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Ship waves on an elastic floating ice plate
No full textWe study a wave wake produced by a finite-size source uniformly moving on an ice plate overlying deep water. We describe the kinematic characteristics of source-generated flexural-gravity waves in terms of isophase patterns scaled by the minimum phase speed and show that the wave picture is determined by ad hoc defined Mach and Bond numbers. Then, we show that several bifurcations occur in the wave wake when the source speed increases. In particular, cusps appear in the wake patterns at certain speeds due to the merging at specific points of two or three wave characteristics (wave rays) which correspond to longitudinal (divergent), transverse, and fan waves. To describe the distribution of wave amplitudes in the wake accounting for the source size and shape, we use the reference solution approach. This approach agrees with the stationary phase method in the far-field zone and reproduces also specific wave dynamics at short and intermediate distances from the source. The effect of a source shape on the wave wake pattern is graphically illustrated for the elliptic source as the dependence on the dimensionless Bond number and the source length-to-beam ratio. The illustrations of the source speed and shape effects are given for low-speed regimes when wave patterns are conformed by single-ray solutions. Ways of the generalization of the suggested approach are outlined
Too close to home? Stubbornness, spite, and sheer bloody-mindedness as contributors to perseverance in doctoral study
Get PDFCompleting a higher degree is a complex and demanding undertaking for doctoral students. Along with the cognitive demands of study, there are competing personal and contextual factors which contribute to stress for students during the process. This study seeks to contribute to existing literature by acknowledging the characteristics of the interplay of various roles and identities, along with the doctoral topic on student’s mental health and perseverance with higher degree studies. Through a collaborative autoethnographic approach, three academics used an arts-based methodology to reflect on our experiences of completing a doctorate which focused on a topic of disability, of which we had lived experience as carers. Data was examined through Pekrun’s control value theory to explore the roles and identities we held during our study, the impact of our unique positionality, as well as the emotional impact from investigating a topic which may have been too close to home
Financing sources for mitigation of adverse climate change: a systematic review
Get PDFAccelerating climate change has harmed food and water security and affected both terrestrial and aquatic systems, hindering efforts to meet many Sustainable Development Goals [SDGs]. Climate finance can help mobilize financial resources and tackle the effects of climate change. This study analyzes existing literature on climate finance more broadly from its beginning to its current status. It reviewed 311 relevant articles from 2005 to 2023 using qualitative content analysis [QCA] and meta-analysis to identify common themes and their classification based on pre-determined article criteria. We also identify research gaps within each theme and suggest priority finance areas. Our result suggests that the periodic publications have drastically increased in the past few years, especially after the Paris Agreement in 2015. With content analysis of prior research, most of the research used quantitative and econometric approaches. With the review of papers, it can be concluded that climate finance is mostly constrained in vulnerable regions in which the risk of climate change and its adverse impacts are delicate, including low-lying coastal areas, SIDS, deserts, mountains, and Polar Regions. Innovative climate finance funding should focus on renewable energy, energy efficiency, and infrastructure that aids adaptation in vulnerable communities. Emphasis should be placed on initiatives that provide both mitigation and adaptation advantages, ensuring a resilient and sustainable future. While research primarily focuses on adaptation and mitigation, the interplay between these two areas requires further exploration. We highlight the knowledge gap in this research domain examining the financing sources for mitigation of adverse climate change from private and public sectors
A Systematic Review of Needleless Connector Function and Occlusion Outcomes: Evidence Leading the Way
No full textVascular access devices (VADs) are essential to intravenous (IV) therapy in acute care. The Centers for Disease Control
and Prevention recommends using needleless connectors (NCs) to provide IV access and eliminate the need for
needles. Approximately 17 NCs are currently available in the United States, with 3 basic designs. The Infusion Nurses
Society Standards of Practice established NC classifications of negative, positive, and anti-reflux NCs. Evidence indicates
a relationship between NC fluid displacement, blood reflux, and occlusion. A systematic review of the literature was
performed to ascertain whether the functional design of anti-reflux NCs results in reduced catheter occlusion.
A literature search of design types, function, and incidence of occlusion complications with peripheral and central
venous access devices yielded 24 334 publications, with 61 studies meeting inclusion criteria. Results from available
in vitro and in vivo evidence suggest using anti-reflux NCs with the lowest levels of fluid displacement may result in
fewer complications of occlusion and longer catheter dwell times. This review correlates current research to update
scientific knowledge of NC displacement performance and outcomes of NCs
Modelling soil physical and chemical properties using existing datasets across grain growing regions of Australia
No full textAccurate characterisation and understanding of soil properties are crucial for evaluating soil fertility, crop water management, and sustaining crop production. Soil properties exhibit significant spatiotemporal variation due to the complex effects of natural and human factors. Major limitations to crop production in Australian soils include salinity, sodicity, acidity, alkalinity, and elemental toxicities (such as boron (B), chloride (Cl), and aluminium (Al)). Despite their importance, information on the extent and impact of these soil properties on Australian agriculture is often based on extrapolations from soil surveys and expert opinions specific to regions or is unavailable. There are several methods to assess soil properties: direct measurement, remote sensing, proximal soil sensing, and modelling. Direct measurement, involving soil sampling and laboratory analysis, is accurate but costly and time-consuming. Remote sensing, though useful, is limited to land surface observations and can be hindered by clouds and dense vegetation. Proximal sensing collects effective soil data but needs extensive lab-measured data for sensor calibration. Soil modelling combines data from various sources to understand soil systems. Modelling provides a useful method for estimating soil properties, though its accuracy often depends on data quality and may require recalibration for different regions or soil types. Additionally, many models are computationally demanding and rely on simplified assumptions, which can limit accessibility and affect reliability. This thesis explores the potential of a modelling approach using existing datasets to predict soil physical and chemical properties across Australian grain-growing regions. Firstly, this research develops a plant available water capacity (PAWC) prediction framework using Agricultural Production Systems sIMulator (APSIM), crop yield, farm management information and initial soil estimates. The framework involves configuring the APSIM model and optimising the lower limit of the soil profile to minimise the residual sum of squares between observed and predicted yields. A sensitivity test of the prediction framework against various input data sources was carried out. The result showed that the proposed PAWC prediction framework adequately predicts PAWC (R2 = 0.75, CCC = 0.75, RMSE = 47.95 mm, bias = -19.32 mm). When testing the framework’s sensitivity with different sources of climate and soil data, the results varied. Secondly, the cubist (CU) machine learning algorithm was applied to observed soil datasets to predict pH, Cl, electrical conductivity (EC), effective cation exchange capacity (ECEC) and exchangeable sodium percentage (ESP). The model demonstrated good accuracy for predicting soil pH, EC and ECEC, but faced challenges with Cl and ESP prediction. Thirdly, five machine learning models namely Random Forest (RF), CU, Extreme Gradient Boosting (XGB), Support Vector Machine (SVM) and K-Nearest Neighbour (kNN) are used to identify suitable models to predict Al, B, Cl and ESP. Extensive datasets including point-based soil data and raster-based environmental data, are used to model soil properties. Environmental covariates include climate variables such as annual precipitation and evaporation; parent material attributes like radiometric data, silica content, and weathering index; relief features derived from digital elevation models (DEM), including aspect, slope, and elevation; soil properties such as the Australian Soil Classification and mineral clays; and spectral information from barest Landsat imagery, including bands like blue, green, and SWIR1. Results revealed that the choice of machine learning model significantly impacts the uncertainty in predicting soil chemical properties. Specifically, RF algorithms exhibited the best accuracy in predicting these properties. The PAWC framework helps to predict PAWC, which is useful for making agronomic decisions but needs testing in constrained soils before future application. Machine learning models of soil chemical properties provide cost-effective alternatives to lab measurements, aiding in the identification of soil constraints. Accurate prediction of soil properties across diverse landscapes cost effectively promotes their widespread adoption. However, user-friendly interfaces or software tools should be developed in the future for practical application
MXenes and its composite structures: synthesis, properties, applications, 3D/4D printing, and artificial intelligence; machine learning integration
Get PDFMXenes, a revolutionary class of two-dimensional transition metal carbides and nitrides, have emerged as exceptional materials for advanced composite applications due to their remarkable properties. MXene-based composites exhibit electrical conductivities exceeding 15,000 S/cm, thermal conductivities up to 60 W/m·K, and mechanical strengths surpassing 500 MPa, making them ideal for applications in energy storage, aerospace, and biomedical engineering. This review explores the synthesis of MXene-filled composites via chemical etching, intercalation (enhancing layer spacing by 20–50%), and functionalization (improving compatibility by 70%), and highlights how these processes shape the material’s properties. Applications are discussed, including lithium-ion batteries with capacities exceeding 300 mAh/g and supercapacitors achieving energy densities over 60 Wh/kg. Furthermore, the integration of MXene composites into 3D printing technology enables resolutions as fine as 100 microns, offering unprecedented customization and precision in manufacturing. Machine learning plays a pivotal role in optimizing synthesis protocols, accelerating material discovery by 30–50%, and achieving predictive modeling accuracies above 90%, thereby revolutionizing the design and performance of MXene-based materials. This review will also presents a data-driven perspective on the synthesis, properties, and applications of MXene-filled composites, bridging advanced research and practical innovation to inspire transformative advancements across multiple industries
Stochastic Analysis of Safety Factors for Buried Box Pipelines in Spatially Random Clay
No full textA significant aspect of offshore pipeline engineering involves evaluating the uplift resistance and failure probability of buried pipelines in clay, which are affected by factors such as pipeline geometry, soil characteristics, material properties, and loading conditions. Subsea marine clay is generally not homogeneous, leading to variations in undrained shear strength vertically and horizontally. As a result, the stochastic analysis method is suitable for accurately modeling such soil conditions. This study addresses these challenges using the Random Adaptive Finite Element Limit Analysis (RAFELA) to analyze the mean uplift resistance factor and the probability of failure for buried rectangular box pipelines in random clay. Seven key parameters are considered in the parametric study: the embedment depth ratio (H/B = 0.5, 1, 2, 4, and 6), width-to-height ratio (L/B = 0.5, 1, 2, 3, and 4), overburden factor (γH/μc = 0, 0.5, and 1), adhesion factor (α = 0, 0.5, and 1), load inclination (β = 0°, 45°, and 90°), coefficient of variation (CoVμc = 25% and 60%), and spatial correlation length (Θc = 0.125, 0.5, 1, 2, 4, and 8). The results are presented as dimensionless uplift resistance factors (μNran), probability of failure (Pf), as well as the corresponding safety factor (FS) for designing pipelines in random clay, ensuring practical designs that are both efficient and reliable. Additionally, this study compares its findings with pullout capacity factors derived from deterministic analyses reported in the literature. This study incorporates machine learning, specifically the Random Forest (RF) algorithm, to predict Pf based on parametric data. The RF model, trained on 500 samples (70% training, 30% testing), achieves high predictive accuracy, with R2 values of 99.12% and 97.29%, respectively. The Shapley Additive Explanations (SHAP) analysis identifies FS as the most influential factor, directly contributing to the reliability of the pipeline design, while α has the least impact. The analysis emphasizes the practical significance of FS in reducing failure probabilities while contextualizing its influence alongside other factors. The integration of the RAFELA with the RF offers a robust framework to address uncertainties in soil properties, enhancing reliability and efficiency in offshore pipeline engineering
Effectiveness of blood flow restriction training during a taper phase in basketball players
Get PDFThis study investigates the effectiveness of blood flow restriction (BFR) training in maintaining athletic performance during a taper phase in basketball players. The taper phase aims to reduce external load while maintaining training intensity. Seventeen experienced basketball players were randomised into two groups: a placebo group (n = 8, 22.0 ± 2.1 years, mean ± SD) and BFR group (n = 9, 21.1 ± 1.5 years). The training schedule included strength trainings, team trainings, individual skill sessions, and competitive games. During the 4-week taper period, lifting volume was reduced while either maintaining (placebo) or reducing (BFR) lifting load. The BFR group lifted with 60% arterial occlusion pressure at 25-30% of their 1RM, whereas the placebo group trained at 80% of their 1RM with BFR cuffs inflated to only 20%. Compared to the placebo group, BFR participants improved 5 m (-1.4 ± 1.5% mean ± 95% CI p = 0.03) and 10 m (-1.1 ± 0.5%, p = < 0.01) sprint performance along with barbell back squat (9.6 ± 8.0%, p = 0.013) and countermovement jump (1.1 ± 0.8%, p = 0.0035). BFR during the taper phase enabled a reduction in lifting load with no reduction in subsequent performance measures
Cost Analysis of COVID-19 in Australia
Get PDFAccess to accurate and reliable information on the cost of COVID-19 is essential for informed socio-economic policy decisions. This paper analyses the economic costs associated with the COVID-19/SARS-CoV-2 pandemic, with a particular focus on Australia. This study examined both the macroeconomic costs measured as the foregone gross domestic product attributable to the pandemic and the direct and indirect costs to society. Using a bottom-up costing approach and the WHO-CHOICE model, this study estimates the direct and indirect economic impacts of COVID-19 on the Australian economy. The analysis draws on quarterly and fortnightly data from 2020 to 2022, the period during which the pandemic exerted its most severe economic effects. The results indicate that the per-day inpatient unit cost is estimated at AUD 836, representing the minimum benchmark for direct health costs. The WHO-CHOICE model identifies key determinants of inpatient hospital costs, including hospital bed occupancy, GDP per capita, and hospital admissions, which are found to be highly responsive to changes in inpatient costs. In terms of indirect effects, GDP fell by 1.9 percent below its projected no-COVID level in the first quarter of 2021. Based on these empirical findings, this study proposes several important policy recommendations to enhance economic resilience and healthcare preparedness in future public health crises
47th Higher Education Research and Development Society of Australasia Annual Conference(HERDSA 2025)
No full textFocus of the Showcase: This presentation showcases the outcomes of a recent large-scale multi-university research project about students’ relationship with GenAI. It highlights how GenAI is reshaping integrity discussions and emphasises the necessity of including student views.
Background/context: A growing number of surveys have explored students’ views on AI’s use and usefulness, academic integrity, and trust (e.g., Chan & Hu, 2023; Kelly, Sullivan & Strampel, 2023; Polyportis, 2024; Ravšelj et al., 2025). Student perspectives are crucial in these discussions (Liu et al., 2024).
Description: The project, a collaboration between four major Australian universities, explored how the complexity and diversity of students’ values, beliefs, backgrounds, and learning context shapes their use of GenAI. This presentation focuses on responses related to personal, academic and professional integrity. Method: We created and utlised a cross-sectional online survey with descriptive analysis as part of the larger mixed method research project.
Evidence: The survey of 8021 students (August to October 2024) revealed that most used AI for brainstorming, creation, Q&A and analysis. Only 27% trusted its output, 71% believed it increased cheating, and 91% worried about breaking university rules. Forty percent admitted to using AI in assessments when not allowed. Just 32% felt their university provided enough guidance on using AI, and only 23% felt prepared to use it professionally (Chung et al., 2024).
Contribution: This research broadens the integrity discussions, highlighting the challenges institutions face in maintaining integrity with advanced technologies. The survey shows that while students use GenAI for academic tasks, many are concerned about cheating and feel unprepared to use it professionally. It also emphasises the need for institutions to adapt their policies and practices. By doing so, they can better support students in navigating the ethical use of these technologies. Engagement: There will be time for questions from the audience and discussion