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Artificial Intelligence for automatic attachment assessment in school-age children: an approach based on language and paralanguage
No full textAbstract not currently available
Ray tracing in multimode sapphire fibre tapers
No full textThis study explores a novel sapphire fibre taper-based photodynamic therapy (PDT) system, utilising a diode laser as the light source. The system consists of a diode laser, a silica delivery fibre and a tapered sapphire fibre, where the tapered segment functions as a probe for targeted light delivery in PDT applications. The research focuses on the numerical simulation of light propagation within the sapphire fibre taper, analysing the behaviour of reflected and transmitted light under varying conditions to evaluate system performance and determine the optimum geometry for sapphire fibre tapers to ensure efficient light utilisation and effective treatment coverage.
To validate the performance of the proposed sapphire fibre taper, this study employs mathematical methods of ray tracing and simulations using MATLAB. The modelling focuses on optimizing light propagation within the taper and controlling the transmission of light outside the taper, ensuring efficient delivery of therapeutic light to the target area. This research provides valuable insights into the design and optimisation of sapphire fibre tapers for PDT, enhancing light delivery precision while minimising unnecessary loss. The findings contribute to the development of more efficient and adaptable PDT systems, improving treatment effectiveness for various clinical applications
Britain as a contractor state: Cooperation between the Navy and private shipbuilders (1688-1714)
Get PDFThis study argues for the importance of a mutually beneficial relationship between the English/British navy and private shipbuilders for the state’s efforts in naval shipbuilding at the turn of the eighteenth century. Historically, Britain has been viewed as a ‘fiscal-military state’ that efficiently gathered funds via Parliament-endorsed taxation and debt. More recent scholars introduced the term ‘contractor state’ to describe Britain’s strategy of mobilising private resources flexibly with many contractors, compared to its rivals like France and Spain. Britain’s broad contractor network enabled its naval power to grow efficiently.
This thesis investigates the Navy’s collaboration with private shipbuilders, especially their role in constructing frigates, which was crucial to Britain’s maritime control and economic growth. Key research questions address who these contractors were, how they managed large-scale naval shipbuilding, and their motivations for engaging in naval projects. Through a synthesis of contractors’ correspondence, navy records, and existing historical studies, the thesis re-examines the Navy-private yard relationship, previously characterised by conflicts over resource procurement and quality. Instead, it emphasises cooperative aspects that highlight contractors as ‘military entrepreneurs’, using extensive shipbuilding resources, business networks, and personal ties to exploit the high demand for warships.
The thesis is structured around specific inquiries, with early chapters providing a literature review and historical context. Chapter 3 investigates contractors’ profiles and defines them as military entrepreneurs who exploited naval demand. Chapter 4 examines the Navy’s interactions with private shipbuilders and argues that the Navy Board, a department responsible for constructing and maintaining warships, assisted the contractors beyond its formal obligations. Although practices like impressments disrupted operations, the Navy generally cooperated with private yards, enabling a rapid expansion of warship production. Chapter 5 analyses the motivations behind private yards’ involvement in warship contracts, underlining that a wartime recession of mercantile shipbuilding drove many shipyards to naval shipbuilding as an alternative revenue source.
Chapter 6 synthesises the thesis’ findings, proposing three main factors behind the expansion of warship contracts: the presence of large-scale entrepreneurs, wartime change in the shipbuilding market, and the active Navy Board’s support. The thesis portrays Britain’s ‘contractor state’ as an ‘embedded state’, wherein mutual benefits allowed both maritime and naval interests to flourish, creating a sustainable ecosystem that underpinned Britain’s eighteenth-century maritime success
Data-driven weaker mixed formulation for diffusion problems
Get PDFThis thesis presents a data-driven mixed finite element framework for solving elliptic equations, i.e. nonlinear diffusion problems, focusing on heat transfer in porous media such as nuclear graphite. Traditional computational approaches rely on phenomenological material models, which contain empirically estimated parameters. However, the models carry uncertainty about the values of these parameters, which we lose by choosing a single value per parameter, which often leads to inaccuracy or overconfidence with complex or inherently stochastic materials. In contrast, the data-driven (DD) approach, as first introduced by Kirchdoerfer and Ortiz [2016], leverages material data directly, avoiding the need for fitted models and allowing uncertainty estimation from data imperfections, such as noise or missing values.
The framework, which employs a weaker DD formulation, is designed to ensure adherence to conservation laws and boundary conditions. The temperature and its gradient are approximated in the L2 (discontinuous) space while the heat flux is approximated in the H(div) space, which enforces normal flux continuity across internal boundaries and provides a posteriori error estimates. An algorithm for adaptive mesh and order refinement is proposed, guided by error indicators and the proximity of the computed fields to the material dataset, assessing the suitability of the material dataset to the problem at hand while minimising computational effort. In contrast to the original “stronger” DD approach, weaker mixed DD formulation results in problems with fewer unknowns and searches through the material dataset while reaching the same accuracy.
The developed framework is applied to a nonlinear problem where heat flux depends on both temperature and its gradient. Uncertainty quantification is performed by perturbing the resulting fields and repeating the iterative process, akin to Monte Carlo simulations, to obtain the standard deviation of the results. Knowing the standard deviation of the results and the distances of the resulting fields from the dataset, the information of the quality of the material dataset for the problem is obtained, suggesting where the material data does not cover the ranges of the fields in the analysis or where the data is too noisy or missing.
The developed framework reformulates the original DD approach and can be further extended to include more complex material datasets and physical phenomena. Allowing for the control and minimisation of FE errors before quantifying the uncertainty of the results propagating from the dataset, the framework can be applied to challenging industrial problems.
This thesis is one of the steps towards the development of a data-driven finite element framework for the analysis of graphite bricks in the advanced gas cooled nuclear reactors (AGRs), which is a complex and evolving environment. The current work introduces and tests the "weaker" DD approach on diffusion problems and provides a foundation for future research and development of the DD framework for more complex problems, such as fracture analysis in nuclear graphite bricks. To this end, a preliminary study is performed on a nuclear graphite brick slice with synthetically generated material datasets.
This thesis is written for engineers, and all of the work is open and reproducible. The implemented framework is an independently developed module [Kulikova, 2024a] in MoFEM, an open-source parallel finite element library, and all examples are collected in another open repository [Kulikova, 2024b] for the reproducibility of the results presented in this thesis
Genomic surveillance and biogeography of vampire bat rabies in Central America and Mexico
No full textAbstract not currently available
How can Health Technology Assessment (HTA) support the optimal use of high-cost devices? A case study of robotic-assisted surgery in Scotland
Get PDFBackground and aims: Health technology assessment is a multidisciplinary process that evaluates the safety, efficacy, and cost-effectiveness of healthcare interventions, guiding evidence-based decisions and addressing social, organisational, and ethical issues. Traditionally, health technology assessment has been effectively applied to new drugs for reimbursement decisions post regulatory approval, leveraging clear evidence on costs and consequences. However, high-cost medical devices present unique challenges that complicate the health technology assessment process, such as incremental development, context dependency (i.e. organisational impact), quality variation (i.e. evidence uncertainties) and physical mode of action (i.e. device-operator interaction). Additionally, health technology assessment is often overlooked at the time of equipment purchase, leading to uncertainties in assessing long-term value and impact.
Surgical robotics, specifically robotic-assisted surgery, exemplifies these challenges. Robotic-assisted surgery systems are expensive, complex, and have been rapidly adopted despite ongoing debates about their evidence base. In Scotland, significant investments in surgical robots were made in 2021, aiming to improve access to minimally invasive procedures and reduce health inequalities. Decision-makers now face the challenge of expanding robotic assisted surgery services amidst these uncertainties.
Given the ongoing adoption and expansion of robotic-assisted surgery in Scotland, this thesis proposes there could be a role for health technology assessment methodology in addressing these associated uncertainties and helping decisionmakers to prioritise the expansion.
This thesis aims to investigate whether health technology assessment can support the optimal use and implementation decisions for high-cost devices by taking a case study of robotic-assisted surgery in Scotland. This study seeks to determine how health technology assessment can guide future investment and expansion decisions, ultimately informing strategies for integrating innovative technologies into healthcare systems effectively.
Methods: To explore the role of health technology assessment in optimising the use of robotic-assisted surgery for Scottish decision-makers, this research employed a multi-step approach.
First, an overview review of clinical effectiveness evidence was conducted to identify which specialties and procedures were most likely to benefit from the expansion of robotic-assisted surgery. This review aimed to map the current landscape of evidence across intra-cavity procedures and pinpoint areas with the most robust comparative outcomes, thereby guiding where the use of robotic-assisted surgery may be most clinically appropriate.
Secondly, a scoping review of economic evaluations of robotic-assisted surgery was undertaken to identify what economic methods have been used in analysing robot-assisted surgery, to investigate how they addressed the challenges of robotic-assisted surgery in economic evaluation research and to explore what opportunity there is to improve methods of evaluation. Insights gained from this analysis informed the design of a tailored approach for economic evaluation of high-cost devices.
Third, a two-stage economic model was developed, informed by both the evidence reviews and stakeholder consultations which helped shape the model's scope and assumptions to ensure it addressed relevant policy questions and practical constraints. The first-stage model was a short-term, procedure-specific costutility analysis comparing robotic-assisted surgery with laparoscopic and open surgery across selected procedures: prostatectomy, colorectal resection, hysterectomy, and pancreaticoduodenectomy. The second-stage model integrated this into a system-level platform model, allowing for exploration of case-mix strategies, annual procedure volumes, and surgical replacement proportions across specialties. This experimental model enables decision- makers to simulate various utilisation scenarios and identify more efficient strategies for shared robotic-assisted surgery platform use under different capacity and investment constraints.
Results: From the overview of systematic reviews, most evidence was available in urology, colorectal, hepatopancreaticobiliary and gynaecology. A total of 165 systematic reviews were included comparing robotic-assisted surgery to laparoscopic and open surgery. In my developed novel evidence map, it presented the strength of evidence and its orientation. Within the selected procedures, the evidence (such as conversion rate, estimated blood loss, length of hospital stay, and postoperative complication) was largely neutral or positive for robotic-assisted surgery compared to both laparoscopic and open approaches with the exception of operative time. Evidence was more positive compared with open surgery. I found that most systematic reviews were of low quality due to a failure to deal with the inherent bias in observational evidence.
In the scoping review of economic evaluations, a total of 50 studies addressing the economic analysis of robotic-assisted surgery were identified. Cost-utility analysis (46%) was the most commonly applied economic evaluation method, followed by cost-consequence analysis (32%). Generally, I found the evidence on the costeffectiveness of robotic-assisted surgery compared to open and/or laparoscopic surgery was mixed, with evaluations having a high degree of heterogeneity including multiple indications, outcomes, comparators, time horizons, perspectives and settings. Distinctive features related to the assessment of robotic-assisted surgery were under-addressed in economic evaluations. Only 40% of the included studies considered learning curve and organisational impact including capital cost investment, annual volume of procedures and platform sharing, and less than 12% of the included studies reflected on incremental innovation and dynamic pricing. Only two studies addressed the fact that the surgical platform was shared. Overall, a large proportion of economic evaluations did not explicitly account for the specific characteristics of robotic-assisted surgery. It is clear that to have a more realistic assessment of the costeffectiveness of robotic-assisted surgery, economic analysis should consider these distinctive features to ensure its optimal utilisation in clinical practice.
In the two-stage economic model evaluation, stage one demonstrated that while robotic-assisted surgery consistently offered higher utility gains compared to laparoscopic and open surgery, its cost-effectiveness varied significantly by procedure. Among the procedures studied, robotic-assisted surgery was not cost-effective against laparoscopic surgery, but showed more favourable results when replacing open surgery, particularly in prostatectomy. Scenario analyses indicated that removing capital costs, representing settings where surgical robots are donated or externally funded, substantially improved the cost-effectiveness of robotic-assisted surgery. However, even in such cases, the opportunity cost of the capital investment must still be considered, especially when viewed from a system or national perspective. Sensitivity analyses identified utility values, length of stay, and operative time as the most influential drivers of cost-effectiveness. These variables also helped explain the findings of the threshold analyses, which showed that increasing the proportion of open surgery replaced by robotic-assisted surgery consistently reduced incremental cost-effectiveness ratios. Stage two extended the analysis to the system level, showing that cost-effectiveness depends on both procedural mix and surgical volume. Economies of scale were critical, with most strategies only becoming cost-effective at ≥350 cases annually, or when focused on high-impact procedures. These findings highlight that RAS can represent value for money if strategically deployed at sufficient volumes and targeted to procedures with the greatest marginal benefit.
Conclusion: This thesis highlights the critical role of health technology assessment in supporting the optimal adoption and utilisation of high-cost medical devices, with a specific focus on robotic-assisted surgery. The research demonstrates that health technology assessment provides a vital tool for decision-makers, facilitating a structured approach to assess the clinical effectiveness, costeffectiveness, and broader implications of innovative technologies.
The overview of clinical effectiveness narratively summarises the evidence and maps it into a novel evidence spectrum. It showed that evidence for robotic-assisted surgery is largely neutral or positive compared to laparoscopic and open approaches. This suggests that selective adoption of robotic-assisted surgery could improve patient outcomes through strategic replacement of more invasive techniques.
The scoping review of economic evaluations revealed that key features unique to robotic-assisted surgery, such as the learning curve, platform-sharing potential, volume sensitivity, and dynamic pricing, are often neglected in existing models. Incorporating these elements can offer a more realistic and comprehensive understanding of robotic-assisted surgery’s value, guiding more efficient decisions around adoption and scale-up.
Building on these insights, a two-stage system-level economic model was developed, offering a novel approach to guide resource allocation and utilisation strategies post-acquisition. Stage one assessed procedure-level cost-effectiveness; stage two allowed decision-makers to test alternative configuration scenarios, such as case-mix, annual volumes, and replacement strategies, based on their local context. The framework provides decision-makers with a practical tool for planning, emphasising that the role of RAS lies not only in clinical outcomes but also in enabling broader access to minimally invasive surgery and guiding resourceefficient service delivery.
While the model provides recommended prioritisation strategies, successful implementation depends on operational realities such as workforce capacity, procedural demand, and existing infrastructure. Nonetheless, its adaptability allows for iterative refinement as new data and service constraints emerge. Ultimately, this thesis demonstrates that HTA can be applied not only at the point of adoption but throughout the technology’s lifecycle, from early evaluation to post-investment optimisation. Though centred on RAS, the insights and methods presented here are generalisable to other high-cost, cross-specialty platform technologies. The research provides a robust and adaptable framework for ensuring that such innovations are integrated into healthcare systems in a costeffective, evidence-informed, and context-sensitive manner
Insomnia after stroke: understanding its impact, assessment, and treatment.
Get PDFBackground: Insomnia – broadly characterised by difficulties with initiating or maintaining sleep – is common after stroke, and is associated with poorer outcomes and greater risk of stroke recurrence. Yet, it has historically received less attention than other post-stroke sequelae. Consequently, current understandings of insomnia’s aetiology, impact, assessment, and treatment after stroke are limited. Seeking to address this, the present studies set out to answer four key questions:
1. What is the experience and impact of insomnia after stroke?
2. What are stroke survivors’ experiences and preferences relating to treatment?
3. How can we accurately identify which individuals are living with insomnia after stroke?
4. How can we best go about treating insomnia after stroke?
Methods: This thesis adopts a mixed-methods approach, and comprises three studies and one study protocol. Study 1 (n = 10) employed semi-structured interviews and interpretive phenomenological analysis to explore stroke survivors’ experiences of insomnia, and their preferences surrounding treatment. Building on the findings of this, Study 2 (n = 180) used receiver operating characteristic (ROC) analysis to assess the discriminant validity of a self-report measure of insomnia after stroke. Study 3 systematically reviewed 15 studies examining the efficacy of stimulus control and sleep restriction, independently and combined. Due to an absence of relevant studies within the context of stroke, older adults were selected as an appropriate adjacent population. Finally, Chapter 5 integrates the findings of all previous chapters, and presents a protocol for a single-case experimental design study evaluating the feasibility, acceptability, and efficacy of a behavioural intervention for insomnia, adapted to meet the needs of stroke survivors.
Results: Findings from Chapter 2 suggest that post-stroke insomnia arises from a complex network of psychological, physical, cognitive, and social factors, each deleteriously influencing one another. Participants described shortcomings in current clinical understanding and management of sleep after stroke, and expressed a strong preference for non-pharmacological approaches to treatment; largely due to scepticism over the safety and efficacy of hypnotic medications. Results of Chapter 3 demonstrate that the insomnia measure effectively distinguished between stroke survivors with and without insomnia, though the optimal diagnostic cut-off (≤ 13) was lower than the conventional cut-off (≤ 16). Finally, stimulus control and sleep restriction significantly alleviated insomnia and depression symptoms, particularly when combined, with benefits observed in as few as two sessions. Collectively, these findings establish a robust point of departure for future research seeking to investigate sleep after stroke
Circulating microRNA expression in canine apocrine gland anal sac adenocarcinoma
Get PDFAberrant blood or tissue miRNA profiles have been documented in multiple different diseases in both humans and animals. There is growing interest in positively exploiting the different miRNA expression profiles for research and medical purposes by identification of biomarkers that link to specific diseases and can be used for diagnostic, monitoring or prognostic purposes.
The aims of this study were to examine the plasma microRNA expression in dogs diagnosed with apocrine gland anal sac adenocarcinoma (AGASACA) and to identify potential miRNA biomarkers for diagnosis and disease staging. The second aim was to validate selected candidate miRNAs using RT-PCR.
Initial miRNA profiling was performed using single-end sequencing on an Illumina NovaSeq 6000 System at LC Sciences, comparing dogs with AGASACA to healthy controls, as well as across different clinical stages of disease (non-metastatic, lymph node metastasis and distant metastasis). Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed overexpression of genes involved in PI3K and Wnt signalling, cell cycle control and cell adhesion; concepts important in tumorigenesis and epithelial-mesenchymal transition (EMT).
Several differentially expressed miRNAs were identified and shortlisted for further analysis based on statistical significance and known oncological relevance. Validation using RT-qPCR focused on promising candidates. MiR-92b emerged as a reliable and reproducible diagnostic biomarker showing consistent upregulation in AGASACA cases across two independent cohorts. MiR-1246 demonstrated potential for detection of distant metastasis and also diagnosis of AGASACA. Interpretation of miR-1246 was limited by low case numbers and possible interference of different isoforms. Despite these challenges, the study provides compelling evidence for the use of circulating miRNAs as non-invasive biomarkers for the diagnosis (and possible staging) of AGASACA in dogs
Representations of the past in political discourse: comparing Britain, France and West Germany during Britain’s first application to the European Economic Community (1961–63)
No full textAbstract not currently available
An exploration of spatio-temporal statistical models for landslide hazard assessment and prediction
Get PDFThis thesis develops and applies statistical modelling techniques for complex spatial and spatio-temporal geophysical datasets, with a particular focus on the estimation and assessment of landslide hazard and surface deformation. The bulk of this thesis’s methodological framework is grounded in Bayesian inference, utilising the integrated nested Laplace approximation (INLA) for efficient computation. The models employed are of the latent Gaussian class, wherein observations are conditioned on an unobserved latent field that captures residual spatial or spatio-temporal variation. This latent field is represented via a Mat´ern Gaussian Random Field, approximated through the Stochastic Partial Differential Equation (SPDE) approach. Domain-specific covariates - geographical, geological and meteorological - are incorporated within the hierarchical structure. In doing so, the thesis explores landslide hazard in terms of where landslides occur, when they occur, and how large they are.
The statistical approaches developed span a range of modelling strategies, including susceptibility models (presence/absence), Poisson and log-Gaussian Cox processes (LGCPs), functional generalised additive models (FGAMs), and a custom space-time SPDE smoother implemented within the Mixed GAM Computation Vehicle with Automatic Smoothness Estimation package (mgcv), which is a flexible framework for modelling non-linear relationships within GAMs. This enables the integration of high-resolution environmental covariates and a functional precipitation predictor, with various continuous distributions used to model landslide size.
Chapter 2 introduces a unified landslide hazard framework through a Hurdle model, jointly modelling occurrence (via a Bernoulli process) and size (via a log-Gaussian model for planimetric extent). This enables the creation of hazard maps that provide probabilistic estimates of large-event exceedances, along with their associated uncertainty.
Chapter 3 presents an updated landslide susceptibility model for Scotland, developed for the British Geological Survey (BGS). It includes a proposed LGCP extension and provides the first data-driven landslide susceptibility framework for the BGS, benchmarked against their previous heuristic model, GeoSure.
Chapter 4 addresses a key methodological challenge: the influence of mesh resolution and integration scheme in SPDE-based point process models with fine-scale covariates. Motivated by issues encountered in the BGS application, a series of simulation studies explores the effects of mesh specification, culminating in a case study where a marked LGCP is fitted to a Japanese landslide inventory using landslide size as the mark.
Chapter 5 explores the temporal dimension of landslide hazard through a spatio-temporal model of surface deformation in a region of China over a two-month period. This chapter introduces a functional precipitation predictor and transitions from a Bayesian to a frequentist framework, motivated by limitations in earlier precipitation representations. It implements, for the first time, a space-time SPDE Matern smoother within mgcv, enabling flexible modelling of deformation using high-resolution covariates and functional data analysis techniques
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