The Commonwealth Scientific and Industrial Research Organisation

Accurate dielectric constant measurements are crucial in Internet of Things (IoT) sensing applications to characterise materials and their properties. This paper introduces an innovative capacitor-based chipless radio frequency identification (RFID) sensory array tailored specifically for precise measurements of dielectric constants in IoT contexts. The array incorporates Pi-shaped resonators and cylindrical capacitors, addressing challenges such as low accuracy, limited εr measuring range, and the reliance on bulky vector network analysers (VNA) as readers. Theoretical modelling, design considerations, sensor calibration, and validation processes are detailed, highlighting the array’s precision and adaptability. Real-world IoT applications are demonstrated, showcasing the array’s potential with a low-cost portable multiple-input multiple-output (MIMO) reader, Walabot. This cost-effective solution overcomes conventional limitations, offering a versatile approach to dielectric constant measurements and opening up new possibilities for diverse IoT sensing applications. The integration of this sensory array with IoT technologies demonstrates the feasibility of smarter material characterisation.

Episodic deposition of light absorbing impurities on glaciers reduces albedo and exacerbates snow melt. In 2019/2020 a devastating Australian bushfire and desert dust event combined with favorable meteorological conditions transported an unprecedented mass of impurities across the Tasman Sea turning the Southern Alps of Aotearoa New Zealand red. Here we use time lapse cameras, airmass back trajectories, snow impurity geochemistry, and remote sensing to quantify the timing, provenance, and mass deposition of the event. Deposited in late November 2019, the impurities were dominated by mineral dust with a distinct southeastern Australian geochemical fingerprint. The event deposited ∼4,500 ± 500 tons of red dust to Southern Alps permanent snow and ice with a mean dust mass concentration of 6.5 ± 0.7 g m⁻². A southeast Australian desert dust storm generated by the same type of meteorological conditions as the 2020 New Year bushfires was the main driver of the glacier discoloration.

The Plasmodium falciparum cytoplasmic tyrosine tRNA synthetase ( Pf TyrRS) is an attractive drug target that is susceptible to reaction-hijacking by AMP-mimicking nucleoside sulfamates. We previously identified an exemplar pyrazolopyrimidine ribose sulfamate, ML901, as a potent reaction hijacking inhibitor of Pf TyrRS. Here we examined the stage specificity of action of ML901, showing very good activity against the schizont stage, but lower trophozoite stage activity. We explored a series of ML901 analogues and identified ML471, which exhibits improved potency against trophozoites and enhanced selectivity against a human cell line. Additionally, it has no inhibitory activity against human ubiquitin-activating enzyme (UAE) in vitro . ML471 exhibits low nanomolar activity against asexual blood stage P . falciparum and potent activity against liver stage parasites, gametocytes and transmissible gametes. It is fast-acting and exhibits a long in vivo half-life. ML471 is well-tolerated and shows single dose oral efficacy in the SCID mouse model of P . falciparum malaria. We confirm that ML471 is a reaction hijacking inhibitor that is converted into a tight binding Tyr-ML471 conjugate by the Pf TyrRS enzyme. A crystal structure of the Pf TyrRS/ Tyr-ML471 complex offers insights into improved potency, while molecular docking into UAE provides a rationale for improved selectivity.

The analysis of how biological shape changes across ontogeny can provide us with valuable information on how species adapt behaviorally, physiologically, and ecologically. The white shark Carcharodon carcharias is one of the largest and most widely distributed apex predators globally, yet an understanding of ontogenetic changes in body shape and relative scaling of length and weight measures is limited, especially in relation to foraging ecology. Through analysis of a suite of shape‐related metrics, we identified ontogenetic patterns of scaling throughout development. Isometric growth was exhibited for most metrics, failing to show a significant deviation from an isometric slope of 1.0 for length–length relationships, and 3.0 for weight–length relationships. The most notable difference from this trend was the negative allometric growth observed for the upper caudal‐fin lobe length, trunk length, and the mouth length. The surface area of the fins also presented a strong, positive relationship with precaudal length (PCL) and the girth at the pectoral fin. Negative allometric growth was exhibited for three of the fins (pectoral, upper caudal fin, and lower caudal fin) against PCL, exhibiting a significant deviation from the expected isometric growth of 2.0 for area–length relationships. There were no significant differences in morphometric relationships between geographic regions within Australia that samples were collected from. No differences between the sexes were identified; however, this may be an artifact of the lack of mature animal samples. Conversely, life stage was found to have a significant effect on the girth–length and weight–length relationships. The development of regression equations for morphometric measures allows the assessment of white shark body condition and may serve as an assessment tool to understand the potential impacts of human‐induced environmental change on white sharks.

Waterbirds are highly mobile and have the ability to respond to environmental conditions opportunistically at multiple scales. Mobility is particularly crucial for aggregate‐nesting species dependent on breeding habitat in arid and semi‐arid wetlands, which can be ephemeral and unpredictable. We aimed to address knowledge gaps about movement routes for aggregate‐nesting nomadic waterbird species by tracking them in numbers sufficient to make robust assessment of their movement patterns. We hypothesised that analysis of long‐distance movements would identify common routes with consistent environmental features that would be useful as context for conservation management. We used GPS satellite telemetry to track the movements of 73 straw‐necked ibis (Threskiornis spinicollis) and 42 royal spoonbills (Platalea regia) over 7 years (2016‐2023). We used these data to identify long‐distance movements and to demarcate and characterise movement routes. We identified common routes used by both species, including a ‘flyway’ over 2000 km long, spanning Australia's Murray–Darling Basin from the south‐west to the north‐east. This flyway connects important breeding sites and is characterised by flat, open/unforested areas with low elevations of < 350 m and mid to high rainfall. The flyway corresponds to an area west of Australia's Great Dividing Range, which appears to act as a low‐permeability barrier to the movement of both species. Identification of an inland flyway for waterbirds in Australia provides important context for multi‐jurisdictional cooperation and strategic management. Where resources are limited, water and wetland management efforts (e.g., environmental watering) should be preferentially located within this route. Similarly, targeting threat mitigation within common movement routes may have disproportionate importance for long‐term population viability. Given the widespread distribution of similar species globally, there are likely to be other flyways worthy of scientific and conservation management attention that could be identified using our approach.

Cardiovascular arrhythmia, characterized by irregular heart rhythms, poses significant health risks, including stroke and heart failure, making accurate and early detection critical for effective treatment. Traditional detection methods often struggle with challenges such as imbalanced datasets, limiting their ability to identify rare arrhythmia types. This study proposes a novel hybrid approach that integrates ConvNeXt-X deep learning models with advanced data balancing techniques to improve arrhythmia classification accuracy. Specifically, we evaluated three ConvNeXt variants—ConvNeXtTiny, ConvNeXtBase, and ConvNeXtSmall—combined with Random Oversampling (RO) and SMOTE-TomekLink (STL) on the MIT-BIH Arrhythmia Database. Experimental results demonstrate that the ConvNeXtTiny model paired with STL achieved the highest accuracy of 99.75%, followed by ConvNeXtTiny with RO at 99.72%. The STL technique consistently enhanced minority class detection and overall performance across models, with ConvNeXtBase and ConvNeXtSmall achieving accuracies of 99.69% and 99.72%, respectively. These findings highlight the efficacy of ConvNeXt-X models, when coupled with robust data balancing techniques, in achieving reliable and precise arrhythmia detection. This methodology holds significant potential for improving diagnostic accuracy and supporting clinical decision-making in healthcare.

The Gnangara groundwater system is a highly productive water resource in southwestern Australia. However, it is considered one of the most vulnerable groundwater systems to climate change, due to consistent declines in precipitation and recharge, and regional climate models project further declines into the future. This study introduces a new framework underpinned by machine learning techniques to provide reliable estimates of precipitation‐based recharge over the whole Perth Basin (including the Gnangara system). By combining estimates of baseflow, groundwater evaporation, and extraction, groundwater recharge was estimated over the Perth (testing site) and Gnangara (calibration site) systems using downscaled Groundwater Storage Anomalies (GWSA) from the Gravity Recovery and Climate Experiment (GRACE) mission. The random forest regression (RFR) model was used to downscale the spatial resolution of GRACE to 0.05° (approx. 5 km), providing estimable signals over the relatively small calibration site (∼2,200 km²) in order to discern any meaningful signals from the original GRACE resolution. Our study reveals that downscaled signals from GRACE can be used to provide precipitation‐based recharge estimates for groundwater systems accurately. However, the growing impacts of climate change, which has led to sporadic precipitation patterns over Western Australia, can limit the efficiency of satellite remote sensing methods in estimating recharge, especially in deep and complex aquifers.

Managing vegetation to sequester carbon in biomass requires estimates to meet standards for accuracy, with methods that are transparent, verifiable and cost‐effective. Allometric models are commonly used to predict biomass from non‐destructive field inventory data. Although a number of studies have addressed biomass error propagation, none have provided a general set of methods for linking errors all the way from initial allometric model development through to the final site‐based biomass prediction, for both above‐ and below‐ground biomass. Error sources in total biomass (above‐ + below‐ground) were quantified using a combination of analytical and Monte Carlo methods, illustrated with four contrasting case studies using either site‐ and‐species‐specific, species‐specific or generalised allometric models. Sampling error was found to be the most important contributor to site‐level biomass uncertainty, arising from the interaction between spatial variability and the field sampling design. The contribution of allometric model covariance to total error was also quantified, with errors in the determination of moisture content during allometric model development identified as a potentially important yet often overlooked error source. Application of different allometric models to the same inventory data suggested the error from generalised models was no greater than that from site‐ or species‐specific models, with increases in the generalised model prediction error balanced by decreases in other error sources associated with the increased sample size on which generalised models are based. Recommendations for reducing errors in predicted biomass include increasing field survey sample size, adopting field survey designs that ensure spatial representativeness and improving moisture content measurement protocols and increasing the moisture content sample size during allometric model development. To reduce costs while maintaining acceptable accuracy, the use of generalised allometric models is recommended, with the caveat that additional biomass sampling for model validation may be required to limit the potential for biased predictions.

Diel partitioning of animals within ecological communities is widely acknowledged, yet rarely quantified. Investigation of most ecological patterns and processes involves convenient daylight sampling, with little consideration of the contributions of nocturnal taxa, particularly in marine environments. Here we assess diel partitioning of reef faunal assemblages at a continental scale utilizing paired day and night visual census across 54 shallow tropical and temperate reefs around Australia. Day–night differences were most pronounced in the tropics, with fishes and invertebrates displaying distinct and opposing diel occupancy on coral reefs. Tropical reefs in daytime were occupied primarily by fishes not observed at night (64% of all species sighted across day and night, and 71% of all individuals). By night, substantial emergence of invertebrates not otherwise detected during sunlit hours occurred (56% of all species, and 45% of individuals). Nocturnal emergence of tropical invertebrates corresponded with significant declines in the richness and biomass of predatory and herbivorous diurnal fishes. In contrast, relatively small diel changes in fishes active on temperate reefs corresponded to limited nocturnal emergence of temperate invertebrates. This reduced partitioning may, at least in part, be a result of strong top‐down pressures from fishes on invertebrate communities, either by predation or competitive interference. For shallow reefs, the diel cycle triggers distinct emergence and retreat of faunal assemblages and associated trophic patterns and processes, which otherwise go unnoticed during hours of regular scientific monitoring. Improved understanding of reef ecology, and management of reef ecosystems, requires greater consideration of nocturnal interactions. Without explicit sampling of nocturnal patterns and processes, we may be missing up to half of the story when assessing ecological interactions.

Giant pre-trained code models (PCMs) start coming into the developers’ daily practices. Understanding the type and amount of software knowledge in PCMs is essential for integrating PCMs into software engineering (SE) tasks and unlocking their potential. In this work, we conduct the first systematic study on the SE factual knowledge in the state-of-the-art PCM CoPilot, focusing on APIs’ Fully Qualified Names (FQNs), the fundamental knowledge for effective code analysis, search and reuse. Driven by FQNs’ data distribution properties, we design a novel lightweight in-context learning on Copilot for FQN inference, which does not require code compilation as traditional methods or gradient update by recent FQN prompt-tuning. We systematically experiment with five in-context learning design factors to identify the best configuration for practical use. With this best configuration, we investigate the impact of example prompts and FQN data properties on CoPilot's FQN inference capability. Our results confirm that CoPilot stores diverse FQN knowledge and can be applied for FQN inference due to its high accuracy and non-reliance on code analysis. Additionally, our extended study shows that the in-context learning method can be generalized to retrieve other SE factual knowledge embedded in giant PCMs. Furthermore, we find that the advanced general model GPT-4 also stores substantial SE knowledge. Comparing FQN inference between CoPilot and GPT-4, we observe that as model capabilities improve, the same prompts yield better results. Based on our experience interacting with Copilot, we discuss various opportunities to improve human-CoPilot interaction in the FQN inference task.

Prediction of surface freshwater flux (precipitation or evaporation) in a CO2‐enriched climate is highly uncertain, primarily depending on the hydrological responses to physiological and radiative forcings of CO2 increase. Using the 1pctCO2 (a 1% per year CO2 increase scenario) experiments of 12 CMIP6 models, we first decouple and quantify the magnitude of global hydrological sensitivity to CO2 physiological and radiative forcings. Results show that the direct global hydrological sensitivity (for land plus ocean precipitation) to CO2 increase only is −0.09 ± 0.07% (100 ppm) ⁻¹ and to CO2‐induced warming alone is 1.54 ± 0.24% K⁻¹. The latter is about 10% larger than the global apparent hydrological sensitivity (i.e., including all effects, not only direct responses to warming, ηa ${\eta }_{a}$ = 1.39 ± 0.22% K⁻¹). These hydrological sensitivities are relatively stable over transient 2× to 4 × CO2 scenario. The intensification of the global water cycle are dominated by the CO2 radiative effect (79 ± 12%) with a smaller positive contribution from the interaction between the two effects (6 ± 12%), but are reduced by the CO2 physiological effect (−10 ± 8%). This finding underlines the importance of CO2 vegetation physiology in global water cycle projections under a CO2‐enriched and warming climate.

Introduction This paper examines the role of agricultural advisors as key partners for scaling adoption of long-term climate information. Agri-food sectors across the world face significant challenges in responding to climate change, which intersect with broader pressures driving transitions to more climate resilient and sustainable agri-food systems. Making better climate information available to farmers is a key part of responding to these challenges, since relevant and usable climate information can help farmers to adapt to future climate conditions. The development of climate services, which seek to provide climate information to assist with decision making, has therefore increased significantly over the last decade. The Climate Services for Agriculture (CSA) program provides long-term climate projections to help the Australian agriculture sector prepare for and adapt to future climate conditions. ‘My Climate View’ is an online tool produced by CSA, which provides localised and contextualised, commodity-specific climate information, through historic weather data and multi-decadal projections of future climate, aimed at Australian famers and farm advisors. Agricultural advisors have a critical yet often underutilised role as climate information intermediaries, through assisting farmers translate climate information into action. Methods This paper uses CSA as a case study to examine farmer-advisor interactions as a key adoption pathway for My Climate View. We interviewed 52 farmers and 24 advisors across Australia to examine the role of advisors as key partners in helping farmers to understand climate information and explore on-farm climate adaptation options. Results and discussion Interactions between farmers and their trusted advisors are an essential part of the enabling environment required to ensure that this long-term climate information can be used at the farm scale to inform longer-term decisions about climate adaptation. We use the concept of an interaction space to investigate farmer-advisor interactions in the adoption and sustained use of My Climate View. We find that although My Climate View is not a transformational technology on its own, its ability to enable farmers and advisors to explore and discuss future climate conditions and consider climate adaptation options has the potential to support transformational changes on-farm that are needed to meet the sustainability transition pressures that climate change presents.