Drama and languages education : authentic assessment through process drama

In this chapter I explore the ways process drama can enrich and enliven the assessment regime of a middle school beginner language program. The chapter draws on five months’ language teaching which I did to collect data during my doctoral research. I taught a secondary co-educational class of 12-13 year olds (first year secondary school) for their German lessons while the teacher who had invited me in observed the lessons. Throughout the project there was an emphasis on student participation through questionnaire, discussion and interview...

Observation of new particle formation in subtropical urban environment

The aim of this study was to characterise the new particle formation events in a subtropical urban environment in the southern hemisphere. The study measured the number concentration of particles and its size distribution in Brisbane, Australia during 2009. The variation of particle number concentration and nucleation burst events were characterised as well as the particle growth rate which was first reported in urban environment of Australia. The annual average NUFP, NAitken and NNuc were 9.3 x 103, 3.7 x 103 and 5.6 x 103 cm-3, respectively. Weak seasonal variation in number concentration was observed. Local traffic exhaust emissions were a major contributor of the pollution (NUFP) observed in morning which was dominated by the Aitken mode particles, while particles formed by secondary formation processes contributed to the particle number concentration during afternoon. Overall, 65 nucleation burst events were identified during the study period. Nucleation burst events were classified into two groups, with and without particles growth after the burst of nucleation mode particles observed. The average particle growth rate of the nucleation events was 4.6 nm hr-1 (ranged from 1.79 – 7.78 nm hr-1). Case studies of the nucleation burst events were characterised including i) the nucleation burst with particle growth which is associated with the particle precursor emitted from local traffic exhaust emission, ii) the nucleation burst without particle growth which is due to the transport of industrial emissions from the coast to Brisbane city or other possible sources with unfavourable conditions which suppressed particle growth and iii) interplay between the above two cases which demonstrated the impact of the vehicle and industrial emissions on the variation of particle number concentration and its size distribution during the same day.

Exposure to airborne fungi and bacteria in Brisbane houses after the 2011 flood

A series of flooding events occurred in Queensland, Australia during December 2010 and January 2011. The state’s capital city of Brisbane experienced major flooding in January 2011, when the Brisbane River broke its bank and inundated low lying areas.

A flexible asset maintenance decision-making process model

Optimal Asset Maintenance decisions are imperative for efficient asset management. Decision Support Systems are often used to help asset managers make maintenance decisions, but high quality decision support must be based on sound decision-making principles. For long-lived assets, a successful Asset Maintenance decision-making process must effectively handle multiple time scales. For example, high-level strategic plans are normally made for periods of years, while daily operational decisions may need to be made within a space of mere minutes. When making strategic decisions, one usually has the luxury of time to explore alternatives, whereas routine operational decisions must often be made with no time for contemplation. In this paper, we present an innovative, flexible decision-making process model which distinguishes meta-level decision making, i.e., deciding how to make decisions, from the information gathering and analysis steps required to make the decisions themselves. The new model can accommodate various decision types. Three industrial case studies are given to demonstrate its applicability.

Improving asset management process modelling and integration

Asset management (AM) processes play an important role in assisting enterprises to manage their assets more efficiently. To visualise and improve AM processes, the processes need to be modelled using certain process modelling methodologies. Understanding the requirements for AM process modelling is essential for selecting or developing effective AM process modelling methodologies. However, little research has been done on analysing the requirements. This paper attempts to fill this gap by investigating the features of AM processes. It is concluded that AM process modelling requires intuitive representation of its processes, ‘fast’ implementation of the process modelling, effective evaluation of the processes and sound system integration.

Utilising Reliability and Condition Monitoring Data for Asset Health Prognosis

The ability to forecast machinery health is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models which attempt to forecast machinery health based on condition data such as vibration measurements. This paper demonstrates how the population characteristics and condition monitoring data (both complete and suspended) of historical items can be integrated for training an intelligent agent to predict asset health multiple steps ahead. The model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function estimator. The trained network is capable of estimating the future survival probabilities when a series of asset condition readings are inputted. The output survival probabilities collectively form an estimated survival curve. Pump data from a pulp and paper mill were used for model validation and comparison. The results indicate that the proposed model can predict more accurately as well as further ahead than similar models which neglect population characteristics and suspended data. This work presents a compelling concept for longer-range fault prognosis utilising available information more fully and accurately.

Optimising preventive maintenance strategy for production Lines

Preventive Maintenance (PM) is often applied to improve the reliability of production lines. A Split System Approach (SSA) based methodology is presented to assist in making optimal PM decisions for serial production lines. The methodology treats a production line as a complex series system with multiple (imperfect) PM actions over multiple intervals. The conditional and overall reliability of the entire production line over these multiple PM intervals are hierarchically calculated using SSA, and provide a foundation for cost analysis. Both risk-related cost and maintenance-related cost are factored into the methodology as either deterministic or random variables. This SSA based methodology enables Asset Management (AM) decisions to be optimised considering a variety of factors including failure probability, failure cost, maintenance cost, PM performance, and the type of PM strategy. The application of this new methodology and an evaluation of the effects of these factors on PM decisions are demonstrated using an example. The results of this work show that the performance of a PM strategy can be measured by its Total Expected Cost Index (TECI). The optimal PM interval is dependent on TECI, PM performance and types of PM strategies. These factors are interrelated. Generally, it was found that a trade-off between reliability and the number of PM actions needs to be made so that one can minimise Total Expected Cost (TEC) for asset maintenance.

Machine prognostics based on health state estimation using SVM

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation, and can also improve productivity and enhance system safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and an assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of machines based on health state probability estimation and involving historical knowledge embedded in the closed loop diagnostics and prognostics systems. The technique uses a Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation, which can affect the accuracy of prediction. To validate the feasibility of the proposed model, real life historical data from bearings of High Pressure Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life. The results obtained were very encouraging and showed that the proposed prognostic system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

Professional development using informal learning networks : an empirical study in Australia’s digital content industry

Informal learning networks play a key role in the skill and professional development of professionals working in micro-businesses within Australia’s digital content industry as they do not necessarily have access to a learning and development or a human resources section that can assist in mapping their learning pathway. Professionals working in this environment would typically adopt an informal learning approach to their skill and professional development by utilising their social and business networks. The overall aim of this PhD research project is to study how these professionals manage their skill and professional development, and to explore what role informal learning networks play in this professional learning context. This paper will describe the theme of the research project and how it fits with previous research and other relevant studies. Secondly, it will present the study’s research focus, and the research questions. It will also present relevant theories and perspectives, and the methods for empirical data collection. Data collection will be through three distinct phases using a mixed methods research design: an online survey, interviews, and case studies. It should be noted the findings presented in this paper offer some early results of the research project.

Leveraging R&D Investment for the Australian Built Environment : Industry Report

The overarching goal of this project is to better match funding strategies to industry needs to maximise the benefits of R&D to Australia’s infrastructure and building industry. Project partners are: Queensland Department of Public Works; Queensland Transport and Main Roads; Western Australian Department of Treasury and Finance; John Holland; Queensland University of Technology; Swinburne University of Technology; and VTT Technical Research Centre of Finland (Prof Göran Roos). This project has been endorsed by the Australian Built Environment Industry Innovation Council (BEIIC) with Council member Prof Catherin Bull serving on this project’s Steering Committee. This project seeks to: (i) maximise the value of R&D investment in this sector through improved understanding of future industry research needs; and (ii) address the perceived problem of a disproportionately low R&D investment in this sector, relative to the size and national importance of the sector. This research will develop new theory built on open innovation, dynamic capabilities and absorptive capacity theories in the context of strategic foresighting and roadmapping activities. Four project phases have been designed to address this research: 1: Audit and analysis of R&D investment in the Australian built environment since 1990 - access publically available data relating to R&D investments across Australia from public and private organisations to understand past trends. 2: Examine diffusion mechanisms of research and innovation and its impact on public and private organisations – investigate specific R&D investments to determine the process of realising research support, direction-setting, project engagement, impacts and pathways to adoption. 3: Develop a strategic roadmap for the future of this critical Australian industry - assess the likely future landscapes that R&D investment will both respond to and anticipate. 4: Develop policy to maximise the value of R&D investments to public and private organisations – through translating project learnings into policy guidelines.

Graphene-like nano-sheets based LiTaO3 surface acoustic wave NO2 gas sensor

Thin films consisting of graphene-like nano-sheets were deposited onto LiTaO3 surface acoustic wave transducers. A thickness of less than 10 nm and the existence of C-C bond were observed during the characterization of graphene-like nano-sheets. Frequency shift of 18.7 kHz and 14.9 kHz towards 8.5 ppm NO2 at two different operating temperature, 40°C and 25°C, respectively, was observed.

Not just another ball game : young adult and adult football fictions are different

Current media attention on the crossover novel highlights the increasing permeability of the boundaries between young adult and adult fiction. This paper will focus upon some of the difficulties around definitions of young adult fiction before considering the fiction of football, or soccer as it is more commonly known in Australia. The football genre exhibits a number of discrete and identifiable differences between young adult and adult readerships including, for example, the role of the protagonist, and the narrative’s distance from the game. This paper will use Franco Moretti’s Mapping as Distant Reading model of abstraction to highlight and unpack these and other characteristic differences in the narratological and stylistic techniques employed across adult and young adult texts. Close reading analysis of the adult football fiction Striker (1992) by Hunter Davies and young adult football fiction Lucy Zeezou’s Goal (2008) by Liz Deep-Jones’ will further illustrate the range of tensions and divergences as they are reflected across those readerships. The texts have been selected because they speak to themes of fear and safety; Joe Swift (Striker) is driven by a need to move away from childhood poverty and insecurity, while Lucy Zeezou shelters a homeless friend. With both protagonists being kidnapped for ransom for example, the texts have also been selected for their striking similarities in form and content.

Controlling buoyancy-driven profiling floats for applications in ocean observation

Establishing a persistent presence in the ocean with an autonomous underwater vehicle (AUV) capable of observing temporal variability of large-scale ocean processes requires a unique sensor platform. In this paper, we examine the utility of vehicles that can only control their depth in the water column for such extended deployments. We present a strategy that utilizes ocean model predictions to facilitate a basic level of autonomy and enables general control for these profiling floats. The proposed method is based on experimentally validated techniques for utilizing ocean current models to control autonomous gliders. With the appropriate vertical actuation, and utilizing spatio–temporal variations in water speed and direction, we show that general controllability results can be met. First, we apply an A* planner to a local controllability map generated from predictions of ocean currents. This computes a path between start and goal waypoints that has the highest likelihood of successful execution. A computed depth plan is generated with a model-predictive controller (MPC), and selects the depths for the vehicle so that ambient currents guide it toward the goal. Mission constraints are included to simulate and motivate a practical data collection mission. Results are presented in simulation for a mission off the coast of Los Angeles, CA, USA, that show encouraging results in the ability of a drifting vehicle to reach a desired location.