GIS-based mapping and statistical analysis of air pollution and mortality in Brisbane, Australia

In this thesis, the relationship between air pollution and human health has been investigated utilising Geographic Information System (GIS) as an analysis tool. The research focused on how vehicular air pollution affects human health. The main objective of this study was to analyse the spatial variability of pollutants, taking Brisbane City in Australia as a case study, by the identification of the areas of high concentration of air pollutants and their relationship with the numbers of death caused by air pollutants. A correlation test was performed to establish the relationship between air pollution, number of deaths from respiratory disease, and total distance travelled by road vehicles in Brisbane. GIS was utilized to investigate the spatial distribution of the air pollutants. The main finding of this research is the comparison between spatial and non-spatial analysis approaches, which indicated that correlation analysis and simple buffer analysis of GIS using the average levels of air pollutants from a single monitoring station or by group of few monitoring stations is a relatively simple method for assessing the health effects of air pollution. There was a significant positive correlation between variable under consideration, and the research shows a decreasing trend of concentration of nitrogen dioxide at the Eagle Farm and Springwood sites and an increasing trend at CBD site. Statistical analysis shows that there exists a positive relationship between the level of emission and number of deaths, though the impact is not uniform as certain sections of the population are more vulnerable to exposure. Further statistical tests found that the elderly people of over 75 years age and children between 0-15 years of age are the more vulnerable people exposed to air pollution. A non-spatial approach alone may be insufficient for an appropriate evaluation of the impact of air pollutant variables and their inter-relationships. It is important to evaluate the spatial features of air pollutants before modeling the air pollution-health relationships.

Probability based data analysis for road asset management

Road agencies require comprehensive, relevan and quality data describing their road assets to support their investment decisions. An investment decision support system for raod maintenance and rehabilitation mainly comprise three important supporting elements namely: road asset data, decision support tools and criteria for decision-making. Probability-based methods have played a crucial role in helping decision makers understand the relationship among road related data, asset performance and uncertainties in estimating budgets/costs for road management investment. This paper presents applications of the probability-bsed method for road asset management.

Testing a belief-based intervention encouraging sun safety among adolescents in a high risk area

Objective. To provide a preliminary test of a Theory of Planned Behavior (TPB) belief-based intervention to increase adolescents’ sun protective behaviors in a high risk area, Queensland, Australia. Methods. In the period of October-November, 2007 and May-June, 2008, 80 adolescents (14.53 ± 0.69 years) were recruited from two secondary schools (one government and one private) in Queensland after obtaining student, parental, and school informed consent. Adolescents were allocated to either a control or intervention condition based on the class they attended. The intervention comprised three, one hour in-school sessions facilitated by Cancer Council Queensland employees with sessions covering the belief basis of the TPB (i.e., behavioral, normative, and control [barrier and motivator] sun-safe beliefs). Participants completed questionnaires assessing sun-safety beliefs, intentions, and behavior pre- and post-intervention. Repeated Measures Multivariate Analysis of Variance was used to test the effect of the intervention across time on these constructs. Results. Students completing the intervention reported stronger sun-safe normative and motivator beliefs and intentions and the performance of more sun-safe behaviors across time than those in the control condition. Conclusion. Strengthening beliefs about the approval of others and motivators for sun protection may encourage sun-safe cognitions and actions among adolescents.

A hidden Markov model and relative entropy rate approach to vision-based dim target detection for UAV sense-and-avoid

Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.

A two-step mutual authentication protocol based on randomized hash-lock for small RFID networks

RFID has been widely used in today's commercial and supply chain industry, due to the significant advantages it offers and the relatively low production cost. However, this ubiquitous technology has inherent problems in security and privacy. This calls for the development of simple, efficient and cost effective mechanisms against a variety of security threats. This paper proposes a two-step authentication protocol based on the randomized hash-lock scheme proposed by S. Weis in 2003. By introducing additional measures during the authentication process, this new protocol proves to enhance the security of RFID significantly, and protects the passive tags from almost all major attacks, including tag cloning, replay, full-disclosure, tracking, and eavesdropping. Furthermore, no significant changes to the tags is required to implement this protocol, and the low complexity level of the randomized hash-lock algorithm is retained.

Developing creativity and innovation in management education : an artful event for transformative learning

This paper argues that management education needs to consider a trend in learning design which advances more creative learning through an alliance with art-based pedagogical processes. A shift is required from skills training to facilitating transformational learning through experiences that expand human potential, facilitated by artistic processes. In this paper the authors discuss the necessity for creativity and innovation in the workplace and the need to develop better leaders and managers. The inclusion of arts-based processes enhances artful behaviour, aesthetics and creativity within management and organisational behaviour, generating important implications for business innovation. This creative learning focus stems from an analysis of an arts-based intervention for management development. Entitled Management Jazz the program was conducted over three years at a large Australian University. The paper reviews some of the salient literature in the field. It considers four stages of the learning process: capacity, artful event, increased capability, and application/action to produce product. One illustrative example of an arts-based learning process is provided from the Management Jazz program. Research findings indicate that artful learning opportunities enhance capacity for awareness of creativity in one’s self and in others. This capacity correlates positively with a perception that engaging in artful learning enhances the capability of managers in changing collaborative relationships and habitat constraint. The authors conclude that it is through engagement and creative alliance with the arts that management education can explore and discover artful approaches to building creativity and innovation. The illustration presented in this paper will be delivered as a brief workshop at the Fourth Art of Management Conference. The process of bricolage and articles at hand will be used to explore creative constraints and prototypes while generating group collaboration. The mini-workshop will conclude with discussion of the arts-based process and capability enhancement outcomes.

Re-generating the praxis of academic staff development through arts-based learning : beyond paradox

Effective staff development remains a challenge in higher education. This paper examines the non-traditional methodology of arts-based staff development, its potential to foster transformational learning and the practice of professional artistry, through perceptions of program impact. Over a three year period, eighty academics participated in one metropolitan Australian university’s arts-based academic development program. The methodology used one-on-one hermeneutic-based conversations with fifteen self-selected academics and a focus group with twenty other academics from all three years. The paper presents a learning model to engender academic professional artistry. The findings provide developers with support for using a non-traditional strategy of transformational learning.

Internet-Based Interventions for Psychological Problems

Presentation about internet based interventions for depression, substance and alcohol abuse.

Structural health monitoring : vibration-based damage detection and condition assessment of bridges

Structural health is a vital aspect of infrastructure sustainability. As a part of a vital infrastructure and transportation network, bridge structures must function safely at all times. However, due to heavier and faster moving vehicular loads and function adjustment, such as Busway accommodation, many bridges are now operating at an overload beyond their design capacity. Additionally, the huge renovation and replacement costs are a difficult burden for infrastructure owners. The structural health monitoring (SHM) systems proposed recently are incorporated with vibration-based damage detection techniques, statistical methods and signal processing techniques and have been regarded as efficient and economical ways to assess bridge condition and foresee probable costly failures. In this chapter, the recent developments in damage detection and condition assessment techniques based on vibration-based damage detection and statistical methods are reviewed. The vibration-based damage detection methods based on changes in natural frequencies, curvature or strain modes, modal strain energy, dynamic flexibility, artificial neural networks, before and after damage, and other signal processing methods such as Wavelet techniques, empirical mode decomposition and Hilbert spectrum methods are discussed in this chapter.

Labour rights as human rights : workers' safety at work in Australian-based supply chains

The increase of buyer-driven supply chains, outsourcing and other forms of non-traditional employment has resulted in challenges for labour market regulation. One business model which has created substantial regulatory challenges is supply chains. The supply chain model involves retailers purchasing products from brand corporations who then outsource the manufacturing of the work to traders who contract with factories or outworkers who actually manufacture the clothing and textiles. This business model results in time and cost pressures being pushed down the supply chain which has resulted in sweatshops where workers systematically have their labour rights violated. Literally millions of workers work in dangerous workplaces where thousands are killed or permanently disabled every year. This thesis has analysed possible regulatory responses to provide workers a right to safety and health in supply chains which provide products for Australian retailers. This thesis will use a human rights standard to determine whether Australia is discharging its human rights obligations in its approach to combating domestic and foreign labour abuses. It is beyond this thesis to analyse Occupational Health and Safety (OHS) laws in every jurisdiction. Accordingly, this thesis will focus upon Australian domestic laws and laws in one of Australia’s major trading partners, the Peoples’ Republic of China (China). It is hypothesised that Australia is currently breaching its human rights obligations through failing to adequately regulate employees’ safety at work in Australian-based supply chains. To prove this hypothesis, this thesis will adopt a three- phase approach to analysing Australia’s regulatory responses. Phase 1 will identify the standard by which Australia’s regulatory approach to employees’ health and safety in supply chains can be judged. This phase will focus on analysing how workers’ rights to safety as a human right imposes a moral obligation on Australia to take reasonablely practicable steps regulate Australian-based supply chains. This will form a human rights standard against which Australia’s conduct can be judged. Phase 2 focuses upon the current regulatory environment. If existing regulatory vehicles adequately protect the health and safety of employees, then Australia will have discharged its obligations through simply maintaining the status quo. Australia currently regulates OHS through a combination of ‘hard law’ and ‘soft law’ regulatory vehicles. The first part of phase 2 analyses the effectiveness of traditional OHS laws in Australia and in China. The final part of phase 2 then analyses the effectiveness of the major soft law vehicle ‘Corporate Social Responsibility’ (CSR). The fact that employees are working in unsafe working conditions does not mean Australia is breaching its human rights obligations. Australia is only required to take reasonably practicable steps to ensure human rights are realized. Phase 3 identifies four regulatory vehicles to determine whether they would assist Australia in discharging its human rights obligations. Phase 3 then analyses whether Australia could unilaterally introduce supply chain regulation to regulate domestic and extraterritorial supply chains. Phase 3 also analyses three public international law regulatory vehicles. This chapter considers the ability of the United Nations Global Compact, the ILO’s Better Factory Project and a bilateral agreement to improve the detection and enforcement of workers’ right to safety and health.