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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 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]

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]

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]

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]

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]

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]

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. [ABSTRACT FROM AUTHOR]

Abstract: 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. [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: 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]

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]

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]

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]

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 crossprogram 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. [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]

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]

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]

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]

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]

Abstract: Fire danger indices are used by fire management agencies to assess fire weather conditions and issue public warnings. The most widely used fire danger indices in Australia are the McArthur Fire Forest Danger Index and the Grassland Fire Danger Index. These indices are calculated at weather stations using measurements of weather variables and fuel information. For a vast country like Australia when assessing the risk of severe fire weather events, it is also important to calculate the spatial distribution of these indices considering the extreme tail of the distribution. The spatial distribution of one of the fire weather danger indices regularly used in Australia is presented in this paper. In particular, we present the spatial distribution of the long-term tendency of extreme values of the McArthur Forest Fire Danger Index (FFDI). This indicator of fire weather conditions was assessed by calculating the return period of its extreme values by fitting extreme value distributions to data sets of FFDI at 78 recording stations around Australia. The spatial distribution of these return periods was obtained by using spatial interpolation algorithms with the recording stations measurements. Two conventional and two new algorithms based on machine-learning techniques were tested. This study shows that the best interpolation results for the FFDI can be obtained by using a combination of random forest and inverse distance weighting interpolation algorithms. The spatial distribution of the seasonal FFDI return period shows that the highest FFDI over large parts of southern Australia occurs during the summer months whilst in northern Australia it occurs in spring. The results also show that the FFDI in eastern Australia, the most populated region of the country, is higher inland than in the coastal areas particularly during spring and summer. [Copyright &y& Elsevier]

Background and purpose Joint Replacement Registries play a significant role in monitoring arthroplasty outcomes by publishing data on survivorship of individual prostheses or combinations of prostheses. The difference in outcomes can be device- or non-device-related, and these factors can be analyzed separately. Although registry data indicate that most prostheses have similar outcomes, some have a higher than anticipated rate of revision when compared to all other prostheses in their class. This report outlines how the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) has developed a method to report prostheses with a higher than expected rate of revision. These are referred to as 'outlier' prostheses. Material and methods Since 2004, the AOANJRR has developed a standardized process for identifying outliers. This is based on a 3-stage process consisting of an automated algorithm, an extensive analysis of individual prostheses or combinations by registry staff, and finally a meeting involving a panel from the Australian Orthopaedic Association Arthroplasty Society. Outlier prostheses are listed in the Annual Report as (1) identified but no longer used in Australia, (2) those that have been re-identified and that are still used, and (3) those that are being identified for the first time. Results 78 prostheses or prosthesis combinations have been identified as being outliers using this approach (AOANJRR 2011 Annual Report). In addition, 5 conventional hip prostheses were initially identified, but after further analysis no longer met the defined criteria. 1 resurfacing hip prosthesis was initially identified, subsequently removed from the list, and then re-identified the following year when further data were available. All unicompartmental and primary total knee prostheses identified as having a higher than expected rate of revision have continued to be re-identified. Interpretation It is important that registries use a transparent and accountable process to identify an outlier prosthesis. This paper describes the development, implementation, assessment, and impact of the approach used by the Australian Registry. [ABSTRACT FROM AUTHOR]

Summary: This paper describes an algorithm for disaggregating daily rainfall into sub-daily rainfall ‘fragments’ (fine-resolution rainfall sequences) under a future, warmer climate. The algorithm uses a combined generalised additive model (GAM) and method of fragments (MoFs) framework to resample sub-daily rainfall fragments from the historical record conditional on daily rainfall amount and a range of atmospheric covariates. The rationale is that as the atmosphere warms, future rainfall patterns will be more reflective of historical rainfall patterns corresponding to warmer days at the same location, or to locations which have an atmospheric profile more reflective of expected future climate. It was found that the daily to sub-daily scaling relationship varied significantly by season and by location, with rainfall patterns on warmer seasons or at warmer locations typically showing more intense rainfall occurring over shorter periods compared with cooler seasons and stations. Importantly, by regressing against atmospheric covariates such as temperature, this effect was substantially reduced, suggesting that the approach may also be valid when extrapolating to a future climate. The GAM–MoF algorithm was then applied to nine stations around Australia, with the results showing that relative to the daily rainfall amount, the maximum intensity of short duration rainfall increased by between 4.1% and 13.4% per degree change in temperature for the maximum six minute burst, and by between 3.1% and 6.8% for the maximum 1h burst. The fraction of each wet day with no rainfall also increased by between 1.5% and 3.5%. This highlights that a significant proportion of the change to the distribution of rainfall is likely to occur at sub-daily timescales, with important implications for many hydrological systems. [Copyright &y& Elsevier]

Abstract: Fire detection using satellites is an important source of information for fire management, ecological studies and emission estimates. However, little is known about the minimum sizes of fires that are being detected. This paper presents an approach using fire radiative power estimated from MODIS satellite data to determine the detection threshold for fire-prone savannas in Northern Australia. The results indicate that fires with an active flaming area 100–300m2 can be detected in the study region. It is also shown that the algorithm is slightly more sensitive at night. As expected the detection threshold shows strong view angle dependence. While this study has been undertaken in the savannas of Northern Australia, the results should be transferable to other savanna regions worldwide and other areas where fires are not obscured by a dense tree canopy. [Copyright &y& Elsevier]

Abstract: This paper provides a novel Exceptional Object Analysis for Finding Rare Environmental Events (EOAFREE). The major contribution of our EOAFREE method is that it proposes a general Improved Exceptional Object Analysis based on Noises (IEOAN) algorithm to efficiently detect and rank exceptional objects. Our IEOAN algorithm is more general than already known outlier detection algorithms to find exceptional objects that may be not on the border; and experimental study shows that our IEOAN algorithm is far more efficient than directly recursively using already known clustering algorithms that may not force every data instance to belong to a cluster to detect rare events. Another contribution is that it provides an approach to preprocess heterogeneous real world data through exploring domain knowledge, based on which it defines changes instead of the water data value itself as the input of the IEOAN algorithm to remove the geographical differences between any two sites and the temporal differences between any two years. The effectiveness of our EOAFREE method is demonstrated by a real world application – that is, to detect water pollution events from the water quality datasets of 93 sites distributed in 10 river basins in Victoria, Australia between 1975 and 2010. [Copyright &y& Elsevier]

The spectral line datacubes obtained from the Square Kilometre Array (SKA) and its precursors, such as the Australian SKA Pathfinder (ASKAP), will be sufficiently large to necessitate automated detection and parametrisation of sources. Matched filtering is widely acknowledged as the best possible method for the automated detection of sources. This paper presents the Characterised Noise Hi (cnhi) source finder, which employs a novel implementation of matched filtering. This implementation is optimised for the 3-D nature of the Hi spectral line observations of the planned Wide-field ASKAP Legacy L-band All-sky Blind surveY (WALLABY). The cnhi source finder also employs a novel sparse representation of 3-D objects, with a high compression rate, to implement the Lutz one-pass algorithm on datacubes that are too large to process in a single pass. WALLABY will use ASKAP's phenomenal 30 square degree field of view to image ~70% of the sky. It is expected that WALLABY will find 500 000 Hi galaxies out to z ~ 0.2. The Characterised Noise Hi source finder uses a novel approach to find sources in Hi spectral line datacubes using matched filtering. The concept, initial implementation and testing are presented here. [ABSTRACT FROM AUTHOR]

ABSTRACT We present a detailed analysis of four of the most widely used radio source-finding packages in radio astronomy, and a program being developed for the Australian Square Kilometer Array Pathfinder telescope. The four packages: sextractor, sfind, imsad and selavy are shown to produce source catalogues with high completeness and reliability. In this paper we analyse the small fraction (∼1 per cent) of cases in which these packages do not perform well. This small fraction of sources will be of concern for the next generation of radio surveys which will produce many thousands of sources on a daily basis, in particular for blind radio transients surveys. From our analysis we identify the ways in which the underlying source-finding algorithms fail. We demonstrate a new source-finding algorithm aegean, based on the application of a Laplacian kernel, which can avoid these problems and can produce complete and reliable source catalogues for the next generation of radio surveys. [ABSTRACT FROM AUTHOR]

Abstract: The prospect that decision support systems (DSS) can help farmers adjust their management to suit seasonal conditions by putting scientific knowledge and rational risk management algorithms at farmers’ fingertips continues to challenge the science and extension community. A number of reviews of agricultural DSS have called for a re-appraisal of the field and for the need to reflect on past mistakes and to learn from social and management theory. The objective of this paper was to investigate whether there is an emerging consensus, among stakeholders in DSS for Australian agriculture, about the lessons learned from past experience with DSS tools. This investigation was conducted in three parts. The first part was a distillation of suggestions for best practice from the relevant literature. The second part was a reflection on what the champions of five current DSS development and delivery efforts in Australia learned from their recent efforts. The third part tested the level of support for the combined findings from the first and second approaches by surveying 23 stakeholders in the research, development, delivery and funding of DSS. The key propositions relating to best practice that were supported by the survey, listed according to the strength of support, were: 1. It is essential to have a plan for delivery of the DSS beyond the initial funding period. 2. DSS need to be embedded in a support network consisting of farmers, consultants and researchers. 3. DSS development requires the commitment of a critical mass of appropriately skilled people. 4. A DSS should aim to educate farmers’ intuition rather than replace it with optimised recommendations. 5. A DSS should enable users to experiment with options that satisfy their needs rather than attempt to present ‘optimised’ solutions. 6. DSS tools stand on the quality and authority of their underlying science and require ongoing improvement, testing and validation. 7. DSS development should not commence unless it is backed by marketing information and a plan for delivery of the DSS beyond the initial funding period. While the DSS stakeholders supported the proposition that it is essential to have a plan for delivery of a DSS beyond the funding period, the majority resisted the notion of DSS development being market-driven and especially commercial delivery of DSS. We argue that since public funding of the delivery of DSS for farmers’ management of climate risk is highly unlikely, reaping the benefits of lessons learned from past efforts will require that DSS stakeholders change their perception of the commercial delivery model or find an alternative way to fund the delivery of DSS beyond the R&D phase. [Copyright &y& Elsevier]

Abstract: Accurate high-resolution soil moisture data are needed for a range of agricultural and hydrologic activities. To improve the spatial resolution of ∼40km resolution passive microwave-derived soil moisture, a methodology based on 1km resolution MODIS (MODerate resolution Imaging Spectroradiometer) red, near-infrared and thermal-infrared data has been implemented at 4km resolution. The three components of that method are (i) fractional vegetation cover, (ii) soil evaporative efficiency (defined as the ratio of actual to potential evaporation) and (iii) a downscaling relationship. In this paper, 36 different disaggregation algorithms are built from 3 fractional vegetation cover formulations, 3 soil evaporative efficiency models, and 4 downscaling relationships. All algorithms differ with regard to the representation of the nonlinear relationship between microwave-derived soil moisture and optical-derived soil evaporative efficiency. Airborne L-band data collected over an Australian agricultural area are used to both generate ∼40km resolution microwave pixels and verify disaggregation results at 4km resolution. Among the 36 disaggregation algorithms, one is identified as being more robust (insensitive to soil, vegetation and atmospheric variables) than the others with a mean slope between MODIS-disaggregated and L-band derived soil moisture of 0.94. The robustness of that algorithm is notably assessed by comparing the disaggregation results obtained using composited (averaged) Terra and Aqua MODIS data, and using data from Terra and Aqua separately. The error on disaggregated soil moisture is systematically reduced by compositing daily Terra and Aqua data with an error of 0.012vol./vol. [ABSTRACT FROM AUTHOR]

This paper presents and evaluates artificial neural network (ANN) models used for macrocell path-loss prediction. Measurement data obtained by utilizing the IS-95 pilot signal from a commercial code-division multiple-access (CDMA) mobile network in rural Australia are used to train and evaluate the models. A simple neuron model and feed-forward networks with different numbers of hidden layers and neurons are evaluated regarding their training time, prediction accuracy, and generalization properties. Furthermore, different backpropagation training algorithms, such as gradient descent and Levenberg--Marquardt, are evaluated. The artificial neural network inputs are chosen to be distance to base station, parameters easily obtained from terrain path profiles, land usage, and vegetation type and density near the receiving antenna. The path-loss prediction results obtained by using the ANN models are evaluated against different versions of the semi-terrain based propagation model Recommendation ITU-R P.1546 and the Okumura--Hata model. The statistical analysis shows that a non-complex ANN model performs very well compared with traditional propagation models with regard to prediction accuracy, complexity, and prediction time. The average ANN prediction results were 1) maximum error: 22 dB; 2) mean error: 0 dB; and 3) standard deviation: 7 dB. A multilayered feedforward network trained using the standard backpropagation algorithm was compared with a neuron model trained using the Levenberg--Marquardt algorithm. It was found that the training time decreases from 150 000 to 10 iterations, while the prediction accuracy is maintained. [ABSTRACT FROM AUTHOR]

Abstract: With the introduction of restructuring into the electric power industry, the price of electricity has become the focus of all activities in the power market. Electricity price forecast is key information for electricity market managers and participants. However, electricity price is a complex signal due to its non-linear, non-stationary, and time variant behavior. In spite of performed research in this area, more accurate and robust price forecast methods are still required. In this paper, a new forecast strategy is proposed for day-ahead price forecasting of electricity markets. Our forecast strategy is composed of a new two stage feature selection technique and cascaded neural networks. The proposed feature selection technique comprises modified Relief algorithm for the first stage and correlation analysis for the second stage. The modified Relief algorithm selects candidate inputs with maximum relevancy with the target variable. Then among the selected candidates, the correlation analysis eliminates redundant inputs. Selected features by the two stage feature selection technique are used for the forecast engine, which is composed of 24 consecutive forecasters. Each of these 24 forecasters is a neural network allocated to predict the price of 1h of the next day. The whole proposed forecast strategy is examined on the Spanish and Australia’s National Electricity Markets Management Company (NEMMCO) and compared with some of the most recent price forecast methods. [Copyright &y& Elsevier]

Transmission system structure has an essential effect on the reliability of the power system and electricity market performance, especially when producers bid strategically. As part of on-going research on the design of a robust algorithm for expansion planning of the transmission grid in the Australian electricity market, this paper presents a framework which addresses: (1) the security of power delivery to the load points of the transmission system in case of single line outages; (2) the minimization of transmission system lost load; (3) an efficient electricity market for market participants; (4) construction and maintenance costs of transmission augmentation options; and (5) operation efficiency of the transmission grid. The suggested algorithm benefits from the dynamic programming and sensitivity analysis approaches along with the aggregation method in its multi-criteria decision-making to locate the optimum configuration of a future transmission system. A set of indices, which account for impacts of the augmentation options of the transmission grid on five aforementioned reliability and market criteria, are proposed and used in the optimum framework for expansion planning of the transmission grid. Although the methodology is promising for expansion planning of the transmission system, considering the sensitivity analysis concept employed, the proposed methodology would be suitable to detect the sensitive areas of the transmission system to be expanded. The tool would be very useful in the case of large scale power systems for a smart reduction of the transmission expansion options. The proposed methodology has been applied to a 6-bus and a modified IEEE 30-bus test system to show the effectiveness of the sensitivity-based algorithm. [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]

Monitoring of surface soil moisture (SM) through microwave radiometry typically relies on the inversion of a radiative transfer model. Conventional inversion algorithms require proper calibration of surface roughness and radiometric parameters of the overlying canopy, including the scattering albedo and optical depth. However, uncertainty in global characterization of these parameters is one of the main sources of error in satellite SM retrievals. To cope with this uncertainty, this paper presents a new algorithm, called "temporal polarization ratio algorithm" (TPRA), that enables retrieval of SM independent of surface roughness and vegetation parameters. This approach uses the temporal differences of polarized emissivity observations assuming that the surface roughness and vegetation parameters are invariant over a window of time. Unlike the classical dual channel algorithms (DCA), TPRA is not only free of surface calibration but also robust to systematical errors arising from surface soil temperature errors. One caveat is that the algorithm is unable to retrieve SM when surface emissivity does not change appreciably in time. The performance of TPRA is evaluated through several controlled numerical experiments and validated using the Soil Moisture Active Passive (SMAP) satellite retrievals as well as in-situ SM measurements over Australia. The results show improved SM retrievals compared with the DCA and SMAP official products as long as the surface emissivity changes sufficiently over the retrieval time period. • A new algorithm is designed for the retrieval of soil moisture at L-band. • Soil moisture retrievals are independent of surface roughness. • Soil moisture retrievals are independent of vegetation parameters. • Soil moisture retrievals are robust to systematical errors in soil temperature. [ABSTRACT FROM AUTHOR]

Study Objective: Continuous Positive Airway Pressure (CPAP) is considered to be the gold standard treatment for obstructive sleep apnoea (OSA). CPAP monitoring systems allow tracking of patient CPAP adherence and treatment efficacy, by measuring residual sleep-disordered breathing, hours of CPAP use, and mask leak etc. The American Thoracic Society (ATS) published a position paper in 2013 highlighting issues of interpreting CPAP data such as a lack of consistency between CPAP manufacturers data algorithms, legal implications of CPAP data and implications for CPAP adherence. This paper extends on this work by investigating these issues in an Australasian context.Method: A review of current literature on CPAP monitoring systems, privacy and security of CPAP data for major Australasian CPAP providers, and CPAP adherence was undertaken. A legal review was also commissioned for issues related to privacy and security of CPAP data.Results: CPAP manufacturers' utilize different algorithms for respiratory event detection and clinicians need to be aware the implications for interpreting CPAP data. Australasian CPAP manufacturers have created security/privacy policies with the intent to follow relevant legislation to protect patients' CPAP data, however they do need to be constantly reviewed and updated to avoid data breaches and changes to agreements. No guarantees can be provided by the Australasian Sleep Association on CPAP manufacturers' compliance with these policies and there is the potential for some degree of liability for physicians and CPAP providers associated with CPAP data. Lastly, providing patients with feedback on their CPAP usage and OSA management appears to have positive influence CPAP adherence.Conclusions: CPAP data provides many opportunities to increase OSA patient care and to help patients self-manage this chronic condition. However, issues relating to lack of standardization of CPAP parameters, privacy, security, and legal implications will need to be managed in this changing technologic and clinical environment. [ABSTRACT FROM AUTHOR]

This paper aims to assess the performance of generalized aboveground biomass (AGB) allometric models in the Australian context by investigating the correlation between the AGB estimates and the lidar-based individual tree parameters. A hybrid tree segmentation algorithm is proposed to segment an airborne lidar point cloud into individual trees. Although the diameter at breast height (DBH) of a tree is a crucial parameter for the AGB estimation, a typical airborne lidar data contains only a few points representing partial DBH, hence a localized DBH regression model is proposed. Principal component analysis is applied to examine the multicollinearity in the input variables and ridge regression is applied to remove the less important variables. Four machine learning techniques, namely random forest, support vector regression, multilayer perceptron and radial basis function, are applied to generate AGB regression models. The qualities of the calibrated AGB models are assessed by calculating the adjusted-coefficient-of-determination, leave-one-out cross-validation, the Akaike information criterion, normalized-mean-square-error and the model efficiency index. The test results indicate that the random forest-based AGB model outperforms other machine learning techniques. It is concluded that, if the environmental conditions of tree samples resemble the study region, the generalized AGB allometric model would perform well with the tree samples regardless of their geographical context. [ABSTRACT FROM AUTHOR]