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Third‐party automatic weather stations (TPAWS) provide a compelling data source for scientists and practitioners to observe and estimate more accurate fine‐scale atmospheric conditions, including daily maximum and minimum temperature (denoted as Tmax and Tmin, respectively), than the current primary weather observation network can offer. Several uncertainties and errors arise in data from TPAWS as the quality control applied to these stations may be inadequate or ad hoc. In this study, we develop a statistical approach to evaluate the quality of daily Tmax and Tmin observations collected from TPAWS in Australia. Our approach compares a target observation with multiple types of reliable reference data, including neighbouring primary weather observations from the official Bureau of Meteorology of Australia stations, Australian Gridded Climate Data, and numerical weather prediction data. Guided by the operational requirements in terms of automation, interpretability, and simplicity as well as expandability, a separate test is formed for each type of reference data and then all the individual tests are merged to generate a single result based on a Gaussian mixture model that is used to provide the final overall assessment for each TPAWS observation. The overall assessment is made in the form of a p‐value‐based confidence score that measures the difference between the target observation and trusted reference data. Our method is validated by synthetic datasets based on high‐quality observations and is also applied to daily Tmax and Tmin observations from 184 TPAWS owned by the Department of Primary Industries and Regional Development of Western Australia. The framework can be readily applied to different regions with different reliable or trusted data sources. [ABSTRACT FROM AUTHOR]

Research in automated creation of test items for assessment purposes became increasingly important during the recent years. Due to automatic question creation it is possible to support personalized and self-directed learning activities by preparing appropriate and individualized test items quite easily with relatively little effort or even fully automatically. In this paper, which is an extended version of the conference paper of Gutl, Lankmayr and Weinhofer (2010), we present our most recent work on the automated creation of different types of test items. More precisely, we describe the design and the development of the "Enhanced Automatic Question Creator" (EAQC) which extracts most important concepts out of textual learning content and creates single choice, multiple-choice, completion exercises and open ended questions on the basis of these concepts. Our approach combines statistical, structural and semantic methods of natural language processing as well as a rule-based AI solution for concept extraction and test item creation. The prototype is designed in a flexible way to support easy changes or improvements of the above mentioned methods. EAQC is designed to deal with multilingual learning material and in its recent version English and German content is supported. Furthermore, we discuss the usage of the EAGC from the users' viewpoint and also present first results of an evaluation study in which students were asked to evaluate the relevance of the extracted concepts and the quality of the created test items. Results of this study showed that the concepts extracted and questions created by the EAQC were indeed relevant with respect to the learning content. Also the level of the questions and the provided answers were appropriate. Regarding the terminology of the questions and the selection of the distractors, which had been criticized most during the evaluation study, we discuss some aspects that could be considered in the future in order to enhance the automatic generation of questions. Nevertheless the results are promising and suggest that the quality of the automatically extracted concepts and created test items is comparable to human generated ones. (Contains 9 figures and 2 tables.)

Australia has been widely condemned for its harsh and comprehensive external border controls that seek to control the inward mobility of would-be asylum seekers through visa denial, interdiction and offshore detention. Less widely discussed is the fact that internal controls have been repeatedly ramped up over the past two decades. This includes the administrative removal of lawfully-present non-citizens following visa cancellation on character grounds under s501 of the Migration Act 1958 (Cth). Automatic visa cancellation was introduced in 2014 for non-citizens sentenced to a prison term of 12  months or more, or for certain offences, bypassing individualised decision-making and raising the spectre of a visa cancellation pipeline feeding a highly automated deportation machinery. In an age of increasingly automated forms of governance, a key question that arises is the role that has been played by automated systems in achieving what has been a seismic shift in practice, and the normative implications of any developments towards automation within the visa cancellation and removal systems. This paper outlines the shift towards automation in other systems of governance in Australia—most notably the notorious Robodebt scheme—before examining automation in Australia’s visa cancellation system. Documentary analysis of recent parliamentary inquiries, independent reports and government policy is used to piece together the development of inter-agency data exchange practices and automation over three specific periods—historical practice pre-2014, post-2014 to the present, and proposed future developments. We conclude that Australia’s s501 visa cancellation system is neither automated nor automatic. Rather, the 2014 law reform gave rise to a ‘surveillance fantasy’ with immense consequences for non-citizens, particularly those who face long days in immigration detention at the conclusion of their prison sentence. We show that while concerns about increasing automation are well-founded, systems based on less sophisticated forms of information handling and reliant on human decision-making nevertheless continue to raise age-old questions concerning efficiency, accuracy and fairness. [ABSTRACT FROM AUTHOR]

Third-party rainfall observations could provide an improvement of the current official observation network for rainfall monitoring. Although third-party weather stations can provide large quantities of near-real-time rainfall observations at fine temporal and spatial resolutions, the quality of these data is susceptible due to variations in quality control applied and there is a need to provide greater confidence in them. In this study, we develop an automatic quality evaluation procedure for daily rainfall observations collected from third-party stations in near real time. Australian Gridded Climate Data (AGCD) and radar Rainfields data have been identified as two reliable data sources that can be used for assessing third-party observations in Australia. To achieve better model interpretability and scalability, these reference data sources are used to provide separate tests rather than a complex single test on a third-party data point. Based on the assumption that the error of a data source follows a Gaussian distribution after a log-sinh transformation, each test issues a p-value-based confidence score as a measure of quality and the confidence of the third-party data observation. The maximum of confidence scores from individual tests is used to merge these tests into a single result which provides overall assessment. We validate our method with synthetic datasets based on high-quality rainfall observations from 100 Bureau of Meteorology (BoM) of Australia stations across Australia and apply it to evaluate real third-party rainfall observations owned by the Department of Primary Industries and regional development (DPIRD) of Western Australia. Our method works well with the synthetic datasets and can detect 76.7% erroneous data while keeping the false alarm rate as low as 1.7%. We also discuss the possibility of using other reference datasets, such as numerical weather prediction data and satellite rainfall data. [ABSTRACT FROM AUTHOR]

Semantic priming effects are popularly explained in terms of an automatic spreading activation process, according to which the activation of a node in a semantic network spreads automatically to interconnected nodes, preactivating a semantically related word. It is expected from this account that semantic priming effects should be routinely observed when the prime identity is veiled from conscious awareness, but the extant literature on masked semantic priming effects is notoriously mixed. The authors use the same prime--target pairs in the lexical decision task and the semantic categorization task and show that although masking the prime eliminates the semantic priming effect in lexical decision, reliable semantic priming effects are observed with both masked and unmasked primes in the semantic categorization task. The authors explain this task dependence in terms of their account of semantic priming effects based on notions of evidence accumulation and source confusion and support their account by means of reaction time distribution analyses.

Semantic priming effects at a short prime-target stimulus onset asynchrony are commonly explained in terms of an automatic spreading activation process. According to this view, the proportion of related trials should have no impact on the size of the semantic priming effect. Using a semantic categorization task ("Is this a living thing?"), we show that on the contrary, there is a robust effect of relatedness proportion on the size of the semantic priming effect. This effect is not due to the participants using the prime to predict the target category/response, as manipulating the proportion of category/response-congruent trials produces a very different pattern. Taken together with response time distribution analysis, we argue that the semantic priming effect observed here is best explained in terms of an evidence accumulation process and source confusion between the prime and target.

University food environments have a strong influence on the dietary choices of students and staff. The aim of this study was to assess the food environment at a large university in Sydney, Australia. Data were collected between March and July 2022 from 27 fixed food outlets and 24 vending machines. The healthiness of the food environment was evaluated using the Healthy Food and Drink in NSW Health Facilities for Staff and Visitors Framework ('Framework'), which assesses food environment parameters including the availability, placement, and promotion of 'Everyday' (healthy) and 'Occasional' (less healthy) products. Each parameter was evaluated overall and across each food outlet type. Across all outlets, Everyday foods and drinks made up 43.9% of all products. Only two outlets met the Framework's product availability benchmark of ≥75% Everyday foods and drinks. A total of 43 outlets (84.3%) sold sugary drinks as part of their product range. Occasional products made up 68.4%, 53.3%, and 59.9% of all items for sale at checkout areas, countertops, and eye-level shelves, respectively. Finally, 79.7% of meal deals included Occasional products. Our findings highlight the need to improve the availability, placement, and promotion of foods and drinks sold at a major university campus in Sydney, Australia.

Background: While most Australian children are vaccinated, delays in vaccination can put them at risk from preventable infections. Widespread mobile phone ownership in Australia could allow automated short message service (SMS) reminders to be used as a low-cost strategy to effectively 'nudge' parents towards vaccinating their children on time., Methods: AuTOMATIC is an adaptive randomised trial which aims to both evaluate and optimise the use of SMS reminders for improving the timely vaccination of children at primary care clinics across Australia. The trial will utilise high levels of digital automation to effect, including eligibility assessment, randomisation, delivery of intervention, data extraction and analysis, thereby allowing healthcare-embedded trial delivery. Up to 10,000 parents attending participating primary care clinics will be randomised to one of 12 different active SMS vaccine reminder content and timing arms or usual practice only (no SMS reminder). The primary outcome is vaccine receipt within 28 days of the scheduled date for the index vaccine (the first scheduled vaccine after randomisation). Secondary analyses will assess receipt and timeliness for all vaccine occasions in all children. Regular scheduled analyses will be performed using Bayesian inference and pre-specified trial decision rules, enabling response adaptive randomisation, suspension of any poorly performing arms and early stopping if a single best message is identified., Discussion: This study will aim to optimise SMS reminders for childhood vaccination in primary care clinics, directly comparing alternative message framing and message timing. We anticipate that the trial will be an exemplar in using Bayesian adaptive methodology to assess a readily implementable strategy in a wide population, capable of delivery due to the levels of digital automation. Methods and findings from this study will help to inform strategies for implementing reminders and embedding analytics in primary health care settings., Trial Registration: ANZCTR: ACTRN12618000789268 ., (© 2023. The Author(s).)

Purpose: This study aimed to develop and assess an automated segmentation framework based on deep learning for metastatic prostate cancer (mPCa) lesions in whole-body [ 68 Ga]Ga-PSMA-11 PET/CT images for the purpose of extracting patient-level prognostic biomarkers., Methods: Three hundred thirty-seven [ 68 Ga]Ga-PSMA-11 PET/CT images were retrieved from a cohort of biochemically recurrent PCa patients. A fully 3D convolutional neural network (CNN) is proposed which is based on the self-configuring nnU-Net framework, and was trained on a subset of these scans, with an independent test set reserved for model evaluation. Voxel-level segmentation results were assessed using the dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity. Sensitivity and PPV were calculated to assess lesion level detection; patient-level classification results were assessed by the accuracy, PPV, and sensitivity. Whole-body biomarkers total lesional volume (TLV auto ) and total lesional uptake (TLU auto ) were calculated from the automated segmentations, and Kaplan-Meier analysis was used to assess biomarker relationship with patient overall survival., Results: At the patient level, the accuracy, sensitivity, and PPV were all > 90%, with the best metric being the PPV (97.2%). PPV and sensitivity at the lesion level were 88.2% and 73.0%, respectively. DSC and PPV measured at the voxel level performed within measured inter-observer variability (DSC, median = 50.7% vs. second observer = 32%, p = 0.012; PPV, median = 64.9% vs. second observer = 25.7%, p < 0.005). Kaplan-Meier analysis of TLV auto and TLU auto showed they were significantly associated with patient overall survival (both p < 0.005)., Conclusion: The fully automated assessment of whole-body [ 68 Ga]Ga-PSMA-11 PET/CT images using deep learning shows significant promise, yielding accurate scan classification, voxel-level segmentations within inter-observer variability, and potentially clinically useful prognostic biomarkers associated with patient overall survival., Trial Registration: This study was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12615000608561) on 11 June 2015., (© 2022. The Author(s).)

This study proposes an automatic method for the detection of pauses and identification of pause types in conversational speech for the purpose of measuring the effects of Friedreich's Ataxia (FRDA) on speech. Speech samples of [approximately] 3 minutes were recorded from 13 speakers with FRDA and 18 healthy controls. Pauses were measured from the intensity contour and fit with bimodal lognormal distributions using the Expectation-Maximization algorithm in Matlab[C]. In the speakers with FRDA, both modes in the pause distributions had significantly larger means, with disproportionately fewer pauses associated with the first mode. From this preliminary study, it is concluded that distributional analysis of pause duration holds promise as a useful method of measuring the effects of FRDA on functional speech. (Contains 3 figures, 2 tables and 4 notes.)

Most studies on the Stroop effect (unintentional automatic word processing) have been restricted to English speakers using vocal responses. Little is known about this effect with deaf signers. The study compared Stroop task responses among four different samples: deaf participants from a Japanese-language environment and from an English-language environment; and hearing individuals from Japan and from Australia. Color words were prepared in both English and Japanese and were presented in three conditions: congruent (e.g., the word "red" printed in red), incongruent (e.g., "red" printed in blue), and neutral. The magnitude of the effect was greater with the deaf participants than with the hearing participants. The deaf individuals experienced more interference in English than in Japanese. (Contains 2 tables.)

There is growing interest in research on the non-modal voice quality, creaky voice; however, its identification often relies on time-consuming manual annotation, leading to a recent focus on automatic creak detection methods. Various automatic methods have been proposed, which rely on varying types and combinations of acoustic cues for creak detection. In this paper, we compare the performance of three automatic tools, the AntiMode method, the Creak Detector algorithm, and the Roughness algorithm, against manual annotation of creak using data from 80 Australian English speakers. We explore the possibility that tools used in combination may yield more accurate creak detection than individual tools used alone. Based on method comparisons, we present options for researchers, including an "out-of-the-box" approach, which supports combining automatic tools, and propose additional steps to further improve creak detection. We found restricting analysis to sonorant segments significantly improves automatic creak detection, and tools performed consistently better on female speech than male speech. Findings support previous work showing detection may be optimised by performing a creak probability threshold sweep on a subset of data prior to applying the Creak Detector algorithm on new datasets. Results provide promising solutions for advancing efficient large-scale research on creaky voice.

There is a large variability in profitability and productivity between farms operating with automatic milking systems (AMS). The objectives of this study were to identify the physical factors associated with profitability and productivity of pasture-based AMS and quantify how changes in these factors would affect farm productivity. We utilised two different datasets collected between 2015 and 2019 with information from commercial pasture-based AMS farms. One contained annual physical and economic data from 14 AMS farms located in the main Australian dairy regions; the other contained monthly, detailed robot-system performance data from 23 AMS farms located across Australia, Ireland, New Zealand, and Chile. We used linear mixed models to identify the physical factors associated with different profitability (Model 1) and partial productivity measures (Model 2). Additionally, we conducted a Monte Carlo simulation to evaluate how changes in the physical factors would affect productivity. Our results from Model 1 showed that the two main factors associated with profitability in pasture-based AMS were milk harvested/robot (MH; kg milk/robot per day) and total labour on-farm (full-time equivalent). On average, Model 1 explained 69% of the variance in profitability. In turn, Model 2 showed that the main factors associated with MH were cows/robot, milk flow, milking frequency, milking time, and days in milk. Model 2 explained 90% of the variance in MH. The Monte Carlo simulation showed that if pasture-based AMS farms manage to increase the number of cows/robot from 54 (current average) to ∼ 70 (the average of the 25% highest performing farms), the probability of achieving high MH, and therefore profitability, would increase from 23% to 63%. This could make AMS more attractive for pasture-based systems and increase the rate of adoption of the technology., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

The power of Machine Learning is demonstrated for automatic interpretation of well logs and determining reservoir properties for volume of shale, porosity, and water saturation respectively for tight clastic sequences. Random Forest algorithms are reputed for their efficiency as they belong to a class of algorithms called ensemble methods, which are traditionally seen as weak learners, but can be transformed into strong performers and they promise to deliver highly accurate results. The study area is located offshore Australia in the Poseidon and Crown fields situated in the Browse Basin, which are gas fields in tight complex clastic reservoirs. There are 5 wells used in this study with one well manually interpreted which is subsequently used in developing a machine learning model which predicts the output for the other 4 wells. The basic open hole logs namely Natural gamma ray, Resistivity, Neutron Porosity, Bulk Density, P-wave and S-wave sonic travel-time, are used in interpretation. One of the wells has a missing S-wave travel-time log which was also predicted by developing a Random Forest Machine Learning model. The results indicate a very robust improvement in performance when Random Forest algorithm was combined with Adaptive Boosting when interpreting the well logs. The training accuracy using Random Forest alone was 98.21%, but testing was 77.62% which suggested over-fitting by the Random Forest model. The Adaptive Boosting of the Random Forest algorithm resulted in the overall training accuracy of 99.40% and an overall testing accuracy of 97.03%, indicating a drastic improvement in performance. S-wave travel-time log was predicted by preparing a training set consisting of Natural gamma ray, Resistivity, Neutron Porosity, Bulk Density, and P-wave travel-time logs for the 4 wells using Random Forest which gave a training accuracy of 99.79% and a testing accuracy of 98.54%. Machine learning algorithms can be successfully applied for interpreting well log data in complex sedimentary environment and their performance can be drastically improved using Adaptive Boosting. [ABSTRACT FROM AUTHOR]

Purpose: Individuals with neurogenic speech disorders require ongoing therapeutic support to achieve functional communication goals. Alternative methods for service delivery, such as tablet-based speech therapy applications, may help bridge the gap and bring therapeutic interventions to the patient in an engaging way. The purpose of this study was to evaluate an iPad-based speech therapy app that uses automatic speech recognition (ASR) software to provide feedback on speech accuracy to determine the ASR's accuracy against human judgment and whether participants' speech improved with this ASR-based feedback. Method: Five participants with apraxia of speech plus aphasia secondary to stroke completed an intensive 4-week at-home therapy program using a novel word training app with built-in ASR. Multiple baselines across participants and behaviors designs were employed, with weekly probes and follow-up at 1 month posttreatment. Four sessions a week of 100 practice trials each were prescribed, with 1 being clinician-run and the remainder done independently. Dependent variables of interest were ASR-human agreement on accuracy during practice trials and human-judged word production accuracy over time in probes. Also, user experience surveys were completed immediately posttreatment. Results: ASR-human agreement on accuracy averaged ~80%, which is a common threshold applied for interrater agreement. All participants demonstrated improved word production accuracy over time with the ASR-based feedback and maintenance of gains after 1 month. All participants reported enjoying using the app with support of a speech pathologist. Conclusion: For these participants with apraxia of speech plus aphasia due to stroke, satisfactory gains were made in word production accuracy with an app-based therapy program providing ASR-based feedback on accuracy. Findings support further testing of this ASR-based approach as a supplement to clinician-run sessions to assist clients with similar profiles in achieving higher amount and intensity of practice as well as empowering them to manage their own therapy program. [ABSTRACT FROM AUTHOR]

Integrated distribution models (IDMs) predict where species might occur using data from multiple sources, a technique thought to be especially useful when data from any individual source are scarce. Recent advances allow us to fit such models with latent terms to account for dependence within and between data sources, but they are computationally challenging to fit.We propose a fast new methodology for fitting integrated distribution models using presence/absence and presence‐only data, via a spatial random effects approach combined with automatic differentiation. We have written an R package (called scampr) for straightforward implementation of our approach.We use simulation to demonstrate that our approach has comparable performance to INLA—a common framework for fitting IDMs—but with computation times up to an order of magnitude faster. We also use simulation to look at when IDMs can be expected to outperform models fitted to a single data source, and find that the amount of benefit gained from using an IDM is a function of the relative amount of additional information available from incorporating a second data source into the model. We apply our method to predict 29 plant species in NSW, Australia, and find particular benefit in predictive performance when data from a single source are scarce and when compared to models for presence‐only data.Our faster methods of fitting IDMs make it feasible to more deeply explore the model space (e.g. comparing different ways to model latent terms), and in future work, to consider extensions to more complex models, for example the multi‐species setting. [ABSTRACT FROM AUTHOR]

Automatic facial recognition technology (AFR) is increasingly used in criminal justice systems around the world, yet to date there has not been an international survey of public attitudes toward its use. In Study 1, we ran focus groups in the UK, Australia and China (countries at different stages of adopting AFR) and in Study 2 we collected data from over 3,000 participants in the UK, Australia and the USA using a questionnaire investigating attitudes towards AFR use in criminal justice systems. Our results showed that although overall participants were aligned in their attitudes and reasoning behind them, there were some key differences across countries. People in the USA were more accepting of tracking citizens, more accepting of private companies' use of AFR, and less trusting of the police using AFR than people in the UK and Australia. Our results showed that support for the use of AFR depends greatly on what the technology is used for and who it is used by. We recommend vendors and users do more to explain AFR use, including details around accuracy and data protection. We also recommend that governments should set legal boundaries around the use of AFR in investigative and criminal justice settings. [ABSTRACT FROM AUTHOR]

Background: For the recruitment and monitoring of subjects for therapy studies, it is important to predict whether mild cognitive impaired (MCI) subjects will prospectively develop Alzheimer's disease (AD). Machine learning (ML) is suitable to improve early AD prediction. The etiology of AD is heterogeneous, which leads to high variability in disease patterns. Further variability originates from multicentric study designs, varying acquisition protocols, and errors in the preprocessing of magnetic resonance imaging (MRI) scans. The high variability makes the differentiation between signal and noise difficult and may lead to overfitting. This article examines whether an automatic and fair data valuation method based on Shapley values can identify the most informative subjects to improve ML classification., Methods: An ML workflow was developed and trained for a subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The validation was executed for an independent ADNI test set and for the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) cohort. The workflow included volumetric MRI feature extraction, feature selection, sample selection using Data Shapley, random forest (RF), and eXtreme Gradient Boosting (XGBoost) for model training as well as Kernel SHapley Additive exPlanations (SHAP) values for model interpretation., Results: The RF models, which excluded 134 of the 467 training subjects based on their RF Data Shapley values, outperformed the base models that reached a mean accuracy of 62.64% by 5.76% (3.61 percentage points) for the independent ADNI test set. The XGBoost base models reached a mean accuracy of 60.00% for the AIBL data set. The exclusion of those 133 subjects with the smallest RF Data Shapley values could improve the classification accuracy by 2.98% (1.79 percentage points). The cutoff values were calculated using an independent validation set., Conclusion: The Data Shapley method was able to improve the mean accuracies for the test sets. The most informative subjects were associated with the number of ApolipoproteinE ε4 (ApoE ε4) alleles, cognitive test results, and volumetric MRI measurements., (© 2021. The Author(s).)

Background: Guideline recommendations state oxygen should be administered to acutely unwell patients to achieve a target oxygen saturation (SpO 2 ) range. The current practice of manual oxygen titration frequently results in SpO 2 outside of a prescribed range. The aim of this study was to assess the efficacy of automatic oxygen titration using a closed-loop feedback system to achieve SpO 2 within a prescribed target range METHODS: An open-label randomised parallel group trial was undertaken comparing automatic oxygen titration using a novel nasal high-flow device to manual oxygen titration using nasal high flow. Medical inpatients requiring oxygen therapy in Wellington Regional Hospital, New Zealand with a prescribed target SpO 2 range of 88%-92% or 92%-96% were recruited and randomised equally between the interventions for a period of 24 hours. The primary outcome was the proportion of time spent with SpO 2 within the prescribed range., Results: 20 patients were included in the analysis. Automatic oxygen titration resulted in a median (IQR) 96.2% (95.2-97.8) of time within the target range compared with 71% (59.4-88.3) with manual titration; difference (95% CI) 24.2% (7.9% to 35%), p<0.001. There was a reduction in the time spent with SpO 2 ≥2% above and ≥2% below range in the automatic titration group, although the point estimate for the differences were small; -1% (-8.2% to -0.04%), p=0.017 and -2.4% (-11.5% to 0.3%), p=0.05 respectively., Conclusions: Nasal high-flow with automatic oxygen titration resulted in a greater proportion of time spent with SpO 2 in target range compared with manual titration., Trial Registration: The trial was registered with the Australian and New Zealand Clinical Trials Registry (ACTRN12619000901101)., Competing Interests: Competing interests: JH reports personal fees from Fisher and Paykel Healthcare, outside the submitted work. RB reports a grant from Fisher and Paykel Healthcare to undertake this study., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: This study explored the feasibility of cortical automatic threshold estimation (CATE), a fully automated late auditory evoked potential (AEP) test, as an alternative to pure-tone audiometry for hearing threshold estimation for adults with dementia living in aged care. Design: A single group cross-sectional study was conducted. Participants' dementia severity was determined through the Clinical Dementia Rating scale. Hearing thresholds were obtained for four audiometric frequencies in at least one ear by using both pure-tone audiometry and CATE. Study sample: Sixteen participants enrolled in the study, of which 14 completed at least one of the hearing tests. Twelve ears, from six participants, were included in the final correlation analysis. Results: Pearson correlation coefficients were significant between CATE and pure-tone audiometry for all frequencies: r2 = 0.52 (p = 0.008) for 500 Hz, r2 = 0.79 (p = 0.0001) for 1000 Hz, r2 = 0.71 (p = 0.0005) for 2000 Hz, and r2 = 0.92 (p < 0.0001) for 4000 Hz. Cortical thresholds were within 10 dB of behavioural thresholds for all four frequencies. Conclusions: Findings are encouraging for the feasibility of CATE as an alternative diagnostic test to pure-tone audiometry for adults living with dementia in aged care. [ABSTRACT FROM AUTHOR]

The current report explores how well vending machines are meeting the needs of health care organizations and their staff and visitors in Australia. Hospital vending machines often provide the only source of food through the night to staff and visitors and traditionally offer less-healthy options. Findings presented in this report suggest that vending machines are not meeting current statewide policies and guidelines for healthier food environments in health care. This is despite widespread support for healthier refreshments in hospitals by staff, visitors, and patients. Alternatives to traditional vending and opportunities for nutrition educators and researchers are discussed., (Copyright © 2020 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.)

Regional understanding of severe surface winds produced by convective processes [severe convective winds (SCWs)] is important for decision-making in several areas of society, including weather forecasting and engineering design. Meteorological studies have demonstrated that SCWs can occur due to a number of different mesoscale and microscale processes, in a range of large-scale atmospheric environments. However, long-term observational studies of SCW characteristics often have not considered this diversity in physical processes, particularly in Australia. Here, a statistical clustering method is used to separate a large dataset of SCW events, measured by automatic weather stations around Australia, into three types, associated with strong background wind, steep lapse rate, and high moisture environments. These different types of SCWs are shown to have different seasonal and spatial variations in their occurrence, as well as different measured wind gust, lightning, and parent-storm characteristics. In addition, various convective diagnostics are tested in their ability to discriminate between measured SCW events and nonsevere events, with significant variations in skill between event types. Differences in environmental conditions and wind gust characteristics between event types suggests potentially different physical processes for SCW production. These findings are intended to improve regional understanding of severe wind characteristics, as well as environmental prediction of SCWs in weather and climate applications, by considering different event types. Significance Statement: The purpose of this study is to improve regional understanding of different types of severe wind events in Australia, specifically those associated with atmospheric convection. We did this by constructing a dataset of 413 severe convective wind events, using weather station and radar data within 20 regions around Australia. We then split those events into three different types, based on the environmental conditions that they occur within. We found that each event type tends to occur at different times of the year and in different regions, while also having different wind gust and lightning characteristics. In addition, the atmospheric conditions that are helpful for prediction of severe wind events differs between each type. These results are intended to be useful for prediction of severe wind events associated with convection and assessing their variability, characteristics, and impacts, in both weather forecasting and climate analysis. [ABSTRACT FROM AUTHOR]

There are several differences in how an automatic milking system (AMS; milking equipment not requiring human intervention for the milk harvesting process) and a conventional milking system (CMS) are managed, where the effect of milking system type on the human-animal relationship has remained unexplored. A survey and observations from 5 Australian dairy farms transitioning from CMS to AMS were taken twice, 1 yr apart, before and after transition to an AMS. The farmers completed a survey and had all farmer-cow interactions documented for 3 d. In addition, a random selection of lactating cows had their avoidance distance (the distance at which they move away from an approaching person) recorded and were involved in a handling test during both visits. The survey findings indicated that basic management practices remained mostly unchanged, whereas records of farmer-cow interactions showed 4 out of 5 farms had less interaction time after AMS transition. This was caused by a reduction in milk harvesting tasks, where a small portion of this time was re-invested into time that farmers spent around the cows without directly interacting with them and into tasks involving close cow contact. Overall, an approximate 27% decline was observed in avoidance distances of cows from an AMS compared with the CMS. A handling test was performed on 4 of the 5 farms before and after AMS transition, where the farmers were asked to move a selection of cows through a gate one at a time. In the AMS more vocal effort was required to move the cows, and the cows responded with a reduced occurrence of running past the farmer and reduced occurrence of slipping in an attempt to avoid the farmers compared with the CMS. Overall, results show that farmers spent less time interacting with cows in the AMS, and that cows were less fearful around people as seen by reduced avoidance distances and reduced stress responses to close handling., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

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

A method for automatic 3D model generation derived from airborne gravity gradient data was illustrated. The proposed method computed a volumetric distribution of curvatures that form a 3D image of possible density distributions. The method relies on spectral analysis and the equivalence of the power spectra of the classical mean and differential curvatures of equipotential surfaces to create pseudodepth slices of a quantity that describes the geometry of the surfaces: the shape index. The method was carried out in three steps. First, the pseudodepth slices of the vertical gravity gradient and the magnitude of the differential curvature components were generated. Second, equivalent pseudodepth slices of the shape index were generated. Finally, 3D interpolation was carried out to obtain the final model. The synthetic model data indicated that the vertical density contrasts were well modeled. A 3D model derived from FALCON airborne gravity gradiometer data from the Canning Basin, Australia, was compared to an independently interpreted integrated 3D earth geologic model. [ABSTRACT FROM AUTHOR]

The article examines the Australian Commonwealth fisheries jurisdiction and the general regulatory and enforcement powers mandated by the Fisheries Management Act 1991. The authors discuss the scope of the authority given to enforcement officers and takes a close look at the provisions regarding the automatic forfeiture of vessels, catch, and equipment as stipulated by the Act. The authors argue for the repeal of the automatic forfeiture provisions in the Act.

Ticks (Ixodida) are haematophagous arthropods that transmit a number of pathogenic organisms, including bacteria, protozoa and viruses, to humans and animals. Globally, there are over 900 species of ticks and Australia has 73 described species, including five introduced and 68 native species. With the exception of only a few Australian tick species, there are still many unanswered questions regarding their taxonomy and systematics, and the phylogeny of Australian ticks is not properly resolved. In recent years, a putative link between tick bites and poorly defined tick-borne illness(es) has been identified (Graves Stenos 2017) and was the subject of a 2015 Australian Senate Inquiry into Lyme-like illnesses in Australia. There is an urgent need to further categorise Australian ticks, specifically hard ticks (Ixodidae), and accurate identification of Australian ticks is therefore of high importance.

Extreme wind gusts cause major socioeconomic damage, and the rarity and localised nature of those events make their analysis challenging by either modelling or empirical approaches. A 23-year long data record from 29 automatic weather stations located in New South Wales (eastern Australia) is used to study the distribution, frequency and average recurrence intervals (ARIs) of extreme gusts via a peaks-over-threshold approach. We distinguish between gust events generated by synoptic phenomena (e.g. cyclones and frontal systems), hereafter called "synoptic events", and convective phenomena (i.e. thunderstorms), hereafter called "convective events", using the wind time series. For synoptic events the frequency of gusts > 25 m/s decreases systematically inland from the coast, in contrast to convective gusts which are more uniformly distributed geographically and occur more often than synoptic gusts at nearly all inland locations. At inland locations the most extreme wind gusts are likewise dominated by convective events, whereas at coastal stations both gust types have similar intensities at low ARIs but convective events again dominate at the highest ARIs. Extreme gust directions were found to be predominantly westerly at inland locations and southerly at coastal ones, with more variable direction for convective than synoptic events. This study confirms the dominant role of thunderstorms in producing the most extreme gusts in the region, and shows that wind risk varies strongly with distance from the coast. [ABSTRACT FROM AUTHOR]