Bayesian methodology for genetics of complex diseases

Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.

A methodology to assess the competitiveness of real estate developers in China

Real estate, or property development, is considered one of the pillar industries of the Chinese economy. As a result of the opening up of the economy as well as the "macro-control" policy of the Central Chinese Government to moderate the frenetic pace of growth of the economy, the real estate industry has faced fierce competition and ongoing change. Real estate firms in China must improve their competitiveness in order to maintain market share or even survive in this brutally competitive environment. This study developed a methodology to evaluate the competitiveness of real estate developers in the China and then used a case study to illustrate the effectiveness of the evaluation method. Four steps were taken to achieve this. The first step was to conduct a thorough literature review which included a review of the characteristics of real estate industry, theories about competitiveness and the competitive characteristics of real estate developers. Following this literature review, the competitive model was developed based on seven key competitive factors (the 'level 1') identified in the literature. They include: (1) financial competency; (2) market share; (3) management competency; (4) social responsibility; (5) organisational competency; (6) technological capabilities; and, (7) regional competitiveness. In the next step of research, the competitive evaluation criteria (the 'level 2') under each of competitive factors (the 'level 1') were evaluated. Additionally, there were identified a set of competitive attributes (the 'level 3') under each competitive criteria (the 'level 2'). These attributes were initially recognised during the literature review and then expanded upon through interviews with multidisciplinary experts and practitioners in various real estate-related industries. The final step in this research was to undertake a case study using the proposed evaluation method and attributes. Through the study of an actual real estate development company, the procedures and effectiveness of the evaluation method were illustrated and validated. Through the above steps, this research investigates and develops an analytical system for determining the corporate competitiveness of real estate developers in China. The analytical system is formulated to evaluate the "state of health" of the business from different competitive perspectives. The result of empirical study illustrates that a systematic and structured evaluation can effectively assist developers in identifying their strengths and highlighting potential problems. This is very important for the development of an overall corporate strategy and supporting key strategic decisions. This study also provides some insights, analysis and suggestions for improving the competitiveness of real estate developers in China from different perspectives, including: management competency, organisational competency, technological capabilities, financial competency, market share, social responsibility and regional competitiveness. In the case study, problems were found in each of these areas, and they appear to be common in the industry. To address these problems and improve the competitiveness and effectiveness of Chinese real estate developers, a variety of suggestions are proposed. The findings of this research provide an insight into the factors that influence competitiveness in the Chinese real estate industry while also assisting practitioners to formulate strategies to improve their competitiveness. References for studying the competitiveness of real estate developers in other countries are also provided.

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.

Structural damage identification with noise polluted Frequency Response Functions (FRFs)

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage with noise polluted data which is unavoidable effect in real world. The measurement data are contaminated by noise because of test environment as well as electronic devices and this noise tend to give error results with structural damage identification methods. Therefore it is important to investigate a method which can perform better with noise polluted data. This paper introduces a new damage index using principal component analysis (PCA) for damage detection of building structures being able to accept noise polluted frequency response functions (FRFs) as input. The FRF data are obtained from the function datagen of MATLAB program which is available on the web site of the IASC-ASCE (International Association for Structural Control– American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group. The proposed method involves a five-stage process: calculation of FRFs, calculation of damage index values using proposed algorithm, development of the artificial neural networks and introducing damage indices as input parameters and damage detection of the structure. This paper briefly describes the methodology and the results obtained in detecting damage in all six cases of the benchmark study with different noise levels. The proposed method is applied to a benchmark problem sponsored by the IASC-ASCE Task Group on Structural Health Monitoring, which was developed in order to facilitate the comparison of various damage identification methods. The illustrated results show that the PCA-based algorithm is effective for structural health monitoring with noise polluted FRFs which is of common occurrence when dealing with industrial structures.

Key competences of design-build clients in China

Purpose-- DB clients play a vital role in the delivery of DB system and the clients’ competences are critical to the success of DB projects. Most of DB clients, however, remain inexperienced with the DB system. This study, therefore, aims to identify the key competences that DB clients should possess to ensure the success of DB projects in the construction market of China. Design/Methodology/Approach -- Five semi-structured face-to-face interviews and two rounds Delphi questionnaire survey were conducted in the construction market of China to identify the key competences of DB clients. Rankings have been assigned to these key competences on the basis of their relative importance. Findings-- Six ranked key competences of DB clients have been identified, which are, namely, (1) the ability to clearly define project scope and objectives; (2) financial capacity for the projects; (3) capacity in contract management; (4) adequate staff or consulting team; (5) effective coordination with DB contractors and (6) experience with similar design-build projects. Calculation of Kendall’s Coefficient of Concordance (W) indicates a statistically significant consensus of panel experts on these top six key competences. Practical implications—Clients should clearly understand the competence requirements in DB projects and should assess their DB capability before going for the DB option. Originality/Value-- The examination of DB client’s key competences will help the client deepen the understanding of the DB system. DB clients can also make use of the research findings as guidelines to improve their DB competence.

Action research as foresight methodology

To the action researcher, who laboriously spends his or her hours working within the local contexts of communities or organisations to co-generate meaningful research, and who’s theories are hardened on the anvil of creating meaningful social change; futures studies might seem the discipline the most peripheral to its interests, and the most ill equipped to deal with the local and intimate domain of community existence. To the futurist, who laboriously spends his or her hours understanding the nuances of history and social change, who through persistent work, begins to make sense of the weak signals and the subtle shifts, action research would seem as simply an auxiliary field, inappropriate for understanding the greater scheme. I invite the reader, however, whether they belong to one camp or the other, to let go of their respective disciplinary perspectives, and see both belonging to each other. [Introduction] .

An analysis of holding cost impact on housing affordability in relation to midsized Greenfield residential property developments in South East Queensland

The position of housing demand and supply is not consistent. The Australian situation counters the experience demonstrated in many other parts of the world in the aftermath of the Global Financial Crisis, with residential housing prices proving particularly resilient. A seemingly inexorable housing demand remains a critical issue affecting the socio-economic landscape. Underpinned by high levels of population growth fuelled by immigration, and further buoyed by sustained historically low interest rates, increasing income levels, and increased government assistance for first home buyers, this strong housing demand level ensures problems related to housing affordability continue almost unabated. A significant, but less visible factor impacting housing affordability relates to holding costs. Although only one contributor in the housing affordability matrix, the nature and extent of holding cost impact requires elucidation: for example, the computation and methodology behind the calculation of holding costs varies widely - and in some instances completely ignored. In addition, ambiguity exists in terms of the inclusion of various elements that comprise holding costs, thereby affecting the assessment of their relative contribution. Such anomalies may be explained by considering that assessment is conducted over time in an ever-changing environment. A strong relationship with opportunity cost - in turn dependant inter alia upon prevailing inflation and / or interest rates - adds further complexity. By extending research in the general area of housing affordability, this thesis seeks to provide a detailed investigation of those elements related to holding costs specifically in the context of midsized (i.e. between 15-200 lots) greenfield residential property developments in South East Queensland. With the dimensions of holding costs and their influence over housing affordability determined, the null hypothesis H0 that holding costs are not passed on can be addressed. Arriving at these conclusions involves the development of robust economic and econometric models which seek to clarify the componentry impacts of holding cost elements. An explanatory sequential design research methodology has been adopted, whereby the compilation and analysis of quantitative data and the development of an economic model is informed by the subsequent collection and analysis of primarily qualitative data derived from surveying development related organisations. Ultimately, there are significant policy implications in relation to the framework used in Australian jurisdictions that promote, retain, or otherwise maximise, the opportunities for affordable housing.

Using BIM for smarter and safer scaffolding and formwork construction: a preliminary methodology

The goal of this research project is to develop specific BIM objects for temporary construction activities which are fully integrated with object design, construction efficiency and safety parameters. Specifically, the project will deliver modularised electronic scaffolding and formwork objects that will allow designers to easily incorporate them into BIM models to facilitate smarter and safer infrastructure and building construction. This research first identified there is currently a distinct lack of BIM objects for temporary construction works resulting in productivity loss during design and construction, and opportunities for improved consideration of safety standards and practices with the design of scaffolding and formwork. This is particularly relevant in Australia, given the “harmonisation” of OHS legislation across all states and territories from 1 January 2012, meaning that enhancements to Queensland practices will have direct application across Australia. Thus, in conjunction with government and industry partners in Queensland, Australia, the research team developed a strategic three-phase research methodology: (1) the preliminary review phase on industrial scaffolding and formwork practices and BIM implementation; (2) the BIM object development phase with specific safety and productivity functions; and (3) the Queensland-wide workshop phase for product dissemination and training. This paper discusses background review findings, details of the developed methodology, and expected research outcomes and their contributions to the Australian construction industry.

Efficient safety information retrieval on construction sites: a preliminary methodology

IT-supported field data management benefits on-site construction management by improving accessibility to the information and promoting efficient communication between project team members. However, most of on-site safety inspections still heavily rely on subjective judgment and manual reporting processes and thus observers’ experiences often determine the quality of risk identification and control. This study aims to develop a methodology to efficiently retrieve safety-related information so that the safety inspectors can easily access to the relevant site safety information for safer decision making. The proposed methodology consists of three stages: (1) development of a comprehensive safety database which contains information of risk factors, accident types, impact of accidents and safety regulations; (2) identification of relationships among different risk factors based on statistical analysis methods; and (3) user-specified information retrieval using data mining techniques for safety management. This paper presents an overall methodology and preliminary results of the first stage research conducted with 101 accident investigation reports.

Reading research methodology and the reading research quarterly : a question of gender

The paper discusses an aspect of reading research methodology as represented by papers published by the Reading Research Quarterly from the beginning of 1989(volume 24, Number 1) to the end of 1993 (volume 28, Number 4). The discussion suggests some points of departure between this research community and an Australian community broadly defined as poststructural. A focus for this investigation is the function of “gender” within the methodological approaches of the two communities. Suggestions are made regarding some potentially productive points of intersection between the work of American and Australian reading researchers.

Integrating driving and traffic simulators to study railway level crossing safety interventions : a methodology

Safety at Railway Level Crossings (RLXs) is an important issue within the Australian transport system. Crashes at RLXs involving road vehicles in Australia are estimated to cost $10 million each year. Such crashes are mainly due to human factors; unintentional errors contribute to 46% of all fatal collisions and are far more common than deliberate violations. This suggests that innovative intervention targeting drivers are particularly promising to improve RLX safety. In recent years there has been a rapid development of a variety of affordable technologies which can be used to increase driver’s risk awareness around crossings. To date, no research has evaluated the potential effects of such technologies at RLXs in terms of safety, traffic and acceptance of the technology. Integrating driving and traffic simulations is a safe and affordable approach for evaluating these effects. This methodology will be implemented in a driving simulator, where we recreated realistic driving scenario with typical road environments and realistic traffic. This paper presents a methodology for evaluating comprehensively potential benefits and negative effects of such interventions: this methodology evaluates driver awareness at RLXs , driver distraction and workload when using the technology . Subjective assessment on perceived usefulness and ease of use of the technology is obtained from standard questionnaires. Driving simulation will provide a model of driving behaviour at RLXs which will be used to estimate the effects of such new technology on a road network featuring RLX for different market penetrations using a traffic simulation. This methodology can assist in evaluating future safety interventions at RLXs.

Methodology to assess safety effects of future Intelligent Transport Systems on railway level crossings

There is consistent evidence showing that driver behaviour contributes to crashes and near miss incidents at railway level crossings (RLXs). The development of emerging Vehicle-to-Vehicle and Vehicle-to-Infrastructure technologies is a highly promising approach to improve RLX safety. To date, research has not evaluated comprehensively the potential effects of such technologies on driving behaviour at RLXs. This paper presents an on-going research programme assessing the impacts of such new technologies on human factors and drivers’ situational awareness at RLX. Additionally, requirements for the design of such promising technologies and ways to display safety information to drivers were systematically reviewed. Finally, a methodology which comprehensively assesses the effects of in-vehicle and road-based interventions warning the driver of incoming trains at RLXs is discussed, with a focus on both benefits and potential negative behavioural adaptations. The methodology is designed for implementation in a driving simulator and covers compliance, control of the vehicle, distraction, mental workload and drivers’ acceptance. This study has the potential to provide a broad understanding of the effects of deploying new in-vehicle and road-based technologies at RLXs and hence inform policy makers on safety improvements planning for RLX.