Response to "a step in the right direction : commentary on expected values for pedometer-determined physical activity in youth"

The figure Beets took exception to displays sex‐ and age‐specific median values of aggregated published expected values for pedometer determined physical activity.

Overloading truck traffic study and strategy to minimise it

Overloaded truck traffic is a significant problem on highways around the world. Developing countries in particular, overloaded truck traffic causes large amounts of unexpected expenditure in terms of road maintenance because of premature pavement damage. Overloaded truck traffic is a common phenomenon in developing countries, because of inefficient road management and monitoring systems. According to the available literature, many developing countries are facing the same problem, which is economic loss caused by the existence of overloaded trucks in the traffic stream. This paper summarizes the available literature, news reports, journal articles and traffic research regarding overloaded traffic. It examines the issue of overloading and the strategies and legislation used in developed countries.

The relative impact of work-related stress, life stress and driving environment stress on driving outcomes

Previous research has shown the association between stress and crash involvement. The impact of stress on road safety may also be mediated by behaviours including cognitive lapses, errors, and intentional traffic violations. This study aimed to provide a further understanding of the impact that stress from different sources may have upon driving behaviour and road safety. It is asserted that both stress extraneous to the driving environment and stress directly elicited by driving must be considered part of a dynamic system that may have a negative impact on driving behaviours. Two hundred and forty-seven public sector employees from Queensland, Australia, completed self-report measures examining demographics, subjective work-related stress, daily hassles, and aspects of general mental health. Additionally, the Driver Behaviour Questionnaire (DBQ) and the Driver Stress Inventory (DSI) were administered. All participants drove for work purposes regularly, however the study did not specifically focus on full-time professional drivers. Confirmatory factor analysis of the predictor variables revealed three factors: DSI negative affect; DSI risk taking; and extraneous influences (daily hassles, work-related stress, and general mental health). Moderate intercorrelations were found between each of these factors confirming the ‘spillover’ effect. That is, driver stress is reciprocally related to stress in other domains including work and domestic life. Structural equation modelling (SEM) showed that the DSI negative affect factor influenced both lapses and errors, whereas the DSI risk-taking factor was the strongest influence on violations. The SEMs also confirmed that daily hassles extraneous to the driving environment may influence DBQ lapses and violations independently. Accordingly, interventions may be developed to increase driver awareness of the dangers of excessive emotional responses to both driving events and daily hassles (e.g. driving fast to ‘blow off steam’ after an argument). They may also train more effective strategies for self-regulation of emotion and coping when encountering stressful situations on the road.

A biologically inspired object spectral-texture descriptor and its application to vegetation classification in power-line corridors

The use of appropriate features to represent an output class or object is critical for all classification problems. In this paper, we propose a biologically inspired object descriptor to represent the spectral-texture patterns of image-objects. The proposed feature descriptor is generated from the pulse spectral frequencies (PSF) of a pulse coupled neural network (PCNN), which is invariant to rotation, translation and small scale changes. The proposed method is first evaluated in a rotation and scale invariant texture classification using USC-SIPI texture database. It is further evaluated in an application of vegetation species classification in power line corridor monitoring using airborne multi-spectral aerial imagery. The results from the two experiments demonstrate that the PSF feature is effective to represent spectral-texture patterns of objects and it shows better results than classic color histogram and texture features.

Leveraging R&D investment for the Australian built environment

This paper discusses a current research project building new understandings and knowledge relevant to R&D funding strategies in Australia. Building on a retrospective analysis of R&D trends and industry outcomes, an industry roadmap will be developed to inform R&D policies more attuned to future industry needs to improve research investment effectiveness. The project will also include analysis of research team formation and management (involving end users from public and private sectors together with research and knowledge institutions), and dissemination of outcomes and uptake in the Australian building and construction industry. The project will build on previous research extending open innovation system theory and network analysis and procurement, focused on R&D. Through the application of dynamic capabilities and strategic foresighting theory, an industry roadmap for future research investment will be developed, providing a stronger foundation for more targeted policy recommendations. This research will contribute to more effective construction processes in the future through more targeted research funding and more effective research partnerships between industry and researchers.

Improved salinity management for the Nebine / Mungallala / Wallam catchment

The low stream salinity naturally in the Nebine-Mungallala Catchment, extent of vegetation retention, relatively low rainfall and high evaporation indicates that there is a relatively low risk of rising shallow groundwater tables in the catchment. Scalding caused by wind and water erosion exposing highly saline sub-soils is a more important regional issue, such as in the Homeboin area. Local salinisation associated with evaporation of bore water from free flowing bore drains and bores is also an important land degradation issue particularly in the lower Nebine, Wallam and Mungallala Creeks. The replacement of free flowing artesian bores and bore drains with capped bores and piped water systems under the Great Artesian Basin bore rehabilitation program is addressing local salinisation and scalding in the vicinity of bore drains and preventing the discharge of saline bore water to streams. Three principles for the prevention and control of salinity in the Nebine Mungallala catchment have been identified in this review: • Avoid salinity through avoiding scalds – i.e. not exposing the near-surface salt in landscape through land degradation; • Riparian zone management: Scalding often occurs within 200m or so of watering lines. Natural drainage lines are most likely to be overstocked, and thus have potential for scalding. Scalding begins when vegetation is removed, and without that binding cover, wind and water erosion exposes the subsoil; and • Monitoring of exposed or grazed soil areas. Based on the findings of the study, we make the following recommendations: 1. Undertake a geotechnical study of existing maps and other data to help identify and target areas most at risk of rising water tables causing salinity. Selected monitoring should then be established using piezometers as an early warning system. 2. SW NRM should financially support scald reclamation activity through its various funding programs. However, for this to have any validity in the overall management of salinity risk, it is critical that such funding require the landholder to undertake a salinity hazard/risk assessment of his/her holding. 3. A staged approach to funding may be appropriate. In the first instance, it would be reasonable to commence funding some pilot scald reclamation work with a view to further developing and piloting the farm hazard/risk assessment tools, and exploring how subsequent grazing management strategies could be incorporated within other extension and management activities. Once the details of the necessary farm level activities have been more clearly defined, and following the outcomes of the geotechnical review recommended above, a more comprehensive funding package could be rolled out to priority areas. 4. We recommend that best-practice grazing management training currently on offer should be enhanced with information about salinity risk in scald-prone areas, and ways of minimising the likelihood of scald formation. 5. We recommend that course material be developed for local students in Years 6 and 7, and that arrangements be made with local schools to present this information. Given the constraints of existing syllabi, we envisage that negotiations may have to be undertaken with the Department of Education in order for this material to be permitted to be used. We have contact with key people who could help in this if required. 6. We recommend that SW NRM continue to support existing extension activities such as Grazing Land Management and the Monitoring Made Easy tools. These aids should be able to be easily expanding to incorporate techniques for monitoring, addressing and preventing salinity and scalding. At the time of writing staff of SW NRM were actively involved in this process. It is important that these activities are adequately resourced to facilitate the uptake by landholders of the perception that salinity is an issue that needs to be addressed as part of everyday management. 7. We recommend that SW NRM consider investing in the development and deployment of a scenario-modelling learning support tool as part of the awareness raising and education activities. Secondary salinity is a dynamic process that results from ongoing human activity which mobilises and/or exposes salt occurring naturally in the landscape. Time scales can be short to very long, and the benefits of management actions can similarly have immediate or very long time frames. One way to help explain the dynamics of these processes is through scenario modelling.

A literature review on research methodologies of gross pollutant traps

This paper presents a comprehensive review of scientific and grey literature on gross pollutant traps (GPTs). GPTs are designed with internal screens to capture gross pollutants—organic matter and anthropogenic litter. Their application involves professional societies, research organisations, local city councils, government agencies and the stormwater industry—often in partnership. In view of this, the 113 references include unpublished manuscripts from these bodies along with scientific peer-reviewed conference papers and journal articles. The literature reviewed was organised into a matrix of six main devices and nine research areas (testing methodologies) which include: design appraisal study, field monitoring/testing, experimental flow fields, gross pollutant capture/retention characteristics, residence time calculations, hydraulic head loss, screen blockages, flow visualisations and computational fluid dynamics (CFD). When the fifty-four item matrix was analysed, twenty-eight research gaps were found in the tabulated literature. It was also found that the number of research gaps increased if only the scientific literature was considered. It is hoped, that in addition to informing the research community at QUT, this literature review will also be of use to other researchers in this field.

Robust suspicious behaviour detection for smart surveillance systems

Video surveillance technology, based on Closed Circuit Television (CCTV) cameras, is one of the fastest growing markets in the field of security technologies. However, the existing video surveillance systems are still not at a stage where they can be used for crime prevention. The systems rely heavily on human observers and are therefore limited by factors such as fatigue and monitoring capabilities over long periods of time. To overcome this limitation, it is necessary to have “intelligent” processes which are able to highlight the salient data and filter out normal conditions that do not pose a threat to security. In order to create such intelligent systems, an understanding of human behaviour, specifically, suspicious behaviour is required. One of the challenges in achieving this is that human behaviour can only be understood correctly in the context in which it appears. Although context has been exploited in the general computer vision domain, it has not been widely used in the automatic suspicious behaviour detection domain. So, it is essential that context has to be formulated, stored and used by the system in order to understand human behaviour. Finally, since surveillance systems could be modeled as largescale data stream systems, it is difficult to have a complete knowledge base. In this case, the systems need to not only continuously update their knowledge but also be able to retrieve the extracted information which is related to the given context. To address these issues, a context-based approach for detecting suspicious behaviour is proposed. In this approach, contextual information is exploited in order to make a better detection. The proposed approach utilises a data stream clustering algorithm in order to discover the behaviour classes and their frequency of occurrences from the incoming behaviour instances. Contextual information is then used in addition to the above information to detect suspicious behaviour. The proposed approach is able to detect observed, unobserved and contextual suspicious behaviour. Two case studies using video feeds taken from CAVIAR dataset and Z-block building, Queensland University of Technology are presented in order to test the proposed approach. From these experiments, it is shown that by using information about context, the proposed system is able to make a more accurate detection, especially those behaviours which are only suspicious in some contexts while being normal in the others. Moreover, this information give critical feedback to the system designers to refine the system. Finally, the proposed modified Clustream algorithm enables the system to both continuously update the system’s knowledge and to effectively retrieve the information learned in a given context. The outcomes from this research are: (a) A context-based framework for automatic detecting suspicious behaviour which can be used by an intelligent video surveillance in making decisions; (b) A modified Clustream data stream clustering algorithm which continuously updates the system knowledge and is able to retrieve contextually related information effectively; and (c) An update-describe approach which extends the capability of the existing human local motion features called interest points based features to the data stream environment.

Strategic management practices in the construction industry : a study of Indonesian enterprises

Since the establishment of the first national strategic development plan in the early 1970s, the construction industry has played an important role in terms of the economic, social and cultural development of Indonesia. The industry’s contribution to Indonesia’s gross domestic product (GDP) increased from 3.9% in 1973 to 7.7% in 2007. Business Monitoring International (2009) forecasts that Indonesia is home to one of the fastest-growing construction industries in Asia despite the average construction growth rate being expected to remain under 10% over the period 2006 – 2010. Similarly, Howlett and Powell (2006) place Indonesia as one of the 20 largest construction markets in 2010. Although the prospects for the Indonesian construction industry are now very promising, many local construction firms still face serious difficulties, such as poor performance and low competitiveness. There are two main reasons behind this problem: the environment that they face is not favourable; the other is the lack of strategic direction to improve competitiveness and performance. Furthermore, although strategic management has now become more widely used by many large construction firms in developed countries, practical examples and empirical studies related to the Indonesian construction industry remain scarce. In addition, research endeavours related to these topics in developing countries appear to be limited. This has potentially become one of the factors hampering efforts to guide Indonesian construction enterprises. This research aims to construct a conceptual model to enable Indonesian construction enterprises to develop a sound long-term corporate strategy that generates competitive advantage and superior performance. The conceptual model seeks to address the main prescription of a dynamic capabilities framework (Teece, Pisano & Shuen, 1997; Teece, 2007) within the context of the Indonesian construction industry. It is hypothesised that in a rapidly changing and varied environment, competitive success arises from the continuous development and reconfiguration of firm’s specific assets achieving competitive advantage is not only dependent on the exploitation of specific assets/capabilities, but on the exploitation of all of the assets and capabilities combinations in the dynamic capabilities framework. Thus, the model is refined through sequential statistical regression analyses of survey results with a sample size of 120 valid responses. The results of this study provide empirical evidence in support of the notion that a competitive advantage is achieved via the implementation of a dynamic capability framework as an important way for a construction enterprise to improve its organisational performance. The characteristics of asset-capability combinations were found to be significant determinants of the competitive advantage of the Indonesian construction enterprises, and that such advantage sequentially contributes to organisational performance. If a dynamic capabilities framework can work in the context of Indonesia, it suggests that the framework has potential applicability in other emerging and developing countries. This study also demonstrates the importance of the multi-stage nature of the model which provides a rich understanding of the dynamic process by which asset-capability should be exploited in combination by the construction firms operating in varying levels of hostility. Such findings are believed to be useful to both academics and practitioners, however, as this research represents a dynamic capabilities framework at the enterprise level, future studies should continue to explore and examine the framework in other levels of strategic management in construction as well as in other countries where different cultures or similar conditions prevails.

Acknowledging social justice and equity through good practice for monitoring student learning engagement in FYE

As higher education institutions respond to government targets to widen participation, their student populations will become increasingly diverse, and the issues around student success and retention will be more closely scrutinised. The concept of student engagement is a key factor in student achievement and retention and Australasian institutions have a range of initiatives aimed at monitoring and intervening with students who are at risk of disengaging. Within the widening participation agenda, it is absolutely critical that these initiatives are designed to enable success for all students, particularly those for whom social and cultural disadvantage have been a barrier. Consequently, for the sector, initiatives of this type must be consistent with the concept of social justice and a set of principles would provide this foundation. This session will provide an opportunity for participants to examine a draft set of principles and to discuss their potential value for the participants’ institutional contexts.

Asset life prediction and maintenance decision-making using a non-linear non-Gaussian state space model

Estimating and predicting degradation processes of engineering assets is crucial for reducing the cost and insuring the productivity of enterprises. Assisted by modern condition monitoring (CM) technologies, most asset degradation processes can be revealed by various degradation indicators extracted from CM data. Maintenance strategies developed using these degradation indicators (i.e. condition-based maintenance) are more cost-effective, because unnecessary maintenance activities are avoided when an asset is still in a decent health state. A practical difficulty in condition-based maintenance (CBM) is that degradation indicators extracted from CM data can only partially reveal asset health states in most situations. Underestimating this uncertainty in relationships between degradation indicators and health states can cause excessive false alarms or failures without pre-alarms. The state space model provides an efficient approach to describe a degradation process using these indicators that can only partially reveal health states. However, existing state space models that describe asset degradation processes largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires that failures and inspections only happen at fixed intervals. The discrete state assumption entails discretising continuous degradation indicators, which requires expert knowledge and often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This research proposes a Gamma-based state space model that does not have discrete time, discrete state, linear and Gaussian assumptions to model partially observable degradation processes. Monte Carlo-based algorithms are developed to estimate model parameters and asset remaining useful lives. In addition, this research also develops a continuous state partially observable semi-Markov decision process (POSMDP) to model a degradation process that follows the Gamma-based state space model and is under various maintenance strategies. Optimal maintenance strategies are obtained by solving the POSMDP. Simulation studies through the MATLAB are performed; case studies using the data from an accelerated life test of a gearbox and a liquefied natural gas industry are also conducted. The results show that the proposed Monte Carlo-based EM algorithm can estimate model parameters accurately. The results also show that the proposed Gamma-based state space model have better fitness result than linear and Gaussian state space models when used to process monotonically increasing degradation data in the accelerated life test of a gear box. Furthermore, both simulation studies and case studies show that the prediction algorithm based on the Gamma-based state space model can identify the mean value and confidence interval of asset remaining useful lives accurately. In addition, the simulation study shows that the proposed maintenance strategy optimisation method based on the POSMDP is more flexible than that assumes a predetermined strategy structure and uses the renewal theory. Moreover, the simulation study also shows that the proposed maintenance optimisation method can obtain more cost-effective strategies than a recently published maintenance strategy optimisation method by optimising the next maintenance activity and the waiting time till the next maintenance activity simultaneously.

Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

Blackboard as an online learning environment : what do teacher education students and staff think?

Abstract: As online learning environments now have an established presence in higher education we need to ask the question: How effective are these environments for student learning? Online environments can provide a different type of learning experience than traditional face-to-face contexts (for on-campus students) or print-based materials (for distance learners). This article identifies teacher education student and staff perceptions of teaching and learning using the online learning management system, Blackboard. Perceptions of staff and students are compared and implications for teacher education staff interested in providing high quality learning environments within an online space are discussed.

Structural reliability prediction of a steel bridge element using Dynamic Object Oriented Bayesian Network (DOOBN)

Different from conventional methods for structural reliability evaluation, such as, first/second-order reliability methods (FORM/SORM) or Monte Carlo simulation based on corresponding limit state functions, a novel approach based on dynamic objective oriented Bayesian network (DOOBN) for prediction of structural reliability of a steel bridge element has been proposed in this paper. The DOOBN approach can effectively model the deterioration processes of a steel bridge element and predict their structural reliability over time. This approach is also able to achieve Bayesian updating with observed information from measurements, monitoring and visual inspection. Moreover, the computational capacity embedded in the approach can be used to facilitate integrated management and maintenance optimization in a bridge system. A steel bridge girder is used to validate the proposed approach. The predicted results are compared with those evaluated by FORM method.

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.