An integrated information management model for ecologically sustainable development

Ecologically sustainable development has become a major feature of legal systems at the international, national and local levels throughout the world. In Australia, governments have responded to environmental crises by enacting legislation imposing obligations and restrictions over privately-owned land. Whilst these obligations and restrictions may well be necessary to achieve sustainability, the approach to management of information concerning these instruments is problematic. For example, management of information concerning obligations and restrictions in Queensland is fragmented, with some instruments registered or recorded on the land title register, some on external registers, and some information only available in the legislation itself. This approach is used in most Australian jurisdictions. This fragmented approach has led to two separate but interconnected problems. First, the Torrens system is no longer meeting its goal of providing a complete and accurate picture of title. Second, this uncoordinated approach to the management of land titles, and obligations and restrictions on land use, has created a barrier to sustainable management of natural resources. This is because compliance with environmental laws is impaired in the absence of easily accessible and accurate information. These problems demonstrate a clear need for reform in this area. To determine how information concerning these obligations and restrictions may be most effectively managed, this thesis will apply a comparative methodology and consider three case studies, which each utilise different models for management of this information. These jurisdictions will be assessed according to a set of guidelines for comparison to identify which features of their systems provide for effective management of information concerning obligations and restrictions on title and use. Based on this comparison, this thesis will devise a series of recommendations for an effective system for the management of information concerning obligations and restrictions on land title and use, taking into account any potential legal issues and barriers to implementation. This series of recommendations for reform will be supplemented by suggested draft legislative provisions.

Are SC-FDE systems robust to CFO?

This paper investigates the impact of carrier frequency offset (CFO) on Single Carrier wireless communication systems with Frequency Domain Equalization (SC-FDE). We show that CFO in SC-FDE systems causes irrecoverable channel estimation error, which leads to inter-symbol-interference (ISI). The impact of CFO on SC-FDE and OFDM is compared in the presence of CFO and channel estimation errors. Closed form expressions of signal to interference and noise ratio (SINR) are derived for both systems, and verified by simulation results. We find that when channel estimation errors are considered, SC-FDE is similarly or even more sensitive to CFO, compared to OFDM. In particular, in SC-FDE systems, CFO mainly deteriorates the system performance via degrading the channel estimation. Both analytical and simulation results highlight the importance of accurate CFO estimation in SC-FDE systems.

Community knowledge and beliefs about ADHD

Accurate knowledge and positive attitudes within the community are important for the effective diagnosis, treatment and support of people with ADHD. Most previous research about knowledge and attitudes has focused only on professional groups and parents of children with ADHD. The aim of this study was to explore knowledge about ADHD characteristics and causes, and attitudes towards issues such as medication in the general population. Six hundred and forty-five members of the Australian community, all of whom were parents, completed a questionnaire. The findings showed that the core features of ADHD were well-known, but there were misconceptions and considerable uncertainty about many aspects. Most respondents failed to recognise the genetic basis of the disorder and its potentially lifelong nature. Fathers were less knowledgeable than mothers. Although most participants believed that ADHD is a genuine disorder and recognised the benefits of medication, the majority believed that it is diagnosed too frequently and that medication is prescribed too readily. The study concluded that, in many respects, the public is not well-informed about ADHD and suggested that the media may have an important role in enhancing community awareness of the disorder through responsible, sensitive and accurate reporting.

Visual motion estimation for an autonomous underwater reef monitoring robot

Performing reliable localisation and navigation within highly unstructured underwater coral reef environments is a difficult task at the best of times. Typical research and commercial underwater vehicles use expensive acoustic positioning and sonar systems which require significant external infrastructure to operate effectively. This paper is focused on the development of a robust vision-based motion estimation technique using low-cost sensors for performing real-time autonomous and untethered environmental monitoring tasks in the Great Barrier Reef without the use of acoustic positioning. The technique is experimentally shown to provide accurate odometry and terrain profile information suitable for input into the vehicle controller to perform a range of environmental monitoring tasks.

Visual motion estimation for an autonomous underwater reef monitoring robot

Performing reliable localisation and navigation within highly unstructured underwater coral reef environments is a difficult task at the best of times. Typical research and commercial underwater vehicles use expensive acoustic positioning and sonar systems which require significant external infrastructure to operate effectively. This paper is focused on the development of a robust vision-based motion estimation technique using low-cost sensors for performing real-time autonomous and untethered environmental monitoring tasks in the Great Barrier Reef without the use of acoustic positioning. The technique is experimentally shown to provide accurate odometry and terrain profile information suitable for input into the vehicle controller to perform a range of environmental monitoring tasks.

Vibration isolation for autonomous helicopter flight

The mechanisms of helicopter flight create a unique, high-vibration environment which can play havoc with the accurate operation of on-board sensors. Vibration isolation of electronic sensors from structural borne oscillations is paramount to their reliable and accurate use. Effective isolation is achieved by realising a trade-off between the properties of the suspended instrument package, and the isolation mechanism. This is made more difficult as the weight and size of the sensors and computing hardware decreases with advances in technology. This paper presents a history of the design, challenges, constraints and construction of an integrated isolated vision and sensor platform and landing gear for the CSIRO autonomous X-Cell helicopter. The results of isolation performance and in-flight tests of the platform in autonomous flight are presented.

Creation of a validated 3D finite element model of an ovine tibia

Introduction Ovine models are widely used in orthopaedic research. To better understand the impact of orthopaedic procedures computer simulations are necessary. 3D finite element (FE) models of bones allow implant designs to be investigated mechanically, thereby reducing mechanical testing. Hypothesis We present the development and validation of an ovine tibia FE model for use in the analysis of tibia fracture fixation plates. Material & Methods Mechanical testing of the tibia consisted of an offset 3-pt bend test with three repetitions of loading to 350N and return to 50N. Tri-axial stacked strain gauges were applied to the anterior and posterior surfaces of the bone and two rigid bodies – consisting of eight infrared active markers, were attached to the ends of the tibia. Positional measurements were taken with a FARO arm 3D digitiser. The FE model was constructed with both geometry and material properties derived from CT images of the bone. The elasticity-density relationship used for material property determination was validated separately using mechanical testing. This model was then transformed to the same coordinate system as the in vitro mechanical test and loads applied. Results Comparison between the mechanical testing and the FE model showed good correlation in surface strains (difference: anterior 2.3%, posterior 3.2%). Discussion & Conclusion This method of model creation provides a simple method for generating subject specific FE models from CT scans. The use of the CT data set for both the geometry and the material properties ensures a more accurate representation of the specific bone. This is reflected in the similarity of the surface strain results.

Exercise, appetite, and energy balance: The interactions between energy expenditure and intake, and the implications for weight management

This book disseminates current information pertaining to the modulatory effects of foods and other food substances on behavior and neurological pathways and, importantly, vice versa. This ranges from the neuroendocrine control of eating to the effects of life-threatening disease on eating behavior. The importance of this contribution to the scientific literature lies in the fact that food and eating are an essential component of cultural heritage but the effects of perturbations in the food/cognitive axis can be profound. The complex interrelationship between neuropsychological processing, diet, and behavioral outcome is explored within the context of the most contemporary psychobiological research in the area. This comprehensive psychobiology- and pathology-themed text examines the broad spectrum of diet, behavioral, and neuropsychological interactions from normative function to occurrences of severe and enduring psychopathological processes

Merging fact with fiction : a study of year 9 students' developing scientific literacy through the writing of hybridised scientific narratives on a socioscientific issue

International assessments of student science achievement, and growing evidence of students' waning interest in school science, have ensured that the development of scientific literacy continues to remain an important educational priority. Furthermore, researchers have called for teaching and learning strategies to engage students in the learning of science, particularly in the middle years of schooling. This study extends previous national and international research that has established a link between writing and learning science. Specifically, it investigates the learning experiences of eight intact Year 9 science classes as they engage in the writing of short stories that merge scientific and narrative genres (i.e., hybridised scientific narratives) about the socioscientific issue of biosecurity. This study employed a triangulation mixed methods research design, generating both quantitative and qualitative data, in order to investigate three research questions that examined the extent to which the students' participation in the study enhanced their scientific literacy; the extent to which the students demonstrated conceptual understanding of related scientific concepts through their written artefacts and in interviews about the artefacts; and the extent to which the students' participation in the project influenced their attitudes toward science and science learning. Three aspects of scientific literacy were investigated in this study: conceptual science understandings (a derived sense of scientific literacy), the students' transformation of scientific information in written stories about biosecurity (simple and expanded fundamental senses of scientific literacy), and attitudes toward science and science learning. The stories written by students in a selected case study class (N=26) were analysed quantitatively using a series of specifically-designed matrices that produce numerical scores that reflect students' developing fundamental and derived senses of scientific literacy. All students (N=152) also completed a Likert-style instrument (i.e., BioQuiz), pretest and posttest, that examined their interest in learning science, science self-efficacy, their perceived personal and general value of science, their familiarity with biosecurity issues, and their attitudes toward biosecurity. Socioscientific issues (SSI) education served as a theoretical framework for this study. It sought to investigate an alternative discourse with which students can engage in the context of SSI education, and the role of positive attitudes in engaging students in the negotiation of socioscientific issues. Results of the study have revealed that writing BioStories enhanced selected aspects of the participants' attitudes toward science and science learning, and their awareness and conceptual understanding of issues relating to biosecurity. Furthermore, the students' written artefacts alone did not provide an accurate representation of the level of their conceptual science understandings. An examination of these artefacts in combination with interviews about the students' written work provided a more comprehensive assessment of their developing scientific literacy. These findings support extensive calls for the utilisation of diversified writing-to-learn strategies in the science classroom, and therefore make a significant contribution to the writing-to-learn science literature, particularly in relation to the use of hybridised scientific genres. At the same time, this study presents the argument that the writing of hybridised scientific narratives such as BioStories can be used to complement the types of written discourse with which students engage in the negotiation of socioscientific issues, namely, argumentation, as the development of positive attitudes toward science and science learning can encourage students' participation in the discourse of science. The implications of this study for curricular design and implementation, and for further research, are also discussed.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

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

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

Image acquisition and manipulation protocols for CT-PET fusion to improve the accuracy of gross tumour volume localisation for 3D conformal radiotherapy for lung cancer

Aims: To develop clinical protocols for acquiring PET images, performing CT-PET registration and tumour volume definition based on the PET image data, for radiotherapy for lung cancer patients and then to test these protocols with respect to levels of accuracy and reproducibility. Method: A phantom-based quality assurance study of the processes associated with using registered CT and PET scans for tumour volume definition was conducted to: (1) investigate image acquisition and manipulation techniques for registering and contouring CT and PET images in a radiotherapy treatment planning system, and (2) determine technology-based errors in the registration and contouring processes. The outcomes of the phantom image based quality assurance study were used to determine clinical protocols. Protocols were developed for (1) acquiring patient PET image data for incorporation into the 3DCRT process, particularly for ensuring that the patient is positioned in their treatment position; (2) CT-PET image registration techniques and (3) GTV definition using the PET image data. The developed clinical protocols were tested using retrospective clinical trials to assess levels of inter-user variability which may be attributed to the use of these protocols. A Siemens Somatom Open Sensation 20 slice CT scanner and a Philips Allegro stand-alone PET scanner were used to acquire the images for this research. The Philips Pinnacle3 treatment planning system was used to perform the image registration and contouring of the CT and PET images. Results: Both the attenuation-corrected and transmission images obtained from standard whole-body PET staging clinical scanning protocols were acquired and imported into the treatment planning system for the phantom-based quality assurance study. Protocols for manipulating the PET images in the treatment planning system, particularly for quantifying uptake in volumes of interest and window levels for accurate geometric visualisation were determined. The automatic registration algorithms were found to have sub-voxel levels of accuracy, with transmission scan-based CT-PET registration more accurate than emission scan-based registration of the phantom images. Respiration induced image artifacts were not found to influence registration accuracy while inadequate pre-registration over-lap of the CT and PET images was found to result in large registration errors. A threshold value based on a percentage of the maximum uptake within a volume of interest was found to accurately contour the different features of the phantom despite the lower spatial resolution of the PET images. Appropriate selection of the threshold value is dependant on target-to-background ratios and the presence of respiratory motion. The results from the phantom-based study were used to design, implement and test clinical CT-PET fusion protocols. The patient PET image acquisition protocols enabled patients to be successfully identified and positioned in their radiotherapy treatment position during the acquisition of their whole-body PET staging scan. While automatic registration techniques were found to reduce inter-user variation compared to manual techniques, there was no significant difference in the registration outcomes for transmission or emission scan-based registration of the patient images, using the protocol. Tumour volumes contoured on registered patient CT-PET images using the tested threshold values and viewing windows determined from the phantom study, demonstrated less inter-user variation for the primary tumour volume contours than those contoured using only the patient’s planning CT scans. Conclusions: The developed clinical protocols allow a patient’s whole-body PET staging scan to be incorporated, manipulated and quantified in the treatment planning process to improve the accuracy of gross tumour volume localisation in 3D conformal radiotherapy for lung cancer. Image registration protocols which factor in potential software-based errors combined with adequate user training are recommended to increase the accuracy and reproducibility of registration outcomes. A semi-automated adaptive threshold contouring technique incorporating a PET windowing protocol, accurately defines the geometric edge of a tumour volume using PET image data from a stand alone PET scanner, including 4D target volumes.

Crowd counting using group tracking and local features

In public venues, crowd size is a key indicator of crowd safety and stability. In this paper we propose a crowd counting algorithm that uses tracking and local features to count the number of people in each group as represented by a foreground blob segment, so that the total crowd estimate is the sum of the group sizes. Tracking is employed to improve the robustness of the estimate, by analysing the history of each group, including splitting and merging events. A simplified ground truth annotation strategy results in an approach with minimal setup requirements that is highly accurate.

Heavy vehicle suspensions : testing and analysis

Transport regulators consider that, with respect to pavement damage, heavy vehicles (HVs) are the riskiest vehicles on the road network. That HV suspension design contributes to road and bridge damage has been recognised for some decades. This thesis deals with some aspects of HV suspension characteristics, particularly (but not exclusively) air suspensions. This is in the areas of developing low-cost in-service heavy vehicle (HV) suspension testing, the effects of larger-than-industry-standard longitudinal air lines and the characteristics of on-board mass (OBM) systems for HVs. All these areas, whilst seemingly disparate, seek to inform the management of HVs, reduce of their impact on the network asset and/or provide a measurement mechanism for worn HV suspensions. A number of project management groups at the State and National level in Australia have been, and will be, presented with the results of the project that resulted in this thesis. This should serve to inform their activities applicable to this research. A number of HVs were tested for various characteristics. These tests were used to form a number of conclusions about HV suspension behaviours. Wheel forces from road test data were analysed. A “novel roughness” measure was developed and applied to the road test data to determine dynamic load sharing, amongst other research outcomes. Further, it was proposed that this approach could inform future development of pavement models incorporating roughness and peak wheel forces. Left/right variations in wheel forces and wheel force variations for different speeds were also presented. This led on to some conclusions regarding suspension and wheel force frequencies, their transmission to the pavement and repetitive wheel loads in the spatial domain. An improved method of determining dynamic load sharing was developed and presented. It used the correlation coefficient between two elements of a HV to determine dynamic load sharing. This was validated against a mature dynamic loadsharing metric, the dynamic load sharing coefficient (de Pont, 1997). This was the first time that the technique of measuring correlation between elements on a HV has been used for a test case vs. a control case for two different sized air lines. That dynamic load sharing was improved at the air springs was shown for the test case of the large longitudinal air lines. The statistically significant improvement in dynamic load sharing at the air springs from larger longitudinal air lines varied from approximately 30 percent to 80 percent. Dynamic load sharing at the wheels was improved only for low air line flow events for the test case of larger longitudinal air lines. Statistically significant improvements to some suspension metrics across the range of test speeds and “novel roughness” values were evident from the use of larger longitudinal air lines, but these were not uniform. Of note were improvements to suspension metrics involving peak dynamic forces ranging from below the error margin to approximately 24 percent. Abstract models of HV suspensions were developed from the results of some of the tests. Those models were used to propose further development of, and future directions of research into, further gains in HV dynamic load sharing. This was from alterations to currently available damping characteristics combined with implementation of large longitudinal air lines. In-service testing of HV suspensions was found to be possible within a documented range from below the error margin to an error of approximately 16 percent. These results were in comparison with either the manufacturer’s certified data or test results replicating the Australian standard for “road-friendly” HV suspensions, Vehicle Standards Bulletin 11. OBM accuracy testing and development of tamper evidence from OBM data were detailed for over 2000 individual data points across twelve test and control OBM systems from eight suppliers installed on eleven HVs. The results indicated that 95 percent of contemporary OBM systems available in Australia are accurate to +/- 500 kg. The total variation in OBM linearity, after three outliers in the data were removed, was 0.5 percent. A tamper indicator and other OBM metrics that could be used by jurisdictions to determine tamper events were developed and documented. That OBM systems could be used as one vector for in-service testing of HV suspensions was one of a number of synergies between the seemingly disparate streams of this project.

Development of pollutant build-up parameters for MIKE URBAN for Southeast Queensland, Australia

Accurate estimation of input parameters is essential to ensure the accuracy and reliability of hydrologic and water quality modelling. Calibration is an approach to obtain accurate input parameters for comparing observed and simulated results. However, the calibration approach is limited as it is only applicable to catchments where monitoring data is available. Therefore, methodology to estimate appropriate model input parameters is critical, particularly for catchments where monitoring data is not available. In the research study discussed in the paper, pollutant build-up parameters derived from catchment field investigations and model calibration using MIKE URBAN are compared for three catchments in Southeast Queensland, Australia. Additionally, the sensitivity of MIKE URBAN input parameters was analysed. It was found that Reduction Factor is the most sensitive parameter for peak flow and total runoff volume estimation whilst Build-up rate is the most sensitive parameter for TSS load estimation. Consequently, these input parameters should be determined accurately in hydrologic and water quality simulations using MIKE URBAN. Furthermore, an empirical equation for Southeast Queensland, Australia for the conversion of build-up parameters derived from catchment field investigations as MIKE URBAN input build-up parameters was derived. This will provide guidance for allowing for regional variations in the estimation of input parameters for catchment modelling using MIKE URBAN where monitoring data is not available.