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Deleterious alkali silica reaction and delayed ettringite in concrete, the role of accelerated laboratory test methods
No full textThe durability of concrete as a construction material is important to its widespread usage in modern infrastructure. The alkali silica reaction (ASR) and delayed ettringite formation (DEF) are two known causes of reduced durability of concrete, both of which are chemical processes with the potential for deleterious expansion leading to micro-cracking and strength loss. DEF is of most concern in large precast concrete elements. ASR is of concern when using unknown or suspect aggregate materials, which can be assessed using accelerated laboratory tests such as AS 1141.60.2. Several Australian standards and specifications for risk of deleterious DEF are based on laboratory test methods using mortar specimens and have overlooked the role of the aggregates in concrete. Accurately assessing the risk of DEF in a timely and economical fashion is crucial for cement and concrete suppliers. However, there are knowledge gaps in understanding ASR & DEF in combination, contributing risk factors, and the link between laboratory results and real-world concrete infrastructure. This study investigates the role of different test methods in determining the risk of deleterious DEF in the presence of ASR, specifically in the use of corresponding concrete and mortar specimens and the effect of accelerated ASR testing. Concrete and mortar specimens containing reactive aggregates were manufactured under conditions that promoted DEF and monitored for expansion and strength over four years. This paper contributes towards the understanding of ASR-DEF mechanisms and the development of appropriate risk assessment guidelines for deleterious DEF in Australia
Roadmap for Australian wastewater nutrient recovery – Towards a sustainable circular economy
Get PDFTapping into wastewater for nutrient recovery is largely missing from water policy and circular economy (CE) conversations, and in particular, its incorporation of machine learning (ML). Past nutrient roadmap studies have either ignored or largely unaccounted for advancements in AI and ML for CE wastewater treatment plants (WWTP). This nutrient roadmap paper provides technology and ML evaluation guidance, data collection practices to prime the industry for smarter treatment processes, financial opportunities and assessments, social acceptance drivers, and guidance on navigating the current environmental and regulatory landscape for the implementation of ML CE WWTPs. Finally, further policy improvements are needed surrounding CE WWTPs to incentivise local production and recycling of critical nutrients (i.e. phosphorus) which would support the creation of new economic growth opportunities, meet environmental targets while securing and stabilising food supply chains
Entanglement Measure-Based Sliding Mode Control for Quantum State Preparation.
No full textEntangled states are fundamental to quantum information processing. However, many existing quantum control methods rely on predefined target states, limiting their flexibility in accommodating diverse entanglement structures. This article introduces a sliding mode control framework that utilizes an entanglement measure as the sliding surface, enabling the generation of entangled states without specifying a fixed target. By adjusting the desired entanglement level, the proposed method can generate a wide range of states, including both bipartite and multipartite configurations, as well as pure and mixed states. Since the entanglement measure is scalar-valued, the resulting control law is inherently independent of the number of subsystems-an important advantage of the proposed approach. Among various entangled states, maximally entangled states (MESs) are of particular interest. Lyapunov stability of the control scheme is established, and numerical simulations confirm its effectiveness in robustly generating MESs in both bipartite and multipartite systems
Optimisation of Ensemble Learning Algorithms for Geotechnical Applications: A Mathematical Approach to Relative Density Prediction
Get PDFThe challenge of predicting relative dry density (Dr) in granular materials is addressed through advanced mathematical modelling and machine learning (ML) techniques. A novel approach to optimise ensemble learning algorithms is presented, with a focus placed on the mathematical foundations of these methods. An experimental dataset obtained from a mobile pluviator was utilised to develop and analyse various ML models. The mathematical analysis was centred on the optimisation and comparative performance of ensemble methods, with particular emphasis given to gradient boosting regression (GBR), AdaBoost regression, and extreme gradient boosting (XGBoost). The mathematical formulation of the GBR model was rigorously examined and optimised using advanced tuning functions, achieving exceptional performance metrics (mean squared error [MSE] = 11.91, mean absolute error [MAE] = 1.93, R2 = 0.997). Through sensitivity analysis, it was revealed that the distance between the shutter plate and the top sieve is the most significant factor affecting Dr prediction. A computational platform was developed within the Google Colab environment, demonstrating the practical application of the mathematical models. This research contributes to applied mathematics by showcasing advanced algorithmic approaches to solving complex geotechnical engineering problems while providing a rigorous mathematical foundation for future developments
Uropathogenic Escherichia coli proliferate as a coccoid morphotype inside human host cells.
Get PDFEscherichia coli is arguably one of the most studied bacterial model systems in modern biology. While E. coli are normally rod-shaped gram-negative bacteria, they are known to undergo conditional morphology changes under environmental and nutrient stress. In this study, using an infection-based in-vitro infection model system combined with advanced dynamical imaging, we present the first molecular details of uropathogenic E. coli (UPEC) dividing to form and proliferate as coccoid-shaped cells inside human host cells. For these intracellular UPEC cells, the frequency of cell division outpaced the rate of cell growth, resulting in a morphological transition from traditional rod-shape to coccobacilli. We also visualized the subcellular protein dynamics in these cells and noted that the division proteins follow the similar localization and constriction patterns that have been demonstrated for vegetative growth. However, unlike for fast-growing rod-shaped cells, FtsZ constriction in intracellular UPEC occurs prior to visual nucleoid segregation. Our results suggest that the modulation of division rate contributes to morphological adaptability of intracellular UPEC at the single-cell level
Plug-and-play dynamic optimization for three-dimensional Gaussian generation
No full textRecent advancements in Three-Dimensional (3D) asset generation have demonstrated remarkable progress in generation efficiency, enabling transformative applications across creative industries and mission-critical domains including autonomous systems. Current 3D asset generation primarily employs Score Distillation Sampling (SDS) to derive 3D priors from Two-Dimensional (2D) diffusion models. While this contribution ensures high generation quality, it is time-consuming. Recent methods have utilized 3D Gaussian Splatting for image rendering, which, despite enhancing generation speed, compromised on quality. Our method aims to balance the quality and speed of 3D asset generation by designing a plug-and-play optimization process that combines the strengths of both methods. We propose a rapid 3D Gaussian generation framework that begins with constructing a pipeline to generate multi-view images from text input using pre-trained generative models. Then our method utilizes 3D Gaussian Splatting for quick 3D asset initialization and subsequently performs detail optimization using Gaussian Filter and SDS-based 2D diffusion model optimizer. Additionally, we have optimized the loss function for 3D Gaussian Splatting and ensured the entire optimization process is plug-and-play, offering high generation quality and speed. Our method demonstrates strong adaptability in representative single-object 3D Gaussian generation tasks, indicating promising generalization potential. Achieving high-quality 3D generation on a single Graphics Processing Unit (GPU), our framework outperforms most popular optimization-based models in generation speed (5× speedup +). Furthermore, when juxtaposed with the latest inference-based models, our optimization architecture offers a notable enhancement in generation quality (Contrastive Language-Image Pre-Training Score 33.8 vs. 27.3) within an acceptable amount of time
Fuel moisture moderates wildfire resistance in rainforests of south-east Australia
Get PDFIn fire-prone forests of south-east Australia, rainforests have longer fire-return-intervals than the dominant and adjoining eucalypt forests, because rainforests occur in topographic positions which are typically too wet to burn. Thus, rainforests often act as natural barriers to fire spread. Although rare, severe drought can make rainforests available to burn, and this can promote very large and intense wildfires by increasing fuel availability across landscapes. Here, we explore how ten fuel moisture indices impact wildfire occurrence in rainforest patches of south-east Australia, when compared with wet and dry sclerophyll eucalypt forest types which are drier and have shorter fire-return-intervals. Vapour pressure deficit was the strongest and most ubiquitous moisture index predicting wildfire occurrence across all forest types, followed by soil moisture and live fuel moisture. Vapour pressure deficit thresholds facilitating a wildfire probability >0.5 also did not differ between forest types. However, the percentage of days exceeding vapour pressure deficit thresholds increased from rainforests to wet eucalypt forests and peaked in dry eucalypt forests. Collectively, our results suggest that the same fuel moisture thresholds promote wildfire in rainforests and fire-prone eucalypt forests; however, wildfire is less common in rainforests because they experience less time in a dry combustible state. Our results provide a framework to forecast wildfire probability across wet and dry forests at large spatial scales
How Do Different Health States Impact Preferences for Social-Care-Related Quality of Life: An Exploration of EQ-5D-5L and Adult Social Care Outcomes Toolkit Using a Vignette Approach.
Get PDFOBJECTIVES: Social-care-related quality-of-life instruments, such as the Adult Social Care Outcomes Toolkit (ASCOT), are important for valuing interventions, particularly when they include utility weights. Utility weights may vary by population and health conditions; yet, most value sets are developed without considering contexts. This study aims to explore this issue by assessing whether the valuation of ASCOT social states differs in the context of poor physical or mental health versus full health. METHODS: An online discrete choice experiment was conducted with a representative sample of 654 respondents in Australia. Respondents completed choice tasks choosing between ASCOT social care states, while imagining living in a poor health (EQ-5D-5L) state for half of the tasks and in full health for the other half. Respondents were evenly assigned to 2 study arms (poor physical or mental health). Data were analyzed using multinomial and mixed logit models, with interaction terms assessing the impact of context. RESULTS: In the mental health arm, the poor health context had no statistically significant impact on ASCOT estimates compared with the full health context. In the physical health arm, there were significant differences in the coefficients of "control" (level 4), "safety" (level 2), and "accommodation" (level 4). However, in all cases, "control," "safety," and "cleanliness" accounted for more than 50% of the relative attribute importance. CONCLUSIONS: This study found limited influence of health context on valuation of social-care-related quality-of-life dimensions, providing evidence supporting development of ASCOT value sets independent of context
Robust optimization of multiscale rainbow metamaterials under additive manufacturing defects
No full textThe broad application of elastic metamaterials (EMMs) is restricted by two major challenges: limited low-frequency wave attenuation and insufficient consideration of manufacturing imperfections and system uncertainties. To address these issues, this research incorporates multiscale rainbow metamaterials (RMs), with spatially varying structural parameters, into EMM design to improve wave attenuation performance over traditional periodic configurations. A novel robust design optimization framework is proposed to simultaneously optimize the first two statistical moments of structural responses, to ensure reliable wave attenuation under various uncertainties. To reduce computational cost in estimating statistical information, a new machine learning algorithm, twin extended support vector regression plus (TX-SVR+), is introduced to construct efficient surrogate models that map structural parameters to relevant structural responses. Furthermore, a self-adjusting mutation-based particle swarm optimization (SMPSO) algorithm is developed to enhance optimization efficiency. The combined TX-SVR+ and SMPSO framework is validated through the robust optimization of a chiral RM. Numerical results demonstrate that the optimized designs exhibit exceptional low-frequency wave attenuation, which highlights the potential of the proposed robust design methodology for wide engineering applications
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