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Recent negotiations of 'data' in schools place focus on student assessment and NAPLAN. However, with the rise in artificial intelligence (AI) underpinning educational technology, there is a need to shift focus towards the value of teachers' digital data. By doing so, the broader debate surrounding the implications of these technologies and rights within the classroom as a workplace becomes more apparent to practitioners and educational researchers. Drawing on the Australian Human Rights Commission's "Human Rights and Technology final report," this conceptual paper focusses on teachers' rights alongside emerging technologies that use or provide predictive analytics or artificial intelligence, also called 'personalisation'. The lens of Postdigital positionality guides the discussion. Three potential consequences are presented as provocations: (1) What might happen if emerging technology uses teachers' digital data that represent current societal inequality? (2) What might happen if insights provided by such technology are inaccurate, insufficient, or unrepresentative of our teachers? (3) What might happen if the design of the AI system itself is discriminatory? This conceptual paper argues for increased discourse about technologies that use or provide predictive analytics complemented by considering potential consequences associated with algorithmic bias.

In the recent Australian 2021 census, the socio-technical construct of algorithmically driven decision-making processes made LGBTQI+ data as a category of diversity, inclusion and belonging an absent presence. In this paper, we position the notion of 'data justice' in relation to the entrenchment of inequalities and exclusion of LGBTQI+ lives and consider the implications of LGBTQI+ data being missing in action. As we look at the notion of 'data justice', we consider five critical socio-technical imaginaries with different kinds of data to think through the implications of technical democracy, data justice and post-automation. Finally, we consider the imaginary of citizenship when LGBTQI+ data is habitually missing in action from systematic power integrated into forms of governance. This paper positions 'data' not as a static 'object or process' but as a dynamic ecology that carries with it a multi-faceted set of coded meanings requiring constant review and reconsideration.

As we move further into the digital age, the acquisition of digital literacy (DL) and computational thinking (CT) skills is emerging internationally as an essential goal for students in contemporary school curricula. As the world becomes more uncertain and volatile due to impacts of artificial intelligence (AI), international unrest, climate change, global economic instability and unforeseen catastrophes such as the Coronavirus (COVID-19) pandemic, it is necessary to review, revise and refine school education curricula and policies. The issue of what is essential for students to learn, and how they learn it, is of growing importance to international organisations such as the Organisation for Economic Co-operation and Development (OECD) and the United Nations Educational, Scientific and Cultural Organisation (UNESCO) and is emerging as a significant driver for educational reform across the globe. The growing prominence of CT and DL skills across many industry sectors has prompted recent changes to international assessment frameworks such as the Programme for International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS). This paper will briefly discuss specific examples of alternative approaches to addressing CT in national curricula for the compulsory years of schooling and explain how CT has been adopted as a driver for mathematics curriculum change in Australia.

The Chameleon Educational Policy Reforms' (CEPR) 25th anniversary was celebrated at the Global General Assembly with the 2075 Decennial Analysis of Schooling (DecAS) announcement of the attainment of a benefit-cost ratio of >1.0. The attainment of a global positive net value of education is directly linked to the educational reforms established to 'provide the right access to the right education for all people' (CEPR, 2050). The Chameleon reforms, informed by The Algorithm, produce policies that instantly adapt to the learning environment and needs of students. Barriers that effect students' learning are removed, in stark contrast to historic processes that viewed students as the barrier to be removed from learning environments. The case studies presented in this paper are guided by three questions: 'Where are we now?', 'How did we get here?', and 'Are we there yet?'. Questioning the 'here and now', directs a look back from educational engagement currently governed by the CEPR to key moments and movements in the attainment of past grand policy announcements that no child be left behind to live in poverty. And 'yet', leads to critical consideration of the ongoing engagement with The Algorithm. Artifacts detailing the 2045 commencement of annual donations from the richest two percent to fully fund global education remain sealed. The results of the annual donations are, however, publicly available; and some would say exploited (Gerve, 2072). Achieving self-sustainable economic cost-benefit status for the CEPR may release the Donators from those agreements and untether society from the Donators.

Multiple myeloma (MM) is a disease of older people, yet factors relating to comorbidity and frailty may threaten treatment tolerability for many of this heterogenous group. There has been increasing interest in defining specific and clinically relevant frailty assessment tools within the MM population, with the goal of using these frailty scores, not just as a prognostic instrument, but also as a predictive tool to allow for a frailty‐adapted treatment approach. This paper reviews the various frailty assessment frameworks used in the evaluation of patients with MM, including the International Myeloma Working Group Frailty Index (IMWG‐FI), the Mayo Frailty Index and the simplified frailty scale. While the IMWG‐FI remains the most widely accepted tool, the simplified frailty scale is the most user‐friendly in busy day‐to‐day clinics based on its ease of use. This paper summarises the recommendations from the Myeloma Scientific Advisory Group (MSAG) of Myeloma Australia, on the use of frailty assessment tools in clinical practice and proposes a frailty‐stratified treatment algorithm to aid clinicians in tailoring therapy for this highly heterogeneous patient population. [ABSTRACT FROM AUTHOR]

The high accuracy of short-term power load forecasting has a pivotal role in helping power companies to construct reasonable production scheduling plans and avoid resource waste. In this paper, a multi-model short-term power load prediction method based on Variational mode decomposition (VMD)-improved whale optimization algorithm (IWOA)-wavelet temporal convolutional network (WTCN)-bidirectional gated recurrent unit (BiGRU)-attention and CatBoost model fusion is proposed. First, VMD was employed to decompose the load data into different intrinsic mode functions. Second, a WTCN was utilized to extract the load data features, and multi-dimensional feature factors were integrated into the BiGRU network for model training. Moreover, an attention mechanism was added to enhance the influence degree of important information. The WTCN-BiGRU-attention model is improved by the WOA algorithm to optimize the hyperparameters of the network. Finally, the model was fused with the predicted data of the CatBoost network by the mean absolute percentage error-reciprocal weight (MAPE-RW) algorithm to construct the best fusion model. Compared with other forecasting models, the proposed multi-model fusion method has higher accuracy in short-term power load forecasting using the public data set for an Australian region.

The objective of this paper is to assess whether existing pavement temperature prediction algorithms can be used to reliably determine Performance Grade (PG) design temperatures for Australian asphalt pavements. The results show good agreement between internationally and locally developed pavement temperature algorithms for the prediction of high pavement design temperature. There is more variability between models in the prediction of low pavement design temperature. The paper also provides a set of PG grading results for a range of Australian asphalt binders. The findings indicate that for a given design situation harder bitumen, ormore highly modified binder would be specified in Australia than in jurisdictions using the PG system. [ABSTRACT FROM AUTHOR]

To solve the problem of feature selection and error correction after mode decomposition and improve the ability of power load forecasting models to capture complex time series information, a two-stage short-term power load forecasting method based on recursive feature elimination with a cross validation (RFECV) algorithm and time convolution network–efficient channel attention mechanism–long short-term memory network (TCN–ECA–LSTM) is presented. First, the load sequence is decomposed into a relatively stable set of modal components using variational mode decomposition. Then, the RFECV-based method filters the feature set of each modal component to construct the best feature set. Finally, a two-stage prediction model based on TCN–ECA–LSTM is established. The first stage predicts each modal component and the second stage reconstructs the load forecast based on the predicted value of the previous stage. This paper takes actual data from New South Wales, Australia, as an example, and the results show that the method proposed in this paper can build the feature set reliably and efficiently and has a higher accuracy than the conventional prediction model. [ABSTRACT FROM AUTHOR]

Chronic disease identification and management is a significant issue in Australia, with general practice being the primary contact point for those at risk of, or living with, chronic disease. However, there is a well-described gap between guideline recommendations for chronic disease management and translation in the general practice setting. In 2018, a group of researchers, clinicians and software developers collaborated to develop a tool to support the identification and management of chronic disease in general practice, with the aim to create a platform that met the needs of general practice. The co-design process drew together core principles and expectations for the establishment of a technological platform, called Future Health Today (FHT), which would sit alongside the electronic medical record (EMR) management system within general practice. FHT used algorithms applied to EMR data to identify patients with, or at risk of, chronic disease and requiring review. Using chronic kidney disease as a clinical focus, the FHT prototype was piloted in a large, metropolitan general practice, and a large regional general practice. Based on user feedback, the prototype was further developed and improved. This paper provides a report on the key features and functionalities that participants identified and implemented in practice. Future Health Today is a new platform, co-designed by general practice for general practice, which aims to streamline the identification and management of chronic disease to improve health outcomes. This paper describes the development of the technology platform and how it was optimised through an implementation study in general practice. [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]

Timely monitoring of pavement sub-surface layer thickness and condition evaluation is essential to ensure stable pavement performance and safety under heavy traffic loading. In addition, accurate estimation of pavement layer thicknesses is required for condition evaluation, overlay design/quality control assurance, and structural capacity evaluation of existing pavements to predict its remaining service life. Traditionally this vital information is ascertained through coring/drilling and visual inspections. In contrast to these current techniques, ground-penetrating radar (GPR) is a nondestructive technique gaining popularity in pavement asphalt layer thickness estimation and structural condition monitoring. Its high-quality data contains vital pavement condition information, and survey costs are reasonably economic. In this work, GPR data were acquired along a toll road in Queensland, Australia, using the GSSI 4-channel SIR30 GPR unit. Asphalt layer thickness information is considered an important input parameter for condition assessment, pavement performance, and lifetime modelling. This work presents an automated segmentation framework to evaluate pavement conditions for a large pavement network. The developed algorithm uses GPR asphalt thickness data as input and generates segments with decision boundaries utilising a cascaded k-means and DBSCAN approach that works in two steps: 1) centroid initialisation using k-means algorithm, 2) clustering using DBSCAN algorithm. Presented in this paper is the workflow of the cascaded method that is applicable to automated analysis of GPR asphalt thickness data. The performance of the cascaded kmeans and DBSCAN algorithm was evaluated in terms of entropy compared with traditional k-means and traditional DBSCAN algorithms. The results show that the proposed method outperforms its constituents. Based on the results of this study, the method presented in this paper is cost-effective, economical and robust for segmenting large pavement network with GPR data. [ABSTRACT FROM AUTHOR]

Accurate short-term load forecasting (STLF) is of positive significance to the effective management of power companies and the stable operation of society. In spite of many studies conducted in this field, there are few to consider the inherent disadvantages of an individual module, which results in sub-optimal forecasting accuracy. Therefore, by integrating data preprocessing module and optimization module into support vector regression (SVR) forecasting module, this paper successfully presents a novel model (AHO-Lasso-SVR). The data preprocessing module, which is comprised of feature construction and Lasso feature selection, is used to construct and select meaningful features. An adaptive hybrid optimization (AHO) algorithm is proposed by introducing two strategies on the basis of standard particle swarm optimization (PSO). The AHO algorithm inevitably increases the computational complexity of model learning, thus, this paper proposes a subsampling technology to improve the optimization efficiency of the algorithm based on the sparsity of SVR. The proposed model is used to forecast the load at 48 points in the next day. To verify the properties of the proposed model, power load data from New South Wales, Australia are adopted as a case study. The results reveal that our model positively exceeds all comparison models in terms of forecasting accuracy and stability. [ABSTRACT FROM AUTHOR]

Across the globe, remote image data is rapidly being collected for the assessment of benthic communities from shallow to extremely deep waters on continental slopes to the abyssal seas. Exploiting this data is presently limited by the time it takes for experts to identify organisms found in these images. With this limitation in mind, a large effort has been made globally to introduce automation and machine learning algorithms to accelerate both classification and assessment of marine benthic biota. One major issue lies with organisms that move with swell and currents, such as kelps. This paper presents an automatic hierarchical classification method local binary classification as opposed to the conventional flat classification to classify kelps in images collected by autonomous underwater vehicles. The proposed kelp classification approach exploits learned feature representations extracted from deep residual networks. We show that these generic features outperform the traditional off-the-shelf CNN features and the conventional hand-crafted features. Experiments also demonstrate that the hierarchical classification method outperforms the traditional parallel multi-class classifications by a significant margin (90.0% vs. 57.6% and 77.2% vs. 59.0%) on Benthoz15 and Rottnest datasets respectively. Furthermore, we compare different hierarchical classification approaches and experimentally show that the sibling hierarchical training approach outperforms the inclusive hierarchical approach by a significant margin. We also report an application of our proposed method to study the change in kelp cover over time for annually repeated AUV surveys.

This paper proposes a spatiotemporal clustering algorithm and its implementation in the R package spotoroo. This work is motivated by the catastrophic bushfires in Australia throughout the summer of 2019-2020 and made possible by the availability of satellite hotspot data. The algorithm is inspired by two existing spatiotemporal clustering algorithms but makes enhancements to cluster points spatially in conjunction with their movement across consecutive time periods. It also allows for the adjustment of key parameters, if required, for different locations and satellite data sources. Bushfire data from Victoria, Australia, is used to illustrate the algorithm and its use within the package. [ABSTRACT FROM AUTHOR]

Evidence supporting the practice of skin-to-skin contact and breastfeeding soon after birth points to physiologic, social, and psychological benefits for both mother and baby. The 2009 revision of Step 4 of the WHO/UNICEF "Ten Steps to Successful Breastfeeding" elaborated on the practice of skin-to-skin contact between the mother and her newly born baby indicating that the practice should be "immediate" and "without separation" unless documented medically justifiable reasons for delayed contact or interruption exist. While in immediate, continuous, uninterrupted skin-to-skin contact with mother in the first hour after birth, babies progress through 9 instinctive, complex, distinct, and observable stages including self-attachment and suckling. However, the most recent Cochrane review of early skin-to-skin contact cites inconsistencies in the practice; the authors found "inadequate evidence with respect to details … such as timing of initiation and dose." This paper introduces a novel algorithm to analyse the practice of skin to skin in the first hour using two data sets and suggests opportunities for practice improvement. The algorithm considers the mother's Robson criteria, skin-to-skin experience, and Widström's 9 Stages. Using data from vaginal births in Japan and caesarean births in Australia, the algorithm utilizes data in a new way to highlight challenges to best practice. The use of a tool to analyse the implementation of skin-to-skin care in the first hour after birth illuminates the successes, barriers, and opportunities for improvement to achieving the standard of care for babies. Future application should involve more diverse facilities and Robson's classifications., (© 2017 The Authors. Maternal and Child Nutrition Published by John Wiley & Sons, Ltd.)

Automatic processing of ionograms to extract ionospheric propagation characteristics and electron density profile parameters is an important enabling step toward constructing real time data‐assimilative models of the ionosphere. A robust and reliable feature extraction and fitting algorithm that supports both oblique and vertical incidence ionograms has recently been developed to enhance the regional ionospheric model for Australia's Jindalee Operational Radar Network. The so‐called DST‐IIP algorithm is an autoscaling technique based on a constrained quasi‐parabolic segment profile, which is modified from its climatological estimate to fit a set of key peak, edge, and line features in the ionogram image, representing the E, sporadic‐E (Es), and F2 layers. The F2 fitting, in particular, is performed in the ionogram domain, using analytic ray‐tracing and homing to produce a synthetic trace that reproduces the image features. Following thorough testing on a large database of midlatitude Australian ionograms, the DST‐IIP algorithm now runs routinely on‐board DST Group's Digital Oblique Receiving System. This paper provides an overview of the technique, along with sample results and performance statistics. Plain Language Summary: An ionogram image captures the path losses and delays for radio propagation via the ionosphere, an outer charged layer of the Earth's atmosphere that refracts high‐frequency radio waves. In order to relate ionograms from multiple ionospheric paths, the usual approach is to automatically convert these images to a parameterized form representing the midpoint electron density profiles. This paper describes such a technique, based on fitting the parameterized profile to a set of key peak, edge, and line features in the ionogram image. It is shown to be robust and reliable across a variety of path geometries and propagation conditions and now runs routinely on‐board DST Group's own ionospheric sounder (or ionosonde), the Digital Oblique Receiving System. Key Points: An algorithm for automatically scaling oblique and vertical incidence ionograms has been developed in conjunction with a new ionosondeThe algorithm extracts key features describing the E, Es, and F2 layers, to which a parameterized profile of electron density is fittedPerformance is shown to be robust under a variety of ionospheric conditions, and results compare well to other nearby sounders [ABSTRACT FROM AUTHOR]

Introduction: Quality of life is included in the economic evaluation of health services by measuring the preference for health states, i.e. health state utilities. However, most intervention studies include a disease-specific, not a utility, instrument. Consequently, there has been increasing use of statistical mapping algorithms which permit utilities to be estimated from a disease-specific instrument. The present paper provides such algorithms between the MacNew Heart Disease Quality of Life Questionnaire (MacNew) instrument and six multi-attribute utility (MAU) instruments, the Euroqol (EQ-5D), the Short Form 6D (SF-6D), the Health Utilities Index (HUI) 3, the Quality of Wellbeing (QWB), the 15D (15 Dimension) and the Assessment of Quality of Life (AQoL-8D)., Methods: Heart disease patients and members of the healthy public were recruited from six countries. Non-parametric rank tests were used to compare subgroup utilities and MacNew scores. Mapping algorithms were estimated using three separate statistical techniques., Results: Mapping algorithms achieved a high degree of precision. Based on the mean absolute error and the intra class correlation the preferred mapping is MacNew into SF-6D or 15D. Using the R squared statistic the preferred mapping is MacNew into AQoL-8D., Implications for Research: The algorithms reported in this paper enable MacNew data to be mapped into utilities predicted from any of six instruments. This permits studies which have included the MacNew to be used in cost utility analyses which, in turn, allows the comparison of services with interventions across the health system., (© The European Society of Cardiology 2014.)

Introduction: Many people at high risk of fractures are not following traditional guidelines and not being recommended for intervention. This study aimed to propose and evaluate a new set of intervention thresholds., Methods: Participants were 213,375 men and women aged ≥50 years living in New South Wales, Australia. Fracture Risk Assessment Paper Charts (Australia) was used to estimate the 10-year fracture risk. The standardized rates (to Australia population distribution 2007) for intervention were calculated for different thresholds: our proposed new thresholds (i.e. 10-year probability of hip fracture: ≥3%, 5% or 7% for 50-69, 70-79 and ≥80 years respectively), thresholds by the National Osteoporosis Guideline Group (NOGG) approach, UK thresholds and US thresholds., Results: The NOGG, UK and US thresholds did not work well in the Australian population. For example, the NOGG and UK thresholds respectively qualified only 1 in 12 (8.1%) and 1 in 9 (11.3%) Australian men aged ≥70 years and the US thresholds qualified about 9 in 10 (90.6%) Australian women aged ≥ 70 years. For men or women aged ≥70 years, our proposed new thresholds gave more realistic treatment rates of 21.6% for men and 70.5% for women. Compared to the current Australian guidelines (i.e. T-score ≤ -2.5 and age ≥ 70 years or a fragility fracture), our thresholds identified an additional 4.9% of men and 18.2% of women aged ≥ 70 years for treatment., Conclusion: The proposed new thresholds could identify currently under-recognised high-risk individuals for treatment. It should be considered as a recommendation for osteoporosis management in Australia., (Copyright © 2014 Elsevier Inc. All rights reserved.)

As more and more photovoltaic (PV) systems are integrated into the grid, the intelligent operation of the grid system is facing significant challenges. Therefore, accurately forecasting PV power output at various time scales is particularly urgent. To meet this demand, this paper proposes an LSTM-Informer model based on an improved Stacking ensemble algorithm (ISt-LSTM-Informer). The proposed model improves the k-fold cross validation in the traditional Stacking algorithm to a time-series cross validation for integrating time-series forecasting models. Simultaneously, it utilizes long short-term memory (LSTM) and Informer as the base models. By integrating the advantages of the two base models, the ISt-LSTM-Informer achieves accurate short and medium-term PV power forecasting. To validate the effectiveness of the model, a historical dataset from a PV system located in Uluru, Australia, is used for various types of experiments. Among them, comparative experiments validate the superiority of the model. Compared with five other methods, the ISt-LSTM-Informer obtains 21 optimal results for the four evaluation metrics of RMSE, MAE, MAPE, and R 2 across eight forecasting time scales. In addition, different combinations of base models are conducted to verify the advantages of the Stacking ensemble algorithm and the two base models, respectively. • A novel multi-timescale photovoltaic power forecasting model is proposed. • Time-series cross validation is introduced into the Stacking algorithm. • LSTM and Informer are utilized as the base models of the Stacking algorithm. • Various methods are compared to verify the proposed model's effectiveness. • The model's predictive accuracy is illustrated through various visualization methods. [ABSTRACT FROM AUTHOR]

The question of which type of computer science (CS) publication-0conference or journal-0is likely to result in more citations for a published paper is addressed. A series of data sets are examined and joined in order to analyze the citations of over 195,000 conference papers and 108,000 journal papers. Two means of evaluating the citations of journals and conferences are explored: h5 and average citations per paper; it was found that h5 has certain biases that make it a difficult measure to use (despite it being the main measure used by Google Scholar). Results from the analysis show that CS, as a discipline, values conferences as a publication venue more highly than any other academic field of study. The analysis also shows that a small number of elite CS conferences have the highest average paper citation rate of any publication type, although overall, citation rates in conferences are no higher than in journals. It is also shown that the length of a paper is correlated with citation rate. [ABSTRACT FROM AUTHOR]

This paper asks why there is so little collective dissent and mobilised resistance in the gig economy, especially when labour-based digital platforms are used. We suggest part of the answer lies with 'management by algorithm'. Drawing on an empirical study of Uber drivers in Australia, we found that algorithms function as a form of biopower, a concept introduced by Michel Foucault. As Uber drivers 'life processes' are put to work, fragmentation, isolation and resignation ensue. We explore the implications that our findings have for appreciating how biopower operates within platform capitalism and beyond. [ABSTRACT FROM AUTHOR]

In semiarid and arid environments, leakage from rivers is a major source of recharge to underlying unconfined aquifers. Differential river gauging is widely used to estimate the recharge. However, the methods commonly applied are limited in that the temporal resolution is event-scale or longer. In this paper, a novel method is presented for quantifying both the total recharge volume for an event, and variation in recharge rate during an event from hydrographs recorded at the upstream and downstream ends of a river reach. The proposed method is applied to river hydrographs to illustrate the method steps and investigate recharge processes occurring in a sub-catchment of the Murray Darling Basin (Australia). Interestingly, although it is the large flood events which are commonly assumed to be the main source of recharge to an aquifer, our analysis revealed that the smaller flow events were more important in providing recharge., (© 2013, National Ground Water Association.)

Objective: To present our data evaluating the feasibility of simultaneous cochlear implantation with resection of acoustic neuroma. Methods: This paper describes a case series of eight adult patients with a radiologically suspected acoustic neuroma, treated at a tertiary referral centre in Newcastle, Australia, between 2012 and 2015. Patients underwent cochlear implantation concurrently with removal of an acoustic neuroma. The approach was translabyrinthine, with facial nerve monitoring and electrically evoked auditory brainstem response testing. Standard post-implant rehabilitation was employed, with three and six months' follow-up data collected. The main outcome measures were: hearing, subjective benefit of implant, operative complications and tumour recurrence. Results: Eight patients underwent simultaneous cochlear implantation with resection of acoustic neuroma over a 3-year period, and had 25–63 months' follow up. There were no major complications. All patients except one gained usable hearing and were daily implant users. Conclusion: Simultaneous cochlear implantation with resection of acoustic neuroma has been shown to be a safe treatment option, which will be applicable in a wide range of clinical scenarios as the indications for cochlear implantation continue to expand. [ABSTRACT FROM AUTHOR]

An accurate predictive model is essential for monitoring the energy produced by a solar system based on different meteorological parameters. In the present paper, a novel machine-learning model named DNN-RODDPSO is proposed to improve the real-time prediction accuracy of the hourly energy produced by four dual-axis solar trackers. This model integrates a new deep neural network (DNN) model with a recent variant of PSO algorithm referred to as a randomly occurring distributed delayed particle swarm optimization (RODDPSO) algorithm. This algorithm is adopted to enhance the training process of the DNN model by reducing the risk of being trapped into local optima and for the search space diversification. Furthermore, to develop the DNN-RODDPSO model, the hourly observations of seven meteorological parameters including time variable measured during 2014–2015 in Alice Springs city, Australia, are used. This model integrates two novel hidden layers, the first one is a selective layer based on daytime/nighttime data selection. The second one named automatic inputs relevance determination to point out the most relevant inputs for an accurate prediction. The obtained results demonstrate the high performance of the two novel hidden layers and the RODDPSO algorithm to improve significantly the prediction accuracy compared to the actual literature standards. • A novel hybrid ANN based on RODDPSO algorithm is developed. • A novel selective layer based on daytime/nighttime data selection is introduced. • A novel automatic inputs relevance determination layer is proposed and validated. • The energy potential produced by four solar trackers is monitored. [ABSTRACT FROM AUTHOR]

Video-based human action recognition is currently one of the most active research areas in computer vision. Various research studies indicate that the performance of action recognition is highly dependent on the type of features being extracted and how the actions are represented. Since the release of the Kinect camera, a large number of Kinect-based human action recognition techniques have been proposed in the literature. However, there still does not exist a thorough comparison of these Kinect-based techniques under the grouping of feature types, such as handcrafted versus deep learning features and depth-based versus skeleton-based features. In this paper, we analyze and compare 10 recent Kinect-based algorithms for both cross-subject action recognition and cross-view action recognition using six benchmark datasets. In addition, we have implemented and improved some of these techniques and included their variants in the comparison. Our experiments show that the majority of methods perform better on cross-subject action recognition than cross-view action recognition, that the skeleton-based features are more robust for cross-view recognition than the depth-based features, and that the deep learning features are suitable for large datasets. [ABSTRACT FROM AUTHOR]

Wildfires are one of the most destructive disasters on the planet. They also significantly impact the land surface. Satellite data have been widely used to detect the outbreak and monitor the expansion of fire incidents for damage assessment and disaster management. Polar-orbiting satellite data have been used for several decades but data from geostationary satellites, which can provide observations with a high temporal resolution, have received much less attention. This paper utilizes data from FengYun-2G, a Chinese geostationary satellite, to detect wildfires in two selected research regions in January 2016. The detection algorithm systemizes image-based analysis to filter out obvious nonfire pixels and temporal analysis to confirm the true detections. Fire detection is based on comparisons between predicted and observed values. The results show that the proposed method has some advantages compared with the use of polar-orbiting satellite data, including early detection and continuous observation. The validation work is conducted based on the collection 6.1 Global Monthly Fire Location Product generated from fire detections by Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. The average accuracy within the target time is 56%, while the omission error rate is over 78%. In detail, the algorithm has a lower omission error rate in Australia while it fails in detecting most of the fire pixels in India. The dominance of small fire incidents, as well as low spatial resolution greatly limit the detection ability. Many small fires were beyond the ability of Stretched Visible and Infrared Spin Scan Radiometer (S-VISSR) data when no significant fire characteristics could be captured. Future development of the algorithm will focus on improving the results by enhancing the adaption to different regions, as well as, including multisource data sets. [ABSTRACT FROM AUTHOR]

Objective. To examine the effectiveness of telephone-based coaching services for the management of patients with chronic diseases. Methods. A rapid scoping review of the published peer reviewed literature, using Medline, Embase, CINAHL, PsychNet and Scopus. We included studies involving people aged 18 years or over with one or more of the following chronic conditions: type 2 diabetes, congestive cardiac failure, coronary artery disease, chronic obstructive pulmonary disease and hypertension. Patients were identified as having multi-morbidity if they had an index chronic condition plus one or more other chronic condition. To be included in this review, the telephone coaching had to involve two-way conversations by telephone or video phone between a patient and a provider. Behaviour change, goal setting and empowerment are essential features of coaching. Results. The review found 1756 papers, which was reduced to 30 after screening and relevance checks. Most coaching services were planned, as opposed to reactive, and targeted patients with complex needs who had one or more chronic disease. Several studies reported improvements in health behaviour, self-efficacy, health status and satisfaction with the service. More than one-third of the papers targeted vulnerable people and telephone coaching was found to be effective for these people. Conclusions. Telephone coaching for people with chronic conditions can improve health behaviour, self-efficacy and health status. This is especially true for vulnerable populations who had difficulty accessing health services. There is less evidence for improvements in quality of life and patient satisfaction with the service. The evidence for improvements in health service use was limited. This rapid scoping review found that telephone-based coaching can enhance the management of chronic disease, especially for vulnerable groups. Further work is needed to identify what models of telephone coaching are most effective according to patients' level of risk and co-morbidity. What is known about the topic? With the increasing prevalence of chronic diseases more demands are being made of limited health services and resources. Telephone health coaching for people with or at risk of chronic diseases is seen as a means of supporting people to manage their health and reducing the burden on the healthcare system. What does this paper add? Telephone coaching interventions were effective for vulnerable people with chronic disease(s). Often the vulnerable populations had worse control of their chronic condition at baseline and demonstrated the greatest improvement compared with those with better control at baseline. Planned (i.e. weekly or monthly telephone calls to support the patients with chronic disease) and unscripted telephone coaching interventions appear to be most effective for improving self-management skills in people from vulnerable groups: the planned telephone coaching services had the advantage of regular contact and helping people develop their skills over time, whereas the unscripted aspect allowed the coach to tailor support to the patient's individual needs What are the implications for practitioners? Telephone coaching is an effective means of supporting people with chronic diseases to manage their own health. Further work is needed to embed telephone coaching within existing services. Good linkages with the patient's general practitioner are important. This might be a regular report, updates via the patient e-health record, or provision for contact if a problem is identified or linking to the patient e-health record. [ABSTRACT FROM AUTHOR]

Background: In Australia, many community service program data collections developed over the last decade, including several for aged care programs, contain a statistical linkage key (SLK) to enable derivation of client-level data. In addition, a common SLK is now used in many collections to facilitate the statistical examination of cross-program use. In 2005, the Pathways in Aged Care (PIAC) cohort study was funded to create a linked aged care database using the common SLK to enable analysis of pathways through aged care services. Linkage using an SLK is commonly deterministic. The purpose of this paper is to describe an extended deterministic record linkage strategy for situations where there is a general person identifier (e.g. an SLK) and several additional variables suitable for data linkage. This approach can allow for variation in client information recorded on different databases., Methods: A stepwise deterministic record linkage algorithm was developed to link datasets using an SLK and several other variables. Three measures of likely match accuracy were used: the discriminating power of match key values, an estimated false match rate, and an estimated step-specific trade-off between true and false matches. The method was validated through examining link properties and clerical review of three samples of links., Results: The deterministic algorithm resulted in up to an 11% increase in links compared with simple deterministic matching using an SLK. The links identified are of high quality: validation samples showed that less than 0.5% of links were false positives, and very few matches were made using non-unique match information (0.01%). There was a high degree of consistency in the characteristics of linked events., Conclusions: The linkage strategy described in this paper has allowed the linking of multiple large aged care service datasets using a statistical linkage key while allowing for variation in its reporting. More widely, our deterministic algorithm, based on statistical properties of match keys, is a useful addition to the linker's toolkit. In particular, it may prove attractive when insufficient data are available for clerical review or follow-up, and the researcher has fewer options in relation to probabilistic linkage.

In various real-world applications, it is very useful mining unanticipated episodes where certain event patterns unexpectedly lead to outcomes, e.g., taking two medicines together sometimes causing an adverse reaction. These unanticipated episodes are usually unexpected and infrequent, which makes existing data mining techniques, mainly designed to find frequent patterns, ineffective. In this paper, we propose unexpected temporal association rules (UTARs) to describe them. To handle the unexpectedness, we introduce a new interestingness measure, residual-leverage, and develop a novel case-based exclusion technique for its calculation. Combining it with an event-oriented data preparation technique to handle the infrequency, we develop a new algorithm MUTARC to find pairwise UTARs. The MUTARC is applied to generate adverse drug reaction (ADR) signals from real-world healthcare administrative databases. It reliably shortlists not only six known ADRs, but also another ADR, flucloxacillin possibly causing hepatitis, which our algorithm designers and experiment runners have not known before the experiments. The MUTARC performs much more effectively than existing techniques. This paper clearly illustrates the great potential along the new direction of ADR signal generation from healthcare administrative databases.

Some serious radiation accidents have occurred around the world during the delivery of radiotherapy treatment. The regrettable incident in Panama clearly indicated the need for independent monitor unit (MU) verification. Indeed the International Atomic Energy Agency (IAEA), after investigating the incident, made specific recommendations for radiotherapy centres which included an independent monitor unit check for all treatments. Independent monitor unit verification is practiced in many radiotherapy centres in developed countries around the world. It is mandatory in USA but not yet in Australia. This paper describes development of an independent MU program, concentrating on the implementation of the Enhanced Dynamic Wedge (EDW) component. The difficult case of non centre of field (COF) calculation points under the EDW was studied in some detail. Results of a survey of Australasian centres regarding the use of independent MU check systems is also presented. The system was developed with reference to MU calculations made by Pinnacle 3D Radiotherapy Treatment Planning (RTP) system (ADAC - Philips) for 4MV, 6MV and 18MV X-ray beams used at the Newcastle Mater Misericordiae Hospital (NMMH) in the clinical environment. A small systematic error was detected in the equation used for the EDW calculations. Results indicate that COF equations may be used in the non COF situation with similar accuracy to that achieved with profile corrected methods. Further collaborative work with other centres is planned to extend these findings.

Problem: Fatigue affects a driver's ability to proceed safely. Driver-related fatigue and/or sleepiness are a significant cause of traffic accidents, which makes this an area of great socioeconomic concern. Monitoring physiological signals while driving provides the possibility of detecting and warning of fatigue. The aim of this paper is to describe an EEG-based fatigue countermeasure algorithm and to report its reliability., Method: Changes in all major EEG bands during fatigue were used to develop the algorithm for detecting different levels of fatigue., Results: The software was shown to be capable of detecting fatigue accurately in 10 subjects tested. The percentage of time the subjects were detected to be in a fatigue state was significantly different than the alert phase (P<.01)., Discussion: This is the first countermeasure software described that has shown to detect fatigue based on EEG changes in all frequency bands. Field research is required to evaluate the fatigue software in order to produce a robust and reliable fatigue countermeasure system., Impact on Industry: The development of the fatigue countermeasure algorithm forms the basis of a future fatigue countermeasure device. Implementation of electronic devices for fatigue detection is crucial for reducing fatigue-related road accidents and their associated costs.

This paper presents and discusses the results of basic source finding tests in three dimensions (using spectroscopic data cubes) with duchamp, the standard source finder for the Australian Square Kilometre Array Pathfinder. For this purpose, we generated different sets of unresolved and extended Hi model sources. These models were then fed into duchamp, using a range of different parameters and methods provided by the software. The main aim of the tests was to study the performance of duchamp on sources with different parameters and morphologies and assess the accuracy of duchamp's source parametrisation. Overall, we find duchamp to be a powerful source finder capable of reliably detecting sources down to low signal-to-noise ratios and accurately measuring their position and velocity. In the presence of noise in the data, duchamp's measurements of basic source parameters, such as spectral line width and integrated flux, are affected by systematic errors. These errors are a consequence of the effect of noise on the specific algorithms used by duchamp for measuring source parameters in combination with the fact that the software only takes into account pixels above a given flux threshold and hence misses part of the flux. In scientific applications of duchamp these systematic errors would have to be corrected for. Alternatively, duchamp could be used as a source finder only, and source parametrisation could be done in a second step using more sophisticated parametrisation algorithms. This paper discusses the results of basic source finding tests with the duchamp source finder on different source models. We find duchamp to be a powerful source finder, capable of reliably detecting sources down to low signal-to-noise ratios. duchamp's measurements of basic source parameters, however, are affected by systematic errors. [ABSTRACT FROM AUTHOR]

National voluntary reporting systems generate large volumes of clinical data pertinent to drug safety. Currently descriptive statistical techniques are used to assist in the detection of drug safety 'signals'. Australian data have been coded according to guidelines formulated almost 30 years ago and which have resulted in many drugs which are not associated with an adverse drug reaction or 'innocent bystander' drugs being recorded as 'suspected' in individual reports. In this paper we explore the application of an iterative probability filtering algorithm titled 'PROFILE'. This serves to identify the 'signals' and remove the 'innocent bystander' drugs, thus providing a clearer view of the drugs most likely to have caused the reactions. Reaction terms analysed include neutropenia, agranulocytosis, hypotension, hypertension, myocardial infarction, neuroleptic malignant syndrome, and rectal haemorrhage. In this version of PROFILE, Fishers exact test has been used as the statistical tool but other methods could be used in future. Advantages and limitations of the method and its assumptions are discussed together with the rationale underlying the method and suggestions for further enhancements.

Performance evaluation of multi-target tracking algorithms is of great practical importance in the design, parameter optimization and comparison of tracking systems. The goal of performance evaluation is to measure the distance between two sets of tracks: the ground truth tracks and the set of estimated tracks. This paper proposes a mathematically rigorous metric for this purpose. The basis of the proposed distance measure is the recently formulated consistent metric for performance evaluation of multi-target filters, referred to as the OSPA metric. Multi-target filters sequentially estimate the number of targets and their position in the state space. The OSPA metric is therefore defined on the space of finite sets of vectors. The distinction between filtering and tracking is that tracking algorithms output tracks and a track represents a labeled temporal sequence of state estimates, associated with the same target. The metric proposed in this paper is therefore defined on the space of finite sets of tracks and incorporates the labeling error. Numerical examples demonstrate that the proposed metric behaves in a manner consistent with our expectations. [ABSTRACT FROM PUBLISHER]

The aim of this paper was to test the capabilities of the Sentinel-1 radar data in downscaling Soil Moisture Active Passive (SMAP) radiometer data for high-resolution soil moisture estimation. Three different active–passive downscaling algorithms, including the brightness temperature-based downscaling algorithm (BTBDA), the soil moisture-based downscaling algorithm (SMBDA), and a change detection method (CDM), were analyzed using pairs of Sentinel-1 active and SMAP passive observations collected over a semiarid landscape in southeastern Australia from May 2015 to May 2016. While these algorithms have been tested previously, this is the first study to evaluate the three algorithms using real Sentinel-1 radar and SMAP radiometer data. The SMAP passive observations were disaggregated to 9-, 3-, and 1-km scales and then compared with ground soil moisture measurements. The results suggest that the root-mean-square error (RMSE) in downscaled soil moisture at 9-km resolution was 0.057, 0.056, and 0.067 cm3/cm3 for the BTBDA, SMBDA, and CDM, respectively. The accuracy of downscaling methods was generally decreased when applied at the finer spatial resolution. The SMBDA had overall better performance in terms of correctly detecting the soil moisture pattern and relatively lower RMSE values, and is, therefore, recommended for the combined Sentinel-1 radar and SMAP radiometer setup for soil moisture monitoring. The influence of incidence angle normalization of Sentinel-1 SAR data on downscaled soil moisture was also investigated and found to be minimal. [ABSTRACT FROM AUTHOR]

Modelling workflow for BRF-LSTM and BRF-GRU predictive models. [Display omitted] • Deep learning hybrid predictive model (BRF-LSTM) is built for streamflow forecasting. • Climate indices, rainfall and periodicity are factored for accurate predictions. • Adopts feature algorithm (i.e. Boruta) with long short-term memory and gated recurrent unit. • Six gauging sites tested; BRF-LSTM yields > 98% errors in ± 0.015 m, ~1.30% relative error. • The proposed model is useful for hydrological and strategic water resources planning. Long-term forecasting of any hydrologic phenomena is essential for strategic environmental planning, hydrologic and other forms of structural design, agriculture, and water resources management. Climate mode indices, utilising machine learning methods, are frequently considered as predictor variables in order to forecast several different hydrological variables. In this study, a feature selection algorithm based on two different deep learning models, i.e., long short-term memory and a gated recurrent unit, is applied to improve the forecasting capability of streamflow water levels at six gauging stations in the Murray Darling Basin of Australia. This paper therefore aggregates the significant antecedent lag memory of climate mode indices, rainfall, and the monthly factor based on the periodicity as the predictor variables to attain significantly accurate stream water level forecasts. This novel method identifies an improved relationship between the stream water level and climate mode indices through the aggregation of the significant lagged datasets capturing the historical features to predict the future streamflow water level. The boruta feature selection algorithm (BRF) was then applied in a two phase process before and after attaining the significant lagged inputs to screen the optimum predictor variables. The merits of the forecast models were evaluated through different performance evaluation criteria. The results show that the accumulated significant lagged inputs based on climate mode indices, along with the rainfall and periodicity factors are seen to provide improved forecasting of the SWL over the non-BRF deep learning approaches where no prior feature selection was applied. The hybrid LSTM method (i.e., BRF-LSTM model) achieved a unique advantage in terms of SWL forecasting, particularly attaining over 98% of the predictive errors lying within a band of +/-0.015 m with relatively low relative errors (RRMSE ≈1.30% and RMAE ≈ 0.882%), outperforming all of the benchmark models. It is also found that the periodicity factor has a potential influence on the accuracy of the forecast models for the four monitored study stations. This study concludes that the newly developed hybrid deep learning approaches, coupled with the BRF feature selection, provide improved forecasting performance. The hybrid approach developed in this paper can therefore be used to provide a strong provide predictive response algorithm for the hydrological variables that were influenced by the low-frequency variability of the climate model indices in respect to streamflow water level. [ABSTRACT FROM AUTHOR]

Grass establishment on railway embankment steep slopes for erosion control in Central Queensland, Australia, is aided by drip lateral irrigation systems. The effective field values of the lateral parameters may be different from the manufacturer supplied ones due to manufacturing variations of the emitters, environmental factors, and water quality. This paper has provided a methodology for estimating drip lateral effective parameter values under field conditions. The hydraulic model takes into account the velocity head change and a proper selection of the friction coefficient formula based on the Reynolds number. Fittings and emitter insertion head losses were incorporated into the hydraulic model. Pressure measurements at some locations within the irrigation system, and the inlet discharges, were used to calibrate the lateral parameters in a statistical framework that allows estimation of parameter uncertainties using the Metropolis algorithm. It is observed that the manufacturer’s supplied parameters were significantly different from the calibrated ones, underestimating pressures within the irrigation system for a given inlet discharge, stressing the need for field testing. The parameter posterior distributions were found to be unimodal and nearly normally distributed. The emitter head loss coefficient distribution being very significant suggests the need to incorporate it into the hydraulic modeling. Although the example given in this paper relates to steep slopes, the methodologies are general and can be applied to any use of drip laterals. [ABSTRACT FROM AUTHOR]

Background: The Pediatric Quality of Life Inventory™ 4.0 Short Form 15 Generic Core Scales (hereafter the PedsQL) and the Child Health Utility-9 Dimensions (CHU9D) are two generic instruments designed to measure health-related quality of life in children and adolescents in the general population and paediatric patient groups living with specific health conditions. Although the PedsQL is widely used among paediatric patient populations, presently it is not possible to directly use the scores from the instrument to calculate quality-adjusted life-years (QALYs) for application in economic evaluation because it produces summary scores which are not preference-based.Objective: This paper examines different econometric mapping techniques for estimating CHU9D utility scores from the PedsQL for the purpose of calculating QALYs for cost-utility analysis.Methods: The PedsQL and the CHU9D were completed by a community sample of 755 Australian adolescents aged 15-17 years. Seven regression models were estimated: ordinary least squares estimator, generalised linear model, robust MM estimator, multivariate factorial polynomial estimator, beta-binomial estimator, finite mixture model and multinomial logistic model. The mean absolute error (MAE) and the mean squared error (MSE) were used to assess predictive ability of the models.Results: The MM estimator with stepwise-selected PedsQL dimension scores as explanatory variables had the best predictive accuracy using MAE and the equivalent beta-binomial model had the best predictive accuracy using MSE.Conclusions: Our mapping algorithm facilitates the estimation of health-state utilities for use within economic evaluations where only PedsQL data is available and is suitable for use in community-based adolescents aged 15-17 years. Applicability of the algorithm in younger populations should be assessed in further research. [ABSTRACT FROM AUTHOR]

Child abuse commonly occurs within the context of multiple risk factors, in particular parental mental health and/or drug and alcohol problems. As no one agency can address all these factors, inter‐agency collaboration is paramount to the protection of vulnerable children, especially in families with a complex array of problems. This paper outlines a range of recommendations to improve collaboration between child protection workers and mental health/drug & alcohol (MH/D&A) clinicians from the perspective of Keep Them Safe‐Whole Family Team (KTS‐WFT) clinicians. Taking referrals from child protection, the KTS‐WFT offers interventions to families with parental MH/D&A problems where there are child protection concerns. As part of a larger evaluation of a KTS‐WFT site, 10 KTS‐WFT clinicians participated in in‐depth interviews. Analysis of the interviews identified collaboration with child protection as a primary theme. Participants reported a number of barriers to effective collaboration; in particular, participants reported challenges with information sharing and confidentiality, inconsistency in terms of the level and style of collaboration, tensions between the different theoretical paradigms that underpin practice for MH/D&A clinicians vs. child protection workers, and insufficient clarity around processes and expectations. Consistent with the identified barriers, primary recommendations to improve collaboration were to improve information sharing, overcome silo ways of thinking, manage risk together more consistently, and develop consistent processes and expectations. [ABSTRACT FROM AUTHOR]

Background and Objectives: Current practices and available resources for nutrition therapy in paediatric intensive care units (PICUs) in the Asia Pacific-Middle East region are expected to differ from western countries. Existing guidelines for nutrition management in critically ill children may not be directly applicable in this region. This paper outlines consensus statements developed by the Asia Pacific-Middle East Consensus Working Group on Nutrition Therapy in the Paediatric Critical Care Environment. Challenges and recommendations unique to the region are described.Methods and Study Design: Following a systematic literature search from 2004-2014, consensus statements were developed for key areas of nutrient delivery in the PICU. This review focused on evidence applicable to the Asia Pacific-Middle East region. Quality of evidence and strength of recommendations were rated according to the Grading of Recommendation Assessment, Development and Evaluation approach.Results: Enteral nutrition (EN) is the preferred mode of nutritional support. Feeding algorithms that optimize EN should be encouraged and must include: assessment and monitoring of nutritional status, selection of feeding route, time to initiate and advance EN, management strategies for EN intolerance and indications for using parenteral nutrition (PN). Despite heterogeneity in nutritional status of patients, availability of resources and diversity of cultures, PICUs in the region should consider involvement of dieticians and/or nutritional support teams.Conclusions: Robust evidence for several aspects of optimal nutrition therapy in PICUs is lacking. Nutritional assessment must be implemented to document prevalence and impact of malnutrition. Nutritional support must be given greater priority in PICUs, with particular emphasis in optimizing EN delivery. [ABSTRACT FROM AUTHOR]

In contrast to the recent proliferation of studies incorporating ordinal methods to generate health state values from adults, to date relatively few studies have utilised ordinal methods to generate health state values from adolescents. This paper reports upon a study to apply profile case best worst scaling methods to derive a new adolescent specific scoring algorithm for the Child Health Utility 9D (CHU9D), a generic preference based instrument that has been specifically designed for the estimation of quality adjusted life years for the economic evaluation of health care treatment and preventive programs targeted at young people. A survey was developed for administration in an on-line format in which consenting community based Australian adolescents aged 11–17 years (N = 1982) indicated the best and worst features of a series of 10 health states derived from the CHU9D descriptive system. The data were analyzed using latent class conditional logit models to estimate values (part worth utilities) for each level of the nine attributes relating to the CHU9D. A marginal utility matrix was then estimated to generate an adolescent-specific scoring algorithm on the full health = 1 and dead = 0 scale required for the calculation of QALYs. It was evident that different decision processes were being used in the best and worst choices. Whilst respondents appeared readily able to choose ‘best’ attribute levels for the CHU9D health states, a large amount of random variability and indeed different decision rules were evident for the choice of ‘worst’ attribute levels, to the extent that the best and worst data should not be pooled from the statistical perspective. The optimal adolescent-specific scoring algorithm was therefore derived using data obtained from the best choices only. The study provides important insights into the use of profile case best worst scaling methods to generate health state values with adolescent populations. [ABSTRACT FROM AUTHOR]

Data from the Australasian Bone Marrow Transplant Recipient Registry show a steady increase in the number of allogeneic haemopoietic stem cell transplantations (HSCT) performed annually in Australia and New Zealand. In 2012, 629 allogeneic HSCT were performed. Allogeneic HSCT is associated with numerous potential complications, including chronic graft-versus-host disease (cGVHD). The oral cavity is one of the most frequent sites affected by cGvHD, often leading to significant disability and reduced quality of life. Management strategies are often complex, of variable efficacy and influenced by the availability of various therapeutic agents, access to compounding pharmacies and associated costs. This paper summarises the current status of allogeneic HSCT in Australia and New Zealand with a focus on oral cGvHD and the associated challenges in its management. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of a carbon price on wholesale electricity prices and carbon-pass-through rates in the states comprising the Australian National Electricity Market (NEM). The methodology utilize an agent-based model, which contains many features salient to the NEM including intra-state and inter-state transmission branches, regional location of generators and load centres and accommodation of unit commitment features. The model uses a Direct Current Optimal Power Flow (DC OPF) algorithm to determine optimal dispatch of generation plant, power flows on transmission branches and wholesale prices. The results include sensitivity analysis of carbon prices on wholesale prices and carbon pass-through rates for different states within the NEM. [ABSTRACT FROM AUTHOR]

The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957–2008 and the monthly EDI predicted over the period 2009–2011. The predictive variables for the ELM model were the rainfall and mean, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed data-driven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg–Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0.277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0.578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning algorithms tested, the ELM was the more expeditious tool for prediction of drought and its related properties. [ABSTRACT FROM AUTHOR]

The Australian Informatics Competition (AIC) is the entry-level Informatics competition in Australia. It is a pen-and-paper competition, requiring no programming experience. Most of the questions focus on testing algorithmic ability, with others testing students' ability to analyse algorithms, apply rules and use logic to solve problems. The algorithmic questions include standard algorithms such as breadth first search, dynamic programming and two person games, as well as ad-hoc algorithms specific to a particular scenario. Currently about 7,000 students enter the competition, but there are plans to expand it with on-line entries. The AIC is becoming more relevant with the introduction of algorithmic thinking as a component of the Digital Technologies strand of the Australian Curriculum. [ABSTRACT FROM AUTHOR]