The research project, Optimization of an array of Peptidic Indicator Displacement Assays for the Discrimination of Cabernet Sauvignon Wines, describes the multiple step lab trials conducted to optimize an array of ensembles composed of synthesized peptides and PCV:Cu+2 complexes for the differentiation of seven Cabernet Sauvignon wines with different tannin levels. This report also includes the methods and analysis used. The analysis interpreted by principal component analysis. / text

The forecasted proportions of urban population to total population in Malaysia are steadily increasing from 26% in 1965 to 70% in 2020. Therefore, there is a need to fully appreciate the legacy of the urbanization of Malaysia by providing affordable housing. The main aim of this study is to focus on developing a model to forecast the demand of low cost housing in urban areas. The study is focused on eight states in Peninsular Malaysia, as most of these states are among the areas predicted to have achieved the highest urbanization level in the country. The states are Kedah, Penang, Perlis, Kelantan, Terengganu, Perak, Pahang and Johor. Monthly time-series data for six to eight years of nine indicators including: population growth; birth rate; child mortality rate; unemployment rate; household income rate; inflation rate; GDP; poverty rate and housing stocks have been used to forecast the demand on low cost housing using Artificial Neural Network (ANN) approach. The data is collected from the Department of Malaysian Statistics, the Ministry of Housing and the Housing Department of the State Secretary. The Principal Component Analysis (PCA) method has been adopted to analyze the data using SPSS 18.0 package. The performance of the Neural Network is evaluated using R squared (R2) and the accuracy of the model is measured using the Mean Absolute Percentage Error (MAPE). Lastly, a user friendly interface is developed using Visual Basic. From the results, it was found that the best Neural Network to forecast the demand on low cost housing in Kedah is 2-16-1, Pahang 2-15-1, Kelantan 2-25-1, Terengganu 2-30-1, Perlis 3-5-1, Pulau Pinang 3-7-1, Johor 3-38-1 and Perak 3-24-1. In conclusion, the evaluation performance of the model through the MAPE value shows that the NN model can forecast the low-cost housing demand very good in Pulau Pinang, Johor, Pahang and Kelantan, where else good in Kedah and Terengganu while in Perlis and Perak it is not accurate due to the lack of data. The study has successfully developed a user friendly interface to retrieve and view all the data easily.

The subject of this paper is microfinance and the question: Do self-sustainable MFI:s alleviate poverty?. A MFI is a micro financial institution, a regular bank or a NGO that has transformed into a licensed financial institutions, focused on microenterprises. To answer the question data has been gathered in Ecuador, South America. South America have a large amount of self sustainable MFI:s. Ecuador was selected as the country to be studied as it has an intermediate level of market penetration in the micro financial sector. To determine relative poverty before and after the access to microcredit, interviews were used. The data retrieved in the interviews was used to determine the impact of micro credit on different aspects of relative poverty using the Difference in Difference method. Significant differences are found between old and new clients as well as for the change over time. But no significant results are found for the difference in change over time for clients compared to the non-clients. The author argues that the insignificant result can either be a result of a too small sample size, disturbances in the sample selection or that this specific kind of institution have little or no affect on the current clients economical development.

No description available.

Substance abuse is the overindulgence in and dependence on a drug or chemical leading to detrimental effects on the individual’s health and the welfare of those surrounding him or her. Logistic regression analysis is an important tool used in the analysis of the relationship between various explanatory variables and nominal response variables. The objective of this study is to use this statistical method to determine the factors which are considered to be significant contributors to the use or abuse of substances in school-aged children and also determine what measures can be implemented to minimize their effect. The logistic regression model was used to build models for the three main types of substances used in this study; Tobacco, Alcohol and Drugs and this facilitated the identification of the significant factors which seem to influence their use in children.

Obesity is a highly cited risk factor for knee osteoarthritis (OA) associated with increased risk of development of OA and accelerated disease progression. Rates of obesity are increasing internationally, and while obesity is well established as a risk factor, the precise role of obesity in knee OA pathogenesis and progression is not as clearly understood. Mechanical loading has been implicated as an important factor in knee OA initiation and progression. The purpose of this thesis was to further examine the roles of moderate knee OA disease presence and obesity on knee joint mechanics during gait, and to characterize their mechanical interaction. Two methods have been applied. First, principal component analysis has been applied to resultant waveforms from gait analysis and second, a sagittal plane joint contact force model has been applied. Using both methods, statistical differences in biomechanical loading has been associated with obesity, moderate knee OA, and their interaction.

This thesis investigates the application of principal component analysis to the Australian stock market using ASX200 index and its constituents from April 2000 to February 2014. The first ten principal components were retained to present the major risk sources in the stock market. We constructed portfolio based on each of the ten principal components and named these “principal portfolios

Substance abuse is the overindulgence in and dependence on a drug or chemical leading to detrimental effects on the individual’s health and the welfare of those surrounding him or her. Logistic regression analysis is an important tool used in the analysis of the relationship between various explanatory variables and nominal response variables. The objective of this study is to use this statistical method to determine the factors which are considered to be significant contributors to the use or abuse of substances in school-aged children and also determine what measures can be implemented to minimize their effect. The logistic regression model was used to build models for the three main types of substances used in this study; Tobacco, Alcohol and Drugs and this facilitated the identification of the significant factors which seem to influence their use in children.

A novel imaging technology, digital breast tomosynthesis (DBT), is a technique that overcomes the tissue superposition limitation of conventional mammography by acquiring a limited number of X-ray projections from a narrow angular range, and combining these projections to reconstruct a pseudo-3D image. The emergence of DBT as a potential replacement or adjunct to mammographic screening mandates that solutions be found to two of its major limitations, namely X-ray scatter and mono-energetic reconstruction methods. A multi-faceted software-based approach to enhance the image quality of DBT imaging has the potential to increase the sensitivity and specificity of breast cancer detection and diagnosis. A scatter correction (SC) algorithm and a spectral reconstruction (SR) algorithm are both ready for implementation and clinical evaluation in a DBT system and exhibit the potential to improve image quality. A principal component analysis (PCA) based model of breast shape and a PCA model of X-ray scatter optimize the SC algorithm for the clinical realm. In addition, a comprehensive dosimetric characterization of a FDA approved DBT system has also been performed, and the feasibility of a new dual-spectrum, single-acquisition DBT imaging technique has also been evaluated.

Behavioural syndromes, defined as suites of correlated behaviours across different contexts, are used to characterize individual variability in behaviours. Males of the weakly electric fish species, Apteronotus leptorhynchus, produce electro-communication signals called chirps. Chirps are thought to be involved in agonistic signalling, as their relative incidence increases during agonistic conspecific interactions. However, high levels of individual variability in aggression obscure the role of chirps in mediating aggression. Here, I tested the presence of an aggression-boldness behavioural syndrome, and then considered the implications such a syndrome would have on chirping behaviours. Behavioural tests in anti-predation, object novelty, feeding, conspecific intrusion and novel environment exploration contexts revealed a syndrome involving only object novelty and feeding. We found no correlation between chirping behaviour and the assessed behaviours. Our results demonstrate that chirps represent a more complex communication system than previously suggested.