Populations are aging and the prevalence of chronic disease, persisting for many years, is increasing. The most common, non-communicable chronic diseases in developed countries are; cardiovascular disease (CVD), type 2 diabetes, obesity, arthritis and specific cancers. Chronic diseases such as cardiovascular disease, type 2 diabetes and obesity have high prevalence and develop over the course of life due to a number of interrelated factors including genetic predisposition, nutrition and lifestyle. With the development and completion of human genome sequencing, we are able to trace genes responsible for proteins and metabolites that are linked with these diseases. A computerized model focused on organizing knowledge related to genes, nutrition and the three chronic diseases, namely, cardiovascular disease, type 2 diabetes and obesity has been developed for the Ontology-Based Personalized Risk Evaluation for Chronic Disease Project. This model is a Protégé-based ontological representation which has been developed for entering and linking concepts and data for these three chronic diseases. This model facilitates to identify interrelationships between concepts. The ontological representation provides the framework into which information on individual patients, disease symptoms, gene maps, diet and life history can be input, and risks, profiles, and recommendations derived. Personal genome and health data could provide a guide for designing and building a medical health administration system for taking relevant annual medical tests, e.g. gene expression level changes for health surveillance. One method, called transductive neuro-fuzzy inference system with weighted data normalization is used to evaluate personalized risk of chronic disease. This personalized approach has been used for two different chronic diseases, predicting the risk of cardiovascular disease and predicting the risk of type 2 diabetes. For predicting the risk of cardiovascular disease, the National Nutrition Health Survey 97 data from New Zealand population has been used. This data contains clinical, anthropometric and nutritional variables. For predicting risk of type 2 diabetes, data from the Italian population with clinical and genetic variables has been used. It has been discovered that genes responsible for causing type 2 diabetes are different in male and female samples. A framework to integrate the personalized model and the chronic disease ontology is also developed with the aim of providing support for further discovery through the integration of the ontological representation in order to build an expert system in genes of interest and relevant dietary components.

Populations are aging and the prevalence of chronic disease, persisting for many years, is increasing. The most common, non-communicable chronic diseases in developed countries are; cardiovascular disease (CVD), type 2 diabetes, obesity, arthritis and specific cancers. Chronic diseases such as cardiovascular disease, type 2 diabetes and obesity have high prevalence and develop over the course of life due to a number of interrelated factors including genetic predisposition, nutrition and lifestyle. With the development and completion of human genome sequencing, we are able to trace genes responsible for proteins and metabolites that are linked with these diseases. A computerized model focused on organizing knowledge related to genes, nutrition and the three chronic diseases, namely, cardiovascular disease, type 2 diabetes and obesity has been developed for the Ontology-Based Personalized Risk Evaluation for Chronic Disease Project. This model is a Protégé-based ontological representation which has been developed for entering and linking concepts and data for these three chronic diseases. This model facilitates to identify interrelationships between concepts. The ontological representation provides the framework into which information on individual patients, disease symptoms, gene maps, diet and life history can be input, and risks, profiles, and recommendations derived. Personal genome and health data could provide a guide for designing and building a medical health administration system for taking relevant annual medical tests, e.g. gene expression level changes for health surveillance. One method, called transductive neuro-fuzzy inference system with weighted data normalization is used to evaluate personalized risk of chronic disease. This personalized approach has been used for two different chronic diseases, predicting the risk of cardiovascular disease and predicting the risk of type 2 diabetes. For predicting the risk of cardiovascular disease, the National Nutrition Health Survey 97 data from New Zealand population has been used. This data contains clinical, anthropometric and nutritional variables. For predicting risk of type 2 diabetes, data from the Italian population with clinical and genetic variables has been used. It has been discovered that genes responsible for causing type 2 diabetes are different in male and female samples. A framework to integrate the personalized model and the chronic disease ontology is also developed with the aim of providing support for further discovery through the integration of the ontological representation in order to build an expert system in genes of interest and relevant dietary components.

In 1802, a French scientific expedition under the command of Nicolas Baudin made an uscheduled visit to the British colony at Port Jackson, New South Wales. It was a pivotal episode in the course of Baudin's Australian voyage. The commander had already fulfilled most of his instructions, though imperfectly, and only the north coast of New Holland remained unexamined. He and his men stayed at anchor in Port Jackson for over five months. When they set sail once more, they embarked on what historians agree was a new phase of the expedition. Baudin and his men did not proceed directly to the north coast, but returned to the southern and western coasts, where they perfected and augmented the work in geography and natural history that they had carried out earlier. This thesis examines what occurred during the sojourn at Port Jackson, as well as the circumstances that led up to it, in order to determine in precise terms why and how this episode came to be a turning point in Baudin's voyage. It asks: was the second campaign just an extension of the first or was it an opportunity for Baudin to redefine the voyage? The Port Jackson sojourn thus serves as a site of interrogation regarding the nature of Nicolas Baudin's leadership and the construction, on British colonial territory, of a French scientific voyage. However, the opportunity to gain real insight into the sojourn of the voyagers at Port Jackson has been limited by a perceived scarcity of resources. The fact that Baudin's journal falls silent here has meant that there is no one privileged source of information on the commander's role or on the day-to-day activities of the expeditioners, and that scholars examining this episode have tended to focus on the details of the larger picture rather than on the larger picture itself. This is not to say that the presence of the Baudin expedition in Port Jackson has left no material traces. In fact, there is a diverse range of archival records – expense accounts, correspondence, inventories of specimens, journals kept by officers and savants and the logbooks of the Géographe – from which the day-to-day life of the commander and his men at Port Jackson can be reconstructed. Commencing with an analysis of the events that led up to the sojourn and influenced Baudin's approach to it, this study examines the relationships that Baudin built in the colony, his manner of command aboard the Géographe and the scientific results of the stay. After then analysing the way in which Baudin managed the sojourn and planned the second campaign, we conclude that Baudin did not simply seek to satisfy the expectations of his superiors but in fact he seized this opportunity to create the “perfect” scientific voyage. / Thesis (Ph.D.) -- University of Adelaide, School of Humanities, 2010

A fundamental problem in building large scale Grid resource sharing system is the need for efficient and scalable techniques for discovery and provisioning of resources for delivering expected Quality of Service (QoS) to users’ applications. The current approaches to Grid resource sharing based on resource brokers are non-coordinated since these brokers make scheduling related decisions independent of the others in the system. Clearly, this worsens the load-sharing and utilisation problems of distributed Grid resources as sub-optimal schedules are likely to occur. Further, existing brokering systems rely on centralised information services for resource discovery. Centralised or hierarchical resource discovery systems are prone to single-point failure, lack scalability and fault-tolerance ability. In the centralised model, the network links leading to the server are very critical to the overall functionality of the system, as their failure might halt the entire distributed system operation.

Populations are aging and the prevalence of chronic disease, persisting for many years, is increasing. The most common, non-communicable chronic diseases in developed countries are; cardiovascular disease (CVD), type 2 diabetes, obesity, arthritis and specific cancers. Chronic diseases such as cardiovascular disease, type 2 diabetes and obesity have high prevalence and develop over the course of life due to a number of interrelated factors including genetic predisposition, nutrition and lifestyle. With the development and completion of human genome sequencing, we are able to trace genes responsible for proteins and metabolites that are linked with these diseases. A computerized model focused on organizing knowledge related to genes, nutrition and the three chronic diseases, namely, cardiovascular disease, type 2 diabetes and obesity has been developed for the Ontology-Based Personalized Risk Evaluation for Chronic Disease Project. This model is a Protégé-based ontological representation which has been developed for entering and linking concepts and data for these three chronic diseases. This model facilitates to identify interrelationships between concepts. The ontological representation provides the framework into which information on individual patients, disease symptoms, gene maps, diet and life history can be input, and risks, profiles, and recommendations derived. Personal genome and health data could provide a guide for designing and building a medical health administration system for taking relevant annual medical tests, e.g. gene expression level changes for health surveillance. One method, called transductive neuro-fuzzy inference system with weighted data normalization is used to evaluate personalized risk of chronic disease. This personalized approach has been used for two different chronic diseases, predicting the risk of cardiovascular disease and predicting the risk of type 2 diabetes. For predicting the risk of cardiovascular disease, the National Nutrition Health Survey 97 data from New Zealand population has been used. This data contains clinical, anthropometric and nutritional variables. For predicting risk of type 2 diabetes, data from the Italian population with clinical and genetic variables has been used. It has been discovered that genes responsible for causing type 2 diabetes are different in male and female samples. A framework to integrate the personalized model and the chronic disease ontology is also developed with the aim of providing support for further discovery through the integration of the ontological representation in order to build an expert system in genes of interest and relevant dietary components.

Encouraging the use of computer algorithms by developing new algorithms and introducing uncommonly known algorithms for use on environmental science problems is a significant contribution, as it provides knowledge discovery tools to extract new aspects of results and draw new insights, additional to those from general statistical methods. Conducting analysis with appropriately chosen methods, in terms of quality of performance and results, computation time, flexibility and applicability to data of various natures, will help decision making in the policy development and management process for environmental studies. This thesis has three fundamental aims and motivations. Firstly, to develop a flexibly applicable attribute selection method, Tree Node Selection (TNS), and a decision tree assessment tool, Tree Node Selection for assessing decision tree structure (TNS-A), both of which use decision trees pre-generated by the widely used C4.5 decision tree algorithm as their information source, to identify important attributes from data. TNS helps the cost effective and efficient data collection and policy making process by selecting fewer, but important, attributes, and TNS-A provides a tool to assess the decision tree structure to extract information on the relationship of attributes and decisions. Secondly, to introduce the use of new, theoretical or unknown computer algorithms, such as the K-Maximum Subarray Algorithm (K-MSA) and Ant-Miner, by adjusting and maximizing their applicability and practicality to assess environmental science problems to bring new insights. Additionally, the unique advanced statistical and mathematical method, Singular Spectrum Analysis (SSA), is demonstrated as a data pre-processing method to help improve C4.5 results on noisy measurements. Thirdly, to promote, encourage and motivate environmental scientists to use ideas and methods developed in this thesis. The methods were tested with benchmark data and various real environmental science problems: sea container contamination, the Weed Risk Assessment model and weed spatial analysis for New Zealand Biosecurity, air pollution, climate and health, and defoliation imagery. The outcome of this thesis will be to introduce the concept and technique of data mining, a process of knowledge discovery from databases, to environmental science researchers in New Zealand and overseas by collaborating on future research to achieve, together with future policy and management, to maintain and sustain a healthy environment to live in.

The thesis analyses the continuum of European activity that preceded establishment of an effective pastoral industry in Australia's north-west. Two strands - physical activity and evolution of legislation - are interwoven, examining growth in geographical knowledge, proposals for colonisation and the outcome of interplay between government officials and landholders over land policy. Growth in geographical knowledge gave rise to colonisation proposals from 1828. The thesis relates these proposals to events affecting northern Australia to show that promotion and occupation of north-west lands constituted an integral part of the outgrowth of colonial settlement in Australia. Europeans occupied the north-west in two waves, abortively during the 1860s and continuously from 1879. The existing literature identifies these waves but provides inadequate analysis of events to 1884. The thesis fills this gap by showing that land hunger, misinformation, land speculation, manipulation of legislation and exploitation of political power for private commercial gain determined the shape of north-west settlement. Moreover, by relating land policy to tenure and occupation, it shows that private individuals influenced land policy and impeded official plans for rapid settlement. Thus, the thesis provides a fresh perspective not only on the prelude to effective pastoral settlement in the north-west but on the management of Western Australia's outlying lands in the period before responsible government.

Thesis (MA(Psychology))-University of Pretoria, 2008. / Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (leaves [243]-256).

Thesis (Ph. D.)--University of Wisconsin--Madison, 1969. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 136-140).