Comprehensive standardized data definitions for acute coronary syndrome research in emergency departments in Australasia

Patients with chest discomfort or other symptoms suggestive of acute coronary syndrome (ACS) are one of the most common categories seen in many Emergency Departments (EDs). While the recognition of patients at high-risk of ACS has improved steadily, identifying the majority of chest pain presentations who fall into the low-risk group remains a challenge. Research in this area needs to be transparent, robust, applicable to all hospitals from large tertiary centres to rural and remote sites, and to allow direct comparison between different studies with minimum patient spectrum bias. A standardised approach to the research framework using a common language for data definitions must be adopted to achieve this. The aim was to create a common framework for a standardised data definitions set that would allow maximum value when extrapolating research findings both within Australasian ED practice, and across similar populations worldwide. Therefore a comprehensive data definitions set for the investigation of non-traumatic chest pain patients with possible ACS was developed, specifically for use in the ED setting. This standardised data definitions set will facilitate ‘knowledge translation’ by allowing extrapolation of useful findings into the real-life practice of emergency medicine.

Integrated Bayesian network frameworks for modelling complex ecological issues

Ecological problems are typically multi faceted and need to be addressed from a scientific and a management perspective. There is a wealth of modelling and simulation software available, each designed to address a particular aspect of the issue of concern. Choosing the appropriate tool, making sense of the disparate outputs, and taking decisions when little or no empirical data is available, are everyday challenges facing the ecologist and environmental manager. Bayesian Networks provide a statistical modelling framework that enables analysis and integration of information in its own right as well as integration of a variety of models addressing different aspects of a common overall problem. There has been increased interest in the use of BNs to model environmental systems and issues of concern. However, the development of more sophisticated BNs, utilising dynamic and object oriented (OO) features, is still at the frontier of ecological research. Such features are particularly appealing in an ecological context, since the underlying facts are often spatial and temporal in nature. This thesis focuses on an integrated BN approach which facilitates OO modelling. Our research devises a new heuristic method, the Iterative Bayesian Network Development Cycle (IBNDC), for the development of BN models within a multi-field and multi-expert context. Expert elicitation is a popular method used to quantify BNs when data is sparse, but expert knowledge is abundant. The resulting BNs need to be substantiated and validated taking this uncertainty into account. Our research demonstrates the application of the IBNDC approach to support these aspects of BN modelling. The complex nature of environmental issues makes them ideal case studies for the proposed integrated approach to modelling. Moreover, they lend themselves to a series of integrated sub-networks describing different scientific components, combining scientific and management perspectives, or pooling similar contributions developed in different locations by different research groups. In southern Africa the two largest free-ranging cheetah (Acinonyx jubatus) populations are in Namibia and Botswana, where the majority of cheetahs are located outside protected areas. Consequently, cheetah conservation in these two countries is focussed primarily on the free-ranging populations as well as the mitigation of conflict between humans and cheetahs. In contrast, in neighbouring South Africa, the majority of cheetahs are found in fenced reserves. Nonetheless, conflict between humans and cheetahs remains an issue here. Conservation effort in South Africa is also focussed on managing the geographically isolated cheetah populations as one large meta-population. Relocation is one option among a suite of tools used to resolve human-cheetah conflict in southern Africa. Successfully relocating captured problem cheetahs, and maintaining a viable free-ranging cheetah population, are two environmental issues in cheetah conservation forming the first case study in this thesis. The second case study involves the initiation of blooms of Lyngbya majuscula, a blue-green algae, in Deception Bay, Australia. L. majuscula is a toxic algal bloom which has severe health, ecological and economic impacts on the community located in the vicinity of this algal bloom. Deception Bay is an important tourist destination with its proximity to Brisbane, Australia’s third largest city. Lyngbya is one of several algae considered to be a Harmful Algal Bloom (HAB). This group of algae includes other widespread blooms such as red tides. The occurrence of Lyngbya blooms is not a local phenomenon, but blooms of this toxic weed occur in coastal waters worldwide. With the increase in frequency and extent of these HAB blooms, it is important to gain a better understanding of the underlying factors contributing to the initiation and sustenance of these blooms. This knowledge will contribute to better management practices and the identification of those management actions which could prevent or diminish the severity of these blooms.

Analysing Seasonal Health Data

Seasonal patterns have been found in a remarkable range of health conditions, including birth defects, respiratory infections and cardiovascular disease. Accurately estimating the size and timing of seasonal peaks in disease incidence is an aid to understanding the causes and possibly to developing interventions. With global warming increasing the intensity of seasonal weather patterns around the world, a review of the methods for estimating seasonal effects on health is timely. This is the first book on statistical methods for seasonal data written for a health audience. It describes methods for a range of outcomes (including continuous, count and binomial data) and demonstrates appropriate techniques for summarising and modelling these data. It has a practical focus and uses interesting examples to motivate and illustrate the methods. The statistical procedures and example data sets are available in an R package called ‘season’. Adrian Barnett is a senior research fellow at Queensland University of Technology, Australia. Annette Dobson is a Professor of Biostatistics at The University of Queensland, Australia. Both are experienced medical statisticians with a commitment to statistical education and have previously collaborated in research in the methodological developments and applications of biostatistics, especially to time series data. Among other projects, they worked together on revising the well-known textbook "An Introduction to Generalized Linear Models," third edition, Chapman Hall/CRC, 2008. In their new book they share their knowledge of statistical methods for examining seasonal patterns in health.

Methods and preliminary results for a data linkage project to determine long-term survival after intensive care unit admission

Aims: To describe a local data linkage project to match hospital data with the Australian Institute of Health and Welfare (AIHW) National Death Index (NDI) to assess longterm outcomes of intensive care unit patients. Methods: Data were obtained from hospital intensive care and cardiac surgery databases on all patients aged 18 years and over admitted to either of two intensive care units at a tertiary-referral hospital between 1 January 1994 and 31 December 2005. Date of death was obtained from the AIHW NDI by probabilistic software matching, in addition to manual checking through hospital databases and other sources. Survival was calculated from time of ICU admission, with a censoring date of 14 February 2007. Data for patients with multiple hospital admissions requiring intensive care were analysed only from the first admission. Summary and descriptive statistics were used for preliminary data analysis. Kaplan-Meier survival analysis was used to analyse factors determining long-term survival. Results: During the study period, 21 415 unique patients had 22 552 hospital admissions that included an ICU admission; 19 058 surgical procedures were performed with a total of 20 092 ICU admissions. There were 4936 deaths. Median follow-up was 6.2 years, totalling 134 203 patient years. The casemix was predominantly cardiac surgery (80%), followed by cardiac medical (6%), and other medical (4%). The unadjusted survival at 1, 5 and 10 years was 97%, 84% and 70%, respectively. The 1-year survival ranged from 97% for cardiac surgery to 36% for cardiac arrest. An APACHE II score was available for 16 877 patients. In those discharged alive from hospital, the 1, 5 and 10-year survival varied with discharge location. Conclusions: ICU-based linkage projects are feasible to determine long-term outcomes of ICU patients

`Getting the city right' : the city as crucible for social reform

The connection between social reform and urban management is evident throughout the history of the city. This article maps out how ideas about social reform and social housing were established historically, during the development of the nineteenth century city. The second part examines contemporary shifts in thinking about homelessness through a case study of two Brisbane City Council initiatives in Brisbane: The Homeless Shelter Trial and Footprints along Kurilpa.

Data-driven background dataset selection for SVM-based speaker verification

The recently proposed data-driven background dataset refinement technique provides a means of selecting an informative background for support vector machine (SVM)-based speaker verification systems. This paper investigates the characteristics of the impostor examples in such highly-informative background datasets. Data-driven dataset refinement individually evaluates the suitability of candidate impostor examples for the SVM background prior to selecting the highest-ranking examples as a refined background dataset. Further, the characteristics of the refined dataset were analysed to investigate the desired traits of an informative SVM background. The most informative examples of the refined dataset were found to consist of large amounts of active speech and distinctive language characteristics. The data-driven refinement technique was shown to filter the set of candidate impostor examples to produce a more disperse representation of the impostor population in the SVM kernel space, thereby reducing the number of redundant and less-informative examples in the background dataset. Furthermore, data-driven refinement was shown to provide performance gains when applied to the difficult task of refining a small candidate dataset that was mis-matched to the evaluation conditions.

Exploiting multiple feature sets in data-driven impostor dataset selection for speaker verification

This study assesses the recently proposed data-driven background dataset refinement technique for speaker verification using alternate SVM feature sets to the GMM supervector features for which it was originally designed. The performance improvements brought about in each trialled SVM configuration demonstrate the versatility of background dataset refinement. This work also extends on the originally proposed technique to exploit support vector coefficients as an impostor suitability metric in the data-driven selection process. Using support vector coefficients improved the performance of the refined datasets in the evaluation of unseen data. Further, attempts are made to exploit the differences in impostor example suitability measures from varying features spaces to provide added robustness.

Performance pressure an empirical analysis using world and Euro cups data

There is a notable shortage of empirical research directed at measuring the magnitude and direction of stress effects on performance in a controlled environment. One reason for this is the inherent difficulties in identifying and isolating direct performance measures for individuals. Additionally most traditional work environments contain a multitude of exogenous factors impacting individual performance, but controlling for all such factors is generally unfeasible (omitted variable bias). Moreover, instead of asking individuals about their self-reported stress levels we observe workers' behavior in situations that can be classified as stressful. For this reason we have stepped outside the traditional workplace in an attempt to gain greater controllability of these factors using the sports environment as our experimental space. We empirically investigate the relationship between stress and performance, in an extreme pressure situation (football penalty kicks) in a winner take all sporting environment (FIFA World Cup and UEFA European Cup competitions). Specifically, we examine all the penalty shootouts between 1976 and 2008 covering in total 16 events. The results indicate that extreme stressors can have a positive or negative impact on Individuals' performance. On the other hand, more commonly experienced stressors do not affect professionals' performances.

Mobile data technologies and SME adoption and diffusion : an empirical study on barriers and facilitators

The technological environment in which Australian SMEs operate can be best described as dynamic and vital. The rate of technological change provides the SME owner/manager a complex and challenging operational context. Wireless applications are being developed that provide mobile devices with Internet content and e-business services. In Australia the adoption of e-commerce by large organisations has been relatively high, however the same cannot be said for SMEs where adoption has been slower than other developed countries. In contrast however mobile telephone adoption and diffusion is relatively high by SMEs. This exploratory study identifies attitudes, perceptions and issues for mobile data technologies by regional SME owner/managers across a range of industry sectors. The major issues include the sector the firm belongs to, the current adoption status of the firm, the level of mistrust of the IT industry, the cost of the technologies and the applications and attributes of the technologies.

Mobile data technology for small business : needs, uses, and adoption

The technological environment in which contemporary small and medium-sized enterprises (SMEs) operate can only be described as dynamic. The exponential rate of technological change, characterised by perceived increases in the benefits associated with various technologies, shortening product life cycles and changing standards, provides the SME a complex and challenging operational context. The primary aim of this research was to identify the needs of SMEs in regional areas for mobile data technologies (MDT). In this study a distinction was drawn between those respondents who were full-adopters of technology, those who were partial-adopters and those who were non-adopters and these three segments articulated different needs and requirements for MDT. Overall the needs of regional SMEs for MDT can be conceptualised into three areas where the technology will assist business practices, communication, e-commerce and security.

Exploring the needs for SMEs for mobile data technologies : the role of qualitative research techniques

The seemingly exponential nature of technological change provides SMEs with a complex and challenging operational context. The development of infrastructures capable of supporting the wireless application protocol (WAP) and associated 'wireless' applications represents the latest generation of technological innovation with potential appeals to SMEs and end-users alike. This paper aims to understand the mobile data technology needs of SMEs in a regional setting. The research was especially concerned with perceived needs across three market segments : non-adopters, partial-adopters and full-adopters of new technology. The research was exploratory in nature as the phenomenon under scrutiny is relatively new and the uses unclear, thus focus groups were conducted with each of the segments. The paper provides insights for business, industry and academics.

Mobile data technologies and small business adoption and diffusion : an empirical study of barriers and facilitators

The technological environment in which contemporary small- and medium-sized enterprises (SMEs) operate can only be described as dynamic. The exponential rate of technological change, characterised by perceived increases in the benefits associated with various technologies, shortening product life cycles and changing standards, provides for the SME a complex and challenging operational context. The primary aim of this research was to identify the needs of SMEs in regional areas for mobile data technologies (MDT). In this study a distinction was drawn between those respondents who were full-adopters of technology, those who were partial-adopters, and those who were non-adopters and these three segments articulated different needs and requirements for MDT. Overall, the needs of regional SMEs for MDT can be conceptualised into three areas where the technology will assist business practices; communication, e-commerce and security

Who needs it? An exploratory investigation into the reaction of small and medium enterprises to mobile data technologies

The technological environment in which contemporary small and medium-sized enterprises (SMEs) operate can only be described as dynamic. The seemingly exponential nature of technological change, characterised by perceived increases in the benefits associated with various technologies, shortening product life cycles and changing standards, provides for the small and medium-sized enterprise a complex and challenging operational context. The development of infrastructures capable of supporting the Wireless Application Protocol (WAP)and associated 'wireless' applications represents the latest generation of technological innovation with potential appeal to SMEs and end-users alike. The primary aim of this research was to understand the mobile data technology needs of SMEs in a regional setting. The research was especially concerned with perceived needs across three market segments; non-adopters of new technology, partial-adopters of new technology and full-adopters of new technology. Working with an industry partner, focus groups were conducted with each of these segments with the discussions focused on the use of the latest WP products and services. Some of the results are presented in this paper.

A parametric study of imaging subsurface objects using ground penetrating radar

A parametric study was carried out to investigate the effects on reconstructed images from a ground penetrating radar (GPR) due to (a) the centre frequency of the GPR excitation pulse, (b) the height of transmitting and receiving antennas above ground level, and (c) the proximity of the buried objects. An integrated software package was developed to streamline the computer simulation based on synthetic data generated by GPRMax.

Data muling over underwater wireless sensor networks using an autonomous underwater vehicle

We present algorithms, systems, and experimental results for underwater data muling. In data muling a mobile agent interacts with static agents to upload, download, or transport data to a different physical location. We consider a system comprising an Autonomous Underwater Vehicle (AUV) and many static Underwater Sensor Nodes (USN) networked together optically and acoustically. The AUV can locate the static nodes using vision and hover above the static nodes for data upload. We describe the hardware and software architecture of this underwater system, as well as experimental data. © 2006 IEEE.