Emplacement of pyroclastic deposits offshore Montserrat : insights from 3D seismic data

During the current (1995-present) eruptive phase of the Soufrière Hills volcano on Montserrat, voluminous pyroclastic flows entered the sea off the eastern flank of the island, resulting in the deposition of well-defined submarine pyroclastic lobes. Previously reported bathymetric surveys documented the sequential construction of these deposits, but could not image their internal structure, the morphology or extent of their base, or interaction with the underlying sediments. We show, by combining these bathymetric data with new high-resolution three dimensional (3D) seismic data, that the sequence of previously detected pyroclastic deposits from different phases of the ongoing eruptive activity is still well preserved. A detailed interpretation of the 3D seismic data reveals the absence of significant (> 3. m) basal erosion in the distal extent of submarine pyroclastic deposits. We also identify a previously unrecognized seismic unit directly beneath the stack of recent lobes. We propose three hypotheses for the origin of this seismic unit, but prefer an interpretation that the deposit is the result of the subaerial flank collapse that formed the English's Crater scarp on the Soufrière Hills volcano. The 1995-recent volcanic activity on Montserrat accounts for a significant portion of the sediments on the southeast slope of Montserrat, in places forming deposits that are more than 60. m thick, which implies that the potential for pyroclastic flows to build volcanic island edifices is significant.

Towards the retrieval of accurate OD matrices from Bluetooth data : lessons learned from 2 years of data

The Bluetooth technology is being increasingly used to track vehicles throughout their trips, within urban networks and across freeway stretches. One important opportunity offered by this type of data is the measurement of Origin-Destination patterns, emerging from the aggregation and clustering of individual trips. In order to obtain accurate estimations, however, a number of issues need to be addressed, through data filtering and correction techniques. These issues mainly stem from the use of the Bluetooth technology amongst drivers, and the physical properties of the Bluetooth sensors themselves. First, not all cars are equipped with discoverable Bluetooth devices and the Bluetooth-enabled vehicles may belong to some small socio-economic groups of users. Second, the Bluetooth datasets include data from various transport modes; such as pedestrian, bicycles, cars, taxi driver, buses and trains. Third, the Bluetooth sensors may fail to detect all of the nearby Bluetooth-enabled vehicles. As a consequence, the exact journey for some vehicles may become a latent pattern that will need to be extracted from the data. Finally, sensors that are in close proximity to each other may have overlapping detection areas, thus making the task of retrieving the correct travelled path even more challenging. The aim of this paper is twofold. We first give a comprehensive overview of the aforementioned issues. Further, we propose a methodology that can be followed, in order to cleanse, correct and aggregate Bluetooth data. We postulate that the methods introduced by this paper are the first crucial steps that need to be followed in order to compute accurate Origin-Destination matrices in urban road networks.

The financial cost of hamstring strain injuries in the Australian Football League

Background/Aim Hamstring strain injuries (HSIs) have remained the most prevalent injury in the Australian football league (AFL) over the past 21 regular seasons. The impact of HSIs in sport is often expressed as regular season games missed due to injury. However the financial cost of athletes missing games due to injury has not been investigated. The aim of this report is to estimate the financial cost of games missed due to HSIs in the AFL. Method Data was collected using publically available information from the AFL’s injury report and the official AFL annual report for the past 10 competitive AFL seasons. Average athlete salary and injury epidemiology data was used to determine the average yearly financial cost of HSIs for AFL clubs and the average financial cost of a single HSI over this time period. Results Across the observed period, average yearly financial cost of HSIs per club increased by 71% compared to a 43% increase in average yearly athlete salary. Over the same time period the average financial cost of a single HSI increased by 56% from $25,603 in 2003 to $40,021 in 2012, despite little change in HSI rates during the period. Conclusion The observed increased financial cost of HSIs was ultimately explained by the failure of teams to decrease HSI rates, but coupled with increases in athlete salaries over the past 10 season. The information presented in this report will highlight the financial cost of HSIs and other sporting injuries, raising greater awareness and the need for further funding for research into injury prevention strategies to maximise economical return for investment in athletes.

A data mining framework for relevance feature discovery

This thesis is a study for automatic discovery of text features for describing user information needs. It presents an innovative data-mining approach that discovers useful knowledge from both relevance and non-relevance feedback information. The proposed approach can largely reduce noises in discovered patterns and significantly improve the performance of text mining systems. This study provides a promising method for the study of Data Mining and Web Intelligence.

Developing targets and strategies for school-based injury prevention programs to reduce alcohol associated transport risks

Background There is considerable and ongoing debate about the role and effectiveness of school-based injury prevention programs in reducing students’ later involvement in alcohol associated transport injuries. Most relevant literature is concerned with pre-driving and licensing programs for middle age range adolescents (15-17 years). This research team is concerned with prevention at an earlier stage by targeting interventions to young adolescents (13-14 years). There is strong evidence that young adolescents who engage in unsafe and illegal alcohol associated transport risks are significantly likely to incur serious related injuries in longitudinal follow up. For example, a state-wide representative sample of male adolescents (mean age 14.5 years) who reported being passengers of drink drivers were significantly more likely to have incurred a hospitalised injury related to traffic events at a 20 year follow up. Aim This paper reports on first aid training integrated with peer protection and school connectedness within the Skills for Preventing Injury in Youth (SPIY) program. A component of the intervention is concerned with providing strategies to reduce the likelihood of being a passenger of a drink driver and effectiveness is followed up at six months post-intervention. Method In early 2012 the study was undertaken in 35 high schools throughout Queensland that were randomly assigned to intervention and control conditions. A total of 2,521 Year 9 students (mean age 13.5years, 43% male) completed surveys prior to the intervention. Results Of these students 316 (13.7%) reported having ridden in a car with someone who has been drinking. This is a traffic safety behaviour that is particularly relevant to a peer protection intervention and the findings of the six month follow up will be reported. Discussion and conclusions This research will provide evidence as to whether this approach to the introduction of first aid skills within a school-based health education curriculum has traffic safety implications.

Fundamental understanding on the use of Bluetooth scanner as a complementary transport data

Literature is limited in its knowledge of the Bluetooth protocol based data acquisition process and in the accuracy and reliability of the analysis performed using the data. This paper extends the body of knowledge surrounding the use of data from the Bluetooth Media Access Control Scanner (BMS) as a complementary traffic data source. A multi layer simulation model named Traffic and Communication Simulation (TCS) is developed. TCS is utilised to model the theoretical properties of the BMS data and analyse the accuracy and reliability of travel time estimation using the BMS data.

Prospective imaging study of asymptomatic patellar tendinopathy in elite junior basketball players

To evaluate the ability of ultrasonography to predict eventual symptoms in an at-risk population, 52 elite junior basketball players' patellar tendons were studied at baseline and again 16 months later. The group consisted of 10 study tendons (ultrasonographically hypoechoic at baseline) and 42 control tendons (ultrasonographically normal at baseline). By design, all tendons were asymptomatic at baseline. No differences were noted between subjects and controls at baseline for age, height, weight, training hours, and vertical jump. Functional (P < 0.01) and symptomatic outcome (P < 0.05) were poorer for subjects' tendons than for controls. Relative risk for developing symptoms of jumper's knee was 4.2 times greater in case tendons than in control tendons. Men were more likely to develop ultrasonographic changes than women (P < 0.025), and they also had significantly increased training hours per week (P < 0.01) in the study period. Half (50%) of abnormal tendons in women became ultrasonographically normal in the study period. Our data suggest that presence of an ultrasonographic hypoechoic area is associated with a greater risk of developing jumper's knee symptoms. Ultrasonographic patellar tendon changes may resolve, but this is not necessary for an athlete to become asymptomatic. Qualitative or quantitative analysis of baseline ultrasonographic images revealed it was not possible to predict which tendons would develop symptoms or resolve ultrasonographically.

Improving the PLDA based speaker verification in limited microphone data conditions

A significant amount of speech data is required to develop a robust speaker verification system, but it is difficult to find enough development speech to match all expected conditions. In this paper we introduce a new approach to Gaussian probabilistic linear discriminant analysis (GPLDA) to estimate reliable model parameters as a linearly weighted model taking more input from the large volume of available telephone data and smaller proportional input from limited microphone data. In comparison to a traditional pooled training approach, where the GPLDA model is trained over both telephone and microphone speech, this linear-weighted GPLDA approach is shown to provide better EER and DCF performance in microphone and mixed conditions in both the NIST 2008 and NIST 2010 evaluation corpora. Based upon these results, we believe that linear-weighted GPLDA will provide a better approach than pooled GPLDA, allowing for the further improvement of GPLDA speaker verification in conditions with limited development data.

Travel time prediction on signalised urban arterials by applying SARIMA modelling on Bluetooth data

Travel time prediction has long been the topic of transportation research. But most relevant prediction models in the literature are limited to motorways. Travel time prediction on arterial networks is challenging due to involving traffic signals and significant variability of individual vehicle travel time. The limited availability of traffic data from arterial networks makes travel time prediction even more challenging. Recently, there has been significant interest of exploiting Bluetooth data for travel time estimation. This research analysed the real travel time data collected by the Brisbane City Council using the Bluetooth technology on arterials. Databases, including experienced average daily travel time are created and classified for approximately 8 months. Thereafter, based on data characteristics, Seasonal Auto Regressive Integrated Moving Average (SARIMA) modelling is applied on the database for short-term travel time prediction. The SARMIA model not only takes the previous continuous lags into account, but also uses the values from the same time of previous days for travel time prediction. This is carried out by defining a seasonality coefficient which improves the accuracy of travel time prediction in linear models. The accuracy, robustness and transferability of the model are evaluated through comparing the real and predicted values on three sites within Brisbane network. The results contain the detailed validation for different prediction horizons (5 min to 90 minutes). The model performance is evaluated mainly on congested periods and compared to the naive technique of considering the historical average.

Recognising team activities from noisy data

Recently, vision-based systems have been deployed in professional sports to track the ball and players to enhance analysis of matches. Due to their unobtrusive nature, vision-based approaches are preferred to wearable sensors (e.g. GPS or RFID sensors) as it does not require players or balls to be instrumented prior to matches. Unfortunately, in continuous team sports where players need to be tracked continuously over long-periods of time (e.g. 35 minutes in field-hockey or 45 minutes in soccer), current vision-based tracking approaches are not reliable enough to provide fully automatic solutions. As such, human intervention is required to fix-up missed or false detections. However, in instances where a human can not intervene due to the sheer amount of data being generated - this data can not be used due to the missing/noisy data. In this paper, we investigate two representations based on raw player detections (and not tracking) which are immune to missed and false detections. Specifically, we show that both team occupancy maps and centroids can be used to detect team activities, while the occupancy maps can be used to retrieve specific team activities. An evaluation on over 8 hours of field hockey data captured at a recent international tournament demonstrates the validity of the proposed approach.

The effects of risk factors and protective factors on influencing engagement in risky behaviours and injury experiences for high-risk adolescents

High-risk adolescents are most vulnerable to the negative outcomes of risk taking behaviour, such as injury. It has been theorised by Jessor (1987) that adolescent risk behaviours (e.g. violence, alcohol use) can be predicted by assessing the risk factors (e.g. peer models for violence) and protective factors (e.g. school connectedness) in a young person’s life. The aim of this research is to examine the influence of risk factors and protective factors on the proneness of high-risk adolescents to engage in risky behaviour. 2,521 Grade 9 students (13-14 years of age) from 35 schools in Queensland, Australia participated in this study. The findings examine the influence of risk factors and protective factors on self-reported risky behaviour and injury experiences for adolescents who have been categorized as high-risk. Thereby, providing insight that may be used to target preventive interventions aimed at high-risk adolescents.

Faster than the speed of print : reconciling ‘big data’ social media analysis and academic scholarship

The promise of ‘big data’ has generated a significant deal of interest in the development of new approaches to research in the humanities and social sciences, as well as a range of important critical interventions which warn of an unquestioned rush to ‘big data’. Drawing on the experiences made in developing innovative ‘big data’ approaches to social media research, this paper examines some of the repercussions for the scholarly research and publication practices of those researchers who do pursue the path of ‘big data’–centric investigation in their work. As researchers import the tools and methods of highly quantitative, statistical analysis from the ‘hard’ sciences into computational, digital humanities research, must they also subscribe to the language and assumptions underlying such ‘scientificity’? If so, how does this affect the choices made in gathering, processing, analysing, and disseminating the outcomes of digital humanities research? In particular, is there a need to rethink the forms and formats of publishing scholarly work in order to enable the rigorous scrutiny and replicability of research outcomes?

The need for and appropriate application of spatial data for disaster management and strengthening community resilience

It is only in recent years that the critical role that spatial data can play in disaster management and strengthening community resilience has been recognised. The recognition of this importance is singularly evident from the fact that in Australia spatial data is considered as soft infrastructure. In the aftermath of every disaster this importance is being increasingly strengthened with state agencies paying greater attention to ensuring the availability of accurate spatial data based on the lessons learnt. For example, the major flooding in Queensland during the summer of 2011 resulted in a comprehensive review of responsibilities and accountability for the provision of spatial information during such natural disasters. A high level commission of enquiry completed a comprehensive investigation of the 2011 Brisbane flood inundation event and made specific recommendations concerning the collection of and accessibility to spatial information for disaster management and for strengthening community resilience during and after a natural disaster. The lessons learnt and processes implemented were subsequently tested by natural disasters during subsequent years. This paper provides an overview of the practical implementation of the recommendations of the commission of enquiry. It focuses particularly on the measures adopted by the state agencies with the primary role for managing spatial data and the evolution of this role in Queensland State, Australia. The paper concludes with a review of the development of the role and the increasing importance of spatial data as an infrastructure for disaster planning and management which promotes the strengthening of community resilience.

A novel representation of bioacoustic events for content-based search in field audio data

Bioacoustic data can provide an important base for environmental monitoring. To explore a large amount of field recordings collected, an automated similarity search algorithm is presented in this paper. A region of an audio defined by frequency and time bounds is provided by a user; the content of the region is used to construct a query. In the retrieving process, our algorithm will automatically scan through recordings to search for similar regions. In detail, we present a feature extraction approach based on the visual content of vocalisations – in this case ridges, and develop a generic regional representation of vocalisations for indexing. Our feature extraction method works best for bird vocalisations showing ridge characteristics. The regional representation method allows the content of an arbitrary region of a continuous recording to be described in a compressed format.

Development of school connectedness as a component of an injury prevention program for early adolescents

The current program of research addresses the need for multi-level programs to target the major increase in injury rates that occurs throughout adolescence. Specifically, it involves the investigation of school connectedness as a protective factor for adolescent injury, and the development of school connectedness as a component of an injury prevention program. To date, school-based risk taking and injury prevention has frequently been limited to addressing adolescents' knowledge and attitudes to risk behaviours, and has largely overlooked the importance of the wider school social context as a protective factor in adolescent development. Additionally, school connectedness has been primarily studied in terms of its impact on student achievement, wellbeing and risk taking behaviour, and research has not yet addressed possible links with injury. Further, school connectedness intervention programs have targeted risk taking behaviours without evaluating their potential impact on injury outcomes. This is the first reported research to develop strategies to increase school connectedness as part of a school-based injury prevention program. The research program was conceptualised as three distinct stages. The development of these research stages was informed by a comprehensive review of the literature on adolescent risk taking, injury and school-based prevention, as well as on school connectedness and its importance in adolescence. A review of the school connectedness literature indicated that students' connectedness is largely influenced by relationships within the school context including with teachers and other school staff, and is therefore a potentially modifiable factor that may be targeted in school-based programs. Overall, the literature shows school connectedness to be a key protective factor in adolescent development. This review established a foundation from which the current program of research was designed. The first stage of the research involved an empirical investigation of the relationship between adolescent risk taking-related injuries and school connectedness. Stage one incorporated two studies. The first involved the development of a measure of adolescent injury, the Extended Adolescent Injury Checklist (E-AIC), for use in the current research as well as in future school-based studies and program evaluation. The results of this study also highlighted the extent of the problem of risk-related injury in adolescence. The second study in Stage one examined the relationship between students' reports of school connectedness, risk taking behaviour and risk taking-related injuries on the E-AIC. The results of this study showed significant relationships between increased school connectedness and reduced reported engagement in transport and violence risk taking, and fewer associated injuries. This study therefore suggested the potential for school-based injury prevention programs to incorporate strategies targeting increased adolescent connectedness to school. The second stage of this research involved the compilation of an evidence base to inform the design of a school connectedness intervention. Stage two also incorporated two studies. The first study in Stage two involved a systematic review of programs that have targeted school connectedness for reduced risk taking and injury. The results of this study revealed that interventions targeting school connectedness can be effective in reducing adolescent risk taking behaviour, and also provided an evidence base for the design of the current school connectedness intervention. The second study in Stage two examined teachers' understanding and perceptions of school connectedness. This qualitative study indicated that teachers consider students' connectedness to be an important factor that relates to their risk taking behaviour; and also provided directions and content for the intervention design stage. The third stage of this research built upon the findings of each of the previous studies, and involved the design, implementation and evaluation of a school connectedness intervention as a component of an adolescent injury prevention program, Skills for Preventing Injury in Youth (SPIY). This connectedness intervention was designed as a professional development workshop for teachers of 13 to 14 year old adolescents, and was developed as a complementary component to the curriculum-based SPIY program. The SPIY connectedness component was implemented and evaluated using process and six-month impact evaluation methodologies. The results of this study revealed that teachers saw value in the program and made use of the strategies presented, and that program school students' self-reported violence risk behaviour was reduced at six-month follow-up. Despite these promising findings, the results of this study did not demonstrate a significant impact of the program on change in students' connectedness to school, relative to comparison schools. The positive impact on self-reported violence risk behaviour was however replicated in additional analyses comparing students participating in the connectedness version of SPIY with students participating in an earlier curriculumonly version of the program. This finding indicated that the connectedness component has additional benefits relating to reduction in violence risks, over and above a curriculum-only version of the program. This research was the first reported to address the relationship between school connectedness and adolescent injury outcomes, and to develop school connectedness as a component of an adolescent injury prevention program. Overall, the results of this program of research have demonstrated the importance of incorporating strategies targeting the wider school social context, including school connectedness, in adolescent injury prevention programs. This research has important implications for future research and practice in adolescent injury prevention.