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Family presence in paediatric resuscitation settings is now accepted practice. However, there is limited evidence to guide psychosocial care of these families. This study aimed to increase the understanding of family demographics and needs for psychosocial care at the Royal Children's Hospital, Melbourne, Australia and to identify the factors that impact on families at this time of emotional and social distress. A retrospective clinical data-mining methodology was utilised (n = 1123). Descriptive and inferential data analysis was undertaken using STATA software to identify population demographics, patterns in service usage and family experience, and to identify factors associated with parents' emotional responses. Four areas were identified that support evidence-informed psychosocial care for families in paediatric resuscitation contexts: 1) family and parent dynamics 2) families' experiences of distress in relation to patient diagnostic category 3) systemic nature of family response, and 4) the identification and analysis of family emotional support needs, which is central to the psychosocial response. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power has attracted widespread attention from many countries around the world due to its clean and renewable characteristics. To ensure the stable operation of the power system, accurate PV power forecasting has become a mandatory and challenging task. Currently, deep learning methods have become a vital approach in the field of PV power forecasting. In this work, a multistage attention neural network based on neural ordinary differential equation (MANODE) is proposed to address the main limitations of previous deep learning methods applied to PV power forecasting. Based on the neural ordinary differential equation (NODE), MANODE optimizes the long short-term memory network (LSTM) and temporal convolutional neural network (TCN), and combines the attention mechanism to achieve fine-grained spatio-temporal information extraction of PV series. In addition, the proposed MANODE model is applied to three different PV series collected from the Alice Springs meteorological station. Compared to previous state-of-the-art methods, the proposed method reduces the PV power forecasting error by at least 12.05%, 13.15%, and 9.71% on three different PV datasets, in terms of mean absolute error metric. The average errors of the MANODE method in four-hour-ahead PV power forecasting on the three datasets are 0.321, 0.350, and 0.567. • A novel neural network method called MANODE is applied in PV power forecasting. • MANODE addresses drawbacks of previous PV forecasting methods using LSTMs or CNNs. • Three multivariate datasets located in Australia are adopted in this work. • MANODE outperforms previous state-of-the-art neural network method in PV forecasting. [ABSTRACT FROM AUTHOR]

• Bus crashes are investigated by crash dataset of the State of Victoria, Australia. • Data mining used to discover chain of factors that lead to fatality in bus crashes. • Bus crashes with non-motor vehicles and on weekends increase fatality risk. • Old driver, intersection and vulnerable user increase fatality in non-motor crashes. • Fatal crash on weekend is linked to darkness, speed, pedestrian and female driver. Introduction: Buses are different vehicles in terms of dimensions, maneuverability, and driver's vision. Although bus traveling is a safe mode to travel, the number of annual bus crashes cannot be neglected. Moreover, limited studies have been conducted on the bus involved in fatal crashes. Therefore, identification of the contributing factors in the bus involved fatal crashes can reduce the risk of fatality. Method: Data set of bus involved crashes in the State of Victoria, Australia was analyzed over the period of 2006–2019. Clustering of crash data was accomplished by dividing them into homogeneous categories, and by implementing association rules discovery on the clusters, the factors affecting fatality in bus involved crashes were extracted. Results: Clustering results show bus crashes with all vehicles except motor vehicles and weekend crashes have a high rate of fatality. According to the association rule discovery findings, the factors that increase the risk of bus crashes with non-motor vehicles are: old bus driver, collision with pedestrians at signalized intersections, and the presence of vulnerable road users. Likewise, factors that increase the risk of fatality in bus involved crashes on weekends are: darkness of roads in high-speed zones, pedestrian presence at highways, bus crashes with passenger car by a female bus driver, and the occurrence of multi-vehicle crashes in high-speed zones. Practical Applications: The study provides a sequential pattern of factors, named rules that lead to fatality in bus involved crashes. By eliminating or improving one or all of the factors involved in rules, fatal bus crashes may be prevented. The recommendations to reduce fatality in bus crashes are: observing safe distances with the buses, using road safety campaigns to reduce pedestrians' distracted behavior, improving the lighting conditions, implementing speed bumps and rumble strips in high-speed zones, installing pedestrian detection systems on buses and setting special bus lanes in crowded areas. [ABSTRACT FROM AUTHOR]

• This paper is dedicated to vehicle-bicycle crash severity modeling. • The class-imbalanced issue in vehicle-bicycle crash severity analysis is handled with the imbalanced data resampling process. • To address the complexity of crash dataset, the learning-based feature extraction process is adopted in an iterative manner, such that the the most significant contributing factors to the severity of vehicle-bicycle crashes are determined and the trade-off between computation time and model performance is catered for. • The vehicle-bicycle crash dataset in this paper contains a large number of discrete variables. The gradient boosting algorithm is applied to handle the large number of categories and does not rely on strict statistical assumptions. • The impact of the most significant contributing factors on the severity of vehiclebicycle crashes are explained with the marginal effect analysis. • The result can provide some implications for policies and counter-measures for fatal and serious vehicle-bicycle crashes. Introduction: Although cycling is increasingly being promoted for transportation, the safety concern of bicyclists is one of the major impediments to their adoption. A thorough investigation on the contributing factors to fatalities and injuries involving bicyclist. Method: This paper designs an integrated data mining framework to determine the significant factors that contribute to the severity of vehicle-bicycle crashes based on the crash dataset of Victorian, Australia (2013–2018). The framework integrates imbalanced data resampling, learning-based feature extraction with gradient boosting algorithm and marginal effect analysis. The top 10 significant predictors of the severity of vehicle-bicycle crashes are extracted, which gives an area under ROC curve (AUC) value of 0.8236 and computing time as 37.8 s. Results: The findings provide insights for understanding and developing countermeasures or policy initiatives to reduce severe vehicle-bicycle crashes. [ABSTRACT FROM AUTHOR]

Introduction: Psychosocial risk and protective factors specific to acute burn patients have been shown to impact on longer-term health outcomes. Yet the nature and impact of such factors throughout the acute hospital admission phase have not been extensively examined to date. This study analysed the psychosocial pre-admission, hospitalisation and discharge factors for a sample of burn patients at an Australian specialist acute burns unit (The Alfred) and the relationship with their admission length.Methods: A retrospective audit was undertaken of psychosocial factors documented in patient medical records, Victorian Adult Burns Service registry data, and social work files (January-December 2014).Results: Two hundred and forty-nine patients were identified, with a mean age of 42.83 years and a preponderance of men (73.9%). The majority (71%) had a low burn severity (<10%), and an average of eight psychosocial factors. Independent of the severity of the burn, two psychosocial domains were strongly predictive of length of stay - coping with treatment and discharge planning - along with pre-existing psychosocial factors and family support.Conclusion: A diversity of psychosocial factors was identified, highlighting the clinical complexity of this patient group. Given three domains of admission-related psychosocial factors were predictive of the length of patient stay, more targeted psychosocial interventions in these areas may ensure patients and their families are supported more effectively throughout an admission, and length of stay costs may also be reduced. [ABSTRACT FROM AUTHOR]

The Great Resignation has brought significant challenges to the recovery of the hospitality industry from the depression caused by the coronavirus pandemic (COVID-19). Prior studies have revealed that the leading cause of the Great Resignation is negative employee experience. However, few empirical studies have been conducted to obtain deep insights into the negative experiences of hospitality employees. Hotel managers still lack the knowledge to help them resolve the workforce problem and maintain competitiveness during the pandemic. This study proposes a novel framework, named HENEX, that uses data-mining technologies and employees' online reviews about hotels to identify the factors that lead to hospitality employees' negative experiences and changes in these factors caused by COVID-19. We demonstrate the effectiveness of HENEX through a case study that involves major hotels in Australia. The findings could help hotel managers develop strategies to resolve the workforce problem and maintain competitiveness during the Great Resignation period. • A framework that could understand employee experience is proposed. • The aspects that lead to negative employee experience are identified. • The changes in factors that lead to negative employee experiences are revealed. • The effectiveness of the framework is demonstrated by data from Glassdoor. • Knowledge that could help hoteliers respond to the Great Resignation is discovered. [ABSTRACT FROM AUTHOR]

Objective: To examine whether a structured data quality report (SDQR) and feedback sessions with practice principals and managers improve the quality of routinely collected data in EHRs.Methods: The intervention was conducted in four general practices participating in the Fairfield neighborhood electronic Practice Based Research Network (ePBRN). Data were extracted from their clinical information systems and summarised as a SDQR to guide feedback to practice principals and managers at 0, 4, 8 and 12 months. Data quality (DQ) metrics included completeness, correctness, consistency and duplication of patient records. Information on data recording practices, data quality improvement, and utility of SDQRs was collected at the feedback sessions at the practices. The main outcome measure was change in the recording of clinical information and level of meeting Royal Australian College of General Practice (RACGP) targets.Results: Birth date was 100% and gender 99% complete at baseline and maintained. DQ of all variables measured improved significantly (p<0.01) over 12 months, but was not sufficient to comply with RACGP standards. Improvement was greatest with allergies. There was no significant change in duplicate records.Conclusions: SDQRs and feedback sessions support general practitioners and practice managers to focus on improving the recording of patient information. However, improved practice DQ, was not sufficient to meet RACGP targets. Randomised controlled studies are required to evaluate strategies to improve data quality and any associated improved safety and quality of care. [ABSTRACT FROM AUTHOR]

Soil calcium carbonate is a key component of the regolith, particularly in arid and semi-arid regions. It influences soil properties, is an important terrestrial carbon store, and is used in mineral exploration. Previous national overviews of soil carbonate distribution have been compiled from regional soil, regolith or geological maps with varying degrees of confidence and consistency. Here we develop a decision tree approach based on a piecewise linear regression model to map soil calcium carbonate abundance at the continental scale. The model is based on relationships established from 1311 field sites sampled across Australia at two depths (0–10 and ~ 60–80 cm) and 49 national environmental predictor datasets reflecting fundamental soil formation processes (e.g., climate, parent material, relief). The resulting map of soil calcium carbonate distribution is based on the median results of twenty model runs generated by randomly selecting 90% training and 10% validation splits of the input data. Results present an average coefficient of determination (R 2 ) of 0.5 on the validation dataset. The predictors used in the modelling are consistent with our understanding of the controls on the sources (inputs), formation, and preservation of soil calcium carbonate across Australian landscapes. The model produces a continuous, internally consistent, quantitative prediction of soil calcium carbonate abundance in surficial regolith at a resolution of 90 m with associated uncertainty estimates. This type of analysis provides a framework for better understanding environmental controls on regolith processes and has the potential to also be used to predict other soil geochemical properties. Furthermore, the methodology has utility globally where geochemical survey data are available. [ABSTRACT FROM AUTHOR]

Microseismic monitoring is a widely used technique in coal mine safe production. Microseismic events classification is one of the most important steps in microseismic monitoring. CNN has been proven to be the potential tool to identify microseismic events automatically. When the noise is serious and the signal is weak, the recognition ability of CNN is limited. Some scholars use denoising methods, such as wavelet transform, to enhance the signal-to-noise ratio (SNR) of the input data. However, the effective signal will be broken if the incorrect threshold parameters are set in this wavelet transform-based denoising method. In this paper, we intend to make the data volume of the time-frequency graph obtained by WPT as the input of CNN. But it may lead to a dimensional disaster if taking the time-frequency domain signals as input directly. To utilize the effective information extraction and reduce the data dimension, we propose a dual-channel CNN model by combining time domain information and wavelet packet decomposition coefficients (T-WPD CNN). The wavelet packet decomposition coefficients highlight the characteristics of the signal and suppress the characteristics of noise. In addition, the coefficients have the same dimension as the original signal. These advantages are useful for enhancing the performance of CNN. Two field datasets are used to test the network. One from Australia and another from Jiayang coal mine, Sichuan, China. The feature map of the convolutional layer with different inputs are shown to illustrate the influence effect of raw time domain signal and WPD coefficient on the classification performance. The final results show that the T-WPD CNN is superior to the traditional CNN method in accuracy and robustness. • The network combines time-domain information and wavelet packet decomposition coefficients of the microseismic data. • The more obvious feature map of the microseismic is extracted, which makes the classification performance better. • The wavelet packet decomposition coefficients can avoid the dimensional disaster of the input data. [ABSTRACT FROM AUTHOR]

Abstract: Age-coded multi-layered geological datasets are becoming increasingly prevalent with the surge in open-access geodata, yet there are few methodologies for extracting geological information and knowledge from these data. We present a novel methodology, based on the open-source GPlates software in which age-coded digital palaeogeographic maps are used to “data-mine” spatio-temporal patterns related to the occurrence of Australian opal. Our aim is to test the concept that only a particular sequence of depositional/erosional environments may lead to conditions suitable for the formation of gem quality sedimentary opal. Time-varying geographic environment properties are extracted from a digital palaeogeographic dataset of the eastern Australian Great Artesian Basin (GAB) at 1036 opal localities. We obtain a total of 52 independent ordinal sequences sampling 19 time slices from the Early Cretaceous to the present-day. We find that 95% of the known opal deposits are tied to only 27 sequences all comprising fluvial and shallow marine depositional sequences followed by a prolonged phase of erosion. We then map the total area of the GAB that matches these 27 opal-specific sequences, resulting in an opal-prospective region of only about 10% of the total area of the basin. The key patterns underlying this association involve only a small number of key environmental transitions. We demonstrate that these key associations are generally absent at arbitrary locations in the basin. This new methodology allows for the simplification of a complex time-varying geological dataset into a single map view, enabling straightforward application for opal exploration and for future co-assessment with other datasets/geological criteria. This approach may help unravel the poorly understood opal formation process using an empirical spatio-temporal data-mining methodology and readily available datasets to aid hypothesis testing. [Copyright &y& Elsevier]

Abstract: This paper explores the application of fuzzy causal networks (FCNs) to evaluating effect of health warnings in influencing Australian smokers’ psychosocial and quitting behaviour. The sample data used in this study are selected from the International Tobacco Control Policy Evaluation Survey project. Our research findings have demonstrated that new health warnings implemented in Australia have obvious impacts on smokers’ psychosocial and quitting behaviours. FCN is a useful framework to investigate such impacts that overcome the limitation of using traditional statistical techniques, such as linear regression and logistics regression, to analyse non-linear data. [Copyright &y& Elsevier]

Summary: Genetic Programming is able to systematically explore many alternative model structures of different complexity from available input and response data. We hypothesised that Genetic Programming can be used to test the structure of hydrological models and to identify dominant processes in hydrological systems. To test this, Genetic Programming was used to analyse a data set from a lysimeter experiment in southeastern Australia. The lysimeter experiment was conducted to quantify the deep percolation response under surface irrigated pasture to different soil types, watertable depths and water ponding times during surface irrigation. Using Genetic Programming, a simple model of deep percolation was recurrently evolved in multiple Genetic Programming runs. This simple and interpretable model supported the dominant process contributing to deep percolation represented in a conceptual model that was published earlier. Thus, this study shows that Genetic Programming can be used to evaluate the structure of hydrological models and to gain insight about the dominant processes in hydrological systems. [ABSTRACT FROM AUTHOR]

Abstract: Accurate yield forecasts are pivotal for the success of any agricultural industry that plans or sells ahead of the annual harvest. Biophysical models that integrate information about crop growing conditions can give early insight about the likely size of a crop. At a point scale, where highly detailed knowledge about environmental and management conditions are known, the performance of reputable crop modelling approaches like APSIM have been well established. However, regional growing conditions tend not to be homogenous. Heterogeneity is common in many agricultural systems, and particularly in sugarcane systems. To overcome this obstacle, hundreds of model settings (‘models’ for convenience) that represent different environmental and management conditions were created for Ayr, a major sugarcane growing region in north eastern Australia. Statistical data mining methods that used ensembles were used to select and assign weights to the best models. One technique, called a lasso approximation produced the best results. This procedure, produced a predictive correlation (r cv) of 0.71 when predicting end of season sugarcane yields some 4 months prior to the start of the harvest season, and 10 months prior to harvest completion. This continuous forecasting methodology based on statistical ensembles represents a considerable improvement upon previous research where only categorical forecast predictions had been employed. [Copyright &y& Elsevier]

Abstract: Environmental factors which influence the distributions of malaria vectors in northern Australia (Anopheles farauti ss, A. farauti 2 and A. farauti 3) were investigated by ecological niche modelling and data mining using an extensive data set of species presence and absence records obtained by systematic field surveys. Models were generated with GARP (the genetic algorithm for rule-set prediction) using geographical coverages of 41 climatic and topographic parameters for the north of the continent. Environmental variables associated with species records were identified with the ranking procedures of the decision tree software packages CART and KnowledgeSeeker. There was consistent agreement in the variables ranked by both methods. This permitted the selection of reduced sets of environmental variables to develop GARP models for the three target species with equivalent predictive accuracy to those which used all of the environmental information. The environmental parameters which define the realised distributions of A. farauti ss and A. farauti 3 were well described by this approach but the results were less satisfactory for A. farauti 2. Atmospheric moisture was shown to be a critical variable for each species which accords with many field and laboratory observations concerning the influence of humidity on adult mosquito survival. [Copyright &y& Elsevier]

Abstract: The concept of knowledge and knowledge management is not new. Researchers identified the practice of knowledge management as early as 4000 years ago. However, knowledge management systems (KMS), which involve the application of IT systems and other organizational resources to manage knowledge strategically, are a relatively recent phenomenon. While the literature on knowledge management covers various issues, it lacks comprehensive studies of factors and variables of adoption and diffusion of KMS. This paper studies these factors and variables in the context of some Australian organizations. A qualitative field study is undertaken in this research, where six organizations of various sizes, all in various stages of KMS adoption and diffusion, are studied via interviews with key personnel. Content analysis is then performed to extract the factors and variables and a comprehensive model of KMS adoption and diffusion is developed. The results of the interviews identify four major variables affecting KMS diffusion as: organizational culture, top management support, benefits to individuals, and dream of KMS. The paper also highlights the research and managerial implications of the KMS diffusion model. [Copyright &y& Elsevier]

• Developing a novel approach for using trip sequences instead of separate trips. • Developing a unique similarity measure for the trip sequences. • Focusing only on the temporal attributes of the trip sequences. • Running the method on real-world case studies. • Evaluating the performance of the developed method. • Comparing the outcome with the existing literature. • Achieving a considerable improvement in inferring the trip purpose. Smart card transactions are known as a rich and continuous source of public transit data, but they miss some important attributes about trips and passengers. One of these missing attributes is the trip purpose attribute. This paper proposes a novel method to infer the trip purpose attribute from the sequences of trips of passengers instead of separate trips. The proposed method infers the trip purpose attribute (a missing attribute in the smart card data) from the temporal attributes (available attributes in the smart card data). First, the relation between the temporal attributes and the trip purpose attribute is learnt by discovering clusters of passengers in the Household Travel Survey dataset while each passenger is represented by one sequence of trips. Then, the discovered clusters are utilized to infer the trip purpose of smart card transactions by allocating each passenger to the closest clusters. The proposed method is implemented on the smart card and HTS datasets from southeast Queensland, Australia. The evaluation results showed a considerable improvement in inferring the trip purpose compared to the results published in the literature. Notably, the effect of considering the trip sequence was more significant than considering land use variables. [ABSTRACT FROM AUTHOR]

Open-source intelligence (OSINT) is a technique involving the use of publicly available information for the purpose of addressing a specific intelligence issue. Open-source, user-reported data on ecstasy pills was extracted from a clear-web site known as Pill Reports (available at www.pillreports.net) and analysed to establish whether drug intelligence could be derived to assist law enforcement and policymakers in taking effective action against the illicit ecstasy market. A comprehensive database of 4358 ecstasy pills reported by users in Australia and New Zealand between September 2005 and April 2020 was established. Using this database, intelligence was gathered under two categories: general summary data and geographic movement trends. General summary data included pill characteristics (i.e., logo, colour and shape variations), regional distribution and temporal usage trends. Geographic movement data was used to ascertain whether a common sequence of flow (i.e., trafficking patterns) could be identified both (i) across state lines and (ii) east to west across Australia. Open-source investigation of online ecstasy report data proved to be most effective for obtaining general summary data which was concordant with other more costly and onerous population-wide approaches like wastewater analyses and population surveys. There was some evidence to suggest where MDMA is entering Australian borders, and an east-to-west onshore distribution pathway is suggested, taking approximately one year to run its course. This OSINT technique illustrates a novel strategic approach that can be used to monitor drug trends in real-time. • Uses a novel strategic technique whereby drug trends can be monitored in real-time. • 512 different ecstasy pills variations were identified over 15 years in Australia. • User-reported data provides insight into drug characteristics. • There is evidence to suggest that MDMA is being imported via NSW and VIC. • Average trafficking delay from the east to west coast of Australia is 363 days. [ABSTRACT FROM AUTHOR]

Over-prediction of subsidence levels of mined land may be an issue for getting mining approval and tighter conditions in some places can be imposed to better inform mining operations and planning. Under-prediction of subsidence, present safety, social and environmental consequences during or after longwall extractions have occurred. In this study we collected subsidence data from reports related to underground coal mining environmental impact statements (EIS) and end of panel reports, and compared predicted subsidence data with the observed data to compare the accuracy of prediction. From the limited data, while it was difficult to substantiate a strong relationship between the predicted and observed subsidence data and mining characteristics, we found that where mining is less than 220 m below the surface subsidence prediction generally matches well with the observed subsidence and the ratio is closer to 1 than those mined at greater depths. Subsidence prediction analysed for mines in the Gunnedah Basin are found to be generally better than those in the Sydney Basin. The results presented here are based on limited data available in the public domain and while more data is needed to prove or disprove the discussed hypotheses, we consider there to be value in extending EIS requirements. Importantly, publication of both prediction and observed subsidence measurements would provide suitable transparency for improved, future predictions, and to provide social and stakeholder assurances that would meet growing legislative requirements. • Lack of publicly available mine data creates an unintended lack of transparency. • Reporting styles are found to present ambiguity in some of the published mine data. • Compared predicted subsidence data with the mine monitored subsidence data. • Limited analysed data show that there is a value in extending EIS requirements. [ABSTRACT FROM AUTHOR]

• Empirical modelling is used for mapping soil thickness across Australia at 3 arc second grid cell resolution. • Soil thickness estimates are informed from the data mining of three separate site datasets. • Our model accommodates right-censored data. • Our data mining model derives plausible realities of soil thickness. • From the modelling, one can derive exceedance probabilities of soil thickness given define depth thresholds. Soil thickness is not easily measured in situ, making it also a challenging variable to reliably map. This study improves on previous digital mapping of soil thickness across Australia using an approach suited to the continent's unique pedo-geomorphic history. Leveraging three large, in situ observation datasets and a wide range of spatial environmental variables, we developed three models depicting rock outcrops, intermediate and deep soils respectively. Our modelling approach addressed right-censored data, which is a common attribute of soil thickness data, and we applied an iterative, data re-sampling framework to quantify prediction uncertainties. We integrated the three models to create soil thickness maps and associated products of soil thickness exceedance probabilities. Using data excluded from model calibrations, we achieved an overall accuracy of 99% for the binary outcome rock outcrops model, and 85% for the binary outcome deep soils model. Modelling soil thickness of shallow to deep soils resulted in a concordance coefficient of 0.77. Of all the environmental variables considered in this study, those associated with climate data (including topo-climate) were consistently the most often used and important. We associate this finding with the direct and indirect effects of climate on biota and weathering of parental materials along with other factors driving spatial heterogeneity in soil thickness across Australia. While the products generated by this research are not without error, the overall pattern of soil thickness is consistent with previous observations from historical soil surveys across Australia and the results are demonstrably more skilful than previous digital soil mapping efforts. [ABSTRACT FROM AUTHOR]

The National Broadband Network (NBN) is the largest public infrastructure project in the history of Australia. The goal of the NBN is to provide Australians with broadband internet access by using a mix of technologies, ranging from fibre and hybrid fibre-coaxial to fixed wireless and satellite platforms. Although the NBN is a public project, one of the more vexing aspects of its evolution is its lack of data transparency. There is virtually no information on platform use or footprint distribution throughout the country. Not only does this drastically limit evidence-based telecommunications policy analysis, it also limits the ability to evaluate equity in the spatial distribution of broadband connectivity and infrastructure quality. The purpose of this paper is to uncover the geographic presence of the various NBN technologies using data mining techniques and census-based socio-spatial data (SEIFA). The results portray the complexity of the NBN footprint across three metropolitan regions of Sydney, Melbourne, and Brisbane – with a focus on how the mixed-technology platform adopted divides major Australian cities. • The National Broadband Network (NBN) is the largest public infrastructure project in the history of Australia. • There is limited information on the geographic distribution of the various NBN technologies throughout the country. • This paper aims to uncover the NBN footprint using data mining techniques and census-based socio-spatial data (SEIFA). • The results portray the complexity of the NBN footprint across three metropolitan regions of Sydney, Melbourne, and Brisbane. [ABSTRACT FROM AUTHOR]