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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]

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]

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]

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]

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]

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]

• 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]

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]

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]

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]

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]

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]

Secondary fracture prevention programs mostly identify patients with symptomatic non-vertebral fractures, whereas asymptomatic vertebral fractures are usually missed. We here describe the development and validation of a simple method to systematically identify patients with radiographic vertebral fractures using simple text-based searching of free-text radiology reports. The study consisted of two phases. In the development phase (DP), twelve search terms were used to identify vertebral fractures in all X-ray and CT reports issued over a period of 6 months. Positive reports were manually reviewed to confirm whether or not a vertebral fracture had in fact been reported. The three search terms most effective in detecting vertebral fractures during the DP were then applied during the implementation phase (IP) over several weeks to test their ability to identify patients with vertebral fractures. The search terms 'Loss of Height' (LoH), 'Compression Fracture' (CoF) and 'Crush Fracture' (CrF) identified the highest number of imaging reports with a confirmed vertebral fracture. These three search terms identified 581 of 689 (84%) of all true vertebral fractures with a positive predictive value of 76%. Using these three terms in the IP, 126 reports were identified of which 100 (79%) had a vertebral fracture confirmed on manual review. Amongst a sample of 587 reports in week 1 of the IP, 7 (1.2%) were false negatives. Many patients with vertebral fractures can be identified via a simple text-based search of electronic radiology reports. This method may be utilised by secondary fracture prevention programs to narrow the 'care gap' in osteoporosis management. [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]

Background: The Sexual Assault Resource Center (SARC) in Perth, Western Australia provides free 24-hour medical, forensic, and counseling services to persons aged over 13 years following sexual assault. Objective: The aim of this research was to design a data management system that maintains accurate quality information on all sexual assault cases referred to SARC, facilitating audit and peer-reviewed research. Methods: The work to develop SARC Medical Services Clinical Information System (SARC-MSCIS) took place during 2007–2009 as a collaboration between SARC and Curtin University, Perth, Western Australia. Patient demographics, assault details, including injury documentation, and counseling sessions were identified as core data sections. A user authentication system was set up for data security. Data quality checks were incorporated to ensure high-quality data. Results: An SARC-MSCIS was developed containing three core data sections having 427 data elements to capture patient’s data. Development of the SARC-MSCIS has resulted in comprehensive capacity to support sexual assault research. Four additional projects are underway to explore both the public health and criminal justice considerations in responding to sexual violence. The data showed that 1,933 sexual assault episodes had occurred among 1881 patients between January 1, 2009 and December 31, 2015. Sexual assault patients knew the assailant as a friend, carer, acquaintance, relative, partner, or ex-partner in 70% of cases, with 16% assailants being a stranger to the patient. Conclusion: This project has resulted in the development of a high-quality data management system to maintain information for medical and forensic services offered by SARC. This system has also proven to be a reliable resource enabling research in the area of sexual violence. [ABSTRACT FROM AUTHOR]

Effective and accurate use of routinely collected health data to produce Key Performance Indicator reporting is dependent on the underlying data quality. In this research, Process Mining methodology and tools were leveraged to assess the data quality of time-based Emergency Department data sourced from electronic health records. This research was done working closely with the domain experts to validate the process models. The hospital patient journey model was used to assess flow abnormalities which resulted from incorrect timestamp data used in time-based performance metrics. The research demonstrated process mining as a feasible methodology to assess data quality of time-based hospital performance metrics. The insight gained from this research enabled appropriate corrective actions to be put in place to address the data quality issues. [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]

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]

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]

The relationships between team performance indicators and match outcome have been examined in many team sports, however are limited in Australian Rules football. Using data from the 2013 and 2014 Australian Football League (AFL) regular seasons, this study assessed the ability of commonly reported discrete team performance indicators presented in their relative form (standardised against their opposition for a given match) to explain match outcome (Win/Loss). Logistic regression and decision tree (chi-squared automatic interaction detection (CHAID)) analyses both revealed relative differences between opposing teams for “kicks” and “goal conversion” as the most influential in explaining match outcome, with two models achieving 88.3% and 89.8% classification accuracies, respectively. Models incorporating a smaller performance indicator set displayed a slightly reduced ability to explain match outcome (81.0% and 81.5% for logistic regression and CHAID, respectively). However, both were fit to 2014 data with reduced error in comparison to the full models. Despite performance similarities across the two analysis approaches, the CHAID model revealed multiple winning performance indicator profiles, thereby increasing its comparative feasibility for use in the field. Coaches and analysts may find these results useful in informing strategy and game plan development in Australian Rules football, with the development of team-specific models recommended in future. [ABSTRACT FROM PUBLISHER]

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]

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]

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]

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]

What is known and Objective: Pharmacists frequently see patients with asthma in the community who have suboptimal management. This study aimed to compare the uptake and effectiveness of pharmacist-initiated mailed and face-to-face interventions for patients whose asthma may not be well managed. Methods: Seventy-one community pharmacies in South Australia, Tasmania and Victoria (Australia) installed a software application that data-mined dispensing records, generating a list of patients who had received six or more asthma reliever inhalers in the preceding 12 months. The pharmacists were randomized, by pharmacy, to perform either a mailed or face-to-face intervention, whereby these patients received educational material and a referral to their general practitioner (GP) for an asthma management review. Matching patients from each pharmacy were also randomly assigned to a control group for 'usual care'. Results and Discussion: A total of 1483 patients were identified and grouped as follows: 510 (34·4%) mailed intervention, 480 (32·4%) face-to-face intervention and 493 (33·2%) controls. Significantly fewer face-to-face interventions were offered than mailed interventions (66·6% vs. 89·4%, respectively; χ2 = 64·2, P < 0·0001). There were significant improvements in the preventer-to-reliever ratio after the intervention period ( P < 0·0001) in each group. In a per-protocol analysis, the magnitude of improvement in the face-to-face intervention group was greater than in the mailed intervention group. The reverse was true in an intention-to-treat analysis. The improvement in the P : R ratios was mainly due to significant decreases in reliever usage. What is new and Conclusion: Community pharmacy dispensing records can effectively identify patients with suboptimal asthma management, who can then be referred to their GP for review. Time constraints in busy pharmacies may limit the uptake and effectiveness of face-to-face interventions in the 'real world' setting, making mailed interventions an attractive option. [ABSTRACT FROM AUTHOR]

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]

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]

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]

Data-mining is a method for practice-based research that draws on existing organizational data to inform practice issues and build social work knowledge. This article reports on how a study investigating social work practice questions about the nature and extent of single session work in hospitals was enhanced by accessing the extensive hospital patient data system. Useful descriptive statistics on the level, purpose, location, and activities of single session social work in nine hospitals in a region of Australia were generated from the patient data. The researchers also report on the importance of a consultative and collaborative process for effective data-mining outcomes. [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]

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-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]

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]

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]

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]

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]

Tilapia lake virus (TiLV) has caused mass mortalities in farmed and wild tilapia with serious economic and ecological consequences. Until recently, this virus was the sole member of the Amnoonviridae, a family within the order Articulavirales comprising segmented negative-sense RNA viruses. We sought to identify additional viruses within the Amnoonviridae through total RNA sequencing (meta-transcriptomics) and data mining of published transcriptomes. Accordingly, we sampled marine fish species from both Australia and China and discovered several segments of two new viruses within the Amnoonviridae, tentatively called Flavolineata virus and Piscibus virus, respectively. In addition, by mining vertebrate transcriptome data, we identified nine additional virus transcripts matching to multiple genomic segments of TiLV in both marine and freshwater fish. These new viruses retained sequence conservation with the distantly related Orthomyxoviridae in the RdRp subunit PB1, but formed a distinct and diverse phylogenetic group. These data suggest that the Amnoonviridae have a broad host range within fish and that greater animal sampling will identify additional divergent members of the Articulavirales. [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]