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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]

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 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]