http://hdl.handle.net/10453/19979 2026-04-03T20:51:10Z http://hdl.handle.net/10453/194448 Title: 3D Micro-Engineered Models to Study Cancer Biology Authors: Nazari, Hojjatollah Abstract: Breast carcinoma metastasis remains the primary cause of cancer-related mortality, with small extracellular vesicles (sEVs) emerging as key mediators of communication between tumors (seed) and metastatic niches (soil). This thesis investigates the distinct yet complementary roles of seed- and soil-derived sEVs in orchestrating critical steps of the metastatic cascade, including luminal-to-basal transformation, metastatic adaptation, vascular attachment, extravasation, and organotropism. To address this, advanced three-dimensional microfluidic platforms, known as microphysiological systems, were developed to replicate physiologically relevant environments of metastasis. In the bone-on-a-chip model, basal carcinoma cell-derived sEVs were shown to induce luminal-to-basal transformation and promote bone invasion. In subsequent studies, brain organoid-derived sEVs were examined using brain organoid-on-a-chip and vascular lumen-on-a-chip platforms to assess their impact on metastatic adaptation, vascular attachment, blood-brain barrier integrity, and extravasation. Building upon this, the first vascularized brain organoid-on-a-chip system was engineered, integrating functional vasculature with brain organoids to investigate metastatic colonization in a complex, dynamic microenvironment. Finally, a multi-compartment device was fabricated to evaluate organotropism, particularly in brain and bone contexts. Employing cellular and molecular assays, real-time imaging, confocal microscopy, and quantitative analyses, this thesis advances understanding of how seed- and soil-derived sEVs drive tumor progression, providing innovative platforms for therapeutic exploration. Description: University of Technology Sydney. Faculty of Engineering and Information Technology. 2025-01-01T00:00:00Z http://hdl.handle.net/10453/194447 Title: Understanding Bacterial Evolution and Adaptation in Natural Microbial Environments Authors: Krishnan, Sidaswar Abstract: Microbial evolution is underpinned by the processes of mutation and recombination, yet the dynamics of these processes within natural microbiomes remain poorly characterised. The growing application of metagenomics to study prokaryotes in their native habitats presents an excellent opportunity for learning more about recombination and mutation. However, a major bottleneck relates to the paucity of tools that can precisely measure recombination and mutation rates using next-generation sequencing datasets. This thesis has delivered a new tool for quantifying mutation and recombination rates from metagenomic datasets, which has opened the door to understanding these important evolutionary forces in natural microbiomes. Using this new tool, I was able to demonstrate the heterogeneous dynamics of recombination and mutation within the marine microbiome for key bacteria on both local and global scales. This work highlighted the complex interplay between evolutionary processes and environmental conditions that shape marine bacterioplankton communities. Description: University of Technology Sydney. Faculty of Science. 2025-01-01T00:00:00Z http://hdl.handle.net/10453/194445 Title: mmWave Sensing for Vital Sign Monitoring and User Identification: Enhancing Accuracy and Robustness via Deep Learning Authors: Wang, Yingqi Abstract: Millimeter-wave (mmWave) sensing has emerged as a powerful sensing modality for non-contact, privacy-preserving human monitoring applications. However, such technology could still lose the efficiency in real-world sensing scenarios. This thesis explores the integration of mmWave sensing for vital sign monitoring and user identification, leveraging deep learning and advanced signal processing to enhance accuracy and robustness in dynamic environments. The first focus of this research is on vital sign monitoring, where mmWave radar is employed to detect respiratory rate, heart rate, arterial pulse, and blood pressure (BP). A multi-modal sensing technology and a novel multi-channel variational mode decomposition (VMD) method are investigated to separate vital sign components from interference caused by body movements and environmental noise. Further, a robust heartbeat and wrist localization algorithm and a physics-driven deep learning approach are developed, incorporating Neural Ordinary Differential Equations (Neural ODEs) and Temporal Convolutional Networks (TCNs) to reconstruct high-level physiological signals from radar Doppler shifts. The system achieves high-accuracy estimation of physiological parameters, demonstrating its feasibility for continuous, non-invasive health monitoring. The second focus is user identification, aiming to associate the user’s ID with the previous vital signs sensing. A novel Inverse Synthetic Aperture Radar (ISAR) sensing is investigated to reconstruct body profiles of individuals walking past the radar. A ResNet-based deep learning model with Additive Angular Margin Loss is developed to enhance feature discrimination, enabling high-precision user identification in long-term. Unlike traditional biometric methods, this approach identifies individuals based on their radio imaging profiles, making it robust to occlusions and appearance variations. The contributions of this work include: (1) a novel multi-modal mmWave localization and signal decomposition is proposed and address the challenges of low accuracy in practical vital sign monitoring, (2) an adaptive mmWave heart and wrist detection, and the physics-embedded learning scheme is proposed to improve flexibility, robustness and explainability in ECG (Electrocardiogram), pulse and BP monitoring, and (3) an ISAR-based user identification method optimized with deep learning for real-world scenarios, significantly improved the performance in long-term mmWave user Re-ID. This research paves the way for future applications in smart healthcare, security, and human-computer interaction. Description: University of Technology Sydney. Faculty of Engineering and Information Technology. 2025-01-01T00:00:00Z http://hdl.handle.net/10453/194415 Title: Investigating the Factors Influencing Adoption, Completion, and Continued Use of MOOCs: A TUE Framework and Behavioural Intention Approach Authors: Alzahrani, Mansor Abstract: Massive Open Online Courses (MOOCs) have transformed access to education, yet persistent issues such as low adoption, high dropout, and poor completion rates continue to challenge their effectiveness. Understanding what drives individuals to adopt, complete, and continue using MOOCs is essential for improving engagement, retention, and overall learning outcomes. This study investigates the behavioural intentions that shape users’ engagement with MOOCs across these three stages. A quantitative design was employed using Partial Least Squares Structural Equation Modelling (PLS-SEM) to test relationships within the Technology–User–Environment (TUE) framework. The model was expanded by introducing new constructs—interface and navigation, learning style preferences, perceived workload, MOOC content, and lack of learning resources. Data were gathered through CloudResearch, Prolific, and social media channels, yielding 1,246 valid responses. The findings show that perceived usefulness, learning style preferences, and MOOC content are significant predictors of users’ intention to adopt, complete, and continue using MOOCs. In contrast, learning tradition negatively affects both completion and continuance intentions. Additionally, performance-to-cost value and instruction quality uniquely influence adoption, while perceived workload primarily affects completion. The influence of peers is evident in both adoption and continuance stages, underscoring the importance of social factors in sustaining participation. Multi-group analysis (MGA) further revealed that constructs often considered insignificant—such as interactivity, accessibility, interface and navigation, and lack of learning resources—can become significant when examined across different user groups. By extending the TUE framework, this research provides a more comprehensive understanding of the factors shaping MOOC engagement across multiple behavioural stages. The findings offer valuable insights for educators, policymakers, and organisations seeking to enhance online learning environments and promote lifelong learning. Ultimately, this study highlights that addressing both technological and contextual aspects is key to improving MOOC adoption, completion, and continuance, ensuring these platforms remain effective, inclusive, and sustainable. Description: University of Technology Sydney. Faculty of Engineering and Information Technology. 2025-01-01T00:00:00Z