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Simple Summary: In radiotherapy treatment, organs at risk and target volumes are contoured by the clinicians to prepare a dosimetry plan. In retrospective data, these structures are not often standardised to universal names across the patients plans, which is required to enable data mining and analysis. In this paper, a new method was proposed and evaluated to automatically standardise radiotherapy structures names using machine learning algorithms. The proposed approach was deployed over a dataset with 1613 patients collected from Liverpool & Macarthur Cancer Therapy Centres, New South Wales, Australia. It was concluded that machine learning techniques can standardise the dosimetry plan structures, taking into consideration the integration of multiple modalities representing each structure during the training process. In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume. [ABSTRACT FROM AUTHOR]

The objectives of the study were: 1) Describe the thematic structure and evolution of the field of physical therapy; 2) identify the main research producers (i.e. countries and institutions); and 3) compare their research output and citation impact. Papers related to physical therapy indexed in Web of Science (2000–2018) were identified to delineate the field, using keywords, journals, and citation networks. VOSviewer software, advanced bibliometric text mining, and visualization techniques were used to evaluate the thematic structure. We collected data about the country and institutional affiliation of all the authors and calculated production and citation impact indicators. 85,697 papers were analyzed. Eleven thematic clusters were identified: 1) "health care and education"; 2) "biomechanics"; 3) "psychosocial, chronic pain and quality of life outcomes"; 4) "evidence-based physical therapy research methods"; 5) "traumatology and orthopedics"; 6) "neurological rehabilitation"; 7) "psychometrics and cross-cultural adaptation"; 8) "gait-balance analysis and Parkinson's disease"; 9) "exercise"; 10) "respiratory physical therapy"; and 11) "back pain." The United States, the United Kingdom, and Australia were the most productive countries. Netherlands, Norway, and Sweden had the highest citation impact. Our bibliometric visualization approach makes it possible to comprehensively study the thematic structure of physical therapy. The ranking of producers has evolved and now includes China and Brazil. High research production does not imply a high citation impact. [ABSTRACT FROM AUTHOR]

Annual turnover of home care workers represents a huge loss of revenue and is a key source of inefficiency in the home health care industry. In this article, we propose a data-driven approach to monitor employee churn and to capture the evolution of employee intent to leave. Unlike most papers in the literature, we use machine learning techniques to analyze over 2 million visits in the US, Canada, and Australia between 2016 and 2019. Results show that the gap between the number of hours worked and in the contract is the most important factor to predict employee intent to leave, which means an employee should be given as many hours as requested in the contract to improve retention. Secondary results show that having diverse shift lengths and continuity in services and patients seem to be associated with less turnover. [ABSTRACT FROM AUTHOR]

Graphs are commonly used to represent objects, such as images and text, for pattern classification. In a dynamic world, an object may continuously evolve over time, and so does the graph extracted from the underlying object. These changes in graph structure with respect to the temporal order present a new representation of the graph, in which an object corresponds to a set of time-variant graphs. In this paper, we formulate a novel time-variant graph classification task and propose a new graph feature, called a graph-shapelet pattern, for learning and classifying time-variant graphs. Graph-shapelet patterns are compact and discriminative graph transformation subsequences. A graph-shapelet pattern can be regarded as a graphical extension of a shapelet—a class of discriminative features designed for vector-based temporal data classification. To discover graph-shapelet patterns, we propose to convert a time-variant graph sequence into time-series data and use the discovered shapelets to find graph transformation subsequences as graph-shapelet patterns. By converting each graph-shapelet pattern into a unique tokenized graph transformation sequence, we can measure the similarity between two graph-shapelet patterns and therefore classify time-variant graphs. Experiments on both synthetic and real-world data demonstrate the superior performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This study introduces data mining methods to accounting education scholarship to explore the relationship between accounting students' current academic performance (grades), demographic information, pre-university entrance scores and predicted academic performance. It adopts a C4.5 classification algorithm based on decision-tree analysis to examine 640 accounting students enrolled in an undergraduate accounting program at an Australian university. A significant contribution of this study is improved prediction of academic performance and identification of characteristics of students deemed to be at risk. By partitioning students into sub-groups based on tertiary entrance scores and employing clustering of study units, this study facilitates a more nuanced understanding of predictor attributes. Key findings were the dominance of a cluster of second year units in predicting students' later academic performance; that gender did not influence performance; and that performance in first year at university, rather than secondary school grades, was the most important predictor of subsequent academic performance. [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]

Group recommendation has become highly demanded when users communicate in the forms of group activities in online sharing communities. These group activities include student group study, family TV program watching, friends travel decision, etc. Existing group recommendation techniques mainly focus on the small user groups. However, online sharing communities have enabled group activities among thousands of users. Accordingly, recommendation over large groups has become urgent. In this paper, we propose a new framework to accomplish this goal by exploring the group interests and the connections between group users. We first divide a big group into different interest subgroups, each of which contains users closely connected with each other and sharing the similar interests. Then, for each interest subgroup, our framework exploits the connections between group users to collect a comparably compact potential candidate set of media-user pairs, on which the collaborative filtering is performed to generate an interest subgroup-based recommendation list. After that, a novel aggregation function is proposed to integrate the recommended media lists of all interest subgroups as the final group recommendation results. Extensive experiments have been conducted on two real social media datasets to demonstrate the effectiveness and efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

Knowledge on the determinants and patterns of water demand for different consumers supports the design of customized demand management strategies. Smart meters coupled with big data analytics tools create a unique opportunity to support such strategies. Yet, at present, the information content of smart meter data is not fully mined and usually needs to be complemented with water fixture inventory and survey data to achieve detailed customer segmentation based on end use water usage. In this paper, we developed a data‐driven approach that extracts information on heterogeneous water end use routines, main end use components, and temporal characteristics, only via data mining existing smart meter readings at the scale of individual households. We tested our approach on data from 327 households in Australia, each monitored with smart meters logging water use readings every 5 s. As part of the approach, we first disaggregated the household‐level water use time series into different end uses via Autoflow. We then adapted a customer segmentation based on eigenbehavior analysis to discriminate among heterogeneous water end use routines and identify clusters of consumers presenting similar routines. Results revealed three main water end use profile clusters, each characterized by a primary end use: shower, clothes washing, and irrigation. Time‐of‐use and intensity‐of‐use differences exist within each class, as well as different characteristics of regularity and periodicity over time. Our customer segmentation analysis approach provides utilities with a concise snapshot of recurrent water use routines from smart meter data and can be used to support customized demand management strategies. Key Points: We investigate which water use behaviors can be identified from nonintrusive, single‐point, smart meter dataWe identify and cluster primary water use behaviors of single‐family households from disaggregated end use dataWe reveal the main water end uses driving different behaviors, usage patterns, and regularity, to support customized demand management [ABSTRACT FROM AUTHOR]

Text and Data Mining (hereinafter, TDM) issue for the purpose of scientific research or for any other purpose which is included in the provisions of the new EU Directive on Copyright in the Digital Single Market (hereinafter, DSM). TDM is a term that includes Web harvesting and Web Archiving activities. Web harvesting and archiving pertains to the processes of collecting from the web and archiving of works that reside on the Web. In the following analysis we will elaborate briefly upon provisions in EU Copyright law which were discussed during the proposal for a new Directive on Copyright in the DSM as well as provisions which are included in the text of art.3 and art.4 of the new Directive 2019/790/EU per TDM. In addition, the following analysis presents legislation in very few EU Member States which pertains to TDM and preceded the rulings of Directive 2019/790/EU. Digital legal deposit remarkable examples from EU Member States are also presented in this paper. The example of Australia is also presented below hereto because it is one of the oldest and most successful worldwide. The National Library of Australia's digital legal deposit is state-of-the-art. [ABSTRACT FROM AUTHOR]

Today's information systems log vast amounts of data. These collections of data (implicitly) describe events (e.g. placing an order or taking a blood test) and, hence, provide information on the actual execution of business processes. The analysis of such data provides an excellent starting point for business process improvement. This is the realm of process mining, an area which has provided a repertoire of many analysis techniques. Despite the impressive capabilities of existing process mining algorithms, dealing with the abundance of data recorded by contemporary systems and devices remains a challenge. Of particular importance is the capability to guide the meaningful interpretation of 'oceans of data' by process analysts. To this end, insights from the field of visual analytics can be leveraged. This article proposes an approach where process states are reconstructed from event logs and visualised in succession, leading to an animated history of a process. This approach is customisable in how a process state, partially defined through a collection of activity instances, is visualised: one can select a map and specify a projection of events on this map based on the properties of the events. This paper describes a comprehensive implementation of the proposal. It was realised using the open-source process mining framework ProM. Moreover, this paper also reports on an evaluation of the approach conducted with Suncorp, one of Australia's largest insurance companies. [ABSTRACT FROM AUTHOR]

Abstract-Harmonic monitoring has become an important tool for harmonic management in distribution system. A comprehensive harmonic monitoring program has been designed and implemented on a typical electrical medium-voltage distribution system in Australia. The monitoring program involved measurements of the three-phase harmonic currents and voltages from the residential, commercial, and industrial load sectors. Data over a three year period have been downloaded and available for analysis. The large amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. More sophisticated analysis methods are required to automatically determine which part of the measurement data are of importance. Based on this information, a closer inspection of smaller data sets can then be carried out to determine the reasons for its detection. In this paper, we classify the measurement data using unsupervised learning based on clustering techniques using the minimum message length technique, which can provide the engineers with a rapid, visually oriented method of evaluating the underlying operational information contained within the clusters. Supervised learning is then used to describe the generated clusters and to predict the occurrences of unusual clusters in future measurement data. [ABSTRACT FROM AUTHOR]

The national health insurance system in Australia records details on medical services and claims provided to its population. An effective method to the discovery of temporal behavioral patterns in the data set is proposed in this paper. The method consists of a two-step approach which is applied recursively to the data set. First, a clustering algorithm is used to segment the data into classes. Then, hidden Markov models are employed to find the underlying temporal behavioral patterns. These steps are applied recursively to features extracted from the data set until convergence. The main objective is to minimize the misclassification of patient profiles into various classes. This results in a hierarchical tree model consisting of a number of classes; each class groups similar patient temporal behavioral patterns together. The capabilities of the proposed method are demonstrated through the application to a subset of the Australian national health insurance data set. It is shown that the proposed method not only clusters data into various categories of interest, but it also automatically marks the periods in which similar temporal behavioral patterns occurred. [ABSTRACT FROM AUTHOR]

Copyright of Regional Studies is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper reports on the results of an overlap study of Australian research library collections. The study used OCLC's WorldCat Collection Analysis software to mine data recording Australian holdings on the WorldCat database. The data is analysed according to the results obtained for six 'groups' which represent various coalitions of academic, national/state, and special libraries. The data is focussed on the incidence and distribution of overlap and unique items, and the analysis considers the implications for the future management of the nation's research collections. [ABSTRACT FROM AUTHOR]

This paper emerged from an earlier survey of developments in knowledge management within the Australian Public Service (APS). The earlier study pointed to future research possibilities in an emerging relationship between knowledge management and archives and records. The resultant case-based research reported here shows that this relationship operates most strongly in the context of key drivers of knowledge management within government agencies and departments, and in linkages between the work of the National Archives of Australia (NAA) and that of other Commonwealth organisations. Knowledge management also emerges as an important strategic issue for the National Archives of Australia. Eleven issues emerging as common in the previous study were discussed at the NAA: Perceptions, Approaches, Drivers, Links to core business, Obstacles, Enablers, Structures, Frameworks, Metrics, Champions and inter-agency networks. In overall terms the NAA emerged as an organisation that was at the development stage for knowledge management and one that was taking conscious strides in this direction. [ABSTRACT FROM AUTHOR]

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]

Given that metals, minerals and energy resources extracted through mining are fundamental to human society, it follows that accurate data describing mine production are equally important. Although there are often national statistical sources, this typically includes data for metals (e.g., gold), minerals (e.g., iron ore) or energy resources (e.g., coal). No such study has ever compiled a national mine production data set which includes basic mining data such as ore processed, grades, extracted products (e.g., metals, concentrates, saleable ore) and waste rock. These data are crucial for geological assessments of mineable resources, environmental impacts, material flows (including losses during mining, smelting-refining, use and disposal or recycling) as well as facilitating more quantitative assessments of critical mineral potential (including possible extraction from tailings and/or waste rock left by mining). This data set achieves these needs for Australia, providing a world-first and comprehensive review of a national mining industry and an exemplar of what can be achieved for other countries with mining industry sectors. [ABSTRACT FROM AUTHOR]

Lakes commonly form in mine pits following the end of mining. A good understanding of the pit lake water balance over future decades to centuries is essential to understand and manage environmental risks from the lake. Evaporation is often the major or only outflow from the lake, thus being an important determinant of equilibrium lake level and environmental risks. A general lack of in situ measurements of pit lake evaporation has meant that estimates have usually been based on pan coefficients derived for other contexts or on alternative unvalidated evaporation models. Our research used data from an evaporation pan and weather station that were floated on a pit lake in semi-arid central Queensland, Australia. A deterministic aerodynamic evaporation model was developed from these data to infill missing values, and an adjusted aerodynamic model was used to reconstruct long-term historical daily evaporation data. With an average bias of 6.5% during the measurement period, this long-term model was found to be more accurate than alternative simple models (e.g., using the commonly used pan coefficient of 0.7 gave a bias of 45%). The reconstructed data were then used to fit and assess a stochastic model for the generation of future evaporation and rainfall realisations, assuming a stationary climate. Fitting stochastic models at a monthly time step was found to accurately represent the monthly evaporation statistics. For example, the cross-correlation between historical rainfall and evaporation was within the 25 and 75 percentiles of the modelled values in 11 of 12 months and always within the 2.5 and 97.5 percentiles. However, the stationary nature of the model presented limitations in capturing interannual anomalies, with continuous periods of up to 6 years, where the modelled annual rainfall was consistently lower and modelled annual evaporation consistently higher than the historical values. Fitting stochastic models at a daily time step had problems capturing a range of statistics of both rainfall and evaporation. For example, in 6 of the 12 months, the cross-correlation between historical rainfall and evaporation was outside the modelled 2.5 and 97.5 percentiles. This likely arises from the complex patterns in transitions from wet to dry days in the semi-arid climate of the case study. While the long-term model and monthly stochastic model are promising, further work is needed to understand the significance of the observed errors and refine the models. [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]

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]

Social workers provide services in many cancer settings however there has been limited research into Australian oncology social work practice. This multisite study investigated the reasons those affected by a cancer diagnosis were referred to social workers and the types of intervention undertaken. Clinical data-mining methodology was used to complete a retrospective review of 250 patient medical records. Qualitative and descriptive analyses were undertaken with comparisons to existing classification systems. Findings of significance were that residential location, socio-economic status, gender, relationship status, responsibilities for dependent children, and other complex individual circumstances were key reasons for referral to social workers. Interventions included psychosocial assessment, financial management, counselling, service coordination, and advocacy across multiple systems. Complex circumstances reflecting systemic social inequalities led to higher levels of social worker engagement. The study outcomes indicate that social workers have a significant role in the provision of effective psychosocial cancer care. Poorer cancer outcomes are linked to socio-economic, cultural, and systemic disadvantage, all of which are domains of social work practice. Working across multiple systems, social workers provide a range of skilled interventions to patients, families, carers, and others in complex social situations exacerbated by a diagnosis of cancer. Improving availability and access to social workers will address many of the social circumstances that have a detrimental impact on those affected by cancer. [ABSTRACT FROM AUTHOR]

Objective. Suicide prevention, including among youth, has been a national priority in Australia for some time. Yet despite this, rates of suicide, and related behaviour, remain high. The aim of this study was to review all suicide-prevention research that had been conducted in Australia between January 2007 and December 2011, with a specific emphasis on studies relating to young people, in order to determine whether or not we are prioritising the sort of research that can adequately inform policy development and guide 'best practice'. Methods. Data were collected from two sources. First, several electronic databases were searched in October 2012 in order to identify published journal articles relating to suicide, written by Australian authors. Second, summary data obtained from the National Health and Medical Research Council, the Australian Rotary Health Research Fund and the Australian Research Council were examined in order to identify currently funded studies that relate to suicide. Studies were then classified according to whether or not they had a focus on youth, and according to research type, type of suicide-related behaviour under investigation and method of suicide. Results. There were 224 articles published and 12 grants funded that specifically focussed on suicide-related behaviour over the period January 2007 to December 2011. Of these, 47 articles (21%) and five funded grants (42%) focussed on young people. Youth studies, in particular those reported in the published articles, tended to be epidemiological in nature and only six of the published articles (13%) and two of the funded grants related to intervention studies. Conclusions. Although the focus on youth is welcome, the lack of intervention studies is disappointing. Given that rates of suicide and related behaviour remain high, there is a clear need for a stronger body of intervention research that can inform national policy, if we are to successfully develop effective approaches to reducing suicide risk. [ABSTRACT FROM AUTHOR]

Assessing OpenStreetMap (OSM) data quality against authoritative data sources may not always be viable. This is primarily because of the multi-dimensional nature and heterogeneity of the maps, yet the activity is pivotal for targeted data cleansing and quality enhancement undertakings in these data sets. A salient facet of OSM, allowing contributors to flag potential problems encountered during the mapping process, is the FIXME tag. In this article, we examine and discuss OSM data quality through the vast expanse of issues (knowledge) documented via FIXME. We present a classification and analysis of these quality issues, exposed as topic models and grounded in the ISO-19157 standard, across USA and Australia. Regional distributions of these topics are further qualitatively analyzed to ascertain the variation of key issues in OSM. We also present a comparison of the intrinsic issue classification against those identified in an issue corpus of an authoritative map data source. Due to the considerable heterogeneity in user mapping and reporting, OSM issue detection and classification remains problematic. This research presents a flexible and intrinsic data-mining approach, linking established ISO data quality standards to OSM issue categorization. Our work, thus informs the development of automated error correction methods for VGI datasets. [ABSTRACT FROM AUTHOR]

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]

The article focuses on the use of Web 2.0 technology to assess the risk of depression in young people in Australia, Great Britain and New Zealand. It discusses the use of data warehousing technologies and text mining in determining and monitoring depressed bloggers who are prone to suicide risk. It provides a literature review on the affective and emotional content text mining as well as suicide research. Studies show that text mining technologies can be used in determining depressed bloggers. It points out that identifying at-risk bloggers and offering appropriate interventions can prevent tragic consequences.

The article presents a study regarding the contribution of qualitative methods and techniques in increasing the understanding of relationship marketing theory in Australia. The study states that the methods provided data which allowed a number of commonalities to emerge relating to the adoption of the relationship marketing within an industry. The study suggests that researchers consider methods that add quality to their findings when trying to develop theory. The study also mentions that triangulation and multiple case study can improve the robustness and transferability of qualitative research.

This paper presents the results of a data-driven; field-modeling (DDFM) evaluation applied to a high-temperature reservoir in Australia for the purpose of determining the significance of chemistry, reservoir, well, and hydraulic-fracture characteristics on well production. The DDFM approach has identified key production drivers for a gas-well field in Australia. This information has been useful in explaining hydraulic-fracture well production and providing guidelines for future fracture stimulation success. A DDFM process is used to develop a model for 32 wells completed in a complex, 250 to 350°F gas reservoir in Australia. This type modeling technique uses data from the field, including chemical formulation, geology, reservoir, well, completion, hydraulic-fracture stimulation, and production results. The data is integrated into a common format and resolution, and then a visual and statistical evaluation is performed. Relevant correlations and useful trends are noted. Next, an effort is made to develop a predictive model that can be used to provide an overall explanation as to what parameters drive production in the well field. Or, in effect, derive a high-level understanding about the effect of the fracturing process on the reservoir. This is accomplished by the use of data modeling/optimization technologies, including artificial neural network (ANN) and genetic algorithms. The resulting ANN model can then be used to evaluate the production associated with various hydraulic-fracturing scenarios and/or characteristics. Validation of conclusions and/or resolution of difficult interpretation issues are done by detailed evaluation and modeling of key wells. Hydraulic-fracture stimulation scenario evaluations performed by the ANN model have yielded some expected as well as unexpected results. As expected, reservoir characteristics, such as pay thickness, porosity, and water saturation have a dominant effect on well production. What was unexpected is the significance of well operations and stimulation fluid chemistry on well production. The practice of killing the well after stimulation and using inappropriate perforation techniques can reduce gas production by as much as one-half, while the use of a high-temperature gel breaker in combination with a reduction in base-gel polymer load can provide a 67% increase in production. [ABSTRACT FROM AUTHOR]

Faults represent important analytical targets for the identification of perceptual ground motions and associated seismic hazards. In particular, during oil production, important data such as the path and flow rate of fluid flows can be obtained from information on fault location and their connectivity. Seismic attributes are conventional methods used for fault detection, whereby information obtained from seismic data are analyzed using various property processing methods. The analyzed data eventually provide information on fault properties and imaging of fault surfaces. In this study, we propose an efficient workflow for fault detection and extraction of requisite information to construct a fault surface model using 3D seismic cubes. This workflow not only improves the ability to detect faults but also distinguishes the edges of a fault more clearly, even with the application of fewer attributes compared to conventional workflows. Thus, the computing time of attribute processing is reduced, and fault surface cubes are generated more rapidly. In addition, the reduction in input variables reduces the effect of the interpreter's subjective intervention on the results. Furthermore, the clustering method can be applied to the azimuth and dip of the fault to be extracted from the complexly intertwined fault faces and subsequently imaged. The application of the proposed workflow to field data obtained from the Vincentian oil field in Australia resulted in a significant reduction in noise compared to conventional methods. It also led to clearer and continuous edge detection and extraction. [ABSTRACT FROM AUTHOR]

• 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]

Background: Implementation research has delved into barriers to implementing change and interventions for the implementation of innovation in practice. There remains a gap, however, that fails to connect implementation barriers to the most effective implementation strategies and provide a more tailored approach during implementation. This study aimed to explore barriers for the implementation of professional services in community pharmacies and to predict the effectiveness of facilitation strategies to overcome implementation barriers using machine learning techniques.Methods: Six change facilitators facilitated a 2-year change programme aimed at implementing professional services across community pharmacies in Australia. A mixed methods approach was used where barriers were identified by change facilitators during the implementation study. Change facilitators trialled and recorded tailored facilitation strategies delivered to overcome identified barriers. Barriers were coded according to implementation factors derived from the Consolidated Framework for Implementation Research and the Theoretical Domains Framework. Tailored facilitation strategies were coded into 16 facilitation categories. To predict the effectiveness of these strategies, data mining with random forest was used to provide the highest level of accuracy. A predictive resolution percentage was established for each implementation strategy in relation to the barriers that were resolved by that particular strategy.Results: During the 2-year programme, 1131 barriers and facilitation strategies were recorded by change facilitators. The most frequently identified barriers were a 'lack of ability to plan for change', 'lack of internal supporters for the change', 'lack of knowledge and experience', 'lack of monitoring and feedback', 'lack of individual alignment with the change', 'undefined change objectives', 'lack of objective feedback' and 'lack of time'. The random forest algorithm used was able to provide 96.9% prediction accuracy. The strategy category with the highest predicted resolution rate across the most number of implementation barriers was 'to empower stakeholders to develop objectives and solve problems'.Conclusions: Results from this study have provided a better understanding of implementation barriers in community pharmacy and how data-driven approaches can be used to predict the effectiveness of facilitation strategies to overcome implementation barriers. Tailored facilitation strategies such as these can increase the rate of real-time implementation of innovations in healthcare, leading to an industry that can confidently and efficiently adapt to continuous change. [ABSTRACT FROM AUTHOR]

The lack of personal and economic attributes in emerging public transit big data (such as smart card data) is a general issue that needs to be addressed. Passengers in the public transit network are from different socioeconomic classes, and their trip attributes usually depend on their personal and economic attributes. For instance, age as a demographic attribute plays an important role in trip attributes; adolescent passengers travel to school, young professionals travel to work, and old passengers travel to medical facilities more often. Relations between the socioeconomic and trip attributes of the passengers can be examined by developing a Bayesian network that represents the relations between the attributes by directed acyclic graphs, and calculating the joint and conditional probability values in the graph. This study infers the socioeconomic attributes of the public transit passengers from the trip attributes through developing a Bayesian network. Considered socioeconomic attributes are age, gender, and income; considered trip attributes are start time and duration of the trip, stay duration, and available origin and destination land use types. First, potential structures of the Bayesian network are examined by comparing network scores and arc strength test. After learning the network's parameters, the reasoning is done through both prediction and diagnosis in the network. Also, the most likely combinations of the socioeconomic and trip attributes are discovered. The case study for developing the Bayesian network is a Household Travel Survey dataset from Queensland, Australia, that contains both socioeconomic and trip attributes. Results clearly show how the socioeconomic attributes can be inferred from the trip attributes. Discovered probability distributions can be used to enrich the smart card datasets with the socioeconomic attributes. Moreover, the Bayesian classifier is applied to the dataset to validate the capability of the model in predicting the socioeconomic attributes. In the end, the developed network is implemented on a set of smart card records to discuss the potential applications. [ABSTRACT FROM AUTHOR]

Background: Individuals experiencing severe and persistent mental illness report a desire to gain and sustain work. Individual Placement and Support (IPS) is an evidence-based approach to vocational rehabilitation to support competitive employment outcomes. Aim/Objective: This study aimed to evaluate whether a joint-governance management partnership, between a clinical adult mental health and an employment service, could deliver a sustained IPS program in Australia. Materials and Method: The methodology entailed a Clinical Data Mining approach, to examine records from seven years of implementation of IPS in one setting within an Australian public mental health service context. Results/Findings: Despite the prevalence of schizophrenia spectrum diagnoses and an older mean age (39 years), indicating that a large proportion of the cohort had experienced serious mental illness for over twenty years, findings were that 46.3% of participants achieved employment. Conclusions: This is an excellent result and is comparable to the only randomised control trial, with adult services, in the Australian context, which found a 42.5% employment rate possible under IPS compared with just 23.5% with referral to external employment services. Significance: More extensive trialling of IPS across clinical services is required, in Australia and internationally, including fidelity protocols, for knowledge translation to be achieved. [ABSTRACT FROM AUTHOR]

This research-on-research study describes efforts to develop non-Cochrane systematic reviews (SRs) by analyzing demographical and time-course collaborations between international institutions using protocols registered in the International Prospective Register of Systematic Reviews (PROSPERO) or published in scientific journals. We have published an a priori protocol to develop this study. Protocols published in scientific journals were searched using the MEDLINE and Embase databases; the query terms “Systematic review” [Title] AND “protocol” [Title] were searched from February 2011 to December 2017. Protocols registered at PROSPERO during the same period were obtained by web scraping all non-Cochrane records with a Python script. After excluding protocols that had a fulfillment or duplication rate of less than 90%, they were classified as published “only in PROSPERO”, “only in journals”, or in “journals and PROSPERO”. Results of data and metadata extraction using text mining processes were curated by two reviewers. These Datasets and R scripts are freely available to facilitate reproducibility. We obtained 20,814 protocols of non-Cochrane SRs. While “unique protocols” by reviewers’ institutions from 60 countries were the most frequent, a median of 6 (2-150) institutions from 130 different countries were involved in the preparation of “collaborative protocols”. The highest Ranked countries involved in overall protocol production were the UK, the U.S., Australia, Brazil, China, Canada, the Netherlands, Germany, and Italy. Most protocols were registered only in PROSPERO. However, the number of protocols published in scientific journals (924) or in both PROSPERO and journals (807) has increased over the last three years. Syst Rev and BMJ Open published more than half of the total protocols. While the more productive countries were involved in “unique” and “collaborative protocols”, less productive countries only participated in “collaborative protocols” that were mainly published in PROSPERO. Our results suggest that, although most countries were involved in solitary production of protocols for non-Cochrane SRs during the study period, it would be useful to develop new strategies to promote international collaborations, especially with less productive countries. [ABSTRACT FROM AUTHOR]

Background: Bali, Indonesia, presents significant infectious and non‐infectious health risks for Australian travellers. Understanding this spectrum of illnesses has the potential to assist clinicians in evaluating unwell returning travellers and guide provision of pre‐travel advice. Aim: To describe the spectrum of illnesses seen in returned travellers from Bali. Methods: Using a novel text mining approach, we performed a retrospective, observational study of all adult emergency department presentations to a metropolitan health service in Melbourne, from 2011 to 2015. Outcome measures included demographic, clinical and laboratory features of travel‐related illnesses. Results: A total of 464 patients met inclusion criteria. Gastroenteritis (119/464, 26%), systemic febrile illness (88/464, 19%) and respiratory tract infection (51/464, 11%) were the most common diagnoses. Dengue was the most common laboratory‐confirmed diagnosis (25/464, 5%). No cases of malaria were identified. Common non‐infectious presentations included traumatic injury (47/464, 10%) and animal bites requiring rabies post‐exposure prophylaxis (29/464, 6%). A total of 110 patients (24%) was admitted to the hospital; those presenting with systemic febrile illness were more likely to be admitted compared to those presenting with other illnesses (odds ratio 3.42, 95% confidence interval 2.02–5.75, P < 0.001). Conclusion: This is the first study to use a text mining approach to identify and describe emergency department presentations related to diseases acquired in Bali by Australian travellers. Although infections are important causes of illness, trauma and animal bites account for a significant number of hospital presentations. Our findings contribute to the knowledge on the health risks for travellers to Bali, and will assist clinicians in relevant pre‐ and post‐travel evaluations. [ABSTRACT FROM AUTHOR]

Because of the inefficiency in analyzing the comprehensive travel data, tourism managers are facing the challenge of gaining insights into travelers’ behavior and preferences. In most cases, existing techniques are incapable of capturing the sequential patterns hidden in travel data. To address these issues, this article proposes to analyze the travelers’ behavior through geotagged photos and sequential rule mining. Travel diaries, constructed from the photo sequences, can capture comprehensive travel information, and then sequential patterns can be discovered to infer the potential destinations. The effectiveness of the proposed framework is demonstrated in a case study of Australian outbound tourism, using a data set of more than 890,000 photos from 3,623 travelers. The introduced framework has the potential to benefit tourism researchers and practitioners from capturing and understanding the behaviors and preferences of travelers. The findings can support destination-marketing organizations (DMOs) in promoting appropriate destinations to prospective travelers. [ABSTRACT FROM AUTHOR]

Although research has examined population growth and decline using functional specialisation, little attention has been paid to the possible combined effects of functional specialisation and socio-economic factors on population change. Using the Australian Bureau of Statistics Census Data 2001–2006 for statistical local areas, this study presents an investigation of the role of both functional specialisation and socio-economic factors in population change in non-metropolitan areas under the sustenance framework. The uniqueness of the study is twofold. Conceptually it develops a framework to compare the combined role of functional specialisation and socio-economic factors on population change; and, empirically it uses data mining (cluster analysis) techniques to investigate the extent of this combined role. The results show the significance of both functional specialisation and socio-economic factors. Policy implications of the study indicate the need to examine regional development and population change in relation to functional specialisation and socio-economic factors and their impact on viability of non-metropolitan areas. [ABSTRACT FROM AUTHOR]

Previous research has established that student outcomes are strongly associated with the socioeconomic composition of a school, also known as school socioeconomic status. Less is known, however, about the ways in which the relationship varies for different students, schools and national education systems. Here, we conduct a secondary analysis of an international dataset to examine the strength of the relationship between school socioeconomic status and achievement in math and reading for Canada and Australia. The history, economy and culture of these two countries are similar, as are many aspects of their education systems. One important difference, however, is the degree to which their education systems are marketised. Our findings show that in both countries, school socioeconomic status is strongly associated with academic achievement for all students, regardless of their individual socioeconomic status. Nevertheless, the relationship between school socioeconomic status and academic achievement is substantially stronger in Australia than in Canada. We conclude that student outcomes are more equitable in Canada than in Australia, and suggest that this may be due to differences in the ways in which the two education systems are funded and structured. [ABSTRACT FROM PUBLISHER]

Australia produces over 90% of the world's precious opal from highly weathered Cretaceous sedimentary rocks within the Great Artesian Basin. Since opal was first discovered around 1870 until the present day, opal mining has been carried out by private operators working a claim no larger than 50 × 50 m, usually in the direct vicinity of areas that have yielded precious opal in the past. Currently there is no formal exploration model for opal and its formation in the geological environment is poorly understood. Here we make the first systematic attempt to formulate a predictive model for opal exploration using a powerful data mining approach, which considers almost the entire Great Artesian Basin as a potential reservoir for precious opal. Our methodology uses all known locations where opal has been mined to date. Its formation and preservation in weathered Cretaceous host rocks is evaluated by a joint analysis of large digital data sets that include topography, regional geology, regolith and soil type, radiometric data and depositional environments through time. By combining these data sets as layers enabling spatio-temporal data mining using the GPlates PaleoGIS software, we produce the first opal prospectivity map for the Great Artesian Basin. Our approach reduces the entire area of the Great Artesian Basin to a mere 6% that is deemed to be prospective for opal exploration. It successfully identifies two known major opal fields (Mintabie and Lambina) that were not included as part of the classification dataset owing to lack of documentation regarding opal mine locations, and it significantly expands the prospective areas around known opal fields particularly in the vicinity of Coober Pedy in South Australia and in the northern and southern sectors of the Eromanga Basin in Queensland. The combined characteristics of these areas also provide a basis for future work aimed at improving our understanding of opal formation. [ABSTRACT FROM AUTHOR]

As many sensor networks are currently being deployed for environmental monitoring, there is a growing need to develop systems and applications for managing, processing and retrieving massive amounts of data generated from those networks. In this research, a query answering system with pattern mining techniques is investigated specifically for marine sensor data. We consider three applications of pattern mining: similar pattern search, predictive query and query by clustering. In pattern mining for query answering, we adopt the dynamic time warping (DTW) method for similarity measurement. We also propose the use of a query relaxation approach that recommends users change parameters of a given query to get an answer. Finally, we show implementation results of pattern query answering in a marine sensor network deployed in the South East of Tasmania, Australia. Pattern query answering system benefits in accessing and discovering knowledge from sensor data for decision making purposes. [ABSTRACT FROM AUTHOR]

Purpose -- The purpose of this study is to investigate the relationship between leadership behaviors and knowledge management (KM) practices. More specifically, it aims to examine the influence of transformational and transactional leadership behaviors on KM, and the moderating effect of organizational culture on this relationship, in the context of small-to-medium sized enterprises (SMEs) operating in Australia. Design/methodology/approach -- A total of four hypotheses were proposed for testing. It also provides is a succinct review of KM basics relevant to the study, the relationship between leadership and KM, and leadership and organizational culture. Findings -- The results suggest that both transformational and transactional leadership are positively related to KM practices. They also reveal that charismatic leadership and contingent reward leadership behaviors have greater influence on all the dimensions of KM practices. Research limitations/implications -- A key limitation of this study is its cross-sectional nature. It is possible that at least certain aspects of leadership and organizational culture, and its impact on KM practices emerge with some kind of time lag. A longitudinal treatment of data might yield additional insights into the impact of leadership behaviors and organizational culture. This study was also unable to actually observe managers interacting with followers. Practical implications -- The results of the study are generally consistent with theoretical predictions based on extant research. Originality/value -- The results of this study provide compelling evidence in support of the moderating role of organizational culture on the relationship between transactional leadership and KM and will be of interest to those in the field. [ABSTRACT FROM AUTHOR]

We present a piecewise linear decision tree model for predicting percent of soil organic C (SOC) in the agricultural zones of Australia generated using a machine learning approach. The inputs for the model are a national database of soil data, national digital surfaces of climate, elevation, and terrain variables, Landsat multispectral scanner data, lithology, land use, and soil maps. The model and resulting map are evaluated, and insights into biogeological surficial processes are discussed. The decision tree splits the overall data set into more homogenous subsets, thus in this case, it identifies areas where SOC responds closely to climatic and other environmental variables. The spatial pattern of SOC corresponds well to maps of estimated primary productivity and bioclimatic zones. Topsoil organic C levels are highest in the high rainfall, temperate regions of Tasmania, Victoria, and Western Australia, along the coast of New South Wales and in the wet tropics of Queensland; and lowest in arid and semiarid inland regions. While this pattern broadly follows continental vegetation, soil moisture, and temperature patterns, it is governed by a spatially variable hierarchy of different climatic and other variables across bioregions of Australia. At the continental scale, soil moisture level, rather than temperature, seems most important in controlling SOC. [ABSTRACT FROM AUTHOR]

With the systematic archiving of qualitative data emerging as a distinct possibility in Australia, both the practices of qualitative research and how subsequent outputs are 'used' are coming under increased scrutiny and reflection. Drawing on a series of focus groups with qualitative researchers, this article critically explores the meanings ascribed to qualitative research practice and the perceived challenges posed by contemporary innovations in data management, access, and analysis through electronic archiving. The accounts presented provide much needed insight into key debates (and divergences) within the qualitative community regarding the values and meanings of qualitative practice, but also how contemporary innovations may come to challenge these core values. [ABSTRACT FROM AUTHOR]