Hilbert-Huang Transform (HHT) is a data analysis tool, first developed in 1998, which can be used to extract the periodic components embedded within oscillatory data. This thesis is dedicated to the understanding, application, and development of this tool. First, the background theory of HHT will be described and compared with other spectral analysis tools. Then, a number of applications will be presented, which demonstrate the capability for HHT to dissect and analyze the periodic components of different oscillatory data. Finally, a new algorithm is presented which expands HHT ability to analyze discontinuous data. The sum result is the creation of a number of useful tools developed from the application of HHT, as well as an improvement of the HHT tool itself.

Notions of corporate social responsibility (CSR) have been suggested to be consistent with an Islamic view of society. Indeed, values and principles that have been central to Islam since the time of the holy Prophet Mohammed (Peace and Blessings be upon him) may serve as a foundation for notions of corporate social responsibility (CSR) similar to those in the West. Much contemporary discussion of CSR, however, has not recognized this. These discussions have largely been based on a Western orientation informed by Western religious values. Moreover, CSR has evolved literally in response to particular issues or problems that are specific to businesses in a Western context. This led to a lack of a comprehensive global context within which a wider perspective of CSR should be positioned. On the other hand, the notion of social responsibility and justice has been an integral part of Islamic society for nearly 14 centuries. However, the Islamic literature remains scattered, fragmented and lacks a coherent framework that would allow such a concept in Islam to be systematized. While Islamic philosophy is rich in precepts pertinent to CSR, these precepts have not yet been formally synthesized to present a systematic model with an explicit notion of CSR in Islam. Thus, there exists a gap in both the Western and Islamic literature. This was fruitfully exploited in this study to advance the understanding of the concept of CSR in a wider cultural and religious setting. This study explored this new territory and presented a conceptual framework of CSR in Islam based on Shariah (the Islamic legal and social system) derived from the holy Qur'an and Hadith. It provided both, a counterpart and a comparable base in the study of various issues relevant to CSR and international business from a much wider global perspective. It also provided significant insights into Islamic jurisprudence (Fiqh) regarding business practice. The consistency of the conceptual framework of CSR in Islam with contemporary business practices was explored using a survey of Islamic banks located in different parts of the world. The survey revealed that many current practices of Islamic banks mirror the expected behaviours or practices generated in the Islamic framework. In fact, it was possible to discern that the organizations surveyed implement the Islamic code of conduct rather extensively. Against this background, a consistency with the framework of CSR in Islam presented in this study was identified. Such consistency, however, was driven by legal requirements in adherence to Shariah rather than an explicit understanding or pursuit of CSR. The lack of a systematic framework with explicit notions of CSR from an Islamic perspective caused hindrance in implementing CSR practices in Islamic organizations. It follows that this study was a modest step towards filling this lacuna by presenting a systematic and coherent framework of CSR in Islam.

The ultimate goal of most prognostic systems is accurate prediction of the remaining useful life (RUL) of individual systems or components based on their use and performance. This class of prognostic algorithms is termed Degradation-Based, or Type III Prognostics. As equipment degrades, measured parameters of the system tend to change; these sensed measurements, or appropriate transformations thereof, may be used to characterize degradation. Traditionally, individual-based prognostic methods use a measure of degradation to make RUL estimates. Degradation measures may include sensed measurements, such as temperature or vibration level, or inferred measurements, such as model residuals or physics-based model predictions. Often, it is beneficial to combine several measures of degradation into a single parameter. Selection of an appropriate parameter is key for making useful individual-based RUL estimates, but methods to aid in this selection are absent in the literature. This dissertation introduces a set of metrics which characterize the suitability of a prognostic parameter. Parameter features such as trendability, monotonicity, and prognosability can be used to compare candidate prognostic parameters to determine which is most useful for individual-based prognosis. Trendability indicates the degree to which the parameters of a population of systems have the same underlying shape. Monotonicity characterizes the underlying positive or negative trend of the parameter. Finally, prognosability gives a measure of the variance in the critical failure value of a population of systems. By quantifying these features for a given parameter, the metrics can be used with any traditional optimization technique, such as Genetic Algorithms, to identify the optimal parameter for a given system. An appropriate parameter may be used with a General Path Model (GPM) approach to make RUL estimates for specific systems or components. A dynamic Bayesian updating methodology is introduced to incorporate prior information in the GPM methodology. The proposed methods are illustrated with two applications: first, to the simulated turbofan engine data provided in the 2008 Prognostics and Health Management Conference Prognostics Challenge and, second, to data collected in a laboratory milling equipment wear experiment. The automated system was shown to identify appropriate parameters in both situations and facilitate Type III prognostic model development.

This research utilizes the imbibition experiments and X-ray tomography results for modeling fluid flow in naturally fractured reservoirs. Conventional dual porosity simulation requires large number of runs to quantify transfer function parameters for history matching purposes. In this study empirical transfer functions (ETF) are derived from imbibition experiments and this allows reduction in the uncertainness in modeling of transfer of fluids from the matrix to the fracture. The application of the ETF approach is applied in two phases. In the first phase, imbibition experiments are numerically solved using the diffusivity equation with different boundary conditions. Usually only the oil recovery in imbibition experiments is matched. But with the advent of X-ray CT, the spatial variation of the saturation can also be computed. The matching of this variation can lead to accurate reservoir characterization. In the second phase, the imbibition derived empirical transfer functions are used in developing a dual porosity reservoir simulator. The results from this study are compared with published results. The study reveals the impact of uncertainty in the transfer function parameters on the flow performance and reduces the computations to obtain transfer function required for dual porosity simulation.

Self-compassion has recently been introduced to Western psychology literature and is defined as a kind, understanding, and nonjudgmental toward oneself (Neff, 2003a). While self-compassion has been conceptualized as a construct that is important to ones overall sense of self, it might also be relevant to more specific self-attitudes, including ones attitude toward the body. Body-related attitudes have received much attention from sport and exercise psychology researchers in kinesiology and it was anticipated that body self-compassion would be relevant to women who exercise, as women often exercise for body-related reasons. The purpose of this study was to explore the meaning of body self-compassion for young adult women who exercise and have experienced a change in their attitude toward their body over time; and to discover the essential structure of the womens experiences. <p>Five women between the ages of 23 and 28 years participated in this study. The women identified themselves as Caucasian and middle-class, were university students, and indicated that they exercised at least four times a week. Each woman participated in an individual interview in which she was asked to describe two instances where she experienced body self-compassion. The womens interviews were analyzed using an empirical phenomenology method (Giorgi, 1985; Giorgi & Giorgi, 2003) to identify the components of the womens stories that were essential to their experience of body self-compassion. A follow-up focus group discussion provided the women with the opportunity to offer feedback on the essential structures. Four essential structures emerged from these interviews: appreciating ones unique body, taking ownership of ones body, engaging in less social comparison, and body self-compassion as a dynamic process. A facilitating structure, the importance of others, also emerged. The findings of this study are generally consistent with Neffs (2003a) conceptualization of self-compassion as they reflect Neffs overall description of self-compassion without merely replicating the three components of self-compassion: self-kindness, common humanity, and mindfulness. <p>The findings of this study provide support for the exploration of more specific domains of self-compassion, such as the body. This study also makes a significant contribution to the body image literature, which has been criticized for being pathology-oriented and for focusing mainly on appearance-related attitudes (Blood, 2005; Grogan, 2006). This study explored a positive body attitude and highlighted the womens attitudes toward their physical capabilities in addition to their appearance. Further research is needed to develop the body self-compassion construct by exploring the generalizability of the essential structures that emerged in this study to broader populations.

High prices in any industry, agricultural especially, tend to spur new investment opportunities. Recent prices for pecans have been high relative to their historical pattern, suggesting investment opportunities for pecans. Prior to any investment, the investor needs to know what products consumers are demanding and how profitable it is to grow those products. This study assessed Texas consumers' preferences for pecan products and the profitability of growing pecans in the central Texas region. A choice experiment was conducted amongst Texas consumers to reveal consumers' preferences and determine their willingness-to-pay for the attributes comprising pecan products. A stochastic production model was formulated to determine the profitability of three different types of pecan orchards: a native orchard with no irrigation, an improved varieties orchard with irrigation, and an improved varieties orchard without irrigation. Results from the choice experiment indicated that consumers preferred large size pecans, native variety pecans, pecan halves, United States-grown pecans, and Texas-grown pecans. The choice experiment also found that consumers were heterogeneous in their preferences for all attributes except pecan variety and U.S. origin. Results from the stochastic production model indicated that the most profitable pecan orchard in central Texas was the irrigated improved orchard.

In statistics it is of interest to find a better interval estimator of the absolute mean deviation. In this thesis, we focus on using the jackknife, the adjusted and the extended jackknife empirical likelihood methods to construct confidence intervals for the mean absolute deviation of a random variable. The empirical log-likelihood ratio statistics is derived whose asymptotic distribution is a standard chi-square distribution. The results of simulation study show the comparison of the average length and coverage probability by using jackknife empirical likelihood methods and normal approximation method. The proposed adjusted and extended jackknife empirical likelihood methods perform better than other methods for symmetric and skewed distributions. We use real data sets to illustrate the proposed jackknife empirical likelihood methods.

Self-compassion has recently been introduced to Western psychology literature and is defined as a kind, understanding, and nonjudgmental toward oneself (Neff, 2003a). While self-compassion has been conceptualized as a construct that is important to ones overall sense of self, it might also be relevant to more specific self-attitudes, including ones attitude toward the body. Body-related attitudes have received much attention from sport and exercise psychology researchers in kinesiology and it was anticipated that body self-compassion would be relevant to women who exercise, as women often exercise for body-related reasons. The purpose of this study was to explore the meaning of body self-compassion for young adult women who exercise and have experienced a change in their attitude toward their body over time; and to discover the essential structure of the womens experiences. <p>Five women between the ages of 23 and 28 years participated in this study. The women identified themselves as Caucasian and middle-class, were university students, and indicated that they exercised at least four times a week. Each woman participated in an individual interview in which she was asked to describe two instances where she experienced body self-compassion. The womens interviews were analyzed using an empirical phenomenology method (Giorgi, 1985; Giorgi & Giorgi, 2003) to identify the components of the womens stories that were essential to their experience of body self-compassion. A follow-up focus group discussion provided the women with the opportunity to offer feedback on the essential structures. Four essential structures emerged from these interviews: appreciating ones unique body, taking ownership of ones body, engaging in less social comparison, and body self-compassion as a dynamic process. A facilitating structure, the importance of others, also emerged. The findings of this study are generally consistent with Neffs (2003a) conceptualization of self-compassion as they reflect Neffs overall description of self-compassion without merely replicating the three components of self-compassion: self-kindness, common humanity, and mindfulness. <p>The findings of this study provide support for the exploration of more specific domains of self-compassion, such as the body. This study also makes a significant contribution to the body image literature, which has been criticized for being pathology-oriented and for focusing mainly on appearance-related attitudes (Blood, 2005; Grogan, 2006). This study explored a positive body attitude and highlighted the womens attitudes toward their physical capabilities in addition to their appearance. Further research is needed to develop the body self-compassion construct by exploring the generalizability of the essential structures that emerged in this study to broader populations.

Revenue Management (RM) is an important tool for matching supply and demand by segmenting customers into different segments based on their willingness-to-pay and allocating scarce capacity to the different segments in a way that maximizes firm revenues. The benefits of RM are well accepted in the hospitality industry, and the technical aspects of RM form a rich analytical research stream. However, the research is missing a holistic examination of important elements of effective RM. The literature shows that market segmentation, pricing, forecasting, capacity allocation, IT use, organizational focus, aligned incentives, organizational structure, and education and training contribute to effective RM. We group these elements into two concepts: RM technical capability and RM social support capability and propose that these nine elements positively impact RM performance. We develop scales to measure our constructs and collect responses in the hotel industry. Our survey yields interesting results. In line with expectations, we find evidence that forecasting and organizational focus positively impact RM performance. On the other hand, the results show evidence that improved organizational structure negatively impacts RM performance. We provide a few explanations for this non-intuitive result and proposals for future research.

In this thesis, a novel technique based on the empirical mode decomposition (EMD) methodology is proposed and examined for the noise-robustness of automatic speech recognition systems. The EMD analysis is a generalization of the Fourier analysis for processing nonlinear and non-stationary time functions, in our case, the speech feature sequences. We use the intrinsic mode functions (IMF), which include the sinusoidal functions as special cases, obtained from the EMD analysis in the post-processing of the log energy feature. We evaluate the proposed method on Aurora 2.0 and Aurora 3.0 databases. On Aurora 2.0, we obtain a 44.9% overall relative improvement over the baseline for the mismatched (clean-training) tasks. The results show an overall improvement of 49.5% over the baseline for Aurora 3.0 on the high-mismatch tasks. It shows that our proposed method leads to significant improvement.