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As a trending mobility choice, e-scooters have become popular in many cities. A number of authorities have initiated shared e-scooter trial schemes to assess the feasibility of the vehicles prior to enacting official legalisation. This paper aims to provide an evidence review of shared e-scooters and investigate how existing evidence may inform long-term policies. This carries significant relevance for jurisdictions that are in a conflicting position with e-scooters, such as New South Wales (NSW), Australia whose context motivates this study. The evidence review focuses on three themes derived from experience with shared e-scooters within the broader micromobility landscape, namely: safety; where shared e-scooters fit into the modal landscape; and the environmental impacts. Findings confirm that ensuring the safety of shared e-scooters requires complex solutions, which may include a clear regulatory framework for e-scooters, safety education and skill training, innovative data collection and analysis methods, and an approach to safety management that is user-based, location-based, and time-based. In terms of modal fit policymakers should encourage first and last-mile combinations with public transport, with consideration of user characteristics; while climate impact is strongly correlated to the mode replaced by e-scooter trips. The paper provides insights for policymakers on the regulation and positioning of shared e-scooters. • Many jurisdictions are struggling to determine policies for e-scooters. • Existing e-scooter evidence sheds light on possible long-term policies. • Safety management should consider different users, locations, and times. • First and last-mile combinations with public transport should be encouraged. • Climate impact is strongly correlated to the mode replaced by e-scooter trips. [ABSTRACT FROM AUTHOR]

Loneliness contributes to mortality risk, comparable to the risks associated with smoking and obesity, although the causal determinants of loneliness remain less clear. This paper leverages mandatory stay-at-home orders in Australia as a natural experiment and employs data from a panel study to investigate the causal link between physical isolation and loneliness. By analyzing variations in the number of lockdown days experienced by respondents up until their interview dates, and utilizing difference-in-differences analyses with individual, region, and year fixed-effects estimations, we find, contrary to expectations, that the number of days in lockdown does not significantly impact loneliness. Our study examines cumulative, concurrent, and non-linear effects, and assesses external validity through community morale and peer effects during lockdowns using spatial analysis. Additionally, we delve into heterogeneous effects across various factors, such as income, age, personality, living arrangements, and remoteness, finding statistically and empirically insignificant effects. However, for extroverts and young people, we observe weak statistical significance. We investigate exclusion restrictions by analyzing factors including social contacts, internet access, job industry, and household characteristics in relation to loneliness; as well as time use and relationship satisfaction to better understand the underlying mechanism. Our study challenges the notion that 'being alone' and 'being lonely' are interchangeable concepts, providing the first empirical causal evidence of no links between the two. Furthermore, our findings refine earlier understandings of social isolation, highlighting that it likely encompasses factors beyond physical isolation. [ABSTRACT FROM AUTHOR]

• Driver preference of roadwork lane merging methods studied using a survey. • Early merge is preferred over Joint lane merge or Signalised merge. • Drivers with work zone experience prefer non-conventional merging strategies. • Experienced drivers prefer Signalised merge over Conventional merge. • Drivers with full license preferred Early merge over Conventional merge. Merging at work zones with lane closures is associated with delays and conflicts that often result in crashes. To minimize these undesirable effects of merging, researchers have studied and evaluated various merging strategies, such as Early Merge (EM), Late Merge (LM), Joint Lane Merge (JLM) and Signalized Merge (SM). While these studies provided important insights on the efficiency and safety aspects, a key gap exists in the literature where limited knowledge is available on driver preference and perceptions of the merging strategies. This paper aims to examine drivers' preferences of merging strategies and how demographic and travel characteristics influence their preference. Using an online survey of 455 drivers in Australia, data on these strategies and the Australian Conventional Merging (CM) setup were collected. This survey sample is equally split between drivers with and without work zone-related employment experience, thus capturing insights and viewpoints of both groups. Results from an ordered probit model showed that drivers with less driving experience drivers preferred CM more and SM less than the experienced drivers. The JLM strategy was less preferred by female drivers than male drivers. Non-conventional merging strategies like EM and SM were preferred over the CM strategy by those without any work zone-related employment experience than those who have such experience. These findings regarding drivers' merging strategy preferences have important implications for implementing merging strategies in future work zones, including potential improvement in driver compliance rates. [ABSTRACT FROM AUTHOR]

[Display omitted] • The geographic distribution of Rhipicephalus linnaei is much wider than previously known. • Rhipicephalus linnaei was recorded on a dingo. • The record of R. linnaei on a dingo suggest that dingoes might aid the spread of Ehrlichia canis in Australia. • We discuss possible incursion routes and how E. canis has apparently spread rapidly in Australia. The tropical brown dog tick, Rhipicephalus linnaei , is a tick of much medical, veterinary, and zoonotic importance. This tick has a nearly world-wide distribution due to its ability to survive and propagate in kennels and houses. Rhipicephalus linnaei is the vector of Ehrlichia canis , the causative agent of canine monocytic ehrlichiosis, an often debilitating disease of canids and, occasionally, humans. To prevent incursion of E. canis into Australia, dogs entering Australia have been required to have a negative immunofluorescence antibody test for E. canis. In May 2020 however, E. canis was detected in Western Australia. The detection of E. canis in Australia prompted disease investigation and concerted surveillance for R. linnaei and E. canis in regions across Australia. These investigations revealed that R. linnaei was established far beyond the previously recognised geographic range limits of this tick. In the present paper, using records from various collections, published data, and data from our network of veterinarian collaborators and colleagues, we update the current geographic range of R. linnaei in Australia. Our analyses revealed that the geographic range of R. linnaei in Australia is much wider than was previously supposed, particularly in Western Australia, and in South Australia. We also map, for the first time, where E. canis has been detected in Australia. Last, we discuss the possible routes of incursion and subsequently the factors which may have aided the spread of E. canis in Australia which led to the establishment of this pathogen in Australia. [ABSTRACT FROM AUTHOR]

A dedicated grid-tied offshore hybrid energy system for hydrogen production is a promising solution to unlock the full benefit of offshore wind and solar energy and realize decarbonization and sustainable energy security targets in electricity and other sectors. Current knowledge of these offshore hybrid systems is limited, particularly in the integration, component control, and allocation aspects. Therefore, a grid-integrated analytical model with a power dispatch allocation strategy between the grid and electrolyzer for the co-production of hydrogen from the offshore hybrid energy system is developed in this paper. While producing hydrogen, the proposed offshore hybrid energy system supplies a percentage of its capacity to the onshore grid facility, and the amount of the electricity is quantified based on the electricity market price and available total offshore generation. The detailed controls of each component are discussed. A case study considers a hypothetical hybrid offshore energy system of 10 MW situated in a potential offshore off the NSW of Australia based on realistic metrological data. A grid-scale proton-exchange membrane electrolyzer stack is used and a model predictive power controller is implemented on the distributed hydrogen generation scheme. The model is helpful for the assessment or optimization of both the economics and feasibility of the dedicated offshore hybrid energy farm for hydrogen production systems. • Modeling of a grid-integrated analytical model of an OHES for hydrogen co-production. • Development of a power dispatch allocation strategy for the proposed OHES. • Development of detailed controls of each component used in the proposed OHES. • Demonstrations of a case study considering 10 MW OHES situated in offshore off the NSW of Australia. [ABSTRACT FROM AUTHOR]

Green hydrogen, produced from renewable electricity, can be transported long distances in multiple mediums. In this paper, a Techno-economic model is proposed to show the economic viability of global green hydrogen transport along 15 major regional routes such as Australia to Asia and Europe for the first time. A Monte Carlo-based technique is presented to assess the levelised cost of green hydrogen for different regions for the next 30 years. Depending on the specific route, green hydrogen can be transported in different forms, including ammonia, Liquid Organic Hydrogen Carrier (LOHC), Hydrogen Slush, compressed or liquefied hydrogen, etc. form and using different modes including shipping, truck, train, and pipeline. A linear programming analysis has been conducted for different regions, showing that among the global projected 300Mt of green hydrogen demand in 2050, 85% will need to be transported between regions to achieve the most economically optimal distribution. • Techno-economic analysis of green H 2 export via 15 worldwide regional trade routes. • Export model includes H2 generation, conversion, transport, and carbon prices. • In 2030, North America will be the largest exporter, followed by Australia. • Higher carbon prices allow for rapid growth in the green hydrogen industry. • Shipping is the most likely and cheapest H 2 export mode for distant transport. [ABSTRACT FROM AUTHOR]

Supporters of hydrogen energy urge scaling up technology and reducing costs for competitiveness. This paper explores how hydrogen energy technologies (HET) are perceived by Australia's general population and considers the way members of the public imagine their role in the implementation of hydrogen energy now and into the future. The study combines a nationally representative survey (n = 403) and semi-structured interviews (n = 30). Results show age and gender relationships with self-reported hydrogen knowledge. Half of the participants obtained hydrogen information from televised media. Strong support was observed for renewable hydrogen, while coal (26%) and natural gas (41%) versions had less backing. Participants sought more safety-related information (41% expressed concern). Most felt uncertain about influencing hydrogen decisions and did not necessarily recognise they had agency beyond their front fence. Exploring the link between political identity and agency in energy decision-making is needed, with energy democracy a potentially productive direction. • Hydrogen's strongest appeal to Australians is its benefit for climate change mitigation. • Although widely accepted, there is limited knowledge about hydrogen in terms of how it is produced or used. • The more people know about hydrogen, the more willing they are to participate in the decision-making process. [ABSTRACT FROM AUTHOR]

Accurate land valuation is necessary for tax purposes, land resources allocation, real estate management and urban development and planning. Since various factors from different domains affect land prices through non-linear relationships, automating the land valuation process on a large scale is a complex task. Advanced technologies in big data analysis and artificial intelligence have demonstrated superior capabilities in knowledge extraction in such cases. Accordingly, this paper develops and compares the performance of four Automated Valuation Models (AVMs) based on machine learning and deep learning techniques utilizing physical, geographical, socio-economic, environmental, legal and planning factors in Melbourne Metropolitan, Australia. According to the results, the eXtreme Gradient Boosting (XGBoost) method outperforms other algorithms of Support Vector Regression (SVR), random forest and Deep Neural Network (DNN). This method has achieved the coefficient of determination (R2) of 0.862, Mean Absolute Percentage Error (MAPE) of 0.139, and normalized Root Mean Square Error (nRMSE) of 0.281. The achieved high accuracy is due to incorporating a wide range of driving factors and applying innovative feature selection and hyperparameter tuning procedures evaluating various possible feature sets and hyperparameters. Accordingly, this paper can contribute to research, governmental and industry-based activities in terms of developing AVMs for mass land valuation. • Automated land valuation models using machine learning and deep learning • Comprehensive feature set and hybrid feature selection process for robust model performance • Spatial mapping of land values in large urban areas • Contribution to fair housing policies and urban development by accurate land valuation • Practical insights for housing affordability strategies in terms of land management [ABSTRACT FROM AUTHOR]

• A novel approach for modelling subsidence induced by fluid extraction from coal seams. • Transient groundwater and subsidence models are linked. • Coal shrinkage induced by gas desorption can significantly impact subsidence. Several coal seams are contained within the formations of Great Artesian Basin (GAB), the largest natural underground water reservoir in Australia and the world. The extraction of coal seam gas (CSG) and its associated water from thousands of wells within the coal measures of the GAB has led to tens of millimetres of subsidence in CSG development areas. Since highly developed farming systems located in these areas rely on very low slope landforms, even this scale of subsidence has caused significant community concern about the potential for CSG extraction to impair farming operations and productivity through changes in land slope and drainage. Coal seam compaction associated with dewatering and gas extraction has two key components: poromechanical compaction and desorption-induced bulk shrinkage. The former results from pore pressure depletion (due to dewatering and gas extraction) and an increase of vertical effective stress, while the latter is induced by gas desorption from the coal matrix, leading to further deformation. While poromechanical compaction of fluid-bearing formations has been extensively addressed in the literature, relatively little research has been conducted on the role of coal shrinkage in CSG-induced subsidence. This paper introduces an innovative practical modelling approach for assessing CSG-induced subsidence at a subregional/regional scale. The approach utilises an analytical model for CSG-induced subsidence derived from constitutive stress–strain relations for poroelastic-sorptive media. This is a novel approach in the context of CSG-induced subsidence which considers both poromechanical compaction and desorption-induced shrinkage. A further distinction to previous work is the integration of the geomechanical-sorptive subsidence model with a numerical groundwater model. Based on this approach, this paper examines the effect of coal shrinkage on subsidence, and its proportions with respect to total compaction for one of the major coal measures in the Surat Basin. Input data are derived from three-dimensional geological, geomechanical, and groundwater models, as well as methane adsorption and desorption reports. Results show that (a) the impact of coal shrinkage on CSG-induced subsidence is likely to be significant in the study area and (b) the contribution of coal shrinkage to CSG-induced subsidence depends on gas content, Langmuir isotherm, shrinkage strain parameters, and the saturation state of coal. This study provides important insights into CSG-induced subsidence and lays the foundation for the development of robust, efficient, and localised predictive models to support environmental impact assessment and management. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the empirical correlations between inter-city population distribution, intra-city urban economic structures, and networked inter-city relationships to propose a conceptual classification framework for the Australian urban system. This paper sets the pre-COVID-19 benchmark for future research which will investigate the impact of COVID-19 on the economic geography of the Australian urban system. This research confirms that the form of Australia's urban structure is informed by a combination of population distribution, economic concentration, specialisation and diversity of industries and occupations, and networks between cities. Based on these results, we propose an urban structure framework characterised by hierarchical sub-systems and typologies of cities. The framework consists of standalone regional centres and larger cities which form a networked megaregion. Additionally, we propose five typologies of cities based on their functional roles. The Australian urban system presents a unique hybrid economic geography where its structure of networked megaregions of cities and standalone regional centres each with its complementary functional roles, presents itself an interesting case study to inform population policies for other geographies. • This study proposes a method to synthesise city size, economic structure and their networked relationships. • Principles of economic agglomeration mostly apply within the Australian context however, there are nuances and exceptions. • Proximity has a role to play when determining the economic roles of cities and the strength of interactions between cities. • Two urban sub-systems exist in Australia: standalone regional centres and megaregions. • Five city types in Australia which considers their size, economic structure and geography. [ABSTRACT FROM AUTHOR]

This is the fourth in a series of papers that provides an historical record in this journal of contributions made by the most influential figures in the field of stuttering. Ann Packman is an Australian researcher who will retire shortly. This paper reflects on her long and productive career, and her contributions to the field. With a background in literature, linguistics, philosophy, psychology, and the brain and language, she became well equipped to contribute to understanding stuttering causality. That work, and an accompanying collection of basic and applied clinical research, was constantly grounded with the thoughts and feelings of those who stutter in the community. • Ann Packman is an Australian researcher who will retire shortly. • This paper reflects on her career and her contributions to the field. • Her work was grounded with the thoughts and feelings of those who stutter. [ABSTRACT FROM AUTHOR]

The understanding of the stratigraphic evolution of marine rift basins is important for both "pure" scientific and economic reasons. From a hydrocarbon exploration point of view the main goal is to develop tools to predict reservoir location, quality, and extent. Stratigraphic models try to do that, and only historical cases can be used to test those models. On the other hand, depositional model cannot precisely predict reservoir location as factors unique to the specific basin under analysis affect sediment distribution (basin physiography, type of flows, among others). This aspect can be assessed only on a case-by-case basis and can be used for initial hypothesis generation based on analogues. This paper shows a case history study based on the integration of well logs, and 3D seismic data across the Eastern Dampier sub-basin, Northwester Shelf of Australia. Advanced seismic interpretation techniques, calibrated to well data, enable the definition of a chrono-stratigraphic framework of the late syn-rift sequence (Late Jurassic) of the Lewis Trough, and the description of the sediment distribution through the interpretation of geomorphological features. During the Late Jurassic three different channel systems deliver sandstones to the deep-water part of the basin through multiple entry points. The timing of the sediment input seems to be linked to variation of sediment input as during that time relative sea-level was slowly rising. The location of the entry points is related to local physiography and defies any attempt of prediction based on general "rules." From a stratigraphic point view, the findings of the study support the idea that, in rift basin, mechanical subsidence is the primary control for accommodation space, with episodic pulses of extension followed by longer periods of tectonic quiescence. It is during these episodic movements along the basin's bounding faults that space for sedimentation is created. This interpretation is in striking contrast with the basic assumptions of the conventional sequence stratigraphy model. • Understanding the stratigraphic evolution of marine rift basins allows to predict reservoir location and extent. • The present paper analyzes the late syn-rift succession of the Lewis Trough to reconstruct the stratigraphic evolution of the basin infill. • Accommodation space in the Lewis Trough is primarily controlled by mechanical subsidence related to asymmetrical fault movement. • Turbiditic sedimentation occurs during sea-level rise, and it is related to an Increase in sediment supply. • IConventional sequence stratigraphic modeling cannot be applied to rift-basins. [ABSTRACT FROM AUTHOR]

[Display omitted] • Mathematical modeling of membrane distillation considering concentration polarization. • Integration of membrane distillation into a liquid desiccant air conditioning system. • The hybrid system driven by solar energy with a batch-wise operation. • Feasibility analysis of the hybrid system in a hot and humid climate area. • System performance evaluation in terms of air dehumidification and water production. Membrane distillation (MD) is a promising method for liquid desiccant (LD) regeneration as it can mitigate LD carryover and produce freshwater as a by-product. Previous research on MD regeneration was mainly focused on performance evaluation and optimization of the regenerator itself. This paper proposed a hybrid solar-driven direct contact MD (DCMD) regeneration-assisted liquid desiccant air conditioning (LDAC) system for air dehumidification, cooling, and freshwater production. A mathematical model for the DCMD regenerator was first developed by considering both temperature and concentration polarizations. This model was validated against the experimental data in terms of the outlet channel temperature and LD solution concentration in the feed tank with relative deviations of ± 9.8 % and ± 0.5 %, respectively. The DCMD model was further integrated into an LDAC system with a batch-wise operation. To investigate the technical feasibility of the proposed system, a one-week simulation was conducted in a residential house during summer in a tropical climate area of Australia. The results showed that the latent heat load was effectively removed from the process air and the regenerated liquid desiccant solution can meet the dehumidification requirement with a concentration over 30.0 wt%. The dehumidification rate and regeneration rate were at 0.49–1.25 kg/h and 0.95–4.25 kg/h, respectively. Solar energy contributed to 52 %-78 % of the total system thermal energy consumption. Furthermore, this system can produce 11.0–12.8 kg of water daily. [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]

Turritelline gastropod-dominated assemblages (TDAs) are common in siliciclastic facies from the mid-Cretaceous to the Recent, but their distribution in carbonate facies has been thought to be limited to the Cretaceous and Paleogene, except for "cool-water" carbonates from the Miocene of Australia. This paper describes a TDA in a Pliocene limestone in northern Chile, the only known Neogene TDA limestone from outside Australia. This Chilean TDA probably formed in conditions of relative low temperatures and high productivity, and thus appears to support the hypothesis that TDAs largely disappeared from warm waters after the Oligocene. It is also possible, however, that it formed in conditions of warmer temperatures associated with upwelling during the Early Pliocene warm interval. • A turritelline gastropod-dominated assemblage from Chile is Early or Late Pliocene. • The limestone assemblage is a rarity in the Neogene, when most are in siliciclastics. • The assemblage formed under high-productivity conditions. • If Late Pliocene, conditions were cool water upwelling, unusual for a limestone. • If Early Pliocene, they were non-uniformitarian warm water upwelling in a warm ocean. [ABSTRACT FROM AUTHOR]

Kakadu plum (Terminalia ferdinandiana) is a plant species endemic to northern Australia, attracting increasing consumer interest due to its multiple nutritional qualities. As a consumer product at a premium price point, the Kakadu plum may be susceptible to food fraud. This paper determines the prevalence of food fraud in the e-commerce Kakadu plum market. We applied stable isotope analysis (SIA) and elemental profiling using X-ray fluorescence (XRF) through Itrax to evaluate the authenticity of 13 commercially available Kakadu plum powdered samples purchased from Australian and overseas suppliers against four powdered samples directly provided by First Nations harvesters. Overseas and Australian-sourced powders were found to have distinct isotopic and elemental profiles. All overseas powders showed highly enriched δ13C values indicating they are fakes, not derived from Kakadu plum. Non-metric multi-dimensional scaling (nMDS) of elements also displayed distinct groupings between Australian-sourced and overseas powders, whilst analysis of similarity percentages (SIMPER) differentiated the elemental composition between groups. It was also observed that 89% of overseas products sold as Kakadu plum were deceptively labelled as other products. These results showed food fraud occurred along the supply chain of overseas-sourced product. Given the complexities of multi-national food systems, utilising a combination of stable isotopes and elemental profiling are straightforward applications for detecting fraudulent products. [ABSTRACT FROM AUTHOR]

Close-kin mark-recapture (CKMR) data can be used on their own to provide estimates of fish spawning stock biomass, adult mortality rate and fecundity-at-age. However, CKMR data can also be integrated directly into a stock assessment. We present a simplified example of integrating CKMR data within the Stock Synthesis assessment framework, and use simulation to evaluate management strategies based on the harvest control rule used to set catch limits for fish stocks off southeast Australia. The simulations are tailored to jackass morwong, Nemadactylus macropterus, a moderately-long lived (up to 46 years) fish species that is believed to have exhibited a regime-shift reduction in recruitment. We identify the sample sizes needed for a CKMR-based management strategy to achieve similar performance to an (optimistically parameterized) catch-per-unit-effort (CPUE)-based approach for this species. A CKMR-based management strategy can perform at least as well as a CPUE-based approach and CKMR data provide information to better estimate parameters such as adult natural mortality. Moreover, CKMR-based management strategies are not subject to the risk associated with CPUE-based approaches related to trends in catchability. The performances of CKMR-based assessment methods and management strategies are likely case-specific, but this paper shows the viability of including CKMR data in next generation stock assessments and harvest control rules. [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]

• The energy and cost benefits of dynamic PV-integrated shading devices are assessed. • Both energy efficiency and on-site energy generation benefits are considered. • Optimal design and operation of PV-integrated shading systems are determined. • PV-integrated overhangs can allow office spaces to achieve net zero energy operation. • For Australian office buildings, PV-integrated shading systems are highly cost-effective. The paper summarizes a comprehensive analysis to determine the energy and cost benefits of static and dynamic photovoltaic (PV)-integrated shading devices when applied to windows of office spaces located in different climate zones across Australia. The evaluated dynamic shading systems consist of rotating overhangs placed above the windows and operated to minimize the annual energy demands of office spaces. For the first time, the study determines through optimization-based controls the best angle settings for the rotating overhangs on hourly, daily, or monthly basis to minimize the energy consumption of office spaces in Australia. The analysis results indicate that both optimally designed static and optimally operated dynamic PV-integrated overhangs have substantial potential to reduce the annual energy needs of office spaces for all Australian climates with annual energy savings ranging from 45% to over 100% depending on the building orientation, window size, glazing type, and overhang depth. This high energy benefits are attributed to the multi-function capability of the PV-integrated shading systems to minimize cooling and space thermal loads while maximizing on-site electricity generation. Unlike the case of static shading devices, it is found that dynamic PV-integrated overhangs allow office spaces to reach net-zero energy operation for certain climate zones in Australia. [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]

Australia's prisons have a high chronic hepatitis C (HCV) prevalence (8 %). Antiviral therapies and prison-based hepatitis services are available, but only a minority of those eligible are being treated. Improving the HCV public health literacy of the prison sector via targeted education may overcome key barriers to scale-up treatment. This paper describes the: i) HCV public health literacy of the prison setting; ii) barriers and solutions for HCV education and service engagement; iii) HCV education program co-design and development processes; and iv) HepPEd resources. A national needs assessment was conducted to analyse the HCV public health literacy of the target audience groups in the prisons (healthcare providers; custodial officers; people in prison) to inform development of a prison-specific HCV education program (HepPEd). Structured interviews were conducted with key informants (n = 40). Three National Steering Committees, one for each target group, were convened to co-design and develop HepPEd. Only healthcare providers involved with hepatitis care were considered to have 'good' to 'very good' HCV health literacy (including knowledge, attitudes, and capabilities), with all other groups considered less favourably. Key barriers identified included being time poor (healthcare providers), poor motivation (custodial officers) and stigma (people in prison). Peer education delivery was considered a key facilitator for custodial officers and people in prison. A suite of multi-modal resources addressing the perceived gaps in HCV health literacy was developed, with a broad theme of 'Let's talk about hep C'. Delivery of HepPEd was designed to overcome key barriers and utilise facilitators for each group. Significant gaps in HCV health literacy were perceived amongst the target audience groups. The comprehensive co-design and development processes utilised in HepPEd suggest the program will be well-placed to improve the HCV public health literacy of the prison sector and thereby enhance HCV testing and treatment rates amongst people in prison. [ABSTRACT FROM AUTHOR]

The synergistic operation of integrated electricity and natural gas systems (IEGS) in the presence of wind power necessitates a distributed optimization framework that ensures information privacy. However, incorporating wind power penetration leads to distributed optimization problems including uncertainty and unstable distributed algorithm's convergence. With the increasing proportion of wind power in IEGS, fluctuating wind power penetration highly affects the convergence of distributed optimization solutions, resulting in uncontrollable optimization time of the distributed algorithm. Accordingly, this paper investigates the impact of wind fluctuation and uncertainty on distributed IEGS optimization and proposes a novel fast distributed co-optimization framework. Specifically, an adaptive alternating direction method of multipliers (ADMM) is developed to accommodate wind fluctuation. Based on designed rules, the penalty parameter is updated in every step to maximize the gradient of the optimization objective. The experiments are conducted using real wind power generation data sourced from a wind farm in Australia and a classical IEGS framework composed of the IEEE 24-bus electricity system and a 12-node natural gas system. Compared to original ADMM and residual balancing ADMM, the proposed framework achieves an average reduction of 0–91.4% in the number of iterative steps for multiple solution iterations across various scenarios, with a corresponding decrease in the standard deviation by 13.8%–93.0%. • Fast and stable convergence of IEGS distributed optimization problem. • ADMM with an adaptive penalty parameter guarantees the convergence. • Update rate further improves the sensibility of the penalty parameter. • Mean value and standard deviation of running iterations significantly decrease. [ABSTRACT FROM AUTHOR]

Hydrogen is considered a key energy vector and carrier for the decarbonization of global energy systems. However, the economics of green hydrogen systems hinder their widespread application. This paper presents a techno-economic analysis of a green hydrogen production system using high-temperature water electrolysis integrated with a concentrated solar power system (CSP-SOEC) for Western Australia. Real-time solar resource data with 30-min resolution for a typical meteorological year were used to assess the performance of the entire system. The intermittent nature of solar resources is accounted for by integrating the system with a thermal energy storage medium and performing the analysis in off-design mode. The validity of the electrolysis stack model is crucial for overall system performance, which was confirmed through experimental testing conducted on a 15-cell stack. The system was designed to generate a 1 MWe output, and the results showed that a field area of 29,000 m2 and thermal energy storage capacity of 382,500 kWh can fulfil the design criteria. The system generates 0.86 tonne/day of hydrogen at a cost of 8.87 US$/kg-H 2 with a solar-to-hydrogen efficiency of 13.80 %. The cost breakdown revealed that the storage medium has the most significant contribution. Moreover, the sensitivity of the system to the production capacity was analyzed, which showed that larger-scale hydrogen production systems have the potential to further reduce the cost. An 8 MWe system has the capacity to produce 7.18 tonne/day of hydrogen at a cost of 6.1 US$/kg-H 2. The molten salt is currently utilized only 39.3 % for the hydrogen production process. To optimize resource utilization, a cogeneration system is devised and assessed for simultaneous steam and hydrogen production. The results reveal that the cogeneration system can achieve an LCOH reduction of 9 % by reaching 8.07 US$/kg-H 2. These findings are invaluable for academic and industry stakeholders in making informed decisions and fostering the green hydrogen sector in Australia. • A techno economic assessment of green H 2 production for Australia is done. • The electrolysis model is corroborated using experimental results. • The cost of green H 2 for such system in Australia is 8.87 US$/kg-H 2. • Higher production capacity favors the system economics. • The cogeneration system reduces the cost by 9 %. [ABSTRACT FROM AUTHOR]

Fishery-independent scientific surveys are highly valuable for monitoring lobsters and other fisheries. Yet, there are relatively few long-term monitoring surveys to support the provision of management advice. A number of challenges can prevent the continuity of long-term monitoring series. This paper uses the 35-year continuous annual dive surveys for Australia's Torres Strait tropical rock lobster (TRL) to overview strengths and limitations of a long-term scientific survey, as well as lessons learnt from having overcome a range of challenges. Fishery-independent surveys are more reliable than fishery-dependent data and are more robust to external shocks such as market drivers and fuel prices. The primary purpose of the surveys is to provide a reliable index of stock abundance together with data to inform on size and age structure, plus model parameters and reference points. Here, we also highlight additional survey benefits including: yielding positive gains relative to cost, facilitating an adaptive management approach that serves as an early warning system, reducing and resolving inter-sector conflicts, reduced frequency of stock assessments, providing data from associated habitat monitoring, improved stakeholder buy-in, utility for development of Operating Models for Management Strategy Evaluation, contribution to achieving certification, contributing to biodiversity agreements and consistent cross-jurisdictional monitoring. We analyse lessons learnt from challenges such as changes in resource management, budget cuts, increased health and safety requirements, maintenance of capability and staff capacity, stakeholder buy-in, need to validate survey results, a pandemic and changing climate. Our experiences highlight why long-term scientific surveys are considered the gold standard for fisheries monitoring and we provide insights for building and maintaining key marine monitoring series. [Display omitted] • Fishery-independent surveys are valuable but challenging to maintain long-term. • Challenges include costs, capacity, management changes, COVID19, climate change. • Survey data robust to extreme events and external system shocks. • Benefits include reliability, positive benefit:cost, habitat data, science buy-in. • Long-term scientific surveys considered the gold standard for fisheries monitoring. [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 response to the high uncertainty associated with residential net load due to the coupling of distributed photovoltaic generation and user demand, this paper proposed a novel cluster-based stacking ensemble learning model for net load interval prediction. Firstly, the k-means algorithm is employed to discover the similarity in user electricity consumption patterns. Then, a RIME optimization algorithm with local enhancement (LRIME) is developed to optimize the parameters and weights of the base learners in stacking ensemble learning. Subsequently, base learners with strong predictive capabilities and significant diversity are chosen as the first-layer predictive models, extreme learning machine (ELM) is utilized as the second-layer predictive model, ultimately resulting in the proposed stacking ensemble learning prediction model. And utilizing the bootstrap method to fit the volatility of point predictions, different prediction intervals are obtained at varying confidence levels, aiming to quantify the integrated uncertainty in photovoltaic generation and load. Testing on the open Ausgrid electricity load data in Australia provided robust validation of the proposed method's effectiveness. In comparison with other outstanding prediction models, the proposed ensemble model can effectively capture the uncertainty in integrating photovoltaic generation and user load. • A RIME optimization algorithm with local enhancement (LRIME) is developed. • A stacking ensemble model is proposed for residential net load interval prediction. • The proposed model demonstrates higher forecasting accuracy and reliability. [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]

• Nationwide streamflow projections for Australia informed by CMIP5 and CMIP6. • Vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. • Lower winter rainfall translates to a significant reduction in annual runoff. • Modelled median projection of runoff shows a 50% reduction in far southwest Australia. • Modelled median projection of runoff shows a 20% reduction in far southeast Australia. Projections of streamflow under future climate are essential for developing adaptation strategies in the water and related sectors. This paper presents nationwide streamflow projections for Australia informed by climate change signals in CMIP6 GCMs and compares the projections with those from CMIP5 GCMs. The modelled future runoff projections driven by CMIP5 and CMIP6 GCMs are relatively similar for far southern Australia. The median projection is a 50% reduction in mean annual runoff in far southwest Australia and 20% reduction in far southeast Australia by 2046–2075 relative to 1976–2015 under the high SSP5-8.5/RCP8.5 global warming scenario. The vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. As most of the runoff in far southern Australia occurs in winter and spring, the lower winter rainfall translates to a significant reduction in annual runoff. It is therefore prudent to plan and manage for a reduction in future water resources, particularly in the densely populated and important agricultural regions in southeast Australia. There is less agreement between GCMs in the summer rainfall projection, with the CMIP6 GCMs generally projecting a wetter future (or smaller decrease in rainfall) than the CMIP5 GCMs. The modelled median projection for mean annual runoff is a 10% reduction in the south-east coast, 5% reduction in the north-east coast and little change in northern Australia. More GCMs project an increase rather than a decrease in the interannual variability of rainfall, and this would further amplify multi-year hydrological droughts. [ABSTRACT FROM AUTHOR]

Subsea flowlines are flexible or rigid pipelines that are used in oil and gas fields commonly for transporting production fluids from wells. The majority of the flowlines operating in basins across the world (including e.g. Australia's 1700 km infield flowlines) are approaching the end of their productive life, and will need to be decommissioned over the next 5 decades. This paper, for the first time, investigates the potential of repurposing decommissioned flowlines to dynamically installed anchors for mooring floating facilities. Five shapes of anchor have been developed using segments of flowline. Two series of tests were subsequently carried out on models of those shapes. The first series of tests involved releasing the models in still water to assess free-fall trajectory (i.e. verticality), terminal velocity, and anchor drag coefficient. The second series of tests were carried out at 50 g in clay to investigate the performance of the anchors in terms of tip embedment depth during dynamic installation and capacity under vertical loading. The results confirmed performance similar to conventional dynamically installed anchors, suggesting that there is potential to fabricate offshore anchors using segments of flowline. • Decommissioned flowlines. • Repurposing decommissioned flowlines to dynamically installed anchors. • Verticality, terminal velocity, and drag coefficient in water. • Tip embedment depth during dynamic installation in clay. • Capacity under monotonic vertical loading in clay. [ABSTRACT FROM AUTHOR]

• This paper presents a review on the state-of-the-art electric vehicle charging technologies i.e., fast, super-fast, and ultra-super-fast charging stations that are under development. These have the potential to cause power quality issues such as charging transients, rapid voltage fluctuations, and harmonics in the power grids. • EVs can participate as mobile storage to provide vehicle-to-grid (V2G) support and ancillary services. There are still some barriers to the wide implementation of V2G systems. This paper has highlighted these issues and their impacts to power grids. • This paper investigates the impacts of random and fluctuating EV fast-charging loads on the electric power grids, mainly considering the random connection of EVs to the power grids through DC fast-charging stations as the principal source of fluctuating EV loads. • The charging stations are modeled for three different commercial DC fast charger connections (CHAdeMO, SAE CCS, and Chargepoint Express 20), with separate CC-CV charging modes of the DC fast chargers incorporated. To quantify the impacts, case studies are conducted on the simulation platform for two different scenarios. • The impact on the electric power grid is evaluated in terms of voltage drops, maximum power demand, current, and voltage total harmonic distortion (THD) experienced by the corresponding transformer connected to the charging stations. • The results have revealed that random and fluctuating EV fast-charging loads pose much more intense impacts on the power systems than the normal EV slow charging loads. The era of the electrified transportation system is fast approaching. Although the socioeconomic and environmental benefits of electric vehicles (EVs) have contributed to their large-scale utilization, it has also created a huge load demand on the existing power grids throughout the world. Moreover, fast, super-fast, and ultra-super-fast charging stations are under development, some of which are now in the markets. These have the potential to cause power quality issues such as charging transients, rapid voltage fluctuations, and harmonics in the power grids. Moreover, EVs can participate as mobile storage to provide vehicle-to-grid (V2G) support and ancillary services. There are still some barriers to the wide implementation of V2G systems. This paper addresses these issues and provides a review of the state-of-the-art EV technologies and their impacts on power grids. This paper also investigates the impacts of random and fluctuating EV fast-charging loads on the electric power grids, mainly considering the random connection of EVs to the power grids through DC fast-charging stations as the principal source of fluctuating EV loads. A practical electrical grid of Wollongong, New South Wales, Australia has been considered in this work to separately analyze the impacts of constant current (CC) and constant voltage (CV) charging modes upon the grid. Furthermore, design and modeling of three different commercial DC fast charger connections (CHAdeMO, SAE CCS, and ChargePoint Express 200), with separate CC-CV charging modes of the DC fast chargers have been incorporated. To quantify the impacts, two separate scenarios were examined using a simulation platform, with case studies conducted to determine the impacts on the power grid. The first scenario involved three fast charging stations, while the second scenario featured ten stations that were able to charge six and twenty electric vehicles respectively, with various load combinations considered. Each of these scenarios was analyzed under different conditions to evaluate their impact on the grid, using factors such as voltage drops, maximum power demand, current, and voltage total harmonic distortion (THD) for the transformer that was connected to the charging stations. The study results indicated that the power systems were affected more significantly by random and fluctuating EV fast-charging loads, compared to normal EV slow-charging loads. [ABSTRACT FROM AUTHOR]

Australia has significant offshore wind resources that can support ever-increasing power demand and hence provide a long-term contribution to the net-zero target by 2050. Although some offshore wind farm projects are proposed in Australia, the industry is not mature enough to justify the benefits and display economic opportunities. More specifically, the techno-economic analysis of offshore wind energy has not been systematically mapped in Australia. Therefore, the investigation of the spatial variation of technical feasibility and economic potential of offshore wind is vital to identify the potential regions and to develop strategies for future offshore wind farm developments in Australia. This paper presents detailed mappings of the techno-economic potential of offshore wind energy in Victoria-Tasmania's exclusive economic zone. Offshore wind energy resources are assessed in the paper in terms of energy availability and variability and the feasibility of various offshore wind technologies is investigated, including turbines, foundation types and transmission topologies. In addition, the high-fidelity life-cycle cost models and two innovative evaluation matrices are developed to assess the economic potential of offshore energy farms in South-eastern Australia. The impact of local power system regulation costs and future carbon prices on the viability of offshore wind is also investigated in this paper. It is concluded that the detailed mapping approach and analysis in the paper can provide a systematic tool for industry partners, investors and policymakers at the pre-planning stage of developing offshore renewable energy systems. • The technical feasibility of offshore wind is mapped under various constraints. • The novel LACE and high-fidelity LCC assessment models are developed. • The techno-economic potential is systemically mapped in South-eastern Australia. • The impacts of electricity market regulation and future carbon prices are studied. [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]

A palynological record from Girraween Lagoon sediments (Darwin region of the Northern Territory, Australia) provides detailed long-term insight into tropical savanna vegetation community patterns, climatic and fire relationships, through Marine Isotope Stage 4 (MIS 4: 71–57 thousand years ago, ka) and Marine Isotope Stage 3 (MIS 3: 57–29 ka). Owing to a lack of data in reconstructing northern Australian environments, this paper looks to define and describe to a greater degree the nature and scope of these stadial and interstadial stages for the region. Girraween Lagoon simultaneously provides proximal palaeoecological data for the time and region of Aboriginal people's first arrival into Australia, also encompassing the late Pleistocene continental decline of megafauna. This study provides a dataset enabling full exploration of long-term people-landscape and faunal-floral interactions. Sea levels and associated variations imposed on the transportation of moisture and heat, held implications for MIS 4 and MIS 3 monsoon strength, which was particularly consequential for Girraween regional ecology. Results reveal a prolonged transition from wooded- to grassy-savanna, into a cool drier semi-arid savanna. Increasingly episodic delivery of moisture influenced the permanency of freshwater in the landscape. • A detailed picture of vegetation in monsoonal north Australia during MIS 4 and MIS 3. • An ultimate transition from grassy eucalypt savanna into semi-arid savanna revealed. • Changed monsoonal delivery particularly consequential for regional ecology. • An indication of baseline landscapes during people's first arrival into Australia. • Assisting to place megafaunal decline within an ecological landscape. [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]

Preference weights are widely used to score generic health states into utility indexes for estimation of quality adjusted life years (QALYs) and to aid health care funding decisions. To date, health state utilities are predominantly derived using stated preference methods based on decision utility. This paper tests an alternative and generates preference weights using experienced utility for children based on the Child Health Utility 9D (CHU9D) descriptive system. We estimate the relative values of the CHU9D health states with regard to experienced utility, where experienced utility is approximated by self-reported happiness. A nationally-representative longitudinal survey was used including 6090 Australian children aged 12–17 years surveyed over 2014–2018. The derived weights were then applied to calculate the utility decrements for a few common child health conditions. We found that the estimated utility decrements are largely similar to those estimated using the published CHU9D Australian adolescent weights based on decision utility, except for pain and depression. A smaller utility decrement for pain and a larger utility decrement for depression were indicated by experienced utility. We contribute to the literature by showing that using experienced utility methods to generate preference weights for health states is possible, and we discuss some important methodological challenges for future studies such as the impracticability of anchoring to 'dead' when utilizing experienced utility. • Established methods of valuing health states rely on decision utility. • We use experienced utility to generate preference weights for children. • The estimated decrements for health conditions are similar to published weights. • We see smaller impact for pain, larger for depression compared to published weights. • Feasibility is demonstrated using child experienced utility for preference weights. [ABSTRACT FROM AUTHOR]

The complexity and time requirements for contemporary ART laboratory activities has increased compared with traditional cycle requirements from 9 personnel hours, to 20 hours for completion [1]. Concurrently, the emergence of machines for data capturing and API integrations have appeared yet the two are rarely utilized in combination. Instead, labs are encouraged to consider admin support to complete important, time-consuming tasks which do not require specific training. This is labour intensive often with double data entry, prone to human error and costly. An alternative is to implement the promise of this digital ecosystem utilizing API's and automatic data capture. The study involved six fertility centres in the UK, Australia, and the US, utilizing Embie's clinic management software integrated with Cooper's RI Witness, Vitrolife's Embryoscope, and Fairtility's CHLOE EQ. a comprehensive analysis of current procedures in fertility centers across three continents was conducted. Manual data entry vs machines and API data points available in the market was modeled. This involved interviews with embryologists, data mapping, exploring API endpoints and gathering feedback for a user interface meant to improve the process as well as an analysis of time saved per cycle when comparing manual vs automatic data entry/capture. The siloed data systems showed a moderate improvement, averaging 209 manual entry points. However, the disconnected nature of these systems still posed challenges in data consistency and effective communication between different platforms, leading to duplicate data entry. A fully integrated process, demonstrated a drastic reduction, with an average of just 18 manual data entry points which was performed in real time e.g. the number of eggs collected was inputted into the RI witness system at the end of the procedure and pushed into the EMR. This minimized the potential for human error, streamlined workflow, improved overall efficiency and accuracy. This study highlights the transformative potential of digital integration in ART laboratories, suggesting significant improvements in efficiency and accuracy. It emphasizes the need for centers to transition from traditional methods like pen-and-paper, Excel sheets, or outdated electronic medical records systems to adopting newer, more advanced technologies. [ABSTRACT FROM AUTHOR]

Globally, school leaders express a need for ongoing professional learning to support teacher's knowledge and skills in inclusive education. This paper reports on interview data collected from four school leaders and 18 teachers who have various roles in Australian schools (four school sites) about their experiences of professional learning to operationalise inclusive schooling. Most of the teachers did not have leadership titles or designated leadership roles but the descriptions of their experience indicated teacher leadership in action. Our research draws on Transformative Leadership Theory and aims to identify how transformative leadership tenets and an additional tenet—Relatedness, are evident in descriptions of experience in professional learning to lead and support inclusive education. Each school site supports a different model of professional learning: Coaching, Collegial Partnership, Community of Practice (CoP), and Independent Learning. The findings were analysed to consider how the model of professional learning impacted on participant's perceptions of influence to operationalise inclusive practices. Our findings indicate that the Transformative Leadership Theory provided a comprehensive framework to elucidate the majority of the findings in this study. Relatedness as an additional tenet was an important consideration for leading transformative change for inclusive education. The majority of participants contributed to a school model of professional learning and fostered an environment, where teachers could expand their knowledge and skills, and work critically and collaboratively with their colleagues, to promote inclusive education practices in their school communities. [ABSTRACT FROM AUTHOR]

• River plumes are a source of pollutants in the Great Barrier Reef, Australia. • Plume dispersion is simulated using a coupled Lagrangian-Eulerian-ocean model. • Lagrangian particle density is computed from application of Voronoi diagrams. • Lagrangian particle densities and Eulerian tracer concentrations are comparable. • Lagrangian model is validated using satellite imagery and satellite Chlorophyll-a. In this paper, we describe methods to verify the adequacy and accuracy of Lagrangian particles from a Lagrangian model to reproduce the concentrations of a passive tracer from an Eulerian-model in river plumes. The modelling simulates plumes from two major rivers discharging in the Great Barrier Reef (GBR), Australia, under real-world scenarios. The study has been a part of a major project to aid in the protection of the GBR system from the impacts of extreme events and climate change. We employ the Regional Ocean Modelling System (ROMS) activated with its built-in Lagrangian model, and forced with wind fields from global models and recorded river volume discharges. The ROMS-Lagrangian model tracks the Lagrangian particles using the spatially interpolated velocities computed on the Eulerian ROMS three-dimensional (3D) grid. The Lagrangian particles are released in the river in proportion to the measured river volume flux. We apply a novel technique that exploits Voronoi polygon areas to convert Lagrangian particle separation into a concentration field. This facilitates comparison with the passive tracer concentrations driven by the Eulerian velocities computed on the ROMS 3D grid. We evaluate the fate of Lagrangian particles activated on a 4-km grid resolution with those of equivalent Eulerian tracer concentrations. For validation, we compare the Lagrangian particles with the Eulerian passive tracer simulated using a higher model grid resolution of 500-m, and found that the computed Lagrangian particle concentrations showed similar overall patterns compared to the passive tracer concentrations from both the coarse 4-km, and high-resolution 500-m Eulerian models. The spatial extent of the particles was in better agreement with the coarse 4-km model than with the higher resolution 500-m model. The clustering of particles resulted in structures at finer scales than the Eulerian model, over the high particle density areas that compared well against observations. The Lagrangian particles are able to capture the general river plume patterns seen in the satellite MODIS images and the satellite derived Chlorophyll-a. We further compare the online ROMS model with an offline particle tracker, OceanPARCELS and determined the similarity of results from both online and offline methods. Overall, our study demonstrates the viability to use Lagrangian particles to reproduce the Eulerian tracer properties, which can be combined with particle properties include tracer age providing to enhance the guidance on the distribution and concentration of effluent from localised flooding river events. [ABSTRACT FROM AUTHOR]

• The compressive behaviour of square RACFSST stub columns after exposure to fire is investigated. • Twelve post-fire compression tests are conducted. • The influences of heating durations and RCA replacement ratios are discussed. • 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. Experimental and numerical studies on the cross-section compressive behaviour and residual resistances of square recycled aggregate concrete-filled stainless steel tube (RACFSST) stub columns after exposure to fire are reported in this paper. An experimental programme was firstly carried out on twelve stub column specimens with three recycled coarse aggregate replacement ratios (0%, 35% and 70%) after exposure to the ISO-834 standard fire for 0 min (i.e. at ambient temperature), 15 min, 30 min and 45 min. The test results, including load–end shortening curves, failure loads and failure modes, were presented, with the initial compressive stiffness and confinement effect analysed. The experimental programme was followed by a numerical modelling programme, where thermal and mechanical finite element models were developed and validated against the test results and afterwards used to conduct parametric studies to generate additional numerical data over a wide range of cross-section dimensions. Based on the test and numerical data, the relevant design rules for square natural aggregate concrete-filled carbon steel tube stub columns at ambient temperature, as specified in the European code, Australia/New Zealand standard and American specification, were evaluated, using post-fire material properties, for their applicability to square RACFSST stub columns after exposure to fire. The evaluation results generally revealed that the European code and Australian/New Zealand standard led to a good level of design accuracy, while the American specification resulted in slightly conservative post-fire cross-section compression resistance predictions. [ABSTRACT FROM AUTHOR]

• Most of the injured adults had either short-term, low-dose or no opioid use following their injury hospitalisation. • Longer durations and higher doses of opioids were seen for those with pre-injury opioid use, more readmissions and surgeries. • If dispensing occurred prior to injury, duration increased 10-fold and oral morphine equivalents doubled. • Approximately 45 % of adults with pre-injury opioid use remained at a higher dose than they were pre-injury. • A small but concerning number of adults were diagnosed or treated as drug dependant over the two-year post-injury follow-up period. Despite a focus of opioid-related research internationally, there is limited understanding of long-term opioid use in adults following injury. We analysed data from the 'Community Opioid Dispensing after Injury' data linkage study. This paper aims to describe the baseline characteristics of the injured cohort and report opioid dispensing patterns following injury-related hospitalisations. Retrospective cohort study of adults hospitalised after injury (ICD-10AM: S00-S99, T00-T75) in Queensland, Australia between 1 January 2014 and 31 December 2015, prior to implementation of opioid stewardship programs. Data were person-linked between hospitalisation, community opioid dispensing and mortality collections. Data were extracted for 90-days prior to the index hospital admission, to establish opiate naivety, to 720 days after discharge. Median daily oral morphine equivalents (i.e., dose) were averaged for each 30-day interval. Cumulative duration of dispensing and dose were compared by demographic and clinical characteristics, stratified by drug dependency status. Of the 129,684 injured adults, 61.3 % had no opioids dispensed in the 2-year follow-up period. Adults having any opioids dispensed in the community (38.7 %) were more likely older, female, to have fracture injuries and injuries with a higher severity, compared to those with no opioids dispensed. Longer durations and higher doses of opioids were seen for those with pre-injury opioid use, more hospital readmissions and repeat surgeries, as well as those who died in the 2-year follow-up period. Median dispensing duration was 24-days with a median daily end dose of 13 oral morphine equivalents. If dispensing occurred prior to the injury, duration increased 10-fold and oral morphine equivalents doubled. Adults with a documented dependency prior to, or after, the injury had significantly longer durations of use and higher doses than the rest of the cohort receiving opioids. Approximately 7 % of the total cohort continued to be dispensed opioids at 2-years post injury. This is a novel population-level profile of opioid dispensing patterns following injury-related hospitalisation, described for the time period prior to the implementation of opioid stewardship programs and regulatory changes in Queensland. Detailed understanding of this pre-implementation period is critical for evaluating the impact of these changes moving forward. [ABSTRACT FROM AUTHOR]

Placement stability is an important indicator of the functioning of an out-of-home care system. Previous research suggests that frequent placement changes have a negative impact on the outcomes for children and young people in out-of-home care. This paper examines the association between placement stability in out-of-home care and children's socio-emotional, cognitive and physical health outcomes. The Pathways of Care Longitudinal Study (POCLS) is the first large-scale prospective longitudinal study of children in out-of-home care in Australia. The sample consists of any study child who participated in any of the first three waves of the POCLS interview. Unweighted data from the first three waves of the POCLS interview and administrative data was used. A measure of placement stability was developed that accounted for both number of placements and length of time in care. Mixed effect modelling was used to examine the link between placement stability and children's developmental outcomes. Placement stability was found to have a significant association with socio-emotional, cognitive (non-verbal) and physical health (gross and fine motor skill) development. A number of other factors were also found to be associated with positive development. The findings support the existing evidence that placement stability is an important factor for children's development. Other factors including placement type, carer wellbeing and carer support are also important for positive development. Appropriate policy and practice intervention to support children and families to improve placement stability is fundamental to achieving positive developmental outcomes for children in out-of-home care. [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]

The Australian financial planning sector has seen recent regulatory changes introduced to address misconduct. One such introduction surrounds increased levels of mandatory financial adviser education. This paper examines the Banning orders between March 2015 and March 2023 to identify relationships between levels of misconduct and education. Analysis of findings supports the validation of education requirements and outlines identifying properties assisting consumer decisions to avoid those advisers affiliated with misconduct. This research further explores avenues to develop the sector's professionalism. [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]

Hydrogen has become an important component of the global transition to zero-carbon economies. Low-carbon and green hydrogen gas and fuel cell technology for domestic household use will depend on skilled practitioners, particularly gasfitters, to convert, install, and maintain hydrogen-based appliances. Upskilling gasfitters to work with hydrogen is critical to transitioning from natural gas to hydrogen for heating and cooking. Yet, limited research exists on training and upskilling trade practitioners in the context of renewable energy and low-carbon technologies. This paper makes a novel contribution to research on upskilling for renewable and low-carbon technologies by drawing on the findings from a survey of 1001 plumbers in Australia. The survey, designed using the Theory of Planned Behavior, aimed to predict behaviors regarding hydrogen training and ascertain social and structural enablers for such behavior. The results show that plumbers have limited awareness of hydrogen, yet have positive attitudes towards upskilling to work with the low-carbon fuel. Perceived benefits to business sustainability, customer service and safety underpin the positive attitudes. The research shows that while plumbers are enthusiastic about upskilling for hydrogen, upskilling policies and programs must ensure key stakeholders who inform plumbers' ongoing practice are on board and informed about hydrogen training opportunities. • Plumber/gasfitters expressed a strong positive attitude towards future hydrogen training. • Colleagues/peers and technical regulators were key stakeholders in supporting training. • Behavioral controls do not strongly influence the intention to undertake hydrogen training. • Plumber/gasfitters have limited awareness of hydrogen and transition plans. • Hydrogen training has a dual purpose of upskilling and educating the sector. [ABSTRACT FROM AUTHOR]

Comparing the United Kingdom's Renewables Obligation and Australia's Renewable Energy Target, this paper reconstructs the market for green certificates in which penalties are imposed on missed certificates. Our analysis shows that excess demand in the Renewables Obligation makes the penalty the minimum certificate price, but excess supply in the Renewable Energy Target makes the penalty the maximum certificate price. Further, excess supply also implies that the sales of certificates are not guaranteed in the Renewable Energy Target. Therefore, compared to the Renewables Obligation, generators face greater risk under the Renewable Energy Target as there is more uncertainty about the price of certificates and the possibility of sales. • Reconstruct demand and supply curves for TGC models. • Excess demand (supply) makes the penalty the minimum (maximum) certificate price. • The recycling mechanism pushes up the certificate price in the RO. • The refunding mechanism pushes down the certificate price in the RET. • Generators under TGC with excess supply face greater price risk and volume risk. [ABSTRACT FROM AUTHOR]

Are political leaders less popular among voters than they used to be? If so, why? Many studies have mapped a decline in citizens' political trust and satisfaction with democracy, but we know less about whether leader popularity is in decline, and if so, what drives this trend. This paper examines these questions using unique data on major party leader popularity and leadership traits from the Australian Election Study collected over four decades. The results show that Australia's major party leaders have become less popular over time. We test a theoretical model which explains declining leader popularity by partisan dealignment and the role of leader integrity. The analyses show that the effect of partisanship on leader popularity has weakened over time, while leader integrity traits have become more important. These findings apply to both major party leaders that have won elections as well as those leaders that have lost elections. [ABSTRACT FROM AUTHOR]