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Thin-film flows involving deformable and porous interfaces
Full text linkThis thesis investigates thin-film flows involving deformable and porous interfaces, addressing both the fundamental fluid mechanics of free-surface flows prone to a novel class of instability and applications of related flows to biomedical transport.
The first part of this thesis focuses on viscous gravity currents spreading over lubricated substrates. Such a free-surface flow involves two immiscible viscous fluids, the interface between them being deformable, with the upper-most surface in contact with the atmosphere. A theoretical framework is developed for such flows using the principles of lubrication theory. We find similarity solutions and perform asymptotic analyses to characterise various flow regimes and a stress singularity near the intrusion front. Building upon this foundation, a linear stability analysis reveals that such flows are prone to a new class of viscous fingering instabilities, arising from hydrostatic interactions between the two viscous fluids. Despite fundamental differences in the type of flow, this new class of fluid-mechanical instabilities curiously resembles a number of features typical of its closest predecessor: the well-known Saffman-Taylor instability, or what simply became known as viscous fingering. This challenges the perception that viscous fingering is limited to porous media, or Hele-Shaw cells, as was popularised in the decades of research since Saffman and Taylor in the 1950s. This thesis highlights how free-surface flows of fluids of unequal viscosity can exhibit similar fingering to that seen in porous media, widening the definition of what the fluid mechanical community perceives to be viscous fingering. We explore how this new class of instabilities depends on contrasts in the viscosity, density, and source flux, and what determines wavelength selection. Extending the problem to inclined substrates demonstrates that the onset and mechanism of instability are robust and not tied to geometric configuration.
The second part of this thesis turns to biomedical transport in haemodialysis – a treatment option for patients affected by kidney failure. Such treatment, in itself, is a rich fluidmechanical problem, involving the flow of two viscous fluids (blood and a sterile solution, known as dialysate) in an artificial kidney known as a dialyser. Dialysers involve thousands of long and thin hollow fibres that facilitate the removal of toxins and excess fluid from the blood. On the scale of a single fibre, the length scales involved are such that both blood and dialysate behave as thin films of viscous fluid, separated by a semipermeable fibre membrane. We use lubrication theory to develop a consistent mathematical framework modelling the fluid flow and solute transport within a single fibre of a typical dialyser, characterising both diffusive and convective transport of toxins from the blood to the dialysate. By performing asymptotic analyses, we obtain analytical expressions for the clearance (characterising treatment efficiency) and recover classical results from the literature as special cases in various asymptotic limits. By incorporating fluid flow, our framework is faithful to the underlying hydrodynamics and provides a systematic foundation for improving dialyser design and exploring new treatment modalities
Equivariant periodic cyclic homology for ample groupoids
Full text linkWe develop an equivariant version of bivariant periodic cyclic homology for actions of Hausdorff ample groupoids, extending the classical bivariant theory of Cuntz and Quillen and its equivariant refinement for groups. For an ample groupoid G, we construct a monoidal category of modules over its convolution algebra and study structural features of its objects, the G-modules. In parallel, we present an equivalent comodule formulation and prove the equivalence between the module and comodule pictures. We introduce G-algebras and give some important examples. After reviewing pro-categories, we define the equivariant X-complex, which is central to the construction of the bivariant equivariant periodic cyclic homology for G-algebras. In analogy with the classical and group-equivariant settings, we establish homotopy invariance, stability, and excision for the resulting theory
Investigating KRASG12C driven tumorigenesis and targeted therapy in colorectal cancer
No full textAbstract not currently available
Making it Together: communities of care in Scotland’s craft ecology – Maker-led organisations and the prospects for good work and social impact in the creative and cultural industries
Full text linkThis thesis investigates the role and impact of maker-led organisations within Scotland's craft ecology through qualitative multi-sited case studies of three national craft organisations. Drawing on Hesmondhalgh and Baker's concept of 'good work,' (2011) the research examines how these organisations enhance individual maker experiences of craft work and generate broader social impacts within the creative and cultural industries. Through analysis of participant interviews, field observations, and organisational documents, the study reveals these organisations serve as crucial intermediaries of care, reducing maker isolation and developing collective responses to systemic challenges, while simultaneously advocating for craft's broader social value.
The findings, drawing on the work of Tronto (1998), demonstrate maker-led organisations act as communities of care-practice. They generate caring other-oriented behaviours, enhance skills and competencies that improve both the quality of craft work and the capacity to maintain common infrastructures, creating surplus social value in the process. However, the research also identifies significant challenges, including inequitable access to support structures, and sustainability concerns stemming from reduced cultural funding, aging memberships, and reducing volunteer labour that largely underpins their operation. This research argues that these organisations exemplify an alternative to growth-focused industrial production models, demonstrating how practice-led peer communities can address needs unmet by current policy frameworks. It draws attention to the deficiencies of the creative industries policy context that views creative individuals as the primary producers of social and economic value, and often disregards non-financial exchange, and collective endeavours.
This research proposes maker-led organisations could, if provided with enhanced status and resources, better develop and maintain both craft and non-craft infrastructures for the common good. This thesis illuminates the extra/ordinary characteristics of care ethics in practice-led communities. It points to the generative role of care as a theoretical framework to explore nonfinancial value exchange and hidden organisational practices within the creative and cultural industries more broadly
Flexoelectricity-driven triboelectrification models accounting for adhesion and contact unloading
No full textAbstract not currently available
Towards a Black feminist epistemology. The myth of exceptionalism and Black Scottish feminism, 1950s-1980s
No full textAbstract not currently available
Flexible joint modelling of multivariate extreme and non-extreme events
Full text linkIn fields such as finance and environmental science, modelling the entire distribution of events with a particular focus on extremes is critical for risk management. Extreme Value Theory (EVT) offers a rigorous framework for such modelling. Initially developed to study the asymptotic behaviour of maxima of i.i.d. sequences, EVT was later extended to characterise the tails of distributions. A widely used result in univariate EVT is the peak-over-threshold (PoT) method, which approximates the tail of a distribution using the Generalised Pareto Distribution (GPD) above a sufficiently high threshold. This has motivated a “sliced” modelling framework that combines a separate distribution for the bulk (below threshold) with a GPD for the tail.
This thesis extends the sliced model to the multivariate setting and proposes three frameworks to either address the practical challenges arising in such extensions or provide alternate approaches for joint modelling of the bulk and tail.
Our first contribution is a multivariate analogue of the sliced model, combining a parametric bulk distribution with a multivariate GPD (mGPD) for the tail. The threshold separating bulk and tail is treated as a free parameter to avoid manual specification. Simulation studies demonstrate that the model robustly estimates marginal behaviour and both bulk and tail dependence, even under misspecification (e.g., when data are asymptotically independent but the model assumes asymptotic dependence). However, three limitations hinder scalability and realism in higher dimensions or large datasets. First, the mGPD is infinitely parameterised, with only a few closedform representations available, risking bias if the true dependence deviates from these forms.
Second, the piecewise construction introduces discontinuities at the bulk-tail boundary, both in the margins and in the dependence structure, which are unrealistic for large datasets. Third, structural inconsistency arises: while the mGPD is always asymptotically dependent, the bulk model (e.g., Gaussian) may be asymptotically independent, leading to conflicts in dependence representation. Moreover, the fixed dependence class of the mGPD limits its applicability in contexts such as spatial modelling, where tail dependence may vary with distance.
To address the first issue, we introduce GPDFlow, a novel mGPD framework in which the dependence structure is modelled using normalising flows, which is a flexible class of generative models. Unlike classic mGPDs, GPDFlow avoids closed-form constraints and instead learns a parameterised dependence structure through flows, with density evaluation performed numerically. GPDFlow explicitly transforms light-tailed distributions into heavy-tailed ones, overcoming typical limitations of generative models. It performs well in describing the data where only subsets of variables are extreme and outperforms standard mGPDs in estimating both marginal and tail dependence.
To address the issues of discontinuity and fixed asymptotic dependence, we develop a second framework combining the extended GPD (eGP) with a latent Gaussian model, implemented in the R-INLA package using the integrated nested Laplace approximation (INLA). The eGP is a sub-asymptotic distribution that retains the key properties of the GPD while avoiding the need for threshold specification, yielding a fully continuous model. Dependence is captured through latent Gaussian fields, ensuring coherence and continuity across the entire distribution. We illustrate this approach in a one-month-ahead spatio-temporal wildfire forecast application in Portugal, focusing on moderate and extreme burn areas. A two-stage ensemble design integrates environmental and historical data: in the first stage, an XGBoost model learns complex covariate patterns, producing pseudo-covariates that feed into the second-stage latent Gaussian model. This addresses key limitations of the INLA framework in handling high-dimensional covariates and obtaining future environmental inputs in retrospective analyses. The eGP model and associated priors are now fully implemented in R-INLA and publicly available to users.
Finally, to explore a purely deep learning-based solution without asymptotic constraints or rigid latent structures, we propose a model tailored to the EVA Data Challenge 2025, which involves estimating the expected number of daily precipitation extremes across a 5×5 spatial grid over 165 years. We use a long short-term memory (LSTM) network to encode spatio-temporal patterns and condition a denoising diffusion probabilistic model (DDPM) on the resulting hidden states. The model operates on log-transformed, zero-adjusted precipitation data. For comparison, we also develop a sliced model with conditional independent margins, using aWeibull distribution for the bulk and a GPD for the tail. The diffusion-based model performs better for five out of six target quantities in the data challenge evaluated at lower thresholds, and accurately captures tail heaviness, as validated by marginal GEV shape parameter analysis on simulated and real data
The role of musculoskeletal ultrasonography (MSUS) in investigating pain in patients with inflammatory arthritis
Full text linkRheumatoid arthritis (RA) is a prevalent and enduring inflammatory joint disorder characterised by autoimmune responses and persistent joint inflammation. This inflammation results in pain, which is regarded as the hallmark symptom of RA. Numerous patients with RA continue to experience pain despite effectively controlled inflammation. This chronic pain is linked to musculoskeletal conditions that can diminish quality of life. Various mechanisms underlie the clinical pain experienced in RA, including nociceptive and nociplastic pathways. Nociceptive pain in RA has predominantly been associated with inflammatory changes within the joints, whereas nociplastic pain is considered non-inflammatory and related to central sensitisation. A comprehensive understanding of the nature of pain in RA could facilitate the development of improved pain management strategies.
The primary objective of this thesis is to investigate the pain mechanisms in RA through the utilisation of musculoskeletal ultrasonography (MSUS). MSUS is valuable for detecting peripheral inflammation that causes nociceptive pain. Furthermore, when pain persists despite inflammation management, MSUS can be used to confirm the absence of inflammatory changes within the joints.
The data used in this thesis were collected from six clinical studies. The initial phase involved assessing the prevalence of persistent pain in early RA using the SERA dataset. Subsequently, the study examined the relationship between Ultrasound power Doppler (USPD) and pain intensity, as measured by the pain visual analogue scale (VAS), within the TaSER dataset. Given that the TaSER dataset comprises a reduced sample of patients with early RA who underwent ultrasound examinations, the third chapter focused on analysing the correlation between MSUS findings and pain measures using the larger Spanish RA cohort dataset (Naredo), which also includes diverse MSUS metrics and a wider range of joints.
In the following chapter, the validation of the findings from the previous chapter (the correlation between pain and MSUS) within a population afflicted by a different form of inflammatory arthritis, specifically Psoriatic Arthritis (PsA), has been explored, utilising the CENTAUR dataset. This dataset includes subjective pain measures, such as the pain VAS, as well as semi-objective pain assessments, namely Quantitative Sensory Testing (QST).
In the concluding chapter, the relationship between pain and MSUS metrics was analysed, this time utilising objective pain measurement tools, specifically neuroimaging via functional Magnetic Resonance Imaging (fMRI), as objective indicators of pain signal processing. The capability of ultrasound to detect nociceptive pain was evaluated by establishing correlations with the traditional pain pathway, specifically the sensorimotor network (SMN)-Thalamus connectivity. Furthermore, the effectiveness of MSUS in identifying inflammation that contributes to the mixed pain state was assessed by correlating MSUS metrics with the nociplastic pain marker, namely the Default Mode Network (DMN)-Insula.
Within the SERA chapter, it was observed that a portion of patients reported experiencing pain during follow-up. Among those who reported pain, few participants exhibited a negative swollen joint count (SJC), while a notable proportion had normal erythrocyte sedimentation rate (ESR) levels. In the TaSER chapter, no significant correlation was identified between ultrasound power Doppler (USPD) and the pain visual analogue scale (VAS). These findings might be due to the reduced sample size and the limited number of joints included in the TaSER dataset. However, within the Spanish RA cohort, which had a larger sample size, more MSUS metrics, and a broader range of joints included, a notable correlation was established between MSUS metrics, namely USPD, ultrasound synovial hypertrophy (USSH), and ultrasound joint effusion (USJE), and pain VAS at both baseline and follow-up visits.
In the CENTAUR analysis, no substantive correlations emerged between USSH, enthesitis metrics, and pain VAS scores, nor between QST, namely pressure pain threshold algometry (PPT) and MSUS parameters. Furthermore, no significant associations were detected linking MSUS findings with fibromyalgia (FM). FM serves as a prototype for nociplastic pain. In a distinct examination of the CENTAUR dataset, a negative correlation was observed between US enthesitis and SMN-thalamus connectivity. A correlation was not observed between MSUS and DMN-insula connectivity. Notably, significant correlations were established in the SOAR and TEMPO datasets between MSUS parameters, including USPD and ultrasound bone erosion (USBE), and DMN-insula connectivity, albeit no significant association was found between MSUS and SMN-thalamus metrics. This thesis substantiates the reliability of ultrasound as an investigative tool in assessing inflammatory changes within the joints of patients with inflammatory arthritis. It demonstrates that MSUS can effectively evaluate nociceptive pain, evidenced by significant correlations in the extensive Spanish RA cohort. Furthermore, MSUS holds promise as a diagnostic tool to discern the presence or absence of peripheral inflammation contributing to non-inflammatory pain in this patient population. Finally, the findings indicate that MSUS can identify the contributions of nociceptive pain to the overall mixed pain experience in patients with RA and FM
Investigating the role of CBFβ in the mouse heart post-myocardial infarction
No full textAbstract not currently available
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