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Public Architecture as Therapy
No full textThe notion of well-being as a social sustainability strategy has also become a timely issue in the post-pandemic context, which reshapes the interactions between people and public urban spaces. The shifting paradigm of social interactions with urban public spaces requires a renewed understanding of public architecture. Although “public architecture” is not an emerging term, the evolving social context indicates further conceptualisation is necessary, emphasising the therapeutic effect of public architecture in promoting subjective well-being(SWB).
This thesis investigates the expanded conceptualisation of public architecture and the
potential application of curatorship in designing socially resilient public architecture, which encourages a new framework that specifically fosters SWB by promoting positive interactions and overcoming some of the negative impacts of loneliness, anxiety, and stress associated with urban dwelling. The field research was conducted based on the premise that
public architecture is a spatial practice in a neighbourhood-scale urban open space that
sensitively considers the existing social demands, which creates a social gathering space for
the neighbourhood or a network of neighbourhoods, inviting interaction without barriers. As a result, this thesis proposes a design framework under a new terminology -therapeutic public architecture, which aims to engage in critical and aesthetic reasoning that emphasises the impacts of public architecture on social resilience and well-being.
The main objectives of this thesis are as follows:
1. Examine and redefine the conceptualisation of public architecture in an urban built environment.
2. Establish a design framework for public architecture promoting positive social
engagement and SWB.
3. Identify relevant concepts and initiatives across disciplines to develop a combined
understanding regarding how public architecture could foster SWB as a potential
social sustainability strategy
Data-driven Renewable Energy and Storage Optimization in Integrated Energy Systems
No full textThe growing integration of renewable energy sources into the power grid necessitates innovative approaches to system energy operation and scheduling. Integrated electricity and gas networks provide a promising solution to this challenge, enabling the efficient, reliable, and sustainable operation of energy systems. This thesis presents a novel data-driven framework for the optimal scheduling of integrated energy networks, addressing the challenges posed by high penetration of renewable energy sources.
A learning-assisted methodology is developed by integrating Graph Convolutional Network (GCN) and Bayesian-based uncertainty models to improve the accuracy and efficiency of scheduling integrated energy systems. The proposed GCN effectively captures complex interactions within the integrated energy networks, facilitating accurate predictions of nodal power and gas flows. Meanwhile, the Bayesian-based model adeptly manages the inherent uncertainties associated with renewable energy generation, employing a chance-constrained approach to ensure system reliability.
The effectiveness of the proposed methodology is validated through extensive simulations on an IEEE 39-bus electricity network coupled with a 22-node gas network. The results indicate significant improvements in predictive accuracy and computational efficiency compared to traditional model-based methods and existing data-driven techniques. This thesis contributes a hybrid learning-assisted framework that provides a foundation for intelligent and scalable energy management in integrated energy systems
Cardiovascular disease in older people with diabetes in Southeast Asia
No full textThe countries in Southeast Asia are going through an epidemiological transition, with an increasing
number of older adults. As age is a contributing risk factor, older people with diabetes face an
increased risk of developing cardiovascular diseases. This thesis aims: (1)To examine the
prevalence of comorbidities and
common geriatric syndromes in older people with type 2 diabetes in Southeast Asia, and (2) To
examine the management of cardiovascular risk factors and cardiovascular diseases in older people
with type 2 diabetes in the region
Standardising Initial Emergency Nursing Care: A Multicentre Implementation Evaluation
No full textBackground
Emergency nurses are crucial for safe, high-quality care and are the first clinicians to assess and treat patients who present to the emergency department (ED), especially in rural settings. The HIRAID emergency nursing framework provides a standardised approach to post-triage patient assessment and management.
Methods
A multimethod implementation study evaluated HIRAID in 11 rural Australian EDs. A mixed-methods survey of emergency nurses identified enablers and barriers to implementation, using the Theoretical Domains Framework (TDF). Quantitative data were analysed using descriptive statistics and qualitative data using content analysis. Results were integrated and mapped to Behaviour Change Techniques (BCTs).
HIRAID implementation was evaluated through a multimethod approach using the RE-AIM (reach, effectiveness, adoption, implementation quality, maintenance) framework. Evaluation included nurse surveys and interviews, audits of site implementation records, and medical records. Quantitative data were analysed with descriptive and inferential statistics, while inductive content analysis was used to analyse qualitative interview data.
Results
Enablers and barriers mapped to 10 TDF domains, and 20 BCTs, operationalised through 12 delivery modes, such clinical champions, and medical record modifications. HIRAID was implemented in 11 EDs, achieving high reach. Over 90% of nurses engaged in HIRAID education. Nurses (83%) reported using HIRAID documentation templates. Fidelity to strategies was low to moderate, but use was sustained at over 6 months. Improved accuracy of nursing documentation was demonstrated through medical record review (n=222), with audit scores increasing significantly across all areas of nursing documentation audited.
Conclusion
An evidence-based implementation strategy was effective for the sustained uptake of HIRAID in rural EDs. The intervention supported nursing practice and demonstrated clinical effectiveness
The Role of Bronchial Epithelial Cell-Derived Extracellular Vesicles in Modulating Macrophage Function and COPD Progression
No full textLong-term exposure to fine particulate matter (PM2.5) is a major environmental risk factor for chronic obstructive pulmonary disease (COPD). PM2.5 directly damages bronchial epithelial cells and promotes pro-inflammatory signaling. Emerging evidence indicates that extracellular vesicles (EVs) released by stressed epithelial cells contribute to immune dysregulation. This study established an in vitro model to examine how PM2.5-induced epithelial EVs modulate macrophage function.
BEAS-2B cells were exposed to low and high PM2.5 concentrations, and EVs were isolated and characterised using nanoparticle tracking analysis, BCA protein quantification, and Western blotting. Time-dependent uptake of EVs by PMA-differentiated THP-1 macrophages was confirmed by fluorescence tracing. EVs alone did not alter macrophage viability or cytokine secretion, but under lipopolysaccharide (LPS) stimulation, EV pre-exposure modulated macrophage responses. These effects depended on EV source and exposure duration rather than concentration, and strong inflammatory stimulation (100 ng/mL LPS) diminished EV-mediated regulation. Notably, IL-6 and IL-8 secretion showed biphasic responses to EV dose, and the duration of EV treatment influenced macrophage signalling outcomes.
While a definitive macrophage polarization pattern could not be established, this work provides a basis for future studies into EV-mediated immune regulation. Overall, PM2.5 reduced epithelial viability, increased pro-inflammatory cytokine release, and may have altered EV cargo, thereby influencing macrophage behaviour in an inflammation-dependent manner. These findings highlight both the direct effects of PM2.5 on epithelial cells and the potential role of EV-mediated epithelial–macrophage communication in air-pollution-induced airway inflammation, offering insights relevant to COPD pathogenesis
Twenty years of PMI’s Pulse of the Profession (2006–2025): A review
No full textThis review examines two decades of the Project Management Institute’s Pulse of the Profession series (2006–2025), the flagship global survey of project, program, and portfolio management. Forty reports were analysed, comprising 14 annual global editions, 23 thematic studies, and three practitioner-focused outputs. The findings show that Pulse has served both as an industry barometer and as an advocacy instrument. While the central message across all editions is consistent, poor project management wastes resources, the framing of this message has shifted over time: from cost-and-control narratives to capability-driven emphases on agility, digital fluency, power skills, and business acumen.
Using text mining (Voyant Tools) and qualitative coding (ATLAS.ti), the study identifies five clusters of project management approaches, governance, process, adaptive, people-centred, and purpose-driven, and traces how PMI’s discourse has repositioned project management as a strategic, human-centred discipline with societal impact. The analysis underscores the value of Pulse as a directional indicator of industry priorities, while also highlighting its limitations as empirical evidence due to shifting metrics, selective transparency, and advocacy framing.
For scholarship, this review offers the first comprehensive synthesis of the Pulse series. For practice, it reinforces the importance of governance, agility, and people skills in sustaining performance. For doctoral research, it provides both a typology and a conceptual scaffold for examining how project management approaches contribute to the sustainability and scalability of public health programs
From Recurrent Adaptation to Hebbian Plasticity: Biologically Plausible Networks of Typical and Atypical Sensory Processing
No full textMost computational approaches utilized to study the brain today avoid explicit incorporation of biologically plausible mechanisms, instead prioritizing performance benchmarks. While these networks might display responses like those exhibited by animals, it is often by converging on mechanistic solutions that lack biological correlates. In doing so, findings derived from such models cannot be directly validated against physiological recordings, and inferences drawn from such a network might thereby not hold true mechanistic value. Recognizing this hurdle, a growing number of recent studies have adopted biophysical mechanisms into their computational simulations, resulting in findings that account for perceptual phenomena that cannot be studied by traditional computational approaches alone. Following a similar strategy, this thesis explores two studies designed with enough resolution to reproduce specific biological phenomena while at the same time remaining computationally tractable. The first study introduces AdaptNet, a motion processing network that learns from natural sequences while implementing neuronal adaptation — a mechanism long implicated in efficient coding and perceptual aftereffects. The second project builds a spiking model of the superior colliculus (SC) with explicit AMPA/NMDA conductances, GABA inhibition, and spike timing dependent plasticity. The study initially validates the network’s responses against established metrics of multisensory integration and then analyses how perturbations in the model’s mechanics lead to the altered responses observed in conditions like autism spectrum disorder (ASD). Taken together, these models advocate for ‘minimal realism’ — careful adherence to key biologically grounded mechanisms, when balanced alongside planned abstraction of secondary mechanisms, can produce network architectures that can be used to derive useful insights about neural activity as well as behavioural responses, in both health and dysfunction
Parametrising Neural Feedback Policies with Stability and Robustness Guarantees
No full textLearning-based control is a powerful tool for nonlinear control in complex dynamical systems. Driven by the rise of deep Reinforcement Learning (RL), most approaches parametrise control policies with black-box neural networks, which are universal approximators for nonlinear systems. These can easily be trained in simulation with simple, gradient-based optimisation schemes. However, black-box approaches such as deep RL suffer a fundamental limitation: they lack certifiable guarantees of closed-loop stability, robustness to disturbances, and sensitivity to model error.
This thesis introduces novel parametrisations of neural feedback policies with built-in stability and robustness guarantees. Instead of relying on black-box networks, we parametrise policies with state-of-the-art robust neural networks that automatically satisfy stability and robustness constraints of their own.
Our main contribution combines robust neural networks with a nonlinear version of the Youla-Kucera parametrisation. We propose a theoretically-motivated framework that fuses classical and learning-based control with model information under a single policy architecture. The resulting policy parametrisation: (1) automatically guarantees closed-loop stability and robustness; (2) allows for plug-and-play optimisation with standard gradient-based training pipelines; and (3) is not restrictive in the controllers it covers (for certain classes of systems). We derive rigorous theoretical certificates for our parametrisation in partially-observed, nonlinear systems with incremental stability requirements (contraction and Lipschitzness), and demonstrate its capability for stability-certified RL in numerical experiments.
We extend our study of robust learning-based control with two further contributions: an empirical study of robustness in deep RL with Lipschitz-bounded policy networks; and a new parametrisation of contracting and Lipschitz networks which are scalable to high-dimensional models
Multi-Agent Reinforcement Learning for Optimal Network and Market Operations in Active Distribution Networks
No full textThe global energy transition toward decarbonization, decentralisation, and digitalisation is driving rapid growth of distributed energy resources (DERs) such as photovoltaics, battery energy storage, electric vehicles, and flexible loads. Their widespread adoption reshapes electricity distribution networks, enabling higher renewable utilisation, local flexibility, and active prosumer participation. Yet it also introduces new challenges in system coordination, operational reliability, and economic efficiency.
To fully capture DER potential, tightly coupled system–market frameworks are urgently needed. Market operations not only guide resource allocation and price formation but also provide a scalable basis for coordinating numerous heterogeneous, agent-based entities at the distribution level. This thesis proposes a layered market design that distinguishes between internal and external markets within virtual power plants (VPPs), enabling hierarchical and flexible energy trading. A carbon-aware market-clearing mechanism is further developed to jointly optimise economic cost and carbon emissions, embedding environmental considerations into operational decision-making.
However, advanced market mechanisms create additional technical hurdles, including unbalanced power flow management, constraint satisfaction under decentralised control, and learning under uncertainty. To address these issues, the thesis develops a multi-agent reinforcement learning (MARL) suite featuring: safe policy learning to satisfy nonlinear voltage and emission constraints,
decentralised training to preserve data privacy, and large language model assistance to enhance robustness against exogenous uncertainties.
All proposed mechanisms and algorithms are validated on standard IEEE distribution test systems. Results show that coordinated system and market operations empowered by MARL can significantly improve the scalability, security, and sustainability of next-generation distribution networks
Design and development of multiscale organic/inorganic interfaces for biosensing applications
No full textElectrochemical biosensors have broad applications including clinical diagnostics, industrial process monitoring, environmental monitoring, and agricultural analysis. Electrochemical biosensors integrate a miniaturized biorecognition element with an electrochemical transducer, such as an electrode or a field-effect transistor. This design facilitates device integration, enabling easy miniaturization, batch manufacturing, and incorporation with electronic acquisition modules on a single chip. Nonetheless, several limitations of electrochemical biosensors hinder their broader application in biomedical sensing. Nanomaterials are critical elements of electrochemical biosensor design due to their unique physical, chemical, and electrical properties that significantly enhance sensor performance. The biofunctionalization process is another important step in determining the overall
performance of a biosensor. While manufacturing techniques and substrate prototyping for biosensors have reached a mature stage, scalable, fast, and cost-effective methods for interface engineering are missing. The techniques used for modifying electrode surfaces with nanomaterials and bio-recognition elements come with many challenges and drawbacks, which hinder the large-scale, cost-effective production of biosensors and ultimately limit their
practical application in real-world settings. In this thesis, we developed novel methods and strategies to overcome these limitations without compromising key performance metric