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Background Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities. Aims In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated. Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes. Results Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers. Conclusion The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.

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The objective of this research was to develop a model to estimate future freeway pavement construction costs in Henan Province, China. A comprehensive set of factors contributing to the cost of freeway pavement construction were included in the model formulation. These factors comprehensively reflect the characteristics of region and topography and altitude variation, the cost of labour, material, and equipment, and time-related variables such as index numbers of labour prices, material prices and equipment prices. An Artificial Neural Network model using the Back-Propagation learning algorithm was developed to estimate the cost of freeway pavement construction. A total of 88 valid freeway cases were obtained from freeway construction projects let by the Henan Transportation Department during the period 1994−2007. Data from a random selection of 81 freeway cases were used to train the Neural Network model and the remaining data were used to test the performance of the Neural Network model. The tested model was used to predict freeway pavement construction costs in 2010 based on predictions of input values. In addition, this paper provides a suggested correction for the prediction of the value for the future freeway pavement construction costs. Since the change in future freeway pavement construction cost is affected by many factors, the predictions obtained by the proposed method, and therefore the model, will need to be tested once actual data are obtained.

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This special issue of the International Journal of Technology Policy and Law considers recent developments in the reconfiguration of communication regulation to account for the impact of media convergence. It is readily apparent that media worldwide are going through a series of transformations, associated with the rise of the internet, user-created content and social media. The papers in the collection draw upon legal and policy developments in Australia, the European union and South Korea, and consider such issues as public participation in media policy and regulation, civic media governance for online platforms, the future copyright laws, the roles and responsibilities of internet intermediaries, and regulatory frameworks for internet protocol television (IPTV).

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We describe the X-series impulse facilities at The University of Queensland and show that they can produce useful high speed flows of relevance to the study of high temperature radiating flow flields characteristic of atmospheric entry. Two modes of operation are discussed: (a) the expansion tube mode which is useful for subscale aerodynamic testing of vehicles and (b) the non-reflected shock tube mode which can be used to emulate the nonequilibrium radiating region immediately following the bow shock of a flight vehicle.

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The present study focused on simulating a trajectory point towards the end of the first experimental heatshield of the FIRE II vehicle, at a total flight time of 1639.53s. Scale replicas were sized according to binary scaling and instrumented with thermocouples for testing in the X1 expansion tube, located at The University of Queensland. Correlation of flight to experimental data was achieved through the separation, and independent treatment of the heat modes. Preliminary investigation indicates that the absolute value of radiant surface flux is conserved between two binary scaled models, whereas convective heat transfer increases with the length scale. This difference in the scaling techniques result in the overall contribution of radiative heat transfer diminishing to less than 1% in expansion tubes from a flight value of approximately 9-17%. From empirical correlation's it has been shown that the St √Re number decreases, under special circumstances, in expansion tubes by the percentage radiation present on the flight vehicle. Results obtained in this study give a strong indication that the relative radiative heat transfer contribution in the expansion tube tests is less than that in flight, supporting the analysis that the absolute value remains constant with binary scaling.

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Bandwidths and offsets are important components in vehicle traffic control strategies. This article proposes new methods for quantifying and selecting them. Bandwidth is the amount of green time available for vehicles to travel through adjacent intersections without the requirement to stop at the second traffic light. The offset is the difference between the starting-time of ``green'' periods at two adjacent intersections, along a given route. The core ideas in this article were developed during the 2013 Maths and Industry Study Group in Brisbane, Australia. Analytical expressions for computing bandwidth, as a function of offset, are developed. An optimisation model, for selecting offsets across an arterial, is proposed. Arterial roads were focussed upon, as bandwidth and offset have a greater impact on these types of road as opposed to a full traffic network. A generic optimisation-simulation approach is also proposed to refine an initial starting solution, according to a specified metric. A metric that reflects the number of stops, and the distance between stops, is proposed to explicitly reduce the dissatisfaction of road users, and to implicitly reduce fuel consumption and emissions. Conceptually the optimisation-simulation approach is superior as it handles real-life complexities and is a global optimisation approach. The models and equations in this article can be used in road planning and traffic control.

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This paper examines co-creative video outputs that have originated from, or relate to, remote Indigenous communities in Australia. Scholarly work on remote media has mostly operated at the interface of media studies and anthropology, seeking to identify how cultural systems shape the production, distribution and reception of media in Aboriginal communities. This paper looks instead at content themes, funding sources and institutions during the 2010-2013 period, and examines the factors that may be determining the quantity of co-creative outputs, as well as the types of stories that get produced. I argue that the focus on culture has obscured important shifts in remote media policy and funding, including a trend towards content designed to address social disadvantage.

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Children and adolescents with intellectual disability have higher rates of mental health problems compared with there typically developing peers. Social support has been identified as an important protective factor for psychological well - being. In this paper we discuss the benefits of social support networks, and consider approaches for promoting children’s perceptions of the availability of social support. We describe an evidence-based intervention that has been specially adapted and implemented for students with intellectual disability in school settings. In a randomised controlled trial, the Aussie Optimism Resilience Skills Program was associated with improved perceptions of social support following a 10-week intervention. Educators need to be aware of the increased vulnerability of students with intellectual disability to the development mental health problems and the proactive ways in which they can promote psychological well - being within their classrooms.

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More children are now being diagnosed with chromosome abnormalities. Some chromosome disorder syndromes are relatively well known; while others are so rare that there is only limited evidence about their likely impact on learning and development. For educators, a basic level of knowledge about chromosome abnormalities is important for understanding the literature and communicating with families and professionals. This paper describes chromosomes, and the numerical and structural anomalies that can occur, usually spontaneously during early cell division. Distinctive features of various chromosome syndromes are summarised before a discussion of the rare chromosome disorders that are labelled, not with a syndrome name, but simply by a description of the chromosome number, size and shape. Because of the potential within-group variability that characterises syndromes, and the scarcity of literature about the rare chromosome disorders, expectations for learning and development of individual students need to be based on the range of possible outcomes that may be achievable.

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This paper explores methodological turning points in researching narratives of early career resilience mediated by the complexities of remote teaching. Innovative, flexible and discursive research design facilitated exploration of emerging narratives using digital technologies. Data were regularly interrogated with participant-researchers to reveal the undercurrents of imbued meaning. Dialogue with participant-researchers enhanced interpretations of data plots and text-based explanations of narrative turning points, providing valuable insights throughout analysis. Reflections on the affordances and tensions in this process illustrate the significance of innovation but also the complexities associated with online collaboration. Consequently, empowering the participant-researchers throughout the life of the research was critical in understanding their narratives of teaching.

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In the past decade the ‘creative cluster’ has become a driver of urban renewal in China. Many cluster developments attract human capital and investment to post-industrial spaces. This paper looks at two developments which are more post-agricultural than post-industrial: the first is Songzhuang, a large scale contemporary art community situated on the eastern fringe of Beijing, the second is Hangzhou’s White Horse Lake Creative Eco-City, a ‘mixed variety’ cluster model which integrates elements of art, fashion, design and animation. The common factor in both cases is how they came into existence. In both districts urban creative workers moved into a rural environment. Drawing on interviews with planners, officials, and residents we investigate the challenges of sustaining such fringe clusters.

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Sustainability is a key driver for decisions in the management and future development of organisations and industries. However, quantifying and comparing sustainability across the triple bottom line (TBL) of economy, environment and social impact, has been problematic. There is a need for a tool which can measure the complex interactions within and between the environmental, economic and social systems which affect the sustainability of an industry in a transparent, consistent and comparable way. The authors acknowledge that there are currently numerous ways in which sustainability is measured and multiple methodologies in how these measurement tools were designed. The purpose of this book is to showcase how Bayesian network modelling can be used to identify and measure environmental, economic and social sustainability variables and to understand their impact on and interaction with each other. This book introduces the Sustainability Scorecard, and describes it through a case study on sustainability of the Australian dairy industry. This study was conducted in collaboration with the Australian dairy industry.

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The global trend toward university-based journalism education has led to a growing scrutiny of students’ experiences at university and the extent to which professional views may be shaped there. Three main influences have been identified in the literature: students’ preferences for certain news beats, their gender, and students’ stage of progression in a journalism program. Typically, however, analyses have focused on only one potential influence within one particular country at a time. Arguing that a comparative approach is needed, this article examines potential influences on journalism students’ role perceptions across eight countries. Results suggest that students’ motivations, and the amount of time they have spent in a program, play a part in influencing their professional views while gender has little influence.

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Although there was substantial research into the occupational health and safety sector over the past forty years, this generally focused on statistical analyses of data related to costs and/or fatalities and injuries. There is a lack of mathematical modelling of the interactions between workers and the resulting safety dynamics of the workplace. There is also little work investigating the potential impact of different safety intervention programs prior to their implementation. In this article, we present a fundamental, differential equation-based model of workplace safety that treats worker safety habits similarly to an infectious disease in an epidemic model. Analytical results for the model, derived via phase plane and stability analysis, are discussed. The model is coupled with a model of a generic safety strategy aimed at minimising unsafe work habits, to produce an optimal control problem. The optimal control model is solved using the forward-backward sweep numerical scheme implemented in Matlab.