. In this study we used X-ray Computed Tomography (CT), at 45μm resolution under 5 specific soil-water pressure head levels to provide 3-D, high-resolution images that were used to detect the inner pore structure of peat samples under a changing water regime. Pore structure and configuration were found to be irregular, which affected the rate of water transmission through peat soils. The 3-D analysis suggested that pore distribution is dominated by a single large pore-space. At low pressure head, this single large air-filled pore imparted a more effective flowpath compared to smaller pores. Smaller pores were disconnected and the flowpath was more tortuous than in the single large air-filled pore, and their contribution to flow was negligible when the single large pore was active. We quantify the pore structure of peat soil that affects the hydraulic conductivity in the unsaturated condition, and demonstrate the validity of our estimation of peat unsaturated hydraulic conductivity by making a comparison with a standard permeameter-based method. Estimates of unsaturated hydraulic conductivities were made for the purpose of testing the sensitivity of pore shape and geometry parameters on the hydraulic properties of peats and how to evaluate the structure of the peat and its affects on parameterization. We also studied the ability to quantify these factors for different soil moisture contents in order to define how the factors controlling the shape coefficient vary with changes in soil water pressure head. The relation between measured and estimated unsaturated hydraulic conductivity at various heads shows that rapid initial drainage, that changes the air-filled pore properties, creates a sharp decline in hydraulic conductivity. This is because the large pores readily lose water, the peat rapidly becomes less conductive and the flow path among pores, more tortuous

ALTERNATIVE ESTIMATION TECHNIQUES FOR CORRELATED DISCRETE DATA

Binary or multinomial data often occur in agricultural and biological research. Advancements in measurement and video technologies now allow such data to be sequentially recorded through time or space. These data sets, however, can exhibit a serial correlation structure, which in turn, can bias and influence point estimates as well as inferences made regarding the data. Statistical methods using generalized mixed models and probability distributions such as the beta-binomial and correlated binomial have been proposed as potential solutions for estimating the parameters of interest in these cases. In this paper, we will explore the properties of these techniques through simulation studies and demonstrate each scenario using real data related to olfactometer choice tests of a seed eating weevil