27716 research outputs found
Sort by
Advancing Transcranial Electrical Stimulation for Personalised Cognitive Enhancement
Get PDFTranscranial electrical stimulation (tES) demonstrates potential for cognitive enhancement, including improvements in vigilant attention. However, its efficacy across the population is limited by numerous challenges, including inter-individual variability in the electric fields induced by tES and brain-state dependent responses to stimulation.
This thesis first presents a comprehensive literature review critically analysing challenges in non-invasive brain stimulation (NIBS) research, encompassing the principal brain stimulation modalities and sources of variability. Recommendations are presented, including accounting for inter-individual variability, inclusive research practices, improving spatial precision, and the use of multimodal and multi-site stimulation, among others.
Following the literature review, this thesis addresses the challenge of accessible, participant-specific tES dose control through a novel MRI-free approach developed using the largest electric field modelling simulation of its kind. This scalable approach, which utilises readily available demographic and morphological data, significantly reduces peak electric field strength variability across participants compared to fixed-dose stimulation.
Commercial and academic trends within NIBS and tES are analysed to assess the market readiness for the proposed MRI-free tES dosing approach. This includes a systematic search of academic publications across NIBS modalities and of clinical trials utilizing tES. In addition, the current state of the commercial usage of tES and intellectual property challenges are discussed.
This thesis then explores the electrophysiological correlates of vigilant attention during a continuous random-dot motion task, known to measure spatial attention performance. Electroencephalography features associated with arousal, active attentional suppression and off-task thought are identified. Potentially enabling the future development of brain-state dependent stimulation.
Subsequently, a within-subject sham-controlled electroencephalography and transcranial direct current stimulation experiment investigates the effects of transcranial direct current stimulation applied to the right dorsolateral prefrontal cortex on vigilant attention. While no significant effects were observed, the methodological insights of this work are discussed.
Together, the advances presented in this thesis contribute to a multifaceted approach to addressing key challenges in NIBS research. This thesis proposes a scalable approach to personalized tES dosing, insights into the electrophysiological underpinnings of vigilant attention and offers broader NIBS recommendations. Ultimately, these contributions aim to advance translational, accessible, and individualized cognitive enhancement
How Does Exposure to Organic Food Products Affect Consumer Responses? Exploring the Moderating Role of Consumer Impulsiveness
No full textIntegrating Flow Imaging and Deep Learning into Patient-Specific Models Following Myocardial Infarction
Get PDF4D-flow magnetic resonance imaging (MRI) provides non-intrusive blood flow reconstructions in the left ventricle (LV) and other cardiac chambers and has the potential to become a key tool in both research and clinic. However, low spatio-temporal resolution and the presence of significant noise artifacts hamper the accuracy of derived haemodynamic quantities and thus limit the effectiveness of the modality to establish links between haemodynamic abnormalities and pathologies. Furthermore, models that are constrained by boundary conditions are impacted by additional uncertainty which arises due to the low spatial resolution of the structural cine-MRI.
4D-flow MRI data corruption, introduced by low resolution and noise artefacts, may be alleviated through super-resolution and de-noising methods, which have been explored in the literature for haemodynamic flow in the vasculature. In this thesis, a physics-informed neural network (PINN) model is introduced to provide super-resolution and de-noising, specifically of cardiac 4D-flow MRI. The model is constrained through weak enforcement using the low-resolution 4D-flow MRI data, the no-slip boundary condition on the endocardium and the governing physical equations. Model components are compared and incorporated to address specific challenges introduced by modelling haemodynamic flow in the heart chambers, such as flow across a range of length and time scales within a heavily deforming domain. Validation of the model is performed across synthetic and in vivo studies, evaluating the robustness of the model to uncertainty in both the 4D-flow MRI data and the position of the deforming endocardium. Following this, the model is applied to a small cohort of LV remodelling patients.
It is demonstrated that the PINN model is able to effectively upsample and de-noise the velocity field across a range of spatio-temporal resolutions and signal-to-noise ratios (SNR), and is robust to positional uncertainty in the deforming endocardium for flow variables measured away from the domain boundaries. Further, variables that are not directly measured, such as relative pressure and flow derivatives, are reconstructed to an acceptable degree of accuracy. In the dual-resolution in vivo validation study, it is shown the model is generally independent to the spatial resolution and SNR of the input 4D-flow MRI data.
Through synthetic and in vivo validation studies, it is demonstrated that the PINN model introduced in this thesis is effective. It is concluded that the use of this type of model is feasible for super-resolution of cardiac 4D-flow MRI data, although certain limitations should be addressed and the model should be further validated using in vivo or in vitro data
The impact of critical audit matters on market behaviour: analysing reactions from corporate managers, short-sellers, and social media
Get PDFMotivated by the recent changes in auditing regulations and the addition of Critical Audit Matters (CAMs) in the audit report, this thesis aims to investigate the impact of CAM disclosure on three groups of users during the first year of the new audit report mandates. Specifically, the thesis examines the reaction of corporate managers as internal users and short-sellers as sophisticated investors through utilising a difference-in-difference research design, and the Twitter investment community as the wider public by focusing on the relevance of CAMs to Twitter users through both textual and regression analyses.
The three user groups have been chosen based on a careful examination of the standard-setter’s communication as well existing literature to identify gaps in existing knowledge, and to contribute to our understanding of the new auditing CAM regulations. In addition to examining the effect of changes in auditing regulations on the three user groups, the thesis also investigates whether the type, topic and number of CAMs are associated with a significant reaction by the users. To begin with, the Public Company Accounting Oversight Board (PCAOB) acknowledges that the changes in auditing regulations are expected to result in changes to management disclosure behaviour as well even though the new disclosures in the audit report are targeted mainly at investors. Recent literature investigating the reaction of management by studying disclosure behaviour suggests that CAMs are associated with significant changes to both financial and non-financial disclosures. Moreover, recent archival literature suggests that CAMs do not offer much in the way of incremental information, as evidenced by insignificant market reactions. Alternatively, experimental research offers a different inference, where it shows that investors with different levels of sophistication, resources and experience react differently to CAM disclosures. This is consistent with literature suggesting that different investors interpret information in the market differently.
Taking this into account, the thesis presents an overview of the institutional background of the development of the auditing regulations for the three standard setters in Chapter Two, namely the FRC in the U.K., the IAASB and the PCAOB. The Chapter shows the similarities in the approaches taken by the three standard setters, and also shows the differences in the implementation of the relevant standards. The thesis also provides a critical review of the relevant literature in Chapter Three to highlight the rationales behind the mixed results, particularly when investigating the reactions of equity investors, and to underscore the strands of literature the empirical studies will be expanding upon.
The thesis contributes to the debate on the usefulness of CAMs by investigating the reaction of (i) corporate managers in Chapter Five, (ii) short-sellers in Chapter Six, and (iii) Twitter users in Chapter Seven. Drawing from the accounting theory of disclosure, as well as recent relevant literature, the first empirical study investigates how CAM disclosures impact the textual properties, such as length, complexity and tone of item 7 of the 10-K report, the Management Discussion & Analysis (MD&A). The overall results show that while there are significant changes to the textual properties of the MD&A sections of the first group of CAM adopters after implementing the new auditing regulations, these changes cannot be attributable to CAMs. The results are persistent for alternative measures of the textual properties. The results of additional tests provide evidence that the type, topic and number of CAMs are associated with changes in the MD&A textual properties, which is consistent with prior literature that suggests that auditors influence the textual properties of the MD&A text (De Franco et al., 2020).
The second empirical Chapter uses a sample of 3,698 firm-year observations and employs a difference-in-differences research design to investigate if short-sellers, arguably the most sophisticated group of investors in terms of obtaining, processing and reacting to information, react to CAM disclosures. The theoretical rationale of this study is based on the line of literature arguing that investors with different levels of experience, knowledge and education react differently if provided with the same information. Consistent with prior literature, the results show no significant relationship between short-seller interest and CAMs. These results are robust for alternative measures of short-interest. Additional tests do not find evidence that suggests that short-sellers react to the type of CAMs, or the number of CAMs disclosed. There is, however, evidence to suggest that short-sellers may be interested in firms that receive specific CAM topics.
Finally, and following the rationale that users with different levels of sophistication interpret information differently, the third empirical Chapter investigates the discourse on CAMs within the online investing community by using Twitter as a novel research setting due to its popularity within the investing community. Through the Twitter API, 824,916 Tweets discussing 1,870 public U.S. firms within a one-month window before and after the release of their 10-K filings are mined and scraped. Textual analysis methods are used to identify tweets that discuss the same topics as the CAMs received by the firms they mention. The results show that only 1,905 tweets, representing 442 firms, are relevant to the CAM topic of the firms they mention. Overall, the results find little evidence to support the notion that CAMs are new information, implying that what auditors consider “critical” may not always be of interest to Twitter users. Overall, the results of the empirical studies should be informative for auditors and auditing standard setters
Predicting melanoma patient outcomes using digital pathology
Get PDFMelanoma is the most aggressive form of skin cancer and fifth most common cancer in the UK. Although immunotherapy has improved outcomes for patients with advanced stages of the disease, some patients do not respond to these treatments or develop resistance during therapy. Additionally, the current staging system which is used for determining prognosis and guiding the treatment of melanoma patients, shows significant variability, with some early-stage patients progressing to metastatic disease. Therefore, identifying which patients will respond to treatments and discovering new prognostic biomarkers have become crucial research areas for improving patient outcomes. The development of digital slide scanners has meant that tissue slides, which contain a wealth of phenotypic information, are being digitised to generate whole slide images (WSIs). This thesis investigates how artificial neural networks with digital pathology workflows, can be used to stratify patients based on their outcomes.
Firstly, we classified patients' WSIs into subgroups based on the inferred expression of immune cells within their tumors. These immune subgroups, derived from genetic data, present varying potential treatment targets and survival outcomes. WSIs are multi-resolution, multi-gigabyte images containing billions of pixels but often will only have a slide-level label. To handle this, we employed multiple instance learning (MIL) methods, where the image is divided into numerous patches, and the image is classified based on a set of these patches using a single supervisory label. We are among the first to investigate how factors such as patch resolution, feature extraction methods, and MIL techniques influence the classification of melanoma patients into immune subgroups. In a primary melanoma dataset, we achieve a mean area under the receiver operating characteristic curve (AUC) of 0.80 for classifying histopathology images into high or low immune subgroups and a mean AUC of 0.82 in an independent TCGA melanoma dataset. Our findings indicate that using 10x resolution patches, pathology-specific feature extraction methods, and attention-based MIL models improve classification performance.
To build on these MIL methods, we introduce a novel way to represent WSIs, using multi-resolution patch graphs, with resolution-aware node embeddings. These graphs enable us to capture long-range dependencies within the images. By employing graph neural networks to aggregate surrounding node embeddings from the WSI patches, we improved the classification performance beyond the MIL models. Here, we achieved a mean test AUC of 0.81 for classifying low and high immune melanoma subtypes, using graph representations.
Finally, we use survival graph neural networks to identify new prognostic subgroups from patch graph WSI representations. Here we show that the risk groups and immune subgroups generated from the models presented within this thesis were predictive of melanoma-specific survival (Concordance index = 0.73), even when adjusting for known prognostic factors. This suggests that these risk groups could represent novel predictive and prognostic biomarkers. Overall this thesis adds to the evidence that digital pathology workflows are an emerging tool for better understanding melanoma patient outcomes and survival
Towards an Understanding of Quantum and Post-Quantum Correlations in Three Causal Settings
Get PDFUnderstanding quantum and post-quantum correlations in various causal settings, is fundamental to singling out the first set from the latter. We present progress in this study in three directions: correlation self-testing of quantum theory, exploration of post-quantum correlations within an indefinite causal order, and certification of nonlocal quantum correlations in the absence of freedom of choice.
Correlation self-testing refers to identifying a set of tasks optimal performance of which, can only be achieved using quantum theory. The adaptive CHSH game, which was proposed as a candidate task, requires a theory to allow for entanglement swapping, if a post-classical performance is to be achieved. Its performance, however, was not explicitly tested in theories that do. In fact, a theory in which this task can be executed better than quantum theory, has also been proposed. We show that this theory does not violate Chained Bell inequalities and therefore can be ruled out. We present adaptive GHZ and adaptive Chained Bell Games and analyse its performance in various theories. Finally, we show that the existing theories that allow for entanglement swapping can also be ruled out using the adaptive CHSH game.
In the second part, we introduce an operational definition of superposition, consistent with quantum theory. We then construct a toy theory that admits superposition, under this definition, and can generate all non-signalling correlations in the Bell setting. We show, that even in an indefinite causal order, in particular, the switch setting, this theory can generate post-quantum correlations. We certify this using theory-independent techniques.
In the final part, we consider a relaxation of parameter independence in the Bell setting by allowing the parties to communicate with each other over a binary symmetric channel. We manage to show, that unless this channel is perfect, all quantum correlations cannot be reproduced using classical strategies. In addition, one way signalling is just as effective as two way signalling, for this channel model
Temporal graph-based convolutional neural networks for electronic health records
Get PDFGraph theory offers a powerful framework for using the relational dependencies in Electronic Health Records (EHRs) to enhance machine learning (ML) predictions of health outcomes and diagnoses. This thesis explores and advances graph-based ML approaches, with applications for the prediction of future hip and knee replacement risk.
A systematic literature review identified 832 studies, with 18 using patient-level graph representations of EHRs for predicting health outcomes. This review showed that current graph-based EHR models have limited clinical applicability due to high risk of bias.
A novel Temporal Graph-Based Convolutional Neural Network (TG-CNN) model was developed. Initially applied to student dropout prediction in online courses, this approach demonstrated state-of-the-art performance. Extending this method to medical data, TG-CNNs were applied to predict hip and knee replacement risks, one and five years in advance. Temporal graphs, constructed from primary care event codes from EHRs, captured temporal relationships between symptoms, diagnoses, and prescriptions. Models achieved AUROC values up to 0.967 for hip replacement and 0.955 for knee replacement.
To improve model interpretability, four explainable methods were explored, including gradient based and feature-mapping approaches. These methods provided visual insights into TG-CNN predictions, highlighting the influence of key EHR features such as prescriptions. While clinicians found these visualisations informative, further simplification is needed to support real-world clinical decision-making.
This thesis demonstrates that graph-based representations improve the predictive performance and interpretability of ML models in healthcare. The TG-CNN model offers the potential to enhance patient care and management through earlier and more accurate predictions. Future work should focus on improving model explainability and translation into clinical practice
Habituating purity: evangelical Christian purity culture and its impact on young women in Great Britain
Get PDFThis project explores the presence of evangelical Christian purity culture in Britain and
its impact on young women within this context. Purity culture refers to efforts in evangelical Christianity – a movement within Protestant Christianity – to encourage adolescents and young adults to commit to sexual abstinence until heterosexual marriage. This phenomenon emerged in the early 1990s in the USA, typified by the wearing of rings and signing of pledges to demonstrate a commitment to abstinence. Thus far, its presence and influence has mostly been examined within the USA, but there is increasing attention to its international reach; this project constitutes the first substantial study to exclusively investigate purity culture in Great Britain.
Drawing on a survey and interviews, this thesis argues for the presence of purity culture
in a specifically British iteration – less overt than its American counterpart but nonetheless evident through a fervent emphasis on sexual abstinence, correlated to understandings of faithful (and biblical) Christian living. Five themes take centre stage – sin, marriage, the body, sexual violence, and shifting faith – the first two as key concepts within evangelical purity culture in Britain, the latter three as key areas of impact.
This thesis utilises Pierre Bourdieu’s concept of habitus to explain how and why these
impacts can be so profound, despite the fact that purity culture in Britain appears more subdued compared to America: the values and expectations of evangelical purity culture are gradually incorporated into the body as long-lasting dispositions, known as habitus. It is argued that, in evangelicalism, community and relationships are centrally important, but that sexual sin risks damaging these relationships; this is conceptualised as the habitus of purity culture. The impacts of purity culture can thus be particularly potent, as living with this habitus means living with the tension of simultaneously valorised and jeopardised relationships
Hydrogen-rich syngas production from pyrolysis-catalytic steam reforming of wastes using char catalysts
No full textThis study comprehensively investigates the production of hydrogen-rich syngas from waste through pyrolysis-catalytic steam reforming in a two-stage fixed-bed reactor system. Various char catalysts, including tire char, biochar, refuse-derived fuel char, and modified carbon catalysts, were utilized to explore the catalytic behavior under different feedstocks and operating conditions.
Tire char was used as a sacrificial catalyst, simultaneously participating in both catalytic steam reforming and carbon-steam gasification. Optimized process parameters-such as high reforming temperatures (up to 1000 °C), steam space velocities (6-10 g h-1 g-1), and high catalyst: plastic ratios-led to significant improvements in hydrogen and syngas yields from HDPE, with hydrogen yields reaching up to 223 mmol g-1. The presence of inherent metals such as Zn, Fe, Ca, and Mg in tire char contributed to its catalytic activity.
Further investigations on single plastics (e.g., HDPE, LDPE, PP, PS, PET) revealed that polyolefin plastics exhibited the highest hydrogen yields (~130 mmol g-1) due to their favorable decomposition behavior. Additionally, real world mixed waste plastics from drink bottles, household packaging, construction waste plastics, agricultural waste plastics and mixed municipal solid waste plastics were investigated. Theoretical maximum hydrogen yields based on elemental compositions were calculated and compared with experimental results for different feedstocks. Biochar and RDF char proved effective for pyrolysis-catalytic steam reforming of plastics, with RDF char offering higher hydrogen potential at high temperatures owing to its higher inorganic metal content. Ashes derived from tires and RDF were also evaluated as catalytic alternatives, where metal contents played key roles in catalytic performance.
Tire char was further used in the pyrolysis-catalytic steam reforming of waste tires. The higher temperature and steam space velocity increased H2 and CO yields. Elemental analysis, surface morphology and pore structure of the used tire char provided insights into tire char consumption in the reaction. Prolonged reaction time allowed for more thorough reactions between the pyrolysis volatiles and tire char, promoting the production of H2. At a reaction time of 2 h, the H2 yield reached 223 mmol g-1, representing 74 wt.% of the maximum hydrogen yield.
In addition, biochar derived from sawdust was tested for steam reforming of both biomass components (cellulose, hemicellulose, lignin) and real biomass. Among the components, the lignin showed the highest H2 and syngas yields, at around 110 mmol g-1 and 140 mmol g-1, respectively, while mixtures demonstrated synergistic effects. Moreover, the incorporation of K and Ca significantly promoted carbon conversion, increased hydrogen production, and inhibited methane generation. Real biomass showed different behavior from model mixtures, indicating complex interactions beyond simple additive effects.
Finally, the properties of pure carbon were modified via acid treatments, metals doping, and steam gasification. The influence of metal species, char surface area and char acidity on hydrogen yield, syngas quality and catalyst stability were systematically investigated.
This work highlights the potential of waste-derived char catalyst for converting plastic tires and biomass waste into valuable hydrogen-rich syngas. The results provide insights into feedstock-catalyst interactions, catalyst design strategies, and the optimization of process conditions for efficient waste-to-energy conversion
Data sources:
Access Repository Dashboard
Do you manage Open Research Online? Become a CORE Member to access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard! 👇