Incorporating uncertainty in environmental models informed by imagery

In this thesis, the issue of incorporating uncertainty for environmental modelling informed by imagery is explored by considering uncertainty in deterministic modelling, measurement uncertainty and uncertainty in image composition. Incorporating uncertainty in deterministic modelling is extended for use with imagery using the Bayesian melding approach. In the application presented, slope steepness is shown to be the main contributor to total uncertainty in the Revised Universal Soil Loss Equation. A spatial sampling procedure is also proposed to assist in implementing Bayesian melding given the increased data size with models informed by imagery. Measurement error models are another approach to incorporating uncertainty when data is informed by imagery. These models for measurement uncertainty, considered in a Bayesian conditional independence framework, are applied to ecological data generated from imagery. The models are shown to be appropriate and useful in certain situations. Measurement uncertainty is also considered in the context of change detection when two images are not co-registered. An approach for detecting change in two successive images is proposed that is not affected by registration. The procedure uses the Kolmogorov-Smirnov test on homogeneous segments of an image to detect change, with the homogeneous segments determined using a Bayesian mixture model of pixel values. Using the mixture model to segment an image also allows for uncertainty in the composition of an image. This thesis concludes by comparing several different Bayesian image segmentation approaches that allow for uncertainty regarding the allocation of pixels to different ground components. Each segmentation approach is applied to a data set of chlorophyll values and shown to have different benefits and drawbacks depending on the aims of the analysis.

Literacy models across nations : literacy and critical literacy in teacher training programs in India

The thrust towards constructivist learning and critical thinking in the National Curricular Framework (2005) of India implies shifts in pedagogical practices. In this context, drawing on grounded theory, focus group interviews were conducted with 40 preservice teachers to ascertain the contextual situation and the likely outcomes of applying critical literacy across the curriculum. Central themes that emerged in the discussion were: being teacher centred/ learner centred, and conformity/autonomy in teaching and learning. The paper argues that within the present Indian context, while there is scope for changes to pedagogy and learning styles, yet these must be adequately contextualised.

Typical accuracy and quality control of a process for creating CT-Based virtual bone models

A pragmatic method for assessing the accuracy and precision of a given processing pipeline required for converting computed tomography (CT) image data of bones into representative three dimensional (3D) models of bone shapes is proposed. The method is based on coprocessing a control object with known geometry which enables the assessment of the quality of resulting 3D models. At three stages of the conversion process, distance measurements were obtained and statistically evaluated. For this study, 31 CT datasets were processed. The final 3D model of the control object contained an average deviation from reference values of −1.07±0.52 mm standard deviation (SD) for edge distances and −0.647±0.43 mm SD for parallel side distances of the control object. Coprocessing a reference object enables the assessment of the accuracy and precision of a given processing pipeline for creating CTbased 3D bone models and is suitable for detecting most systematic or human errors when processing a CT-scan. Typical errors have about the same size as the scan resolution.

Predicting loss and fragmentation of habitat of the vulnerable subtropical rainforest tree Macadamia integrifolia with models developed from compiled ecological data

Habitat models are widely used in ecology, however there are relatively few studies of rare species, primarily because of a paucity of survey records and lack of robust means of assessing accuracy of modelled spatial predictions. We investigated the potential of compiled ecological data in developing habitat models for Macadamia integrifolia, a vulnerable mid-stratum tree endemic to lowland subtropical rainforests of southeast Queensland, Australia. We compared performance of two binomial models—Classification and Regression Trees (CART) and Generalised Additive Models (GAM)—with Maximum Entropy (MAXENT) models developed from (i) presence records and available absence data and (ii) developed using presence records and background data. The GAM model was the best performer across the range of evaluation measures employed, however all models were assessed as potentially useful for informing in situ conservation of M. integrifolia, A significant loss in the amount of M. integrifolia habitat has occurred (p < 0.05), with only 37% of former habitat (pre-clearing) remaining in 2003. Remnant patches are significantly smaller, have larger edge-to-area ratios and are more isolated from each other compared to pre-clearing configurations (p < 0.05). Whilst the network of suitable habitat patches is still largely intact, there are numerous smaller patches that are more isolated in the contemporary landscape compared with their connectedness before clearing. These results suggest that in situ conservation of M. integrifolia may be best achieved through a landscape approach that considers the relative contribution of small remnant habitat fragments to the species as a whole, as facilitating connectivity among the entire network of habitat patches.

Explaining usage of process modeling grammars : comparing three theoretical models in the study of two grammars

Our objective was to determine the factors that lead users to continue working with process modeling grammars after their initial adoption. We examined the explanatory power of three theoretical models of IT usage by applying them to two popular process modeling grammars. We found that a hybrid model of technology acceptance and expectation-confirmation best explained user intentions to continue using the grammars. We examined differences in the model results, and used them to provide three contributions. First, the study confirmed the applicability of IT usage models to the domain of process modeling. Second, we discovered that differences in continued usage intentions depended on the grammar type instead of the user characteristics. Third, we suggest implications and practice.

Making use of pedagogic models as reflective catalysts for investigating pedagogic practice

The notion of pedagogy for anyone in the teaching profession is innocuous. The term itself, is steeped in history but the details of the practice can be elusive. What does it mean for an academic to be embracing pedagogy? The problem is not limited to academics; most teachers baulk at the introduction of a pedagogic agenda and resist attempts to have them reflect on their classroom teaching practice, where ever that classroom might be constituted. This paper explores the application of a pedagogic model (Education Queensland, 2001) which was developed in the context of primary and secondary teaching and was part of a schooling agenda to improve pedagogy. As a teacher educator I introduced the model to classroom teachers (Hill, 2002) using an Appreciative Inquiry (Cooperrider and Srivastva 1987) model and at the same time applied the model to my own pedagogy as an academic. Despite being instigated as a model for classroom teachers, I found through my own practitioner investigation that the model was useful for exploring my own pedagogy as a university academic (Hill, 2007, 2008). Cooperrider, D.L. and Srivastva, S. (1987) Appreciative inquiry in organisational life, in Passmore, W. and Woodman, R. (Eds) Research in Organisational Changes and Development (Vol 1) Greenwich, CT: JAI Press. Pp 129-69 Education Queensland (2001) School Reform Longitudinal Study (QSRLS), Brisbane, Queensland Government. Hill, G. (2002, December ) Reflecting on professional practice with a cracked mirror: Productive Pedagogy experiences. Australian Association for Research in Education Conference. Brisbane, Australia. Hill, G. (2007) Making the assessment criteria explicit through writing feedback: A pedagogical approach to developing academic writing. International Journal of Pedagogies and Learning 3(1), 59-66. Hill, G. (2008) Supervising Practice Based Research. Studies in Learning, Evaluation, Innovation and Development, 5(4), 78-87

Density variation of different shaped food particulates in fluid bed drying : empirical models

Three particular geometrical shapes of parallelepiped, cylindrical and spheres were selected from potatoes (aspect ratio = 1:1, 2:1, 3:1), cut beans (length:diameter = 1:1, 2:1, 3:1) and peas respectively. The density variation of food particulates was studied in a batch fluidised bed dryer connected to a heat pump dehumidifier system. Apparent density and bulk density were evaluated with non-dimensional moisture at three different drying temperatures of 30, 40 and 50 o C. Relative humidity of hot air was kept at 15% in all drying temperatures. Several empirical relationships were developed for the determination of changes in densities with the moisture content. Simple mathematical models were obtained to relate apparent density and bulk density with moisture content.

Interrogating statistical models of music perception

We have developed a new experimental method for interrogating statistical theories of music perception by implementing these theories as generative music algorithms. We call this method Generation in Context. This method differs from most experimental techniques in music perception in that it incorporates aesthetic judgments. Generation In Context is designed to measure percepts for which the musical context is suspected to play an important role. In particular the method is suitable for the study of perceptual parameters which are temporally dynamic. We outline a use of this approach to investigate David Temperley’s (2007) probabilistic melody model, and provide some provisional insights as to what is revealed about the model. We suggest that Temperley’s model could be improved by dynamically modulating the probability distributions according to the changing musical context.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Bayesian models for longitudinal data

Longitudinal data, where data are repeatedly observed or measured on a temporal basis of time or age provides the foundation of the analysis of processes which evolve over time, and these can be referred to as growth or trajectory models. One of the traditional ways of looking at growth models is to employ either linear or polynomial functional forms to model trajectory shape, and account for variation around an overall mean trend with the inclusion of random eects or individual variation on the functional shape parameters. The identification of distinct subgroups or sub-classes (latent classes) within these trajectory models which are not based on some pre-existing individual classification provides an important methodology with substantive implications. The identification of subgroups or classes has a wide application in the medical arena where responder/non-responder identification based on distinctly diering trajectories delivers further information for clinical processes. This thesis develops Bayesian statistical models and techniques for the identification of subgroups in the analysis of longitudinal data where the number of time intervals is limited. These models are then applied to a single case study which investigates the neuropsychological cognition for early stage breast cancer patients undergoing adjuvant chemotherapy treatment from the Cognition in Breast Cancer Study undertaken by the Wesley Research Institute of Brisbane, Queensland. Alternative formulations to the linear or polynomial approach are taken which use piecewise linear models with a single turning point, change-point or knot at a known time point and latent basis models for the non-linear trajectories found for the verbal memory domain of cognitive function before and after chemotherapy treatment. Hierarchical Bayesian random eects models are used as a starting point for the latent class modelling process and are extended with the incorporation of covariates in the trajectory profiles and as predictors of class membership. The Bayesian latent basis models enable the degree of recovery post-chemotherapy to be estimated for short and long-term followup occasions, and the distinct class trajectories assist in the identification of breast cancer patients who maybe at risk of long-term verbal memory impairment.

Flexural behaviour and design of the new built-up LiteSteel beams

LiteSteel Beam (LSB) is a new cold-formed steel beam produced by OneSteel Australian Tube Mills. The new beam is effectively a channel section with two rectangular hollow flanges and a slender web, and is manufactured using a combined cold-forming and electric resistance welding process. OneSteel Australian Tube Mills is promoting the use of LSBs as flexural members in a range of applications, such as floor bearers. When LSBs are used as back to back built-up sections, they are likely to improve their moment capacity and thus extend their applications further. However, the structural behaviour of built-up beams is not well understood. Many steel design codes include guidelines for connecting two channels to form a built-up I-section including the required longitudinal spacing of connections. But these rules were found to be inadequate in some applications. Currently the safe spans of builtup beams are determined based on twice the moment capacity of a single section. Research has shown that these guidelines are conservative. Therefore large scale lateral buckling tests and advanced numerical analyses were undertaken to investigate the flexural behaviour of back to back LSBs connected by fasteners (bolts) at various longitudinal spacings under uniform moment conditions. In this research an experimental investigation was first undertaken to study the flexural behaviour of back to back LSBs including its buckling characteristics. This experimental study included tensile coupon tests, initial geometric imperfection measurements and lateral buckling tests. The initial geometric imperfection measurements taken on several back to back LSB specimens showed that the back to back bolting process is not likely to alter the imperfections, and the measured imperfections are well below the fabrication tolerance limits. Twelve large scale lateral buckling tests were conducted to investigate the behaviour of back to back built-up LSBs with various longitudinal fastener spacings under uniform moment conditions. Tests also included two single LSB specimens. Test results showed that the back to back LSBs gave higher moment capacities in comparison with single LSBs, and the fastener spacing influenced the ultimate moment capacities. As the fastener spacing was reduced the ultimate moment capacities of back to back LSBs increased. Finite element models of back to back LSBs with varying fastener spacings were then developed to conduct a detailed parametric study on the flexural behaviour of back to back built-up LSBs. Two finite element models were developed, namely experimental and ideal finite element models. The models included the complex contact behaviour between LSB web elements and intermittently fastened bolted connections along the web elements. They were validated by comparing their results with experimental results and numerical results obtained from an established buckling analysis program called THIN-WALL. These comparisons showed that the developed models could accurately predict both the elastic lateral distortional buckling moments and the non-linear ultimate moment capacities of back to back LSBs. Therefore the ideal finite element models incorporating ideal simply supported boundary conditions and uniform moment conditions were used in a detailed parametric study on the flexural behaviour of back to back LSB members. In the detailed parametric study, both elastic buckling and nonlinear analyses of back to back LSBs were conducted for 13 LSB sections with varying spans and fastener spacings. Finite element analysis results confirmed that the current design rules in AS/NZS 4600 (SA, 2005) are very conservative while the new design rules developed by Anapayan and Mahendran (2009a) for single LSB members were also found to be conservative. Thus new member capacity design rules were developed for back to back LSB members as a function of non-dimensional member slenderness. New empirical equations were also developed to aid in the calculation of elastic lateral distortional buckling moments of intermittently fastened back to back LSBs. Design guidelines were developed for the maximum fastener spacing of back to back LSBs in order to optimise the use of fasteners. A closer fastener spacing of span/6 was recommended for intermediate spans and some long spans where the influence of fastener spacing was found to be high. In the last phase of this research, a detailed investigation was conducted to investigate the potential use of different types of connections and stiffeners in improving the flexural strength of back to back LSB members. It was found that using transverse web stiffeners was the most cost-effective and simple strengthening method. It is recommended that web stiffeners are used at the supports and every third points within the span, and their thickness is in the range of 3 to 5 mm depending on the size of LSB section. The use of web stiffeners eliminated most of the lateral distortional buckling effects and hence improved the ultimate moment capacities. A suitable design equation was developed to calculate the elastic lateral buckling moments of back to back LSBs with the above recommended web stiffener configuration while the same design rules developed for unstiffened back to back LSBs were recommended to calculate the ultimate moment capacities.