https://hdl.handle.net/2123/22645 Tue, 14 Jul 2026 18:58:06 GMT 2026-07-14T18:58:06Z https://hdl.handle.net/2123/35576 Hydrogen Trapping and Embrittlement Behaviours in Steels Huang, Chao Hydrogen embrittlement is a major challenge for the durability of structural steels used in the emerging hydrogen economy. This thesis investigates how hydrogen interacts with microstructural defects, how these interactions contribute to embrittlement, and how they can be mitigated through microstructural design, with the aim of establishing microstructure–property relationships in hydrogen-containing environments (Chapters 1 and 2). Using advanced characterization techniques and principles of metal physics, this work reveals how defects such as dislocations, interfaces, and precipitates act as hydrogen traps and influence embrittlement behavior. The concepts of weak and strong hydrogen traps are explored, and a new experimental strategy is introduced to distinguish their respective roles (Chapters 4–6). By correlating atom probe tomography, transmission-based microscopy, and local mechanical characterization, hydrogen distributions at specific defects are directly linked to deformation behavior, providing an atomic-scale understanding of hydrogen–defect interactions (Chapter 7). The results further demonstrate that strong hydrogen traps can be intentionally activated through microstructural optimization, improving resistance to embrittlement (Chapter 8). In addition, this work examines interstitial hydrogen solution behavior in face-centered cubic alloys, with the results showing good agreement with theoretical predictions regarding hydrogen affinity. These findings provide new insights into hydrogen partitioning and alloy design. Finally, future directions are proposed for atomic-scale hydrogen mapping using cryogenic APT and for the development of next-generation hydrogen-resistant alloys for advanced energy applications (Chapters 9 and 10). Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35576 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35575 ADVANCED ALGORITHMS FOR RIS-ASSISTED WIRELESS COMMUNICATION SYSTEMS Li, Weijie Reconfigurable intelligent surface (RIS) has emerged as a promising technology for 6G wireless networks, offering the ability to reshape propagation environments and improve communication performance. Despite its potential, deploying RIS faces challenges in channel estimation, signal processing, and system integration. This thesis develops novel algorithmic solutions for RIS-assisted wireless communication systems, focusing on improving estimation accuracy, reducing complexity, and enabling integrated sensing and communication. First, we develop UAMP-SBL-PCI, exploiting structured sparsity in RIS-assisted channels. By combining partial common support identification with unitary approximate message passing, it reduces complexity while improving accuracy. Simulations demonstrate excellent performance across various environments. Second, we propose a unified framework for ULA RIS systems jointly performing user positioning and channel estimation. A vision transformer (ViT) enables adaptive positioning, while a position-based dictionary design reduces dictionary size and resolves the off-grid problem. A modified sparse Bayesian learning algorithm with early stopping prevents overfitting for static deployment scenarios. Third, we extend this to dynamic environments with a three-stage joint channel estimation and positioning framework for UPA RIS systems, employing a graph attention network (GAT) for robust positioning, location-aware dictionary refinement, and meta-learning for rapid adaptation. An iterative refinement mechanism further reduces pilot overhead. These contributions span from basic channel estimation to advanced integrated sensing and communication, enabling practical RIS implementation with reduced costs and improved accuracy for 6G systems. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35575 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35573 Robust Implied Volatility Surface Construction via B-Spline-Enhanced Graph Neural Operators Ye, Xuan The reliable recovery of implied volatility surfaces from sparse option quotes remains challenging, yet it is essential for pricing, hedging, and market risk measurement. In practice, widely-used parametric specifications can be fast and arbitrage-aware, but their stability depends on repeated calibration and can deteriorate when market data are irregular. Operator learning offers a complementary perspective by modelling a mapping from scattered observations to a continuous surface, though many implementations become expensive because they predict values over dense grids and therefore require large training sets. This thesis develops a graph-based neural operator that targets a low-dimensional spline representation of the surface. A bivariate cubic B-spline is first used to obtain an initial smooth approximation, after which the operator learns residual adjustments to the spline coefficients rather than producing a full grid of volatilities. This coefficient-level formulation embeds smoothness by construction and reduces computational burden. In addition, the analytic derivatives available under the spline representation enable butterfly and calendar no-arbitrage penalties to be imposed without finite-difference noise. Experiments on daily S&P 500 index options show that the resulting framework is more stable under sparse conditions and achieves improved efficiency relative to common parametric baselines and grid-based neural operator models. These results support spline-prior operator learning as a practical route to constructing smooth, arbitrage-regularised volatility surfaces. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35573 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35572 Graph-Based Machine Learning for Real-World Applications Yan, Kuan Graph-based machine learning has emerged as a powerful approach for modeling complex real-world data, particularly in finance and medical research, where data are heterogeneous, noisy, incomplete, and highly structured. This thesis investigates graph-based machine learning methods for addressing practical challenges in financial and medical AI, with an emphasis on robustness, interpretability, and fairness. The first part of the thesis focuses on financial applications. A heterogeneous graph learning framework is developed for credit card fraud detection by modeling complex transactional relationships. Feature importance–based edge weighting is incorporated into a graph neural network to improve predictive performance and model interpretability. The thesis further investigates fairness in graph neural networks by proposing a framework that balances group fairness and individual fairness, demonstrating that these objectives can be jointly improved in graph learning tasks. The second part of the thesis focuses on medical AI for retinal disease research. Machine learning methods are applied to predict subretinal lesion severity and identify genes associated with disease progression from high-dimensional transcriptomic data. Building on this work, a graph pseudotime analysis framework is proposed to infer disease progression trajectories, while neural stochastic differential equations are employed to characterize pathway dynamics and identify transition points. Overall, this thesis demonstrates that graph-based machine learning provides a flexible and effective framework for modeling complex data across financial and biomedical applications. The proposed methods improve predictive performance, interpretability, and fairness while providing biologically meaningful insights into disease progression. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35572 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35571 Lattice-Boltzmann Large Eddy Simulation Solver for Atmospheric Transport and Dispersion Applications Waters, Brendan Ashley Accurate prediction of metropolitan climate conditions is essential for public health and national security, directly influencing commercial, environmental, social, and political activity. High-fidelity dispersion modelling therefore has broad utility, including risk assessment, urban planning, emergency response, and sustainable city management. However, urban morphologies are highly heterogeneous, producing inherently unsteady wind fields with turbulent processes spanning wide spatial and temporal scales. Consequently, industrial-scale simulations involve many degrees of freedom, making prognostic modelling demanding. This thesis presents a wall-modelled large eddy simulation solver for atmospheric transport and dispersion in urban environments at grid resolutions of 1–10m. A hybrid method is considered, wherein the hydrodynamics are modelled using a D3Q27 cumulant lattice Boltzmann method (LBM), while passive scalar transport is modelled via the MUSCL-Hancock finite volume scheme. Following a brief literature review and introduction of the numerical methods, this work systematically the spectral bandwidth of various cumulant LBM-LES formulations, and wall-modelling techniques. The fully integrated solver is then applied to validation cases in both idealised urban environments using the Mock Urban Setting Test database and real-world geometries using the Joint URBAN 2003 database for downtown Oklahoma City. Results show that the WMLES solver predictions are in good agreement with experimental data, exceeding industry-standard performance criteria. In the idealised urban canopy, velocity predictions achieve FAC1.3 accuracy. Concentration statistics exhibit larger deviations but remain within experimental uncertainty. For downtown Oklahoma City, the solver retains strong predictive capability in real urban conditions; although formal accuracy is slightly below FAC1.3 despite minor geometric discrepancies, it remains above the industry-standard FAC2 threshold. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35571 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35570 Deadline Scheduling with Predictions Zhang, Luoyuan Online deadline scheduling has been extensively studied. Most prior works assume a known deadline per job, but this is sometimes impractical, e.g., submission and withdrawal of cloud jobs. Deadline scheduling with unknown deadlines for real-time systems is a challenging problem, where deadlines need to be predicted. This thesis considers the problem of maximizing the number of early jobs under unknown dead lines. In this setting, a job has an unknown deadline, which becomes known only when it is reached, and the job immediately fails after that. The study of this prob lem in this work is within the learning-augmented framework, where the scheduler can access deadline predictions, which may come with errors. A simple error metric is defined to quantify prediction quality and design an algorithm with a competitive ratio as a function of the prediction error. The algorithm achieves performance matching the optimal online scheduler that knows the deadlines and smooth performance degradation as the prediction error increases. Simulations are run to compare the performance of the proposed algorithm with the existing ones. Simulations show that the performance of the proposed algorithm consistently exceeds the conservative theoretical bound; thus, the bound represents a pessimistic view of the algorithm; one can expect exceptional practical performance. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35570 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35568 Sounding the Wallacea: The Siboga Oceanographic Expedition (1899-1900) Maharani, Annissa Ayu This thesis examines the historical context of the research on ocean circulation in the eastern Indonesian waters conducted by the Dutch Siboga Expedition (1899-1900). The region is characterised by abundant and scattered islands, while the seafloor exhibits countless elevations and depressions in close proximity. In the late 19th century, this complex underwater topography in the eastern Indonesia was believed to yield a distinctive physical environment to which marine fauna would respond. The Siboga aimed to investigate deep-layer water communication across basins and to speculate about the effects on the distribution of marine fauna. The thesis emphasises the role of nautical charts, as both scientific instruments and navigational guidance, which were often inaccessible to scientists due to their association with the colonial administration and top-level authorities. It highlights the involvement of naval powers and the mobilisation of the colonial maritime infrastructure in sustaining oceanographic expeditions within the archipelagic colony. The thesis argues that the Siboga made significant contributions to the understanding of physical oceanography in Indonesian waters, producing foundational observations on stratified deep circulation between the neighbouring Pacific and Indian Oceans. It illustrates how knowledge is produced under multi-layered states of technological, logistical, and political factors, including manual sounding technology, changing monsoonal atmospheric conditions, and colonial hydrographic records. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35568 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35566 Evaluating The Impact Of The Speech-Language Pathology Primary Contact Model Of Care In Diagnostic Assessment Of Voice Disorders. Payten, Christopher Letton Voice disorders affect millions and are costly. Early assessment is vital, but access to ENT and multidisciplinary clinics is limited. The speech-language pathology primary contact (SLP-PC) model offers quicker evaluation before or instead of ENT assessments, though its reliability and diagnostic contribution need more evidence. This thesis presents five studies on SLP-PC in adult VD diagnosis: an observational cohort, a literature review, a global MDT survey, a GP referral review, and another cohort study, examining outcomes, frameworks, practice patterns, predictors, and diagnostic agreement. Integrating SLP-PC into an ENT MDT pathway reduced wait times by an average of 277 days compared to traditional pathways. Most patients (81%) managed by SLP didn't need ENT assessment, but 7% of urgent cases required ENT. SLP-PC was estimated to cut staffing costs by 27%. A review of 20 frameworks across 2,675 publications highlighted the need for clearer classification systems for VDs suitable for first-line SLP intervention, a need that was subsequently addressed. A survey of 109 clinicians showed growing support for diverse diagnostic pathways, including SLP-PC, emphasising the importance of case history. An analysis of GP referrals found no strong predictors of assessment approach, underscoring the need for better triage information and GP training. The SLP-PC telehealth model showed promise in predicting urgent ENT needs, with substantial diagnostic agreement. Overall, these studies demonstrate that SLP-PC is a reliable, effective model that improves access and diagnostic efficiency while enhancing clinical skills among ENT and SLPs within an integrated MDT VD service. This research advances global voice assessment practices, supports SLP-first pathways, offers insight for GPs, and highlights the need for standardised classification systems. Further research is needed to understand the diagnostic reasoning of SLPs and ENTs in VDs and the economic impact of SLP-PC. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35566 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35564 Investigating the therapeutic potential of Australian honey as a prebiotic and anti-inflammatory to improve gut health Schell, Kathleen Rose The gut microbiome is a complex microbial community that plays a critical role in host health. Diet is a major determinant of its composition and function, while dysbiosis is associated with intestinal inflammation and numerous chronic diseases. Australian honey has emerged as a potential functional food due to its complex carbohydrate content and reported antimicrobial, prebiotic and anti-inflammatory properties. This thesis investigated the antibacterial, antioxidant, prebiotic and anti-inflammatory potential of 56 Australian honeys from diverse floral sources using in vitro, ex vivo and in vivo models. Australian honeys exhibited minimal antibacterial activity under anaerobic conditions, indicating little potential to adversely affect beneficial gut bacteria, while antioxidant capacity differed significantly between floral types. Ex vivo studies demonstrated that all honeys promoted Lactobacillaceae growth and enhanced suppression of opportunistic pathogens in mixed faecal cultures. In vivo, Australian honey increased the abundance of beneficial taxa, including members of the Lactobacillaceae and Muribaculaceae families, in mice fed a standard chow diet. Following western diet-induced gut microbiome restructuring, honey promoted alternative carbohydrate-metabolising taxa rather than restoring those reduced by the diet. In a dextran sodium sulphate (DSS) model of colitis, honey reduced histopathological indicators of inflammation and promoted immune phenotypes associated with inflammation resolution. Collectively, these findings demonstrate that Australian honey possesses antimicrobial, antioxidant, prebiotic and anti-inflammatory properties that support its potential to beneficially modulate the gut microbiome and attenuate intestinal inflammation. These results provide a foundation for future investigations into Australian honey as a functional food for gastrointestinal health. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35564 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35563 The Thermo-Mechanical Control and Analysis of the TOLIMAN Space Telescope George, Mark Andrew The Telescope for Orbital Locus Interferometric Monitoring of our Astronomical Neighborhood (TOLIMAN) is a microsatellite mission that aims to detect habitable exoplanets in the Alpha Centauri star system. TOLIMAN is designed around a 13 cm Ritchey–Chrétien telescope in low Earth orbit that uses a reformulation of an optical configuration known as a ``diffractive pupil'' to measure the micro-arcsecond angular deflections between stars. TOLIMAN aims to address a critical blind spot in our astronomy cabability; answering the basic question of whether there are Earth-analog exoplanets orbiting our nearest-neighbour star systems. This innovative mission concept requires an optical system with thermo-mechanical stability previously unseen in this class of telescope. This thesis introduces the design and analysis of a ``cold bias'' thermal control system for TOLIMAN. Through extensive simulations, candidate orbits are studied, exploring the trade space between thermal performance and viewing time on the science target. Parametric thermal models and optimisation routines are used for the determination of worst case orbit conditions and optimal design choices. Reduced order thermal models are also created based on finite element models for the design of an active heater control system. These thermal models and their results are integrated with structural models to assess the dimensional stability of the primary mirror. The proposed design can achieve on orbit temperature stability of 20 +/- 0.05 deg C on the primary mirror, and 20 +/- 0.25 deg C on the metering structure through a synthesis of active and passive control techniques. The final outcome of a predicted RMS dimensional stability of better than 5 nm on the primary mirror is highly encouraging for the precision optical measurements required to accomplish the core mission science. Mon, 13 Jul 2026 00:00:00 GMT https://hdl.handle.net/2123/35563 2026-07-13T00:00:00Z https://hdl.handle.net/2123/35561 DEVELOPING PROTEIN-RICH FOODS FROM AUSTRALIAN GROWN FABA BEANS Hopf, Andreas Nikolaus This thesis examined dry fractionated faba bean protein concentrate through systematic investigation of extraction method effects, controlled modification strategies, and application in diverse food structuring systems. Initial characterisation compared dry fractionated and wet fractionated pulse proteins from faba bean, mung bean, chickpea and red lentil against commercial soy protein concentrate. Extraction process critically impacted functional properties and composition. Controlled thermal treatment applied prior to dry fractionation altered functional behaviour of dry fractionated faba bean concentrate. Partial denaturation of the proteins modified gelation mechanism. Thermal treatment mitigated the reduced water-holding capacity associated with elevated Calcium Sulphate coagulant concentrations, revealing synergistic effect of thermal pretreatment and gelation. The gelation properties of faba bean protein were further investigated in the application of tofu-like bean curds. Coagulant selection in faba bean tofu production was critical. Finally, the assessment of dry fractionated faba bean protein under high moisture extrusion conditions using a micro compounder explored the material for fibrous meat analogue products. This research demonstrates that dry fractionated faba bean protein concentrate possesses distinctive functional characteristics. These characteristics can be systematically enhanced through pretreatments, including thermal treatment. The protein concentrate supports implementation in both conventional gel-based and emerging extrusion-based food applications. These outcomes advance industrial adoption pathways for sustainably produced pulse proteins while delivering practical formulation frameworks for manufacturers pursuing alternatives to animal-based and wet fractionated protein ingredients. The findings hold particular relevance to the Australian pulse industry, by exploring pathways to valorise local protein-rich crops for the food industry. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35561 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35560 Inflammation and its Effects on Plaque Cell Fate in Atherosclerosis Lin, Alexander Atherosclerosis, a chronic inflammatory disease of the arteries, causes myocardial infarction and stroke when an unstable atherosclerotic plaque ruptures. Stable plaques consist of a thick fibrous cap, composed of abundant collagen-secreting cells, with fewer inflammatory infiltrates, whereas unstable plaques have a thin fibrous cap with few collagen-producing cells and excessive inflammatory cells. Vascular smooth muscle cells (SMCs) are traditionally considered the primary source of collagen production within the lesion, but studies over the last decade have revealed their capacity to also modulate their phenotype and worsen lesion stability instead. However, the cellular mechanisms which regulate SMC phenotype are not fully understood. This thesis aims to investigate how modulating inflammation, using colchicine as an anti-inflammatory therapy and diabetes as a pro-inflammatory cardiovascular risk factor, influences SMC phenotypic behaviour within the atherosclerotic lesion, and how this ultimately affects plaque stability. First, we demonstrate that colchicine directly promotes a contractile SMC phenotype, an effect which appears independent of its canonical anti-inflammatory properties in immune cell populations. Second, we show that diabetes induces the expansion of multiple SMC clones into the lesion but hinders their trans-differentiation to a fibroblast-like phenotype, ultimately leading to declines in lesion stability. Third, we find that colchicine intervention in diabetic lesions did not alter the stability of plaques and had very little influence on SMC behaviour. Taken together, these findings highlight the importance of SMCs and their phenotypic transitions in atherosclerosis, both in the therapeutic stabilisation of lesions, and in driving disease pathogenesis with co-morbidities. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35560 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35559 Knowledge-Driven Visual Reasoning Under Data Scarcity Zhang, Xu Visual reasoning connects seeing with understanding by requiring vision systems to produce structured, decision-relevant outputs from pixels. These capabilities are crucial for open-world deployment and are viewed as a prerequisite for general intelligence. Yet, despite progress driven by deep learning and pretraining, state-of-the-art visual reasoning pipelines remain constrained by a fundamental bottleneck: they rely heavily on structured supervision. Reasoning-oriented tasks require annotations such as boxes, which are more expensive to obtain than class labels, making data-hungry fine-tuning difficult to extend to specialized applications. This thesis advances data-efficient visual reasoning from the perspective of knowledge-driven learning. Its unifying principle is that when structured labels are scarce, missing supervision can be compensated by making relevant knowledge explicit and using it to guide learning and inference. Here, knowledge is instantiated as explicit guidance signals, including linguistic knowledge, reasoning priors, and exemplar-derived textual conditions. We develop it across increasingly stringent scarcity regimes. First, under intra-domain sparsity with large appearance variability, we improve pose estimation via contrastive alignment that leverages linguistic knowledge to enhance keypoint correspondence learning. Second, for cross-modal reasoning, we improve label efficiency in referring object detection by integrating interpretable priors into transformer-based pipelines. Third, under the regime where data scarcity is compounded by severe domain shift, we propose a training-free paradigm that uses language as a domain-invariant bridge, converting exemplars into textual conditions that guide frozen detectors. Extensive experiments show that explicit knowledge guidance can serve as a practical surrogate for structured supervision, offering a scalable route for applying foundation models to data-scarce, real-world visual reasoning. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35559 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35556 Automated Radiation Dose Monitoring in Computed Tomography: A Comprehensive Evaluation of Clinical Effectiveness Adoption and User Experience Alanazi, Mohammed Aim: This thesis investigated the role of automated dose monitoring systems (DMS) in computed tomography (CT) radiation dose monitoring and optimisation. Method: A multi-method research design comprising two systematic reviews and three empirical studies was undertaken. The first review evaluated the use of DMS high-dose alert functions and their impact on CT dose optimisation, while the second examined DMS applications, benefits, and challenges in CT practice. The third study surveyed CT dose monitoring practices and DMS adoption in Australian radiology facilities. The fourth study explored the experiences of medical physicists and radiographers using DMS in CT. The final study retrospectively assessed a DMS's ability to detect high-dose CT events at an Australian radiology facility. Results: Study 1 showed that DMS systems are valuable for identifying high-dose events and supporting dose optimisation. Study 2 showed that DMS systems facilitate benchmarking, tracking and estimation, although challenges related to data inconsistencies and integration issues were reported. Study 3 found that while most Australian radiology facilities performed CT dose assessments, these were commonly undertaken annually using traditional methods, and 41% used DMS. Study 4 showed that DMS users reported improved workflow efficiency, CT dose assessment, and radiation protection practices, although challenges related to system setup, data validation and IT infrastructure were identified. The final study found that 851 of 11,865 CT examinations (7%) triggered DMS alerts, most commonly in spine, joint, and abdomen–pelvis CT exams, with contributing factors related to patients and operators. Conclusion: Automated DMS systems support CT dose monitoring and optimisation through continuous dose assessment, benchmarking, and identification of high-dose events. Wider adoption, supported by appropriate implementation and training, would strengthen radiation protection and enhance patient safety. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35556 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35555 Multi-Scale Thermo-Mechanical and Microstructural Simulations of Wire Arc Additive Manufacturing (WAAM) of 316L Stainless Steel Valiente Dies, Fernando Wire Arc Additive Manufacturing (WAAM) of 316L stainless steel offers a practical route to large structural components, yet industrial uptake is limited by distortion, residual stresses, and microstructure heterogeneity, together with a lack of experimentally validated simulation workflows. This thesis develops and validates an integrated framework that links WAAM process parameters to thermo-mechanical response, hardness, and microstructure. Purpose-built benchmark walls and T-joints were designed and fabricated to generate co-registered datasets: in-process thermocouples, full-field distortion by GOM, microhardness maps, EBSD textures, and internal residual stresses from neutron diffraction and the contour method. A coupled thermo-mechanical model in MOOSE, with temperature-dependent properties, and progressive element activation, was calibrated to the thermal data and validated against distortion and hardness. A hardness surrogate tied to accumulated equivalent plastic strain, reproduced measured hardness gradients between wall, HAZ, and base plate. For T-joints, simulations captured the sign, magnitude, and spatial distribution of tensile and compressive regions observed by diffraction and contour measurements to clarified their evolution through deposition and cool-down. Microstructure was interrogated with phase-field and Cellular Automata models to reproduced epitaxial columnar growth, grain size trends through height, and texture consistent with EBSD. The outcome is an open, experimentally anchored pathway for pre-build what-if analysis and process design in WAAM 316L. The work contributes: a shared benchmark dataset, a calibrated modelling stack connecting temperature, stress, distortion, and hardness, microstructure-informed interpretation of anisotropy, and actionable guidance on energy input, interpass temperature, bead sequencing, and restraint. The proposed framework is readily extensible to more complex geometries and other alloys. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35555 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35552 On Vassiliev Invariants and Weight Systems of Classical and Welded Knots Lin, Damian Vassiliev invariants are a special class of knot invariant that are analogous to polynomial functions on, for example, the real line. We review how Vassiliev invariants can be constructed from Lie algebra objects in arbitrary monoidal categories and are completely determined by their weight systems. We make some computations of the values of the g2 and f4 exceptional Lie algebra weight systems on a certain special class of chord diagrams. We generalise a construction of Hinich-Vaintrob to welded knots, thereby constructing a universal welded weight system. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35552 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35550 Efficient Edge-AI: Towards the Future of Implantable and Smart Medical Devices Herbozo Contreras, Luis Fernando Epilepsy affects over 1% of the global population and imposes substantial clinical, social, and economic burdens. Although pharmacological therapy is the first line of treatment, approximately 30–40% of patients remain drug-resistant, making neuromodulation one of the few viable options. However, current neuromodulation systems are largely open-loop or depend on cloud-based AI, limited by latency, power, privacy, and scalability. These constraints hinder autonomous, personalised, implantable closed-loop neurostimulation. This thesis investigates neuromorphic computing as a paradigm for next-generation closed-loop neuromodulation, focusing on seizure detection and prediction in epilepsy. It introduces neuromorphic neuromodulation and shows how biologically inspired, on-device intelligence can support self-responsive and personalised therapies. Building on this framework, the thesis develops seizure detection systems using liquid-time constant neurons and dendritic spiking mechanisms with heterogeneous temporal dynamics. These models enable efficient neural-signal representation without expensive feature extraction and show robust out-of-sample generalisation across patients and recording conditions on large-scale clinical EEG datasets. The thesis also addresses edge learning by proposing a neuromorphic learning rule for on-device adaptation toward patient-specific treatment under strict power and memory constraints. This mechanism can be adopted by the developed models to support personalised, low-latency intelligence at the edge. Finally, learnable activation functions are explored within artificial neural networks to improve training efficiency and interpretability, highlighting a pathway for integration with neuromorphic technology. Together, these contributions advance neuromorphic neurotechnology toward autonomous, personalised, and continuously learning closed-loop systems, with implications beyond epilepsy to a broader range of neurological disorders. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35550 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35549 Blockchain-based Advanced Information Infrastructure and its Applications in Demand-Side Energy Systems Yu, Teng The energy sector is moving from centralised one-way power systems to decentralised bidirectional smart grids driven by distributed energy resources (DERs). This shift requires infrastructure for secure coordination, verifiable markets, and prosumer data privacy. Existing blockchain systems have limited scalability, security, and interoperability, restricting high-frequency smart-grid applications. This thesis designs blockchain infrastructure for peer-to-peer (P2P) energy trading and power load forecasting (PLF). It has three theoretical innovation points. First, a dual-blockchain architecture with an Improved Optimistic Rollup (IOR) improves vertical throughput by offloading heavy computation from a primary to a secondary blockchain. Second, a Transaction Batch Generation (TBG) protocol for leaderless Byzantine Fault Tolerance (LBFT) consensus improves horizontal throughput by letting every node broadcast blocks, avoiding redundant transaction rebroadcast, and reducing transaction censorship to nearly zero. Third, a Blockchain-of-Blockchains (BoB) architecture, with Cross-Chain Token Exchange (CCTE) and Cross-Chain Data Interoperability (CCDI), supports asset and data transfer across blockchains with lower latency and memory overhead. The infrastructure is applied to two demand-side problems. For P2P market clearing, it is combined with Trusted Execution Environments (TEEs), D-TASK, and TEAR-DO to address the privacy-robustness-latency trilemma: prosumer data remain private, distributed optimisation preserves optimal convergence, and clearing time is reduced by an order of magnitude over state-of-the-art methods. For PLF, CTP-FL combines the infrastructure with message coding and commitment-based dual consensus to preserve local-model privacy, tolerate Byzantine faults at client and server sides, and keep communication latency below local model training time. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35549 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35548 Towards Geometry-Grounded World Understanding Tang, Liyao Understanding the three-dimensional (3D) world underpins embodied artificial intelligence, enabling robotics, autonomous driving, augmented and virtual reality, and, more broadly, spatial intelligence. Yet the most direct representation of a 3D scene can be deceptively simple: a point cloud, \ie, a set of Cartesian coordinates sampled from scene surfaces, from which objects and semantic structure must be inferred. In practice, point clouds are noisy, incomplete, and irregularly sampled, making reliable interpretation fundamentally challenging. Geometry-grounded world understanding demands semantics that are consistent with metric 3D geometry. Semantic segmentation provides a principled route from geometric measurements to high-level scene understanding. In 3D point clouds, semantic segmentation anchors geometry-grounded world understanding by coupling fine-grained semantics with explicit 3D geometry and confronting a core perceptual challenge: forming structured and coherent interpretation from noisy, incomplete, and irregular samples. This thesis investigates how explicit 3D geometry can be elevated from a passive input to a source of structure for learning and generalization. We treat geometry as a prior that constrains what a plausible segmentation should look like, modulates how noisy supervision should be used, and guides how models adapt when spatial statistics shift. Building on this view, we advance geometry-grounded scene segmentation along three complementary aspects of the learning problem: the output, the supervision, and the adaptation Overall, this thesis approaches scene segmentation by structuring the outcomes of learning, the supervision that guides learning, and the contexts in which learning occurs, all grounded in explicit 3D geometry. In doing so, we advance a geometry-grounded view of world understanding in which explicit 3D geometry shapes both learning and generalization. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35548 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35547 Effects of the Post-Heat Treatment (Furnace Cooling) on the mechanical strength and dimensional accuracy of 3D printed PEEK in FDM method Deng, Yunxiang In recent decades, the production of polymeric parts using fused deposition modelling (FDM) has gained significant attention in the field, owing to its design flexibility, low cost, and time-efficient prototyping capabilities. Nevertheless, the inherently as-built limitation constrains the performance and challenges the broader applications. To address these limitations, the post-heat treatment or annealing has long been applied as one the critical post processing techniques for enhancing the materials properties and its performance. Despite the beneficial effects of the post-heat treatment on the mechanical strength, its effect on the long-term tribological performance with the involvement of complex structures are limited. While it is often assumed that improvements in mechanical properties lead to enhanced tribological performance, tribological properties are not intrinsic material properties. Instead, they are instead dependent strongly on the specific system and operating conditions in which a material or structure has to function. Among the tribological studies, the friction-induced vibration (FIV) is a critical issue, causing unwanted noise, wear, and potential system failure. Although the proposed active or passive controls can mitigate FIV, they inevitably increase the complexity in the design and implementation of the whole system. The re-entrant auxetic structure was employed in this study as the solution, characterized by Negative Poisson’s ratio (NPR). Notably, the performance of AM-fabricated parts remains highly sensitive to the external environmental stimuli, particularly temperature. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35547 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35546 Probabilistic Learning and Inference for Multi-Modal Trajectory Generation and Adaptation Yin, Zeya Robotic systems are fundamental to enhancing efficiency, advancing autonomous processes, and performing tasks that are hazardous or routine in daily life. However, the complexity and unpredictability of various tasks require that robots can adapt effectively to changing environments. This thesis aims to advance the state of the art in robotic motion generation by presenting an ensemble of approaches that combine motion planning, learning from demonstration techniques, and modern probabilistic methods. Our goal is to develop principled frameworks that can produce diverse and reliable multimodal solutions for robotic tasks. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35546 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35545 Attention Calibration for Reducing Hallucination in Large Vision-Language Models Zhu, Younan Large Vision-Language Models (LVLMs) exhibit impressive multimodal reasoning capab- ilities but remain highly susceptible to object hallucination, where models generate responses that are not factually aligned with the visual content. Recent works attribute this issue to an inherent bias of LVLMs where vision token attention map has spurious focus on certain positions, and propose to mitigate this issue by reordering visual tokens. However, we find that different LVLMs exhibit different correlations between attention and spatial position, which makes the existing static solution difficult to generalize to other LVLMs. To begin with, we investigate the attention bias introduced by image tokens through a toy experiment, in which a blank image is fed into the model to capture its position-dependent bias. We then remove this bias from the original attention map, which already leads to a substantial reduction in hallucinations. This proof of concept validates the core intuition behind attention calibration. Building on this insight, we propose Dynamic Attention Calibration (DAC), a lightweight, plug-and-play module that leverages contrastive learning to dynamically enforce positional invariance. Unlike static baselines, DAC adapts to different models and inputs in a robust and learnable manner, offering a generalizable solution to mitigate attention-related hallucinations in LVLMs. Comprehensive experiments across multiple benchmarks demonstrate that DAC significantly reduces object hallucination while improving general multimodal alignment. Our method achieves state-of-the-art performance across diverse LVLM architectures on various metrics. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35545 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35544 Design and optimisation of OpenStride an open-source inexpensive force plate actometer Yang, Yang Quantitative assessment of rodent motor behaviour is central to preclinical neuroscience, and evaluation of naturalistic movement is of particular interest. In the early 2000s, force plate actometry (FPA) was developed to track rodent subject centre-of-mass (COM) with high temporal and spatial precision, enabling varied quantifications relevant to motor performance and behaviour. While FPA was commercialised, these systems cost ~$15,000 AUD and are no longer produced. Thus, despite clear utility, particularly in movement disorders and neuropsychiatric research, FPA adoption has remained limited. In this work, we sought to develop an open-source, low-cost FPA system called OpenStride, and to characterise how its performance is shaped by hardware and signal processing choices, providing users with information needed to use it effectively across varied experimental contexts. We designed OpenStride to be able to be fabricated for ~$800 AUD using standard 3D printing and laser cutting. OpenStride achieves minimal static drift during long recordings and minimal signal displacement in response to environmental perturbation; further, it reliably tracks position and distance and can separate groups of rodents with established motor phenotypes. We characterised the influence of five key variables on measurement quality: applied load, load cell excitation voltage, mechanical damping, platform mass, and signal filtering, providing suggestions on how to optimise setup in a context-dependent manner. Our findings support the interpretation that OpenStride is capable of meaningful motor and behavioural quantification across rodent species and experimental paradigms. Its performance is influenced by modifiable parameters, and understanding these relationships allows users to configure the system to suit their specific needs. All hardware and software files have been distributed freely via GitHub, with the intent of making force plate actometry accessible to the research community. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35544 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35543 Multimodal Learning for Multi-scope Brain Lesion Segmentation Chen, Jiahao Brain lesion-related diseases, including glioblastoma, stroke, and demyelinating disorders, present significant challenges for accurate diagnosis and personalized treatment due to heterogeneity in lesion distribution, morphology, and progression. Early and accurate lesion identification is critical for disease classification, prognosis, and treatment planning. MRI provides rich tissue-level structural information for lesion detection, while microscopy reveals morphology and topology at the cellular level. Together, they offer complementary perspectives on multifocal brain lesion pathology. However, existing deep learning methods for multimodal MRI segmentation often hinder interpretability and introduce redundant parameters, leaving AI decision-making opaque in clinical settings. My contributions are: 1.SwitchNet: A modality-adaptive fusion network that explicitly learns each MRI modality's contribution to different lesion subregions via Adaptive Encoder/Decoder Blocks and modality-specific attention, improving interpretability while maintaining segmentation accuracy. 2.Multi-scale Diffusion Segmentation: I refactor segmentation into stepwise mask reconstruction, constructing a mask pyramid with independent diffusion processes at each scale to establish structural-level image-mask correspondence. 3.Skeleton-Guided Microscopy Pipeline: A cellular-level analysis pipeline for tumor and non-tumor cell segmentation, topology extraction, and quantitative analysis of cell spatial organization. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35543 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35542 Inter-project Learning in Major Infrastructure Projects Liu, Yuhan In project-based organizations (PBOs), the episodic character of projects disrupts durable knowledge retention and organizational memory. While inter-project learning (IPL) has been proposed to improve knowledge flow across projects, institutionalizing knowledge does not automatically activate it. Knowledge can remain dormant in manuals, platforms, and reports unless organizations help people reinterpret and adapt it for new project contexts. Drawing on a longitudinal case study of Beijing Capital International Airport Terminal 3 and Beijing Daxing International Airport, led by Capital Airports Holding Company (CAH), this thesis investigates how organizations can sustain and reactivate IPL. The study develops an integrated framework that explains how project knowledge is identified and externalised, embedded through formal systems and routines, and regenerated across later project contexts. It further examines how dormant knowledge is brought back to life through knowledge translation, including deconstructing prior knowledge, reframing it for local use, embedding it into routines and discourse, and renewing it through feedback loops. Rather than treating knowledge as a static object to be transferred, this thesis conceptualises IPL as a reflexive and recursive process grounded in communicative practices and organizational design. By theorising IPL as organizational reproduction and knowledge translation, the study offers insights into how organizations maintain learning continuity and make past project knowledge meaningful and usable again amid fragmentation, turnover, and temporariness. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35542 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35541 Towards Efficient High Fidelity 3D Reconstruction and Novel View Synthesis Chen, Haodong This thesis studies three-dimensional (3D) reconstruction and novel view synthesis (NVS) under tight hardware and data budgets, a common constraint in VR/AR, robotics, autonomous navigation, and cultural heritage. Existing methods often rely on multi-camera rigs, dense imagery, calibrated cameras, or heavy compute, limiting embedded and dynamic deployment. The thesis addresses this through three largely orthogonal pipeline-stage contributions: E2V for event-stream sensing, 3DLS for splatting-kernel representation, and HDGS for sparse-view depth supervision. First, E2V shows that a single neuromorphic event camera can support full volumetric reconstruction. It uses an end-to-end model to map raw events to voxel occupancy in one pass, without known intrinsics/extrinsics or multi-stage processing. A synthetic dataset of 39,739 object scans generated with an event-camera simulator is also released for training and benchmarking. Second, 3D Linear Splatting (3DLS) improves point-based radiance-field rendering by replacing the Gaussian fall-off in 3D Gaussian Splatting (3DGS) with a bounded linear kernel. This reduces blur, preserves high-frequency detail, and improves rendering speed by 30% while maintaining competitive image quality across benchmarks. Third, Hierarchical Depth-Guided Splatting (HDGS) addresses geometric inconsistency in sparse-view splatting. Its Cascade Pearson Correlation Loss (CPCL) supervises depth across scales, improving geometric accuracy and consistently outperforming prior methods when only a few views are available. Together, E2V, 3DLS, and HDGS advance 3D reconstruction and NVS toward practical use under tight energy, compute, and memory budgets. Remaining challenges include the synthetic-to-real gap, broader kernel design for splat-based rendering, and geometric consistency under extreme sparsity, occlusion, and scene dynamics. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35541 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35540 The quantitative significance of membrane lipids to the phosphorus economics of Australian native plants Liang, Grace Phosphorus (P) is an essential macronutrient that commonly limits the productivity of plants in terrestrial ecosystems worldwide. The prevalence of P-impoverished soils across the Australian continent, and throughout much of Australia’s geological history, has played a significant role in the evolution of its native flora. Many native species exhibit a wide range of traits and mechanisms that allow them to persist and thrive on some of the most P-impoverished soils, including reducing their allocation of P into membrane phospholipids. Membrane phospholipids, which account for around one-third of organic P in leaves, is a unique P compound as they can be substituted by functionally similar non-P lipids, such as galactolipid and sulfolipids. This process of substitution allows for lipidbound P to be liberated and reallocated to maintain plant growth and function. Therefore, phospholipids likely play an important role in the P economics of plants. The main focus of this thesis is on foliar membrane lipids and their quantitative significance to the P economics of native Australian flora. In particular, I investigated the contribution of membrane phospholipids in the adaptation of Australian native plants to P-impoverished soils. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35540 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35538 Australian minimum legal drinking age & effects later-in-life Denford, Thomas This thesis examines the long-run effects of Australia’s 1970s minimum legal drinking age (MLDA) reforms, which aligned all states to a uniform age of 18. While alcohol’s harms on adolescents are well-established, less is known about its level of persistence decades on. Using a staggered synthetic difference-in-differences (SDID) framework and HILDA data, I estimate the impact of exposure to MLDA reform on a range of outcomes at ages 55 to 63. Large and highly significant effects are found for annual income (negative) and the prevalence of long-term health conditions (positive). However, a likely violation of the identifying assumption known as ’parallel trends’—driven by structural differences between treated and control states—means these estimates cannot be interpreted as causal. A novel contribution of this thesis is its focus on the long-run outcomes of MLDA policy in an Australian context, in contrast to existing literature which has largely examined short-run effects. Most importantly, however, the study highlights the challenges of using retrospective data to assess historical policy impacts, underscoring the need for caution in formulating research questions. Wed, 01 Jan 2025 00:00:00 GMT https://hdl.handle.net/2123/35538 2025-01-01T00:00:00Z https://hdl.handle.net/2123/35537 Using Genomic Data to Elucidate the Evolution and Historical Biogeography of the Liverwort Family Lepidoziaceae (Jungermanniales; Jungermanniopsida) Rayos, Antonio Jr Luciano Lepidoziaceae, the third-largest family of liverworts, account for almost 10% of liverwort species diversity worldwide, and there are many unanswered questions about the phylogeny and biogeography of this large and diverse family. The broad aims of this thesis were to resolve the phylogenetic relationships among the taxa within Lepidoziaceae, to propose necessary taxonomic revisions based on the resulting phylogenetic analyses, and to provide insights into the spatial and temporal distribution of the family. The thesis opened with an introduction to Lepidoziaceae, the phylogenetic studies that have been done on the family so far, and the potential of genomic data to resolve the phylogeny of the family. The remaining chapters then addressed the aims using a range of methods to analyse data sets with overlapping taxon sampling. Overall, this thesis has presented several interesting findings about the evolutionary relationships among the members of Lepidoziaceae at different taxonomic levels and provided insights into the distribution of the family across space and time. These results have taxonomic implications that can be used as the basis for taxonomic revisions. The findings from this thesis still leave many unanswered questions that can be addressed in future studies. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35537 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35536 WOMEN-FRIENDLY QURʾĀN TRANSLATIONS IN COMPARATIVE PERSPECTIVE: A FEMINIST CRITICAL DISCOURSE ANALYSIS OF LALEH BAKHTIAR’S THE SUBLIME QURʾĀN (Q.2:231, Q.4:11, Q.4:34) Elmir, Mouna This thesis investigates how English translations of the Qurʾān shape gender-conscious interpretations of verses concerning women's rights and roles. It examines Laleh Bakhtiar's The Sublime Qurʾān (2007), focusing on her translation of three Qurʾānic verses (Q.2:231, Q.4:11 and Q.4:34) that have traditionally been associated with debates on marriage, divorce, inheritance and domestic authority. The study argues that Qurʾānic translation plays a significant role in shaping ethical understandings and social attitudes towards women. It explores how alternative translation choices can challenge interpretations that have been used to justify spiritual abuse, domestic violence and patriarchal authority, while remaining grounded in the Qurʾān's ethical framework. Using an interdisciplinary methodology, the study integrates Translation Studies with Critical Discourse Analysis, Feminist Critical Discourse Analysis, Feminist Poststructuralist Discourse Analysis and Contrastive Analysis. Bakhtiar's translation is analysed in comparison with selected classical and modern Qurʾānic commentaries across textual, discursive and contextual levels. The findings demonstrate that Bakhtiar's approach is best understood as an ethically grounded and linguistically informed reinterpretation rather than a feminist ideological project. The thesis makes two original contributions to Qurʾānic translation studies by introducing the analytical category of woman-friendly translators and the concept of woman-friendly commentary as a hybrid exegetical model of tafsīr. Together, these contributions expand contemporary approaches to Qurʾānic translation and interpretation by foregrounding ethical coherence, contextual sensitivity and the Qurʾān's inclusive moral vision. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35536 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35535 Young Children’s Meaning-Making through the Use of Tablets in Digital Play Guo, Zejian In recent decades, digital technologies have become not only prevalent in early childhood school environments but increasingly embedded in home settings. Digital play has become a significant activity where young children engage, explore, and express themselves. This situation raises important questions about how children make meaning through digital play and how parents, educators, and researchers can understand and support these processes. The current study investigated the meaning-making processes of four young children during digital play at home in Australia and China. The children were accompanied by their parents during these processes. Grounded in sociocultural theories, this study explored digital play behaviour using the digital play framework (DPF) (Bird & Edwards, 2015). The research further examined the roles of oral language and child–parent interactions in shaping these processes, and potential sociocultural differences between two distinct contexts. Based on a qualitative case study methodology, the findings suggested that the four children’s meaning-making processes were informed by their use of oral language, their agency during play, the selection and affordances of technologies, and the child–parent interactions. Various types of parental scaffolding were observed as significant in fostering the children’s communication, creativity, and learning within digital play. The application of the DPF highlighted both epistemic and ludic play, with three distinct digital play movements emerging across the four cases. Cross-case analysis suggested that although cultural differences existed between the Australian and Chinese families, more similarities than differences were observed. It concludes by discussing the broader implications for research and practices for educators, policymakers, parents, and technology designers while acknowledging the study’s limitations and identifying directions for future research in this evolving field. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35535 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35534 Leveraging gene-by-environment interactions to identify molecular drivers of diet-induced metabolic disease Cutler, Harry Benjamin Understanding why individuals differ in susceptibility to cardiometabolic disease remains a central challenge in metabolic research. Although caloric excess is a major driver of obesity and disease risk, the threshold at which dysfunction develops varies substantially between individuals. This points to intrinsic biological mechanisms that buffer against adverse dietary environments. To identify such mechanisms, I developed a systematic experimental pipeline integrating genetic diversity across inbred mouse strains with multi-omic profiling of metabolically relevant tissues following high fat high sugar (HFHS) feeding – a perturbation that induces metabolic disease only in a subset of strains. Comprehensive metabolic phenotyping revealed that tissue-specific dysfunction was strongly shaped by genetic background, in some cases fundamentally altering which tissues were affected by HFHS exposure. Deep learning models trained solely on protein abundance in chow-fed mice accurately predicted strain- and tissue-specific responses to HFHS feeding, leading to the hypothesis that dietary stress responses are governed by the pre-existing molecular architecture of each tissue. To test this, I established a bespoke tissue-specific adeno-associated virus platform to overexpress candidate regulators identified by the deep learning models, substantially accelerating functional validation compared to conventional genetic approaches. This enabled testing of seven candidate regulators of diet-induced metabolic dysfunction. Hepatic SLC22A7 emerged as a previously unrecognised mediator of insulin resistance in both liver and white adipose tissue under HFHS conditions. Collectively, this work demonstrates that integrating systems-level molecular profiling with predictive modelling and high-throughput in vivo validation is a powerful strategy to uncouple metabolic disease from caloric excess, providing a framework for precision therapeutic discovery. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35534 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35533 Genetic Architecture and Molecular Mechanisms of Coat Colour Variation in Domestic Dogs Brancalion, Lillian Valmai Coat colour in the domestic dog (Canis lupus familiaris) is a complex trait shaped by several interacting genetic variants, regulatory mechanisms and breed-specific evolutionary history. While multiple pigmentation loci have been identified, the genetic basis of many coat colour phenotypes remains unresolved. This thesis advances knowledge of canine pigmentation genetics by identifying novel genetic variants underlying coat colour variation, resolving complex inheritance patterns, and developing practical frameworks for genetic coat colour prediction. This thesis characterises two variants associated with white-spot modification near USH2A, resolving the molecular basis and inheritance of ticking and roan phenotypes through a three-haplotype allelic series and identifying USH2A as a novel candidate pigmentation gene. Further, this thesis demonstrates that pheomelanin intensity in the Golden Retriever can be accurately predicted using a simple, biologically informed predictive model. Evaluation across multiple dog breeds highlights the importance of population-aware approaches to phenotype prediction. Finally, characterisation of the MITF-A pseudogene and reference genome misassemblies at the MITF locus facilitate more accurate genetic interpretation of this important pigmentation region. Collectively, this research expands understanding of the genetic architecture of canine pigmentation by identifying novel coat colour variants, clarifying complex genomic regions, and demonstrating how biologically informed models can translate complex traits into practical predictive tools. These findings provide valuable Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35533 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35532 Relationship between Metabolome and Survival Characteristics of Rhizobia after Growth in Liquid Media Vathshalyan, Nishanthi Legume inoculation enhances biological nitrogen fixation, but desiccation stress reduces rhizobial survival on seed. This thesis investigated the effect of different liquid media on the rhizobial metabolome and desiccation tolerance. The effect of carbon sources and carbon-to-nitrogen (C:N) ratios in defined media were examined and defined medium (mJMM) was compared with complex media, peat extract (PE) and a food-waste-derived medium (FW). Three rhizobial strains representing clover, pea and soybean symbionts were studied. Carbon source strongly influenced metabolome and desiccation tolerance. Growth on L-arabinose increased trehalose levels and improved survival after drying while lower C:N ratios also improved survival. Early survival was positively associated with trehalose, whereas longer-term survival correlated with metabolites including 3-hydroxybutyric acid. To explore mechanisms in complex media, TA1 survival was monitored during storage at 4 °C in PE, FW and mJMM. Although mJMM maintained the highest viable counts, survival was greatest in FW during early stages and in PE after prolonged storage. Pantothenic acid and ribonic acid were positively associated with survival on beads. Despite upregulation of otsB in complex media, intracellular trehalose accumulation was low and did not predict survival. Improved survival was more closely linked to other metabolites and physiological changes. Membrane integrity increased during storage, particularly in PE-grown TA1. Survival correlated positively with saturated fatty acids, xylitol and related metabolites and negatively with unsaturated fatty acids. These findings suggest that improved desiccation tolerance in TA1may be explained by membrane remodelling triggered by growth and storage in PE. Overall, this study advances understanding of the metabolic and physiological basis of rhizobial desiccation tolerance and informs future development of inoculant formulations for seed survival. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35532 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35516 Complex Individuality: The Spatial Temporal and Agential Dimensions of The Problem of Biological Individuality. Mann, Rebecca This dissertation explores how we understand and demarcate the spatiotemporally unified, cohesive wholes that we call biological individuals. There to be four main points of contention underlying the so-called problem of biological individuality: (1) Whether there is a single kind of biological individual or multiple; (2) How accounts of biological individuality address how different sorts of individuals exist in space and over time; (3) Which biological phenomena or criteria underlie biological individuality; and (4) How different concepts of biological individuality relate to one another and when they come apart. I develop a three-pronged account of biological individuality that recognises three changeable kinds of biological individuals: evolutionary individuals; organisms; and agential individuals. This is based on three core processes in the biological world: evolution, energy, and action. I begin by examining the ambiguity underlying the problem of biological individuality itself, surveying the numerous solutions to the problem. After dispelling monist approaches to biological individuality, I argue that we should treat biological individuality as an umbrella concept, under which there are multiple kinds of biological individuals based on the important ways in which biological processes form unified, cohesive wholes. I then show how the problem of biological individuality has both spatial and temporal dimensions, and that any solution must address both. I then develop a novel metabolic account of the organism, defining the organism as having a centred metabolic network, before arguing for an agential account of biological individuality, which is not coextensive with organismal or evolutionary individuality concepts. This analysis not only sheds new light on many commonly discussed problem cases, such as social insects and aggregative slime moulds, it also illuminates often-overlooked complex cases of biological individuality, including mammals like ourselves. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35516 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35515 Stand to your post: The impact of incentives on retention in the Australian Defence Force Plummer, James The Australian Defence Force has publicly stated its strategic objective to expand operational capability. An integral part of that strategy is an increase in the number of service personnel, which requires higher rates of enlistment and (or) retention. This thesis considers three aspects of military service or reward that are expected to influence the retention decisions of personnel; retention bonuses, housing and relocation. It employs data rarely available to researchers, and uses empirical methods to identify how various factors influence the retention of military personnel. This thesis is intended to further academic understanding of a relatively unexplored aspect of labour economics, namely military workforces. It is also intended to inform decision-making by policymakers on a topic of national importance. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35515 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35514 Assessment of pathological features to improve accuracy of diagnosis, classification and prognosis of primary and metastatic melanoma Rawson, Robert V. The gold standard for the diagnosis of melanocytic lesions and provision of vital prognostic information for primary melanoma, is microscopic examination by a trained pathologist. When interpreting difficult to diagnose primary melanocytic lesions the challenges for the reporting pathologist include subjective diagnostic criteria leading to significant interobserver variability in diagnosis, definitive diagnosis when interpreting an initial partial diagnostic biopsy and, more recently, how to integrate the complex genomic information available from ancillary testing with the morphological clues to diagnosis which have been accumulated and learnt over the last 50 or so years by pathologists. Once the diagnosis is made the pathologist’s role is to report key prognostic information to ensure the lesion is appropriately classified, staged and managed. Traditionally pathologists role in the reporting of patients with advanced (macroscopically detectable metastatic melanoma) has been minimal, usually limited to the initial diagnosis and identification of further adverse features including tumour bulk and extranodal extension4. However, the recent development of effective systemic therapies, particularly immune checkpoint inhibitors, have been tested with proven superior survival in the neoadjuvant setting compared with the adjuvant setting5, 6. This provides the opportunity for pathologists to provide crucial information when analysing these neoadjuvant specimens to assist in predicting clinical outcomes in patients and guide further management. The ensuing body of work studies novel ways in which pathological assessment, through morphological and immunohistochemical techniques, of both primary melanocytic lesions and neoadjuvant specimens in metastatic melanoma can lead to more appropriate management and improved patient outcomes. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35514 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35513 Using Machine Learning to Solve Stochastic Differential Equations Miltchinov, Nikola Dimitrov Scientific machine learning is a rapidly growing field that draws from and combines many disciplines, including computational mathematics, computer science, physics and more. In recent years, great attention has been turned towards a set of methods which allow one to employ machine learning techniques to solve differential equations. These so-called `physics-informed' methods are powerful tools which do not suffer many of the usual limitations of traditional solvers, making them incredibly well-equipped to handle those complex problems which have eluded researchers for years. Up until now, these machine learning models have focused primarily on deterministic differential systems, with little attention paid to those driven by randomness or noise. However, these stochastic problems can be just as interesting, and are by no means less important than those of the deterministic variety. The content presented in this thesis centers around the application of existing machine learning solvers to problems in stochastic calculus, something which has rarely been attempted, and which has up until now excluded a vast range of meaningful problems from consideration. Of particular interest is the Landau-Lifshitz-Gilbert (LLG) equation, which presents unique challenges when attempting to solve. We find that physics-informed solvers are not particularly well suited for dealing with problems of a stochastic nature - at least in their most basic forms - though they do have potential. Whilst there are certain methods which can somewhat bridge this gap, it will ultimately take more research before the models handling stochastic differential equations are able to catch up with those of their deterministic counterparts. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35513 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35512 Molecular and Cellular Characterisation of Non-coding Variants in Inherited Heart Disease Singer, Emma S. Genetic variants outside of protein-coding regions of genes, i.e non-coding variants, are increasingly identified to cause inherited diseases. However, the clinical relevance of these variants is challenging to determine from sequence context alone, leaving many classified as variants of uncertain significance. This thesis aimed to study the functional consequences and clinical relevance of non-coding variants in people with inherited heart disease and sudden cardiac death (SCD) through genetic and cellular studies. A rare variant burden analysis in Chapter 2, found an enrichment of RNA splice-disrupting variants in six cardiac disease genes in over 1000 people with inherited heart disease or SCD compared to population controls, supporting their relevance to disease causation. RNA extracted from blood amplified two-thirds of cardiac disease genes, highlighting the suitability of blood RNA for functional analyses of splicing variants. Chapter 3 presents functional analysis reports confirming the pathogenicity of eight putative splice-disrupting variants following blood RNA analyses. RNA and protein studies using primary heart tissue samples presented in Chapter 4 confirmed that a 5’ untranslated region deletion in TAFAZZIN abrogated protein expression from the gene, leading to haploinsufficiency and causing Barth syndrome. Eight MYBPC3 midigenes were developed in Chapter 5 to functionally assess the impact of putative splicing variants without requiring patient-derived RNA samples. In Chapter 6, RNA sequencing of patient-specific induced pluripotent stem cell cardiomyocytes confirmed the pathogenicity of a minor intron variant in SCN5A. CRISPR-Cas9 correction of this variant in Chapter 7, reversed cryptic splicing to wild-type. Collectively, data presented in this thesis led to a clinically meaningful reclassification of 13/21 (62%) non-coding variants and demonstrated how incorporating RNA analyses in genetic testing shortens the diagnostic odyssey for families. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35512 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35511 A Proposed Framework for an Artificial Intelligence-based Clinical Decision Support System in Dento-Maxillofacial Radiology Delamare, Eduardo Artificial intelligence shows promise for automated image analysis in dento-maxillofacial radiology (DMFR), yet few systems reach routine clinical use. This thesis argues that the gap between benchmark performance and adoption stems from four limitations of deep-learning-only systems—interpretability, generalisability, trustworthiness, and explainability—and proposes an AI-based clinical decision support system (CDSS) addressing them through hybrid deep-learning/rule-based (HDLRB) architectures, modular agentic orchestration, and human-in-the-loop design. Three empirical studies underpin the framework. A systematic review of how panoramic imaging errors are handled during machine-learning development revealed marked inconsistencies, exposing a generalisability gap that motivates automated quality assurance as a CDSS's first stage. Two further studies showed that HDLRB pipelines—pairing deep-learning segmentation with deterministic spatial analytics—can simulate expert reasoning for the surgical management of impacted mandibular third molars and for staging periodontal bone loss on cone-beam computed tomography (CBCT). Both reached strong agreement with expert consensus while preserving full transparency, every recommendation traceable to inspectable measurements and auditable rules. The thesis synthesises these into a framework built on five principles—hybrid architecture, modular auditable orchestration, human-in-the-loop design, quality assurance as the first diagnostic stage, and determinism where possible—structured around a QA–anatomy–pathology pathway mirroring DMFR specialist reasoning. The PerioDetect Registered Report, a supplementary appendix, operationalises these as a fully auditable multi-agent system for periodontal CBCT assessment. This work shows HDLRB pipelines can match expert-level agreement on structured DMFR tasks without sacrificing the transparency clinicians need for trust, and extends to further specialties, modalities, and technologies. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35511 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35510 Engineering DNA origami nanopores: From structural dynamics to selective molecular transport Meppat, Sreelakshmi Cell specificity is essential for effective targeted drug delivery and for minimizing off-target effects. DNA origami nanopores have been developed to interact in highly specific ways with lipid membranes, facilitating controlled membrane transport. The size, shape and chemical functionality strongly influence their in vivo fate, including endosomal escape. In this thesis a Barrel-encapsulated DNA origami nanopore (BN) nanostructure design was investigated. BN consists of a DNA origami barrel linked to a DNA nanopore via single-stranded scaffold tether. The structural properties of BN variants were validated by evaluating tether variants designed using different scaffold sequences and testing the ability to encapsulate the nanopore inside the barrel. Also, BN was tested for selective interaction with lipid membranes and for de-coupling membrane docking from membrane insertion. A Giant unilamellar vesicle (GUV) based dye influx assay was introduced and optimised to validate the membrane interactions of BN. Docking and signal responsive nanopore insertion was validated by testing different configurations of BN with a 6 helix bundle (6hb) nanopore. Toe-hold mediated nanopore switching was employed to validate nanopore activation once the BN was docked onto the membrane, triggering membrane transport of dye. The barrel docking mechanism was further modified for selective docking and competitive docking to different sub-populations of GUVs. The barrel efficiently docked onto the GUV membrane when chol-DNA handles were present on GUVs. BN docking specificity provides an approach for targeting multicellular systems and can be further enhanced through appropriate functional modifications such as aptamer or protein. The 6hb nanopore is capable of separating membrane docking from insertion, which offers a versatile platform for therapeutic cargo delivery with potentially reduced off-target effects. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35510 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35509 Investigating uncharacterised epigenetic regulators in the C. elegans germline Wills, Carlotta Epigenetics refers to the molecular signals that control the activation and repression of genes. These signals are important for several biological processes ranging from gametogenesis to development, and to the development of complex disease. Additionally, there is growing evidence that they can be inherited from one generation to the next, challenging traditional understanding of heredity. To fully comprehend how epigenetic mechanisms contribute to germline development and function, we must understand how the germline regulates, and is regulated by, these epigenetic processes. In this thesis, I use Caenorhabditis elegans as a model to explore understudied players in germline epigenetic regulation. The findings span numerous facets of epigenetic regulation, from chromatin to small RNA and the spatial organisation of biomolecules into phase-separating granules. I provide an overview of the burgeoning intersection between the fields of epigenetics and evolutionary biology, giving broader context for the potential implications of epigenetic inheritance. I synthesise existing data on protein-protein interactions in the germline granule context, developing a resource to better inform future investigations in this area. I investigate two uncharacterised genes in detail, using a range of phenotypic, transcriptomic and proteomic analyses to probe their functions, with a focus on their roles in germline epigenetics. Lastly, I explore the writing and reading of an underexplored histone modification which has been implicated in transgenerational effects but is lacking characterisation of its precise function and regulation in the germline. This thesis broadens the network of genes that play a role in germline epigenetics in C. elegans. It opens several new avenues of investigation that future work can build on to further expand our understanding of how epigenetics is regulated in the germline, and how it can contribute to complex phenotypes. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35509 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35471 Macroecological processes impact soil microbial diversity and functional potential Du, Mingming Soil microorganisms are crucial for nutrient cycling and ecosystem function, yet their large-scale distribution patterns and functional responses to environmental change remain insufficiently resolved. This thesis investigates how climate, soil properties, and land-use intensification shape soil microbial communities across Australian soils, with a particular focus on community composition, ecological traits, and functional potential. By collecting soil samples at local and continental scales and applying advanced bioinformatics and machine learning, this thesis provides a clearer picture of soil microbial biogeography. The results show that broad-scale climatic constraints act as dominant filters of microbial structure and function at the continental scale, whereas soil properties, particularly pH, become the principal controls of microbial variation at the regional scale. Within this abiotic template, land-use intensification further modifies these patterns by reshaping microbial diversity, composition, and community stability, while also promoting biotic homogenisation. Beyond taxonomic patterns, the thesis shows that microbial functional attributes, including antibiotic resistance potential and growthrelated ecological strategies, are non-randomly distributed across pedo-climatic gradients. These functional patterns reveal clear ARG hotspots in climate-stable regions with agricultural activities and a higher microbial potential growth rate in higher-latitude regions. Overall, this thesis advances current understanding of soil microbial ecology by linking biodiversity, functional potential, and environmental filtering within a unified continental framework. It provides new ecological knowledge into how soil microbiomes respond to natural and anthropogenic gradients and offers a foundation for predicting microbial contributions to soil health and ecosystem functioning under ongoing environmental change. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35471 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35470 Mapping porphyry copper prospectivity from Jurassic to recent times using spatio-temporal machine learning Alfonso, Christopher Peter Copper is a crucial material for the global economy, required for the production of a vast array of modern technologies. It is likely that new copper deposits will need to be discovered; however, much of the Earth’s surface has already been explored, driving the search for these deposits to greater depths and necessitating the development of new exploration and prospectivity mapping techniques. Here we present a machine learning method for estimating prospectivity for porphyry copper – the most abundant copper deposit type. Unlike the majority of prospectivity mapping studies, our approach makes use of time-dependent datasets coupled to global plate tectonic reconstructions to quantify the likelihood that a deposit formed at a given place and time in the past. By considering the many factors which may contribute to the formation of these deposits, this method also provides quantitative information reaffirming existing porphyry copper deposit models. These deposit models often emphasise the need for thick continental crust and the subduction of large quantities of volatile material such as water or carbon to stimulate deposit formation. Correspondingly, inspection of the global or regional scale prospectivity models, through techniques such as feature importance and partial dependence analysis, indicates that input variables such as overriding plate thickness and subducting carbonate and silicic sediment volumes are strongly associated with the presence of deposits, suggesting that one or more of these factors may be prerequisite for deposits to form. These models represent a powerful new tool, both for prospectivity mapping and for furthering the understanding of the mechanisms behind the formation of porphyry copper deposits. Furthermore, the methods used to produce the models are predicted to be sufficiently generalisable to allow their application to other deposit types such as porphyry molybdenum and gold, which are also formed along subduction zones. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35470 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35469 Schism and Development: Liberalism in the Contemporary Chinese Context Zhou, Yunfan As one of the most influential political ideologies in the contemporary Chinese public sphere, liberalism not only continued to develop its theoretical tradition during the Reform and Opening-up period but also constructed a unique political vision based on its core concepts, thereby forging a powerful political consensus within civil society. However, with the alteration of the Party’s basic political line following Xi Jinping’s ascent to power, coupled with the re-consolidation of the authoritarian system, the socio political environment for Chinese liberalism has undergone a drastic transformation. Faced with new empirical and ideological challenges, contemporary Chinese liberalism has experienced profound shifts in its theoretical frameworks and discursive paradigms, rendering this intellectual tradition intensely dynamic and highly fluid in the new era. Through an exploration of the controversies and introspections among contemporary Chinese liberal intellectuals regarding critical issues in the public sphere, this thesis seeks to assess the recent developmental trajectory of Chinese liberalism—a major intellectual tradition—in the Xi Jinping era. Spanning domains from the market economy and pluralism to conservatism and nationalism, these pivotal issues form the core of liberal contemplation regarding contemporary Chinese political life. Indeed, it is through relentless introspection on these very topics that contemporary Chinese liberalism has catalyzed both its theoretical evolution and its internal schisms, ultimately articulating a political vision in the new era. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35469 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35468 Understanding Inter-Ethnic Differences in Tyrosine Kinase Inhibitor Response Kyriacou, Nicki Marie Tyrosine kinase inhibitors (TKIs) are a class of targeted drugs that have transformed the landscape of cancer pharmacotherapy, however, they exhibit large inter-individual variability in their systemic exposure and effects. Ethnicity/geographic ancestry has been identified as a factor potentially contributing to inter-individual variability in drug response, primarily driven by population differences in the profiles of intrinsic (e.g., genetics, body weight) and extrinsic factors (e.g., diet, complementary medicine use) influencing drug pharmacokinetics and pharmacodynamics. Investigations of the use of pazopanib in the treatment of advanced ovarian cancer have revealed differences in the tolerability and therapeutic benefit of pazopanib between women of European and East Asian ancestry, although the mechanisms underlying these differences remain unclear. This prompted exploration of extrinsic factors potentially contributing to these inter-ethnic differences, including the effect of green tea consumption on pazopanib pharmacokinetics, given the greater prevalence of green tea consumption in East Asian populations. An open-label, single-dose, fixed-sequence pharmacokinetic study was designed and conducted to evaluate the influence of green tea administration on the pharmacokinetics of pazopanib in healthy participants. Concomitant consumption of a green tea extract tablet significantly decreased the oral systemic exposure of a 200 mg dose of pazopanib by approximately 50%. Exposure-response relationships for pazopanib efficacy are well-established such that decreases in pazopanib systemic exposure arising from concomitant green tea consumption could potentially lead to suboptimal therapeutic outcomes. This research highlights the importance of exploring the factors contributing to inter-individual variability in TKI pharmacokinetics and pharmacodynamics to inform appropriate dosing recommendations and improve therapeutic outcomes. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35468 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35465 The Characteristics of Jockey and Rider Falls and the Development of a Clinical Trial Protocol to Evaluate the Effect of Fall Safety Training on Injury Severity in Equine Sports Nylund, Lindsay Edwin People who engage in equestrian sports have a high risk of falling from their horse, which can result in serious injury. The first study was analysis of injury outcomes for riders who fell wearing or not wearing an air jacket. From Fédération Équestre Internationale data, air jacket usage was found to be associated with an increase in the incidence of serious injuries in falls (p=0.007). Riders wearing an air jacket had 1.7 times increased odds of sustaining a serious or fatal injury in a fall compared to riders not wearing an air jacket; however, there was insufficient data to determine the cause of this counterintuitive association and further research is needed. The second study investigated relationships between fall characteristics and high-risk landings (HRL) at jumps in cross-country eventing. A video analysis protocol was developed to analyse 87 video recordings of HRL — defined as when the rider's head impacted the ground and or where there was potential horse impact with the rider. An Equestrian Fall Assessment Instrument (EFAI) video analysis protocol was developed to examine the characteristics associated with high-risk landings. Based on the EFAI and subsequent data analyses, findings suggest optimised approach speed for correct striding and take-off; jump design to enable run-out; and rider training could help reduce the occurrence of HRLs. In the third study, video footage of 80 racing falls which occurred in UK, Ireland, and NZ, were analysed using the EFAI. Lower race class (p=0.054), hanging onto the reins upon ground impact (p=0.028), and no jockey tuck-and-roll behaviour following ground impact (p=0.001) explained 40.3% of the variance associated with HRLs. The evidence-base and findings in this thesis will enable future research work, including a FALLSAFE clinical trial to be carried out to ascertain potential cost-benefits of a training intervention that may mitigate injury risk for riders who regularly engage in equine activities. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35465 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35464 Wish for the perfect card: Exploring computational metagame design in competitive strategy games Elton-Pym, Alexander This thesis explores how computational metagame design, a new emerging field, can be applied to competitive strategy games. In particular, we explore computational metagame design for the collectable card game Hearthstone, which is a valuable research platform due to the complexity of designing Hearthstone metagames. The research follows a research-through-design methodology whereby we develop three prototypes of computational tools for assisting in metagame design. These prototypes are a user interface, a simulation engine, and a metagame model. We find a number of challenges associated with the design of metagames, contribute a novel method of procedurally generating cards, and a new means of understanding dynamic metagames through modelling. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35464 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35460 Exploring safer mobility behaviour for fall prevention: implications for people with Parkinson's disease Cheung, Daniel Ho Yan Falls continue to be a devastating problem for people with Parkinson’s disease (PwPD). Safer mobility behaviour is an emerging approach to fall prevention that focuses on how safely people move around. However, this concept remains poorly defined and inadequately conceptualised in research and clinical practice. This thesis aims to understand safer mobility behaviour within the context of fall prevention and how it applies to PwPD. Two scoping reviews were conducted to summarise existing literature on the concept of safer mobility behaviour and how it is currently applied in assessment and intervention. A qualitative study explored clinician/researcher perspectives on how safer mobility behaviour is approached in fall prevention for PwPD. Finally, a modified Delphi study was conducted to provide a consensus-based summary amongst people with lived experience and clinician/researchers on key assessment and intervention components targeting safer mobility behaviour. From these studies, a new theoretical conceptualisation was developed. Safer mobility behaivour was defined as any protective action or associated functional cognitive process used to reduce falls. Mobility behaviour is influenced by a dynamic interaction between person, environment and task-related factors. Promoting safer mobility behaviour was understood as empowering people to perform desired activities as safely as possible. This was practically applied through a contextual, individual and collaborative approach. However, there is currently no assessment tool nor optimal intervention addressing safer mobility behaviour in PwPD. Physical and cognitive ability were ranked as the top two components to include in assessment. Exercise, movement strategy training and cognitive strategy training were ranked as the top three components to include in intervention. This thesis establishes a clear and comprehensive foundation towards a safer mobility behavioural approach to fall prevention in PwPD. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35460 2026-01-01T00:00:00Z https://hdl.handle.net/2123/35459 Effectiveness of strengthening interventions in very weak muscles of people with spinal cord injury Chen, Lydia This thesis evaluates the effectiveness of strength training interventions for improving very weak muscles (grade 1–2) in people with recent spinal cord injury (SCI). Neurological weakness is a major consequence of SCI, limiting mobility and functional activity. Strength training is therefore central to rehabilitation, aiming to maximise strength, function, and independence. Two commonly prescribed approaches are high-dose voluntary contractions and strength training combined with electrical stimulation (ES). This thesis investigates the effectiveness of these interventions. Four projects are included: two randomised controlled trials (RCTs) and two secondary analyses. The first multi-centre RCT (n=120) examined whether 10,000 voluntary contractions over eight weeks improved strength. Participants were randomised to high-dose training plus usual care or usual care alone. The mean between-group difference was 0.4/13 points (95% CI −0.5 to 1.4), indicating little to no effect. The second multi-centre RCT (n=60) studied strength training combined with ES versus usual care. The between-group difference was 0.7/13 points (95% CI −0.7 to 2.1), below the pre-specified clinically worthwhile effect (1 point), suggesting minimal effectiveness. The third project pooled data from both RCTs to assess an impression of change scale. A moderate correlation with measured strength (Spearman’s rho = 0.5) supported its validity as a simple patient-reported outcome. The fourth project examined assessor blinding across four RCTs (n=219). Assessors were more likely to correctly guess allocation in the experimental group (OR 2.55, 95% CI 1.47 to 4.43), highlighting challenges in maintaining blinding. Overall, the RCTs showed that the studied interventions provided little to no meaningful improvement in very weak muscles after SCI. Results of the secondary analyses highlight the value of patient-reported outcomes, and challenges of maintaining assessor blinding in clinical trials. Includes publication Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/2123/35459 2026-01-01T00:00:00Z