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Informing Health Economic Decisions: A Framework for Model Calibration and Value of Information Analysis for Target Data
No full textDecision-analytic models (DAMs) are vital tools in public health economics for evaluating healthcare strategies under uncertainty. Their credibility hinges on robust calibration of unobservable parameters. However, the comparative effectiveness of diverse calibration methods—spanning Bayesian (e.g., Incremental Mixture Importance Sampling [IMIS], Hamiltonian Monte Carlo [HMC]) and non-Bayesian (e.g., Random Search, Latin Hypercube Sampling, Nelder-Mead, Simulated Annealing) approaches—and the economic value of improving calibration data remain understudied.
This thesis addresses these gaps through two objectives. First, it evaluates calibration methods using a natural history model across varying complexities, assessing their accuracy in recovering known parameters, influence on cost-effectiveness outcomes, and characterisation of decision uncertainty. Second, it develops a Value of Information (VOI) framework to quantify the economic worth of calibration data, examining how data availability (none, expert-elicited, empirical), richness (number of data points), and calibration method affect the value of acquiring improved data. The VOI framework is theoretically illustrated and then applied to cancer and infectious disease case studies.
Results indicate Bayesian methods, particularly IMIS and HMC, achieve superior parameter estimation and uncertainty quantification in high-dimensional scenarios, offering robust cost-effectiveness conclusions. Non-Bayesian optimisation methods (e.g., Nelder-Mead), while computationally efficient, risk bias and inadequate uncertainty representation, potentially distorting decision uncertainty metrics. Unguided sampling methods (e.g., Random Search) retain substantial residual uncertainty. VOI analyses demonstrate that data value is contingent on data quality, quantity, and calibration approaches.
Ultimately, this work delivers clear evidence favouring Bayesian techniques for calibrating complex health economic models, offering a crucial guide for researchers. It also equips policymakers with a novel analytic framework to formally evaluate and justify expenditure on data collection for calibration. By providing this methodological direction and a tool for strategic data investment, this research directly enhances the transparency, reliability, and decision-relevance of DAMs, fostering more defensible and efficient health resource allocation
Disability, Sibling Relationships and Everyday Life: Exploring Mundane Realities as Counter-Stories
Get PDFThis thesis explores the childhood experiences of siblings of people with learning disabilities in the UK. Research around this topic has often reproduced deficit narratives that centre non-disabled sibling outcomes, reinforcing pathological understandings of learning disability. This work sets out to disrupt these dominant discourses through bringing together understandings from family sociology and critical disability studies in order to generate counter-narratives that speak to the everyday of siblinghood and learning disability. To do this, narrative interviews were conducted with 14 siblings (aged 18-32) of people with learning disabilities. As part of the interviews participants were asked to bring along photographs and a timeline of their childhood. The data are presented through narrative thematic analysis, with narrative portraits being offered as a means to centre participant stories in their own words.
The findings provide stories of the everyday of siblinghood and disability, with participants reflecting on sharing bedrooms, dinnertime and other mundane activities. Within these stories are moments of care, conflict, humour and frustration that speak to wider understandings of siblinghood offered by family sociology. Alongside this, insight is given into the role of wider society in sibling experiences, with discussions of support services and public interactions. These narratives contain nuanced understandings of disability, family and siblinghood with participants reflecting on the role of expectations of siblinghood in how they understand their lives and offering conceptualisations of learning disability that disrupt commonplace narratives. Considering counter-narratives, the participant accounts are full of joy, humour and self-reflection that challenge dominant understandings offering crip accounts of siblinghood. Throughout, the thesis offers new understandings of siblinghood and learning disability, presenting holistic stories of the everyday that reject deficit understandings and centre the human
The Role of Endoscopy in the Diagnosis of Coeliac Disease
Get PDFCoeliac disease is a common autoimmune condition triggered by gluten in genetically predisposed individuals. While endoscopy with duodenal biopsies has traditionally been the diagnostic gold standard, this work demonstrates that serology-based diagnosis is a highly accurate, cost-effective, and environmentally sustainable alternative. A meta-analysis of 18 studies involving >12,000 adults revealed that tissue transglutaminase levels ≥10 times the upper limit of normal have 100% specificity and 98% positive predictive value, enabling a no-biopsy approach that could save the NHS over £2.5 million annually and reduce the carbon footprint by 87 tonnes of CO2. Patient preference study confirmed favourability for serology-based diagnosis over endoscopy and biopsies, emphasising diagnostic accuracy, reduced procedural discomfort, and shorter wait times as the main factors influencing patient choices. Interviews with primary and secondary care physicians identified confidence in serological tests and concerns about missed diagnoses as key factors influencing adoption. Additionally, patients with potential coeliac disease, positive serology and normal biopsies showed mixed outcomes, with some developing overt disease while others normalised serology; most symptomatic individuals benefited from a gluten-free diet. Narrow-band imaging during endoscopy was shown to accurately identify duodenal villous atrophy, reducing the need for random biopsies in patients with a low pre-test probability of coeliac disease. These findings underscore the evolving role of endoscopy, integrating advanced imaging and selective biopsy strategies. Serology-based diagnosis aligns with patient preferences, reduces healthcare burden, and promotes sustainability
Stability analysis of permanent magnet synchronous machines under flux-weakening control with advanced control strategies
No full textFlux-weakening (FW) control is essential for permanent magnet synchronous machine (PMSM) drives in high-speed applications, where the operating speed must extend beyond voltage and current limits. As the back-EMF approaches the DC-link voltage, the inverter operates near its voltage limit, and the reduction in voltage margin introduces strong nonlinearities in the current-regulation loop. This voltage-limited behaviour is the primary source of potential instability. Although various FW strategies, e.g. feedforward, feedback, and hybrid, have been developed based on field-oriented control, direct torque control, and model predictive control, their stability characteristics for different machine types and deep FW operation remain insufficiently understood.
This thesis establishes a unified modeling and control framework to address these issues and proposes advanced FW strategies for robust and efficient PMSM operation. A unified small-signal model is first developed for both surface-mounted PMSM and interior PMSM, enabling systematic stability analysis of representative voltage-feedback methods: d-axis-current-based and current-angle-based control. The study clarifies how saliency, controller structure, and operating point jointly influence loop gain and damping. An adaptive voltage feedback control method is then proposed to integrate the two feedback mechanisms through dynamically tuned weighting factors, ensuring consistent stability across the entire FW region. A model predictive flux control scheme with voltage feedback is further developed, introducing a low-pass-filtered voltage loop and a ripple-suppression cost function to improve transient stability. Finally, a frequency-domain framework based on finite impulse response modeling enables multi-objective optimization of MPC parameters, balancing stability, bandwidth, ripple, and computational cost
How has UK Government policy towards citizenship and character education changed over time? How can a historical policy analysis of these topics contribute to developing a set of recommendations for improving these areas of education in the future?
Get PDFThis thesis considers the policy of the UK Government towards citizenship and character education, the ethics behind the role of schools providing them and policy changes in the early twenty-first century.
Citizenship and character education stem from ancient Greek times. Indeed, Aristotle’s influence is still felt today in the promotion of a traditional over a more progressive form which nearly replaced it from the 1960s. This thesis considers the context for these developments including an early version crafted by T H Marshall. It discusses two contrasting philosophies – progressive and neoliberal – in the emergence of comprehensive education in the 1970s amid fraught debate between Labour and right-wing politicians leading to the first national curriculum in 1988 - enshrining a non-statutory form of citizenship education, focusing on democracy, at national level for the first time whilst introducing elements of character education comprising civic virtues.
The thesis traces New Labour’s attempts to forge one model based on the Crick Report, 1998, and how political events over the succeeding two decades undermined progress resulting in a lesser role for citizenship education and a much-expanded agenda for character education by 2020.
With insight from three key philosophers, the thesis analyses the impact of major phases during the later twentieth century including the neoliberal 1970s and 1980s as a context for the development of citizenship and then character education in the twenty-first century.
It recounts interventions over this period, impacting education during the time of the Conservative-Liberal Democrat Coalition and Conservative Governments and suggests evaluation of the implementation of citizenship and character education.
In the absence of recent work at a national level, the thesis recommends research into application of a civic participatory model in the UK, with evidence of its successful operation elsewhere, and potential for engaging local communities in its administration
Expanding the Enzymatic Toolbox for β-amino Acids and Unnatural Amino Acids Manufacturing
Get PDFβ-Amino acids, characterized by the separation of their terminal carboxylic acid and
amino groups by two carbon atoms (Cα and Cβ), exhibit remarkable structural
versatility. This arrangement allows for R or S isomers at both Cα and Cβ positions,
resulting in up to four diastereomers for a given side chain. Such diversity facilitates
the generation of a broad array of stereo- and regio-isomers, alongside the potential
for di-substitution, making β-amino acids highly valuable in molecular design. Their
incorporation into peptidomimetics has led to compounds with potent biological
activity and enhanced resistance to proteolysis. Furthermore, β-amino acids and their
derivatives serve as essential chiral building blocks in pharmaceutical synthesis,
highlighting their significance in drug discovery and development.
This study focuses on enzymatic approaches to β-amino acid production as a
sustainable alternative to traditional chemical synthesis, which often suffers from low
carbon efficiency. Leveraging both wildtype and engineered variants of aspartase and
3-methylaspartate ammonia-lyase (MAL), this project explores their catalytic
potential for industrial applications. Aspartase catalyzes the reversible deamination of
L-aspartic acid via a carbanion mechanism to produce fumaric acid and ammonium
ion, while MAL facilitates the α,β-elimination of ammonia from 3-methylaspartate to
form mesaconate.
Novel aspartase and MAL enzymes sourced from thermophilic organisms were
identified and characterized. Protocols for their recombinant expression and
purification were established, and their enzymatic activities were validated through
NMR and spectrometric assays. This research provides critical insights into the
catalytic mechanisms and industrial applicability of aspartase and MAL, offering a
foundation for environmentally friendly and efficient production of β-amino acids and
other unnatural amino acids
Enhancing the Explainability of Deep Neural Networks from Causal Perspectives with a Human-in-the-loop Framework
No full textWith the advancement of deep neural networks (DNNs), their surprising performance has sparked widespread interest in exploring the feasibility of their applications across various fields. However, the inherent opacity of DNNs presents a significant barrier to their application in high-stakes fields where demonstrating the inference process of DNNs is as critical as the accuracy of model outputs. Although various methods, including explanation generation, transparent models, and human-model interaction methods, have been proposed to enhance explainability of DNNs for revealing the inference process, their effectiveness in revealing causality, correcting model bias, and guiding system design with multiple interaction scenarios remains limited. To address this research gap, this thesis proposes a concept-based causal explanation generation workflow, as a post-hoc explanation generation method, to illustrate the causality between interested concepts and model outputs with multiple formats of explanations through a global variational autoencoder probe, a causal structure discovery algorithm, a causal effect estimation method, and a set of explanation generation tools. To correct model bias, a causal-guided model fine-tuning workflow is proposed, consisting of causal-based sample selection, concept-based low-rank fine-tuning module, and a do-calculus optimisation strategy for implementing causal inference in DNNs. This workflow converts black-box DNNs into transparent models and corrects model bias through model fine-tuning. Last, a human-in-the-loop framework with explanations is proposed to guide the DNN-based system design and validation on multiple human-model interaction scenarios. In experiments, the proposed workflows outperform state-of-the-art methods in enhancing DNN explainability and correcting model bias in computer vision and natural language processing tasks. Additionally, a case study of pest management in agriculture is completed, demonstrating the feasibility of the proposed frameworks for establishing explainable DNN-based systems
Deep Learning-Based Prediction of Far-Field Sound Directivity in Parametric Array Loudspeakers
Get PDFParametric array loudspeakers (PALs) enable highly directional sound projection through ultrasonic modulation and are widely used in applications such as immersive audio, assistive communication, and spatial sound control. Despite their potential, accurate far-field directivity modelling remains challenging due to nonlinear acoustic propagation and the complexity of sparse array configurations. This dissertation proposes an integrated framework combining analytical, numerical, and deep learning methods to address these challenges. A dual convolution model is developed to improve grating lobe prediction and nonlinear beam characterisation. To optimise array performance under varying steering angles, a particle swarm optimisation (PSO) method is introduced for transducer layout design. Full-wave acoustic simulations using the finite-element method (FEM) are employed to model propagation and validate the optimised arrays, demonstrating up to a 5 dB reduction in peak sidelobe level across the tested steering range. To overcome the computational burden of full-wave simulations, deep learning models, based on generative adversarial network (GAN) architectures are trained to infer far-field patterns from sparse near-field inputs. By integrating physics-informed modelling, numerical optimisation, and data-driven prediction, the proposed approach improves both design efficiency and accuracy. In particular, the GAN-based predictor achieves MAE = 0.0105 and RMSE = 0.0171 on peak-normalised directivity. Experimental measurements were conducted to validate the proposed models and inform their development, ensuring consistency between predicted and observed acoustic directivity patterns. These outcomes address critical limitations in existing PAL modelling techniques and support the feasibility of real-time, adaptive control of acoustic fields for advanced sound applications
Insights into clostridial spore germination and twitching motility using live cell imaging
No full textThe Clostridia are anaerobic, Gram-positive bacteria that sporulate when stressed and produce type-IV-pili that play a key role in biofilm development.
Spores allow bacteria to survive extreme conditions and play a key role in human infection. In this work, we investigated the dynamics of spore germination in Clostridium sporogenes, a non-pathogenic
model of the botulism-causing species Clostridium botulinum (Group I). Using a combination of maleimide-based dyes, time-lapse fluorescent/phase microscopy in anaerobic conditions, and automated image analysis, we resolved the phenotypes of mutants lacking key spore proteins by growing them in co-culture with wild-type cells. We found that spores lacking CsxB, a protein thought to be associated with the exosporium (the outermost layer of the spore), exhibited a transient stall during rehydration, suggesting that CsxB regulates the entry of water into the spore core. Furthermore, we found that in mutants lacking CsxC, which forms a layered structure inside the exosporium, the spore body tends to reside closer to one end of the exosporium compared to wild-type cells. Importantly, this asymmetry plays a key role in germination of vegetative cells, which preferentially emerge from the distal pole of the exosporium in all the strains we tested. These findings provide new insights into regulatory and morphological processes that control spore germination in Clostridia.
We also investigated pili-based motility in Clostridioides difficile, a human pathogen that causes diarrhea and life threatening colitis. On agar surfaces, C. difficile colonies form branch-like structures on their periphery that previous studies have indicated are driven by the extension and retraction of pili. In this work, we sought to directly visualize pili-based movement of both solitary C. difficile cells and those in densely packed colonies using high-resolution, time lapse microscopy.
As many species of Clostridia are pathogenic, elucidating the fundamental processes that underlie spore germination and colonization is critical for developing new treatment strategies
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