Sydney Digital Theses (University of Sydney Access only)

Reconstruction of Visual Experiences from Neural Activity

2026-07-23T07:30:54Z

Reconstruction of Visual Experiences from Neural Activity Das, Tanaya Reconstruction of visual experiences from brain activity is emerging as a key research area in cognitive neuroscience and neurotechnology, paving the way for transformative assistive technologies. While recent advances in deep learning have enabled good reconstructions from predominantly Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG)-based image reconstruction (IR) is particularly sought after due to its high temporal resolution and accessibility. However, current research largely relies on high-density EEG and full-bandwidth signals for IR, increasing computational cost, signal transfer requirements, latency, and hardware complexity. Furthermore, existing models show limited cross-subject and cross-dataset generalisation, often failing to transfer across individuals or datasets. This thesis addresses these challenges through a two-stage investigation. First, we examine the feasibility of efficient EEG-based IR using targeted signal processing. Frequency analysis and Generalised Eigenvalue Decomposition (GED) are used to identify frequency bands associated with visual information, followed by evaluation of reduced electrode configurations containing 8, 5, and 3 electrodes. The results show that selected frequency bands and reduced electrode sets of 8 or 5 channels retain reconstruction performance comparable to 63 or 17-electrode configurations, indicating a more resource-efficient approach. Second, we investigate the development of a foundation model for EEG-based IR to improve generalisation. The proposed architecture combines data harmonisation, vision transformers, and a Mixture of Experts to standardise data across different electrode systems and generate visual representations from EEG. While the work does not achieve full IR, it suggests a potential framework for developing generalisable, non-invasive visual decoding systems in future studies. Includes publication

Design and Decoding of Short Block Codes for URLLC Applications

2026-07-23T07:29:02Z

Design and Decoding of Short Block Codes for URLLC Applications Namadchi, Fatemeh Ultra-reliable low-latency communication (URLLC) requires short-packet transmission under stringent reliability and latency constraints. In the short-block-length regime, coding schemes are evaluated against finite-block-length limits, such as the normal approximation (NA). Designing codes that approach these limits with low decoding complexity is particularly challenging at low code rates. Primitive rateless (PR) codes are promising candidates for short-packet communications due to their strong distance properties, rate compatibility, and flexible block-length design. However, their practical use is limited by the high complexity of near-maximum-likelihood (ML) decoders, especially at low rates. This thesis develops efficient decoding and coding architectures for PR codes. First, a double-bases belief propagation (BP) framework exploiting structured parity-check matrices derived from primitive polynomials is proposed. Building on this framework, a parallel hybrid decoder combining multiple-bases BP with low-order ordered statistics decoding (OSD) is developed, where parity-check matrices are optimized using a genetic algorithm. For very low-rate operation, a parallel concatenated coding scheme combining convolutional and PR codes is introduced. A parallel list Viterbi decoding strategy and an analytical approximation for the required list size are also proposed. Simulation results show that the proposed schemes closely approach the NA benchmark while significantly reducing complexity compared with high-order OSD. Moreover, they outperform several state-of-the-art short block-length coding schemes, particularly in the very low-rate regime, demonstrating the potential of PR codes for URLLC applications. Includes publication

Investigating the aetiology, classification and trends of post-neonatally acquired cerebral palsy

2026-07-23T04:55:07Z

Investigating the aetiology, classification and trends of post-neonatally acquired cerebral palsy Waight, Emma Background: Children with post-neonatally acquired (PNN) cerebral palsy (CP) are a subgroup of children with CP who sustained a brain injury after the prenatal/perinatal period. PNN-CP accounts for 6% of all CP in Australia and remains an important, preventable and understudied group. The aims of this thesis were to improve New South Wales (NSW)/Australian Capital Territory (ACT) CP register ascertainment of children with PNN-CP, characterise PNN-CP in Australia, identify the global causes of PNN-CP and review how injury timing is defined, and evaluate a PNN-CP causal event classification system. Methods: A combination of CP register quality assurance work, CP register population-based research, a systematic review, and survey were conducted to address the aims of this thesis. Results: Application of a new strategy improved ascertainment of PNN-CP to the NSW/ACT CP Register. The prevalence of PNN-CP in Australia has declined over time, with an identified reduction in infectious causes. However, there was no statistically significant decline in head injury or cerebrovascular accidents. Children born to teenage mothers, born in remote areas and those born to Aboriginal and Torres Strait Islander mothers were over-represented in the cohort. Globally, infections were the most common cause of PNN-CP and definitions of brain injury timing varied. The survey study of the causal event classification system found substantial agreement across raters and the participants found it to be acceptable, appropriate and feasible. Conclusion: Further prevention strategies (such as childhood vaccinations) targeted at reducing post-neonatal brain injury are required across all global contexts. Consistent definitions of the timing of PNN-CP will assist in improving global collaboration and surveillance of this understudied group. Includes publication

Does Accessing International Social Media Create a Pathway for Global Citizenship Education? A Study of Mainland Chinese Students in Australian Universities

2026-07-22T05:41:42Z

Does Accessing International Social Media Create a Pathway for Global Citizenship Education? A Study of Mainland Chinese Students in Australian Universities Ding, Zheqi This study explores how Chinese international students understand global citizenship, and how international social media influences their global citizenship during their time abroad. Using a mixed-methods approach, this research draws on a survey of 361 students, alongside single and follow-up interviews with 22 participants from universities across Australia. The findings suggest that most students lack a clear understanding of global citizenship. Many recognize its associated values, demonstrate relevant competencies, and identify themselves as global citizens, yet they often display limited global participation. International social media serves as a pathway for global citizenship education. It provides students with access to global affairs and opportunities for cross-cultural engagement to improve foreign language proficiency, expand global knowledge and cultivate global values. The interplay between international social media exposure and offline cross-cultural interactions strengthens critical thinking skills and intercultural competencies among students. Even so, international social media cannot cultivate global identity directly. Students develop global identity during their studying abroad through a sense of connection generated from cross-cultural interactions. International social media usage and cross-cultural interaction among students are shaped by individual acculturation strategies.

Identifying pathways that promote cardiovascular pathology in chronic kidney disease

2026-07-21T06:29:54Z

Identifying pathways that promote cardiovascular pathology in chronic kidney disease Trinh, Katie Individuals with chronic kidney disease (CKD) have a high risk of cardiovascular (CV) morbidity and mortality, for which there are limited treatment options. Uraemic toxins, such as indoxyl sulfate (IS), are a non-traditional CV risk factor. Enhanced understanding of the underlying molecular mechanisms will provide new pharmacological targets. This thesis explores the role of the cell stress response protein, thrombospondin-1 (TSP1) and its receptor CD47 in the development of uraemic cardiomyopathy and CKD-associated vascular remodelling. Wild-type (WT) C57BL/6 mice with CKD induced by 5/6 nephrectomy (5/6Nx) had upregulated myocardial TSP1 expression and developed features of uraemic cardiomyopathy: left ventricular (LV) hypertrophy (LVH), cardiac fibrosis and LV diastolic dysfunction. This was associated with senescence-associated secretory phenotype, enhanced mitogen activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK) and aryl hydrocarbon receptor (AhR) signalling. These were mitigated in TSP1 knockout (KO) 5/6Nx mice. In vitro, the pathological effects of IS on human cardiomyocytes required intact TSP1 signalling. Furthermore, 5/6Nx mice treated with anti-TSP1 monoclonal antibody were protected against cardiac remodelling, suggesting TSP1 blockade as a potential therapeutic tool. CD47KO-5/6Nx mice showed preserved aortic adventitial wall thickness and limited expression of senescence marker, p53, compared to WT-5/6Nx mice. In vitro, IS and exogenous TSP1 increased TSP1 expression in human aortic vascular smooth muscle cells, reduced cell proliferation, elevated β-galactosidase activity and enhanced MAPK/ERK and AhR signalling, which were attenuated by CD47 blockade. This thesis provides novel insights into the role of TSP1–CD47 signalling in the development of uraemic cardiomyopathy and vascular remodelling in CKD. Interruption of TSP1- and/or CD47-signalling may represent a novel therapeutic strategy to mitigate CV complications in CKD. Includes publication

Severe-Acute Respiratory Infection (SARI) in Children: Towards integrated virological and syndromic surveillance

2026-07-20T04:42:38Z

Severe-Acute Respiratory Infection (SARI) in Children: Towards integrated virological and syndromic surveillance Burrell, Rebecca Anne Acute respiratory infections (ARI) are associated with significant global burden, unequally borne by children. There is significant diversity in pathogenic cause and clinical presentation, however current surveillance is restrictive, limited to select syndromes and detection of influenza, SARS CoV 2, and respiratory syncytial virus (RSV). Through descriptive and statistical analysis of large pre existing health system datasets of viral detections and emergency department (ED) presentations I confirmed high frequency of detection of RSV and substantial burden of bronchiolitis. However, I identified notable gaps current surveillance efforts. Pathogen surveillance of rhinovirus, human metapneumovirus (HMPV), adenovirus, and human parainfluenza virus 3 (hPIV3), is lacking. All of which are detected more frequently than influenza viruses in children. Further, despite large number of ED presentations of croup and asthma/wheeze, they are not included in current syndromic surveillance efforts. Presentations often required urgent treatment, and children presenting with asthma/wheeze were admitted to hospital at similar proportions to bronchiolitis, demonstrating considerable healthcare burden. Finally, statistical analyses confirmed associations of RSV and bronchiolitis. Extension of modelling to include all viruses demonstrated further associations with HMPV and hPIV3. These studies provide contemporary estimates of ARI associated viral detections, as well as age and syndrome specific incidence rates for a variety of ARI syndromes. Further, development of methodology to assess the contribution of a variety of viruses to presentations of bronchiolitis has identified possible future targets to further reduce the burden (HMPV and hPIV3) and has potential to be applied to other syndromes. Data produced in this thesis will be beneficial for assessing effectiveness of targeted public health interventions, including the current roll out of RSV immunisations. Includes publication

A Value-based Approach to Improving Care for Adults Receiving Home Enteral Tube Feeding

2026-07-20T02:43:03Z

A Value-based Approach to Improving Care for Adults Receiving Home Enteral Tube Feeding Korai, Andriana Home Enteral Tube Feeding (HETF) is often required for individuals with chronic conditions that limit their ability to meet their nutritional needs via the oral route. Despite the treatment being available for decades, delivering high-quality care for adults receiving HETF remains challenging due to complex healthcare systems and resource constraints. Value-based health care offers an approach to address these challenges by orienting systems around outcomes of value to patients. Patient-reported measures are validated tools that collect information directly from patients about outcomes and experiences that matter to them, and are key to achieving value-based health care. They offer a promising way to enhance HETF care by influencing system change. The aim of this thesis is to explore how patient perspectives inform current approaches to HETF care, as this knowledge can shape future initiatives to improve this care. A systematic review of fifteen HETF guidelines found poor implementability and limited patient-centred focus. A subsequent scoping review identified 184 patient-reported measures used in HETF care and research, though few were appropriately validated or captured the breadth of HETF experiences. To address this gap, a qualitative study was undertaken and a framework was developed to describe HETF-specific burden and serve as the basis of a patient-reported measure. A Delphi study was then employed which demonstrated strong agreement among clinicians and service-coordinators regarding the content of the proposed framework. The findings of this thesis demonstrate HETF burden is measurable and amenable to capture using a patient-reported measure also perceived as valuable by healthcare providers. Directions of future research to develop and integrate such a tool into routine care are discussed and recommendations are provided for clinical practice and service provision as part of wider efforts to provide more value-based health care to people receiving HETF. Includes publication

A cocktail of confusion: Helping jurors make sense of eyewitness testimony affected by alcohol intoxication

2026-07-17T07:58:36Z

A cocktail of confusion: Helping jurors make sense of eyewitness testimony affected by alcohol intoxication Martin, Erica Victims and witnesses are often intoxicated during crimes, yet courts lack consistent, evidence-based guidance for evaluating intoxication, leaving jurors to rely on common knowledge. Research shows the effect of alcohol on memory is dose-dependent: low to moderate intoxication reduces completeness but not accuracy, whereas severe intoxication impairs both. Thus, low to moderately intoxicated individuals can provide accurate, though less detailed, testimony and should not be dismissed solely due to intoxication. However, jurors tend to view any intoxication as harmful, lowering credibility and conviction rates regardless of dose. This thesis addressed this issue by examining (1) how jurors assess intoxication-related testimony, and (2) strategies to assist jurors to reach conclusions consistent with empirical evidence. Study 1 identified how laypeople conceptualise intoxication using language, drink counts, symptoms, and blood alcohol concentration (BAC) – producing a taxonomy of dose-specific indicators. Participants were more accurate when using drink counts and symptoms than BAC, and symptom-based intoxication evidence was considered most useful in legal contexts. Study 2 found presenting intoxication using these lay indicators helped jurors distinguish between levels of intoxication when assessing credibility and verdicts, improving alignment with empirical evidence. Studies 3A and 3B introduced deliberation and expert testimony. Jurors reverted to treating all intoxication as equally damaging, often misinterpreting expert evidence and dismissing victim testimony. Deliberations revealed victim-focused prejudice and highly conservative standards of proof. Overall, jury decision-making in intoxication-related cases is compromised. The belief that intoxication is common knowledge should be reconsidered. Evidence should be presented using clear, dose-specific lay terminology, supported by multiple indicators and improved expert testimony to promote fairer outcomes.

Main group element based visible-light photocatalyst for biomass conversion to value-added chemicals

2026-07-17T07:34:11Z

Main group element based visible-light photocatalyst for biomass conversion to value-added chemicals Shi, Yujian Biomass valorization offers a sustainable alternative to fossil resources for manufacturing green fuels and platform chemicals. Standard methods for converting sugars into high-value platform molecules, such as 5-hydroxymethylfurfural (HMF), usually rely on thermally driven transition-metal catalysts that require harsh conditions and corrosive additives. An innovative strategy is introduced herein utilizing main-group metal-based photocatalysts (Ga(III) and Sn(IV)) for the selective transformation of sugars under mild, visible-light irradiation. By exploiting the photoresponsive coordination chemistry of main-group elements, it is demonstrated that light can dynamically modulate the local electronic structure and Lewis acidity of site-isolated metal centers, thereby steering reaction selectivity without Brønsted acid additives. Alumina-supported Ga(III) complexes were developed to catalyze the selective conversion of glucose to HMF under visible light. Photoexcitation via ligand-to-metal charge transfer (LMCT) was found to enhance Ga–O interactions, facilitating proton abstraction and lowering the energy barriers for sugar isomerization and dehydration. Chapter 4 expands this paradigm to Sn(IV)-based solid-state catalysts. The immobilized Sn(IV) system exhibited outstanding photocatalytic efficiency and reusability. Mechanistic evaluations, including photoluminescence and action spectrum analyses, confirmed that the visible-light-driven LMCT process significantly decreases the apparent activation energy compared to traditional thermal pathways. Overall, a fundamental framework for main-group-based photocatalysis is established, highlighting the potential to replace conventional thermocatalytic processes and providing guiding principles for designing scalable, energy-efficient, and sustainable green chemical production pathways.

Maneuvering the Sampling Process of Generative Models for Feature Alignment and Accelerated Inference

2026-07-16T00:34:02Z

Maneuvering the Sampling Process of Generative Models for Feature Alignment and Accelerated Inference Dinh, Anh Dung Generative Models (GMs) have become increasingly prominent in both applied and fundamental research, with applications spanning nearly every aspect of daily life. Despite their remarkable success in producing high-quality and realistic outputs, GMs still face fundamental challenges during the sampling process. In many cases, even when using the same model, the generated samples can vary dramatically—some closely align with predefined conditions and exhibit excellent quality, while others deviate significantly and appear poor or unrecognizable. This thesis addresses the central question: How can we control the sampling process to avoid poor outputs and consistently generate high-quality features? We approach this problem through an optimization-based framework. First, we formulate the sampling process as an optimization problem. Then, depending on the specific task, we design different objective functions to systematically improve various aspects of the sampling process across different types of generative models. The proposed methods are benchmarked on multiple baselines, covering both diffusion-based and autoregressive generative models. Our results demonstrate that the optimization-based perspective provides a unified and effective approach for enhancing the quality and consistency of samples across diverse generative modeling paradigms.

RDSplat: Robust Watermarking Against Diffusion Editing for 3D Gaussian Splatting

2026-07-15T23:54:39Z

RDSplat: Robust Watermarking Against Diffusion Editing for 3D Gaussian Splatting Zhao, Longjie 3D Gaussian Splatting (3DGS) has established itself as a leading representation for novel view synthesis, underpinning a rapidly expanding ecosystem of commercially valuable digital assets. The growing circulation of these assets has brought IP protection to the forefront, yet the research landscape remains fragmented. This thesis contributes to 3DGS IP protection along two complementary axes. The first contribution is a systematic survey organising 24 existing methods under a unified bottom-up framework with three layers: Gaussian-based perturbation mechanisms; IP protection tasks spanning passive paradigms (watermarking, steganography, tampering localisation) and active paradigms (editing safeguard); and AIGC-era robustness threats. A central finding is that diffusion-based generative editing is a severe and largely unaddressed vulnerability, as it performs iterative semantic reconstruction that suppresses high-frequency content, effectively erasing embedded watermarks. The second contribution targets this vulnerability with RDSplat, a 3DGS watermarking framework robust to both conventional distortions and diffusion-based editing, by anchoring signals in low-frequency spectral components. Carriers are selected via a visibility-aware Mip Score, restricting perturbations to the lowest-frequency 30% of primitives. Training uses a surrogate exploiting the spectral equivalence between Gaussian blur and diffusion editing, avoiding costly generative models in the loop. Recovery is performed by GeoMark, a geometry-aware ViT-S/16 decoder. Evaluation on Blender, LLFF, Mip-NeRF 360, and Instruct-NeRF2NeRF shows RDSplat outperforms the strongest baseline by +0.17 in average bit accuracy under diffusion-based attacks, confirming low-frequency embedding as an effective strategy for 3DGS IP protection in the AIGC era. Includes publication

Exploring Multi-Modal Representation Learning for Embodied Agents

2026-07-15T23:33:04Z

Exploring Multi-Modal Representation Learning for Embodied Agents Yin, Zhenfei Building intelligent embodied agents requires representations that support grounding, prediction, reasoning, and action. Existing systems show a gap between semantic understanding and embodied competence: they describe scenes and generate futures, yet fail to localize objects, reason over geometry, coordinate agents, or execute actions reliably. Representation learning is a fundamental bottleneck for embodied intelligence. This thesis investigates multi-modal representation learning for embodied agents. We study instruction-tuned multi-modal models across images, point clouds, and videos, showing semantic alignment emerges earlier than geometric grounding. We introduce evaluation protocols probing robustness, hallucination, and failure modes, and explore parameter-efficient adaptation strategies preserving grounding across tasks. We examine predictive models as world representations for embodied generalization. Visual realism alone is insufficient; effective world models must integrate multi-modal structure and physical constraints. Through evaluation combining perceptual assessment and embodied execution, models grounded in multi-modal representations—linking vision, geometry, language, and dynamics—generalize more reliably than unguided approaches. Long-horizon reasoning emerges from systems organizing predictive and control skills into adaptive multi-agent structures. Reward-driven self-organizing frameworks enable task decomposition and coordination. Structured constraints and hierarchical supervision show compositional multi-modal representations enable safer, more generalizable embodied behavior. Embodied intelligence is fundamentally a representation and system design problem. Competence emerges when multi-modal representations are task-conditioned, physically grounded, and compositional. These findings provide guidance for building general-purpose embodied agents. Includes publication

Negotiating Postcolonialism through Whiteface: Performances of Whiteness in Contemporary Senegal and Australia

2026-07-15T22:52:57Z

Negotiating Postcolonialism through Whiteface: Performances of Whiteness in Contemporary Senegal and Australia Okkes-Sane, Charlotte This thesis examines the use of “whiteface” – the mimicry of Whiteness in theatre, television and writing – by Senegalese and Aboriginal and Torres Strait Islander artists as a tool for anticolonial critique. I argue that whiteface often disturbs and challenges colonial Whiteness, but also simultaneously reifies it. Across contexts as diverging as Senegal and Australia, artists resist postcolonial norms upheld by colonialism: the violence of migration bureaucracy, linguistic hierarchies which depreciate local languages, literary repetitions of colonial representations of Indigenous peoples, and the dehumanisation of the colonial gaze. Whiteface becomes an effective tool (for both the artist and the reader/audience) to understand and critique colonial violence. None of these artists, however, are able to fully extricate themselves from complicity with colonial Whiteness. To a certain extent, their work remains tethered to the colonial world through, for example, collaboration with organisations with their own agendas in place, self-censorship, and the reinscription of colonial literary tropes. Thus, whiteface texts demonstrate the complexity of postcolonial critique for marginalised communities, who voice their ideas whilst at the same time trying to survive (economically, artistically) in colonial societies.

From Jets to Mergers: Multiwavelength Signatures of Synchrotron Transients

2026-07-15T22:33:30Z

From Jets to Mergers: Multiwavelength Signatures of Synchrotron Transients Gulati, Ashna Synchrotron transients arise from explosive astrophysical events that launch relativistic outflows into their surrounding environments. Their interaction then produces synchrotron emission which further probes the physics, geometry, and environments of some of the most energetic phenomena in the Universe. This thesis investigates relativistic outflows using wide-field radio surveys and coordinated multi-wavelength follow-up observations. Late-time radio observations with the Australian SKA Pathfinder (ASKAP) are used to search for afterglows associated with eleven well-localised gravitational-wave events detected by LIGO/Virgo. No convincing radio counterparts are found out to approximately 1500 days after merger. These non-detections constrain the inclination angles, circummerger densities, and energetics of relativistic jets, and define the conditions under which late-time radio emission from compact binary mergers can be detected in wide-field surveys. This thesis also presents the discovery and characterisation of ASKAP J0055−2558, a long-lived luminous extragalactic radio transient identified during a search for orphan afterglows. Broadband observations reveal an evolving synchrotron spectrum and a mildly relativistic outflow consistent with either an off-axis long gamma-ray burst afterglow or an off-nuclear tidal disruption event, demonstrating the ability of radio surveys to uncover rare classes of transients. Finally, a radio-to-X-ray study of the afterglow of GRB 240825A reveals evidence for a two-component jet and a hard electron energy distribution ($p<2$), providing new constraints on the structure and microphysics of relativistic jets. Together, these studies demonstrate the power of combining wide-field radio surveys with broadband follow-up observations to constrain the physics, geometry, and environments of explosive astrophysical outflows, establishing radio surveys as a critical tool for exploring the time-domain Universe. Includes publication

Hydrogen Trapping and Embrittlement Behaviours in Steels

2026-07-14T08:19:42Z

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).

ADVANCED ALGORITHMS FOR RIS-ASSISTED WIRELESS COMMUNICATION SYSTEMS

2026-07-14T07:49:56Z

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.

Robust Implied Volatility Surface Construction via B-Spline-Enhanced Graph Neural Operators

2026-07-14T02:59:24Z

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.

Lattice-Boltzmann Large Eddy Simulation Solver for Atmospheric Transport and Dispersion Applications

2026-07-14T01:24:17Z

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

Deadline Scheduling with Predictions

2026-07-13T06:15:47Z

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.

Investigating the therapeutic potential of Australian honey as a prebiotic and anti-inflammatory to improve gut health

2026-07-13T03:43:49Z

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

DEVELOPING PROTEIN-RICH FOODS FROM AUSTRALIAN GROWN FABA BEANS

2026-07-12T23:45:24Z

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

Inflammation and its Effects on Plaque Cell Fate in Atherosclerosis

2026-07-12T23:26:24Z

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

Knowledge-Driven Visual Reasoning Under Data Scarcity

2026-07-12T23:03:48Z

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

Probabilistic Learning and Inference for Multi-Modal Trajectory Generation and Adaptation

2026-07-09T01:01:27Z

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

Multimodal Learning for Multi-scope Brain Lesion Segmentation

2026-07-09T00:19:56Z

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

Inter-project Learning in Major Infrastructure Projects

2026-07-09T00:02:58Z

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

Young Children’s Meaning-Making through the Use of Tablets in Digital Play

2026-07-06T04:11:45Z

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.

Relationship between Metabolome and Survival Characteristics of Rhizobia after Growth in Liquid Media

2026-07-05T22:13:17Z

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.

Assessment of pathological features to improve accuracy of diagnosis, classification and prognosis of primary and metastatic melanoma

2026-07-02T08:18:25Z

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

Molecular and Cellular Characterisation of Non-coding Variants in Inherited Heart Disease

2026-07-02T04:37:37Z

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

A Proposed Framework for an Artificial Intelligence-based Clinical Decision Support System in Dento-Maxillofacial Radiology

2026-07-02T03:47:37Z

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

Investigating uncharacterised epigenetic regulators in the C. elegans germline

2026-07-01T23:31:25Z

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.

Understanding Inter-Ethnic Differences in Tyrosine Kinase Inhibitor Response

2026-06-29T05:52:08Z

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

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

2026-06-29T03:37:55Z

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

Exploring safer mobility behaviour for fall prevention: implications for people with Parkinson's disease

2026-06-26T04:31:33Z

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

Modernisation as Discourse, 1949 to the Great Leap Forward: Zhou Enlai, Chen Yun, and Mao Zedong

2026-06-23T06:27:49Z

Modernisation as Discourse, 1949 to the Great Leap Forward: Zhou Enlai, Chen Yun, and Mao Zedong Chen, Ximeng This thesis examines the discursive and intellectual architecture of modernisation (xiandaihua) in the People’s Republic of China from 1949 to the end of the Great Leap Forward by juxtaposing the ideological and philosophical underpinnings of the discourses of Zhou Enlai, Chen Yun and Mao Zedong. Methodologically, the study employs a discursive analysis that begins by de-sedimenting established scholarly and historiographical frameworks. It combines close textual analysis of both established and more recently released primary sources to foreground temporality, subjectivity, and conceptual differences that animated the discourse of modernisation. It demonstrates that modernisation emerged through three distinct visions: (1) Zhou Enlai’s linear, Soviet-inspired teleology that cast heavy-industry precedence as historical necessity; (2) Chen Yun’s recursive, balance-oriented pragmatism that privileged systemic viability and embodied socialism in the here-and-now; and (3) Mao Zedong’s ruptural ‘Great Leap’ dialectics that re-imagined modernity as a process of continuous, partial qualitative transformations. By recasting xiandaihua as a unifying keyword that masked these deep ideological divisions, the thesis unsettles mainstream narratives that reduce early PRC development to a binary of Stalinist mimicry versus Maoist voluntarism. It positions temporality and agency as central analytics for socialist modernity and demonstrates that divergent readings of dialectics were not academic quarrels but concrete drivers of historical changes.

Behavioral Dataset Compression for Efficient Reinforcement Learning

2026-06-23T05:53:34Z

Behavioral Dataset Compression for Efficient Reinforcement Learning Lei, Shiye Offline reinforcement learning (offline RL) provides a principled framework for learning decision-making policies from fixed datasets without environment interaction, enabling applications in safety-critical, privacy-sensitive, and resource-constrained settings. However, modern offline RL systems often rely on large-scale datasets collected from suboptimal policies, leading to substantial computational overhead and limited scalability. Improving data efficiency is therefore critical for making offline RL practically viable. In this thesis, we develop dataset compression algorithms for offline RL that explicitly account for these intrinsic data properties. From the action perspective, we establish a theoretical equivalence between the policy performance gap and an action-value-weighted decision discrepancy. This insight motivates an action-value-weighted objective for offline behavior distillation (OBD), which distills large offline RL datasets into compact synthetic training sets. From the state perspective, we identify state diversity as a key factor governing the effectiveness of offline behavior distillation. We show that insufficient state coverage in the original dataset limits policy performance after compression. To address this issue, we propose state-weighted OBD, which explicitly incorporates state diversity into the distillation objective and significantly improves robustness to dataset compression. Finally, by jointly considering action-value information, state density, and trajectory-level sequential structure, we propose stepwise dual ranking (SDR), a simple and scalable coreset selection algorithm that constructs compact yet informative subsets from large offline behavioral datasets without additional training overhead. Includes publication

Sonochemical Processing of Gallium-based Soft Composites

2026-06-23T05:46:38Z

Sonochemical Processing of Gallium-based Soft Composites Chiu, Shih-Hao Gallium-enabled soft materials have emerged as promising systems for flexible electronics and sustainable devices due to their metallic conductivity, low melting point, and deformability. This thesis investigates how sonochemical processing and interfacial engineering can address key limitations of gallium-based particles while enabling the design of soft, conductive, and biomass-integrated composites. Thiol-functionalized gallium nano- and microparticles are developed to suppress oxidation and achieve sintering-free conductivity via metal–molecule junctions, with conjugated ligands enabling tunable electrical performance. Liquid gallium nanoparticles are further integrated into PEDOT:PSS, significantly enhancing electrochromic switching kinetics through improved interfacial charge transport. The role of sonochemistry in intensifying mass transport is demonstrated through a coffee-brewing sonoreactor, where extraction efficiency is improved while preserving chemical integrity. Building on this, sustainable composites incorporating spent coffee grounds (SCGs) and gallium nanoparticles are fabricated, forming flexible films with integrated biomass functionality. Preliminary NO₂ sensing results further demonstrate the potential of these materials for functional applications. Overall, this thesis establishes sonochemical interface engineering, molecular functionalization, and biomass integration as complementary strategies for designing next-generation gallium-based soft materials, advancing both fundamental understanding and practical applications in flexible and sustainable systems. Includes publication

Communal identities and legacies of hegemonic intervention: prospects for justpeace in Afghanistan

2026-06-22T07:23:30Z

Communal identities and legacies of hegemonic intervention: prospects for justpeace in Afghanistan Tippett, Diane This thesis seeks to address the following three research questions: 1. How have various communal identity groups and supra-communal understandings of belonging developed and been reproduced in Afghanistan? 2. How have successive hegemonic interventions affected patterns of state-making and civic organisation in Afghanistan? 3. What are the key relational considerations in respect of achieving a future justpeace in Afghanistan? This thesis examines how identity formation in Afghanistan is shaped by relational ontological insecurity—distinctively deepened and institutionalised during repeated hegemonic interventions— and argues that any future justpeace must therefore be grounded in relational, everyday, and structure–process dynamics that address the affective afterlives of the foregoing rather than interest based models. The above research questions situate this thesis not only within the existing case study scholarship on Afghanistan, but also within the following domains: - Politically applied psychology (specifically including discussions of self-making and identity group affiliation when faced with dynamic realities, the ‘prototype clarity’ of in-groups, and applied hegemony ); - Ontological security studies (specifically including discussions of anxiety, myth and narratives, habitus, rituals of state sovereignty and self/other relations which arise from the second wave of ontological security studies scholarship); and, - Justpeace (specifically including discussions of civic sovereignty, a structure-process orientation, and ordering power ). This broad contribution reflects the transdisciplinary nature of peace and conflict studies as a discipline but also the limitless movement and trajectory of belonging, violence, and peace— each of which are not only political processes, but phenomena continually re-grounded in the affective, relational, routinised practices of the everyday.

Chromosome-level, haplotype-phased genome assembly of an Australian isolate of the oat crown rust fungus Puccinia coronata f. sp. avenae provides insights into population structure and genomics

2026-06-22T06:11:35Z

Chromosome-level, haplotype-phased genome assembly of an Australian isolate of the oat crown rust fungus Puccinia coronata f. sp. avenae provides insights into population structure and genomics Guan, Haixia Puccinia coronata f. sp. avenae (Pca), the causal agent of oat crown rust, is one of the most damaging pathogens of oat worldwide. Rapid evolution and extensive virulence variation continually erode host resistance, posing major challenges for disease management and breeding. This thesis investigated the genomic architecture, population structure, and molecular basis of virulence evolution in Pca through integrated genomic analyses. A chromosome-scale, haplotype-phased reference genome was generated for OCR_502, a representative Australian isolate, using PacBio HiFi sequencing and Hi-C scaffolding. The resulting high-quality assembly provides a valuable genomic resource and a foundation for downstream analyses. Using this reference genome, whole-genome resequencing data from Australian isolates collected over multiple decades were analysed to examine population structure and evolutionary dynamics. The population exhibited high genetic diversity and consisted of several distinct lineages. Temporal shifts in lineage composition suggested that mutation, somatic recombination, and/or the introduction of exotic lineages contribute to diversification despite predominantly clonal reproduction. To investigate the genetic basis of virulence, genome-wide association studies (GWAS) and haplotype-resolved comparative genomics were conducted using 361 isolates phenotyped on five oat crown rust resistance genes (Pc38, Pc39, Pc50, Pc56, and Pc68). Comparative analyses indicated that virulence evolution is more likely driven by allelic variation than gene presence–absence variation. Four high-confidence candidate avirulence genes associated with AvrPc50 and AvrPc56 were identified. Overall, this study provides important genomic resources and advances understanding of the mechanisms underlying virulence evolution and population diversification in Pca. Includes publication

Neural substrates underpinning co-occurring posttraumatic stress disorder and alcohol use disorder

2026-06-19T03:24:38Z

Neural substrates underpinning co-occurring posttraumatic stress disorder and alcohol use disorder Towers, Ellen Elizabeth Posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) are highly prevalent and debilitating. Despite this, the neurobiology of comorbid PTSD & AUD remains poorly understood. To address this gap, this thesis aimed to investigate neural and psychological mechanisms that may represent risk factors for, or consequences of, co-occurring PTSD and AUD (PTSD & AUD). Chapter 2 systematically reviewed the genetic, molecular, neural and cognitive mechanisms specific to PTSD & AUD. While evidence for greater hypothalamic-pituitary-adrenal axis dysregulation was limited, findings suggested potentially unique disruptions spanning genetic and neural domains, warranting further investigation. Building on these findings, Chapters 3-5 used pooled neuroimaging datasets to examine the structural and functional underpinnings of PTSD & AUD. Chapters 3 and 4 represents the first large-scale investigation to directly compare current PTSD & AUD with PTSD-only, AUD-only, and controls. Using complementary region-specific and graph-theoretical approaches, these chapters identified shared and comorbidity-specific structural alterations. Chapter 5 identified altered intrinsic functional connectivity patterns associated with depression and stress symptom severity in PTSD & AUD relative to AUD-only. Chapter 6 explored associations between negative affect, drinking motives, craving and use in a large Australian AUD sample with and without PTSD. Findings indicated that alcohol-related processes were more broadly linked to negative affect states (e.g., depression, stress, anxiety) in PTSD & AUD than AUD-only. This thesis provides a comprehensive and highly integrated body of work examining the neural substrates of co-occurring PTSD and AUD and explores how psychological features relate to both clinical and neurobiological phenotypes. Findings suggest that PTSD & AUD is characterised by distinct neural and psychological alterations that may represent risk and/or maintenance mechanisms. Includes publication

Melanoma risk assessment and risk-stratified melanoma screening and surveillance

2026-06-19T03:00:32Z

Melanoma risk assessment and risk-stratified melanoma screening and surveillance Reyes- Marcelino, Gillian Melanoma remains a major public health challenge in Australia, which has one of the highest melanoma incidence rates worldwide. Despite this burden, there is currently insufficient evidence to support the implementation of an organised population-wide melanoma screening program. Early detection largely relies on opportunistic skin checks undertaken in clinical practice. Current clinical guidance recommends identifying individuals at higher personal risk and offering them more regular skin examinations, yet the consistency of this risk-based approach remains uncertain. This thesis examined how melanoma early detection currently operates in Australia and how risk prediction tools might support more consistent risk-based approaches in clinical practice and future screening policy. The research programme combined studies examining clinical skin check behaviours, the acceptability of melanoma risk prediction tools, and the performance of existing melanoma risk prediction models. This thesis provides new evidence relevant to both current clinical practice and ongoing considerations regarding melanoma screening policy in Australia. Includes publication

Three Essays in Corporate Finance and Informed Trading

2026-06-18T06:39:49Z

Three Essays in Corporate Finance and Informed Trading Sarkar, Md. Ahasan Habib This dissertation examines how uncertainty, organizational capabilities, and information networks shape corporate investment, international acquisitions, and institutional investor behavior. Using novel identification strategies and granular data across multiple institutional settings, it provides new evidence on how firms and financial intermediaries respond to uncertainty and exploit private information around major corporate events. The first essay examines how central bank governor transitions influence corporate investment. Exploiting predetermined leadership transitions that generate exogenous policy uncertainty, it shows that firms reduce investment in anticipation of governor turnover, particularly in interest- and exchange-rate-sensitive industries, with effects attenuated in countries with stronger central bank independence. The second essay studies the role of organization capital in international acquisitions. Firms with higher organization capital are more likely to undertake international acquisitions and create greater shareholder value following deal completion, highlighting the importance of intangible organizational capabilities in successful international expansion. The third essay provides transaction-level evidence on mutual fund trading around mergers and acquisitions. Mutual funds accumulate target shares before public announcements and earn substantial profits from these trades. Profitability is significantly higher when trades overlap with corporate insider activity, when affiliated investment banks advise transaction parties, and when funds are geographically closer to event firms. Overall, the dissertation contributes to the literatures on monetary policy uncertainty, organization capital, and informed trading by demonstrating how uncertainty, organizational capabilities, and information networks shape investment decisions, corporate strategy, institutional investor behavior, and ultimately asset prices. Includes publication

Investigating the Mechanisms and Applications of Liquid Metal-Biomolecule Interactions

2026-06-15T23:25:43Z

Investigating the Mechanisms and Applications of Liquid Metal-Biomolecule Interactions Liu, Li Gallium (Ga)-based liquid metals have attracted increasing attention due to their metallic conductivity, fluidity, and unique surface chemistry. However, their interactions with biological macromolecules remain insufficiently understood. This thesis investigates the interfacial interactions between liquid Ga droplets and biomolecules, focusing on protein self-assembly and nucleic acid reactivity. First, a self-standing soy protein isolate (SPI)-Ga composite film is developed to study interactions between Ga droplets and protein fibrils. Protein fibrils reduce Ga surface oxidation, promote droplet coalescence, and facilitate conductive pathway formation without disrupting the β-sheet-rich fibrillar network. The resulting composites exhibit combined electrical conductivity and mechanical robustness, enabling applications in gas sensing and electrically stimulated antibacterial activity. This thesis then investigates the interaction between Ga droplets and DNA. The results demonstrate that Ga droplets can cleave DNA phosphodiester bonds, with preference for adenine- and thymine-rich sequences. Mechanistic studies show that the activity originates from interfacial electron transfer associated with Ga surface oxidation, generating reactive species that cleave the DNA backbone while largely preserving nucleobase integrity. The nuclease-mimicking activity can also be tuned through droplet characteristics and external stimuli. Together, this thesis establishes a fundamental understanding of how liquid Ga interfaces interact with protein fibrils and nucleic acids, providing guidance for the design of liquid metal-based biohybrid materials and future biomedical applications. Includes publication

Essays on Machine Learning in Economics

2026-06-15T10:48:59Z

Essays on Machine Learning in Economics Deng, Sinan This thesis presents three studies exploring how machine learning can be applied to understand and model complex phenomena in economics. Chapter 2 investigates how machine learning can be used to examine geographic diversity within global economics research. Chapter 3, published in Energy Economics (2024), develops a seasonal deep learning model for Great Britain’s electricity imbalance prices, showing that incorporating seasonality improves forecasting accuracy. Chapter 4 extends this framework by applying distributional deep learning methods to model full price distributions and distinguish between aleatoric and epistemic uncertainty. Includes publication

Engineering entanglement in trapped ion quantum harmonic oscillators using spin-dependent interactions

2026-06-15T10:33:09Z

Engineering entanglement in trapped ion quantum harmonic oscillators using spin-dependent interactions Millican, Maverick James Quantum harmonic oscillators are a central resource in quantum technologies. Coupling this resource to a two-level system gives rise to spin-oscillator dynamics that can be used for system calibrations, enhanced sensitivity to signals, and the preparation of oscillator entangled states. Chapter 4 introduces a motional-frequency calibration protocol based on a time-reversal, state-dependent force sequence that maps the effects of motional-frequency miscalibration onto a narrow spin-response feature. The chapter also discusses how thermally occupied motional states enhance sensitivity to particular Hamiltonian terms, making them effective probe states. A two-point feedback servo with processing performed on a field-programmable gate array, or FPGA, is used to track the interaction resonance in real time, enabling sub-second updates and continuous correction of frequency drift during data acquisition. Chapter 5 realizes phase-insensitive displacement sensing by embedding spin-dependent squeezing in a time-reversal interferometry sequence. The method prepares a spin-correlated superposition of orthogonally squeezed motional states, yielding a spin-observable response used to estimate displacement amplitudes while remaining agnostic to the signal phase. Experiments with vacuum and number-state probes demonstrate enhanced sensitivity with increasing motional energy. Chapter 6 extends the spin-oscillator toolkit to multi-oscillator control. Optimized phase modulations applied to Jaynes-Cummings and anti-Jaynes-Cummings couplings between a single spin and two motional modes synthesize an effective two-mode squeezing interaction, enabling preparation of two-mode squeezed vacuum states and a non-Gaussian superposition of those states. Joint phase-space tomography reveals multi-mode correlations, and continuous-variable entanglement is certified by the Einstein-Podolsky-Rosen criterion and a Clauser-Horne-Shimony-Holt-type continuous-variable Bell test. Includes publication

Mesenchymal stem cell senescence: Mechanisms and rejuvenation strategies

2026-06-15T07:40:23Z

Mesenchymal stem cell senescence: Mechanisms and rejuvenation strategies Zhang, Yiran Cellular senescence is a key contributor to organismal ageing and reduces the regenerative capacity of mesenchymal stem cells (MSCs), limiting their therapeutic potential in treating age-related diseases. This thesis aimed to investigate the mechanisms and functional consequences of MSC senescence and to develop strategies to rejuvenate senescent cells. Using adipose-derived mesenchymal stem cells (ASCs), this study first examined the effects of the chemotherapeutic drug doxorubicin (DOX) on senescence. Both short- and long-term DOX exposure induced senescence; however, while long-term exposure impaired osteogenic differentiation, short-term exposure enhanced osteogenesis. Transcriptomic analysis identified insulin-like growth factor 2 (IGF2) as a potential regulator of this dual effect, and IGF2 treatment restored osteogenic potential in senescent ASCs, suggesting a beneficial role of transient stress-induced senescence. To explore rejuvenation strategies, this thesis investigated both biomaterial and drug delivery approaches. Osteopontin (OPN), a major extracellular matrix protein, was shown to reverse senescence-associated phenotypes in replicative senescent ASCs, restoring proliferation, osteogenic differentiation, trophic support, and regulating cell morphology. In parallel, a liposome-based drug delivery system was developed to encapsulate nicotinamide mononucleotide (NMN), an anti-senescence drug. Optimised formulations achieved high encapsulation efficiency and reduced the expression of senescence markers in vitro. Together, these findings advance understanding of MSC senescence and identify OPN-based biomaterials and liposomal delivery systems as promising strategies for MSC rejuvenation and regenerative medicine.

Development of a sustainable acid resistant mortar for back-filling of impressed current cathodic protection (ICCP) anodes

2026-06-15T05:55:51Z

Development of a sustainable acid resistant mortar for back-filling of impressed current cathodic protection (ICCP) anodes Fatemi Nayeri, Sayed Hamid Reza Reinforced concrete (RC) structures in marine environments are highly susceptible to chloride-induced corrosion, for which impressed current cathodic protection (ICCP) is widely applied. The performance of ICCP systems is governed by the durability and electrochemical behaviour of the anode back-fill mortar. Field observations from New South Wales, Australia, show that conventional Portland cement-based mortars can degrade due to electrochemically induced acidification at the anode interface, highlighting the need for more stable and compatible materials. This thesis investigates ICCP-induced acidification through field sampling, laboratory characterisation, and electrochemical testing. Analyses of mortars retrieved from operational marine bridges show that anodic reactions generate acidic species that dissolve calcium-rich phases, promote gypsum formation, and degrade the binder, resulting in non-uniform deterioration. These mechanisms were reproduced under accelerated laboratory conditions. Based on this understanding, hybrid geopolymer mortars incorporating supplementary cementitious materials (SCMs) were developed as acid resistant alternatives. Laboratory results show improved resistance to acidic exposure, reduced mass loss, and enhanced microstructural stability. Field implementation confirmed material integrity and compatibility with electrochemical requirements under marine exposure. Transport properties were also assessed. While higher SCM content improved durability, it increased resistivity, highlighting a trade-off between durability and electrochemical performance. Ionic conductivity governed polarisation behaviour, and its optimisation improved performance in atmospheric zones, while conductive additives were unnecessary in submerged conditions. Overall, this research establishes an exposure-specific framework for designing ICCP back-fill mortars that balance durability and electrochemical functionality in marine environments.

Leveraging deep learning to enable real-time beam-view image-guided radiotherapy

2026-06-15T03:51:29Z

Leveraging deep learning to enable real-time beam-view image-guided radiotherapy Chrystall, Danielle Maria Real-time image-guided radiotherapy (IGRT) is essential for minimising the clinical impact of patient motion during radiotherapy. Existing IGRT systems often rely on additional imaging dose or specialised, expensive technology to enable continuous intrafraction monitoring. Beam-view imaging offers a promising alternative, enabling real-time tumour monitoring directly in the treatment beam without additional imaging dose and with broad compatibility on standard linear accelerators (linacs). However, its clinical use is limited by treatment-beam occlusions and poor megavoltage (MV) image quality. This thesis aims to develop, implement, and evaluate a deep learning-enabled beam-view IGRT framework that is safe, accurate, and compatible with standard linacs. Novel deep learning approaches were leveraged to overcome the aforementioned challenges facing beam-view imaging. Three research objectives are addressed: (i) develop deep learning-enabled beam-view target tracking approaches for abdominopelvic and thoracic treatment sites; (ii) experimentally evaluate real-time beam-view marker tracking using an anthropomorphic pelvic phantom, and develop associated workflows and patient-specific quality assurance procedures to facilitate safe clinical deployment for prostate cancer radiotherapy; and (iii) clinically implement and evaluate real-time beam-view marker tracking during prostate stereotactic body radiotherapy (SBRT). Real-time beam-view IGRT has been developed and investigated, with clinical feasibility demonstrated for prostate SBRT. Key implementation barriers are addressed, establishing a foundation for broader clinical adoption of real-time beam-view IGRT on standard linacs.

Critical point network-based organizational principle of cortical spatiotemporal dynamics

2026-06-04T05:33:37Z

Critical point network-based organizational principle of cortical spatiotemporal dynamics Xu, Yiben Inspired by the field of turbulence and vector field topology in neural activities, this thesis introduces a novel and generalizable organizational principle of cortical spatiotemporal dynamics based on a global network of interacting critical points. Starting from the analysis of human fMRI signals, this thesis highlights the discovery of travelling cortical spiral waves (termed ‘brain spirals’) during both the resting and task states, emphasizing the mechanistic and functional relevance of a novel spiralbased organizational principle of large-scale brain activities. Next, based on human high-density electroencephalography (HdEEG) recordings, this thesis extends the spiral-based organizational principle from wakefulness to non-rapid-eye-movement (NREM) sleep, revealing that this spiralbased organizational principle is also a defining feature of human N2 sleep, which is associated with sleep-dependent-memory-consolidation and aging-related memory decline. Finally, this thesis further expands the spiral-based organizational principle to include other types of critical points (i.e., sinks, sources and saddles), two new recording modalities (Magnetoencephalography/MEG and Electrocorticography/ECoG), and intracranial recordings of non-human primates (marmoset). In two distinct datasets, a global network of interacting critical points can be consistently observed regardless of species, recording modalities and cognitive states. Acting like an organizational skeleton, this network of critical points collectively enables the task-dependent organizations of largescale brain activities, supporting the universal presence of a critical point network-based organizational principle of large-scale brain activities across species.