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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]

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]

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]

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]

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]

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]

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]

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]

Background: Nurses working in intensive care units make autonomous decisions to manage high‐risk vasoactive medications in critically ill patients. Noradrenaline (norepinephrine) is a vasoactive medication commonly administered to patients in intensive care units. The influence of unit culture and environment on nurse‐decision‐making on noradrenaline (norepinephrine) management is unknown. Aims: The study aimed to investigate nurses' perceptions of the impact of interpersonal interactions, socialisation, and the intensive care environment on decision‐making when managing noradrenaline (norepinephrine). Materials & Methods: An exploratory qualitative study applied thematic analysis to focus group data. A purposive sample of nineteen nurses participated in four focus groups at two intensive care units in Melbourne, Australia, from March to June 2021. The COREQ checklist was used to guide study development and no patients or members of the public were involved in focus groups. Results: Three themes were generated from the researcher's interaction with data, Nursing and Medications; Culture and Decision‐making; and a Safe Practice Environment. Nurses reported decision‐making challenges associated with learning to manage noradrenaline (norepinephrine) early in their intensive care career and discussed feelings of isolation due to staffing resources, and the configuration of the intensive care environment. Nurses developed titration and weaning strategies to support decision‐making in the absence of evidence‐based algorithms. Discussion: Empathetic patient allocation early in nurses' intensive care careers facilitated a safer learning environment, and reduced isolation inherent in single room intensive care units. Nurses developed and used titration and weaning strategies, often learnt from other clinicians to manage practice uncertainty. Conclusions: Management of noradrenaline (norepinephrine) is core business for intensive care nurses worldwide. Development of titration and weaning strategies by nurses indicated unmet need for guidelines to support decision‐making. Identifying contextual elements that impact nurse management of high‐risk medications can guide development of environments, resources and policies that support nurse decision‐making, and reduce nurse anxiety and disempowerment. [ABSTRACT FROM AUTHOR]

This study determined stroke and movement accelerometry metrics from a wearable sensor and compared between court surface (grass vs. hard) and match outcome (win vs. loss) during competitive tennis match-play. Eight junior high-performance tennis players wore a trunk-mounted global positioning system, with in-built accelerometer, magnetometer, and gyroscope during singles matches on hard and grass courts. The manufacturer software calculated accelerometer-derived total player load (tPL). A prototype algorithm classified forehands, backhands, serves, and "other" strokes, thereby calculating stroke PL (sPL) from individual strokes. Movement PL (mPL) was calculated as the difference between tPL and sPL, with all metrics reported as absolute and relative (min-1, %, and ·stroke). Analysis of accelerometer load and stroke count metrics was performed through a two-way (surface [grass vs. hard] × match outcome [win vs. loss]) analysis of variance (p < 0.05) and effect sizes (Cohen's d). No interaction effects for surface and match outcome existed for absolute tPL, mPL, and sPL (p > 0.05). Increased mPL% featured on grass courts, whereas sPL% was increased on hard courts (p = 0.04, d = 1.18[0.31-2.02]). Elevated sPL·min-1 existed on hard courts (p = 0.04, d = 1.19[0.32-2.04]), but no differences in tPL·min-1 and mPL·min-1 were evident for surface or outcome (p > 0.05). Relative forehand sPL (FH-sPL·min-1) was higher on hard courts (p = 0.03, d = 1.18[0.31-2.02]) alongside higher forehand counts (p = 0.01, d = 1.29[0.40-2.14]). Hitting demands are heightened on hard courts from increased sPL and counts. Conversely, increased mPL% on grass courts likely reflects the specific movement demands from point-play. Physical preparation strategies during training blocks can be tailored toward movement or hitting loads to suit competitive surfaces. [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]

Background: The Northern Territory (NT) of Australia is currently experiencing a syphilis epidemic. Neurosyphilis is commonly considered in the differential diagnosis for patients presenting with neurologic conditions such as dementia and stroke in the NT. Aims: To explore the local epidemiologic, diagnostic and treatment complexities of neurosyphilis in the NT and produce a guideline for clinical practice. Methods: A database search was undertaken and local and global neurosyphilis guidelines were analysed. A guideline was created based on findings of the critical review and consultation with local multidisciplinary experts. Results: Neurosyphilis is frequently encountered in the NT but studies suggest it is often undertreated. Dementia is the most common clinical presentation locally. Establishing a diagnosis of neurosyphilis is complex and requires stepwise evaluation of clinical, laboratory and radiological findings. Conclusions: A clinical guideline and algorithm have been developed for the diagnosis and management of patients with neurosyphilis. [ABSTRACT FROM AUTHOR]

To increase data availability and accuracy in the coastal zone, especially in the last 5 km to the coast, we present a SCMR (Seamless Combination of Multiple Retrackers) processing strategy to combine sea surface height (SSH) estimates from waveform retrackers of SGDR MLE4, ALES, WLS3 and MB4 for Jason-3 and Saral missions, and of SAMOSA and SAMOSA+ for Sentinel-3A mission in the Australian coastal zone. The SCMR does not require the waveform classification result. It includes two steps: (1) estimating and removing the SSH bias due mainly to the significant wave height (SWH) difference-dependent height differences, and (2) determining the optimal along-track SSH profile by using the Dijkstra algorithm. In the study region, the results show that the SCMR increases the data availability by up to 15% in the last 5 km to the coast and reduces the noise level by 28–34% at the spatial scales < 2.5 km. The validation results against tide gauges show that SCMR-derived SSH estimates achieve a better accuracy than that from any single retracker, with the improvement percentage of 6.26% and 4.94% over 0–10 km and 20–100 km distance bands, respectively. [ABSTRACT FROM AUTHOR]

Live streaming of child sexual abuse (CSA) involves the procurement and viewing of sexual abuse of children across the internet in real time, in exchange for money. These offenses leave little tangible evidence of the offense beyond a financial transaction, and metadata relating to the live-streaming session. This research analyzed the demographic, criminal history, and financial transaction characteristics of 209 individuals who live streamed child sexual abuse. A machine learning clustering technique was implemented to consider whether there were sub-groups present among these offenders, and in particular the prevalence of contact sexual offending among any detected sub-groups. Findings revealed that offenders tend to engage in live streaming around the same age, before making regular transactions with facilitators at brief intervals, with the majority of offenders featuring limited criminal history. This analysis identified a notable sub-group of live-streaming offenders that also engaged in contact sexual offending. This is the first study to empirically demonstrate an intersection between live streaming of CSA, and contact sexual offenses against children and adults. This research highlighted the importance of financial transactions data in detecting, and disrupting this crime type. Further, the identification of an intersection between live-streaming CSA offenders, and contact sexual offenders suggests that these individuals may pose a risk to both local and international communities. [ABSTRACT FROM AUTHOR]

This article presents the experimental design of ClaimCheck, an artificial intelligence tool for detecting repeated falsehoods in political discourse using a semantic similarity model developed by the fact-checking organization Newtral in collaboration with ABC Australia. The study reviews the state of the art in algorithmic fact-checking and proposes a definition of claim matching. Additionally, it outlines the scheme for annotating similar sentences and presents the results of experiments conducted with the tool. [ABSTRACT FROM AUTHOR]

Timely and accurate statistics on the labour market enable policymakers to rapidly respond to changing economic conditions. Estimates of job vacancies by national statistical agencies are highly accurate but reported infrequently and with time lags. In contrast, online job postings provide a high-frequency indicator of vacancies with less accuracy. In this study we develop a robust signal averaging algorithm to measure job vacancies using online job postings data. We apply the algorithm using data on Australian job postings and show that it accurately predicts changes in job vacancies over a 4.5-year period. We also show that the algorithm is significantly more accurate than using raw counts of job postings to predict vacancies. The algorithm therefore offers a promising approach to the timely and reliable measurement of changes in vacancies. [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]

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]

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]

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]

Background: Assessment of occupational exposures is an integral component of population-based studies investigating the epidemiology of occupational diseases. However, all the available methods for exposure assessment have been developed, tested and used in high-income countries. Except for a few studies examining pesticide exposures, there is limited research on whether these methods are appropriate for assessing exposure in LMICs. The aim of this study is to compare a task-specific algorithm-based method (OccIDEAS) to a job-specific matrix method (OAsJEM) in the assessment of asthmagen exposures among healthcare workers in a high-income country and a low- and middle- income country (LMIC) to determine an appropriate assessment method for use in LMICs for future research.Methods: Data were obtained from a national cross-sectional survey of occupational asthmagens exposure in Australia and a cross-sectional survey of occupational chemical exposure among Bhutanese healthcare workers. Exposure was assessed using OccIDEAS and the OAsJEM. Prevalence of exposure to asthmagens and inter-rater agreement were calculated.Results: In Australia, the prevalence was higher for a majority of agents when assessed by OccIDEAS than by the OAsJEM (13 versus 3). OccIDEAS identified exposures to a greater number of agents (16 versus 7). The agreement as indicated by κ (Cohen's Kappa coefficient) for six of the seven agents assessed was poor to fair (0.02 to 0.37). In Bhutan, the prevalence of exposure assessed by OccIDEAS was higher for four of the seven agents and κ was poor for all the four agents assessed (-0.06 to 0.13). The OAsJEM overestimated exposures to high-level disinfectants by assigning exposures to all participants from 10 (Bhutan) and 12 (Australia) ISCO-88 codes; whereas OccIDEAS assigned exposures to varying proportions of participants from these ISCO-codes.Conclusion: There was poor to fair agreement in the assessment of asthmagen exposure in healthcare workers between the two methods. The OAsJEM overestimated the prevalence of certain exposures. As compared to the OAsJEM, OccIDEAS appeared to be more appropriate for evaluating cross-country exposures to asthmagens in healthcare workers due to its inherent quality of assessing task-based determinants and its versatility in being adaptable for use in different countries with different exposure circumstances. [ABSTRACT FROM AUTHOR]

This study aims to understand the effects that role‐diverse online communities have on informal carers, particularly in providing support. Australian Facebook communities used to support those involved in the National Disability Insurance Scheme (NDIS) were explored. Social network analysis of an NDIS‐centred community was conducted, based on 909 publicly visible interactions that occurred in May–June and August–September 2019. Two managers of informal NDIS communities were interviewed, the transcripts of which were analysed using NVivo. Results from the two analyses suggest that both an individual carer's attributes and the collective attributes of the network defined the capability of the network to support the carer, often depending on the experiences and expertise of those offering support. Support was unconstrained by role, though differing goals and expectations often impeded collaboration between roles. The outcomes of support provision were shown to affect not only individuals but also the collective network. However, while effective, community spaces currently lack organisational backing and resources available to informal communities are constrained. Findings drawn from this study, which we believe are applicable to a broader, international context, are three‐fold. Firstly, it is recommended that informal support communities clearly define purpose and create multiple channels to ensure that all participants can meet their needs. Secondly, the benefits of participation to organisations should be further explored. Finally, the use of social network analysis as a method in this study has provided significant insights into the communication patterns and activities of the community under study. Future use of SNA in similar studies may provide further insight into the effectiveness and interactions of community‐based support methods. [ABSTRACT FROM AUTHOR]

Introduction: Transimpedance measurements from cochlear implant electrodes have the potential to identify anomalous electrode array placement, such as tip fold-over (TFO) or fold-back, basal electrode kinking, or buckling. Analysing transimpedance may thus replace intraoperative or post-operative radiological imaging to detect any potential misplacements. A transimpedance algorithm was previously developed to detect deviations from a normal electrode position with the aim of intraoperatively detecting TFO. The algorithm had been calibrated on 35 forced, tip folded electrode arrays in six temporal bones to determine the threshold criterion required to achieve a sensitivity of 100%. Our primary objective here was to estimate the specificity of this TFO algorithm in patients, in a prospective study, for a series of electrode arrays shown to be normally inserted by post-operative imaging. Methods: Intracochlear voltages were intraoperatively recorded for 157 ears, using Cochlear's Custom Sound™ EP 5 electrophysiological software (Cochlear Ltd., Sydney, NSW, Australia), for both Nucleus® CI512 and CI532 electrode arrays. The algorithm analysed the recorded 22 × 22 transimpedance matrix (TIM) and results were displayed as a heatmap intraoperatively, only visible to the technician in the operating theatre. After all clinical data were collected, the algorithm was evaluated on the bench. The algorithm measures the transimpedance gradients and corresponding phase angles (θ) throughout the TIM and calculates the gradient phase range. If this was greater than the predetermined threshold, the algorithm classified the electrode array insertion as having a TFO. Results: Five ears had no intraoperative TIM and four anomalous matrices were identified from heatmaps and removed from the specificity analysis. Using the 148 remaining data sets (n = 103 CI532 and n = 45 CI512), the algorithm had an average specificity of 98.6% (95.80%–99.75%). Conclusion: The algorithm was found to be an effective screening tool for the identification of TFOs. Its specificity was within acceptable levels and resulted in a positive predictive value of 76%, with an estimated incidence of fold-over of 4% in perimodiolar arrays. This would mean 3 out of 4 cases flagged as a fold-over would be correctly identified by the algorithm, with the other being a false positive. The measurements were applied easily in theatre allowing it to be used as a routine clinical tool for confirming correct electrode placement. [ABSTRACT FROM AUTHOR]

A novel canonical duality theory (CDT) is presented for solving general bilevel mixed integer nonlinear optimization governed by linear and quadratic knapsack problems. It shows that the challenging knapsack problems can be solved analytically in term of their canonical dual solutions. The existence and uniqueness of these analytical solutions are proved. NP-hardness of the knapsack problems is discussed. A powerful CDT algorithm combined with an alternative iteration and a volume reduction method is proposed for solving the NP-hard bilevel knapsack problems. Application is illustrated by benchmark problems in optimal topology design. The performance and novelty of the proposed method are compared with the popular commercial codes. [ABSTRACT FROM AUTHOR]

Purpose: To investigate whether preseason training plans for Australian football can be computer generated using current training-load guidelines to optimize injury-risk reduction and performance improvement. Methods: A constrained optimization problem was defined for daily total and sprint distance, using the preseason schedule of an elite Australian football team as a template. Maximizing total training volume and maximizing Banister-model-projected performance were both considered optimization objectives. Cumulative workload and acute:chronic workload-ratio constraints were placed on training programs to reflect current guidelines on relative and absolute training loads for injury-risk reduction. Optimization software was then used to generate preseason training plans. Results: The optimization framework was able to generate training plans that satisfied relative and absolute workload constraints. Increasing the off-season chronic training loads enabled the optimization algorithm to prescribe higher amounts of "safe" training and attain higher projected performance levels. Simulations showed that using a Banister-model objective led to plans that included a taper in training load prior to competition to minimize fatigue and maximize projected performance. In contrast, when the objective was to maximize total training volume, more frequent training was prescribed to accumulate as much load as possible. Conclusions: Feasible training plans that maximize projected performance and satisfy injury-risk constraints can be automatically generated by an optimization problem for Australian football. The optimization methods allow for individualized training-plan design and the ability to adapt to changing training objectives and different training-load metrics. [ABSTRACT FROM AUTHOR]

Regional flood frequency analysis (RFFA) is widely used to estimate design floods in ungauged catchments. Both linear and non-linear methods are adopted in RFFA. The development of the non-linear RFFA method Adaptive Neuro-fuzzy Inference System (ANFIS) using data from 181 gauged catchments in south-eastern Australia is presented in this study. Three different types of ANFIS models, Fuzzy C-mean (FCM), Subtractive Clustering (SC), and Grid Partitioning (GP) were adopted, and the results were compared with the Quantile Regression Technique (QRT). It was found that FCM performs better (with relative error (RE) values in the range of 38–60%) than the SC (RE of 44–69%) and GP (RE of 42–78%) models. The FCM performs better for smaller to medium ARIs (2 to 20 years) (ARI of five years having the best performance), and in New South Wales, over Victoria. In many aspects, the QRT and FCM models perform very similarly. These developed RFFA models can be used in south-eastern Australia to derive more accurate flood quantiles. The developed method can easily be adapted to other parts of Australia and other countries. The results of this study will assist in updating the Australian Rainfall Runoff (national guide)-recommended RFFA technique. [ABSTRACT FROM AUTHOR]

Clinical free-text data represent a vast, untapped source of rich information. If more accessible for research it would supplement information captured in structured fields. Data need to be de-identified prior to being reused for research. However, a lack of transparency with existing de-identification software tools makes it difficult for data custodians to assess potential risks associated with the release of de-identified clinical free-text data. This case study describes the development of a framework for releasing de-identified clinical free-text data in two local health districts in NSW, Australia. A sample of clinical documents (n = 14 768 965), including progress notes, nursing and medical assessments and discharge summaries, were used for development. An algorithm was designed to identify and mask patient names without damaging data utility. For each note, the algorithm output the (i) note length before and after de-identification, (ii) the number of patient names and (iii) the number of common words. These outputs were used to iteratively refine the algorithm performance. This was followed by manual review of a random subset of records by a health information manager. Notes that were not correctly de-identified were fixed, and performance was reassessed until resolution. All notes in this sample were suitably de-identified using this method. Developing a transparent method for de-identifying clinical free-text data enables informed-decision making by data custodians and the safe re-use of clinical free-text data for research and public benefit. [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]

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]

The equivalent source technique is a powerful and widely used method for processing gravity and magnetic data. Nevertheless, its major drawback is the large computational cost in terms of processing time and computer memory. We present two techniques for reducing the computational cost of equivalent source processing: block-averaging source locations and the gradient-boosted equivalent source algorithm. Through block-averaging, we reduce the number of source coefficients that must be estimated while retaining the minimum desired resolution in the final processed data. With the gradient-boosting method, we estimate the sources coefficients in small batches along overlapping windows, allowing us to reduce the computer memory requirements arbitrarily to conform to the constraints of the available hardware. We show that the combination of block-averaging and gradient-boosted equivalent sources is capable of producing accurate interpolations through tests against synthetic data. Moreover, we demonstrate the feasibility of our method by gridding a gravity data set covering Australia with over 1.7 million observations using a modest personal computer. [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]

The Satin Bowerbird Optimisation (SBO) was inspired by the Satin Bowerbirds living in Australia's rainforests and other mesic habitats. Like other meta-heuristic algorithms, the main problem faced by the SBO is that it has been empirically demonstrated to become easily trapped into local optimal solutions, creating low precision and slow convergence speeds. To overcome these deficiencies, we propose herein the Adaptive Chaotic Satin Bowerbird Optimisation algorithm (AC-SBO). Within the AC-SBO algorithm, a chaotic map is introduced to modify the search process, with which to enhance global convergence speeds, and to obtain better performance. We introduced the chaos theory into the SBO optimisation process, in order to replace the main parameter's greatest step size ( α), which assists in controlling the balance between both exploration and exploitation. The search accuracy and performance of the AC-SBO algorithm were verified on ten classical benchmark functions. In addition, in the experimental CEC2014 results showed that for almost all functions, the AC-SBO technique proved superior to the other comparative algorithms optimisations. The Wilcoxon rank-sum statistical test was performed in order to judge the significance of the results, and further demonstrated the improved performance of the proposed AC-SBO algorithm. [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]

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]

There is a growing concern around the dependency of news organisations on platforms like Facebook for audience traffic. However, scholars are still working out the extent of this dependency and how it manifests in practice. In this article, we draw on interviews with Australian news professionals and industry fieldwork to provide a nuanced account of this phenomenon. We find that news media organisations have recently started to diversify their distribution strategies and the business models associated with them in response to Facebook's algorithm changes. While Facebook remains important, we suggest that greater attention needs to be paid to the complex relationships that news organisations have with platforms. This article ends by considering the implications of these findings for international policy discussions centred around the prospect of platform regulation. [ABSTRACT FROM AUTHOR]

The utility of inertial measurement units (IMUs) for sporting skill and performance analysis during training and competition is advantageous for enhancing the objectivity of athlete monitoring. This study aimed to classify Australian Rules football (AF) kick types in an applied environment using ankle-mounted IMUs. IMUs and video capture of a controlled protocol, including four kick types at varying distances, were recorded during a single testing session with female AF athletes (n = 20). Processed IMU data were modelled using support vector machine classifier, random forest, and k-nearest neighbour algorithms under a 2-Kick, 4-Kick, and kick distance (10, 20, 30 m) conditions. The random forest model showed the highest results for overall classification accuracy (83% 2-Kick and 80% 4-Kick), test F1-score (0.76 2-Kick and 0.81 4-Kick), and AUC score (0.58 2-Kick and 0.60 4-Kick). Kick distance classification showed a model test and class weighted F1-score of 0.63 and overall accuracy of 64%, respectively. This study highlights the potential for an applied semi-automated AF training kick detection and type classification system using IMUs. [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]

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]

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]

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]

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]

Background and aims: Clinical staff are typically poor at predicting alcohol dependence treatment outcomes. Machine learning (ML) offers the potential to model complex clinical data more effectively. This study tested the predictive accuracy of ML algorithms demonstrated to be effective in predicting alcohol dependence outcomes, compared with clinical judgement and traditional linear regression. Design Prospective study. ML models were trained on 1016 previously treated patients (training‐set) who attended a hospital‐based alcohol and drug clinic. ML models (n = 27), clinical psychologists (n = 10) and a 'traditional' logistic regression model (n = 1) predicted treatment outcome during the initial treatment session of an alcohol dependence programme. Setting: A 12‐week cognitive behavioural therapy (CBT)‐based abstinence programme for alcohol dependence in a hospital‐based alcohol and drug clinic in Australia. Participants: Prospective predictions were made for 220 new patients (test‐set; 70.91% male, mean age = 35.78 years, standard deviation = 9.19). Sixty‐nine (31.36%) patients successfully completed treatment. Measurements Treatment success was the primary outcome variable. The cross‐validated training‐set accuracy of ML models was used to determine optimal parameters for selecting models for prospective prediction. Accuracy, sensitivity, specificity, area under the receiver operator curve (AUC), Brier score and calibration curves were calculated and compared across predictions. Findings The mean aggregate accuracy of the ML models (63.06%) was higher than the mean accuracy of psychologist predictions (56.36%). The most accurate ML model achieved 70% accuracy, as did logistic regression. Both were more accurate than psychologists (P < 0.05) and had superior calibration. The high specificity for the selected ML (79%) and logistic regression (90%) meant they were significantly (P < 0.001) more effective than psychologists (50%) at correctly identifying patients whose treatment was unsuccessful. For ML and logistic regression, high specificity came at the expense of sensitivity (26 and 31%, respectively), resulting in poor prediction of successful patients. Conclusions: Machine learning models and logistic regression appear to be more accurate than psychologists at predicting treatment outcomes in an abstinence programme for alcohol dependence, but sensitivity is low. [ABSTRACT FROM AUTHOR]

Background: Competing risk models used for midpregnancy prediction of preterm pre‐eclampsia have shown detection rates (DR) of 85%, at fixed false‐positive rate (FPR) of 10%. The full algorithm used between 19+0 and 24+6 weeks includes maternal factors, mean arterial pressure (MAP), mean uterine artery pulsatility index (UtAPI), serum placental growth factor (PlGF) level in multiples of the median (MoM), and soluble Fms‐like tyrosine kinase‐1 (sFlt‐1) level in MoM. Aims: To assess performance of the Fetal Medicine Foundation (FMF) algorithm at midpregnancy to screen for preterm (<37 weeks) pre‐eclampsia. The outcome measured was preterm pre‐eclampsia. Materials and Methods: This is a prospective study including singleton pregnancies at 19–22 weeks gestation. Maternal bloods were collected and analysed using three different immunoassay platforms. Maternal characteristics, medical history, MAP, mean UtAPI, serum PlGF MoM and serum sFlt‐1 MoM were used for risk assessment. DR and FPR were calculated, and receiver operating characteristic curves produced. Results: Five hundred and twelve patients were included. Incidence of preterm pre‐eclampsia was 1.6%. Using predicted risk of pre‐eclampsia of one in 60 or more and one in 100 or higher, as given by the FMF predictive algorithm, the combination with the best predictive performance for preterm pre‐eclampsia included maternal factors, MAP, UtAPI and PlGF MoM, giving DRs of 100% and 100%, respectively, and FPRs of 9.3 for all platforms and 12.9–13.5, respectively. Addition of sFlt‐1 to the algorithm did not appear to improve performance. sFlt‐1 MoM and PlGF MoM values obtained on the different platforms performed very similarly. Conclusions: Second trimester combined screening for preterm pre‐eclampsia by maternal history, MAP, mean UtAPI and PlGF MoM using the FMF algorithm performed very well in this patient population. [ABSTRACT FROM AUTHOR]

In this article, a continuous terminal sliding mode control algorithm is proposed for servo motor systems. A novel full-order terminal sliding mode surface is proposed based on the bilimit homogeneous property, such that the sliding motion is finite-time stable independent of the system's initial condition. A new continuous terminal sliding mode control algorithm is proposed to guarantee that the system states reach the sliding surface in finite-time. Not only the robustness is guaranteed by the proposed controller but also the continuity makes the control algorithm more suitable for the servo mechanical systems. Finally, a numerical example is presented to depict the advantages of the proposed control algorithm. An application in the rotary servo system is done to validate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

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]