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Decentralised Technologies: ‘Self-Infrastructuring’ Resilience
No full textThis thesis focuses on how groups of people build, use, and experience infrastructure in the digital era by investigating the resilience of decentralised technologies for people that use them. Here, resilience denotes the capacity for adaptability and transformability when faced with external threats, internal vulnerabilities, and opportunities, in relation to shared goals. The concept of resilience encompasses adaptivity across both social and technical dimensions of a system. Public blockchain networks and associated distributed protocols can be considered socio-technical infrastructures, meaning that social and technical components shape one-another and are inextricably linked. While much scholarly attention on decentralised technologies focuses on technical facets of resilience, such as cyber risks and security, decentralised technologies also serve an enabling infrastructure for social coordination. Despite their significance, there is a lack of empirical research concerning the development, application, and utilisation of decentralised technologies. This research addresses the gap by investigating the infrastructural practices occurring within decentralised technology communities. It determines the affordances of these tools in fostering resilience by discerning the circumstances, means, and extent of their resilience-building capacity for users.Drawing on infrastructure studies literature, which contends that the purpose of infrastructure emerges during its use, this research examines decentralised technologies in practice. Through three ethnographic cases, the thesis illustrates people’s conceptualisation, construction, and experience of resilience, as well as the factors leading to its breakdown or failure. The cases traced are a “Decentralised Autonomous Organisation” (DAO), which crowdfunds grants called “GitcoinDAO”, a “crypto state”, which manufactures and distributes open microchip hardware called “Kong Land DAO”, and a peer-to-peer data management protocol, which is used to “content address” data (as opposed to Hypertext Transfer Protocol (HTTP) that “location addresses” data) before it can be stored in a variety of contexts called the “Interplanetary File System” (IPFS). This research identifies a unique practice in decentralised technologies known as “self-infrastructuring”. The term “infrastructuring” describes the ongoing processes involved in infrastructure management, including designing, building, operating, governing, and maintaining. Self-infrastructuring occurs when individuals and groups of people have the ability to participate in designing, owning, operating, governing, and/or maintaining their own infrastructure in relation to perceived threats, opportunities, and goals, by adhering to pre-determined rules. This practice requires attention to the full technological stack of both software and hardware, as well as a strong motivation and commitment to navigate the significant time and effort inherent in running and maintaining one’s own infrastructure. For a decentralised technology community to consider an infrastructure as resilient, the capacity for self-infrastructuring must be possible. While self-infrastructuring can bolster resilience against certain threats, it can also limit resilience by introducing new vulnerabilities. When self-infrastructuring does not occur or is inadequately integrated across the technical and social-institutional facets of decentralised infrastructures for communities to both create and adapt their own boundaries, resilience breaks down. In numerous instances, there remains unresolved challenges in terms of how to implement self-infrastructuring, in terms of both technical and institutional aspects of decentralised organising and infrastructure. This thesis presents an empirical contribution to knowledge, informing the enhancement of resilience in the development and usage of digital infrastructure. It also informs both academic discourses and policy discussions concerning the objectives of, and practices within, decentralised technology communities.</p
"The possibility of meeting the needs of all children": A framework for enhancing inclusive STEAM pedagogy in early childhood settings
No full textThis study explored early childhood teachers’ perceptions and practices of inclusive Science, Technology, Engineering, Arts, and Mathematics (STEAM) pedagogy in China. Drawing on qualitative data from semi-structured interviews and classroom observations, the study examined how teachers define and implement inclusive STEAM learning and how they address the diverse needs of all children. Findings revealed that teachers hold positive conceptual understandings of play-based, child-centered learning and apply thoughtful strategies to promote participation among children with diverse abilities. However, their pedagogical approaches remained predominantly subject-specific and play-focused, with limited interdisciplinary integration of STEAM disciplines, and the results indicated participants’ inclusive practices emphasized enabling participation rather than adapting STEAM content or assessment for diverse learners. In response to these gaps, the study proposes the Inclusive STEAM Pedagogy (ISP) framework for integrating Planning, Action, and Reflection into an ongoing, flexible cycle designed to effectively meet the diverse needs of all EC learners. This framework merges Action and Product into a single phase, reflecting the simultaneous emergence of learning processes and outcomes during play. The study highlights the need for professional learning that supports inclusive, interdisciplinary, and reflective STEAM practices in early childhood education. The discussion emphasizes the utility of the ISP framework for supporting ongoing research and practioner professional development for embedding inclusive STEAM learning experiences in EC programs in China and in other global contexts.</p
A pilot cohort study of a microfluidic-based point-of-care bilirubin measurement system
No full textObjectiveThe concentration of bilirubin in blood or serum is useful for assessing liver function as well as monitoring treatment. This study evaluates the clinical performance of a novel point-of-care (PoC) device for the detection of bilirubin in serum. The PoC device incorporates an integrated miniature optoelectronic sensing module and a microfluidic test cartridge.MethodsPatients’ serum total bilirubin concentrations, ranging from 2 μmol/L to 480 μmol/L, were measured using the PoC device and the standard laboratory method (n=20). Bland-Altman analysis and regression analysis using Passing-Bablok method were used to benchmark the PoC device against the standard laboratory measurements. The diagnostic capability of the PoC device in categorising the serum samples within clinically relevant bilirubin concentration thresholds of 200, 300, and 450 μmol/L was assessed using receiver operating characteristic (ROC) analysis.ResultsThe mean difference between the PoC device and the standard laboratory method was −5.6 μmol/L, with a 95% confidence interval (CI) of −45.1 μmol/L to 33.9 μmol/L. The coefficient of determination (R2) was 0.986. The PoC device achieved a detection sensitivity of 90% and specificity of 97% in categorising bilirubin concentrations within bands used in clinical decision-making.ConclusionsThis study demonstrates that the proposed PoC device is capable of measuring bilirubin levels in patient samples with clinically acceptable accuracy.</p
Social media applications through the lens of DeLone and McLean’s information system success model: does perceived privacy matter?
No full textSocial media applications (SMAs) significantly impact higher education by affecting students, professors, and institutions through various features that may improve learning, communication, and collaboration. However, many studies have focused on the initial use of SMAs, rarely considering their post-adoption and continued usage. Additionally, there are growing concerns about the privacy behaviour of SMA users, which remains inadequately examined. Therefore, this study explores the impact of perceived privacy on the relationship between tripartite quality constructs, user satisfaction, and continual usage using DeLone and McLean’s information system success (ISS) model. This study surveyed 384 SMA users among university lecturers through a convenience sampling approach, and SmartPLS 4, nonparametric software, was used to analyse the data. These findings elucidate the elements affecting SMA usage, suggesting that service and information quality influence continual usage. The tripartite quality constructs correlate with users’ satisfaction, which robustly correlates with continual usage. In addition, perceived privacy influences the links between service quality, system quality, and continual usage. This study finds that perceived privacy is crucial to DeLone and McLean’s ISS model. Hence, information privacy must be ensured to create more secure, functional, and engaging applications. Suppliers and developers should focus on improving app quality, security, and protection, which are precursors to user satisfaction and continual usage. This study examines the moderating role of perceived privacy in DeLone and McLean’s model. Its strong predictive model demonstrates the theoretical robustness of the ISS model for studying the continual usage of SMAs.</p
A hybrid approach to enhance HbA1c prediction accuracy while minimizing the number of associated predictors: A case-control study in Saudi Arabia
No full textType 2 diabetes (T2D) is considered a significant global health concern. Hemoglobin A1c level (HbA1c) is recognized as the most reliable indicator for its diagnosis. Genetic, family, environmental, and health behaviors are the factors associated with the disease. T2D is linked to substantial economic costs and human suffering, making it a primary concern for health planners, physicians, and those living with the disease. Saudi Arabia currently ranks seventh worldwide in terms of prevalence rate. Despite this high rate, the country lacks focused research on T2D. This study aims to develop hybrid prediction models that integrate the strengths of multiple algorithms to enhance HbA1c prediction accuracy while minimising the number of significant Key Performance Indicators (KPIs). The proposed model can help healthcare practitioners diagnose T2D at an early stage. Analyses were conducted in a case-control study in Saudi Arabia involving cases (patients with HbA1c levels ≥ 6.5) and controls with normal HbA1c levels (</p
Enhancing Re-Identification and Object Detection Through Multi-Modal Feature Learning
No full textModern computer vision systems are increasingly tasked with operating in dynamic and complex environments, where challenges such as person Re- Identification (Re-ID) and object detection demand sophisticated and adaptable methodologies. Recent advancements in multi-modal feature learning have shown significant promise in addressing these challenges by integrating complementary data modalities and leveraging cutting-edge computational techniques. These innovations have enabled more robust feature representations, improving precision, adaptability, and efficiency in real-world applications. Advanced approaches, such as ranking-based dictionary learning, transformer-based architectures, and the incorporation of point cloud data, have emerged as key strategies to further enhance system performance in multi-camera and multi-modal frameworks. Building on these advancements, this thesis proposes novel methods to enhance accuracy, robustness, and efficiency in object detection and Re-ID. The research is guided by three interconnected questions: How can ranking-based dictionary learning improve person Re- Identification performance in multi-camera systems? How can transformer-based multi-query methods enhance both detection and Re-Identification through improved feature representations? How can the integration of point cloud data optimize multi-modal frameworks for detection and Re-ID in complex environments? To tackle the first question, a dictionary learning-based framework is introduced, incorporating Top-Push Polynomial Ranking Loss (TPRL) and a ranking graph Laplacian constraint. This approach targets variability in real-world data by enhancing intra-personal compactness and inter-personal dispersion within the feature space. The framework mitigates challenges such as occlusion, lighting inconsistencies, and diverse poses, achieving state-of-the-art results on multiple Re-ID benchmark datasets. By refining ranking relationships between person images, the proposed method significantly improves matching accuracy and system reliability under challenging conditions. Building on this foundation, the thesis addresses the second research question by developing a transformer-based Multi-Query Person Search (MQPS) framework. Unlike traditional methods that rely on single-object queries, MQPS leverages multiple adjacent queries to extract robust, multi-scale feature representations. This design enables the framework to better handle occlusion, small objects, and complex camera configurations, providing enhanced robustness in challenging inference scenarios. Experiments conducted on the CUHK-SYSU and PRW datasets demonstrate the superior performance of MQPS, setting new benchmarks in both detection and Re-ID tasks. Recognizing the importance of efficiency in real-world applications, the thesis explores the third question by introducing Frustum 3DNet (F-3DNet), a novel framework for 3D object detection. F-3DNet takes advantage of the structured nature of LiDAR-generated point clouds, creating pseudo-panoramic images and defining frustum regions of interest to capture both global and local context. By integrating 3D and RGB data, the method effectively manages data variability while maintaining computational efficiency. Experimental results on the KITTI and nuScenes datasets showcase the framework’s ability to achieve state-of-the-art detection accuracy, offering a scalable and efficient solution for dynamic IoT and surveillance environments. This research makes significant contributions to the field of computer vision by addressing core challenges in multi-modal feature learning. Through the integration of advanced ranking methods, transformer-based frameworks, and 3D object detection techniques, the thesis advances the state-of-the-art in person search, object detection, and Re-ID. By providing robust, accurate, and efficient solutions, the proposed methods enable the deployment of vision systems in complex and dynamic real-world scenarios, paving the way for further advancements in intelligent surveillance and IoT applications.</p
Sensor Fusion for Improved Localization in Autonomous Underwater Vehicle
No full textThe ocean has long been a vital resource for humankind, providing essential natural resources such as renewable energy, marine minerals, and seafood. However, marine ecosystems are increasingly threatened by global warming and pollution, which require extensive studies to monitor and mitigate these effects. Additionally, ocean research aids scientists in understanding the Earth's ecosystem and enhances their ability to predict and respond to natural hazards such as earthquakes and tsunamis. Despite its significance, oceanographic research has received comparatively less attention than land and aerial studies due to the challenges associated with accessing and mapping aquatic ecosystems. Therefore, developing efficient ocean survey methods that enable rapid and comprehensive exploration of marine environments is crucial.Traditional ocean surveys conducted by human divers are time-consuming, costly, and inherently hazardous due to unpredictable underwater conditions and the presence of venomous marine life. Autonomous Underwater Vehicles (AUVs) present a viable alternative, offering a robotic platform equipped with power systems and onboard processing units capable of navigating unknown underwater environments without human intervention. AUVs have been successfully deployed for large-scale ocean surveys, such as mapping the Great Barrier Reef to assess reef health and marine biodiversity. However, navigating complex and unstructured environments, such as coral reef systems, requires precise self-localization to avoid obstacles and ensure efficient mission execution. Unlike terrestrial autonomous systems, which rely on the Global Positioning System (GPS), AUVs must depend on onboard sensors for localization, as electromagnetic signals are heavily attenuated underwater.Different types of sensors provide complementary information, but relying on a single sensor often results in significant localization errors due to noise and environmental disturbances. Sensor fusion techniques integrate data from multiple sources to mitigate noise and enhance localization accuracy. A well-designed fusion algorithm is essential to effectively combine sensor data and optimize the AUV’s self-localization performance. Additionally, the dynamic and unpredictable nature of underwater environments poses significant challenges in developing and training localization algorithms. Conducting field tests is often dangerous, resource-intensive, and impractical for large-scale experimentation.To address these challenges, high-fidelity underwater simulation frameworks are essential for creating realistic virtual testing environments. These simulation frameworks accurately model AUV dynamics, sensor behavior, and provide realistic rendering of underwater environment, enabling safe and cost-effective underwater self-localization algorithm development. In this research, we introduce an underwater simulation framework designed for evaluating AUV self-localization algorithms, incorporating a noise modeling structure to simulate various sensor uncertainties. We then investigate the impact of sensor fusion techniques on localization accuracy and systematically compare different fusion algorithms to identify the most effective approach. Our research evaluates four localization algorithms for AUVs—IMU-only, stereo camera, LiDAR-only, and LiDAR-IMU—within a realistic underwater simulation across three mission types: lawnmower, loop-closure, and adaptive path. Results show that LiDAR-only localization performs best in feature-rich environments for short-term missions, while LiDAR-IMU excels in long-term or feature-sparse scenarios. Stereo localization offers a budget-friendly alternative with acceptable accuracy in feature-rich areas. A key limitation is computational constraints due to limited GPU power, restricting simulation realism and runtime to 120 seconds. Future research should develop more realistic noise models and refine AUV hydrodynamics to better capture real-world conditions.</p
A Cost-Based Assignment of Demand-Responsive Transport: A Comparative Study with Public Transportation Alternatives
No full textDemand-responsive transport (DRT), a flexible and dynamic mode, is becoming popular in urban cities but often competes with traditional public transport (PT). This paper explores the integration of DRT and PT, introducing a decision-making strategy that evaluates DRT and PT options based on travel time and fare. By prioritizing routes where DRT offers the most value and relegating less cost-efficient journeys to PT, this strategy aims to optimize the utility of both modes. Implemented and tested using the MATSim simulator in Inner Melbourne, Australia, the findings indicate a significant improvement in system efficiency: 15-18% of requests were economically rejected, reducing overall travel costs by approximately 9-16%, with total fare savings estimated between 14-31%. This study demonstrates the potential trade-offs between system travel time and fare, substantiating the model’s effectiveness in enhancing urban transport systems.</p
Institutional Parasites Publishing Practice at the Intersection of Art and Its Designed Frameworks
No full textInstitutional Parasites is a practice-based enquiry into publishing as an artistic method that interrogates the designed frameworks scaffolding contemporary art. Treating exhibition-making and live publishing as critical methods, it shifts publications – catalogues, wall texts and other paratexts – from peripheral ephemera to primary sites of artistic enquiry. In dialogue with Institutional Critique and post-structuralist theory, the project retools these publications to foreground subjectivity, expose the fiction of institutional neutrality and open plural, situated readings of art. Site-specific interventions and collaborative, often improvised, publishing actions show design itself as both medium and critique, embedded within and responsive to institutional logics. By eroding boundaries between artwork and its supplements, author and reader, critique and complicity, the project proposes a parasitic mode of engagement that is simultaneously dialogic and productive. It advances current debates on publishing as artistic practice and demonstrates how performative design can reconfigure the politics of mediation in contemporary art.</p
Shining a Light on the Dark Side of Pollution: Unravelling the Impact of Emerging Contaminants on the Phototactic Responses of Aquatic Animals
No full textBehavioural bioassays for analysing the movement patterns and direct neurological responses to stimuli such as chemical contaminants, light illumination, and shifts in water temperature, is an emerging field in ecotoxicology. Recent studies have outlined both morphological and neurological shifts in aquatic organisms when exposed to neurotoxic pollution as well as shifts in environmental conditions in the form of light availability and the introduction of light pollution during nighttime hours. The presence of neurotoxic and neuro-modulating contaminants has been shown to have subsequent influences on behavioural patterns of aquatic animals in the forms of phototaxis, chemotaxis, and thermotaxis. Impacted behaviours resulting from exposure to both light and neurotoxic chemical pollution include food searching, predator avoidance, and mating or reproductive activities. Phototaxis is the direct behavioural response to the presence of light stimuli and is utilised for essential functions such as food searching and consumption. Such responses to light stimuli can be observed in aquatic invertebrates and fish through the tracking and monitoring of their movement patterns when exposed to a light stimulus. This is a complex behavioural endpoint that can offer valuable insights into not only the neurological function of organisms, but also any influences resulting from exposure to sublethal concentrations of neurotoxic contaminants. However, the full extent of neurological influences of these neurotoxic chemicals, such as pharmaceutical products remain unknown making this a novel field in ecotoxicology and highlights a need for further behavioural assays and studies. This project aims to contribute to this emerging field of research through the development of an innovative, cutting-edge analysis system called the “Test Chamber System” and employing innovative bioinformatics techniques and methodologies to inform future studies investigating animal behaviour. The Test Chamber System was developed in two different forms - the Horizontal Test Chamber System and the Vertical Test Chamber System. The Test Chamber System was continually developed over the 2-year duration of the research project with various upgrades including the addition of the sheet optic light guide thermal management system and the optimisation of the light stimulus control interface system aimed at eliminating uncontrolled environmental variables from influencing the accuracy and reliability of behavioural data and subsequent findings. Through the development of both the Horizontal and Vertical Test Chamber analysis systems, we can conduct phototactic assays investigating animal movement at both a horizontal and vertical axis, respectively. These Test Chamber Systems also enable the activating of light stimuli to expose subjects to differing wavelengths of light including Cool White, Green, Blue, and Red to accurately monitor the behavioural shifts that occur when the testing arena is illuminated with light. The utilisation of the Horizontal Test Chamber System for three phases of phototactic testing including social comparative, wavelength preferential, and neurotoxic exposure testing using both individual and grouped Daphnia carinata subjects outlined a range of neurological shifts when exposed to light stimulus. The activity of unexposed subjects in a control group was compared to the activity of subjects exposed, for a total of 14 days, to the metallic pollutants of lead and zinc as well as the emerging pharmaceutical pollutants of carbamazepine and ibuprofen. At environmentally relevant concentrations, treatment groups underwent the exposure period and exhibited varying differences in both movement patterns and phototactic responses to different light stimuli. With the completion of this project, the influences of pollutant exposure on both the movement patterns and phototactic behaviours and preferences, which were previously hidden, were uncovered. These insights generated valuable and reliable behavioural data and provided new insights into the influences of anthropogenic activity on aquatic life. Alongside these conclusions, the development and optimisation of both the Horizontal and Vertical Test Chamber Systems provide an exceptionally reliable and cheap technology that facilitates the monitoring and analysis of small aquatic organisms within small aquatic test chambers with the use of animal tracking algorithms to produce accurate behavioural data sets. The optimisation of both the PMMA bonding techniques and bioinformatic analysis workflow procedures provide an efficient and reliable method for both the production and manufacturing of custom-made well plates such as the well plates used for testing during this project as well as the high throughput of phototactic analyses using the Test Chamber System for use in future studies.</p