Creating an effective prevention curriculum to reduce transport risks and injuries

Adolescent injury is a significant health concern and can be a result of the adolescents engagement in transport-related behaviours. There is however significant planning and formative research needed to inform prevention programme design. This presentation reports on the development and evaluation of a curriculum programme that was shown to be effective in reducing transport-related risks and injuries. Early adolescents report injuries resulting from a number of transport-related behaviours including those associated with riding a bicycle, a motorcycle, and as a passenger (survey of 209 Year 9 students). In focus groups, students (n=30) were able to describe the context of transport risks and injuries. Such information provided evidence of the need for an intervention and ecologically valid data on which to base programme design including insights into the language, culture and development of adolescents and their experiences with transport risks. Additional information about teaching practices and implementation issues were explored in interviews with 13 teachers. A psychological theory was selected to operationalise the design of the programmes that drew on such preparatory data. The programme, Skills for Preventing Injury in Youth was evaluated with 197 participating and 137 control students (13–14 year olds). Results showed a significant difference between the intervention and control groups from baseline to 6-month follow-up in a number of transport-related risk behaviours and transport-related injuries. The programme thus demonstrated potential in reduce early adolescents transport risk behaviours and associated harm. Discussion will involve the implications of the development research process in designing road safety interventions.

Discovery & effective use of quality association rules in multi-level datasets

In today’s electronic world vast amounts of knowledge is stored within many datasets and databases. Often the default format of this data means that the knowledge within is not immediately accessible, but rather has to be mined and extracted. This requires automated tools and they need to be effective and efficient. Association rule mining is one approach to obtaining knowledge stored with datasets / databases which includes frequent patterns and association rules between the items / attributes of a dataset with varying levels of strength. However, this is also association rule mining’s downside; the number of rules that can be found is usually very big. In order to effectively use the association rules (and the knowledge within) the number of rules needs to be kept manageable, thus it is necessary to have a method to reduce the number of association rules. However, we do not want to lose knowledge through this process. Thus the idea of non-redundant association rule mining was born. A second issue with association rule mining is determining which ones are interesting. The standard approach has been to use support and confidence. But they have their limitations. Approaches which use information about the dataset’s structure to measure association rules are limited, but could yield useful association rules if tapped. Finally, while it is important to be able to get interesting association rules from a dataset in a manageable size, it is equally as important to be able to apply them in a practical way, where the knowledge they contain can be taken advantage of. Association rules show items / attributes that appear together frequently. Recommendation systems also look at patterns and items / attributes that occur together frequently in order to make a recommendation to a person. It should therefore be possible to bring the two together. In this thesis we look at these three issues and propose approaches to help. For discovering non-redundant rules we propose enhanced approaches to rule mining in multi-level datasets that will allow hierarchically redundant association rules to be identified and removed, without information loss. When it comes to discovering interesting association rules based on the dataset’s structure we propose three measures for use in multi-level datasets. Lastly, we propose and demonstrate an approach that allows for association rules to be practically and effectively used in a recommender system, while at the same time improving the recommender system’s performance. This especially becomes evident when looking at the user cold-start problem for a recommender system. In fact our proposal helps to solve this serious problem facing recommender systems.

Knowledge translation for effective consumers

With the emergence of patient-centered care, consumers are becoming more effective managers of their care—in other words, “effective consumers.” To support patients to become effective consumers, a number of strategies to translate knowledge to action (KTA) have been used with varying success. The use of a KTA framework can be helpful to researchers and implementers when framing, planning, and evaluating knowledge translation activities and can potentially lead to more successful activities. This article briefly describes the KTA framework and its use by a team based out of the University of Ottawa to translate evidence-based knowledge to consumers. Using the framework, tailored consumer summaries, decision aids, and a scale to measure consumer effectiveness were created in collaboration with consumers. Strategies to translate the products into action then were selected and implemented. Evaluation of the knowledge tools and products indicates that the products are useful to consumers. Current research is in place to monitor the use of these products, and future research is planned to evaluate the effect of using the knowledge on health outcomes. The KTA framework provides a useful and valuable approach to knowledge translation.

Evaluating multivariate volatility forecasts : how effective are statistical and economic loss functions?

Multivariate volatility forecasts are an important input in many financial applications, in particular portfolio optimisation problems. Given the number of models available and the range of loss functions to discriminate between them, it is obvious that selecting the optimal forecasting model is challenging. The aim of this thesis is to thoroughly investigate how effective many commonly used statistical (MSE and QLIKE) and economic (portfolio variance and portfolio utility) loss functions are at discriminating between competing multivariate volatility forecasts. An analytical investigation of the loss functions is performed to determine whether they identify the correct forecast as the best forecast. This is followed by an extensive simulation study examines the ability of the loss functions to consistently rank forecasts, and their statistical power within tests of predictive ability. For the tests of predictive ability, the model confidence set (MCS) approach of Hansen, Lunde and Nason (2003, 2011) is employed. As well, an empirical study investigates whether simulation findings hold in a realistic setting. In light of these earlier studies, a major empirical study seeks to identify the set of superior multivariate volatility forecasting models from 43 models that use either daily squared returns or realised volatility to generate forecasts. This study also assesses how the choice of volatility proxy affects the ability of the statistical loss functions to discriminate between forecasts. Analysis of the loss functions shows that QLIKE, MSE and portfolio variance can discriminate between multivariate volatility forecasts, while portfolio utility cannot. An examination of the effective loss functions shows that they all can identify the correct forecast at a point in time, however, their ability to discriminate between competing forecasts does vary. That is, QLIKE is identified as the most effective loss function, followed by portfolio variance which is then followed by MSE. The major empirical analysis reports that the optimal set of multivariate volatility forecasting models includes forecasts generated from daily squared returns and realised volatility. Furthermore, it finds that the volatility proxy affects the statistical loss functions’ ability to discriminate between forecasts in tests of predictive ability. These findings deepen our understanding of how to choose between competing multivariate volatility forecasts.

Designing university, government and industry community engagement to build informed and effective interventions in the area of road safety

Individuals, community organisations and industry have always been involved to varying degrees in efforts to address the Queensland road toll. Traditionally, road crash prevention efforts have been led by state and local government organisations. While community and industry groups have sometimes become involved (e.g. Driver Reviver campaign), their efforts have largely been uncoordinated and under-resourced. A common strength of these initiatives lies in the energy, enthusiasm and persistence of community-based efforts. Conversely, a weakness has sometimes been the lack of knowledge, awareness or prioritisation of evidence-based interventions or their capacity to build on collaborative efforts. In 2000, the Queensland University of Technology’s Centre for Accident Research and Road Safety – Queensland (CARRS-Q) identified this issue as an opportunity to bridge practice and research and began acknowledging a selection of these initiatives, in partnership with the RACQ, through the Queensland Road Safety Awards program. After nine years it became apparent there was need to strengthen this connection, with the Centre establishing a Community Engagement Workshop in 2009 as part of the overall Awards program. With an aim of providing community participants opportunities to see, hear and discuss the experiences of others, this event was further developed in 2010, and with the collaboration of the Queensland Department of Transport and Main Roads, the RACQ, Queensland Police Service and Leighton Contractors Pty Ltd, a stand-alone Queensland Road Safety Awards Community Engagement Workshop was held in 2010. Each collaborating organisation recognised a need to mobilise the community through effective information and knowledge sharing, and recognised that learning and discussion can influence lasting behaviour change and action in this often emotive, yet not always evidence-based, area. This free event featured a number of speakers representing successful projects from around Australia and overseas. Attendees were encouraged to interact with the speakers, to ask questions, and most importantly, build connections with other attendees to build a ‘community road safety army’ all working throughout Australia on projects underpinned by evaluated research. The workshop facilitated the integration of research, policy and grass-roots action enhancing the success of community road safety initiatives. For collaboration partners, the event enabled them to transfer their knowledge in an engaged approach, working within a more personal communication process. An analysis of the success factors for this event identified openness to community groups and individuals, relevance of content to local initiatives, generous support with the provision of online materials and ongoing communication with key staff members as critical and supports the view that the university can directly provide both the leadership and the research needed for effective and credible community-based initiatives to address injury and death on the roads.

Identifying interests of web users for effective recommendations

Search log data is multi dimensional data consisting of number of searches of multiple users with many searched parameters. This data can be used to identify a user’s interest in an item or object being searched. Identifying highest interests of a Web user from his search log data is a complex process. Based on a user’s previous searches, most recommendation methods employ two-dimensional models to find relevant items. Such items are then recommended to a user. Two-dimensional data models, when used to mine knowledge from such multi dimensional data may not be able to give good mappings of user and his searches. The major problem with such models is that they are unable to find the latent relationships that exist between different searched dimensions. In this research work, we utilize tensors to model the various searches made by a user. Such high dimensional data model is then used to extract the relationship between various dimensions, and find the prominent searched components. To achieve this, we have used popular tensor decomposition methods like PARAFAC, Tucker and HOSVD. All experiments and evaluation is done on real datasets, which clearly show the effectiveness of tensor models in finding prominent searched components in comparison to other widely used two-dimensional data models. Such top rated searched components are then given as recommendation to users.

Effective adsorption of sodium dodecylsulfate (SDS) by hydrocalumite (CaAl-LDH-Cl) induced by self-dissolution and re-precipitation mechanism

Hydrocalumite (CaAl-LDH-Cl) were synthesized through a rehydration method involving a freshly prepared tricalcium aluminate (C3A) with CaCl2 solution. To understand the intercalation behaviour of sodium dodecylsulfate (SDS) with CaAl-LDH-Cl, X-ray diffraction (XRD), Fourier transform infrared (FTIR), scanning electron microscopy (SEM), transmission electron microscope (TEM), X-ray photoelectron spectroscopy (XPS), inductively coupled plasma-atomic emission spectrometer (ICP) and elemental analysis have been undertaken. The sorption isotherms with SDS reveal that the maximum sorption amount of SDS by CaAl-LDH-Cl could reach 3.67 mmol•g-1. The results revealed that CaAl-LDH-Cl holds a self-dissolution property, about 20-30% of which is dissolved. And the dissolved Ca2+, Al3+ ions are combined with SDS to form CaAl-SDS or Ca-SDS precipitation. It has been highlighted that the composition of resulting products is strongly dependent upon the SDS concentration. With increasing SDS concentrations, the main resulting product changes from CaAl-SDS to Ca-SDS, and the value of interlayer spacing increased to 3.27 nm.

Effective health communication in the mining industry

Historically, occupational health and safety has primarily presented as attempts to create a safer work environment for employees. The mining industry carries health and safety risks, often greater than other occupations. Whilst the mining industry is regulated by stringent workplace health and safety regulations, the very nature of the work and environmental influences expose employees to a greater number of injury risk factors than many other industries. The application of risk management techniques has resulted in a substantial decline in injury rates observed for mining operations in developed countries (Donoghue, 2004). This essential focus can be complemented by a more comprehensive approach to occupational health and safety that also supports the design and delivery of proactive health promotion programs...

In the pursuit of effective affective computing : the relationship between features and registration

For facial expression recognition systems to be applicable in the real world, they need to be able to detect and track a previously unseen person's face and its facial movements accurately in realistic environments. A highly plausible solution involves performing a "dense" form of alignment, where 60-70 fiducial facial points are tracked with high accuracy. The problem is that, in practice, this type of dense alignment had so far been impossible to achieve in a generic sense, mainly due to poor reliability and robustness. Instead, many expression detection methods have opted for a "coarse" form of face alignment, followed by an application of a biologically inspired appearance descriptor such as the histogram of oriented gradients or Gabor magnitudes. Encouragingly, recent advances to a number of dense alignment algorithms have demonstrated both high reliability and accuracy for unseen subjects [e.g., constrained local models (CLMs)]. This begs the question: Aside from countering against illumination variation, what do these appearance descriptors do that standard pixel representations do not? In this paper, we show that, when close to perfect alignment is obtained, there is no real benefit in employing these different appearance-based representations (under consistent illumination conditions). In fact, when misalignment does occur, we show that these appearance descriptors do work well by encoding robustness to alignment error. For this work, we compared two popular methods for dense alignment-subject-dependent active appearance models versus subject-independent CLMs-on the task of action-unit detection. These comparisons were conducted through a battery of experiments across various publicly available data sets (i.e., CK+, Pain, M3, and GEMEP-FERA). We also report our performance in the recent 2011 Facial Expression Recognition and Analysis Challenge for the subject-independent task.

What makes an LMS effective : a synthesis of current literature

There is a growing number of organizations and universities now utilising e-learning practices in their teaching and learning programs. These systems have allowed for knowledge sharing and provide opportunities for users to have access to learning materials regardless of time and place. However, while the uptake of these systems is quite high, there is little research into the effectiveness of such systems, particularly in higher education. This paper investigates the methods that are used to study the effectiveness of e-learning systems and the factors that are critical for the success of a learning management system (LMS). Five major success categories are identified in this study and explained in depth. These are the teacher, student, LMS design, learning materials and external support.

An assessment for cost effective maintenance and operation of the Callide Power Station electrical system, using reliability evaluation

The reliable operation of the electrical system at Callide Power Station is of extreme importance to the normal everyday running of the Station. This study applied the principles of reliability to do an analysis on the electrical system at Callide Power Station. It was found that the level of expected outage cost increased exponentially with a declining level of maintenance. Concluding that even in a harsh economic electricity market where CS Energy tries and push their plants to the limit, maintenance must not be neglected. A number of system configurations were found to increase the reliability of the system and reduce the expected outage costs. A number of other advantages were identified as a result of using reliability principles to do this study on the Callide electrical system configuration.

Mice selectively bred for High and Low fear behavior show differences in the number of pMAPK (p44/42 ERK) expressing neurons in lateral amygdala following Pavlovian fear conditioning

Individual variability in the acquisition, consolidation and extinction of conditioned fear potentially contributes to the development of fear pathology including posttraumatic stress disorder (PTSD). Pavlovian fear conditioning is a key tool for the study of fundamental aspects of fear learning. Here, we used a selected mouse line of High and Low Pavlovian conditioned fear created from an advanced intercrossed line (AIL) in order to begin to identify the cellular basis of phenotypic divergence in Pavlovian fear conditioning. We investigated whether phosphorylated MAPK (p44/42 ERK/MAPK), a protein kinase required in the amygdala for the acquisition and consolidation of Pavlovian fear memory, is differentially expressed following Pavlovian fear learning in the High and Low fear lines. We found that following Pavlovian auditory fear conditioning, High and Low line mice differ in the number of pMAPK-expressing neurons in the dorsal sub nucleus of the lateral amygdala (LAd). In contrast, this difference was not detected in the ventral medial (LAvm) or ventral lateral (LAvl) amygdala sub nuclei or in control animals. We propose that this apparent increase in plasticity at a known locus of fear memory acquisition and consolidation relates to intrinsic differences between the two fear phenotypes. These data provide important insights into the micronetwork mechanisms encoding phenotypic differences in fear. Understanding the circuit level cellular and molecular mechanisms that underlie individual variability in fear learning is critical for the development of effective treatment of fear-related illnesses such as PTSD.

Effective punishment experiences : the need for a more comprehensive approach to conceptualising behavioural punishers and reinforcers in a road safety context

Determining what consequences are likely to serve as effective punishment for any given behaviour is a complex task. This chapter focuses specifically on illegal road user behaviours and the mechanisms used to punish and deter them. Traffic law enforcement has traditionally used the threat and/or receipt of legal sanctions and penalties to deter illegal and risky behaviours. This process represents the use of positive punishment, one of the key behaviour modification mechanisms. Behaviour modification principles describe four types of reinforcers: positive and negative punishments and positive and negative reinforcements. The terms ‘positive’ and ‘negative’ are not used in an evaluative sense here. Rather, they represent the presence (positive) or absence (negative) of stimuli to promote behaviour change. Punishments aim to inhibit behaviour and reinforcements aim to encourage it. This chapter describes a variety of punishments and reinforcements that have been and could be used to modify illegal road user behaviours. In doing so, it draws on several theoretical perspectives that have defined behavioural reinforcement and punishment in different ways. Historically, the main theoretical approach used to deter risky road use has been classical deterrence theory which has focussed on the perceived certainty, severity and swiftness of penalties. Stafford and Warr (1993) extended the traditional deterrence principles to include the positive reinforcement concept of punishment avoidance. Evidence of the association between punishment avoidance experiences and behaviour has been established for a number of risky road user behaviours including drink driving, unlicensed driving, and speeding. We chose a novel way of assessing punishment avoidance by specifying two sub-constructs (detection evasion and punishment evasion). Another theorist, Akers, described the idea of competing reinforcers, termed differential reinforcement, within social learning theory (1977). Differential reinforcement describes a balance of reinforcements and punishments as influential on behaviour. This chapter describes comprehensive way of conceptualising a broad range of reinforcement and punishment concepts, consistent with Akers’ differential reinforcement concept, within a behaviour modification framework that incorporates deterrence principles. The efficacy of three theoretical perspectives to explain self-reported speeding among a sample of 833 Australian car drivers was examined. Results demonstrated that a broad range of variables predicted speeding including personal experiences of evading detection and punishment for speeding, intrinsic sensations, practical benefits expected from speeding, and an absence of punishing effects from being caught. Not surprisingly, being younger was also significantly related to more frequent speeding, although in a regression analysis, gender did not retain a significant influence once all punishment and reinforcement variables were entered. The implications for speed management, as well as road user behaviour modification more generally, are discussed in light of these findings. Overall, the findings reported in this chapter suggest that a more comprehensive approach is required to manage the behaviour of road users which does not rely solely on traditional legal penalties and sanctions.

The Yd2 resistance to barley yellow dwarf virus is effective in barley plants but not in their leaf protoplasts

The Yd2 gene for “resistance” to barley yellow dwarf virus (BYDV) has been widely used in barley (Hordeum vulgare). We have tested Australian isolates of BYDV of varying severity against barley genotypes with and without the Yd2 gene and report here a positive relationship between symptoms and virus levels determined by ELISA. Cultivar Shannon is the result of backcrossing the resistant line CI 3208 to cultivar Proctor, a susceptible line. It appears to be intermediate in reaction to BYDV between Proctor and CI 3208, although it carries the major gene, Yd2. Unlike the whole plant studies, no significant differences were observed with regard to the ability of protoplasts derived from these various genotypes to support BYDV replication. It is therefore demonstrated for the first time that the Yd2 gene is not among the small number of resistance genes which are effective against virus replication in isolated protoplasts.

The Effective and The Evocative : A Spectrum of Creative Practice Research

In the expanding literature on creative practice research, art and design are often described as a unified field. They are bracketed together (art-and-design), referred to as interchangeable terms (art/design), and nested together, as if the practices of one domain encompass the other. However it is possible to establish substantial differences in research approaches. In this chapter we argue that core distinctions arise out of the goals of the research, intentions invested in the resulting “artefacts” (creative works, products, events), and the knowledge claims made for the research outcomes. Moreover, these fundamental differences give rise to a number of contingent attributes of the research such as the forming contexts, methodological approaches, and ways of evidencing and reporting new knowledge. We do not strictly ascribe these differences to disciplinary contexts. Rather, we use the terms effective practice research and evocative practice research to describe the spirit of the two distinctive research paradigms we identify. In short, effective practice research (often pursued in design fields) seeks a solution (or resolution) to a problem identified with a particular community, and it produces an artefact that addresses this problem by effecting change (making a situation, product or process more efficient or effective in some way). On the other hand, evocative practice research (often pursued by creative arts fields) is driven by individual pre-occupations, cultural concerns or human experience more broadly. It produces artefacts that evoke affect and resonance, and are poetically irreducible in meaning. We cite recent examples of creative research projects that illustrate the distinctions we identify. We then go on to describe projects that integrate these modes of research. In this way, we map out a creative research spectrum, with distinct poles as well as multiple hybrid possibilities. The hybrid projects we reference are not presented as evidence an undifferentiated field. Instead, we argue that they integrate research modes in deliberate, purposeful and distinctive ways: employing effective practice research methods in the production of evocative artefacts or harnessing evocative (as well as effective) research paradigms to effect change.