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Staphylococcus argenteus and Staphylococcus schweitzeri, previously known as divergent Staphylococcus aureus clonal lineages, have been recently established as novel, difficult-to-delimit, coagulase-positive species within the S. aureus complex. Methicillin-resistant strains of S. argenteus are known from Australia and the UK. Knowledge of their epidemiology, medical significance and transmission risk is limited and partly contradictory, hampering definitive recommendations. There is mounting evidence that the pathogenicity of S. argenteus is similar to that of 'classical' S. aureus , while as yet no S. schweitzeri infections have been reported. To provide decision support on whether and how to distinguish and report both species. PubMed, searched for S. argenteus and S. schweitzeri. This position paper reviews the main characteristics of both species and draws conclusions for microbiological diagnostics and surveillance as well as infection prevention and control measures. We propose not distinguishing within the S. aureus complex for routine reporting purposes until there is evidence that pathogenicity or clinical outcome differ markedly between the different species. Primarily for research purposes, suitably equipped laboratories are encouraged to differentiate between S. argenteus and S. schweitzeri. Caution is urged if these novel species are explicitly reported. In such cases, a specific comment should be added (i.e. 'member of the S.aureus complex') to prevent confusion with less- or non-pathogenic staphylococci. Prioritizing aspects of patient safety, methicillin-resistant isolates should be handled as recommended for methicillin-resistant Staphylococcus aureus (MRSA). In these cases, the clinician responsible should be directly contacted and informed by the diagnosing microbiological laboratory, as they would be for MRSA. Research is warranted to clarify the epidemiology, clinical impact and implications for infection control of such isolates. [ABSTRACT FROM AUTHOR]

When undertaking any study of the isotope abundance values of a bulk material, consideration should be given to the source materials and how they are combined to reach the final product being measured. While it is demonstrative to measure and record the values of clean papers, such as the results published as part one of this series, the majority of forensic casework samples would have undergone some form of writing or printing process prior to examination. Understanding the effects of these processes on the δ13C values of paper is essential for interpretation and comparison with clean samples, for example in cases where printed documents need to be compared to paper from an unprinted suspect ream. This study was undertaken so that the source materials, the effects of the production process and the effects of printing and forensic testing could be observed with respect to 80 gsm white office papers. Samples were taken sequentially from the paper production facility at the Australian Paper Mill (Maryvale, VIC). These samples ranged from raw wood chips through the pulping, whitening and refinement steps to the final formed and packed paper. Cellulose was extracted from each sample to observe both fractionation and mixing steps and their effect on the δ13C values. Overall, the mixing steps were observed to have a larger effect on the isotopic values of the bulk materials than any potential fractionation. Printing of papers using toner and inkjet printing processes and forensic testing were observed to have little effect on δ13C. These experiments highlighted considerations for sampling and confirmed the need for a holistic understanding of sample history to inform the interpretation of results. [ABSTRACT FROM AUTHOR]

It is generally accepted that the amino acid reagent consisting of 1,2-indanedione and a catalytic amount of zinc chloride, referred to as IND-Zn, is the single best method for the detection of latent fingermarks on paper substrates and that ninhydrin is of limited value when used in sequence after this reagent. However, recent research has suggested that the sequence 1,8-diazafluoren-9-one (DFO) followed by ninhydrin may actually produce a greater number of fingermarks than IND-Zn on its own or IND-Zn followed by ninhydrin. This study focussed on the evaluation of two fingermark detection sequences for porous surfaces: (1) IND-Zn followed by ninhydrin, physical developer (PD) and the lipid stain nile red; and (2) DFO followed by ninhydrin, PD and nile red. The evaluation was undertaken using a range of latent fingermark donors and on a number of paper substrates that are commonly encountered in Australia. In addition, a pseudooperational trial was completed on 5-year-old university examination booklets. Parallel studies were undertaken at two locations: Sydney (temperate, coastal climate) and Canberra (relatively dry, continental climate). The results of the donor study indicated that there was a negligible difference in performance between the two sequences across all paper types and all time periods evaluated. When considering individual reagents, IND-Zn generally developed better quality fingermarks compared to DFO; however, ninhydrin had a greater enhancement effect on DFO developed marks than after IND-Zn. In the pseudooperational trials, the IND-Zn sequence outperformed the DFO sequence. Nile red did not develop any additional marks at the end of each sequence and, as a result, the use of this technique at the end of a full sequence is of questionable value. The overall outcome was that the sequence IND-Zn followed by ninhydrin and PD is recommended for the processing of common paper substrates under the conditions typically experienced at the two locations studied. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: To describe the paper-based and electronic formats of resident admission forms used in several aged care facilities in Australia and to compare the extent to which resident admission information was documented in paper-based and the electronic health records. Methods: Retrospective auditing and comparison of the documentation quality of paper-based and electronic resident admission forms were conducted. A checklist of admission data was qualitatively derived from different formats of the admission forms collected. Three measures were used to assess the quality of documentation of the admission forms, including completeness rate, comprehensiveness rate and frequency of documented data element. The associations between the number of items and their completeness and comprehensiveness rates were estimated at a general level and at each information category level. Results: Various paper-based and electronic formats of admission forms were collected, reflecting varying practice among the participant facilities. The overall completeness and comprehensiveness rates of the admission forms were poor, but were higher in the electronic health records than in the paper-based records (60% versus 56% and 40% versus 29% respectively, p <0.01). There were differences in the overall completeness and comprehensiveness rates between the different formats of admission forms (p <0.01). At each information category level, varying degrees of difference in the completeness and comprehensiveness rates were found between different form formats and between the paper-based and the electronic records. A negative association between the completeness rate and the number of items in a form was found at each information category level (p <0.01), i.e., more data items designed in a form, the less likely that the items would be completely filled. However, the associations between the comprehensiveness rates and the number of items were highly positive at both overall and individual information category levels (p <0.01), suggesting more items designed in a form, more information would be captured. Conclusion: Better quality of documentation in resident admission forms was identified in the electronic documentation systems than in previous paper-based systems, but still needs to be further improved in practice. The quality of documentation of resident admission data should be further analysed in relation to its specific content. [Copyright &y& Elsevier]

A lifecycle assessment to estimate greenhouse gas emissions in Australia from the paper cycle is summarised. The greenhouse gas emissions from paper in Australia in 1999/2000 were estimated to be 12.1 million tonnes (Mt) of CO2 equivalent. Nearly half of this amount consisted of CH4 emissions from landfilled waste paper. Various waste management options were modelled to investigate the greenhouse impact of a tonne of paper over its whole lifecycle. Options that keep paper out of landfills significantly reduce greenhouse emissions, waste-to-energy recovery being most effective. Recycling is also beneficial, and is of particular interest from a management perspective because it can be controlled by the pulp and paper industry. These findings can be extended to other wood-based and organic wastes. [Copyright &y& Elsevier]

An effective cleaner production programme requires the efficient collection and interpretation of data relating to a facility’s activities, including information of pollutant emissions, wastes generated and raw materials consumed. Many of these data are required to be collected and reported under national Pollutant Release and Transfers Registers (PRTRs) which are an increasingly common environmental policy tool in the OECD countries.This article considers the relationship between cleaner production and PRTR reporting, and discusses whether cleaner production programmes can be enhanced by the use of PRTR data. A case-study of a fertiliser manufacturing facility reporting under the Australian National Pollutant Inventory (NPI) is used to analyse and illustrate the practical issues around linking cleaner production with PRTR reporting.PRTR reporting can assist in identifying cleaner production opportunities and in generating the data sets required to design, implement and monitor cleaner production programmes. There are, however, some limitations in PRTR data that need to be recognised. Specifically, these are issues around data quality and comparability (e.g. PRTRs rely heavily on default emissions factors rather than the monitoring data that are generally required for cleaner production programmes) and the different data sets required for PRTR reporting (which generally require reporting at the site level rather than at the individual process unit level that is required for cleaner production). [Copyright &y& Elsevier]

• Innovative phenotype-genotype association analysis for categorical genomic data having non-ignorable missing values. • Statistical learning method of random forest is used for variable selection in the analysis. • Weighted logistic ridge regression with EM algorithm is used for missing data imputation in the analysis. • Linear statistical hypothesis testing is used for determining the missingness mechanism in the analysis. • An application to analyzing real data from Australia breast cancer genome-wide association study (GWAS). Genomic data arising from a genome-wide association study (GWAS) are often not only of large-scale, but also incomplete. A specific form of their incompleteness is missing values with non-ignorable missingness mechanism. The intrinsic complications of genomic data present significant challenges in developing an effective and efficient procedure of phenotype-genotype association analysis by a statistical variable selection approach. In this paper we develop a coherent procedure of categorical phenotype-genotype association analysis, in the presence of missing values with non-ignorable missingness mechanism in genomic data. It is developed by integrating the statistical learning methods of random forest for variable selection, joint weighted logistic ridge regression with EM algorithm for missing data imputation, and linear statistical hypothesis testing for determining the missingness mechanism. Two simulated genomic datasets are used to undertake the phenotype-genotype association analysis by the proposed procedure, with the performance validated. The proposed procedure is then applied to analyze a real data set from breast cancer GWAS. [ABSTRACT FROM AUTHOR]

This paper examines the strategies that speakers employ in response to children's crying in the remote Aboriginal community of Wadeye, in northern Australia. Drawing on spontaneous interactions amongst Murrinhpatha speaking families, we analyse instances of crying by children aged 0;6 to 8;11 years, and the ways in which they are responded to. Results indicate that adult Murrinhpatha speakers manage children's distress through a variety of verbal and non-verbal strategies, and multimodal combinations thereof. The selection of strategies directly relates to the developmental stage of the crier. Adult caregivers respond differently to the crying of infants, of children who can walk unaided but are producing little if any language, of children who can talk intelligibly, and children more advanced again who have a degree of social independence. In the particular strategies that caregivers apply, they guide children towards a developmentally appropriate self-sufficiency. Caregivers encourage autonomy in ways that reflect a child's current abilities, be it physical, linguistic, emotional, or social. By exploring responses to crying in an under-researched cultural and linguistic context, this paper offers a unique perspective on the pragmatics of managing distress and what this reveals about local constructions of personhood within the context of carer-child interaction. • Analysis examines responses to children's crying in a remote Australian Aboriginal community. • Methods utilize multimodal analysis of spontaneous crying events in early childhood. • Findings show how caregiver responses are situated in relation to local expectations of children across developmental stages. [ABSTRACT FROM AUTHOR]

This paper examines the strategies that speakers employ in response to children's crying in the remote Aboriginal community of Wadeye, in northern Australia. Drawing on spontaneous interactions amongst Murrinhpatha speaking families, we analyse instances of crying by children aged 0;6 to 8;11 years, and the ways in which they are responded to. Results indicate that adult Murrinhpatha speakers manage children's distress through a variety of verbal and non-verbal strategies, and multimodal combinations thereof. The selection of strategies directly relates to the developmental stage of the crier. Adult caregivers respond differently to the crying of infants, of children who can walk unaided but are producing little if any language, of children who can talk intelligibly, and children more advanced again who have a degree of social independence. In the particular strategies that caregivers apply, they guide children towards a developmentally appropriate self-sufficiency. Caregivers encourage autonomy in ways that reflect a child's current abilities, be it physical, linguistic, emotional, or social. By exploring responses to crying in an under-researched cultural and linguistic context, this paper offers a unique perspective on the pragmatics of managing distress and what this reveals about local constructions of personhood within the context of carer-child interaction. • Analysis examines responses to children's crying in a remote Australian Aboriginal community. • Methods utilize multimodal analysis of spontaneous crying events in early childhood. • Findings show how caregiver responses are situated in relation to local expectations of children across developmental stages. [ABSTRACT FROM AUTHOR]

As research has shown, the interactional practice of getting acquainted in initial encounters can play a crucial role in the construction of interpersonal relationships. This exploratory paper focusses on one aspect of the getting acquainted discourse, i.e. how conversational openings are constructed in dyadic and multi-party interactions between previously unacquainted interlocutors. These interactions are examined in a specific context – the launch night episode of the reality television gameshow Big Brother Australia 2012. The findings indicate that, while some conversational openings follow the canonical structure, including preference for self-identification, in most conversational openings in our data, the interactants tend to request other-identification, which, importantly, is not treated as problematic. In addition, this study further confirms that introducing sequences are discursively constructed as essential in conversational openings. This becomes visible when the lack of identification at an early stage is treated as problematic by the interlocutors who orient to resuming or initiating introducing, whether prompted or unprompted, while engaging in the first instances of getting acquainted. • This paper explores how conversational openings are constructed in interaction. • This paper analyses dyadic and multi-party interactions in a reality television gameshow. • Interactants tend to request other-identification, not offer self-identification. • Introducing sequences are essential in conversational openings while getting acquainted. • Lack of self-identification is treated as problematic by interlocutors, and if missing, is resumed at a later stage. [ABSTRACT FROM AUTHOR]

At weirs and dams, the spillway flow is a high-velocity turbulent motion operating at very-large Reynolds numbers and physical measurements are challenging. In these high-speed free-surface flows, the upstream flow is not aerated and some air entrainment takes place downstream of the inception of free-surface aeration. The state-of-the-art knowledge in large dam spillway hydrodynamics currently reaches a major knowledge gap because of a lack of detailed quantitative field measurements in both the non-aerated and air-water flow regions. In this contribution, a review of prototype observations and measurements is presented based upon optical data sets conducted between 2010 and 2022 during floods at several large dam spillways in eastern Australia, with a focus on the key requirements for successful optical velocity measurements in high-velocity (prototype) spillway flows. The experience and expertise gained in these studies provide new insights into the operation of large dam spillways and their modelling. The intricacy of field measurement techniques is discussed using the experience at dam spillways during large floods, encompassing optical velocity measurements of spillway hydrodynamics at two structures. Overall, the paper presents current challenges on the modelling of large dam spillway flows. • Observations and measurements at large dam spillways are too few. • Prototype observations were successfully gained during 2010-2022 flood events. • A review of field experience and optical velocity measurements was conducted. • Successful optical flow velocity measurements of high-Reynolds-number spillway chutes were achieved. [ABSTRACT FROM AUTHOR]

National weather forecasting agencies routinely issue a range of hazard warnings. But to our knowledge, along sandy coastlines where storm waves and storm surge can result in widespread but location-specific beach erosion and beachfront flooding, no national-scale early warning service for these hazards is presently operational. This paper outlines the scientific basis and implementation of a new framework for large area coastal storm hazards forecasting, currently being tested along the southwest (Indian Ocean) and southeast (Pacific Ocean) coasts of Australia. The system provides 7-day rolling predictions of localized beach erosion and/or coastal flooding linked to forecasted extreme weather events. Coastal setting influences the nature and occurrence of these hazards, with sandy beaches along wave-dominated coasts more prone to erosion and at surge-dominated coasts to flooding. An existing nearshore water-level forecasting system and a new inshore wave modeling capability are used to forecast beach erosion and coastal flooding at every 100 m along the shore. At the regional scale O(100–1 000 km of coastline), a threshold-based decision tree model categorises the predicted extent, location, and severity of erosion and flooding. At a more local scale O(100–1 000 m), physics-based modeling using XBeach focuses on vulnerable or high-value locations, providing specific storm hazard indicators tailored to local needs. This two-tier approach is feasible for national implementation due to the reduced computational effort, limiting intensive modeling to pre-identified critical locations. Delft-FEWS manages the data and modeling workflow, ensuring scalability and compatibility with existing forecast infrastructure. Initial evaluations of the system are promising, with a detailed 2-year evaluation in progress. Future enhancements could include the use of satellite imagery for real-time beach width and dune topography assimilation and exploring alternative modeling approaches to further improve forecast accuracy. • A framework for coastal storm hazards early warning over very large areas is developed. • The severity of hazards is predicted every 100 m alongshore by parametric modeling. • Site-specific storm hazard indicators at vulnerable sites are predicted by XBeach. • 7-day rolling forecasts of storm hazards are communicated via a web portal. [ABSTRACT FROM AUTHOR]

Building operations use 45% of the total energy in Australia, and Net-Zero Energy Buildings (NZEBs) are instrumental in achieving net zero emissions by 2050. However, research reveals that various factors hinder NZEB adoption in Australia, and it is imperative to contextualise the barriers, thereby highlighting clear gaps that can be addressed to enhance the implementation of NZEB in Australia. This study presents a systematic literature review and bibliometric analysis to examine the barriers that hinder the implementation of NZEB contextualised within the Australian building industry. The paper's novelty resides in its contextualisation of barriers to NZEB thereby highlighting clear gaps that can be addressed to enhance the implementation of NZEB in Australia. Emergent results reveal the most impactful barriers experienced and the inter-relatedness between barriers and their effects on each other. These identified barriers are also compared with comparable countries, including the UK, China, Italy and the US. New insight and knowledge acquired provide a basis for assessing potential solutions to the main barriers. Findings show that a lack of knowledge, climate issues, and cost issues, in addition to government policy factors, are considered primary barriers encountered by the industry, hindering the achievement of NZEBs. It is imperative that future studies and initiatives counteract these impacts to realise a better implementation of NZEB in the Australian context. [ABSTRACT FROM AUTHOR]

Power grid damage and blackouts are increasing with climate change. Load forecasting methods that integrate climate resilience are therefore essential to facilitate timely and accurate network reconfiguration during periods of extreme stress. Our paper proposes a generalised Wildfire Resilient Load Forecasting Model (WRLFM) to predict electricity load based on operational data of a Distribution Network (DN) in Australia during wildfire seasons in 2015–2020. We demonstrate that load forecasting during wildfire seasons is more challenging than during non-wildfire seasons, motivating an imperative need to improve forecast performance during wildfire seasons. To develop the robust WRLFM, comprehensive comparative analyses were conducted to determine proper Machine Learning (ML) forecast structures and methods for incorporating multiple factors. Bi-directional Gated Recurrent Unit (Bi-GRU) and Vision Transformer (ViT) were selected as they performed the best among all 13 recently trending ML methods. Multi-factors were incorporated to contribute to forecast performance, including input sequence structures, calendar information, flexible correlation-based temperature conditions, and categorical Fire Weather Index (FWI). High-resolution categorical FWI was used to build a forecasting model with climate resilience for the first time, significantly enhancing the average stability of forecast performances by 42%. A sensitivity analysis compared data set patterns and model performances during wildfire and non-wildfire seasons. The improvement rate of load forecasting performance during wildfire seasons was more than two times greater than in non-wildfire seasons. This indicates the significance and effectiveness of applying the WRLFM to improve forecast accuracy under extreme weather risks. Overall, the WRLFM reduces the Mean Absolute Percentage Error (MAPE) of the forecast by 14.37% and 20.86% for Bi-GRU and ViT-based models, respectively, achieving an average forecast MAPE of around 3%. • Novel climate-resilient load forecast model for use during extreme wildfire seasons. • Bi-GRU and ViT outperform 11 recently trending machine learning models. • Categorical FWI with high resolution enhances forecast stability by 42%. • Optimised calendar, temperature and FWI input reduce errors by 17.6%. • Forecast improvement during wildfires is twice that in non-wildfire seasons. [ABSTRACT FROM AUTHOR]

The recent technological advances and their applications to agriculture provide leverage for the new paradigm of smart agriculture. Remote sensing applications can help optimise resources, making agriculture more ecological, increasing productivity and helping farmers to anticipate events that could not otherwise be avoided. Considering that losses caused by anomalies such as diseases, weeds and pests account for 20–40 % of overall agricultural productivity, a successful research effort in this area would be a breakthrough for agriculture. In this paper, we propose a methodology with which to discover and classify anomalies in images of crops, taken from a wide range of distances, using different Convolutional Neural Network architectures. This methodology also deals with several difficulties that usually appear in this kind of problems, such as class imbalance, the insufficient and small variety of images, overtraining or lack of models generalisation. We have implemented four convolutional neural network architectures in a high-performance computing environment, and propose a methodology based on data augmentation with the addition of Gaussian noise to the images to solve the above problems. Our approach was tested using two well-established open datasets that are unalike: DeepWeeds, which provides a classification of 8 weed species native to Australia using images that were taken at a distance of 1 m, and Agriculture-Vision, which classifies 6 types of crop anomalies using multispectral satellite imagery. Our methodology attained accuracies of 98 % and 95.3% respectively, improving the state-of-the-art by several points. In order to ease reproducibility and model selection, we have provided a comparison in terms of computational time and other metrics, thus enabling the choice between architectures to be made according to the resources available. The complete code is available in an open repository in order to encourage reproducibility and promote scientific advances in sustainable agriculture. • A convolutional deep learning model was developed for anomaly detection in agricultural images. • High and low-resolution imagery can be used as input for the model. • Data transformation and augmentation are part of our methodology. • Extensive experiments comparing 4 architectures and 2 datasets are shown. [ABSTRACT FROM AUTHOR]

Microgrids are extensively integrated into electrical systems due to their many technical, economic, and environmental advantages. However, they encounter a challenge as they experience high-frequency fluctuations caused by the stochastic nature of renewable energy generation, electric loads, and the presence of Electric Vehicles (EVs). Therefore, various techniques, algorithms, and controllers have been introduced to ensure effective Load Frequency Control (LFC) and maintain a stable power system in microgrids. These methods aim to ensure that the system's frequency remains stable and within an acceptable range, especially when faced with changing load demands and other factors. This paper presents a novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), to optimize the load frequency model of a microgrid with vehicle-to-grid integration. The results are then compared under various scenarios, including renewable energy integration, EV charging and discharging dynamics, and varying load demands. The comparative analysis involves assessing the performance of the conventional Proportional–Integral–Derivative (PID) controller, the PSO-PID controller, and the newly proposed controlling technique. The suggested controller attains 99.904% efficiency with a negligible mean squared error of 1.1112 × 10−7, decreasing the integrated time absolute error to 1.0 × 10−4. It shows rapid response, precise targeting, and quick peak output ability, with marginal overshoot and undershoot, and a transient time of 28.5626 s, efficiently controlling microgrid frequency. Stability analysis validates the effectiveness of the proposed PSO-TANN controller in ensuring stability within the microgrid's LFC system during uncertainties and disturbances. This establishes resilience, diminishes settling time, and maintains reliable performance while controlling frequency. • An investigation into an islanded microgrid's Load Frequency Control (LFC) model framework has been conducted, comprising diesel generator, solar PV system, wind turbine, and EVs operating in vehicle-to-grid mode to supply power to electric loads, consequently impacting the frequency dynamics of the microgrid system. • A novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN) has been implemented to optimize the load frequency model of a microgrid. • An extensive comparative analysis with other controllers, including PID and PSO-PID, is conducted across diverse scenarios involving renewable energy integration and EV charging and discharging dynamics under various load disturbances to reduce settling time, control system frequency, and ensure reliable performance. • The stability analysis validates the efficiency of the proposed PSO-TANN controller in ensuring reliability during uncertainties and disturbances, maintaining overall robustness and stability of the LFC system within the microgrid. [ABSTRACT FROM AUTHOR]

• Apply python to interface with plaxis software to simulate the embankment settlement for a case study in Australia. • Develope a GEP-based prediction model to estimate the settlement of ground improvement. • Conduct a parametric study to examine the effects of input variables on the settlement of the ground. • Optimize the design of CMC diameters and CMC spacings for specific ground conditions. This paper is going to present the advanced feature in Plaxis using remote scripting with Python wrapper. Python allows engineers to create Plaxis models, calculate construction phases, and plot the results automatically. The settlement estimation of a high embankment, supported by cement mixing columns (CMC), is presented as a case study. Approximately 500 models were created and analyzed within a few hours with different embankment heights, soft soil thickness, CMC spacing, and CMC diameter. This original database was used to develop a regression model using a Gene-expression programming (GEP) algorithm. The proposed GEP-based model with high accuracy could be applied to optimize the CMC design. In detail, the coefficient of correlation (R-value) of all phases is high and fluctuates from 0.967 to 0.976, while the mean absolute error of the model is lower than 0.009 m. The parametric study indicates that increasing the height of the fill embankment, the thickness of the peat layer, or the spacing between CMCs causes high settlement, while increasing the CMC diameter could significantly reduce the settlement of the embankment. The research results demonstrate that using Plaxis remote scripting and the GEP technique could help engineers run the numerical analysis much faster, leading to a more in-depth analysis to create better decision-making. [ABSTRACT FROM AUTHOR]

Although traditional physical models have been used to analyse groundwater systems, the emergence of novel machine learning models can improve the accuracy of the predictions. Deep learning has been prominent in environmental and climate change problems. In this paper, we present a framework for utilising deep learning models to predict groundwater levels based on nearby streamflow and rainfall data. We address the missing data problem using a Bayesian linear regression model within the deep learning framework. Our deep learning framework utilises models such as long-short term memory (LSTM) networks and convolutional neural networks (CNN) for multi-step ahead time series prediction. We examine the fluctuations in groundwater levels at various boreholes located near Middle Creek in New South Wales, Australia. We use the National Collaborative Research Infrastructure Strategy (NCRIS) groundwater database and utilise Bayesian linear regression to impute missing data. We investigate the accuracy of the selected models for individual and regional basins and univariate and multivariate strategies. Our results show that the LSTM-based regional model with multivariate strategy using rainfall data provided the best accuracy. • We predict groundwater levels based on stream flow and rainfall data. • Our framework uses Bayesian data imputation and deep learning for multi-step prediction. • We examine the groundwater fluctuations at various boreholes located in Australia. • Results show a fine-tuned regional model with rainfall data yielded the best accuracy. • We find that LSTM models outperformed CNNs using the multivariate approach. [ABSTRACT FROM AUTHOR]

• This paper is dedicated to vehicle-bicycle crash severity modeling. • The class-imbalanced issue in vehicle-bicycle crash severity analysis is handled with the imbalanced data resampling process. • To address the complexity of crash dataset, the learning-based feature extraction process is adopted in an iterative manner, such that the the most significant contributing factors to the severity of vehicle-bicycle crashes are determined and the trade-off between computation time and model performance is catered for. • The vehicle-bicycle crash dataset in this paper contains a large number of discrete variables. The gradient boosting algorithm is applied to handle the large number of categories and does not rely on strict statistical assumptions. • The impact of the most significant contributing factors on the severity of vehiclebicycle crashes are explained with the marginal effect analysis. • The result can provide some implications for policies and counter-measures for fatal and serious vehicle-bicycle crashes. Introduction: Although cycling is increasingly being promoted for transportation, the safety concern of bicyclists is one of the major impediments to their adoption. A thorough investigation on the contributing factors to fatalities and injuries involving bicyclist. Method: This paper designs an integrated data mining framework to determine the significant factors that contribute to the severity of vehicle-bicycle crashes based on the crash dataset of Victorian, Australia (2013–2018). The framework integrates imbalanced data resampling, learning-based feature extraction with gradient boosting algorithm and marginal effect analysis. The top 10 significant predictors of the severity of vehicle-bicycle crashes are extracted, which gives an area under ROC curve (AUC) value of 0.8236 and computing time as 37.8 s. Results: The findings provide insights for understanding and developing countermeasures or policy initiatives to reduce severe vehicle-bicycle crashes. [ABSTRACT FROM AUTHOR]

Over the past decades, populist propaganda has grown exponentially, triggering the proliferation of nationalistic discourses worldwide. Australia has not been exempt from the populist surge, which has propagated online and offline, yet without receiving enough academic attention. Considering the disintermediated interaction promoted by their communicative formats, Social Networking Sites (SNSs) are believed to have fostered the spread of populist ideologies. While recent research conceptualizes SNSs as socio-discursive sites where ambient affiliations come to be dialogically construed by means of conversational tagging, it has not been investigated whether and how hashtagging enables political dialogue among populist affiliates in virtual contexts. Aiming to fill this gap, the present research relies on a corpus-based mixed methodology that combines the semantic mapping of hashtags, through the quantitative methodologies of Social Network Analysis, with the qualitative approaches of Systemic Functional Linguistics. The article investigates a corpus of tweets posted by selected populist politicians active in Australia. Drawing upon Halliday's metafunctions of language, tweets were closely analyzed to gain insights into the semantic nature and linguistic functions of the collected hashtags. By borrowing Bakhtin's and Austin's terminological framework, the paper ultimately discusses how the Twitter ambient is dialogically built. • Digital populism may proliferate on Twitter through the hashtagging practice. • Hashtags reveal the semantic fulcrums of the populist dialogues enacted onto the social media platform. • Mentions show how the populists' interlocutors are involved into the dialogic field of Twitter. • Twitter ambients can simultaneously be hetero- and mono- glossic. • Social Network Analysis offers an innovative way of investigating digital spaces from a linguistic perspective. [ABSTRACT FROM AUTHOR]

Optimal nutrition care is important in the management of cystic fibrosis (CF). This paper summarises the '2017 Nutrition Guidelines for Cystic Fibrosis in Australia and New Zealand (NZ)'. CF dietitians formulated 68 practice questions which were used to guide a systematic literature search and review of the evidence for nutrition in CF. Identified papers underwent quality and evidence assessment using the American Dietetic Association quality criteria checklist and the National Health and Medical Research Council of Australia (NHMRC) rankings. Evidence statements, graded recommendations and practice points were developed covering core nutrition topics (assessment and nutrition interventions including oral, enteral and micronutrient supplementation); nutrition-related co-morbidities (including pancreatic insufficiency, CF-related diabetes, bone health and distal intestinal obstruction syndrome); and key new topic areas (genetic modulator therapies, overweight/obesity and complementary therapies). This paper showcases highlights from the guidelines, focussing on new topic areas and geographic and climate considerations for vitamin D, salt and hydration. • This paper summarises the 2017 Nutrition guidelines for CF for Australia and NZ • A systematic approach to evidence appraisal and guideline development was used • Nutrition assessment, nutrition support and micronutrient supplementation are covered • Nutrition-related topics including CF-related co-morbidities and PERT are covered • Novel topic areas include genetic modulators, overweight/obesity and probiotics [ABSTRACT FROM AUTHOR]

The Elevation sensitive Oblique Incidence Sounder Experiment (ELOISE) was an extensive experiment undertaken by the Defence Science and Technology (DST) Group that focused on collecting ionospheric sensor data from multiple overlapping ionospheric paths in the Australian region in order to improve understanding of the characteristics of ionospheric variability and its effect on HF radio propagation. The experiment ran from July to October 2015 and included a period of three weeks of increased sample density and three days of dedicated over-the-horizon (OTH) radar operations. It was anticipated that ELOISE would sample a wide range of environmental conditions and present an opportunity to characterise periods of "normal variability" and periods of "exceptional variability" in the ionosphere. This report is a general description of the aims of this experiment and the types of data collected. Particular interest focused on observing and measuring variability in ionospheric electron density gradients and their effect on oblique HF propagation. To this end, ELOISE established a pair of two dimensional HF receiver arrays to directly measure the oblique angle of arrival (AoA) on many overlapping oblique paths. ELOISE also established a dense sub-network of spatially separated quasi-vertical incidence soundings in the vicinity of Alice Springs in central Australia. This enabled a comparison of gradients observed in a dense network of vertical sounders with gradient effects observed in oblique propagation passing overhead. Several additional ionospheric observing systems were also used to give complementary pictures of the fine scale characteristics of ionospheric variability in the region. This paper is an overview of the 2015 Elevation sensitive Oblique Incidence Sounder Experiment (ELOISE), an experiment designed to observe and characterise mid-latitude ionospheric disturbances in the Australian region and understand their impact on high frequency (HF) signal propagation. ELOISE involved the simultaneous operation of a large collection of ionospheric sounders enabling ionospheric variability to be characterised on a finely sampled large scale. Particular efforts were made to provide direct high fidelity measurements of the angle of arrival (AoA) on many oblique HF propagation paths. These direct AoA measurements imply horizontal electron density gradients that can be compared to ionospheric gradients estimated from conventional models of the ionosphere derived from the spatial network of sounder sites. The size and scope of the experiment are detailed in this paper and some preliminary results are presented. [ABSTRACT FROM AUTHOR]

Emerging industries and technologies are made up of objective, practical requirements and ideas that encompass broad hopes for what future newness might enable. The bioeconomy is one such emerging industry. Its recent evolution has involved a complicated and dynamic mix of promises, reality checks and subsequent ambivalences. This article frames and investigates the emerging algae-based bioplastics industry as one niche within this broader system envisaged for social, technological, economic, political and ecological change. As such, our paper presents two categories of knowledge that in practice interrelate: 1) specific and practical recommendations that can assist a future algal-based bioplastics industry to develop in Australia in ways that are conscious of socio-ecological and socio-cultural dimensions, and 2) critical extensions that go beyond the attributes of newness, impact, suddenness and universality often emphasised in both popular and scientific research into emerging green technologies. In particular, our analysis highlights the importance of specific temporal, tonal and spatial factors when framing the contextual factors associated emerging industries, technologies and the different pathways for change they might help support. • Pathways and enabling factors for a algal-based bioplastics industry in regional Australia are presented. • The 'social idea' of the bioeconomy is analysed with reference to algal-based bioplastics. • The algal-based bioplastics industry is contextualised in relation to transition pathways literatureon the bioeconomy. • Themes and recommendations are synthesised from literature review and semi-structured interviews. [ABSTRACT FROM AUTHOR]

In recent years, many FinTech start-up firms have poured into the FinTech landscape. Since FinTech leaders (including founders and chief executive officers (CEOs)) have a major impact on the performance of their respective firms, research studies on FinTech leaders are indispensable. This paper discusses our study to investigate three major dimensions (education, work experience, and eagerness of self-employment) of FinTech leaders in an Australian context. We also compare these dimensions between FinTech founders and CEOs. Overall, we found that: (a) The majority of FinTech leaders are degree holders and one-third of them have a master's degree. (b) Only about one-third of leaders with a master's degree hold an MBA. (c) Only 19.4% and 11.1% of degree-holding leaders were graduated from the IT and finance discipline, respectively. (d) Only 19.0% of leaders have previous IT work experience and the mean years of such experience is only 2.0. (e) 54.8% of leaders have previous finance work experience and the mean years of such experience is 6.7. (f) When compared with CEOs, founders are more likely to hold a master's degree and are more eager to be self-employed. [ABSTRACT FROM AUTHOR]

Hybrid offshore renewable energy platforms have been proposed to optimise power production and reduce the levelised cost of energy by integrating or co-locating several renewable technologies. One example is a hybrid wave-wind energy system that combines offshore wind turbines with wave energy converters (WECs) on a single floating foundation. The design of such systems involves multiple parameters and performance measures, making it a complex, multi-modal, and expensive optimisation problem. This paper proposes a novel, robust and effective multi-objective swarm optimisation method (DMOGWA) to provide a design solution that best compromises between maximising WEC power output and minimising the effect on wind turbine nacelle acceleration. The proposed method uses a chaotic adaptive search strategy with a dynamic archive of non-dominated solutions based on diversity to speed up the convergence rate and enhance the Pareto front quality. Furthermore, a modified exploitation technique (Discretisation Strategy) is proposed to handle the large damping and spring coefficient of the Power Take-off (PTO) search space. To evaluate the efficiency of the proposed method, we compare the DMOGWA with four well-known multi-objective swarm intelligence methods (MOPSO, MALO, MODA, and MOGWA) and four popular evolutionary multi-objective algorithms (NSGA-II, MOEA/D, SPEA-II, and PESA-II) based on four potential deployment sites on the South Coast of Australia. The optimisation results demonstrate the dominance of the DMOGWA compared with the other eight methods in terms of convergence speed and quality of solutions proposed. Furthermore, adjusting the hybrid wave-wind model's parameters (WEC design and PTO parameters) using the proposed method (DMOGWA) leads to a considerably improved power output (average proximate boost of 138.5%) and a notable decline in wind turbine nacelle acceleration (41%) throughout the entire operational spectrum compared with the other methods. This improvement could lead to millions of dollars in additional income per year over the lifespan of hybrid offshore renewable energy platforms. • A new multiobjective optimisation method proposed and enhanced hybrid wave-wind platform. • A new combination of chaotic adaptive exploration and dynamic archive is introduced. • Effective and practical discretisation method improved performance of hybrid platform. • The performance of the proposed optimiser is validated on four real sea studies. [ABSTRACT FROM AUTHOR]

The increased penetration of renewable energy sources (RESs) as an effective mechanism to reduce carbon emissions leads to an increased weather dependency for power and energy systems. This has created dynamic operation and degradation phenomena, which affect the lifetime estimation of the assets operated with RESs. For the reliable and efficient operation of RES it is crucial to monitor the health of its constituent components and feature selection is a crucial step for building robust and accurate health monitoring approaches. In this context, this paper presents a probabilistic feature selection approach, which probabilistically weights and selects features through a heuristic and iterative process for an improved asset lifetime estimation. Power transformers are key power grid assets and they are used to demonstrate the validity and impact of the proposed approach. The approach is tested on two different photovoltaic power plants operated in Spain and Australia. Results consistently show that the proposed feature-selection approach reduces the prediction error and consistently selects relevant features. The approach has been applied to transformer lifetime estimation, but it can be generally applied to assist in the lifetime estimation of other components operated in RESs. Part of the studies presented here as well as source codes are all open-source under the GitHub repository https://github.com/iramirezg/FeatureSelection. • Probabilistic feature selection approach for improved asset lifetime estimation. • Integration of environmental features for improved renewable-operated asset lifetime. • Systematic and robust feature weighting methodology. • Improved transformer lifetime estimation including sensor and environmental data. • Validated on two real photovoltaic power plant case studies. [ABSTRACT FROM AUTHOR]

• Develops a deep Bayesian survival model that integrates deep neural network and Bayesian inference into the survival analysis for rail useful lifetime modelling. • Demonstrates survival analysis is an effective way to handle censored data problems in rail useful lifetime modelling. • Conducts extensive evaluation to verify the effectiveness of this model, using Concordance index, root mean square error, and a proposed CW-index. • The estimated lifetimes obtained by this model are the closest to real lifetimes compared to other commonly used models. • This model can provide the confidence interval of the estimated lifetime, which is more useful than point estimates in decision-making and maintenance planning of railroad systems. Reliable estimation of rail useful lifetime can provide valuable information for predictive maintenance in railway systems. However, in most cases, lifetime data is incomplete because not all pieces of rail experience failure by the end of the study horizon, a problem known as censoring. Ignoring or otherwise mistreating the censored cases might lead to false conclusions. Survival approach is particularly designed to handle censored data for analysing the expected duration of time until one event occurs, which is rail failure in this paper. This paper proposes a deep Bayesian survival approach named BNN-Surv to properly handle censored data for rail useful lifetime modelling. The proposed BNN-Surv model applies the deep neural network in the survival approach to capture the non-linear relationship between covariates and rail useful lifetime. To consider and quantify uncertainty in the model, Monte Carlo dropout, regarded as the approximate Bayesian inference, is incorporated into the deep neural network to provide the confidence interval of the estimated lifetime. The proposed approach is implemented on a four-year dataset including track geometry monitoring data, track characteristics data, various types of defect data, and maintenance and replacement (M&R) data collected from a section of railway tracks in Australia. Through extensive evaluation, including Concordance index (C-index) and root mean square error (RMSE) for evaluating model performance, as well as a proposed CW-index for evaluating uncertainty estimations, the effectiveness of the proposed approach is confirmed. The results show that, compared with other commonly used models, the proposed approach can achieve the best concordance index (C-index) of 0.80, and the estimated rail useful lifetimes are closer to real lifetimes. In addition, the proposed approach can provide the confidence interval of the estimated lifetime, with a correct coverage of 81% of the actual lifetime when the confidence interval is 1.38, which is more useful than point estimates in decision-making and maintenance planning of railroad systems. [ABSTRACT FROM AUTHOR]

• A new energy-as-a-service business model for aggregators of prosumers. • A new techno-economic simulation framework to compute business model benefits. • The new business model increases the average profitability of the aggregator by 438%. • The new business model provides free electricity to prosumers. Traditional retail business models price electricity using volumetric tariffs, which charge customers for the unit of energy consumed. These tariffs were designed for passive consumers with low flexibility. In this paper, we argue that these volumetric tariffs are unsuitable for prosumers with high flexibility since they are unable to adequately value the flexibility of their distributed energy resources in multiple electricity markets. This reduces the interest of prosumers participating in aggregators' business models. To address this issue, we propose a new business model for aggregators of prosumers, based on the concept of energy-as-a-service. In this business model, prosumers pay a monthly fee for aggregators to represent and optimize them in multiple wholesale electricity markets, including in energy and ancillary service markets. The monthly fee is computed by a new techno-economic simulation framework proposed in this paper, which can also be used to estimate the profitability of the new business model from the perspectives of both the aggregator and prosumers. Our experiments on a portfolio of real prosumers from Australia show that the new business model maximizes the economic benefits of both the aggregator and prosumers by increasing the average profit of the aggregator by 438% and reducing the average electricity cost of prosumers from $583/year to $0 when compared to two of the most common retail business models available in the Australian market. In other words, the economic benefit for prosumers is free electricity. In addition to this benefit, the new business model also provides simplicity and predictability to prosumers, as they are offered a guaranteed outcome before providing the services. [ABSTRACT FROM AUTHOR]

• Qualitative outcomes of energy poverty across Australian climate zones are compared. • Outcomes influenced by energy costs and local energy, housing and welfare policies. • Measures of energy poverty and policy responses need to reflect local contexts. Academic interests into energy poverty have been growing since the 1990s. These have ranged from quantitative measures and qualitative reflections, as well as ones that aimed to provide more nuanced definitions. These studies have, however, typically focused on single jurisdictions or across national borders, with very few examining the issue across different climatic and policy contexts within the same nation. This paper aims to address this gap by focussing on differences in climate conditions, local policies and quality of the housing stock across Australia and reflects on how these factors each played a role, to varying degrees, in the prevalence of energy poverty amongst lower-income households across eight metropolitan and regional settings conducted as part of a recent study in Australia. Through a review of focus group, interview and workshop data, it especially highlights the more qualitative impacts of energy poverty, and how each of these factors come to influence the extent to which lower-income households experience energy poverty and the support they were able to access. Additional to the need for more contextualised measures of energy poverty, this paper calls for localised responses to alleviate the negative outcomes reflected by our participants in regard to both energy and housing policies. [ABSTRACT FROM AUTHOR]

This paper argues that attempts to transform Australia's urban environment into a sufficiently sustainable one has been misdirected. The 'green rating tool,' industry's adherence to relevant standards and governmental policies represent the primary means of effecting the sustainability transition. However, only high-profile commercial building owners seem interested in being green-rated; the actual end-users of buildings are far less committed (e.g. employees ensconced in commercial buildings and residential home occupiers). Through a systematic review of 103 journal articles published on the topic of end-users and sustainability transition, original findings are presented. The findings reveal that most residential end-users do not purchase green homes and without their 'buy-in,' sustainability transition across Australia will continue to fail. This paper offers a critical analysis of the status-quo, identifying where the effort to generate a sustainable urban environment has been misdirected, what challenges prevail, and why residential end-users have been overlooked. In looking for a way forward that engages end-users, the paper proposes that financial incentives for the purchase of low-carbon buildings must be introduced into the residential real-estate market. And the modeling for this rebate is discussed in terms of emissions trading schemes or carbon tax. • Attempts to transform Australia's urban environment into a sufficiently sustainable one have not generated desirable outcomes. • A systematic review of 103 journal articles is conducted on the topic of end-users and sustainability transition. • Residential end-users do not purchase green homes and without their 'buy[HYPHEN]in,' sustainability transition will fail. • Financial incentives for the purchase of low-carbon buildings must be introduced into the residential real-estate market. • The modeling for the rebate is discussed in terms of emissions trading schemes or carbon tax. [ABSTRACT FROM AUTHOR]

To fulfil the hunger for sustainable development, Australian construction sector seems to contemplate toward green design. Green-Star environmental rating system, one of the active building rating systems in Australia, has also been employed in New Zealand, and South Africa. To help engineers and designers obtain comprehensive visualisation on sustainable projects, this paper develops a model to evaluate energy consumption and greenhouse-gas emissions based on typical Australian commercial building alternatives. From that, this paper explains how to achieve points for “Energy” and “Life-cycle impacts” categories of this rating system. The model uses GaBi 8.1 platforms and shows relationships between building's energy consumption, and greenhouse-gas emissions released during the building lifetime. This paper provides insight into life-cycle greenhouse-gas emissions assessment for several typical commercial building fabrics in Australia. The developed model can also be conveniently modified to automate calculations for other credits under the Green-Star environmental rating system, and other environmental rating systems around the world. The study suggests that designers should consider the inverse relationship between building envelopes' total R-value with energy consumption and greenhouse-gas emissions. Another finding shows that materials used for building envelopes play a significant role to manage energy consumption as well as greenhouse-gas emissions amount during the building's life-cycle. [ABSTRACT FROM AUTHOR]

This paper presents experimental and numerical investigations into the post-fire cross-section compressive behaviour and resistances of circular recycled aggregate concrete-filled stainless steel tube (RACFSST) stub columns. Twelve stub column specimens, fabricated from concretes with three recycled coarse aggregate replacement ratios (0%, 35% and 70%), were tested after exposure to the ISO-834 standard fire for 0 min (i.e. at ambient temperature), 15 min, 30 min and 45 min. The experimental investigation included heating and cooling of specimens, cylinder tests and post-fire tensile coupon tests and stub column tests. The test results, including load–end shortening curves, failure loads and failure modes, were fully reported and the initial compressive stiffnesses and confinement effect were discussed. The numerical investigation was subsequently conducted, where thermal and mechanical finite element models were developed and validated against the test results and then used to perform parametric studies to generate further numerical data. Given the absence of design codes for stainless steel–recycled aggregate concrete composite structures after exposure to fire, the relevant design rules for circular natural aggregate concrete-filled carbon steel tube stub columns at ambient temperature, as set out in the European code, Australia/New Zealand standard and American specification, were assessed, using post-fire material properties, for their applicability to circular RACFSST stub columns after exposure to fire, based on the test and numerical data. The assessment results generally revealed that the European code and the Australian/New Zealand standard led to an acceptable level of design accuracy and consistency, while the American specification resulted in rather conservative and scattered post-fire cross-section compression resistance predictions. • The compressive behaviour of circular RACFSST stub columns after exposure to fire is investigated. • Post-fire compression tests on twelve circular RACFSST stub column specimens are conducted. • The influences of heating durations and RCA replacement ratios are analysed. • Thermal and mechanical FE models are validated against test results and then used to conduct parametric studies. • The relevant design codes are assessed against the test and FE data. [ABSTRACT FROM AUTHOR]

Australia is a long-term key partner of China in coal trading, which is famous for high-quality coal and its price advantage. However, affected by the evolution of international political and economic situation, China has issued a coal ban on Australian coal since the end of 2020. The potential environmental consequences of this coal ban remain unclear. So, this paper quantifies the ban's impact on air pollution, human health and climate change by simulating the emission difference between the port scenario (simulating the emissions due to Australian coal import under business as usual) and the mine scenario (simulating the emissions due to domestic coal mining that converted from import share of Australian coal), and monetizes the losses into economic costs for direct comparison. The results illustrate the emission of SO 2 , VOCs and GHG in mine scenario grows up to 102, 12 and 6 times the amount of that in port scenario, mainly deriving from coal mining process in the North China Plain and Yunnan-Guizhou Plateau. It shows that the potential alternative of replacing import with mining leads to PM 2.5 pollution exacerbation and O 3 pollution abatement, which causes 148 (95 % CI: 101–183) non-communicable diseases and respiratory disease-related deaths in total. As for GHG, the emission of BC in transportation process and fugitive methane in coal mining process contribute to an impressive effect on warming potential with few quantities, which is worth noting in control process. In total, the environmental deterioration resulted in an economic loss of approximately 934 million yuan. The undiminished demand for high-quality industrial coal as well as growing carbon tax could make the economic value of the coal ban's environmental consequences more considerable in the near future. This research emphasizes the contradiction between economic development and environmental improvement caused by excessive dependence on coal and addresses a critical knowledge gap in assessing the ban's environmental impact by economic value. The result directly demonstrates the environmental consequences of the potential mining-replacing-import alternative and helps better understand environmental influences on the Chinese government's decision. [ABSTRACT FROM AUTHOR]

Spatial predictions of wildfire spread are used operationally and in risk estimation. It is important that their outputs are validated to quantify predictive performance and uncertainty. There are numerous loss functions for this simulation validation. Our paper synthesises ten common and five novel loss functions for evaluating the performance of wildfire predictions against observed or simulated wildfires. We describe each loss function; test their sensitivity to scale, rotation and translation; and demonstrate their application with three case study wildfires in south-eastern Australia. Based on their purpose, there are three categories of loss functions: general overlap, partial overlap and distance. Within these categories, there is functional redundancy as many loss functions are appropriate and perform similarly. Using loss functions from across the three different categories is the most comprehensive for evaluating wildfire models, but the choice depends on the simulation purpose and interpretation. • Spatial predictions of wildfire spread are frequently used but not always evaluated. • We synthesised fifteen loss functions for evaluating predicted vs observed wildfire spread. • There is not a 'best' loss function, it depends on simulation use and interpretation. [ABSTRACT FROM AUTHOR]

Accurately predicting estuaries water quality is essential to support immediate intervention to water quality problem management. Deep learning is used to improve forecasting of water quality parameters in many aquatic systems. However, it is frequently constrained by low data frequency and quality. High-frequency, continuous monitoring using integrated in-situ water quality and environmental sensors can be an input to deep learning models resulting in highly accurate water quality and environmental predictions. This paper proposes a novel approach to improve forecasting of water quality and environmental variables. The results of the real-world data from the Swan Canning Estuary sites show how well the suggested model works. With different sizes of training and testing sets, the model can still predict the increased number of hours in high scores. Eliminating highly correlated variables impact the model's performance, emphasising the usefulness of strongly correlated variables in scarce data scenarios. • This article proposes a framework to enhance water quality variable forecasting. • The framework reframes the time series problem as a supervised learning problem. • The results on the set of data from the estuary show how well the framework works. • The approach is envisaged to support management in detecting water quality issues. [ABSTRACT FROM AUTHOR]

This paper profiles Australian universities’ website communications about sustainability initiatives, especially relating to Talloires Declaration signification and the ten recommended actions. The research involves a content study of Talloires signatories’ websites and their semiotic and signaling theory aspects across time. The source is publicly available information (time period 2014 and 2015 respectively) in context with education for sustainable development (ESD) in society and the transaction model in communication theory. It is found that some of the signatory universities in Australia exemplify a few of the Talloires ten agreed actions in their public communications, but each point is not emphasised with equal priority. The variance may be due to divergent underlying objectives; such as public communication of Talloires toward genuine benefit for the environment, or to create a strategic positioning tool to signal the university as an environmental champion within the broader society. Focusing upon secondary data from signatory university websites in Australia, the paper provides a study of higher education signification of Talloires. It assists the initial understanding of signaling theory in the public communication transactions that promote Talloires action and implementation. The findings encourage further studies of related communication issues in universities in other countries. The paper also deepens local and global understanding of perceptions of sustainability and contributes to the body of comparative literature of sustainability in higher education as a development component of strategic management of ESD. [ABSTRACT FROM AUTHOR]

Forensic science is increasingly used to help exonerate the innocent and establishing links between individuals and criminal activities. With increased reliance on scientific services provided by multi-disciplinary (police, medicine, law, forensic science), and multi-organisational in the private and government sectors (health, justice, legal, police) practitioners, the potential for miscommunication resulting unjust outcomes increases. The importance of identifying effective multi-organisational information sharing is to prevent the 'justice silo effect'; where practitioners from different organisations operate in isolation with minimal or no interaction. This paper presents the findings from the second part of the Interfaces Project, an Australia-wide study designed to assess the extent of the justice silos. We interviewed 121 police, forensic scientists, lawyers, judges, coroners, pathologists and forensic physicians. The first paper published in 2013 presented two key findings: first investigative meetings were rare in adult sexual assault cases; second many medical practitioners were semi-invisible in case decision-making with this low level of visibility being due to lawyers, forensic scientists or police not being aware of the role/expertise medical practitioners offer. These findings led to the development of a flowchart model for adult sexual assault that highlights the range of agencies and practitioners typically involved in sexual assault. The rationale for the flowchart is to produce a visual representation of a typical sexual assault investigative process highlighting where and who plays a role in order to minimise the risk of justice silos. This is the second paper in a series of two. [ABSTRACT FROM AUTHOR]

In Australia, heatwaves are the deadliest natural hazard and a major driver of peak electricity demand. The disproportionately high peak demand increases electricity prices, causes occasional blackouts and exacerbates energy poverty, all of which limit one’s ability to use air conditioning. Meanwhile, increased energy efficiency of dwellings may decrease their heat stress resistance. This paper challenges whether the current Australian Nationwide Energy Rating Scheme encourages heat stress resistance. Cooling consumption, peak cooling demand and the risk of indoor overheating were assessed for a typical single-storey home in Adelaide and Sydney. Design options between 6 and 8 stars, plus two traditional, energy-inefficient homes were simulated with the AccuRate energy simulation program. This paper demonstrates that the NatHERS does not directly encourage heat stress resistance in new homes and can even deliver buildings with worse heat resistance and increased reliance on air conditioning than traditional, energy-inefficient buildings. This overreliance on air conditioning can present a public health hazard, consequently, the integration of heat stress resistance in the Nationwide House Energy Rating Scheme is recommended. Recommendations for the analysis and assessment of heat stress resistance are provided. [ABSTRACT FROM AUTHOR]

Recycled concrete waste in the form of recycled concrete aggregate (RCA) is presently used mostly as a road base filler in Australia. However, instead of producing natural aggregate (NA) to manufacture natural aggregate concrete (NAC) to use in structural concrete, there is potential to use RCA to manufacture recycled aggregate concrete (RAC). While the material performance of RAC compared to NAC is analysed in the existing literature, it is not evident whether the use of RCA in structural concrete results in financial and environmental benefits. Previous literature analysing these aspects mostly focuses on a single area of investigation. This paper presents an integrated methodology for the assessment of the use of RCA replacing NA in structural concrete, considering technical, financial, environmental and social perspectives. Cost-benefit assessment (CBA) has been used to evaluate the internalised impacts as well as external costs concerning the use of both RAC and NAC. The scope of each discipline-focused assessment is presented demarcating the relevant scope for further study, and the specific tools and methodologies to be adopted are specified. Finally, amalgamating the different discipline-focused assessments, a unique approach for comparing a sustainable, alternative raw material for concrete, is presented in this paper. [ABSTRACT FROM AUTHOR]

• We will monitor compliance with the intervention and adverse events. • Data will be analyzed following intention-to-treat principles. • A cost-effectiveness analysis will be performed. This a priori statistical analysis plan describes the methods of analysis for the Trial Of Prevention Strategies for low back pain (TOPS). TOPS aimed to investigate the effectiveness and cost-effectiveness of exercise and education classes compared with a minimal intervention control in preventing recurrence of low back pain (LBP) in people who have recently recovered from an episode of LBP. TOPS is a superiority, pragmatic, parallel-group randomized controlled trial with allocation concealment, blinded outcome assessors, and intention-to-treat analysis. Participants were randomized to a physical therapist-led exercise and education program for 12 weeks or minimal intervention. The primary outcome was days to recurrence of an episode of LBP. The three key secondary outcomes were days to recurrence of an episode of LBP resulting in (1) activity limitation, (2) care seeking for LBP; and (3) work absence of at least 1 day. Differences in survival curves for the primary (days to recurrence) and secondary outcome (days to LBP with activity limitation, days to care seeking due to LBP, and days to work absence due to LBP) will be compared using Cox regression. Hazard ratios (HRs) and median survival times with 95% confidence intervals (CI) will be calculated. The number of adverse events, including serious adverse events will be reported and the proportion of adverse events between groups will be compared using a Chi-squared test. This paper will provide a detailed description of the planned analyses for the TOPS trial. Australian New Zealand Clinical Trials Registry (ACTRN12615000939594). [ABSTRACT FROM AUTHOR]

• The ELM model is applied to predict the ionospheric propagation factor M(3000)F2. • The ELM achieves slightly higher accuracy at a faster training speed than BPNN. • Better average accuracy of ELM over the IRI in high and low solar activity years. This paper presents an novel extreme learning machine (ELM)-based prediction model for the ionospheric propagation factor M(3000)F2 at Darwin station (12.4°S, 131.5°E; −44.5°dip) in Australia. The proposed ELM model is trained with hourly daily values of M(3000)F2 from the period 1998–2014 except 2001 and 2009. The hourly daily values of 2001 (high solar activity) and 2009 (low solar activity) are used for validating the prediction accuracy. The proposed ELM for modeling M(3000)F2 can achieve faster training process and similar testing accuracy compared with backward propagation neural network (BPNN). In addition, the performance of the ELM is verified by comparing the predicted values of M(3000)F2 with observed values and the international reference ionosphere (IRI −2016) model predicted values. Based on the error differences (the root mean square error (RMSE) and the M(3000)F2 percentage improvement values M(3000)F2 IMP (%)), the result demonstrates the effectiveness of the ELM model compared with the IRI-2016 model at hourly, daily, monthly, and yearly in high (2001) and low (2009) solar activity years. The ELM also shows good agreement with observations compared with the IRI during disturbed magnetic activity. [ABSTRACT FROM AUTHOR]

Traditional glass bottles are the wine industry's major source of carbon emissions. Those wishing to reduce the carbon footprint embedded in wine have been looking to alternative packaging, such as PET, aluminium cans, and bag-in-box (BIB) to mitigate the impact. However, these alternative formats pose challenges with consumer acceptance and knowledge of the eco-benefits of each format. This paper uses a discrete choice experiment and latent class analysis to investigate the possible effects of message content and style (abstract vs concrete) on wine purchase in the context of brand size, brand prestige, and pricing. The analysis reveals the presence of two customer segments, which highly emphasise the pack format. Of the four non-glass packs tested, BIB and flat glass lookalike PET bottles score the highest preference after glass bottles. Message content and appeal type score low in their ability to alter preferences, whilst mid-to-low prices increase the chances of alternative packaging to be chosen. • Packaging format is a key choice driver for wine. • Glass is still the most preferred packaging format. • Bag in Box and low-carbon alternatives that look most like glass have the most potential for uptake. • Message content and appeal have little impact on choice. • Alternative packaging is more likely to be chosen for mid-to-low-priced wines. [ABSTRACT FROM AUTHOR]

Although noteworthy efforts towards energy storage participation in electricity markets have been witnessed worldwide during the last decades, market mechanisms for optimal storage integration are yet to be comprehensively studied. This paper presents a novel approach wherein a multi-period look-ahead market is proposed to progressively solve the optimisation problem over a receding horizon, incorporating intertemporal constraints to enable forward planning. The focus is specifically on modelling the bidding behaviour of energy storage, and to address this, we develop two distinct models for the participation of energy storage in the market. The integrated model aims to schedule the future dispatch of the storage to minimise total operational cost, taking into account the fixed costs of the storage, while the strategic model pursues the dual ambition to maximise storage profit by formulating a bilevel optimisation problem. We validate the viability of our proposed market mechanisms through case studies on the Australian National Electricity Market. We conduct case studies which provide evidence that both the strategic and integrated bidding models effectively address the requirement for forward planning, resulting in reduced market costs and volatility. Notably, the strategic model outperforms in terms of generating higher storage revenue, while the integrated model exhibits a slight advantage in terms of lower market costs compared to the strategic model. • Development of a multi-period look-ahead market for storage integration in electricity markets. • Model of strategic bidding using a bilevel optimisation model, vs an integrated bidding model. • Both bidding models can fulfil need for forward planning, reducing market costs and volatility. • The strategic model increases storage revenue, while the integrated model reduces market costs. • Testing is carried out using a high-fidelity model of Australia's NEM and historical data. [ABSTRACT FROM AUTHOR]

• A PV variability tolerant multifunctional control strategy for BESS is proposed. • Four control functions of BESS with possible user-defined extensions were formulated. • A use-case to control PV plant's power ramp rate is demonstrated using real data. • Validation is performed in real-time software-in-loop platform using real-field data. • Evaluation of proposed strategy shows the potential for real-world deployment. This paper proposes a multifunctional control strategy for battery energy storage systems (BESSs) in solar photovoltaic (PV) plants to avoid the unacceptable PV-power ramp-rate caused by PV variability. In addition to the PV variability tolerant ramp-rate control, the proposed multifunctional BESS control strategy is capable of maintaining a user-specified charging profile to store excess PV power while providing backup power support and charge maintenance, by accommodating diverse control functions time to time. Four control functions, namely, Rest (RST), Surplus Energy Storage (STR), Back-up Power Support (SPT) and Charge Maintenance (CMT) functions, are proposed in this article. Besides, extendibility of the proposed multifunctional strategy is presented by adding two action strategies – one with the STR function and another with the SPT function. Validation of the proposed control strategy is conducted by using real historical data from a utility-scale solar PV plant located in Queensland, Australia. Numerical analyses show that the strategy can successfully keep the PV ramp-rate within specified limit along with the user specified charge profile. Further, exploiting the real-field data, the proposed strategy is demonstrated through a real-time co-simulation study conducted in a MATLAB interfaced RSCAD-RTDS platform that can be used as Digital Twin (DT) for the PV plant. [ABSTRACT FROM AUTHOR]

Despite the considerable success of deep learning methods in modelling physical processes, they suffer from a variety of issues such as overfitting and lack of interpretability. In hydrology, conceptual rainfall-runoff models are simple yet fast and effective tools to represent the underlying physical processes through lumped storage components. Although conceptual rainfall-runoff models play a vital role in supporting decision-making in water resources management and urban planning, they have limited flexibility to take data into account for the development of robust region-wide models. The combination of deep learning and conceptual models has the potential to address some of the aforementioned limitations. This paper presents a sub-model hybridization of the GR4J rainfall-runoff model with deep learning architectures such as convolutional neural networks (CNN) and long short-term memory (LSTM) networks. The results show that the hybrid models outperform both the base conceptual model as well as the canonical deep neural network architectures in terms of the Nash–Sutcliffe Efficiency (NSE) score across 223 catchments in Australia. We show that our hybrid model provides a significant improvement in predictive performance, particularly in arid catchments, and generalizing better across catchments. • Hybrid modelling features parsimony of data-driven and conceptual hydrologic models. • We present a deep learning-based hybridization for the GR4J rainfall-runoff model. • We present a hierarchical training approach consisting of gradient-based and evolutionary optimization of the respective models. • The results show that hybrid models outperform the canonical (base) hydrologic and deep learning model. • The addition of surface temperature and antecedent streamflow further improves the predictive performance. [ABSTRACT FROM AUTHOR]

Integrating disparate and distributed vegetation data is critical for consistent and informed national policy development and management. Australia's National Vegetation Information System (NVIS) under the Department of Climate Change, Energy, the Environment and Water (DCCEEW) is the only nationally consistent vegetation database and hierarchical typology of vegetation types in different locations. Currently, this database employs manual approaches for integrating disparate state and territory datasets which is labour intensive and can be prone to human errors. To cope with the ever-increasing need for up to date vegetation data derived from heterogeneous data sources, a Semi-Automated Hybrid Matcher (SAHM) is proposed in this paper. SAHM utilises both schema level and instance level matching following a two-tier matching framework. A key novel technique in SAHM called Multivariate Statistical Matching is proposed for automated schema scoring which takes advantage of domain knowledge and correlations between attributes to enhance the matching. To verify the effectiveness of the proposed framework, the performance of the individual as well as combined components of SAHM have been evaluated. The empirical evaluation shows the effectiveness of the proposed framework which outperforms existing state of the art methods like Cupid, Coma, Similarity Flooding, Jaccard Leven Matcher, Distribution Based Matcher, and EmbDI. In particular, SAHM achieves between 88% and 100% accuracy with significantly better F1 scores in comparison with state-of-the-art techniques. SAHM is also shown to be several orders of magnitude more efficient than existing techniques. • The Integration of the National vegetation database is key for policy development. • A novel semi-automated hybrid schema matching framework, SAHM is proposed. • SAHM is a two-tiered matching solution that uses schema and instance information. • SAHM yields better schema matching results in comparison with the State-of-the-art. [ABSTRACT FROM AUTHOR]

Spotty Liver Disease (SLD) is a serious problem in laying hens farmed in cage-free systems. The causative organism, Campylobacter hepaticus , is regarded as having a fecal-oral method of transmission and hence may build up and spread readily in housing systems which allow ease of direct contact of hens with the flock's fecal material. The epidemiology of SLD has not been thoroughly investigated. An initial cross-sectional analytical epidemiological survey of SLD in free range and barn layer systems was conducted in Australia over 2019 to 2021.The survey involved rearing flocks (n = 32) which were then followed through into laying flocks (n = 24) up to 40 wk of age. Cloacal swabs were collected during rearing and lay for C. hepaticus detection by PCR. Flocks were classified as "Cases" (n = 18) where clinical SLD according to the case definition was observed or "Controls" (n = 6) which were clinically unaffected. No C. hepaticus was detected in cloacal swabs from rearing houses whereas the organism was detected in 18 Case flocks in lay and from 2 Control flocks in lay. All layer houses that incorporated a scratch area (n = 13) were categorized as Cases. Thus, having a scratch area is a key determinant for SLD and no analyses of further contributory factors from these flocks were able to be made. Of the remaining 11 flocks which had floors fully covered by slats, 5 were Cases (45%). Further risk factor analysis was compromised by this small sample size and identification of other significant associations was not possible. A larger survey investigating flocks laying in houses with fully slatted floors was undertaken to further the understanding of SLD epidemiology and is reported in a companion paper. [ABSTRACT FROM AUTHOR]

• Presenting a set of mathematical models for green flowshop scheduling problems. • Considering common carbon reduction policies and their corresponding scenarios. • Providing analysis of models and insights to design a framework for green flowshops. • Industries can use our cost-effective strategies to meet limits mandated by government. • Government can explore our integrated framework to gauge impacts of carbon policies. In this paper we consider, from an environmental policy-maker perspective, how carbon reduction policies impact the economic competitiveness of the manufacturing sector. Specifically, we focus on flowshop scheduling – which typically aims to minimize makespan for purely economic objectives – and consider how three common carbon reduction policies – namely, taxes on emissions, baselines on emissions, and emissions trading schemes – can create competitive green flowshops that balance minimization of makespan and carbon emissions. The goal is to enable policy-makers to understand how to set policies and control parameters to achieve environmental objectives while ensuring global economic competitiveness of industry. We initially present a set of mixed-integer linear programming (MILP) models for flowshop scheduling problems operating in a regulated environment in which each carbon reduction policy is adopted. We then introduce a bi-objective scheduling framework for the corresponding problem to obtain alternative solutions under each policy. These models and their computational results however, are not the main focus of the study, but are presented as a means to demonstrate how green policies co-exist with economic objectives, with policy-makers in control of the balance. To this end, based on financial data from Australia's carbon emissions profile, we provide a policy-oriented analysis of the models, and some managerial insights into the effect of scheduling strategies on carbon emissions under different reduction policies. These insights offer support to both environmental policy-makers and corporate production and sustainability managers to determine whether it is technically feasible and profitable to replace traditional scheduling strategies with environmentally friendly scheduling strategies. [ABSTRACT FROM AUTHOR]

• Mutual information is effective to analyze dependence of foF2 on R 12 and F10.7 12. • Dependence of foF2 on R 12 and F10.7 12 differ in different solar activity years. • Mutual information provides an effective selection of inputs for foF2 prediction. This paper analyzes the dependence of the ionospheric F2 layer critical frequency, f oF2, on two widely used solar activity indices (12-month running mean of the sunspot number (R 12) and the solar radio flux of 10.7 cm wavelength (F10.7 12)) by using mutual information method at the ionospheric station of Darwin (geographic 12.4°S, 131.5°E) in Australia. The results show that the f oF2 have different responses to R 12 and F10.7 12 in different solar activity years. In the high and low solar activity years, the f oF2 is more dependent on R 12 rather than F10.7 12. In the moderate solar activity years, the f oF2 is more dependent on F10.7 12 rather than R 12. We further verify the effectiveness of the mutual information on the selection of the solar activity index by developing the prediction models of the f oF2. Additionally, the results are compared against the International Reference Ionosphere (IRI) model. The data of training period used in the model is from 1998 to 2015, except 2000, 2009 and 2013. Data for 2000, 2009 and 2013 are used to validate the prediction accuracy, for high, low and moderate solar activity years, respectively. The results demonstrate that the favorable prediction results can be obtained when the solar activity index with large mutual information is used as the input variable of the fo F2 prediction model. The proposed method not only can quantitatively analyze the nonlinear relationship between the fo F2 and solar activity indices but also provides an effective way of selecting the solar activity indices for the fo F2 prediction. [ABSTRACT FROM AUTHOR]

There is wide agreement that the US student loan system faces significant problems. Seven million borrowers are in default and many more are not repaying for reasons such as returning to school, or economic hardship. The stress of repayments faced by many students results at least in part from the design of US student loans. Specifically, loans are organised like a mortgage, with fixed monthly repayments over a fixed period of time, creating a high repayment burden on borrowers with low income. This paper draws on the experience of the income-contingent loan (ICL) systems operating in England and Australia, in which monthly or two-weekly repayments are related to the borrower's income in that period, thus building in automatic insurance against inability to repay during periods of low income. We discuss the design of this type of loan in detail since such an exercise seems to be largely absent in the US literature. Drawing on data from the US Current Population Survey (CPS) we provide two main empirical contributions: a stylised illustration of the revenue and distributional implications of different hypothetical ICL arrangements for the USA; and an illustration of repayment problems faced by low-earning borrowers in the US loan system, including a plausible example of adverse outcomes with respect to Stafford loans. Importantly, we compare repayment burdens under the existing and alternative systems. Our illustrations show how US mortgage-type loans can create financial difficulties for a significant minority of US borrowers, difficulties which an ICL is designed to address. We note also that the current small and ineffective income-based repayment system in the US has few of the characteristics of an ideal ICL. [ABSTRACT FROM AUTHOR]