Identifying differences in wet and dry road crashes using data mining

It is commonly accepted that wet roads have higher risk of crash than dry roads; however, providing evidence to support this assumption presents some difficulty. This paper presents a data mining case study in which predictive data mining is applied to model the skid resistance and crash relationship to search for discernable differences in the probability of wet and dry road segments having crashes based on skid resistance. The models identify an increased probability of wet road segments having crashes for mid-range skid resistance values.

Comprehensive Australian Study of Entrepreneurial Emergence (CAUSEE): design, data collection and descriptive results

The Comprehensive Australian Study of Entrepreneurial Emergence (CAUSEE) is a research programme that aims to uncover the factors that initiate, hinder and facilitate the process of emergence of new economic activities and organizations. It is widely acknowledged that entrepreneurship is one of the most important forces shaping changes in a country’s economic landscape (Baumol 1968; Birch 1987; Acs 1999). An understanding of the process by which new economic activity and business entities emerge is vital (Gartner 1993; Sarasvathy 2001). An important development in the study of ‘nascent entrepreneurs’ and ‘firms in gestation’ was the Panel Study of Entrepreneurial Dynamics (PSED) (Gartner et al. 2004) and its extensions in Argentina, Canada, Greece, the Netherlands, Norway and Sweden. Yet while PSED I is an important first step towards systematically studying new venture emergence, it represents just the beginning of a stream of nascent venture studies – most notably PSED II is currently being undertaken in the US (2005– 10) (Reynolds and Curtin 2008).

Using data mining to predict road crash count with a focus on skid resistance values

Road crashes cost world and Australian society a significant proportion of GDP, affecting productivity and causing significant suffering for communities and individuals. This paper presents a case study that generates data mining models that contribute to understanding of road crashes by allowing examination of the role of skid resistance (F60) and other road attributes in road crashes. Predictive data mining algorithms, primarily regression trees, were used to produce road segment crash count models from the road and traffic attributes of crash scenarios. The rules derived from the regression trees provide evidence of the significance of road attributes in contributing to crash, with a focus on the evaluation of skid resistance.

Characterisation of public mobile data networks for aeronautical telemetry purposes

This paper describes the characterisation for airborne uses of the public mobile data communication systems known broadly as 3G. The motivation for this study was to explore how this mature public communication systems could be used for aviation purposes. An experimental system was fitted to a light aircraft to record communication latency, line speed, RF level, packet loss and cell tower identifier. Communications was established using internet protocols and connection was made to a local server. The aircraft was flown in both remote and populous areas at altitudes up to 8500ft in a region located in South East Queensland, Australia. Results show that the average airborne RF levels are better than those on the ground by 21% and in the order of -77 dbm. Latencies were in the order of 500 ms (1/2 the latency of Iridium), an average download speed of 0.48 Mb/s, average uplink speed of 0.85 Mb/s, a packet of information loss of 6.5%. The maximum communication range was also observed to be 70km from a single cell station. The paper also describes possible limitations and utility of using such a communications architecture for both manned and unmanned aircraft systems.

The molecular mechanisms utilised by mesenchymal stem cells in migration to acute myocardial infarction

Heart damage caused by acute myocardial infarction (AMI) is a leading cause of death and disability in Australia. Novel therapies are still required for the treatment of this condition due to the poor reparative ability of the heart. As such, cellular therapies that assist in the recovery of heart muscle are of great current interest. Culture expanded mesenchymal stem cells (MSC) represent a stem and progenitor cell population that has been shown to promote tissue recovery in pre-clinical studies of AMI. For MSC-based therapies in the clinic, an intravenous route of administration would ideally be used due to the low cost, ease of delivery and relative safety. The study of MSC migration is therefore clinically relevant for a minimally invasive cell therapy to promote regeneration of damaged tissue. C57BL/6, UBI-GFP-BL/6 and CD44-/-/GFP+/+ mice were utilised to investigate mMSC migration. To assist in murine models of MSC migration, a novel method was used for the isolation of murine MSC (mMSC). These mMSC were then expanded in culture and putative mMSC were positive for Sca-1, CD90.2, and CD44 and were negative for CD45 and CD11b. Furthermore, mMSC from C57BL/6 and UBI-GFP-BL/6 mice were shown to differentiate into cells of the mesodermal lineage. Cells from CD44-/-/GFP+/+ mice were positive for Sca-1 and CD90.2, and negative for CD44, CD45 and CD11b however, these cells were unable to differentiate into adipocytes and chondrocytes and express lineage specific genes, PLIN and ACAN. Analysis of mMSC chemokine receptor (CR) expression showed that although mMSC do express chemokine receptors, (including those specific for chemokines released after AMI), these were low or undetectable by mRNA. However, protein expression could be detected, which was predominantly cytoplasmic. It was further shown that in both healthy (unperturbed) and inflamed tissues, mMSC had very little specific migration and engraftment after intravenous injection. To determine if poor mMSC migration was due to the inability of mMSC to respond to chemotactic stimuli, chemokine expression in bone marrow, skin injury and hearts (healthy and after AMI) was analysed at various time points by quantitative real-time PCR (qRT PCR). Many chemokines were up-regulated after skin biopsy and AMI, but the highest acute levels were found for CXCL12 and CCL7. Due to their high expression in infarcted hearts, the chemokines CXCL12 and CCL7 were tested for their effect on mMSC migration. Despite CR expression at both protein and mRNA levels, migration in response to CXCL12 and CCL7 was low in mMSC cultured on Nunclon plastic. A novel tissue culture plastic technology (UpCellTM) was then used that allowed gentle non-enzymatic dissociation of mMSC, thus preserving surface expression of the CRs. Despite this the in vitro data indicated that CXCL12 fails to induce significant migration ability of mMSC, while CCL7 induces significant, but low-level migration. We speculated this may be because of low levels of surface expression of chemokine receptors. In a strategy to increase cell surface expression of mMSC chemokine receptors and enhance their in vitro and in vivo migration capacity, mMSC were pre-treated with pro-inflammatory cytokines. Increased levels of both mRNA and surface protein expression were found for CRs by pre-treating mMSC with pro-inflammatory cytokines including TNF-á, IFN-ã, IL-1á and IL-6. Furthermore, the chemotactic response of mMSC to CXCL12 and CCL7 was significantly higher with these pretreated cells. Finally, the effectiveness of this type of cell manipulation was demonstrated in vivo, where mMSC pre-treated with TNF-á and IFN-ã showed significantly increased migration in skin injury and AMI models. Therefore this thesis has demonstrated, using in vitro and in vivo models, the potential for prior manipulation of MSC as a possible means for increasing the utility of intravenously delivery for MSC-based cellular therapies.

Announcement and enactment of legislation improve children’s car seating positions

Purpose: Evaluate effectiveness of new legislation Are children more likely to sit in the rear seat now than previously? ----- Are they more likely to wear an age-appropriate restraint? How easy is it for parents to comply (what are the barriers)?----- What more can be done? ----- ----- Design: 2 studies Study 1-observational 3 time phases (pre-legislation; post announcement; post enactment)----- Study 2-intercept interviews 2 time phases (post announcement; post enactment, same parents)----- Three data collection phases: T1 (before announcement, 2007) T2 (after announcement but before enactment, 2009-10) T3 (after the enactment, 2010)----- Two regional cities: Toowoomba, Rockhampton----- Site types Schools, shopping areas

Injuries across adolescence : an investigation using the Extended Adolescent Injury Checklist (E-AIC)

Injury is the leading cause of death among adolescents, and in many countries, accounts for more deaths than all other causes combined. Rates of death due to injury also increase dramatically across adolescence. The Australian Institute of Health and Welfare reported that, in 2005, there were 954 deaths of young Australians due to injury, which is a rate of 26 deaths per 100,000 young people. Of these deaths, 4% were adolescents aged 12-14, 17% were aged 15-17, and 80% were aged 18-24 years. Issues addressed: Injuries are the leading cause of death among adolescents. The current research examined a measure of adolescent injury in terms of whether it encompasses the diverse injury experiences of Australian adolescents, including high-risk and normative adolescents, and thus determine its utility as a tool for health promotion research. Grade 9 students from two Brisbane high schools (n=202, aged 13-14 years) and adolescents recruited from the Emergency Department waiting rooms of four Brisbane hospitals (n=98, aged 16-18 years) completed the Extended Adolescent Injury Checklist (E-AIC). The most common cause of injury among adolescents was a sports activity, followed by fights for all participants except schoolbased males, who experienced more bicycle injuries. Alcohol use was most frequently reported in association with interpersonal violence injuries. A broad variety of injuries, occurring in context of multiple risk as well as normative behaviours, were reported by adolescents in both school and ED settings, and were captured by the E-AIC. Findings suggest that the E-AIC is a useful measure that captures the injury experiences of adolescents in different contexts. The high occurrence of injuries that do not result in formal medical treatment also indicates scope for interventions to be based around lessons in first aid, while also incorporating injury prevention components.

Secure data aggregation in wireless sensor networks

A Wireless Sensor Network (WSN) is a set of sensors that are integrated with a physical environment. These sensors are small in size, and capable of sensing physical phenomena and processing them. They communicate in a multihop manner, due to a short radio range, to form an Ad Hoc network capable of reporting network activities to a data collection sink. Recent advances in WSNs have led to several new promising applications, including habitat monitoring, military target tracking, natural disaster relief, and health monitoring. The current version of sensor node, such as MICA2, uses a 16 bit, 8 MHz Texas Instruments MSP430 micro-controller with only 10 KB RAM, 128 KB program space, 512 KB external ash memory to store measurement data, and is powered by two AA batteries. Due to these unique specifications and a lack of tamper-resistant hardware, devising security protocols for WSNs is complex. Previous studies show that data transmission consumes much more energy than computation. Data aggregation can greatly help to reduce this consumption by eliminating redundant data. However, aggregators are under the threat of various types of attacks. Among them, node compromise is usually considered as one of the most challenging for the security of WSNs. In a node compromise attack, an adversary physically tampers with a node in order to extract the cryptographic secrets. This attack can be very harmful depending on the security architecture of the network. For example, when an aggregator node is compromised, it is easy for the adversary to change the aggregation result and inject false data into the WSN. The contributions of this thesis to the area of secure data aggregation are manifold. We firstly define the security for data aggregation in WSNs. In contrast with existing secure data aggregation definitions, the proposed definition covers the unique characteristics that WSNs have. Secondly, we analyze the relationship between security services and adversarial models considered in existing secure data aggregation in order to provide a general framework of required security services. Thirdly, we analyze existing cryptographic-based and reputationbased secure data aggregation schemes. This analysis covers security services provided by these schemes and their robustness against attacks. Fourthly, we propose a robust reputationbased secure data aggregation scheme for WSNs. This scheme minimizes the use of heavy cryptographic mechanisms. The security advantages provided by this scheme are realized by integrating aggregation functionalities with: (i) a reputation system, (ii) an estimation theory, and (iii) a change detection mechanism. We have shown that this addition helps defend against most of the security attacks discussed in this thesis, including the On-Off attack. Finally, we propose a secure key management scheme in order to distribute essential pairwise and group keys among the sensor nodes. The design idea of the proposed scheme is the combination between Lamport's reverse hash chain as well as the usual hash chain to provide both past and future key secrecy. The proposal avoids the delivery of the whole value of a new group key for group key update; instead only the half of the value is transmitted from the network manager to the sensor nodes. This way, the compromise of a pairwise key alone does not lead to the compromise of the group key. The new pairwise key in our scheme is determined by Diffie-Hellman based key agreement.

Incidence, severity and correlates of bicycling injuries in a sample of cyclists in Queensland, Australia

Bicycle injuries, particularly those resulting from single bicycle crashes, are underreported in both police and hospital records. Data on cyclist characteristics and crash circumstances are also often lacking. As a result, the ability to develop comprehensive injury prevention policies is hampered. The aim of this study was to examine the incidence, severity, cyclist characteristics, and crash circumstances associated with cycling injuries in a sample of cyclists in Queensland, Australia. A cross-sectional study of Queensland cyclists was conducted in 2009. Respondents (n=2056) completed an online survey about their cycling experiences, including cycling injuries. Logistic regression modelling was used to examine the associations between demographic and cycling behaviour variables with experiencing cycling injuries in the past year, and, separately, with serious cycling injuries requiring a trip to a hospital. Twenty-seven percent of respondents (n=545) reported injuries, and 6% (n=114) reported serious injuries. In multivariable modelling, reporting an injury was more likely for respondents who had cycled <5 years, compared to ≥10 years (p<0.005); cycled for competition (p=0.01); or experienced harassment from motor vehicle occupants (p<0.001). There were no gender differences in injury incidence, and respondents who cycled for transport did not have an increased risk of injury. Reporting a serious injury was more likely for those whose injury involved other road users (p<0.03). Along with environmental and behavioural approaches for reducing collisions and near-collisions with motor vehicles, interventions that improve the design and maintenance of cycling infrastructure, increase cyclists’ skills, and encourage safe cycling behaviours and bicycle maintenance will also be important for reducing the overall incidence of cycling injuries.

Profiling off-road motorcycle and all-terrain riders in Queensland, Australia

The current research aimed to profile off-road riders to identify specific sub-groups in relation to their risk-related behaviours and perceptions. A total of 235 adults from the Australian state of Queensland who had ridden a motorcycle or ATV off-road in the last 12 months were recruited. A cluster analysis was applied to the survey data. Two distinct clusters of riders were identified, which corresponded with the self-report of injury from an off-road riding crash in the prior 12 months. The injured cluster had a significantly higher mean risk propensity and use of safety equipment, though did not differ on self-reported risk taking. The injured cluster as a whole included a higher percentage of males, was younger, and rode more often for recreational or competitive purposes than the non-crash involved cluster. The results indicate that the crash cluster may be both more aware of the potential risks of riding and more willing to ride in a riskier manner.

Data and process requirements for product recall coordination

When an organisation becomes aware that one of its products may pose a safety risk to customers, it must take appropriate action as soon as possible or it can be held liable. The ability to automatically trace potentially dangerous goods through the supply chain would thus help organisations fulfill their legal obligations in a timely and effective manner. Furthermore, product recall legislation requires manufacturers to separately notify various government agencies, the health department and the public about recall incidents. This duplication of effort and paperwork can introduce errors and data inconsistencies. In this paper, we examine traceability and notification requirements in the product recall domain from two perspectives: the activities carried out during the manufacturing and recall processes and the data collected during the enactment of these processes. We then propose a workflow-based coordination framework to support these data and process requirements.

Scaling acoustic data analysis through collaboration and automation

Monitoring and assessing environmental health is becoming increasingly important as human activity and climate change place greater pressure on global biodiversity. Acoustic sensors provide the ability to collect data passively, objectively and continuously across large areas for extended periods of time. While these factors make acoustic sensors attractive as autonomous data collectors, there are significant issues associated with large-scale data manipulation and analysis. We present our current research into techniques for analysing large volumes of acoustic data effectively and efficiently. We provide an overview of a novel online acoustic environmental workbench and discuss a number of approaches to scaling analysis of acoustic data; collaboration, manual, automatic and human-in-the loop analysis.

Using reputation management in participatory sensing for data classification

Participatory sensing enables collection, processing, dissemination and analysis of environmental sensory data by ordinary citizens, through mobile devices. Researchers have recognized the potential of participatory sensing and attempted applying it to many areas. However, participants may submit low quality, misleading, inaccurate, or even malicious data. Therefore, finding a way to improve the data quality has become a significant issue. This study proposes using reputation management to classify the gathered data and provide useful information for campaign organizers and data analysts to facilitate their decisions.