Prediction of structural integrity, robustness and service life using advanced finite element methods

Corrosion is a common phenomenon and critical aspects of steel structural application. It affects the daily design, inspection and maintenance in structural engineering, especially for the heavy and complex industrial applications, where the steel structures are subjected to hash corrosive environments in combination of high working stress condition and often in open field and/or under high temperature production environments. In the paper, it presents the actual engineering application of advanced finite element methods in the predication of the structural integrity and robustness at a designed service life for the furnaces of alumina production, which was operated in the high temperature, corrosive environments and rotating with high working stress condition.

Using Bayesian methods for the estimation of uncertainty in complex statistical models

The research objectives of this thesis were to contribute to Bayesian statistical methodology by contributing to risk assessment statistical methodology, and to spatial and spatio-temporal methodology, by modelling error structures using complex hierarchical models. Specifically, I hoped to consider two applied areas, and use these applications as a springboard for developing new statistical methods as well as undertaking analyses which might give answers to particular applied questions. Thus, this thesis considers a series of models, firstly in the context of risk assessments for recycled water, and secondly in the context of water usage by crops. The research objective was to model error structures using hierarchical models in two problems, namely risk assessment analyses for wastewater, and secondly, in a four dimensional dataset, assessing differences between cropping systems over time and over three spatial dimensions. The aim was to use the simplicity and insight afforded by Bayesian networks to develop appropriate models for risk scenarios, and again to use Bayesian hierarchical models to explore the necessarily complex modelling of four dimensional agricultural data. The specific objectives of the research were to develop a method for the calculation of credible intervals for the point estimates of Bayesian networks; to develop a model structure to incorporate all the experimental uncertainty associated with various constants thereby allowing the calculation of more credible credible intervals for a risk assessment; to model a single day’s data from the agricultural dataset which satisfactorily captured the complexities of the data; to build a model for several days’ data, in order to consider how the full data might be modelled; and finally to build a model for the full four dimensional dataset and to consider the timevarying nature of the contrast of interest, having satisfactorily accounted for possible spatial and temporal autocorrelations. This work forms five papers, two of which have been published, with two submitted, and the final paper still in draft. The first two objectives were met by recasting the risk assessments as directed, acyclic graphs (DAGs). In the first case, we elicited uncertainty for the conditional probabilities needed by the Bayesian net, incorporated these into a corresponding DAG, and used Markov chain Monte Carlo (MCMC) to find credible intervals, for all the scenarios and outcomes of interest. In the second case, we incorporated the experimental data underlying the risk assessment constants into the DAG, and also treated some of that data as needing to be modelled as an ‘errors-invariables’ problem [Fuller, 1987]. This illustrated a simple method for the incorporation of experimental error into risk assessments. In considering one day of the three-dimensional agricultural data, it became clear that geostatistical models or conditional autoregressive (CAR) models over the three dimensions were not the best way to approach the data. Instead CAR models are used with neighbours only in the same depth layer. This gave flexibility to the model, allowing both the spatially structured and non-structured variances to differ at all depths. We call this model the CAR layered model. Given the experimental design, the fixed part of the model could have been modelled as a set of means by treatment and by depth, but doing so allows little insight into how the treatment effects vary with depth. Hence, a number of essentially non-parametric approaches were taken to see the effects of depth on treatment, with the model of choice incorporating an errors-in-variables approach for depth in addition to a non-parametric smooth. The statistical contribution here was the introduction of the CAR layered model, the applied contribution the analysis of moisture over depth and estimation of the contrast of interest together with its credible intervals. These models were fitted using WinBUGS [Lunn et al., 2000]. The work in the fifth paper deals with the fact that with large datasets, the use of WinBUGS becomes more problematic because of its highly correlated term by term updating. In this work, we introduce a Gibbs sampler with block updating for the CAR layered model. The Gibbs sampler was implemented by Chris Strickland using pyMCMC [Strickland, 2010]. This framework is then used to consider five days data, and we show that moisture in the soil for all the various treatments reaches levels particular to each treatment at a depth of 200 cm and thereafter stays constant, albeit with increasing variances with depth. In an analysis across three spatial dimensions and across time, there are many interactions of time and the spatial dimensions to be considered. Hence, we chose to use a daily model and to repeat the analysis at all time points, effectively creating an interaction model of time by the daily model. Such an approach allows great flexibility. However, this approach does not allow insight into the way in which the parameter of interest varies over time. Hence, a two-stage approach was also used, with estimates from the first-stage being analysed as a set of time series. We see this spatio-temporal interaction model as being a useful approach to data measured across three spatial dimensions and time, since it does not assume additivity of the random spatial or temporal effects.

Methods used in cross-cultural comparisons of psychological symptoms and their determinants

This paper is the second in a series of reviews of cross-cultural studies of menopausal symptoms. The goal of this review is to compare and contrast methods which have been previously utilized in Cross-Cultural Midlife Women's Health Studies with a view to (1) identifying the challenges in measurement across cultures in psychological symptoms and (2) suggesting a set of unified questions and tools that can be used in future research in this area. This review also aims to examine the determinants of psychological symptoms and how those determinants were measured. The review included eight studies that explicitly compared symptoms in different countries or different ethnic groups in the same country and included: Australian/Japanese Midlife Women's Health Study (AJMWHS), Decisions At Menopause Study (DAMeS), Four Major Ethnic Groups (FMEG), Hilo Women's Health Survey (HWHS), Penn Ovarian Aging Study (POAS), Study of Women's Health Across the Nation (SWAN), Women's Health in Midlife National Study (WHiMNS), and the Women's International Study of Health and Sexuality (WISHeS). This review concludes that mental morbidity does affect vasomotor symptom prevalence across cultures and therefore should be measured. Based on the review of these eight studies it is recommended that the following items be included when measuring psychological symptoms across cultures, feeling tense or nervous, sleeping difficulty, difficulty in concentrating, depressed and irritability along with the CES-D Scale, and the Perceived Stress Scale. The measurement of these symptoms will provide an evidence based approach when forming any future menopause symptom list and allow for comparisons across studies.

Methods used in cross-cultural comparisons of sexual symptoms and their determinants

This is the fourth in a series of reviews of cross-cultural studies of menopausal symptoms. The purpose of this review is to examine methods used in cross-cultural comparisons of sexual symptoms among women at midlife, and to examine the determinants of sexual symptoms and how those determinants were measured. The goal of this review is to make recommendations that will improve cross-cultural comparisons in the future. The review included nine studies that explicitly examined symptoms in different countries or different ethnic groups in the same country and included: Australian/Japanese Midlife Women's Health Study (AJMWHS), Decisions At Menopause Study (DAMeS), Four Major Ethnic Groups (FMEG), Hilo Women's Health Survey (HWHS), Mid-Aged Health in Women from the Indian Subcontinent (MAHWIS), Penn Ovarian Aging Study (POAS), Study of Women's Health Across the Nation (SWAN), Women's Health in Midlife National Study (WHiMNS), and Women's International Study of Health and Sexuality (WISHeS). Although methods used for assessing sexual symptoms across cultures differed between studies, statistically significant differences were reported. Cross-cultural differences in sexual symptoms exist, and should be measured by including the following symptoms: loss of interest in sex, vaginal dryness, and the Females Sexual Function Index which covers desire, arousal, lubrication, orgasm, satisfaction, and pain on intercourse. The measurement of these symptoms will provide an evidence-based approach when forming any future menopause symptom list and allow for comparisons across studies.

Methods used in cross-cultural comparisons of vasomotor symptoms and their determinants

Methodological differences among studies of vasomotor symptoms limit rigorous comparison or systematic review. Vasomotor symptoms generally include hot flushes and night sweats although other associated symptoms exist. Prevalence rates vary between and within populations, but different studies collect data on frequency, bothersomeness, and/or severity using different outcome measures and scales, making comparisons difficult. We reviewed only cross-cultural studies of menopausal symptoms that explicitly examined symptoms in general populations of women in different countries or different ethnic groups in the same country. This resulted in the inclusion of nine studies: Australian/Japanese Midlife Women's Health Study (AJMWHS), Decisions At Menopause Study (DAMeS), Four Major Ethnic Groups (FMEG), Hilo Women's Health Survey (HWHS), Mid-Aged Health in Women from the Indian Subcontinent (MAHWIS), Penn Ovarian Aging Study (POAS), Study of Women's Health Across the Nation (SWAN), Women's Health in Midlife National Study (WHiMNS), and Women's International Study of Health and Sexuality (WISHeS). These studies highlight the methodological challenges involved in conducting multi-population studies, particularly when languages differ, but also highlight the importance of performing multivariate and factor analyses. Significant cultural differences in one or more vasomotor symptoms were observed in 8 of 9 studies, and symptoms were influenced by the following determinants: menopausal status, hormones (and variance), age (or actually, the square of age, age2), BMI, depression, anxiety, poor physical health, perceived stress, lifestyle factors (hormone therapy use, smoking and exposure to passive smoke), and acculturation (in immigrant populations). Recommendations are made to improve methodological rigor and facilitate comparisons in future cross-cultural menopause studies.

Overview of methods used in cross-cultural comparisons of menopausal symptoms and their determinants : Guidelines for Strengthening the Reporting of Menopause and Aging (STROMA) studies

This paper reviews the methods used in cross-cultural studies of menopausal symptoms with the goal of formulating recommendations to facilitate comparisons of menopausal symptoms across cultures. It provides an overview of existing approaches and serves to introduce four separate reviews of vasomotor, psychological, somatic, and sexual symptoms at midlife. Building on an earlier review of cross-cultural studies of menopause covering time periods until 2004, these reviews are based on searches of Medline, PsycINFO, CINAHL and Google Scholar for English-language articles published from 2004 to 2010 using the terms “cross cultural comparison” and “menopause.” Two major criteria were used: a study had to include more than one culture, country, or ethnic group and to have asked about actual menopausal symptom experience. We found considerable variation across studies in age ranges, symptom lists, reference period for symptom recall, variables included in multivariate analyses, and the measurement of factors (e.g., menopausal status and hormonal factors, demographic, anthropometric, mental/physical health, and lifestyle measures) that influence vasomotor, psychological, somatic and sexual symptoms. Based on these reviews, we make recommendations for future research regarding age range, symptom lists, reference/recall periods, and measurement of menopausal status. Recommendations specific to the cross-cultural study of vasomotor, psychological, somatic, and sexual symptoms are found in the four reviews that follow this introduction.

Methods used in cross-cultural comparisons of somatic symptoms and their determinants

This paper is the third in a series of reviews of cross-cultural studies of symptoms at midlife. The goal of this review is to examine methods used previously in cross-cultural studies of menopause and women's health at midlife to (1) identify challenges in the measurement of somatic symptoms across cultures and (2) recommend questions and tools that can be used in future research. This review also aims to examine the determinants of somatic symptoms. The review concludes that methods used for assessing somatic symptoms differ across studies. Somatic symptoms, particularly, aches, pain, and fatigue have a high prevalence. Statistically significant differences were seen in the prevalence of somatic symptoms across cultures. Based on the number of studies that demonstrated cross-cultural differences in symptom prevalence, we recommend that the following symptoms be included in future studies of symptoms at midlife: headaches, aches/pain, palpitations, dizziness, fatigue, breathing difficulties, numbness or tingling, and gastrointestinal difficulties. We also recommend that objective measures of physical function be administered when possible to supplement subjective self-evaluation.

Tracking employees' twists and turns : describing a mixed methods approach to assessing change in the psychological contract

The psychological contract is a key analytical device utilised by both academics and practitioners to conceptualise and explore the dynamics of the employment relationship. However, despite the recognised import of the construct, some authors suggest that its empirical investigation has fallen into a 'methodological rut' [Conway & Briner, 2005, p. 89] and is neglecting to assess key tenets of the concept, such as its temporal and dynamic nature. This paper describes the research design of a longitudinal, mixed methods study which draws upon the strengths of both qualitative and quantitative modes of inquiry in order to explore the development of, and changes in, the psychological contract. Underpinned by a critical realist philosophy, the paper seeks to offer a research design suitable for exploring the process of change not only within the psychological contract domain, but also for similar constructs in the human resource management and broader organisational behaviour fields.

Comparing cost uplift in infrastructure delivery methods : a case based approach

In 2009 the Australian Federal and State governments are expected to have spent some AU$30 billion procuring infrastructure projects. For governments with finite resources but many competing projects, formal capital rationing is achieved through use of Business Cases. These Business cases articulate the merits of investing in particular projects along with the estimated costs and risks of each project. Despite the sheer size and impact of infrastructure projects, there is very little research in Australia, or internationally, on the performance of these projects against Business Case assumptions when the decision to invest is made. If such assumptions (particularly cost assumptions) are not met, then there is serious potential for the misallocation of Australia’s finite financial resources. This research addresses this important gap in the literature by using combined quantitative and qualitative research methods, to examine the actual performance of 14 major Australian government infrastructure projects. The research findings are controversial as they challenge widely held perceptions of the effectiveness of certain infrastructure delivery practices. Despite this controversy, the research has had a significant impact on the field and has been described as ‘outstanding’ and ‘definitive’ (Alliancing Association of Australasia), "one of the first of its kind" (Infrastructure Partnerships of Australia) and "making a critical difference to infrastructure procurement" (Victorian Department of Treasury). The implications for practice of the research have been profound and included the withdrawal by Government of various infrastructure procurement guidelines, the formulation of new infrastructure policies by several state governments and the preparation of new infrastructure guidelines that substantially reflect the research findings. Building on the practical research, a more rigorous academic investigation focussed on the comparative cost uplift of various project delivery strategies was submitted to Australia’s premier academic management conference, the Australian and New Zealand Academy of Management (ANZAM) Annual Conference. This paper has been accepted for the 2010 ANZAM National Conference following a process of double blind peer review with reviewers rating the paper’s overall contribution as "Excellent" and "Good".

A Study of XML models for data mining : representations, methods, and issues

With the increasing number of XML documents in varied domains, it has become essential to identify ways of finding interesting information from these documents. Data mining techniques were used to derive this interesting information. Mining on XML documents is impacted by its model due to the semi-structured nature of these documents. Hence, in this chapter we present an overview of the various models of XML documents, how these models were used for mining and some of the issues and challenges in these models. In addition, this chapter also provides some insights into the future models of XML documents for effectively capturing the two important features namely structure and content of XML documents for mining.

Towards participatory action design research : adapting action research and design science research methods for urban informatics

This paper proposes a new research method, Participatory Action Design Research (PADR), for studies in the Urban Informatics domain. PADR supports Urban Informatics research in developing new technological means (e.g. using mobile and ubiquitous computing) to resolve contemporary issues or support everyday life in urban environments. The paper discusses the nature, aims and inherent methodological needs of Urban Informatics research, and proposes PADR as a method to address these needs. Situated in a socio-technical context, Urban Informatics requires a close dialogue between social and design-oriented fields of research as well as their methods. PADR combines Action Research and Design Science Research, both of which are used in Information Systems, another field with a strong socio-technical emphasis, and further adapts them to the cross-disciplinary needs and research context of Urban Informatics.

Mapping intermedia news flows : topical discussions in the Australian and French political blogospheres

The growth of technologies and tools branded as =new media‘ or =Web 2.0‘ has sparked much discussion about the internet and its place in all facets of social life. Such debate includes the potential for blogs and citizen journalism projects to replace or alter journalism and mainstream media practices. However, while the journalism-blog dynamic has attracted the most attention, the actual work of political bloggers, the roles they play in the mediasphere and the resources they use, has been comparatively ignored. This project will look at political blogging in Australia and France - sites commenting on or promoting political events and ideas, and run by citizens, politicians, and journalists alike. In doing so, the structure of networks formed by bloggers and the nature of communication within political blogospheres will be examined. Previous studies of political blogging around the world have focussed on individual nations, finding that in some cases the networks are divided between different political ideologies. By comparing two countries with different political representation (two-party dominated system vs. a wider political spectrum), this study will determine the structure of these political blogospheres, and correlate these structures with the political environment in which they are situated. The thesis adapts concepts from communication and media theories, including framing, agenda setting, and opinion leaders, to examine the work of political bloggers and their place within the mediasphere. As well as developing a hybrid theoretical base for research into blogs and other online communication, the project outlines new methodologies for carrying out studies of online activity through the analysis of several topical networks within the wider activity collected for this project. The project draws on hyperlink and textual data collected from a sample of Australian and French blogs between January and August 2009. From this data, the thesis provides an overview of =everyday‘ political blogging, showing posting patterns over several months of activity, away from national elections and their associated campaigns. However, while other work in this field has looked solely at cumulative networks, treating collected data as a static network, this project will also look at specific cases to see how the blogospheres change with time and topics of discussion. Three case studies are used within the thesis to examine how blogs cover politics, featuring an international political event (the Obama inauguration), and local political topics (the opposition to the =Création et Internet‘, or HADOPI, law in France, the =Utegate‘ scandal in Australia). By using a mixture of qualitative and quantitative methods, the study analyses data collected from a population of sites from both countries, looking at their linking patterns, relationship with mainstream media, and topics of interest. This project will subsequently help to further develop methodologies in this field and provide new and detailed information on both online networks and internet-based political communication in Australia and France.

User behaviour modelling in a multi-dimensional environment for personalization and recommendation

Handling information overload online, from the user's point of view is a big challenge, especially when the number of websites is growing rapidly due to growth in e-commerce and other related activities. Personalization based on user needs is the key to solving the problem of information overload. Personalization methods help in identifying relevant information, which may be liked by a user. User profile and object profile are the important elements of a personalization system. When creating user and object profiles, most of the existing methods adopt two-dimensional similarity methods based on vector or matrix models in order to find inter-user and inter-object similarity. Moreover, for recommending similar objects to users, personalization systems use the users-users, items-items and users-items similarity measures. In most cases similarity measures such as Euclidian, Manhattan, cosine and many others based on vector or matrix methods are used to find the similarities. Web logs are high-dimensional datasets, consisting of multiple users, multiple searches with many attributes to each. Two-dimensional data analysis methods may often overlook latent relationships that may exist between users and items. In contrast to other studies, this thesis utilises tensors, the high-dimensional data models, to build user and object profiles and to find the inter-relationships between users-users and users-items. To create an improved personalized Web system, this thesis proposes to build three types of profiles: individual user, group users and object profiles utilising decomposition factors of tensor data models. A hybrid recommendation approach utilising group profiles (forming the basis of a collaborative filtering method) and object profiles (forming the basis of a content-based method) in conjunction with individual user profiles (forming the basis of a model based approach) is proposed for making effective recommendations. A tensor-based clustering method is proposed that utilises the outcomes of popular tensor decomposition techniques such as PARAFAC, Tucker and HOSVD to group similar instances. An individual user profile, showing the user's highest interest, is represented by the top dimension values, extracted from the component matrix obtained after tensor decomposition. A group profile, showing similar users and their highest interest, is built by clustering similar users based on tensor decomposed values. A group profile is represented by the top association rules (containing various unique object combinations) that are derived from the searches made by the users of the cluster. An object profile is created to represent similar objects clustered on the basis of their similarity of features. Depending on the category of a user (known, anonymous or frequent visitor to the website), any of the profiles or their combinations is used for making personalized recommendations. A ranking algorithm is also proposed that utilizes the personalized information to order and rank the recommendations. The proposed methodology is evaluated on data collected from a real life car website. Empirical analysis confirms the effectiveness of recommendations made by the proposed approach over other collaborative filtering and content-based recommendation approaches based on two-dimensional data analysis methods.

Fuzzy methods for analysis of microarrays and networks

Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.