A theological study of hyper-Calvinism in the writings of Joseph Hussey (1660-1726), John Skepp (1675-1721), John Gill (1697-1771), and John Brine (1703-1765)

No abstract available

The creative uses of Irish literature in works by J.R.R. Tolkien

The aim of this thesis is to examine the significant and sustained influence of Irish literature across the corpus of J.R.R. Tolkien’s mythopoeic writings— what is frequently termed as his “legendarium” —and to analyse how he adapted and creatively revised Irish sources in his writings. Some of Tolkien’s statements have given the impression that he did not like, nor was he influenced by, Irish language or literature, and yet scholars have long identified Irish elements in his writings and called for a deeper appreciation of them. This thesis presents the first book-length, systematic critical analysis of the role of Irish literature in Tolkien’s legendarium. It includes the identification and critical analysis of specific works of Irish (and Irish-themed) literature and language study that Tolkien owned, read, is suspected to have read, and/or referenced, including his volumes donated to the Bodleian and English Faculty Libraries at the University of Oxford, and it presents a curated selection of new observations and findings on Irish influences in his writings. As the question of how Tolkien’s work relates to Irish antecedents has been under-researched, this thesis breaks new ground by approaching Irish literature as a major category of Tolkien’s sources and influences

Predicting retrieval failures in conversational recommendation systems

In recent years, the use of dialogue systems and voice assistants commonly implemented in smart devices has shifted the users’ interest towards online shopping. In turn, online shopping platforms are gaining popularity and moving towards allowing an interactive dialogue with users that more accurately depicts a real shopping setting. In this regard, the task of Conversational Image Recommendation is the state-of-the-art task for conversational recommendation in the fashion domain, where a user has a specific fashion item in mind, and interacts with the system with natural language feedback on recommended image items, which guides the system in finding the imagined item in the next turn. Such systems are trained and evaluated with user simulators as a plentiful surrogate for human users. A practical problem with CRS performance is that it is primarily evaluated in terms of successes and is therefore assumed to return the item of interest by a pre-defined number of turns. In practice, often the item is not returned by the end of a conversation, therefore leading to conversational failures; this is our particular setting of interest. In this thesis, we argue that the performance of a Conversational Recommendation System can be predicted to detect when a conversation fails, under different scenarios, across different turns of a conversation. In this regard, Query Performance Prediction (QPP) techniques predict the effectiveness of a ranked list result in response to a query without having access to relevance judgments. We predict the performance of CRS models by treating them as dense retrieval processes, where both the image retrieved items and textual feedback can be represented with dense embedded representations. In particular, we propose a set of coherence-based dense QPPs specifically designed for single-representation dense retrieval models (ANCE and TCT-ColBERT) and show that the examination of the relations among dense embedded representations already contained in the document list is sufficient to provide effective predictions for dense retrieval models. At the same time, by using a multi-level perspective that jointly considers QPPs and types of queries, we explain why some QPPs are better for certain types of queries, thus explaining discrepancies among different evaluation metrics. At the next stage, we predict the effectiveness of a ranking of image items in Conversational Image Recommendation models, which are also based on learned embedded representations of images, and where user feedback takes the place of a textual query. In deed, we create a novel task which we call Conversational Performance Prediction (CPP), which predicts conversation success at the conversation level and taking into account the multi-turn nature of the task, and can differentiate between success predicted over a short-term and a long-term horizon, thereby predicting current user satisfaction or overall satisfaction of a conversation. First, we examine the set of unsupervised predictors developed for dense retrieval models but applied to state-of-the-art Conversational Image Recommendation models; a GRU-based model, which mainly considers the feedback of the previous turn, and an EGE model that considers the entire dialogue history. Our results show that using correlations is not an optimal evaluation strategy for predicting conversational failures, as, while correlations are low to medium mainly for short-term predictions, a lot of inconsistencies are observed among the performance of different predictors across metrics and datasets (similarly to dense retrieval models). Consequently, we propose a supervised CPP approach, which treats CPP as a binary classification task, which predicts whether a target item is returned by a given turn. In this way, we show that by learning the embedded representations already contained in the CRS models, we can predict the accuracy of a conversation success using the retrieved items of both single and multiple turns. In addition, state-of-the-art CRS models are trained using user simulators with a single target item in mind, and at the same time, they are assumed to be infinitely patient. These settings do not reflect a real shopping scenario, where a user might change their mind according to what a shopping assistant is suggesting. For this purpose, we enhance the evaluation completeness of CRS models by obtaining real user opinions in a user study using pooling similar to information retrieval tasks, thus identifying alternative relevance labels for several target items, and in turn, inform the user simulator with an extended target space. This increases the completeness of CRS evaluation, and therefore, creates a more realistic prediction setting for CRS, which leads to improved predictions of user preferences. Indeed, when we reevaluate the CRS models using the updated simulator with the identified alternatives as part of the target space, we show that by the single target setting previously used to evaluate CRS models for a maximum amount of 10 turns was underestimating the effectiveness of CRS models. As a final step, we account for the fact that CRS models assume only one type of recommendation failure, namely the inability of the system to retrieve the target item. In this regard, we introduce the concept of recommendation scenarios, and specifically, we adapt our CPP framework for different types of conversational failures, which are determined by whether the user’s need is clearly defined and whether the target item is available. Therefore, we propose the removed target scenario (the target is not available in the catalogue), and the alternative scenario (a user has a more flexible need, which can be satisfied by either the original target or any of the identified alternatives in the collected datasets). Consequently, we detect different types of conversational failure, such as when a user cannot find an item, versus when the system’s catalogue does not contain the relevant item. By examining the supervised CPP predictors introduced under these two novel scenarios, we find that in both cases, there is a marked difference from the original scenario, and that CPP can indeed be predicted for different recommendation scenarios

Epidemiology of Mycoplasma bovis in Scottish dairy herds

The bacterium Mycoplasma bovis (M. bovis) causes major economic losses to dairy herds resulting from increased mortality and morbidity, treatment costs, and reduced growth of young stock. There was limited knowledge on the prevalence of M. bovis in Scotland and no national monitoring scheme. Two studies were conducted; a longitudinal bulk tank milk (BTM) prevalence study and a cross-sectional seroprevalence study on dairy calves. In the longitudinal BTM prevalence study, one hundred and eighty-one dairy herds across Scotland participated in the study which required them to submit four BTM samples roughly three months apart that were tested for the presence of active M. bovis infection and for recent exposure. A short questionnaire on general herd management practices were issued to farmers to identify potential risk factors associated with seropositivity. At each of the four sampling points, the proportion of antibody positive herds were 76%, 71%, 83%, and 79%, and overall, 86% of herds tested seropositive in at least one of their four samples. Multivariable logistic regression identified herd history of M. bovis as a potential risk factor for the presence of M. bovis antibodies. The questionnaire results also provide an updated overview of the common structures and practices on Scottish dairy farms. Herds were then classified based on the antibody results of their four BTM samples using various methods. Sixty-one percent of herds tested consistently positive for all four samples, 15% consistently negative, and 24% transitional. When classified by k-means clustering of the optical density (OD) trend, the majority of herds had a stable trajectory (44%). A cross-sectional seroprevalence study was then carried out on a subset of herds from the BTM study (n=36) to determine if there was evidence of exposure to M. bovis in youngstock and if there was an association between the BTM and calf seroprevalence. Twenty calves were sampled on each farm (10 animals 4-8 months old and 10 animals 10-14 months old) and a BTM sample collected. There was evidence of youngstock exposure in most herds (58%), and this was associated with the BTM prevalence. The results of this thesis have demonstrated that M. bovis is likely endemic in Scottish dairy herds and has raised further questions on risk factors and within-herd prevalence estimates of M. bovis

Spatial variability of meander characteristics in an avulsing distributive fluvial system

Previous studies of meandering fluvial systems have mainly focused on meanders at a localised ‘reach’ scale within a river system, without consideration of the spatial context. As such, much of the research has focused on exhumed meander deposits instead of active meanders. More research is therefore required on the spatial variability of meander deposits across a single system or sedimentary basin. Recent research has found meandering fluvial systems to be a dominant planform type in modern-day sedimentary basins; meander deposits are consequently assumed to be more dominant than originally perceived in the fluvial rock record. Distributive fluvial systems (DFSs) have also been shown to dominate sedimentation patterns in modern-day aggradational sedimentary basins and therefore warrant further study due to their abundance. Due to the prevalence of meandering systems and distributive fluvial systems in modern-day sedimentary basins, this study aims to fill a critical literature gap with regards to the spatial and temporal variability of meander characteristics across a modern-day distributive fluvial system (i.e., from apex to toe of a DFS). This study uses satellite imagery of Brazil, acquired through Google Earth Engine and analysed in ArcGIS software, to conduct a spatial analysis of the meandering Taquari DFS. The Taquari DFS is a well-documented, dominantly meandering system, which provides a good spatial context for the study of meander characteristics across the DFS. Spatial changes in: channel width, channel belt width, meander deposit dimensions and sinuosity are quantified on the Taquari DFS to explore downstream changes in meander characteristics within this system. Polygons are created in ArcGIS using the available satellite imagery, which allows for detailed measurements of meander dimensions downstream. This study also explores the temporal changes in channel width, channel belt width, meander deposit dimensions, and sinuosity on the Taquari DFS since the initiation of the large Caronal avulsion (initiation between 1996 to 1997) by comparing meander dimensions pre-avulsion and during-avulsion. The Caronal avulsion is ongoing and continues to divert flow from the parent channel to the avulsed channel. Using the oldest and most modern satellite imagery available from 1985 and 2022, respectively, fluvial dimensions are compared between pre-avulsion (1985) and during-avulsion (2022) imagery, to understand the impact of the avulsion on the parent channel (active channel) and its associated channel belt and meander deposits. On the modern Taquari DFS (2022), active variables (i.e., active channel width, active channel belt width, and active meander deposit dimensions) show a decrease in dimensions downstream, with a significant decrease in dimensions downstream of the avulsion point (where flow is diverted to the avulsed channel). Pre avulsion variables were also identified on the 2022 satellite imagery including pre avulsion channel belt width and abandoned meander deposit dimensions. Pre-avulsion channel belt width displays weak downstream trends and abandoned meander deposit dimensions display no downstream trends. Important differences in downstream trends were identified between active and abandoned meander deposit dimensions along the Taquari DFS. The active meander deposits are larger in size than the abandoned meander deposits upstream of the Caronal avulsion point and the abandoned meander deposits are larger than active meander deposits downstream of the avulsion point. The active meander deposits also show clear changes in size and shape downstream as they change from larger, more rounded deposits, to much smaller crescent-shaped deposits. The abandoned deposits however, display a range of shapes and sizes downstream and show no clear decrease in size, especially between medial and distal DFS zones. The decrease in active meander dimensions (active channel width, active channel belt width, and active meander deposit dimensions) is due to a decrease in discharge downstream as a result of typical DFS bifurcation processes in addition to the diversion of flow from the parent channel to the avulsed channel. Active meander deposit size and shape change downstream as sediment load, and therefore deposition, decrease as discharge decreases. The weak downstream trends displayed by the channel belt relate to confinement in the upper DFS where channel belt migration capacity is limited. The lack of downstream trends displayed by the abandoned meander deposits is due to the range of conditions under which these deposits were formed over time. This research has important implications for the understanding of avulsing rivers due to the significant decrease in width of the parent channel and the size of active deposit dimensions downstream, which influence the redistribution of water and sediment resources within modern DFS. In addition, this research creates an important database on the spatial variability of meander deposit dimensions on a modern DFS which can contribute to the understanding of sandstone-body reservoir dimensions which is important for resource exploration or hydrocarbon storage

AI-assisted, Raman Activated Cell Sorting (AI-RACS)

Abstract not currently available

Forces controlling the dynamics of planetary interiors

Convection occurs naturally in the atmospheres of giant planets and within electrically conducting regions of terrestrial planets, such as Earth’s outer core. Over time, increasing attention has been given to these conducting fluid regions in astrophysical and geophysical bodies, as they are believed to generate magnetic fields through dynamo action. Therefore, understanding convection and the dynamo process is fundamental to explaining how magnetic fields are sustained in astrophysical and geophysical bodies. This thesis investigates convective fluid flows under the influence of rotation and magnetic fields. Numerical simulations are conducted using two models: an annulus model with an imposed magnetic field, and a spherical shell model that allows for the self-excitation of magnetic fields. Throughout this thesis, particular attention is given to the forces governing the flow dynamics. The first part presents a literature review of existing work and outlines the methods used in both models. New results from nonlinear simulations of an annulus model with an imposed magnetic field are presented. The study examines how varying the strength of magnetic field and convection affects the prevailing force balances and flow patterns. Additionally, the characteristics of zonal flows and multiple jets within the annulus model are investigated, with particular emphasis on the influence of magnetic field strength and the force balances required to sustain these flows. Zonal flows and multiple jet solutions are typically found at weak magnetic field strength where a strong inertial force is present, although some cases of zonal flows and multiple jets are found at strong magnetic field strength where a strong Lorentz force is present. Force balances occur that are similar to those found in the main regimes of dynamo action. Finally, spherical shell simulations are performed to investigate both forces and solenoidal forces, where flow lengthscales in two distinct directions are examined. Dynamically relevant flow lengthscales are identified by introducing a triple balance point involving key forces characteristic of the main dynamo regimes. These dynamically relevant lengthscales are then successfully compared with energetically dominant scales, highlighting how force balances at particular scales set the size of the flow. The forces and solenoidal forces across different regions of the spherical shell are further analysed. Transitions between the main dynamo regimes are examined, where solenoidal forces are used to explain the mechanisms driving these transition

The application of the Delphi methodology in intervention development for social withdrawal and Hikikomori

Abstract available at each chapter

The effect of emotional intelligence and emotional regulation in elite military units

Despite widespread application of emotional intelligence (EI) assessments in high-stakes occupations, fundamental theoretical and methodological challenges persist regarding how well current measurements represent the nomological network of EI and their predictive validity. The extent to which existing EI instruments capture a unified construct versus distinct psychological phenomena, remains unclear. Simultaneously, while emotional regulation (ER) theory demonstrates clear links to performance under stress, there remains a critical absence of empirically validated intra-personal regulation interventions that can be effectively deployed in real-world, high-pressure contexts. This thesis addresses these substantial theoretical and practical limitations through two complementary studies investigating EI measurement precision and ER intervention efficacy within British Royal Marine Commandos training. Study 1 examined how well current EI measurements represent the construct by investigating convergent validity between two leading ability-based assessments, the MSCEIT and GECo, and their predictive utility for military training success. Analysis revealed weak to moderate correlations between total EI scores, with similarly labelled sub-scales showing negligible correlations, indicating low convergent validity and suggesting these instruments capture different aspects of EI’s nomological network. Comparative analysis demonstrated that Officer recruits possessed significantly higher EI across most domains compared to civilian reference samples, while non-Officer recruits showed mixed profiles. Logistic regression identified only the GECo emotional management sub-scale as predictive of Officer training success, while the MSCEIT showed no predictive utility in either cohort, and no EI measures predicted non-Officer outcomes. Study 2 evaluated a novel three-component ER intervention (The King Strategy®1) combining resonance breathing, cognitive reframing, and vagus nerve reset techniques through a longitudinal experimental design (n=233). Mixed-effects modelling revealed significant improvements in Commando training performance markers for intervention participants. Under acute stress serials, critical findings included enhanced memory recall when ER strategies were actively employed, with optimal heart rate recovery occurring within 99 seconds; beyond this threshold, memory performance declined significantly. Longitudinally, the intervention increased heart rate variability (RMSSD), reduced perceived stress, enhanced interoceptive awareness, and improved EI sub-scales (management, understanding, regulation) while leaving emotional recognition unchanged. These findings contribute to EI theory by demonstrating measurement challenges within the construct’s nomological network while establishing utility for role-specific applications. The study advances ER theory by providing empirical validation of a multi-modal intra-personal regulation strategy and identifying critical physiological thresholds for cognitive performance under stress. Practically, this research informs evidence-based selection processes and provides a deployable intervention for enhancing human performance in high-stakes environments

Investigating the innate immune barriers that constrain the transmission of coronaviruses

Since the turn of the century, the emergence of three highly pathogenic coronaviruses highlights the importance of understanding coronavirus-host interactions. If sufficient cellular factors are available for a virus to complete its life cycle, genome-encoded post-entry blocks to replication may determine whether virus replication is successful. One such barrier is the interferon response, a signalling pathway upregulating hundreds of interferon-stimulated genes (ISGs), many of which encode proteins with specific and potent antiviral activity. The presence and timing of a functional interferon response is important in controlling coronavirus infection. Thus, identifying ISGs with antiviral activity can provide insights into genetic risk factors associated with coronavirus disease severity and the barriers to coronavirus zoonosis. To identify ISGs that inhibit unmodified coronaviruses, I optimised an arrayed ISG expression screening protocol that utilises immunostaining of the dsRNA replication intermediate and quantification of virus infection by plate-based image cytometry. I screened the endemic coronavirus HCoV-OC43 against multiple ISG libraries encoded into lentiviral vectors, including three published species libraries (human, macaque, bovine) and two newly generated libraries (mouse, bat). This revealed ISGs with known and novel antiviral activity against coronaviruses, including 2’-5’-oligoadenylate synthetase 2 (OAS2). OAS proteins classically activate RNase L via the synthesis of 2’-5’-oligoadenylates, resulting in the degradation of cellular and viral RNA. Alternative splicing generates two OAS2 isoforms, p69 and p71, exhibiting differential antiviral activity. I show that the p69 isoform restricts HCoV-OC43,while the p71 isoform restricts the unrelated picornavirus Cardiovirus A (EMCV) via different mechanisms. The OAS gene family thus enhances antiviral breadth in the host genome by both gene duplication and alternative splicing. This research has provided insights into how coronaviruses interact with the innate immune system