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Cities are under intensifying pressure to respond to climate change and growing social inequities. Integrating nature-based solutions, as part of essential city infrastructure, contributes to resilient, liveable and equitable cities, as well as provision of biodiversity habitat. There is increasing focus on how concepts of justice can be embedded in these planning and governance processes. This paper proposes a framework to inform planning for just nature-based cities. It brings together concepts of just city, environmental and climate justice and more-than-human thinking, with research on urban nature-based solutions. It highlights the centrality of epistemic justice, and the importance of Traditional Ecological Knowledge in just nature-based city planning and governance. The just nature-based city framework is applied to the case study of the Yarra River (Melbourne, Australia). In 2017, the Yarra River Protection (Wilip-gin Birrarung murron) Act, the first legislation in Australia to be co-titled in the Traditional Owners' language, legally recognised the river as a single living and integrated natural entity. Applying the just nature-based city framework, we find that there are both opportunities and challenges in this new approach to planning. While the Act and associated programs represent substantial and innovative advances, their scope is limited by exclusion of key policy domains and a focus on waterway corridor rather than catchment. The paper concludes by reinforcing the importance of a broadened understanding of justice, that spans both human and non-human considerations, and foregrounds Traditional Owners' perspectives and aspirations in designing, planning and governing just nature-based cities. • 'Just nature-based cities' integrate theories of just cities, nature-based solutions and epistemic justice. • The 'just nature-based city' framework can be applied as both a planning and an analytical tool. • Yarra River-Birrarung is a case study of new approaches to just nature-based city governance. [ABSTRACT FROM AUTHOR]

Recently, Social Media (SM)'s adoption as educational instruments in Higher Education (HE) has been gaining popularity among researchers and practitioners. Nonetheless it has been adopted to support various learning approaches such as Cooperative Learning (CL). Despite the growing research in this field, few studies have proposed a comprehensive SM integration framework as well as empirical studies to evaluate its effectiveness. This paper examines how SM is being used as a pedagogical tool in HE and identifies major factors that drives SM's inclusion in HE classrooms. In addition, the paper investigates the effectiveness of a previously proposed integration framework which was developed to promote CL using a popular SM platform, LinkedIn. A survey was administered to collect data from students in different subjects at a university in Australia. Structural Equation Modelling (SEM) was adopted for data analysis using IBM's SPSS26 and AMOS26. One of the notable results of this study is the significant positive relation found between the use of the adopted integration framework and CL, SM challenges' mitigation, students' intention to use SM and their learning process. The findings of this study have important implications for practice and pave the way for future empirical studies. [ABSTRACT FROM AUTHOR]

• 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]

Nature-based solutions (NBS) are implemented across multiple cities worldwide and feature as promising solutions in local and global agendas. As solutions that can deal with interlinked urban challenges, NBS are being taken up by cities in different geographies and are considered to be mainstreaming. The process, referred to as mainstreaming, and how this can be achieved needs to be better understood, which is identified as a research gap. In this paper, we examine the roles that actors can undertake that contribute to the mainstreaming of NBS in cities. The aim is to understand the roles that urban actors, especially those within local governments, assume in the process of NBS mainstreaming to mobilise and implement novel and innovative strategic solutions for cities. This topic is explored in a case study of the metropolitan Melbourne region in Australia, where urban forest strategies are gaining traction in local governments for addressing urban resilience concerns. We present how the roles different actors assume contribute to the (re)shaping, building, and/or transformation of institutions to attain climate and ecologically resilient cities. The main contribution of this paper is a pathways framework that illustrates how sustainability norms can move through the mainstreaming process, facilitated by the roles that actors undertake – to champion, advocate, and realise transformative discourses and actions within urban politics and urban practices. Our key findings are framed as success factors of mainstreaming agencies and pathways underpinning transformation, which are: commitment longevity, innovative capacity, collaborative mindset, and on-ground delivery. • Policy, practice, and research actors can facilitate NBS mainstreaming in cities. • Role archetypes describe the mainstreaming actions of specific actors. • The configuration of roles is analysed to explain mainstreaming pathways. • Define mainstreaming pathways framework for actors' roles in NBS mainstreaming. • Success factors are commitment, innovation, collaboration, and on-ground delivery. [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]

[Display omitted] Climate change and dynamic government policy is compounding pressure on water resources in farming landscapes creating water scarcity. Successful adaptation to a more sustainable farming system in the face of water scarcity is not a foregone conclusion and requires choosing between short-term incremental change or more transformational options. In this paper, we propose a framework 'unpacking' four pathways that farmers might use to achieve sustainability: absorb, exploit, adapt and transform. Each pathway has specific attributes that relate to the type of farming system change undertaken. The framework is applied to 28 irrigation farmers from southern Murry Darling Basin, Australia, all facing water scarcity from drought and water policy. Using semi-structured interviews, we test the attributes and found they help understand farmers' pathway choices. We show that farmers use all four pathways, either singularly or simultaneously, to achieve a range of sustainability outcomes when facing a water crisis, although some are maladapted, and result in less sustainable outcomes. The study adds to the literature on pathways to sustainability by illustrating the need to clarify attributes that describe what each pathway means to farmers' decision-making and to consider multiple pathways, not just one, e.g. transformation. Policy implications are clear: there are various pathways to, or away from, farm sustainability. Therefore, policy interventions must be 'fit for purpose' and appropriately consider the diversity of farmer capacities and contexts. A focus on a single pathway or single technological fix will fail to deliver farm sustainability. This paper starts to develop a more specific and nuanced understanding of pathways to farm sustainability that reflect real life farming experiences with insight for policy and practice. [ABSTRACT FROM AUTHOR]

Climate change is expected to seriously affect agricultural livelihoods and food security in all Pacific Island countries. The United Nations Food and Agriculture Organisation contends that, to adapt, both 'modern' and Indigenous agricultural practices and knowledge are needed. A challenge lies in combining agricultural knowledge systems and enabling adaptive knowledge to be shared amongst Pacific Islanders. Opportunities to integrate different types of agricultural knowledge (including 'modern' and Indigenous) are needed. This paper argues that international labour mobility programmes provide an avenue for knowledge exchange that can support climate change adaptation in agriculture. It presents empirical evidence from research with 33 Pacific Islanders who grow crops in their home countries and were employed in Australia's Seasonal Worker Programme (SWP). We demonstrate how SWP participation opened new possibilities for climate change adaptation in agriculture in the Pacific Islands. Adaptive knowledge stems from SWP workers' exposure to different agricultural skills and ideas in Australia, combined with their knowledge and experience of crop production in their countries of origin. SWP participation created opportunities for workers to draw on different agricultural skills and knowledge on return to the Pacific Islands. The ability for SWP workers to then exchange adaptive knowledge with other Pacific Islanders is enabled by their social relationships, the likes of which an official agricultural extension officer might not have to the same degree. So far, such adaptive agricultural knowledge and skills exchanges are serendipitous. There is scope to explicitly build climate change knowledge exchange into agriculture-focused international labour mobility schemes. • International labour mobility schemes facilitate agricultural knowledge exchange. • Labour mobility migrants adapt acquired agricultural knowledge to their countries. • International labour mobility schemes can build adaptive capacity in agriculture. • Adaptive capacity from labour mobility can spread via agricultural extension. [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]

Impact assessment as a discipline continues to evolve and search for ways to address challenges that are wicked, systemic, and unconventional. More guidance however is required on integrating multi-disciplinary and novel approaches without losing the richness of impact assessments. This paper explores integrating the causal network approach, underpinned by a community well-being framework, into environmental and social impact assessments. We do this by using a hypothetical scenario of large-scale green hydrogen industry in Australia and engage an interdisciplinary team of experts representing a broad experience across the social, economic, and environmental sectors, via interviews and a workshop. The result is a complex detailed causal network, depicting both environmental and social risks and opportunities associated with the hydrogen industry. We discuss using simple and complex versions of causal networks as both are needed to when communicatng with all relevant stakeholders. We found that the causal network approach is useful for the initial scoping of potential social and environmental impacts, especially for a multi-disciplinary team. Three areas were identified to further strengthen such an approach to facilitate a fully-fledged impact assessment in a real case. First, causal relationships need to be evaluated to assess the magnitude of the impacts and identify mitigation measures and incentives. Second, stakeholders, including local communities, should be involved throughout the assessment process, beginning from the conceptualisation of such assessments. Third, the causal network approach presented in this paper is likely to benefit from incorporating ecological frameworks (or similar) to expand environmental pathways in the network. • Structured mapping of cause-and-effect relationships of complex systems. • Use of a community well-being framework for social and biophysical dimensions. • Value in developing complex and simplified representations of reality. [ABSTRACT FROM AUTHOR]

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

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

In Australia, trifluralin is one of the commonly used herbicides to manage annual grasses and some broadleaf weeds. However, it may have some ecosystem impacts such as high toxicity to terrestrial and aquatic life, so it is vital to monitor the degradation of trifluralin for a considerable period for environmental safety. For risk assessment purposes, it is necessary to estimate the half-life of trifluralin, which is often evaluated using derived mathematical dissipation models. In the literature, bi-exponential (BEXP) and gamma models were suggested for modelling the dissipation of trifluralin in soil. Both models provide the half-life estimate without discussing the uncertainty of the estimate, which is a shortcoming in the literature. In this paper, we used simulation to illustrate the importance of estimate's uncertainty (standard error) and demonstrated a method to compute the standard error for the half-life estimate mathematically for kinetic dissipation models. Later, we evaluated the performance of the two suggested models using statistical indices. The computation of the half-life and the standard error of the half-life estimate were discussed. This allows us to describe the inference of the half-life parameter and determine whether the half-life estimates are significantly different against the co-variate (moisture) levels. We demonstrated the method to calculate the standard error of the half-life of trifluralin, which allows us to determine the statistical difference between the estimates. In this study, we found that the half-life of trifluralin in soil tends to increase with increasing moisture levels, and the half-life of trifluralin in soil with 100% moisture level is significantly greater than 40% and 70% moisture levels. Our findings suggest that soil moisture levels should be carefully considered before trifluralin application to minimize the non-target environmental damage. [Display omitted] • BEXP and gamma models have been considered to investigate the trifluralin dissipation. • A simulation study illustrated the importance of the standard error of half-life. • Provided mathematical computation of the standard error of the half-life. • The soil moisture levels influenced the half-life estimates of trifluralin. [ABSTRACT FROM AUTHOR]

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

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

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

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

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]

The Australian citizen science research programs, VegeSafe and DustSafe, are novel and wide-reaching. Together, they capture the largest number of community-generated domestic garden soil and indoor house dust samples and associated trace metal analysis of any similar programme globally, totalling 26,500 samples from 7,200 homes in Australia alone. All citizen science research programs need to balance often conflicting expectations and imperatives of the researchers and the participants. This paper assesses VegeSafe and DustSafe participant and researcher outcomes against common goals of citizen science programs, including participant engagement, accessibility, motivations and learning in order to evaluate the programs' impact and usefulness. Questionnaire data from 522 questionnaires were analysed which showed that VegeSafe and DustSafe have: enhanced participants' involvement in science (76%), understanding of science (62%); addressed specific community concerns (91%); and were considered useful (93%). The success of the VegeSafe and DustSafe programs can be measured by the number of samples received, households engaged and its geographic footprint across Australia's most populated cities. The participant questionnaire provided deeper insight into positive participant outcomes, including participant autonomy in the scientific process and changes in attitudes and behaviours towards science. Many participants adopted interventions to mitigate potential toxic trace metal exposure in their domestic spaces after receiving their results. The VegeSafe and DustSafe programs provide valuable examples of how to establish programs to meet community needs effectively, educate the community and bring about positive change to ultimately improve community health. [Display omitted] • 522 citizen scientists were surveyed about participation outcomes. • VegeSafe and DustSafe increased scientific understanding and involvement. • Participants reported ownership, changed attitudes and behaviours. • Participants prefer simplicity in presentation of results. [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]

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]

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]

This paper presents the results of the project conducted within the Research embedded in teaching initiative in collaboration with NostraData. The main objective of the research was to analyse if the peak values of such weather conditions, like rainfall and wind speed, can impact the drug purchase transactions within shorts time periods (one, three, and five days) of time, after the peak values of the weather parameters have appeared. The basis for the automated analysis were the data collected over more than six years, restricted to the Melbourne region in Australia. [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]

• 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]

Alkali-activated concretes have been shown to be an environmentally advantageous construction material as they utilize waste or by-products as precursors, such as fly ash and ground blast furnace slag. These concretes also have the ability to achieve strengths suitable for structural applications. However, each mix design of an alkali-activated concretes is unique and requires a detailed life cycle analysis to determine the environmental impact and cost viability. This study evaluates the feasibility of alkali-activated fly ash or fly ash geopolymer and alkali-activated slag concrete developed for application in Australia currently subject to long term site performance studies. This paper reports a detailed life cycle assessment analysis of these concretes to assess their environmental footprint. The paper considers three distinct allocation methods: baseline, mass, and economic allocation, with two system boundaries: manufacture and construction. The study shows AAS concrete has a lower environmental impact than an equivalent strength PC concrete in two impact categories, global warming potential and eutrophication, while acidification and human toxicity depend on the allocation method applied in the manufacturing stage. The global warming potential (100-years) of AAS is 5.25–35% less than PC concrete. The FAGP concrete has more negative impacts on the environment than PC-based concrete, regardless of which allocation method is applied. The report highlights alkaline activators and transportation of raw materials as the main environmental impact contributors to concrete manufacture. The global warming potential of FAGP is 22–34% higher than the equivalent PC concrete in the baseline method. For the construction stage boundary, machinery appliances for handling concrete and transportation only contributes a small environmental impact (<4%) compared with concrete manufacturing. Cost estimation for concrete production suggests that the cost of the alkali-activated concrete is competitive with the conventional concrete market, dependant on the proximity of the feedstocks and the cost of sodium silicate. Neglecting the transportation cost of feedstocks, the cost of AAS concrete can be 4.8% cheaper, whereas the cost of FAGP concrete is 2.7% more expensive than PC concrete. • AAS concrete has a lower environmental impact than PC concrete. • FAGP concrete production has a greater environmental impact compared to PC concrete. • The most significant contributors to the environmental impact are transportation and the alkaline activators. • The cost of FAGP and AAS concrete are comparable with the conventional concrete. • The primary cost factors are transportation and price of Na 2 SiO 3. [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]

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]

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]

Quantitative assessment of floodplain ecological response to flow regimes is challenging but essential for setting targets and estimating impacts for environmental water management. This paper proposes a model that takes long-term (90 years) and large-scale (9 million grid cells) flood maps as input to estimate the response of floodplain vegetation using infinitely differentiable functions. The model, named Floodplain Ecological Response Model (FERM), is calibrated against 1-D temporal Leaf Area Index (LAI) data from the WAVES energy and water balance model at a daily timestep, and validated on the entire floodplain using condition data of the Icon Sites of the Murray River aggregated to a yearly timestep. Results show that FERM can adequately simulate the response of different types of vegetation on the floodplain, while reducing the data requirements and runtime drastically compared to other approaches. The FERM modeling approach is a first step towards a quantitative modeling of floodplain forest ecosystems at large scale with realistic data and computation requirements. It is intended to indicate the potential of such an approach in semi-arid systems where data availability is limited, and to encourage the further research needed to improve our understanding of floodplain forests and our capacity to model the impact of floods on their ecological response. [Display omitted] • FERM uses infinitely differentiable functions to estimate floodplain vegetation condition. • Model parameters can be calibrated to data or inferred from knowledge. • Drastically reduces data requirements and runtime compared to existing methods. • Tested on a semi-arid floodplain in Australia. • FERM adequately simulates the response of different types of vegetation on the floodplain. [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]

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]

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]

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]

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]

• 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]