Developing a numerical inverse-theory-based extraction of orientation-dependent relaxation rates from partially- relaxed spectra

Second-rank tensor interactions, such as quadrupolar interactions between the spin- 1 deuterium nuclei and the electric field gradients created by chemical bonds, are affected by rapid random molecular motions that modulate the orientation of the molecule with respect to the external magnetic field. In biological and model membrane systems, where a distribution of dynamically averaged anisotropies (quadrupolar splittings, chemical shift anisotropies, etc.) is present and where, in addition, various parts of the sample may undergo a partial magnetic alignment, the numerical analysis of the resulting Nuclear Magnetic Resonance (NMR) spectra is a mathematically ill-posed problem. However, numerical methods (de-Pakeing, Tikhonov regularization) exist that allow for a simultaneous determination of both the anisotropy and orientational distributions. An additional complication arises when relaxation is taken into account. This work presents a method of obtaining the orientation dependence of the relaxation rates that can be used for the analysis of the molecular motions on a broad range of time scales. An arbitrary set of exponential decay rates is described by a three-term truncated Legendre polynomial expansion in the orientation dependence, as appropriate for a second-rank tensor interaction, and a linear approximation to the individual decay rates is made. Thus a severe numerical instability caused by the presence of noise in the experimental data is avoided. At the same time, enough flexibility in the inversion algorithm is retained to achieve a meaningful mapping from raw experimental data to a set of intermediate, model-free

Self concept, acculturation, and fashion orientation

The research begins with a discussion of the worldwide and the Canadian market. The research profiles the examination of the relationship between a person's self concept (as defined by Malhotra) and fashion orientation (as defined by Gutman and Mills), and to understand how these factors are influenced by acculturation, focusing in-depth on their managerial implications. To study these relationships; a random sample of 196 ChineseCanadian female university students living in Canada was given a survey based on Malhotra's self-concept scale, and the SLASIA acculturation scale. Based on multiple regression analysis, findings suggest that the adoption of language and social interaction dimensions of acculturation constructs have significant effects on the relationship between self concept and fashion orientation. This research contributes significantly to both marketing theory and practice. Theoretically, this research develops new insights on the dimensionality of fashion orientation, identifies various moderating effects of acculturation on the relationship of self concept and fashion orientation dimensions, and provides a framework to examine these effects, where results can be generalized across different culture. Practically, marketers can use available findings to improve their understanding of the fashion needs of Chinese-Canadian consumers, and target them based on these findings. The findings provide valuable implications for companies to formulate their fashion marketing strategies for enhance fashion orientation in terms of different dimensions, based on different levels of acculturation.

QUANTIFICATION AND EXTRACTION OF SURFACE FEATURES FROM DIGITAL TERRAIN MODELS

Digital Terrain Models (DTMs) are important in geology and geomorphology, since elevation data contains a lot of information pertaining to geomorphological processes that influence the topography. The first derivative of topography is attitude; the second is curvature. GIS tools were developed for derivation of strike, dip, curvature and curvature orientation from Digital Elevation Models (DEMs). A method for displaying both strike and dip simultaneously as colour-coded visualization (AVA) was implemented. A plug-in for calculating strike and dip via Least Squares Regression was created first using VB.NET. Further research produced a more computationally efficient solution, convolution filtering, which was implemented as Python scripts. These scripts were also used for calculation of curvature and curvature orientation. The application of these tools was demonstrated by performing morphometric studies on datasets from Earth and Mars. The tools show promise, however more work is needed to explore their full potential and possible uses.