Adelaide Research & Scholarship
Adelaide Research & Scholarship (AR&S) is the University of Adelaide’s digital repository. AR&S provides a platform for the collection, organisation, access and preservation of the research and scholarly outputs of the University community in digital formats, as well as digital management of information in physical formats.
University of Adelaide higher degree by research theses are deposited into the AR&S Theses community as part of the final thesis lodgement process.
AR&S also serves as the home of the digital collections of University Library Archives and Special Collections. Items include digitized representations of physical items, such as photographs and full texts, and digital-born materials, allowing worldwide access to our heritage and research collections.
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Recent Submissions
Intrinsic fracture energy of 3D printed cement mortar obtained from snapback behaviour in indirect tensile testing
(Elsevier BV, 2025) Huang, Z.; Nguyen, G.D.; Karakus, M.; Tran, T.T.; Bui, H.H.; 24th European Conference on Fracture (ECF) (26 Aug 2024 - 30 Aug 2024 : Zagreb, Croatia)
Dynamic and abrupt fracture are always the case observed in indirect tensile testing based on Brazilian discs. This dynamic and violent fracture challenges Brazilian disc testing for obtaining intrinsic fracture energy and tensile strength, as the energy is dissipated through not only the creation of new fracture surface, but also ejection of fragments. In this paper, the use of AUSBIT (Adelaide University Snapback Indirect Tensile test) for indirect displacement control allows stabilizing fracturing process to obtain quasi-static behaviour associated with snapback. Post-peak behaviour with snap-back can be successfully captured, allowing obtaining intrinsic fracture properties of 3D printed cement-based materials. Based on the results of load and displacements with good snap-back responses, the values of peak load are used for estimating the peak strength of the disc specimens and the fracture energy can be obtained from the area under load deflection curve. This preliminary result opens potentials to obtain intrinsic fracture energy of 3D printed cement-based mortar using indirect tensile testing.
KDM6A Mediated Regulation of Cranial Frontal Bone Suture Fusion in Mice is Sex-Dependent
(Mary Ann Liebert, 2023) Pribadi, C.; Cakouros, D.; Camp, E.; Anderson, P.J.; Gronthos, S.
The five flat bones of developing cranial plates are bounded by fibrous sutures, which remain open during development to accommodate for the growing brain. Kdm6A is a demethylase which removes the epigenetic repressive mark, tri-methylated lysine 27 on histone 3 (H3K27me3), from the promoters of osteogenic genes, and has previously been reported to promote osteogenesis in cranial bone cells. The present study generated a mesenchyme-specific deletion of a histone demethylase, Kdm6a, to assess the effects of Kdm6a loss, in cranial plate development and suture fusion. The results showed that the loss of Kdm6a in Prx-1+ cranial cells caused increased anterior width and length in the calvaria of both male and female mice. However, the posterior length was further decreased in female mice. Moreover, loss of Kdm6a resulted in suppression of late suture development and calvarial bone formation predominantly in female mice. In vitro assessment of calvaria cultures isolated from female Kdm6a knockout mice, found significantly suppressed calvarial osteogenic differentiation potential, associated with decreased gene expression levels of Runx2 and Alkaline Phosphatase and increased levels of the suppressive mark, H3K27me3 on the respective gene promoters. Conversely, cultured calvaria bone cultures isolated from male Kdm6a knockout mice exhibited an increased osteogenic differentiation potential. Interestingly, the milder effects on cranial suture development in Kdm6a knockout male mice, were associated with an overcompensation of the Kdm6a Y-homolog, Kdm6c and increased expression levels of Kdm6b in calvarial bone cultures. Taken together, these data demonstrate a role for Kdm6a during calvarial development and patterning, predominantly in female mice, and highlights the potential role of Kdm6 family members in patients with unexplained craniofacial deformities.
Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes
(IEEE, 2023) Li, R.; Gong, D.; Yin, W.; Chen, H.; Zhu, Y.; Wang, K.; Chen, X.; Sun, J.; Zhang, Y.; IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (18 Jun 2023 - 22 Jun 2023 : Vancouver, Canada & virtual online)
Multi-frame depth estimation generally achieves high accuracy relying on the multi-view geometric consistency. When applied in dynamic scenes, e.g., autonomous driving, this consistency is usually violated in the dynamic areas, leading to corrupted estimations. Many multi-frame methods handle dynamic areas by identifying them with explicit masks and compensating the multi-view cues with monocular cues represented as local monocular depth or features. The improvements are limited due to the uncontrolled quality of the masks and the underutilized benefits of the fusion of the two types of cues. In this paper, we propose a novel method to learn to fuse the multi-view and monocular cues encoded as volumes without needing the heuristically crafted masks. As unveiled in our analyses, the multiview cues capture more accurate geometric information in static areas, and the monocular cues capture more useful contexts in dynamic areas. To let the geometric perception learned from multi-view cues in static areas propagate to the monocular representation in dynamic areas and let monocular cues enhance the representation of multi-view cost volume, we propose a cross-cue fusion (CCF) module, which includes the cross-cue attention (CCA) to encode the spatially non-local relative intra-relations from each source to enhance the representation of the other. Experiments on real-world datasets prove the significant effectiveness and generalization ability of the proposed method.
Managing Water for Environmental Provision and Horticultural Production in South Australia’s Riverland
(MDPI AG, 2023) Robinson, G.M.; Song, B.
This paper outlines and analyses preliminary research in South Australia’s Riverland, part of Australia’s largest river system, the Murray–Darling Basin, and one of the nation’s most important horticultural production areas. It focuses on the Renmark Irrigation Trust (RIT), which supplies water to c570 irrigators. Management of the Basin is controversial, with conflicting demands from stakeholders, including smallholder irrigators, broadacre farming, indigenous groups, and the environment. Climate change and the water market have contributed to uncertainty over environmental sustainability. Using sequential mixed methods, including a questionnaire survey, focus groups and interviews, we investigate the chief risks perceived by irrigators and their future-plans in face of concerns over variable water flows and economic uncertainty. We highlight the RIT’s contribution to river restoration and investigate its plans for additional on-farm water stewardship. We reveal high levels of uncertainty among irrigators regarding their future viability, including unintended consequences from the water market, the controversial role of water brokers, and environmental viability of the river system. The growth of ‘lifestyle blocks’ occupied by hobby farmers has added both to landscape diversity and fragmentation. To maintain a resilient horticultural industry, there may need to be adjustments to water management in the Basin to protect smallholders’ livelihoods whilst continuing to meet specified environmental needs.
Meta-Model Development for Mine-to-Mill Optimisation Using AI and Simulation
(International Aset Inc., 2025) Nobahar Ghezeljehmeidan, P.; Xu, C.; Dowd, P.; 11th World Congress on Mechanical, Chemical, and Material Engineering (MCM) (19 Aug 2025 - 21 Aug 2025 : Paris, France)
The current demand for mineral resources is higher than it has ever been, and it is expected that, over time, the quality of future resources will decline, and they will become more difficult to extract. Routinely collected on-site data from various mining stages are often neglected in mining operations and are not being used to improve the value of the mining chain. To address this issue, mining companies need to increase the efficiency of their mining processes to achieve sustainable production by using innovative solutions. The primary purpose of the study presented here is to develop an integrated knowledge-based system using advanced AI techniques to simulate, monitor, assess, and optimise mining processes from blasting to downstream products. In this study, publicly available data from the Barrick Cortez Mine in Nevada, USA, was used to model the entire mining process from blasting to SAG mill by using Orica’s Integrated Extraction Simulator (IES) platform. The comparison of real data from the mining site with simulated data on the IES platform demonstrates that the modelled operations closely match the real data. Thirteen parameters related to blasting, screens, crusher, and SAG mill were considered. Given the computational infeasibility of testing all combinations, three million scenarios were simulated to identify key performance drivers. Machine learning models—including linear regression, decision trees, random forest, and XGBoost—were evaluated to determine the most effective for predicting outcomes. The next step involved using input scenarios and outcomes to investigate key features and interpret results using feature importance and SHapley Additive exPlanations (SHAP) techniques, respectively, as powerful tools for determining the influence of individual features of the models. The findings highlight the potential of AI-driven meta-models to enhance decision-making, reduce operational costs, and improve resource usage in mining operations.