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• 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]

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]

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]

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]

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]

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]

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]

• 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]

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]

Evidence is lacking on how inequality in homeowner uptake of solar panels has changed over time. This paper assesses changes in inequality over time using four Australian household surveys from 2012 to 2020. There is evidence of an intuitive transition where average uptake for homeowners with net-wealth just below the median (the fourth and fifth deciles) improved first, with subsequent gains for homeowners in the third net-wealth decile. However, there were still some significant shortfalls by 2020, especially for the first (i.e. lowest) net-wealth decile. In particular, the lowest net-wealth decile had lower uptake by 9.4 percentage points in 2012 and there was a similar gap below the highest decile of 10.6 percentage points in 2019–20. With control variables these corresponding gaps were 8.0 and 8.3 percentage points respectively. Inequality and changes have been less pronounced based on other aspects like education, income, and welfare status. A new policy implication is that Australian support should primarily target the bottom two net-wealth deciles among homeowners. Australia's experience can inform policies for other countries and clean energy technologies. The general implication is that policy approaches should be dynamic and detailed to address inequality changes over time in each context. • We assess the changes in solar-uptake distributions for Australian homeowners. • Changes in solar uptake inequality are most pronounced with respect to net wealth. • Persistent inequality exists for the lowest net-wealth decile from 2012 to 2020. • Deciles 3 to 5 had higher solar uptake following 2012, relative to other deciles. • Policies should be dynamic to account for distributional changes over time. [ABSTRACT FROM AUTHOR]

• A novel CElif system is proposed in this paper. • Decomposition and denoising strategy is designed in the CElif system. • The MSsa mechanism is superior both in search ability and operation efficiency. • The construction of the five ANNs covers the inherent modes of sequence. • Differences in people's behavior on weekdays and weekends are considered. Under the trend of the global green and low-carbon transformation, accurate prediction of electric load is an urgent problem for all countries to maintain the normal operation of production and living activities. Currently, a large number of models based on single-point forecasting of electric load have emerged. However, these models are limited by the high randomness of electric load data, which restricts the further improvement of prediction accuracy. To fill this gap, a CElif (Combined Electric Load Interval Forecasting) system is proposed in this paper, which contains decomposition and denoising module, individual forecasting module, optimization module and evaluation module, aiming to tolerate uncertainty to provide policymakers with more information. Decomposition and denoising strategy is adopted to extract and reconstruct the inherent modes of the original sequence. In order to verify the superiority of the CElif system, the electric load data of 30-minute interval in Australia are used to test. The numerical results conclude the CElif system not only has excellent coverage performance in electric load interval prediction, controls the uncertainty to a great extent, but also provides a basis for power system dispatching management. [ABSTRACT FROM AUTHOR]

Design practices often involve grassroots communities and institutional actors with designers working as or with intermediaries. This paper defines intermediation as a significant design practice, especially when designers engage in commoning and institutioning—concepts that have gained traction in recent discourses in design scholarship. To discuss intermediation, the paper introduces two case studies: a community radio project in Europe and a food supply chain project between Australia and China. The two cases show implicit and explicit ways that intermediation can form an integral part of the practices and competences of designers. The paper discusses the merits of intermediation skills and techniques as well as how intermediation can be better embedded and integrated in design practices. • Expands discourse on institutioning and commoning in participatory design research. • Makes explicit the so far largely implicit design skillset of intermediation practice. • Illustrates the argument with two design cases from Europe and Australia/China. • Argues for the merit of adding intermediaries to interdisciplinary design teams. [ABSTRACT FROM AUTHOR]

When distributed solar photovoltaic systems (PV) provide the majority of supply in electricity grids, system operators and network service providers require increased control of customer-owned assets to maintain system security, reliability and equity. Several programs of reform for distributed energy resources (DER) in Australia, USA and Europe have emphasised the importance of placing customers at the centre of any energy transition, but this has occurred against a haphazard backdrop of proposed solar export taxes, decreasing feed-in tariffs, time-varying export incentives and updated inverter standards. Absent is a conversation of how the social contract of connecting to the electricity grid is evolving beyond the passive expectation of electricity supply of the 19th and 20th centuries to a two-way engagement that must support fairness for both DER-owners and non-owners in grids that are at times dominated by DER. This paper proposes a first attempt at a 'DER Bill of Rights and Responsibilities' to provide a template for this required social-license, distinguishing between passive grid participation (passive loads, traditional use, PV self-consumption) and active grid participation (grid interaction for remuneration). Guiding principles of fairness are presented with practical definitions for implementation, referencing existing instruments including inverter standards, network connection agreements, reliability standards, and central ancillary service markets. The intent is that clarity on rights to self-consumption and passive participation will support customer trust in the responsibilities for active DER integration and control. We highlight how proposed rights are already being breached regularly in Australia, as an example jurisdiction, before outlining a pathway to enshrine them for a DER-dominated future with broad sector endorsement. These questions are critical for Australia to address now; other jurisdictions will likely be required to do so in the near future. [ABSTRACT FROM AUTHOR]

The increasing awareness of the undesirable environmental impact of cement-based products has led to rethinking earth construction within the digitally mechanised framework of Industry 4.0. The attempts to mechanise earth construction started nearly a decade ago, yet, the past four years have seen a surge in the intensity of research on the advanced manufacturing of earth construction. Additive manufacturing of clay-rich soil, like in cob and adobe methods, has been attracting the primary attention, while on the contrary, considerably limited research has been conducted to mechanise other methods such as rammed earth. This paper collected, reviewed, and analysed the state-of-the-art research on the advanced manufacturing of rammed earth construction, with a focus on process design aspects and machine development. Two case studies of recent digitally manufactured rammed earth projects in Australia and Switzerland were comprehensively analysed to provide a realistic feasibility assessment of the potential and technical challenges. The insights from this research provide a holistic and tangible roadmap to the digital manufacturing of rammed earth construction, which aids in making it more desirable and capable of meeting contemporary demands of the architecture and construction of Industry 4.0. • State-of-the-art advanced manufacturing methods for rammed earth construction are critically reviewed. • Two case studies of digitally manufactured rammed earth projects in Australia and Switzerland are comprehensively analysed. • Potential and challenges of advanced manufacturing for rammed earth construction are discussed. • Information for enhanced utilisation of digital techniques in rammed earth construction is provided. [ABSTRACT FROM AUTHOR]

Pretension is the pre-added axial tensile force on fully grouted bolt systems, which include rock bolts and cable bolts. Although a series of studies via lab testing and analytical modelling proved the importance of pretension in cable bolts, there is no systematic analysis to guide the optimal pretension values to be applied on cable bolts in different engineering geological conditions. Complicated geological conditions, such as anisotropic in-situ stress, bedding plane detachment and country rock deformation, cannot be fully represented in the lab or analytical work. In addition, field scale numerical models to investigate cable bolt performance and its interaction with various geological conditions are rare. This paper analyses the effect of pretension on the performance of cable bolts in underground coal mines via field scale numerical models, with considering different geological conditions (in-situ stress, bedding plane stiffness and rock mass properties). Numerical models were developed based on a case study mine in Australia with challenging ground conditions. The in-situ stress, properties of claystone and bedding planes are considered critical variables to be analysed. The results show higher pretension contributes to higher force along the cable bolt. Neutral points, where shear force in grout materials is zero, are used to describe the pretension-induced stress redistribution on cable bolts. Pretension also has a positive effect on roof and bedding plane management, resulting in a more stable roof condition. In addition, high pretension and high in-situ stress are more likely to result in cable-grout (CG) interface failure since the relative vertical displacement difference between cable bolts and the surrounding rock mass is higher. Once CG interface failure is generated, the pretension effect turns out to be negative, leading to severe roof sagging and bedding plane aperture. This paper provides a guide to the application of pretension in coal mining. • Developed a numerical model to illustrate the effect of pretension on cable bolts. • Analysed effects of pretension and cable-grout (CG) interface failure on cable bolts. • Investigated the relationship between pretension applied and CG interface failure generated along cable bolts. • CG interface failure induced by excessive pretension is more sensitive to bedding plane properties. [ABSTRACT FROM AUTHOR]

• Activism has a substantial role within the specialization of Indigenous Australian public relations. • Indigenous women working in public relations utilize their activist skill set in their personal lives. • There are unique cultural connections between the concepts of Indigeneity, professional activism and personal activism. • Indigenous Australian public relations strategies can involve activism through fashion. The role of activism is important in the field of Indigenous Australian public relations as a strategy for creating change and giving back to Indigenous people and communities. However, there is a dearth of information on how, when, and why Indigenous women employed in public relations engage in activist practices. This paper aims to help fill this gap by exploring the activist practices used by Indigenous women working in public relations in their personal lives. By considering personal activism from the perspectives of Indigenous women in public relations, we can further conceptualize activism within the profession. Through the critical lens of Indigenous women's standpoint theory, and utilizing an Indigenous yarning method, five Indigenous women discuss their definitions of activism and the various ways in which they engage in activism within their personal lives. This paper builds upon the ideas of activism within public relations and demonstrates the power of public relations in terms of influencing social change for Indigenous people and others. [ABSTRACT FROM AUTHOR]

• Optimization model for the integrated assessment of the green ammonia value chain. • Case study for ammonia trade between Australia and Germany in 2030. • Sensitivity analysis on ammonia prices for technical and economic parameters. • Cost comparison of conventional and green ammonia production. This paper contributes to understanding the transformation of global energy trade to green energy carriers, focusing on green ammonia as the foreseeable first green hydrogen carrier. We provide a comprehensive overview of today's ammonia trade and assess scaling options for the trade of green ammonia. To that aim, we develop an optimization model for the integrated assessment of the green ammonia value chain that covers all steps from green ammonia production in an exporting country, up to delivery to a harbor in an importing country. The model endogenously chooses among different technology options and determines cost minimal operation. In a case study, we apply the model to the large-scale import of ammonia from Australia to Germany in a scenario for 2030. The results show that green ammonia can reach cost parity with gray ammonia even for moderate gas prices (but not necessarily with blue ammonia) if CO 2 prices are high enough. We also provide a sensitivity analysis with respect to the interest rate and other key technical and economic parameters and show that cracking ammonia to provide pure hydrogen comes at a 45 % cost markup per MWh at the destination. [ABSTRACT FROM AUTHOR]

This paper investigates the least-cost greenhouse gas (GHG) abatement pathway for a coupled energy system. We consider the roles of diverse abatement technologies, such as renewable electricity generation, biomass and carbon capture and storage (CCS), as well as sources of operational flexibility, such as peaking generators, electricity storage, transmission and electrolysis. Scenario and sensitivity analyses are applied to explore plausible pathways to energy system decarbonization. All modelled pathways to a zero-emissions energy system feature an accelerating transition from fossil fuels to renewable energy with diverse technologies playing a role. This includes variable renewable generators (VRG), CCS, and sources of operational flexibility on different timescales. Specifically, synchronous condensers appear to be a cost-effective option to control system frequency, inter-seasonal hydrogen storage in engineered caverns can play a significant and complementary role to that of pumped hydro and battery storage, and technologies that capture and sequester atmospheric carbon, including biomass-to-CCS and direct air capture (DAC), can also play a significant role in the transition to net-zero emissions. [ABSTRACT FROM AUTHOR]

• Circular RNAs are differentially expressed in endometrial cancer. • The overall circRNA abundance is lower in cancer compering to control. • Identified circRNAs may serve as biomarkers for obesity-related endometrial cancer. The present study has investigated the circular RNA (circRNA) transcriptome of twenty obese and postmenopausal women, recruited in Australia, with endometrial cancer (EC). This paper expands on previous findings which evaluated the circRNA transcriptome of a similar cohort of six women recruited in the United States of America. EC is the most common gynaecological malignancy and the fifth most common cancer in women worldwide with obesity as one of its major risk factors. CircRNAs, a class of non-coding RNAs, are involved in many human diseases including cancer. As such the objective of this study was to investigate the circRNA transcriptome of these twenty women and identify circRNAs of interest. We obtained paired samples (EC and adjacent normal tissue) from the cohort of twenty women. Samples were subjected to ribosomal RNA depletion and sequencing performed using Illumina sequencing technology. CircRNAs were identified through CIRI2 and CIRCexplorer2 and common circRNAs extracted for differential expression with edgeR which met the criteria of counts per million > 0.1 and expressed in ≥ 10. We found that the overall abundance of circRNAs was lower in EC compared to adjacent non-cancerous endometrial tissue. We also identified hotspot genes, genes expressing over 10 distinct circRNA isoforms. There were 82 hotspot genes in normal tissue and 23 hotspot genes in EC. There were 174 significantly differentially expressed circRNAs, of which 172 were down-regulated and 2 were up-regulated in EC. The circRNAs identified from this study may act as diagnostic or prognostic biomarkers for EC in obese women. While the circRNA transcriptome of obesity-related EC has been investigated further work is required to determine their functional significance. [ABSTRACT FROM AUTHOR]

Modern power systems are experiencing the challenge of high uncertainty with the increasing penetration of renewable energy resources and the electrification of heating systems. In this paradigm shift, understanding electricity users' demand is of utmost value to the retailers, aggregators, and policymakers. However, behind-the-meter (BTM) equipment and appliances at the household level are unknown to the other stakeholders mainly due to privacy concerns and tight regulations. In this paper, we seek to identify residential consumers based on their BTM equipment, mainly rooftop photovoltaic (PV) systems and electric heating, using imported/purchased energy data from utility meters. To solve this problem with an interpretable, fast, secure, and maintainable solution, we propose an integrated method called Interpretable Refined Motifs And binary Classification (IRMAC). The proposed method comprises a novel shape-based pattern extraction technique, called Refined Motif (RM) discovery, and a single-neuron classifier. The first part extracts a sub-pattern from the long time series considering the frequency of occurrences, average dissimilarity, and time dynamics while emphasising specific times with annotated distances. The second part identifies users' types with linear complexity while preserving the transparency of the algorithms. With the real data from Australia and Denmark, the proposed method is tested and verified in identifying PV owners and electrical heating system users. The performance of the IRMAC is studied and compared with various state-of-the-art methods. The proposed method reached an accuracy of 96% in identifying rooftop PV users and 94.4% in identifying electric heating users, which is comparable to the best solution based on deep learning, while the speed of the inference model is a thousand times faster. Last but not least, the proposed method is a transparent algorithm, which can tackle the concerns regarding the agnostic decision-making process when policies prohibit some machine learning methods. [ABSTRACT FROM AUTHOR]

Managing constrained healthcare resources is an important and inescapable role of healthcare decision makers. Allocative decisions are based on downstream consequences of changes to care processes: judging whether the costs involved are offset by the magnitude of the consequences, and therefore whether the change represents value for money. Process mining techniques can inform such decisions by quantitatively discovering, comparing and detailing care processes using recorded data, however the scope of techniques typically excludes anything 'after-the-process' i.e., their accumulated costs and resulting consequences. Cost considerations are increasingly incorporated into process mining techniques, but the majority of healthcare costs for service and overhead components are commonly apportioned and recorded at the patient (trace) level, hiding event level detail. Within decision-analysis, event-driven and individual-level simulation models are sometimes used to forecast the expected downstream consequences of process changes, but are expensive to manually operationalise. In this paper, we address both of these gaps within and between process mining and decision analytics, by better linking them together. In particular, we introduce a new type of process model containing trace data that can be used in individual-level or cohort-level decision-analytical model building. Furthermore, we enhance these models with process-based micro-costing estimations. The approach was evaluated with health economics and decision modelling experts, with discussion centred on how the outputs could be used, and how similar information would otherwise be compiled. • An event log that can be compiled from linked hospital data that is routinely collected across Australia, with standardised nomenclatures, for the purpose of activity-based funding. • A new approach that combines process modelling with economic decision tree modelling, to express processes with explicit trace attributes, such as demographic data. • Discovery techniques, model visualisations and conformance checking techniques for such models. • Estimation of micro-cost information for activities of interest within the event-log, from aggregate episode-level costs recorded as trace attributes. • An approach to help inform real-world decisions for managing healthcare delivery based on 'value for money', by bringing the mining of processes, and the consideration of costs and outcomes, closer together. [ABSTRACT FROM AUTHOR]

Green buildings are synonymous with environmental sustainability; however, it is unclear what role its occupants, specifically employees, play in promoting sustainability in green office buildings. This paper proposes that Green Human Resource Management (HRM), underpinned by Social Identity Theory (SIT), can maximize the potential of green behaviors to improve employees' outcomes (e.g., job satisfaction and work-related flow) in green office buildings by creating positive workplace behaviors (i.e., green behaviors). We collected multisource data from 549 employees and 91 managers working in 17 organizations in green office buildings across Australia. We confirm the double mediation effects of Green HRM, green behaviors and organizational identification on the relationships between organizational readiness, job satisfaction and work-related flow. The paper makes theoretical contributions by advancing the concept of Green HRM and green behaviors within the realm of SIT, thus taking a multidisciplinary stance in the built environment and sustainability literatures. [ABSTRACT FROM AUTHOR]

Global concerns around climate change and the volatility of conventional fuel prices have prompted researchers and technologists to make significant efforts to identify and exploit alternative energy sources that are cleaner and more sustainable. Wind energy has seen considerable development among these alternative energy sources, mainly due to its abundance and global availability for extraction and the existing knowledge within the aviation and aerospace fields. Many nations, including European countries, already operate offshore wind farms (OWFs) and are progressively carrying out new projects and expanding on other projects. The Australian offshore environment provides unique opportunities for wind energy extraction, particularly along the southern coast of mainland Australia and the regions around Tasmania, where substantially strong winds blow most of the year. A significant challenge to establishing wind farms is the selection of site locations with optimal outputs. This can become a complex decision-making problem if there are numerous options and no information from previous projects. This paper aims to develop a decision-making framework to select the optimal location for installing OWFs while addressing financial, performance-related, and availability-related objectives. This paper adopts a game-theoretical approach to develop a decision-support tool to account for the interdependencies of influencing factors and possible conflicts amongst the parties. The game model is applied to an OWF development case study in the Bass Strait, known for its dominant and strong winds. • A framework to find the optimal location for installing Offshore Wind Farms (OWFs). • Game theory decision-making methodology for offshore renewable energy applications. • Define the influencing factors and conflicts amongst the interested parties. • Apply to OWFs development in Bass Strait, known for its dominant and strong winds. [ABSTRACT FROM AUTHOR]

The rapid urbanisation and infrastructure development projects have increased infinite property regeneration and construction projects in the developed countries. This study aims to examine the current construction waste management literature, including key authors, university networks, and relevant information systems, considering four selected countries, including Australia, Canada, the United Kingdom, and the United States. This paper uses a set of novel metrics, query-based search, and social network analysis techniques for evaluating a dataset of 2337 papers published in a decade by authors from different countries investigating the topic of recycling and reuse of construction and demolition waste in order to identify prominent researchers, influential universities, collaboration practices, and research trends. The dataset of publications is collected from Scopus and analysed using a set of network analysis techniques and statistical analysis. An additional dataset of keywords was collected from the Twitter pages of engineering firms and other relevant industry organisations to assess any possible connection between industry interests and research trends. In order to present a solid analysis of the current investigations in the field, a scientometric analysis along with a rigor statistical method was adopted to carefully identify trends, detailed sub-topics, and the materials investigated in the literature. A probit regression model for testing differences between countries in recycling practices was utilised in the STATA environment. The findings show a set of network analysis and clusters that can help scholars to set their future studies based on the current advances and limitations identified in the literature. Within the dataset, 42% of publications referred to concrete waste, whilst only 2.2% included glass, 2.5% included brick, 4.3% included steel and none included wood, which shows the most common building materials have not been fully covered in the literature. Only 48 publications, or 7.4% of the dataset, originated from the three selected concrete-focused journals. It was also found that information systems appeared in the database lately; however, there is less discussion in the literature on how the integration of Geographic Information System (GIS), Buidling Information Model (BIM), 3D Printing and geo-spatial data analysis can help waste management in the field of construction. The bibliographic analysis of the selected cases shows that universities in Canada and the US exhibit more evidence of domestic collaboration and industry partnerships than universities in Australia and the UK. Additionally, there is evidence to suggest that publications with at least one industry co-author are more likely to address the issue of regulation. The outcome of this paper enables academics, engineers, and regulators to predict research trends, improve industry collaborations and utilise information systems and assign more research resources to address the identified gaps in the literature. • Developed a network of 1408 authors within the dataset and apply Total Publications metrics. • Examined the collaboration of 529 institutions within Australia, Canada, the UK, and the US. • Utilised a probit regression model for testing differences between countries in recycling practices. • Visualised an online GIS-based dashboard of landfills and recycling facilities using geo-spatial data. • Identified gaps in two different directions of material technology and management systems. [ABSTRACT FROM AUTHOR]

This paper asks to what extent host language proficiency can insure immigrants against the risk of ending up in mismatched jobs. Using the 2003-2016 waves of the Household, Income and Labour Dynamics in Australia (HILDA), the paper discriminates between three forms of mismatch, overqualification, underqualification and overskilling. Host language proficiency is instrumented using Bleakley and Chin (Rev Econ Stat 86:481–496, 2004) strategy, which exploits the fact that younger children learn languages more easily than older ones. To differentiate between local average treatment effects (LATE) and average treatment effects (ATE), the paper considers two alternative models, 2SLS instrumental variables and biprobit. We find that treatment effects are heterogeneous. English language proficiency among immigrants in Australia reduces the probability of ending up in overqualified jobs, by between 17.2 (LATE) and 36.7 (ATE) percentage points. The ATE of overskilling is also significant and about -8.9 percentage points. In contrast, language skills tend to raise the probability of being underqualified at the job, by approximately 8.6 percentage points according to the ATE. Local effects of overskilling and underqualification fail to be statistically significant, suggesting that host language proficiency may be innocuous for some workers. Overall, the results indicate that host language proficiency is a country-specific, valuable form of human capital. [ABSTRACT FROM AUTHOR]

[Display omitted] • Pedogenon classes are created by K means clustering on soil forming factors. • Pedogenons define unique soils and characterise the pedodiversity of the study area. • Soil properties of genosoil and phenosoil are predicted using VisNIR spectra. • The result shows that genosoils had twice the variation of phenosoils. Mapping soil classes can support the understanding of soil origin and development, subsequently the soil classes can be used to support monitoring and assessing soil change due to human influence. Pedogenon was proposed as a conceptual soil taxon derived from a set of quantitative state variables representing the soil-forming factors for a given reference time. This study aims to test the pedogenon concept in the Edgeroi region in New South Wales. This paper developed local pedogenons, designed a sampling scheme to capture soil variation under natural conditions and under intensive human activities, and tested the hypothesis that pedogenon is an efficient method of stratifying the landscape to capture soil variation. This study derived the 14 pedogenons by employing layers of soil-forming factors (soil, climate, organism, topography, and parent material and age) using an unsupervised classification technique (k-means clustering). Within each pedogenon, genosoils were identified based on areas with native vegetation, while phenosoils were identified as areas with cropping practises. One meter soil cores were collected for each genosoil and phenosoil, and scanned using Vis-NIR spectrometer for predicting soil properties (clay, sand, cation exchange capacity, pH, and organic carbon). Results show that each pedogenon was characterised by a soil type formed under a dominant parent material occupying a unique position in the landscape. Redundancy discriminant analysis of the soil properties as a function of pedogenon and depth of observations show that pedogenon significantly explained the variation in soil properties. Variance partitioning analysis confirmed that pedogenon explained a large proportion of the variation (49 %) as opposed to landuse (5 %). Principal component analysis of the soil properties shows that genosoils had twice the variation of phenosoils. The results indicate that agricultural activities homogenised the variation of soil profiles. This study demonstrated that pedogenon clasess can effectively characterise soil variation and be used as a benchmark to compare how human activities have altered soil conditions. [ABSTRACT FROM AUTHOR]

• A novel integrated short-term power load forecasting system is proposed. • The fuzzy granulation technique simplifies the large-scale dataset and extracts the effective characteristics of the raw data. • A novel multi-objective variant of the tuna swarm optimizer is utilized to integrate multiple forecasting sequences. • The optimal weight coefficients of the ensemble model are theoretically demonstrated to be the Pareto optimal solutions. • Our proposed forecasting system can gauge the uncertainty of future electricity load. As an irreplaceable power source, electricity is responsible for sustaining the national economy and social development, and the precondition for the power system's stable operation is to perform an accurate short-term load forecast (STLF). However, with the increasing forms of social power consumption and the emergence of large-scale sustainable resources on the grid, which make STLF increasingly challenging as the power load exhibits greater stochasticity and instability. Therefore, a novel STLF system is developed in this paper, which incorporates data fuzzy granulation, a high-performance optimizer for integrating forecasting sequences, point and interval forecasts. Moreover, the performance tests of the optimization algorithm verify that our proposed optimizer can obtain more approximate solution sets to the real Pareto front and outshines the traditional optimization algorithm concerning convergence and diversity. Load data from three regions of Australia demonstrate that our developed system can remarkably contribute to the accuracy and stability of the STLF , and also quantify the volatility and uncertainty of the power load, which allows power workers to better capture the fluctuation interval of future loads and effectively enhance the flexibility of grid operation. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) or particle pollution include the tiny particles of dust and fly ash particles are expelled from coal-burning power plants. Coal combustion is an extremely prevalent source of air pollution, and resulting PM has substantial impacts on human health, especially in industrial zones. This paper aims to design a novel hybrid deep learning framework based on long short-term memory (LSTM) integrated with a two-stage data filtering technique to accurately predict the air quality indices (i.e., PM 2.5 and PM 10) in a chosen study region 'Miles Airport, Queensland,' that meets the needs of the coal seam gas industry in Australia. The data used to construct the novel hybrid two-stage data filtering technique based on LSTM comprising of six meteorological parameters (i.e., wind direction, wind speed, air temperature, relative humidity, solar radiation, and rainfall) and two environmental factors (i.e., ozone and total suspended particulate). In the first stage, two robust feature selection methods, namely, extreme gradient boosting (XGBoost) and the classification and regression tree (CART) approach, were adopted to explore the most significant predictors. Then in the second stage, the best subset regression (BSR) technique is utilized to determine the best subsets input combinations i.e., C1, C2, and C3 based on several particular metrics. The three BSR-based input combinations were employed in the LSTM model to estimate the PM 2.5 and PM 10. Furthermore, to validate the main hybrid framework, two advanced machine learning (ML) methods (i.e., LightGBM and ridge kernel regression (KRR)) and two traditional ML methods (i.e., Adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron neural network (MLP)) were hybridized with the multi-level data filtering strategy by examining the optimal input combinations. Several statistical metrics, graphical tools, and diagnostic analyses evaluated the hybrid models. The outcomes of the PM 2.5 simulation based on 2375 data samples showed that the LSTM-C3 containing all the selected predictors yielded the most promising accuracy, followed by the LightGBM-C3 and MLP-C3 models. On the other hand, the simulation of PM 10 distribution demonstrated that the LSTM-C3 was superior to other models, followed by the KRR-C3 and LightGBM-C3 models. [ABSTRACT FROM AUTHOR]

Control of the superheated steam temperature is a significant technical challenge to coal-fired power plants due to the strong nonlinearity, large inertia and parameter uncertainties with external disturbances. This paper proposes a robust proportion–integration–differentiation (PID) control of the superheated steam temperature with proven stability and the external disturbance rejection. The design is based on the proposed data-driven feedback compensator (DFC) which is a neural network (NN) trained to isolate the nonlinearity and inertia from the PID controller, allowing the system for tuning the PID controller can be simplified as an approximate linear system with the modeling error based-feedback and feed-forward compensations. Then, the PID controller is tuned by the Nyquist stability criterion to place every closed-loop pole to a specific region in the left-half s-plane to guarantee the stability with considering the modeling error. Besides, a NN based feed-forward compensator is trained as the inverse model of the feedback compensator to further smooth the temperature fluctuation caused by the disturbance. The simulations and engineering implementation of the superheated steam temperature controls for a coal-fired power plant in Australia show the effectiveness. • A data-driven compensation based robust control with considering uncertainties. • A neural feedback compensator to compensate the nonlinearity and inertia. • Approximation errors handled by the robust PID control with proven stability. • The effectiveness verified by the implementation in a physical coal-fired plant. [ABSTRACT FROM AUTHOR]

A rapidly warming planet provides a significant challenge to the design, delivery, and maintenance of habitable housing across Australia. Information on the adequacy of current measures for cooling Indigenous houses in regional and remote communities in relation to climate change and rising energy consumption costs is lacking. Such information is needed to better protect low socio-economic status householders, people with pre-existing health conditions, and people living in climatically exposed regions. This article models the thermal performance of both existing and 'improved' (built to current national construction code standards) housing stock in relation to various occupancy density, cooling system, and ventilation practice scenarios. This modelling was undertaken for housing in Australia's tropical, arid, and hot/mild climate zones, matching the sites where most discrete Indigenous communities are located. Results show that retrofitting existing housing stock and even building new stock to meet recommended standards are not adequate measures for current or future climate changes. This paper challenges current knowledge, policy, and practices regarding Indigenous housing, demonstrating that significant reform is necessary to maintain habitable conditions and improve energy resilience. [ABSTRACT FROM AUTHOR]

Environmental issues are becoming more urgent. Biodiversity loss, climate change, extreme events and global pressures on resources place increasing importance on decision making about how natural resources should be managed. Natural capital accounting (NCA) is gaining popularity as a systematic approach to recognise the full value of natural resources such as soil, vegetation, animals, water, and biodiversity. To understand perceptions and opportunities for awareness and behaviour change relating to the use of NCA, we conducted a discourse analysis of 57 interviews with stakeholders across Australia. Our aim is to promote discussion and reflection about perceptions of natural resources as forms of capital, and the role of NCA to underpin management practice change and support sustainability. We identify four key areas of contestation that relate to values, complexity, digital technology, and the desired future vision of NCA in society. Findings include conflicting views around whether NCA should have a diversity of tailored approaches or a consistent approach for all and that digital technology has and will continue to shape the way NCA is conducted. To our knowledge, this is the first paper to take a discourse analysis approach to perceptions of natural capital accounting. [Display omitted] [ABSTRACT FROM AUTHOR]

In Australia, tropospheric ozone measurements in rural locations are scarce with measurements mostly made in cities. This limits the ability to estimate background ozone levels that inform policy development. The few studies that have assessed rural ozone in Australia have been associated with short campaign monitoring or specific, short-term research programs. Recognising this deficit of information, the New South Wales Government has established long-term ozone monitoring at two rural locations. This paper presents results from the first three years of monitoring at Gunnedah. We assess seasonal, diurnal and sectoral patterns of ozone. Several events are analysed, including high ozone associated with the 2019/20 Australian Bushfire Emergency and an extreme heatwave event. We find that ozone levels at Gunnedah exceed the screening standards set by Australia's National Environmental Protection (Ambient Air Quality) Measure, emphasising the need for additional ozone monitoring in rural and regional Australia. Our early results indicate that in NSW, background ozone mixing ratios for airmasses of continental origin is likely in the range of 36–39 ppb, higher than the 14–30 ppb associated with air masses of marine origin and greater than the 30 ppb background mixing ratio used for monitoring design and standard setting in Australia. Maximum 8-hourly ozone in non-bushfire impacted events is as high as 64 ppb, demonstrating the challenges that rural/regional communities may face in always meeting the new Australian 8-h ozone standard of 65 ppb. These results add to our understanding of rural background ozone within Australia and in the southern hemisphere. • New measurements of tropospheric ozone in rural Australia. • Use of k-means clustering to estimate background ozone. • Background ozone in Australia may be higher than previously thought. • Anthropogenic component of ozone may be over-estimated when setting Australian air quality standards. • Challenges in meeting new ozone standards and WHO guidelines in rural Australia. [ABSTRACT FROM AUTHOR]

• The LSTM method is proposed to construct the LUBE approach-based prediction interval for wind power forecast. • An innovative fuzzy approach is suggested to adjust the prediction interval coverage probability. • CDOA is applied to achieve the most optimal prediction intervals. This paper proposes a novel deep learning method based on lower-upper-bound-estimation (LUBE) and long short-term memory (LSTM) models to capture the uncertainty effects in the power generation of wind turbines. The LUBE model deploys the prediction interval concept as well as the LSTM approach to make a robust and resilient model. The LSTM approach is applied to construct the optimal prediction intervals with an appropriate upper bound and lower bound with a certain confidence level. In addition, the fuzzy set theory is proposed in the model to let adjust LSTM parameters based on the decision maker's ideas. This approach empowers operators to satisfy both coverage probability and width indicators of prediction intervals to achieve the optimal solution. The collective decision optimization algorithm is introduced to provide more flexibility in tuning the LSTM parameters. The efficiency and quality of the proposed scheme are studied using some datasets gathered from the Australia wind farms. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid approach for Global Solar Radiation (GSR) prediction problems, based on deep learning approaches. Predictive models are useful ploys in solar energy industries to optimize the performance of photovoltaic power systems. Specifically, in this work we develop a new 4-phase hybrid CXGBRFR framework, which includes a deep learning Convolutional Neural Network, an Extreme Gradient Boosting with Random Forest Regression, and a Harris Hawks Optimization for initial feature selection. The proposed system has been tested in a problem of daily GSR prediction at six solar farms in Australia. Data from three global climate models (GCM) (CSIRO-BOM ACCESS1-0, MOHC Hadley-GEM2-CC and MRI MRI-CGCM3) have been considered as predictive (input) variables for the proposed approach. The variables from these GCMs contain enough information to obtain an accurate prediction of the GSR at each solar farm. The performance of the proposed approach is compared against different deep and shallows learning approaches: Deep Belief Network, Deep Neural Network, Artificial Neural Network, Extreme Learning Machine and Multivariate Auto-Regressive Spline models. We show that the proposed approach exhibits an excellent performance in GSR prediction, against all alternative approaches in all solar farms considered. [Display omitted] • We develop hybrid deep learning model to predict solar radiation • We optimize the algorithm with Harris Hawks, gradient boosting and random forest • We adopt global climate model and observed predictors as learning model inputs • We build an expert system with relevant knowledge in context of solar application • Our methods benefit solar photovoltaic power monitoring in solar engineering problems. [ABSTRACT FROM AUTHOR]

Moisture-related issues in buildings can accelerate the decay of building materials and increase occurrence of mould, resulting in significant economic and health implications. Transient hygrothermal risks assessment proved to be the most reliable approach, yet its application at early design stages is impeded by the high number of assumptions, input and skills required to be robustly applied. Climate-based indicators can assist in determining effective prevention design strategies based on the magnitude of the potential hygrothermal risks. However, in Australia, the National Construction Code relies on a climate zoning originally developed for thermal and energy efficiency provisions, clustering locations that present different moisture profiles. This paper investigates the suitability of the existing climate zones for hygrothermal risk indications by calculating the moisture index for several representative cites across Australia. Results reveal the current climate mapping to be unsuitable and too coarse to reliably provide robust indications for moisture management. Further, a new characterization of Australian climates for hygrothermal assessment purposes is proposed as a first step towards a better representation of moisture risks within the building code. [ABSTRACT FROM AUTHOR]

More timely and accurate air quality forecasting could contribute to better public health protection and air pollution prevention. Particulates are a significant indicator for measuring the degree of air pollution. This paper reports on research to model an early warning tool for coarse particulates when assessing the impact of the 12 satellite-derived and ground-based meteorological pollutants out of 30 pollutants considered using hourly Australian data from January 2018–December 2020. A one-dimensional convolutional neural network (CNN) was integrated with a one-directional fully gated recurrent unit (GRU) to forecast consecutive hours' air quality. The CNN model acts as a spatial feature extractor, whereas the new generation GRU makes it computationally efficient. The resultant hybrid ' CNN-GRU' is then comprehensively benchmarked outperforming an ensemble of six other deep learning models. The proposed model's efficacy is indicated at the four most air polluted Australian postcodes in the testing phase. A detailed error analysis with visual and statistical metrics for air quality forecasting ascertains the proposed model's countermeasure to reduce harm and loss. The practical tool is immensely beneficial and can be widely deployed to the regions of public health concern where air pollution is a significant hazard. [Display omitted] • A novel early warning hybrid PM10 forecasting framework designed for Australia. • 30 satellite and ground-based meteorological pollutants were considered with 7 modeling approaches. • CNN integrated with GRU outperformed the other models. • Early warning AI model show potential in the health, environment sector, and decision-making. [ABSTRACT FROM AUTHOR]

This study proposes a new hybrid deep learning (DL) model, the called CSVR, for Global Solar Radiation (GSR) predictions by integrating Convolutional Neural Network (CNN) with Support Vector Regression (SVR) approach. First, the CNN algorithm is used to extract local patterns as well as common features that occur recurrently in time series data at different intervals. Then, the SVR is subsequently adopted to replace the fully connected CNN layers to predict the daily GSR time series data at six solar farms in Queensland, Australia. To develop the hybrid CSVR model, we adopt the most pertinent meteorological variables from Global Climate Model and Scientific Information for Landowners database. From a pool of Global Climate Models variables and ground-based observations, the optimal features are selected through a metaheuristic Feature Selection algorithm, an Atom Search Optimization method. The hyperparameters of the proposed CSVR are optimized by mean of the HyperOpt method, and the overall performance of the objective algorithm is benchmarked against eight alternative DL methods, and some of the other Machine Learning approaches (LSTM, DBN, RBF, BRF, MARS, WKNNR, GPML and M5TREE) methods. The results obtained shows that the proposed CSVR model can offer several predictive advantages over the alternative DL models, as well as the conventional ML models. Specifically, we note that the CSVR model recorded a root mean square error/mean absolute error ranging between ≈ 2.172–3.305 MJ m2/1.624–2.370 MJ m2 over the six tested solar farms compared to ≈ 2.514–3.879 MJ m2/1.939–2.866 MJ m2 from alternative ML and DL algorithms. Consistent with this predicted error, the correlation between the measured and the predicted GSR , including the Willmott's, Nash-Sutcliffe's coefficient and Legates & McCabe's Index was relatively higher for the proposed CSVR model compared to other DL and Machine Learning methods for all of the study sites. Accordingly, this study advocates the merits of CSVR model to provide a viable alternative to accurately predict GSR for renewable energy exploitation, energy demand or other forecasting-based applications. [Display omitted] • This paper presents a hybrid CSVR algorithm for global solar radiation prediction. • The proposed approach combines Convolutional Neural Network with a Support Vector Machine. • An Atom Search-based feature selection is applied to improve the performance of the system. • Experiments are carried out in data at six solar energy farms in Queensland, Australia. • The results show the efficacy of deep learning CSVR for solar energy prediction problems. [ABSTRACT FROM AUTHOR]

• Welded H-section stainless steel members were tested under combined constant compression and uniaxial cyclic bending. • The hysteretic behavior of welded H-section stainless steel beam-columns was investigated. • The cyclic hardening and effects of loading history were fully discussed. • The applicability of current design codes was evaluated. Previous studies have shown that structural stainless steel material has significant cyclic hardening characterized by strain amplitude dependence and unsaturation under cyclic loadings, which could have a remarkable impact on the seismic performance of stainless steel structures. Whether the current stainless steel structure design codes based on their static performance can accurately predict the seismic capacities of stainless steel structural members is worth studying. This paper investigates the hysteretic behavior of welded H-section stainless steel members under combined constant compression and uniaxial cyclic bending. A total of 24 specimens were tested, which covered a variety of test variables including stainless steel grade, section slenderness, axial compression ratio and loading history. Residual stresses of the welded H-sections considered in this study were measured. Then, the failure mode, hysteretic response, ductility coefficient and energy dissipation of welded H-section stainless steel beam-columns were fully obtained from the tests. Cyclic hardening was observed at the structural member level in the tests. The effect of loading history on seismic behavior was thoroughly discussed. It was found that various loading histories can obviously influence the strengths and deformation capacities of welded H-section stainless steel beam-columns resulting in their discrete seismic behavior, mainly due to the unsaturated cyclic hardening feature of stainless steels. Moreover, the current design codes including European Code, American Specification, Australia/New Zealand Standard and Chinese Specification underestimate the seismic capacities of welded H-section stainless steel beam-columns. [ABSTRACT FROM AUTHOR]