A Bayesian network approach to modelling temporal behaviour of Lyngbya majuscula bloom initiation

Lyngbya majuscula is a cyanobacterium (blue-green algae) occurring naturally in tropical and subtropical coastal areas worldwide. Deception Bay, in Northern Moreton Bay, Queensland, has a history of Lyngbya blooms, and forms a case study for this investigation. The South East Queensland (SEQ) Healthy Waterways Partnership, collaboration between government, industry, research and the community, was formed to address issues affecting the health of the river catchments and waterways of South East Queensland. The Partnership coordinated the Lyngbya Research and Management Program (2005-2007) which culminated in a Coastal Algal Blooms (CAB) Action Plan for harmful and nuisance algal blooms, such as Lyngbya majuscula. This first phase of the project was predominantly of a scientific nature and also facilitated the collection of additional data to better understand Lyngbya blooms. The second phase of this project, SEQ Healthy Waterways Strategy 2007-2012, is now underway to implement the CAB Action Plan and as such is more management focussed. As part of the first phase of the project, a Science model for the initiation of a Lyngbya bloom was built using Bayesian Networks (BN). The structure of the Science Bayesian Network was built by the Lyngbya Science Working Group (LSWG) which was drawn from diverse disciplines. The BN was then quantified with annual data and expert knowledge. Scenario testing confirmed the expected temporal nature of bloom initiation and it was recommended that the next version of the BN be extended to take this into account. Elicitation for this BN thus occurred at three levels: design, quantification and verification. The first level involved construction of the conceptual model itself, definition of the nodes within the model and identification of sources of information to quantify the nodes. The second level included elicitation of expert opinion and representation of this information in a form suitable for inclusion in the BN. The third and final level concerned the specification of scenarios used to verify the model. The second phase of the project provides the opportunity to update the network with the newly collected detailed data obtained during the previous phase of the project. Specifically the temporal nature of Lyngbya blooms is of interest. Management efforts need to be directed to the most vulnerable periods to bloom initiation in the Bay. To model the temporal aspects of Lyngbya we are using Object Oriented Bayesian networks (OOBN) to create ‘time slices’ for each of the periods of interest during the summer. OOBNs provide a framework to simplify knowledge representation and facilitate reuse of nodes and network fragments. An OOBN is more hierarchical than a traditional BN with any sub-network able to contain other sub-networks. Connectivity between OOBNs is an important feature and allows information flow between the time slices. This study demonstrates more sophisticated use of expert information within Bayesian networks, which combine expert knowledge with data (categorized using expert-defined thresholds) within an expert-defined model structure. Based on the results from the verification process the experts are able to target areas requiring greater precision and those exhibiting temporal behaviour. The time slices incorporate the data for that time period for each of the temporal nodes (instead of using the annual data from the previous static Science BN) and include lag effects to allow the effect from one time slice to flow to the next time slice. We demonstrate a concurrent steady increase in the probability of initiation of a Lyngbya bloom and conclude that the inclusion of temporal aspects in the BN model is consistent with the perceptions of Lyngbya behaviour held by the stakeholders. This extended model provides a more accurate representation of the increased risk of algal blooms in the summer months and show that the opinions elicited to inform a static BN can be readily extended to a dynamic OOBN, providing more comprehensive information for decision makers.

Kaleidoscope : shaping a better future through a stakeholder approach to widespread organisational change

The main focus of ‘Kaleidoscope: Reframing evaluation through a stakeholder approach to sustainable, cultural change in Higher Education’ is to develop a set of principles to guide user-led engagement in widespread organisational change and maximise its impact. The word kaleidoscope represents the unique lens through which each institution will need to view their cultural specificity and local context through an extensive process of collaboration and engagement, followed by communication and dissemination. Kaleidoscope has particular relevance when new approaches to learning and teaching evaluation are introduced by tertiary institutions. Building on the Reframe Project, which involved three years of user-led consultation and was designed to meet stakeholders’ needs, QUT successfully introduced a new evaluation framework in 2013 across the university. Reframe was evidence based, involved scholarly reflection and was founded on a strong theoretical framework. The evolution of the evaluation framework included analysis of scholarly literature and environmental scans across the higher education sector (Alderman, et al., 2012), researched development of conceptual theory (Alderman, et al., in press 2013), incorporated the stakeholder voice and framed within project management principles (Alderman & Melanie, 2012). Kaleidoscope’s objectives are for QUT to develop its research-based stakeholder approach to distil the successful experience exhibited in the Reframe Project into a transferable set of guidelines for use by other tertiary institutions across the sectors. These guidelines will assist others to design, develop, and deploy, their own culturally specific widespread organisational change informed by stakeholder engagement and organisational buy-in. It is intended that these guidelines will promote, support and enable other tertiary institutions to embark on their own projects and maximise the impact. In correlation with a our conference paper, this round table presents the Draft Guidelines and Framework ready for external peer review by evaluation practitioners, as part of Kaleidoscope’s dissemination (Hinton & Gannaway, 2011) applying illuminative evaluation theory (Parlett & Hamilton, 1976), through conference workshops and linked round table discussions (Shapiro, et al., 1983; Jacobs, 2000).

REFRAME: a new approach to evaluation in higher education

Evaluation practices in the higher education sector have been criticised for having unclear purpose and principles; ignoring the complexity and changing nature of learning and teaching and the environments in which they occur; relying almost exclusively on student ratings of teachers working in classroom settings; lacking reliability and validity; using data for inappropriate purposes; and focusing on accountability and marketing rather than the improvement of learning and teaching. In response to similar criticism from stakeholders, in 2011 Queensland University of Technology began a project, entitled REFRAME, to review its approach to evaluation, particularly the student survey system it had been using for the past five years. This presentation will outline the scholarly, evidence based methodology used to undertake institution-wide change, meet the needs of stakeholders suitable to the cultural needs of the institution. It is believed that this approach is broadly applicable to other institutions contemplating change with regard to evaluation of learning and teaching.

Effective punishment experiences : the need for a more comprehensive approach to conceptualising behavioural punishers and reinforcers in a road safety context

Determining what consequences are likely to serve as effective punishment for any given behaviour is a complex task. This chapter focuses specifically on illegal road user behaviours and the mechanisms used to punish and deter them. Traffic law enforcement has traditionally used the threat and/or receipt of legal sanctions and penalties to deter illegal and risky behaviours. This process represents the use of positive punishment, one of the key behaviour modification mechanisms. Behaviour modification principles describe four types of reinforcers: positive and negative punishments and positive and negative reinforcements. The terms ‘positive’ and ‘negative’ are not used in an evaluative sense here. Rather, they represent the presence (positive) or absence (negative) of stimuli to promote behaviour change. Punishments aim to inhibit behaviour and reinforcements aim to encourage it. This chapter describes a variety of punishments and reinforcements that have been and could be used to modify illegal road user behaviours. In doing so, it draws on several theoretical perspectives that have defined behavioural reinforcement and punishment in different ways. Historically, the main theoretical approach used to deter risky road use has been classical deterrence theory which has focussed on the perceived certainty, severity and swiftness of penalties. Stafford and Warr (1993) extended the traditional deterrence principles to include the positive reinforcement concept of punishment avoidance. Evidence of the association between punishment avoidance experiences and behaviour has been established for a number of risky road user behaviours including drink driving, unlicensed driving, and speeding. We chose a novel way of assessing punishment avoidance by specifying two sub-constructs (detection evasion and punishment evasion). Another theorist, Akers, described the idea of competing reinforcers, termed differential reinforcement, within social learning theory (1977). Differential reinforcement describes a balance of reinforcements and punishments as influential on behaviour. This chapter describes comprehensive way of conceptualising a broad range of reinforcement and punishment concepts, consistent with Akers’ differential reinforcement concept, within a behaviour modification framework that incorporates deterrence principles. The efficacy of three theoretical perspectives to explain self-reported speeding among a sample of 833 Australian car drivers was examined. Results demonstrated that a broad range of variables predicted speeding including personal experiences of evading detection and punishment for speeding, intrinsic sensations, practical benefits expected from speeding, and an absence of punishing effects from being caught. Not surprisingly, being younger was also significantly related to more frequent speeding, although in a regression analysis, gender did not retain a significant influence once all punishment and reinforcement variables were entered. The implications for speed management, as well as road user behaviour modification more generally, are discussed in light of these findings. Overall, the findings reported in this chapter suggest that a more comprehensive approach is required to manage the behaviour of road users which does not rely solely on traditional legal penalties and sanctions.

A CLASS act : the teaching team approach to subject coordination

Advancing the development of good practice around the teaching team has been the focus of a recently completed, nationally funded Australian grant entitled Coordinators Leading Advancement of Sessional Staff (CLASS). The project focused on developing leadership capacity of subject coordinators to provide supportive contexts for sessional staff to enhance their knowledge of teaching practice and contribute to subject improvement through a team approach. An action learning approach and notions of distributed leadership underpinned the activities of the teaching teams in the program. This paper provides an overview of a practical approach, led by the subject coordinator, to engaging sessional staff through the facilitation of a supportive network within the teaching team. It addresses some of the gaps identified in the recent literature which includes lack of role clarity for all members of the team and provides some examples of initiatives that teams engaged with to address some of the challenges identified. Resources to support this approach were developed and are shared through the project website. Recommendations for future direction include improved policy and practice at the institutional level, better recognition and reward for subject coordinators and resourcing to support the participation and professional development needs of sessional staff.

A proposed approach to monitor private-sector policies and practices related to food environments, obesity and non-communicable disease prevention

Private-sector organizations play a critical role in shaping the food environments of individuals and populations. However, there is currently very limited independent monitoring of private-sector actions related to food environments. This paper reviews previous efforts to monitor the private sector in this area, and outlines a proposed approach to monitor private-sector policies and practices related to food environments, and their influence on obesity and non-communicable disease (NCD) prevention. A step-wise approach to data collection is recommended, in which the first (‘minimal’) step is the collation of publicly available food and nutrition-related policies of selected private-sector organizations. The second (‘expanded’) step assesses the nutritional composition of each organization's products, their promotions to children, their labelling practices, and the accessibility, availability and affordability of their products. The third (‘optimal’) step includes data on other commercial activities that may influence food environments, such as political lobbying and corporate philanthropy. The proposed approach will be further developed and piloted in countries of varying size and income levels. There is potential for this approach to enable national and international benchmarking of private-sector policies and practices, and to inform efforts to hold the private sector to account for their role in obesity and NCD prevention.

Quantitative proteomic approach to study subcellular localization of membrane proteins

As proteins within cells are spatially organized according to their role, knowledge about protein localization gives insight into protein function. Here, we describe the LOPIT technique (localization of organelle proteins by isotope tagging) developed for the simultaneous and confident determination of the steady-state distribution of hundreds of integral membrane proteins within organelles. The technique uses a partial membrane fractionation strategy in conjunction with quantitative proteomics. Localization of proteins is achieved by measuring their distribution pattern across the density gradient using amine-reactive isotope tagging and comparing these patterns with those of known organelle residents. LOPIT relies on the assumption that proteins belonging to the same organelle will co-fractionate. Multivariate statistical tools are then used to group proteins according to the similarities in their distributions, and hence localization without complete centrifugal separation is achieved. The protocol requires approximately 3 weeks to complete and can be applied in a high-throughput manner to material from many varied sources.

A data mining approach to improve the automated quality of data

This thesis describes the development of a robust and novel prototype to address the data quality problems that relate to the dimension of outlier data. It thoroughly investigates the associated problems with regards to detecting, assessing and determining the severity of the problem of outlier data; and proposes granule-mining based alternative techniques to significantly improve the effectiveness of mining and assessing outlier data.

A therapeutic approach to assessing legal capacity in Australia

Australia lacks a satisfactory, national paradigm for assessing legal capacity in the context of testamentary, enduring power of attorney and advance care directive documents. Capacity assessments are currently conducted on an ad hoc basis by legal and/or medical professionals. The reliability of the assessment process is subject to the skill set and mutual understanding of the legal and/or medical professional conducting the assessment. There is a growth in the prevalence of diseases such as dementia. Such diseases impact upon cognition which increasingly necessitates collaboration between the legal and medical professions when assessing the effect of mentally disabling conditions upon legal capacity. Miscommunication and lack of understanding between legal and medical professionals involved could impede the development of a satisfactory paradigm. This article will discuss legal capacity assessment in Australia and how to strengthen the relationship between legal and medical professionals involved in capacity assessments. The development of a national paradigm would promote consistency and transparency of process, helping to improve the professional relationship and maximising the principles of autonomy, participation and dignity.

Rough set based approach to text classification

Textual document set has become an important and rapidly growing information source in the web. Text classification is one of the crucial technologies for information organisation and management. Text classification has become more and more important and attracted wide attention of researchers from different research fields. In this paper, many feature selection methods, the implement algorithms and applications of text classification are introduced firstly. However, because there are much noise in the knowledge extracted by current data-mining techniques for text classification, it leads to much uncertainty in the process of text classification which is produced from both the knowledge extraction and knowledge usage, therefore, more innovative techniques and methods are needed to improve the performance of text classification. It has been a critical step with great challenge to further improve the process of knowledge extraction and effectively utilization of the extracted knowledge. Rough Set decision making approach is proposed to use Rough Set decision techniques to more precisely classify the textual documents which are difficult to separate by the classic text classification methods. The purpose of this paper is to give an overview of existing text classification technologies, to demonstrate the Rough Set concepts and the decision making approach based on Rough Set theory for building more reliable and effective text classification framework with higher precision, to set up an innovative evaluation metric named CEI which is very effective for the performance assessment of the similar research, and to propose a promising research direction for addressing the challenging problems in text classification, text mining and other relative fields.

Video analytics for the detection of near-miss incidents on approach to railway level crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand causal factors that contribute to these accidents, the Cooperative Research Centre for Rail Innovation is running a project entitled Baseline Level Crossing Video. The project aims to improve the recording of level crossing safety data by developing an intelligent system capable of detecting near-miss incidents and capturing quantitative data around these incidents. To detect near-miss events at railway level crossings a video analytics module is being developed to analyse video footage obtained from forward-facing cameras installed on trains. This paper presents a vision base approach for the detection of these near-miss events. The video analytics module is comprised of object detectors and a rail detection algorithm, allowing the distance between a detected object and the rail to be determined. An existing publicly available Histograms of Oriented Gradients (HOG) based object detector algorithm is used to detect various types of vehicles in each video frame. As vehicles are usually seen from a sideway view from the cabin’s perspective, the results of the vehicle detector are verified using an algorithm that can detect the wheels of each detected vehicle. Rail detection is facilitated using a projective transformation of the video, such that the forward-facing view becomes a bird’s eye view. Line Segment Detector is employed as the feature extractor and a sliding window approach is developed to track a pair of rails. Localisation of the vehicles is done by projecting the results of the vehicle and rail detectors on the ground plane allowing the distance between the vehicle and rail to be calculated. The resultant vehicle positions and distance are logged to a database for further analysis. We present preliminary results regarding the performance of a prototype video analytics module on a data set of videos containing more than 30 different railway level crossings. The video data is captured from a journey of a train that has passed through these level crossings.

A web-based approach to data imputation

In this paper, we present WebPut, a prototype system that adopts a novel web-based approach to the data imputation problem. Towards this, Webput utilizes the available information in an incomplete database in conjunction with the data consistency principle. Moreover, WebPut extends effective Information Extraction (IE) methods for the purpose of formulating web search queries that are capable of effectively retrieving missing values with high accuracy. WebPut employs a confidence-based scheme that efficiently leverages our suite of data imputation queries to automatically select the most effective imputation query for each missing value. A greedy iterative algorithm is proposed to schedule the imputation order of the different missing values in a database, and in turn the issuing of their corresponding imputation queries, for improving the accuracy and efficiency of WebPut. Moreover, several optimization techniques are also proposed to reduce the cost of estimating the confidence of imputation queries at both the tuple-level and the database-level. Experiments based on several real-world data collections demonstrate not only the effectiveness of WebPut compared to existing approaches, but also the efficiency of our proposed algorithms and optimization techniques.

A state-wide systems approach to embedding the learning and teaching of sustainability in teacher education

Education systems have a key role to play in preparing future citizens to engage in sustainable living practices and help create a more sustainable world. Many schools throughout Australia have begun to develop whole-school approaches to sustainability education that are supported by national and state policies and curriculum frameworks. Preservice teacher education, however, lags behind in building the capacity of new teachers to initiate and implement such approaches (ARIES, 2010). This proposed project seeks to develop a state-wide systems approach to embedding Education for Sustainability (EfS) in teacher education that is aligned with the Australian National Curriculum and the aspirations for EfS in the Melbourne Declaration and other national documents. Representatives from all teacher education institutions and other agents of change in the Queensland education system will be engaged in a multilevel systems approach, involving collaboration at the state, institutional and course levels, to develop curriculum practices that reflect a shared vision of EfS.