The four paws of robotics: designing a zoomorphic robot for animal welfare education

This doctoral thesis investigates the design of a zoomorphic robot for animal welfare education with children. Animal welfare education can cultivate positive attitudes and behaviours towards animals, thereby promoting positive, safe child-animal interactions and enabling children to experience the many benefits of human-animal relationships. Zoomorphic robots (robots that are animal-like in appearance and behaviour) are promising tools for animal welfare education because children engage with them somewhat like live animals and their interactivity could make interventions more engaging and effective. Since animal welfare education is a novel application domain, the thesis encompasses work to investigate children's perceptions of zoomorphic robots and how to design a zoomorphic robot that is suitable for animal welfare education. First, we aimed to better understand children's attitudes towards and beliefs about a zoomorphic robot, focusing on how small behaviours that implied the robot could feel emotions, like tail wagging, affected their opinions. The findings indicate that these subtle behaviours can influence children's beliefs about a zoomorphic robot. Subsequently, we invited end-users, namely animal welfare educators and schoolchildren, to co-design concepts for a zoomorphic robot for animal welfare education. We aimed to understand what children perceived as important features of pets and what animal welfare educators needed to teach children about safe, appropriate behaviour around pets. From this work, we identify several points of convergence and divergence in design requirements between the two groups, present guidelines for the design of educational pet robots that are applicable beyond animal welfare education, and challenge the widespread emphasis on cuteness in zoomorphic robots. Finally, we created a new design for a zoomorphic robot, implemented it by customising an existing platform, and developed an educational intervention focusing on dogs' emotions and welfare needs and responsible dog ownership. Evaluation in schools showed that the intervention improved children's knowledge and beliefs about dogs. While the zoomorphic robot did not significantly outperform a stuffed toy in terms of learning outcomes, children's experience of the intervention was better, highlighting the potential for robots to enrich animal welfare education in the future. This thesis contributes new insights into children's interactions with zoomorphic robots, design ideas for zoomorphic robots and educational robots, methodologies for eliciting these ideas from children and educators, and a prototyped and evaluated system that can support animal welfare education

The non-canonical role of the outer kinetochore protein KNL-1 in axonal development

The nervous system is composed of specialized cells; glia and neurons, that form an interconnected network to relay information. Proper transmission of information relies on the two main compartments within neurons, dendrites, that receive information, and axons, that relay the information through specialized domains called synapses. Axon development is a multi-step process that involves, axon outgrowth, guidance, termination and synaptogenesis. In every step of axonal development rearrangements in the microtubule and actin cytoskeleton are essential to mediate the morphological changes that the axon undergoes. The molecular mechanism governing cytoskeletal regulation during axon development is still not fully characterized. Recent findings have highlighted a novel, non-canonical role for the outer kinetochore protein network, KMN (Knl-1, Mis12, Ndc80), in neuronal development. KMN, primarily recognized for its role in tethering chromosomes to spindle microtubules during chromosome segregation in cell division, has emerged as a potential key cytoskeletal regulator in neurons. This work investigates the noncanonical neuronal role of the outer kinetochore signalling and scaffolding protein KNL-1, in brain organization and axon development. In my thesis I show that KNL-1 is essential for axon organization and termination in the nervous system of C. elegans. In the first part I show that KNL-1 is required for the organization of the C. elegans nerve ring axons and ganglia organization in the brain. Specifically, loss of KNL-1 affects the correct placement and fasciculation of the axons within the nerve. Structure-function analysis of KNL-1 showed that this function requires both the signalling and microtubule binding domains of KNL-1. The second part of my work reveals an essential role for KNL-1 in axon termination, a process whereby the axonal growth cone is destabilized and stops its growth upon reaching its target. The effect of KNL-1 in axon termination, requires reorganization of F-actin at the axonal tip and is regulated by microtubule dynamics. In the final part, I have used Correlative Light-Electron Microscopy and a GFPTrap of KNL-1 in C. elegans embryonic neurons to identify the neuronal structures and proteins that KNL-1 associates within the axon. KNL-1 associates with endo-lysosomal structures in the cell body and synaptic vesicles in the axon. Mass spectrometry analysis revealed a synaptic protein as a potential interactor-candidate of KNL-1. This work showed a new potential link of KNL-1’s neuronal activity with synaptic organization and function. Overall, this study provides insights into the mechanism by which the outer kinetochore component KNL-1 functions in brain development, identifies a novel role for this protein in axon termination and reveals neuronal interactors of KNL-1 highlighting a potential role of the protein in synapses

Investigating (in)equalities in organisations: a three-paper thesis on gender and expatriation in the Saudi Arabian context

This thesis examines the employment experiences of underrepresented groups, particularly women and non-Western expatriates, within Saudi Arabian organisations. The research contributes to the growing HRM/IHRM literature on employment (in)equalities by examining how socio-cultural and institutional dynamics shape access to opportunities in Saudi Arabia’s distinctive organisational and socio-economic context. The examination of such dynamics with broader cultural and institutional structures is significant because organisations play a pivotal role in either perpetuating or mitigating societal (in)equalities, both locally and globally. This thesis is particularly significant as Saudi Arabia undergoes profound societal and economic reforms under Vision 2030, which prioritises greater workforce diversity and inclusion. Using a qualitative approach and thematic analysis of 85 in-depth interviews conducted in Riyadh, the research investigates how socio-institutional characteristics in the evolving Saudi context shape employment (in)equality. Study 1 applies an intersectional framework to analyse the roles of gender, tribal affiliation, and marital status in shaping women’s employment. The findings reveal that while tribal affiliations and marital status often act as barriers, they also confer certain privileges; for example, tribal networks (wasta) can facilitate job opportunities, and divorce may sometimes offer women increased autonomy. Study 2 explores self-Orientalism among both non-Western expatriates and Saudi nationals, revealing how internalised cultural hierarchies perpetuate discriminatory hiring, pay, and role assignment practices, sometimes reinforced by the Kafala system. This study contributes to IHRM literature by highlighting how socio-cultural dynamics influence employment (in)equalities within Saudi private-sector organisations. Study 3 examines the impact of Islamic religiosity on Saudi women’s workplace experiences, particularly through the lens of various forms of hijab. The findings demonstrate a disconnect between societal support for Islamic modesty and organisational biases, with certain forms of hijab, such as the niqab, hindering career advancement despite social acceptance. Together, these studies contribute to HRM and IHRM literature by examining how intersectionality, Orientalism, and religiosity interact to shape employment (in)equalities. In highlighting the dynamics of employment (in)equalities, the thesis offers valuable insights for understanding the complex interactions between cultural practices, institutional frameworks, and workplace diversity in Saudi Arabia. The thesis provides practical recommendations for organisational leaders and policymakers to promote inclusivity and diversity in the workforce, aligning with the goals of Saudi Vision 2030

Computational complexity in quantum learning tasks

Quantum learning theory is a burgeoning field at the intersection of quantum information and machine learning theory. However, current research for learning quantum objects typically focuses on efficiency with respect to sample complexity, rather than computational complexity. In this thesis, we study classes of quantum processes and classical functions for which computationally efficient quantum learning is achievable. On the other hand, it is known that there exist classes of quantum states that are provably hard to learn in polynomial time, under standard cryptographic assumptions. We consider the converse: can we design useful cryptography from a given class of quantum states that is hard to learn efficiently? We answer this question affirmatively by constructing fundamental cryptographic primitives from the computational hardness of learning. This reflects the deep connection between learning and cryptography in the classical world, which has been widely unexplored in the quantum setting. First, as an example of a computationally efficient quantum learning algorithm, we prove a rigorous quantum advantage against classical gradient methods for learning periodic neurons. Next, we develop a more general formalism, called agnostic process tomography, for approximating an unknown quantum channel by a simpler one in a given class. In this setting, we prove that the correct measure of efficiency is computational complexity and design efficient quantum algorithms for learning a wide variety of processes. Finally, having understood when quantum algorithms can learn efficiently, we study the hardness of learning classes of quantum states and its applications to cryptography

Temporal and regional white matter vulnerability associated with Alzheimer's disease pathology

Dementia is a term used to describe the loss of cognitive function that is sufficiently severe to impact daily activities and affects over 55 million people worldwide. Alzheimer’s disease (AD) is the leading cause of dementia, accounting for ~70% of cases. AD is a devastating age-associated disease that involves brain atrophy, neuronal loss and the accumulation of amyloid beta (Aβ) and tau protein aggregates. In recent years, there has been progress in developing disease-modifying treatments for AD. However, these drugs only show a modest clinical benefit and have significant side effects. Safer and more effective treatments are urgently needed. The white matter becomes compromised up to 20 years before the onset of AD symptoms. Yet, until recently, the contribution of white matter damage to AD has been largely understudied. White matter integrity is crucial for healthy cognitive function and damage to the white matter is predictive of cognitive decline. However, the neuropathological changes to the white matter during AD are unclear. In order to understand AD-associated white matter damage, I examined axon health, myelin integrity and the myelin-producing oligodendrocytes in the AppNL-G-F mouse model of AD. This is a second-generation model of AD that involves Aβ plaque deposition without the non-physiological overexpression of amyloid precursor protein (APP) seen in earlier mouse models. I investigated the cellular and molecular changes in the white matter across different regions and stages of pathology. AppNL-G-F mice showed no changes to oligodendrocyte lineage cell densities at 3 months of age, at the onset of Aβ plaque deposition. However, by 6 months of age, when cognitive deficits have been reported, there was a significant decrease in mature oligodendrocytes in the lateral white matter, which persisted at 9 months of age. Interestingly, the medial white matter was unaffected. This suggests that the lateral white matter region is vulnerable during the accumulation of AD pathology. The reduction in mature oligodendrocytes was followed by a loss of small diameter axons in the lateral white matter of 9-month-old AppNL-G-F mice. This suggests that oligodendrocyte loss leaves axons vulnerable to degeneration. To identify the mechanism underpinning oligodendrocyte vulnerability, digital spatial transcriptomics was performed. The molecular profiles of oligodendrocytes were examined in both medial and lateral white matter regions in AppNL-G-F and WT mice. Differential gene expression analysis revealed oligodendrocytes in the white matter of AppNL-G-F mice were enriched for genes involved in autophagy, an essential protein degradation pathway. Medial oligodendrocytes were enriched in genes associated with chaperone-mediated autophagy (CMA) while lateral oligodendrocytes were enriched in genes associated with macroautophagy and apoptosis. These data indicate that lateral oligodendrocytes undergo changes in protein degradation mechanisms which may leave them vulnerable to apoptotic cell death. The resulting oligodendrocyte loss might drive axon degeneration and contribute to AD-associated cognitive decline. Overall, this work demonstrates marked regional and temporal vulnerability of the white matter in the context of AD pathology. These data reveal novel cellular responses to AD pathology and highlight oligodendrocytes as a potential therapeutic target to prevent disease progression

If all you have is a hammer, everything looks like a nail: spectral trends as a measure of ecological change in the Arctic

The Arctic is warming four times faster than lower latitudes. Observations from spatially limited tundra field sites show increased vegetation growth, expansion of woody shrub cover, decreased snow cover and extreme permafrost thaw disturbances. Satellite imagery is fundamental to our understanding of these land-surface changes across the Arctic and, thereby, to our ability to predict feedbacks to global climate. Positive trends in the satellite-derived normalised difference vegetation index (NDVI) have been broadly observed and attributed to increased vegetation productivity, instigating a discourse of Arctic greening, while negative trends, or browning, are less common and broader in attribution. However, methodological issues such as spectral mixing within satellite pixels, the saturation of the NDVI, and the period of the analyses have emerged as a source of significant uncertainty in the detection and ecological interpretation of spectral greening and browning trends. In this thesis, I use high-resolution drone and satellite imagery to assess the effect of snow, fractional vegetation cover, and permafrost thaw slumps on our ability to detect and interpret spectral trends. Snow cover has decreased in extent and duration across much of the Arctic but is poorly accounted for in spectral trend analyses. By using high-resolution drone imagery from one Arctic and one sub-Arctic site, I found that fine-scale snow persistence within satellite pixels is associated with both reduced magnitude and delayed timing of annual peak NDVI, the base metric of spectral trend analyses. These findings indicate that unaccounted changes in fine-scale snow persistence may contribute to Arctic spectral greening and browning trends through either biotic responses of vegetation to snow cover or abiotic integration of snow within the estimated peak NDVI. Across the Arctic, changing snow persistence may drive both underestimation and overestimation of changes in vegetation productivity. Fractional vegetation cover corresponds with spectral mixing and the saturation of the NDVI. However, vegetation cover is difficult to calculate at the scale of satellite pixels, and the relationship between vegetation cover and spectral trends therefore remains unknown. I found that spectral Sentinel-2 data can predict vegetation cover at a high-latitude and low-latitude tundra site, and subsequently observed that predicted vegetation cover differed significantly between pixels with and without spectral trends. These results suggest that a pixel’s vegetation cover may affect our ability to detect spectral trends, due to spectral mixing within low vegetation cover pixels and saturation of the NDVI within high vegetation cover pixels. Spatial variation in spectral greening and browning across the Arctic may, in places, reflect underlying patterns in fractional vegetation cover more than the presence or absence of vegetation change. Permafrost disturbance events are an often-cited source of spectral browning, however, the effect of their timing and subsequent recovery on trend detection has received limited attention. I use a pan-Arctic dataset of retrogressive thaw slumps to examine the representation of permafrost disturbance events in spectral trends derived from Landsat imagery. I found that spectral browning occurred over less than half of the analysed thaw slumps (~49%) due to post-disturbance vegetation recovery and the time period of analysis. Ultimately, this may lead to an underestimation of permafrost disturbance-related change across the Arctic. Together, my thesis findings demonstrate that spectral trend analyses, although familiar, are a somewhat blunt tool for inferring Arctic vegetation change from satellite imagery. Attributing spectral trends to field observations of ecological change is complicated by a lack of methodological nuance, where unaccounted variation in snow, vegetation cover and dynamic permafrost thaw disturbances may obscure the detection or interpretation of trends. Overall, this thesis highlights three confounding effects on spectral trend analyses that should be considered to improve future assessments of Arctic land-surface change

Utilisation of Artificial Intelligence in Accurate Translation of Peace Agreements: A Practical Assessment

This report explores how Artificial Intelligence (AI) technologies can enhance the translation of peace agreements for the PA-X Peace Agreements Database. Peace agreements are vital instruments in conflict resolution, making accurate translations essential. This report addresses the challenges of translating these complex documents, which has traditionally relied on academic researchers, translation professionals, and domain experts. The report examines AI-driven approaches to improve sustainability, efficiency, and linguistic precision in this critical work

Quantifying stress-induced mutagenesis in bacteria exposed to low-dose antibiotics

Bacteria are frequently exposed to antibiotics, particularly at low doses, which induces stress responses in the cells. Some of these responses increase mutagenesis and thus potentially accelerate resistance evolution. Many studies report increased mutation rates under stress, often using the standard experimental approach of fluctuation assays. In this thesis, I extend the mathematical model behind the fluctuation assay to include within-population heterogeneity in stress responses. Our model is inspired by the DNA-damage response in Escherichia coli (SOS response). It accounts for a subpopulation with high expression of the stress response, which increases the mutation rate and decreases the division rate of a cell. In Chapter 2, I implement maximum likelihood estimation and stochastic simulations of fluctuation assays under existing and our new population dynamic model. Using the simulated data, I show that this new model, in principle, allows for estimating the increase in mutation rate specifically associated with the induction of the stress response. However, I also show that when heterogeneity is neglected, an accurate estimate of the increase in population-mean mutation rate is recovered. Moreover, in many cases, different models can explain the data equally well and, therefore, cannot be distinguished using fluctuation assay data alone. In Chapter 3, I apply our estimation method, which I converted into a user-friendly R tool, to published experimental data. I show that not all experiments that report an increase in mutation rate significantly support the hypothesis of stress-induced mutagenesis. Moreover, I find that DNA-damaging antibiotics particularly increase mutation rates and identify several signals of heterogeneity in stress-induced mutagenesis. In Chapter 4, I study how stress-induced mutagenesis depends on the antibiotic dose. By modelling different antibiotic modes of action, I determine under which conditions within-population heterogeneity can lead to a non-monotonic increase in mutation rate with antibiotic concentration. Such a maximum increase for intermediate concentrations has been previously observed empirically. Overall, this thesis improves the estimation of mutation rates in bacteria under stress, which could contribute to better predictions of the evolution of antibiotic resistance

Machine learning for detecting fetal hypoxia using cardiotocography and pregnancy risk factors

BACKGROUND: Fetal hypoxia during labour is characterised by an insufficient oxygen supply in the womb during active uterine contractions. Although this condition represents a normal physiological compensatory response, certain infants are unable to adapt, resulting in severe consequences such as cerebral palsy, developmental disorders, and neonatal mortality. Cardiotocography (CTG) is a device that records fetal heart rate (FHR) and uterine contractions (UC), generating a graphical representation of these measurements. Clinicians utilise this non-invasive CTG to monitor alterations in FHR in response to UC, thereby identifying fetuses at risk of hypoxia during labour. However, human factors may compromise the quality and consistency of CTG interpretation. Previous research has indicated an increase in the caesarean section rate without corresponding improvement in the incidence of cerebral palsy. Machine learning (ML) has demonstrated the potential for detecting hypoxic fetuses using CTG data. Nonetheless, the majority of studies have relied on the same open-access dataset, and the absence of external validation and inconsistent hypoxia surrogate measures impedes clinical application. Moreover, although pregnancy risk factors can influence fetal hypoxia during labour, there is a paucity of studies employing ML in this domain. Consequently, the objective of this thesis was to develop ML prediction models for fetal hypoxia using CTG and pregnancy risk factors. METHODS: A scoping review was conducted to examine how existing CTG prediction models were studied and developed. In this thesis, CTG data from the UK and Czech Republic were compared. This comparison used exploratory data analysis (EDA) to find differences in CTG patterns between healthy and hypoxic fetuses. This thesis is the first to use Apgar scores as the gold standard for hypoxia. Statistical tests were performed to better understand CTG signal characteristics between hypoxic and normal fetuses. The review also helped to build and validate the CTG-ML models. For pregnancy risk factors modelling, EDA was performed on US pregnancy health records, and ML models were used to predict fetal hypoxia during labour. Logistic regression, a common tool, was used to determine the odds of different pregnancy risk factors. All ML models were checked using various metrics, such as misclassification errors, AUROC, Area Under the Precision-Recall Curve, Brier score, and calibration plots. RESULTS: The scoping review revealed that none of the previous studies incorporated UC when modelling FHR despite clinical recommendations. Additional gaps identified included the use of varying benchmarks for hypoxia surrogate markers, inconsistent analysis of CTG characteristics, and a lack of population generalisability owing to reliance on the same open-access CTG database. For CTG modelling, data were available for 4,909 women. The proportion of low Apgar scores was 2.1% for the UK dataset and 3.4% for the open-access dataset, respectively. In the external dataset, extreme gradient boosting with under-sampling and feature selection achieved the highest recall (sensitivity) of 0.95 and an area under the receiver operating characteristic curve (AUROC) of 0.59. However, the other metrics were suboptimal, with precision and F1 scores below 0.10. In the modelling of pregnancy risk factors, the study included 13,823,214 women, with low Apgar scores comprising 1.3% of the entire dataset. The odds ratio indicated a significant association with smoking before pregnancy, a finding that has not been previously reported. There were no differences in the prediction accuracy among the various data enrichment methods. The best results were obtained using multilayer perceptron, a type of neural network and extreme gradient boosting, with a recall (sensitivity) of 0.66 and an AUROC of 0.64. In addition, the precision and F1 score were below 0.05. CONCLUSION: The overall performance of the CTG and pregnancy risk models was suboptimal, as ML was unable to effectively differentiate between low and normal Apgar scores. Although statistical differences were observed in variables between groups, these differences do not necessarily translate into distinct separability in ML modelling. This limitation may be attributed to overlapping features between groups and/or small effect sizes. Furthermore, the subjective nature of the Apgar score evaluation renders it unsuitable as a benchmark for assessing fetal hypoxia using ML

Equitable Urban Cycleway Routing using Multi-Criteria Analysis: A Case Study of Inner South London

Frequent cycling offers both personal benefits like disease risk reduction and wider city scale benefits like improved air quality and reduced traffic casualties. While this emphasises the importance of active-travel modal shift, perceptions of unsafety limits cycling rates. Segregated cycleways improve perceived safety, encouraging active travel, with cycleway placement and orientation being important in influencing cycling rates. However, demand-orientated routing disproportionately favours demographic groups already overrepresented among cyclists. To meet Transport for London’s (TFL) goal of increasing cycling participation across the wider population it is essential that cycleway alignment considers the needs of underrepresented groups. This dissertation uses Multi-Criteria Evaluation (MCE) to locate routes which expand Inner South London’s cycleway network while considering the needs of underrepresented groups to equitably spread cycleway benefits. The study area is underserved by both public transport and cycle infrastructure. Network Analysis is used to find appropriate routes with road segments weighted to consider locational attributes that reflect inclusivity orientated cycling route choice parameters. The analysis finds several contiguous North-South routes in densely populated areas of South London have high suitability. Routes are characterised by their location along commercial streets with high service density and route connectivity. Analysis also identifies the value of creating a contiguous network by linking adjacent commercial corridors using residential often public park adjacent side-streets to create accessible, secure alignments. When altering impedance weights to favour high street-lighting and cycle path routing is largely consistent suggesting alignment along commercial streets is an inclusive practice