Representative learning design and functionality of research and practice in sport

Egon Brunswik proposed the concept of “representative design” for psychological experimentation, which has historically been overlooked or confused with another of Brunswik’s terms, ecological validity. In this article, we reiterate the distinc­tion between these two important concepts and highlight the relevance of the term representative design for sports psychology, practice, and experimental design. We draw links with ideas on learning design in the constraints-led approach to motor learning and nonlinear pedagogy. We propose the adoption of a new term, repre­sentative learning design, to help sport scientists, experimental psychologists, and pedagogues recognize the potential application of Brunswik’s original concepts, and to ensure functionality and action fidelity in training and learning environments.

Ethics and public health

• recognise that “ethics” is often defined and approached in different ways • describe the foundations and development of public health ethics • summarise some key ethical systems and their relevance to public health practice • outline and critique some codes of ethics, and discuss their application to public health practice • recognise, evaluate and communicate ethical concerns regarding public health, and apply ethical principles in your practice.

The Law and Governance of Water Resources : The Challenge of Sustainability

To achieve the sustainable use and development of water resources is a daunting challenge for both the global and local communities. It requires commitments by all groups within the international, national and local communities from their own particular, possibly conflicting, perspectives. Without a set of coherent legal arrangements designed to ensure effective governance of water resources, their sustainable use and development are unlikely to be achieved. This study looks at how the legal arrangements for managing water resources have evolved across the continents over hundreds of years; their relevance for contemporary society; how the norms of current international and national legal regimes are responding; and, most importantly, how legal rights and duties should be structured so as to achieve sustainability in the future.

An exploration of the role of web and mobile social media in the implementation of e-Government in Malaysia

This paper reports on an exploratory study of the role of web and social media in e-governments, especially in the context of Malaysia, with some comparisons and contrasts from other countries where such governmental efforts have been underway for awhile. It describes the current e-government efforts in Malaysia, and proposes that applying a theoretical framework would help understand the context and streamline these ongoing efforts. Specifically, it lays out a theoretical and cultural framework based on Mary Douglas’ (1996) Grid-Group Theory, Mircea Georgescu’s (2005) Three Pillars of E-Government, and Gerald Grant’s and Derek Chau’s (2006) Generic Framework for E-Government. Although this study is in its early stages, it has relevance to everyone who is interested in e-government efforts across the world, and especially relevant to developing countries.

Perceptions of data quality dimensions and data roles

Several authors stress the importance of data’s crucial foundation for operational, tactical and strategic decisions (e.g., Redman 1998, Tee et al. 2007). Data provides the basis for decision making as data collection and processing is typically associated with reducing uncertainty in order to make more effective decisions (Daft and Lengel 1986). While the first series of investments of Information Systems/Information Technology (IS/IT) into organizations improved data collection, restricted computational capacity and limited processing power created challenges (Simon 1960). Fifty years on, capacity and processing problems are increasingly less relevant; in fact, the opposite exists. Determining data relevance and usefulness is complicated by increased data capture and storage capacity, as well as continual improvements in information processing capability. As the IT landscape changes, businesses are inundated with ever-increasing volumes of data from both internal and external sources available on both an ad-hoc and real-time basis. More data, however, does not necessarily translate into more effective and efficient organizations, nor does it increase the likelihood of better or timelier decisions. This raises questions about what data managers require to assist their decision making processes.

Cellulosic ethanol from sugarcane bagasse in Australia : exploring industry feasibility through systems analysis, techno-economic assessment and pilot plant development

Overcoming many of the constraints to early stage investment in biofuels production from sugarcane bagasse in Australia requires an understanding of the complex technical, economic and systemic challenges associated with the transition of established sugar industry structures from single product agri-businesses to new diversified multi-product biorefineries. While positive investment decisions in new infrastructure requires technically feasible solutions and the attainment of project economic investment thresholds, many other systemic factors will influence the investment decision. These factors include the interrelationships between feedstock availability and energy use, competing product alternatives, technology acceptance and perceptions of project uncertainty and risk. This thesis explores the feasibility of a new cellulosic ethanol industry in Australia based on the large sugarcane fibre (bagasse) resource available. The research explores industry feasibility from multiple angles including the challenges of integrating ethanol production into an established sugarcane processing system, scoping the economic drivers and key variables relating to bioethanol projects and considering the impact of emerging technologies in improving industry feasibility. The opportunities available from pilot scale technology demonstration are also addressed. Systems analysis techniques are used to explore the interrelationships between the existing sugarcane industry and the developing cellulosic biofuels industry. This analysis has resulted in the development of a conceptual framework for a bagassebased cellulosic ethanol industry in Australia and uses this framework to assess the uncertainty in key project factors and investment risk. The analysis showed that the fundamental issue affecting investment in a cellulosic ethanol industry from sugarcane in Australia is the uncertainty in the future price of ethanol and government support that reduces the risks associated with early stage investment is likely to be necessary to promote commercialisation of this novel technology. Comprehensive techno-economic models have been developed and used to assess the potential quantum of ethanol production from sugarcane in Australia, to assess the feasibility of a soda-based biorefinery at the Racecourse Sugar Mill in Mackay, Queensland and to assess the feasibility of reducing the cost of production of fermentable sugars from the in-planta expression of cellulases in sugarcane in Australia. These assessments show that ethanol from sugarcane in Australia has the potential to make a significant contribution to reducing Australia’s transportation fuel requirements from fossil fuels and that economically viable projects exist depending upon assumptions relating to product price, ethanol taxation arrangements and greenhouse gas emission reduction incentives. The conceptual design and development of a novel pilot scale cellulosic ethanol research and development facility is also reported in this thesis. The establishment of this facility enables the technical and economic feasibility of new technologies to be assessed in a multi-partner, collaborative environment. As a key outcome of this work, this study has delivered a facility that will enable novel cellulosic ethanol technologies to be assessed in a low investment risk environment, reducing the potential risks associated with early stage investment in commercial projects and hence promoting more rapid technology uptake. While the study has focussed on an exploration of the feasibility of a commercial cellulosic ethanol industry from sugarcane in Australia, many of the same key issues will be of relevance to other sugarcane industries throughout the world seeking diversification of revenue through the implementation of novel cellulosic ethanol technologies.

Multifractal characterisation and analysis of complex networks

Complex networks have been studied extensively due to their relevance to many real-world systems such as the world-wide web, the internet, biological and social systems. During the past two decades, studies of such networks in different fields have produced many significant results concerning their structures, topological properties, and dynamics. Three well-known properties of complex networks are scale-free degree distribution, small-world effect and self-similarity. The search for additional meaningful properties and the relationships among these properties is an active area of current research. This thesis investigates a newer aspect of complex networks, namely their multifractality, which is an extension of the concept of selfsimilarity. The first part of the thesis aims to confirm that the study of properties of complex networks can be expanded to a wider field including more complex weighted networks. Those real networks that have been shown to possess the self-similarity property in the existing literature are all unweighted networks. We use the proteinprotein interaction (PPI) networks as a key example to show that their weighted networks inherit the self-similarity from the original unweighted networks. Firstly, we confirm that the random sequential box-covering algorithm is an effective tool to compute the fractal dimension of complex networks. This is demonstrated on the Homo sapiens and E. coli PPI networks as well as their skeletons. Our results verify that the fractal dimension of the skeleton is smaller than that of the original network due to the shortest distance between nodes is larger in the skeleton, hence for a fixed box-size more boxes will be needed to cover the skeleton. Then we adopt the iterative scoring method to generate weighted PPI networks of five species, namely Homo sapiens, E. coli, yeast, C. elegans and Arabidopsis Thaliana. By using the random sequential box-covering algorithm, we calculate the fractal dimensions for both the original unweighted PPI networks and the generated weighted networks. The results show that self-similarity is still present in generated weighted PPI networks. This implication will be useful for our treatment of the networks in the third part of the thesis. The second part of the thesis aims to explore the multifractal behavior of different complex networks. Fractals such as the Cantor set, the Koch curve and the Sierspinski gasket are homogeneous since these fractals consist of a geometrical figure which repeats on an ever-reduced scale. Fractal analysis is a useful method for their study. However, real-world fractals are not homogeneous; there is rarely an identical motif repeated on all scales. Their singularity may vary on different subsets; implying that these objects are multifractal. Multifractal analysis is a useful way to systematically characterize the spatial heterogeneity of both theoretical and experimental fractal patterns. However, the tools for multifractal analysis of objects in Euclidean space are not suitable for complex networks. In this thesis, we propose a new box covering algorithm for multifractal analysis of complex networks. This algorithm is demonstrated in the computation of the generalized fractal dimensions of some theoretical networks, namely scale-free networks, small-world networks, random networks, and a kind of real networks, namely PPI networks of different species. Our main finding is the existence of multifractality in scale-free networks and PPI networks, while the multifractal behaviour is not confirmed for small-world networks and random networks. As another application, we generate gene interactions networks for patients and healthy people using the correlation coefficients between microarrays of different genes. Our results confirm the existence of multifractality in gene interactions networks. This multifractal analysis then provides a potentially useful tool for gene clustering and identification. The third part of the thesis aims to investigate the topological properties of networks constructed from time series. Characterizing complicated dynamics from time series is a fundamental problem of continuing interest in a wide variety of fields. Recent works indicate that complex network theory can be a powerful tool to analyse time series. Many existing methods for transforming time series into complex networks share a common feature: they define the connectivity of a complex network by the mutual proximity of different parts (e.g., individual states, state vectors, or cycles) of a single trajectory. In this thesis, we propose a new method to construct networks of time series: we define nodes by vectors of a certain length in the time series, and weight of edges between any two nodes by the Euclidean distance between the corresponding two vectors. We apply this method to build networks for fractional Brownian motions, whose long-range dependence is characterised by their Hurst exponent. We verify the validity of this method by showing that time series with stronger correlation, hence larger Hurst exponent, tend to have smaller fractal dimension, hence smoother sample paths. We then construct networks via the technique of horizontal visibility graph (HVG), which has been widely used recently. We confirm a known linear relationship between the Hurst exponent of fractional Brownian motion and the fractal dimension of the corresponding HVG network. In the first application, we apply our newly developed box-covering algorithm to calculate the generalized fractal dimensions of the HVG networks of fractional Brownian motions as well as those for binomial cascades and five bacterial genomes. The results confirm the monoscaling of fractional Brownian motion and the multifractality of the rest. As an additional application, we discuss the resilience of networks constructed from time series via two different approaches: visibility graph and horizontal visibility graph. Our finding is that the degree distribution of VG networks of fractional Brownian motions is scale-free (i.e., having a power law) meaning that one needs to destroy a large percentage of nodes before the network collapses into isolated parts; while for HVG networks of fractional Brownian motions, the degree distribution has exponential tails, implying that HVG networks would not survive the same kind of attack.

Relevance feature discovery for text analysis

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term- based ones in describing user preferences, but many experiments do not support this hypothesis. This research presents a promising method, Relevance Feature Discovery (RFD), for solving this challenging issue. It discovers both positive and negative patterns in text documents as high-level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the high-level features. The thesis also introduces an adaptive model (called ARFD) to enhance the exibility of using RFD in adaptive environment. ARFD automatically updates the system's knowledge based on a sliding window over new incoming feedback documents. It can efficiently decide which incoming documents can bring in new knowledge into the system. Substantial experiments using the proposed models on Reuters Corpus Volume 1 and TREC topics show that the proposed models significantly outperform both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and other pattern-based methods.

Automata modeling for cognitive interference in users' relevance judgement

Quantum theory has recently been employed to further advance the theory of information retrieval (IR). A challenging research topic is to investigate the so called quantum-like interference in users’ relevance judgement process, where users are involved to judge the relevance degree of each document with respect to a given query. In this process, users’ relevance judgement for the current document is often interfered by the judgement for previous documents, due to the interference on users’ cognitive status. Research from cognitive science has demonstrated some initial evidence of quantum-like cognitive interference in human decision making, which underpins the user’s relevance judgement process. This motivates us to model such cognitive interference in the relevance judgement process, which in our belief will lead to a better modeling and explanation of user behaviors in relevance judgement process for IR and eventually lead to more user-centric IR models. In this paper, we propose to use probabilistic automaton(PA) and quantum finite automaton (QFA), which are suitable to represent the transition of user judgement states, to dynamically model the cognitive interference when the user is judging a list of documents.

Engaging parents in STEM: Coteaching and cogenerative dialoguing in a Queensland high school

Diminished student interest in science, technology, engineering and mathematics (STEM) is recognised by educators, researchers and public policy makers as a concerning global trend. Inviting stakeholders like scientists and industry specialists to discuss their work is one means schools use to facilitate student engagement in the sciences. However, these visits generally comprise one-off sessions with minimal relevance to students’ particular and ongoing learning needs. This case study investigated coteaching and cogenerative dialoguing with parents in teaching a Year-8 multidisciplinary unit with science and technology foci. Two parents cotaught alongside the resident teacher and researcher over eight months. This paper concentrates on one parent, a medical scientist by profession. Data sources included video and audio recordings of cogenerative dialogues and classroom interactions, student work samples and journal entries. Data were interrogated using the sociological constructs of fields and capitals and the dialectic of structure|agency. The findings reveal how (a) the parent’s science and technology knowledge was tailored to the students’ needs initially and continually and (b) student-generated data indicated enhanced engagement in science and technology. The research speaks to schools and governments about enhancing STEM education by furthering collaborative relationships with relevant stakeholders.

Capacity-building workshops on the responsibility to protect (R2P) and the protection of civilians (POC) in armed conflict - Summary Report

In June 2011, a research project team from the Institute for Ethics, Governance and Law (IEGL), Queensland University of Technology, the United Nations University, and the Australian Government’s Asia Pacific Civil-Military Centre of Excellence (APCMCOE) held three Capacity-Building Workshops (the Workshops) on the Responsibility to Protect (R2P) and the Protection of Civilians (POC) in Armed Conflict in Manila, Kuala Lumpur, and Jakarta. The research project is funded by the Australian Responsibility to Protect Fund, with support from APCMCOE. Developments in Libya and Cote d’Ivoire and the actions of the United Nations Security Council have given new significance to the relationship between R2P and POC, providing impetus to the relevance and application of the POC principle recognised in numerous Security Council resolutions, and the R2P principle, which was recognised by the United Nations General Assembly in 2005 and, now, by the Security Council. The Workshops considered the relationship between R2P and POC. The project team presented the preliminary findings of their study and sought contributions and feedback from Workshop participants. Prior to the Workshops, members of the project team undertook interviews with UN offices and agencies, international organisations (IOs) and non-government organisations (NGOs) in Geneva and New York as part of the process of mapping the relationship between R2P and POC. Initial findings were considered at an Academic-Practitioner Workshop held at the University of Sydney in November 2010. In addition to an extensive literature review and a series of academic publications, the project team is preparing a practical guidance text (the Guide) on the relationship between R2P and POC to assist the United Nations, governments, regional bodies, IOs and NGOs in considering and applying appropriate protection strategies. It is intended that the Guide be presented to the United Nations Secretariat in New York in early 2012. The primary aim of the Workshops was to test the project’s initial findings among an audience of diplomats, military, police, civilian policy-makers, practitioners, researchers and experts from within the region. Through dialogue and discussion, the project team gathered feedback – comments, questions, critique and suggestions – to help shape the development of practical guidance about when, how and by whom R2P and POC might be implemented.

New perspectives on context-based chemistry education : using a dialectical sociocultural approach to view teaching and learning

Context-based chemistry education aims to improve student interest and motivation in chemistry by connecting canonical chemistry concepts with real-world contexts. Implementation of context-based chemistry programmes began 20 years ago in an attempt to make the learning of chemistry meaningful for students. This paper reviews such programmes through empirical studies on six international courses, ChemCom (USA), Salters (UK), Industrial Science (Israel), Chemie im Kontext (Germany), Chemistry in Practice (The Netherlands) and PLON (The Netherlands). These studies are categorised through emergent characteristics of: relevance, interest/attitudes motivation and deeper understanding. These characteristics can be found to an extent in a number of other curricular initiatives, such as science-technology-society approaches and problem-based learning or project based science, the latter of which often incorporates an inquiry-based approach to science education. These initiatives in science education are also considered with a focus on the characteristics of these approaches that are emphasised in context-based education. While such curricular studies provide a starting point for discussing context-based approaches in chemistry, to advance our understanding of how students connect canonical science concepts with the real-world context, a new theoretical framework is required. A dialectical sociocultural framework originating in the work of Vygotsky is used as a referent for analysing the complex human interactions that occur in context-based classrooms, providing teachers with recent information about the pedagogical structures and resources that afford students the agency to learn.

An aspect query language model based on query decomposition and high-order contextual term associations

In information retrieval (IR) research, more and more focus has been placed on optimizing a query language model by detecting and estimating the dependencies between the query and the observed terms occurring in the selected relevance feedback documents. In this paper, we propose a novel Aspect Language Modeling framework featuring term association acquisition, document segmentation, query decomposition, and an Aspect Model (AM) for parameter optimization. Through the proposed framework, we advance the theory and practice of applying high-order and context-sensitive term relationships to IR. We first decompose a query into subsets of query terms. Then we segment the relevance feedback documents into chunks using multiple sliding windows. Finally we discover the higher order term associations, that is, the terms in these chunks with high degree of association to the subsets of the query. In this process, we adopt an approach by combining the AM with the Association Rule (AR) mining. In our approach, the AM not only considers the subsets of a query as “hidden” states and estimates their prior distributions, but also evaluates the dependencies between the subsets of a query and the observed terms extracted from the chunks of feedback documents. The AR provides a reasonable initial estimation of the high-order term associations by discovering the associated rules from the document chunks. Experimental results on various TREC collections verify the effectiveness of our approach, which significantly outperforms a baseline language model and two state-of-the-art query language models namely the Relevance Model and the Information Flow model