A Bayesian analysis of an agricultural field trial with three spatial dimensions

Modern technology now has the ability to generate large datasets over space and time. Such data typically exhibit high autocorrelations over all dimensions. The field trial data motivating the methods of this paper were collected to examine the behaviour of traditional cropping and to determine a cropping system which could maximise water use for grain production while minimising leakage below the crop root zone. They consist of moisture measurements made at 15 depths across 3 rows and 18 columns, in the lattice framework of an agricultural field. Bayesian conditional autoregressive (CAR) models are used to account for local site correlations. Conditional autoregressive models have not been widely used in analyses of agricultural data. This paper serves to illustrate the usefulness of these models in this field, along with the ease of implementation in WinBUGS, a freely available software package. The innovation is the fitting of separate conditional autoregressive models for each depth layer, the ‘layered CAR model’, while simultaneously estimating depth profile functions for each site treatment. Modelling interest also lay in how best to model the treatment effect depth profiles, and in the choice of neighbourhood structure for the spatial autocorrelation model. The favoured model fitted the treatment effects as splines over depth, and treated depth, the basis for the regression model, as measured with error, while fitting CAR neighbourhood models by depth layer. It is hierarchical, with separate onditional autoregressive spatial variance components at each depth, and the fixed terms which involve an errors-in-measurement model treat depth errors as interval-censored measurement error. The Bayesian framework permits transparent specification and easy comparison of the various complex models compared.

Classifying external causes of injury : history, current approaches, and future directions

The International Classification of Diseases (ICD) is used to categorise diseases, injuries and external causes, and is a key epidemiological tool enabling the storage and retrieval of data from health and vital records to produce core international mortality and morbidity statistics. The ICD is updated periodically to ensure the classification remains current and work is now underway to develop the next revision, ICD-11. There have been almost 20 years since the last ICD edition was published and over 60 years since the last substantial structural revision of the external causes chapter. Revision of such a critical tool requires transparency and documentation to ensure that changes made to the classification system are recorded comprehensively for future reference. In this paper, the authors provide a history of external causes classification development and outline the external cause structure. Approaches to manage ICD-10 deficiencies are discussed and the ICD-11 revision approach regarding the development of, rationale for and implications of proposed changes to the chapter are outlined. Through improved capture of external cause concepts in ICD-11, a stronger evidence base will be available to inform injury prevention, treatment, rehabilitation and policy initiatives to ultimately contribute to a reduction in injury morbidity and mortality.

The role of law in responding to climate change : emerging regulatory, liability and market approaches

Climate change presents as the archetypal environmental problem with short-term economic self-interest operating to the detriment of the long-term sustainability of our society. The scientific reports of the Intergovernmental Panel on Climate Change strongly assert that the stabilisation of emissions in the atmosphere, to avoid the adverse impacts of climate change, requires significant and rapid reductions in ‘business as usual’ global greenhouse gas emissions. The sheer magnitude of emissions reductions required, within this urgent timeframe, will necessitate an unprecedented level of international, multi-national and intra-national cooperation and will challenge conventional approaches to the creation and implementation of international and domestic legal regimes. To meet this challenge, existing international, national and local legal systems must harmoniously implement a strong international climate change regime through a portfolio of traditional and innovative legal mechanisms that swiftly transform current behavioural practices in emitting greenhouse gases. These include the imposition of strict duties to reduce emissions through the establishment of strong command and control regulation (the regulatory approach); mechanisms for the creation and distribution of liabilities for greenhouse gas emissions and climaterelated harm (the liability approach) and the use of innovative regulatory tools in the form of the carbon trading scheme (the market approach). The legal relations between these various regulatory, liability and market approaches must be managed to achieve a consistent, compatible and optimally effective legal regime to respond to the threat of climate change. The purpose of this thesis is to analyse and evaluate the emerging legal rules and frameworks, both international and Australian, required for the effective regulation of greenhouse gas emissions to address climate change in the context of the urgent and deep emissions reductions required to minimise the adverse impacts of climate change. In doing so, this thesis will examine critically the existing and potential role of law in effectively responding to climate change and will provide recommendations on the necessary reforms to achieve a more effective legal response to this global phenomenon in the future.

Should we still lecture? Reconsidering pedagogical approaches to promote student engagement, challenging the traditional lecture

The dynamic interplay between existing learning frameworks: people, pedagogy, learning spaces and technology is challenging the traditional lecture. A paradigm is emerging from the correlation of change amongst these elements, offering new possibilities for improving the quality of the learning experience. For many universities, the design of physical learning spaces has been the focal point for blending technology and flexible learning spaces to promote learning and teaching. As the pace of technological change intensifies, affording new opportunities for engaging learners, pedagogical practice in higher education is not comparatively evolving. The resulting disparity is an opportunity for the reconsideration of pedagogical practice for increased student engagement in physical learning spaces as an opportunity for active learning. This interplay between students, staff and technology is challenging the value for students in attending physical learning spaces such as the traditional lecture. Why should students attend for classes devoted to content delivery when streaming and web technologies afford more flexible learning opportunities? Should we still lecture? Reconsideration of pedagogy is driving learning design at Queensland University of Technology, seeking new approaches affording increased student engagement via active learning experiences within large lectures. This paper provides an overview and an evaluation of one of these initiatives, Open Web Lecture (OWL), an experimental web based student response application developed by Queensland University of Technology. OWL seamlessly integrates a virtual learning environment within physical learning spaces, fostering active learning opportunities. This paper will evaluate the pilot of this initiative through consideration of effectiveness in increasing student engagement through the affordance of web enabled active learning opportunities in physical learning spaces.

Should we still lecture? Reconsidering pedagogical approaches to promote student engagement, challenging the traditional lecture

The dynamic interplay between existing learning frameworks: people, pedagogy, learning spaces and technology is challenging the traditional lecture. A paradigm is emerging from the correlation of change amongst these elements, offering new possibilities for improving the quality of the learning experience. For many universities, the design of physical learning spaces has been the focal point for blending technology and flexible learning spaces to promote learning and teaching. As the pace of technological change intensifies, affording new opportunities for engaging learners, pedagogical practice in higher education is not comparatively evolving. The resulting disparity is an opportunity for the reconsideration of pedagogical practice for increased student engagement in physical learning spaces as an opportunity for active learning. This interplay between students, staff and technology is challenging the value for students in attending physical learning spaces such as the traditional lecture. Why should students attend for classes devoted to content delivery when streaming and web technologies afford more flexible learning opportunities? Should we still lecture? Reconsideration of pedagogy is driving learning design at Queensland University of Technology, seeking new approaches affording increased student engagement via active learning experiences within large lectures. This paper provides an overview and an evaluation of one of these initiatives, Open Web Lecture (OWL), an experimental web based student response application developed by Queensland University of Technology. OWL seamlessly integrates a virtual learning environment within physical learning spaces, fostering active learning opportunities. This paper will evaluate the pilot of this initiative through consideration of effectiveness in increasing student engagement through the affordance of web enabled active learning opportunities in physical learning spaces.

How do the learning outcomes of students compare in units with different levels of practical work? A comparative study about the relation between Problem-­Based Learning and assessment approaches

The literature supporting the notion that active, student-centered learning is superior to passive, teacher-centered instruction is encyclopedic (Bonwell & Eison, 1991; Bruning, Schraw, & Ronning, 1999; Haile, 1997a, 1997b, 1998; Johnson, Johnson, & Smith, 1999). Previous action research demonstrated that introducing a learning activity in class improved the learning outcomes of students (Mejias, 2010). People acquire knowledge and skills through practice and reflection, not by watching and listening to others telling them how to do something. In this context, this project aims to find more insights about the level of interactivity in the curriculum a class should have and its alignment with assessment so the intended learning outcomes (ILOs) are achieved. In this project, interactivity is implemented in the form of problem- based learning (PBL). I present the argument that a more continuous formative feedback when implemented with the correct amount of PBL stimulates student engagement bringing enormous benefits to student learning. Different levels of practical work (PBL) were implemented together with two different assessment approaches in two subjects. The outcomes were measured using qualitative and quantitative data to evaluate the levels of student engagement and satisfaction in the terms of ILOs.

A Bayesian‐Markov process for reliability prediction

Accurate reliability prediction for large-scale, long lived engineering is a crucial foundation for effective asset risk management and optimal maintenance decision making. However, a lack of failure data for assets that fail infrequently, and changing operational conditions over long periods of time, make accurate reliability prediction for such assets very challenging. To address this issue, we present a Bayesian-Marko best approach to reliability prediction using prior knowledge and condition monitoring data. In this approach, the Bayesian theory is used to incorporate prior information about failure probabilities and current information about asset health to make statistical inferences, while Markov chains are used to update and predict the health of assets based on condition monitoring data. The prior information can be supplied by domain experts, extracted from previous comparable cases or derived from basic engineering principles. Our approach differs from existing hybrid Bayesian models which are normally used to update the parameter estimation of a given distribution such as the Weibull-Bayesian distribution or the transition probabilities of a Markov chain. Instead, our new approach can be used to update predictions of failure probabilities when failure data are sparse or nonexistent, as is often the case for large-scale long-lived engineering assets.

PySSM : a Python module for Bayesian inference of linear Gaussian state space models

PySSM is a Python package that has been developed for the analysis of time series using linear Gaussian state space models (SSM). PySSM is easy to use; models can be set up quickly and efficiently and a variety of different settings are available to the user. It also takes advantage of scientific libraries Numpy and Scipy and other high level features of the Python language. PySSM is also used as a platform for interfacing between optimised and parallelised Fortran routines. These Fortran routines heavily utilise Basic Linear Algebra (BLAS) and Linear Algebra Package (LAPACK) functions for maximum performance. PySSM contains classes for filtering, classical smoothing as well as simulation smoothing.

Alternatives to the behavioural technicist approaches to study skills

The content and approach of study skills courses are critiqued and alternatives are suggested. It is proposed that an approach providing students with knowledge about the cognitive processes involved in mastering complex material would make the study skills teacher an agent of social change aiming for the enlightenment and emancipation of students and lecturers.

A sequential Monte Carlo algorithm to incorporate model uncertainty in Bayesian sequential design

Here we present a sequential Monte Carlo (SMC) algorithm that can be used for any one-at-a-time Bayesian sequential design problem in the presence of model uncertainty where discrete data are encountered. Our focus is on adaptive design for model discrimination but the methodology is applicable if one has a different design objective such as parameter estimation or prediction. An SMC algorithm is run in parallel for each model and the algorithm relies on a convenient estimator of the evidence of each model which is essentially a function of importance sampling weights. Other methods for this task such as quadrature, often used in design, suffer from the curse of dimensionality. Approximating posterior model probabilities in this way allows us to use model discrimination utility functions derived from information theory that were previously difficult to compute except for conjugate models. A major benefit of the algorithm is that it requires very little problem specific tuning. We demonstrate the methodology on three applications, including discriminating between models for decline in motor neuron numbers in patients suffering from neurological diseases such as Motor Neuron disease.

Models of collective cell behaviour with crowding effects : comparing lattice-based and lattice-free approaches

Individual-based models describing the migration and proliferation of a population of cells frequently restrict the cells to a predefined lattice. An implicit assumption of this type of lattice based model is that a proliferative population will always eventually fill the lattice. Here we develop a new lattice-free individual-based model that incorporates cell-to-cell crowding effects. We also derive approximate mean-field descriptions for the lattice-free model in two special cases motivated by commonly used experimental setups. Lattice-free simulation results are compared to these mean-field descriptions and to a corresponding lattice-based model. Data from a proliferation experiment is used to estimate the parameters for the new model, including the cell proliferation rate, showing that the model fits the data well. An important aspect of the lattice-free model is that the confluent cell density is not predefined, as with lattice-based models, but an emergent model property. As a consequence of the more realistic, irregular configuration of cells in the lattice-free model, the population growth rate is much slower at high cell densities and the population cannot reach the same confluent density as an equivalent lattice-based model.

Use of Bayesian MUNE to show differing rate of loss of motor units in subgroups of ALS

Objectives To evaluate differences among patients with different clinical features of ALS, we used our Bayesian method of motor unit number estimation (MUNE). Methods We performed serial MUNE studies on 42 subjects who fulfilled the diagnostic criteria for ALS during the course of their illness. Subjects were classified into three subgroups according to whether they had typical ALS (with upper and lower motor neurone signs) or had predominantly upper motor neurone weakness with only minor LMN signs, or predominantly lower motor neurone weakness with only minor UMN signs. In all subjects we calculated the half life of MUs, defined as the expected time for the number of MUs to halve, in one or more of the abductor digiti minimi (ADM), abductor pollicis brevis (APB) and extensor digitorum brevis (EDB) muscles. Results The mean half life of MUs was less in subjects who had typical ALS with both upper and lower motor neurone signs than in those with predominantly upper motor neurone weakness or predominantly lower motor neurone weakness. In 18 subjects we analysed the estimated size of the MUs and demonstrated the appearance of large MUs in subjects with upper or lower motor neurone predominant weakness. We found that the appearance of large MUs was correlated with the half life of MUs. Conclusions Patients with different clinical features of ALS have different rates of loss and different sizes of MUs. Significance: These findings could indicate differences in disease pathogenesis.