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Investigating the Crashworthiness Performance of Hybrid Composite F1 Nose Cones: A Benchmarked Hierarchical Approach
No full textFormula One (F1) safety structures face a critical design conflict as they require high specific stiffness to minimize mass yet must exhibit progressive crushing to limit deceleration forces. While hybrid Carbon/Kevlar composites offer a solution, their interacting failure mechanisms are poorly predicted by traditional isotropic design methods. This thesis develops and benchmarks a hierarchical computational framework to resolve these mechanisms and optimize hybrid crashworthiness under FIA regulations.
At the micro-scale, Phase Field fracture modelling identifies fundamental damage triggers. A key contribution is the resolution of Kevlar/Epoxy anisotropy; the study demonstrates that standard isotropic models overestimate transverse stiffness by nearly 70%. The developed anisotropic RVE model predicts a corrected modulus of E22=5.15 GPa, aligning with experimental benchmarks. Furthermore, the fracture model captures the crack initiation angle with a deviation of only 1.6% (62° vs. 61° experimental), significantly outperforming standard XFEM approaches (18% error). These calibrated properties inform a meso-scale Continuum Damage Mechanics (CDM) model. Low-Velocity Impact (LVI) simulations identified a “Crack-Arrest” mechanism in hybrid topologies. The Carbon-Outer/Kevlar-Inner configuration matched the peak force of monolithic carbon (+1.6%) while absorbing 38.6% more energy through controlled delamination. To assess the framework for high-energy axial crushing, simulations were benchmarked against Formula Student experiments, achieving a correlation error of less than 3.5%. This analysis quantified the topological trade-off the Carbon-Outer/Kevlar-Inner configuration optimized occupant safety (lowest peak deceleration of 33.11 G vs 42.89 G baseline), while the Kevlar-Outer configuration maximized Specific Energy Absorption (SEA) to 43.98 kJ/kg.
Finally, a parametric optimization workflow identified a hybrid F1 nose cone layup that capped Peak Deceleration at 36.84 G (safely below the FIA 40G limit), achieving a 22.8% reduction in G-forces. This work defines a verified, physics-based strategy that resolves the stiffness-toughness conflict in high-performance safety structures
Differential Expression of Molecules that Contribute to the Pathogenesis of Bladder Pain Syndrome
Full text linkBladder Pain Syndrome (BPS) is a chronic, clinically heterogeneous condition characterised by pelvic pain and urinary urgency, yet it lacks reliable biomarkers and consistently effective treatments. Traditional classification systems—primarily based on cystoscopic findings such as Hunner lesions—do not adequately capture the underlying biological diversity. This thesis investigated whether transcriptomic profiling of bladder tissue could reveal molecular signatures associated with distinct BPS subtypes, thereby informing more targeted diagnostic and therapeutic approaches.
A systematic review of 20 molecular studies was first conducted to identify candidate genes and proteins implicated in BPS pathophysiology. These targets informed analysis of an in- house transcriptomic dataset derived from bladder biopsies of 13 clinically well-characterised female patients, encompassing both BPS and non-BPS cases. Expression data were interpreted in conjunction with cystoscopic findings, histology, trans-epithelial resistance, and O’Leary-Sant symptom scores.
Although differential gene expression between diagnostic groups was limited, individual- level analyses revealed transcriptional variability suggestive of biologically meaningful subtypes. Two samples (Y2336, Y2338) exhibited prominent immune activation—including elevated IL6, TNF, CCL2, and STAT1/3—despite differing cystoscopic phenotypes. A third case (Y2610) displayed high CNR1 expression with minimal inflammatory signalling, consistent with a neuroplasticity-associated profile. Most remaining samples showed muted or heterogeneous expression patterns, potentially reflecting biologically quiescent states, unprofiled mechanisms, or sampling limitations.
These findings provide preliminary support for a stratified model of BPS, including immune- enriched, neurogenic, and oxidative stress–linked subtypes. The heterogeneity observed cautions against population-averaged interpretations and highlights the potential of personalised transcriptomic analysis. This thesis contributes novel data toward molecular characterisation of BPS and sets the stage for future multi-omics studies in larger cohorts. Refining such subtype frameworks could ultimately improve biomarker development and guide personalised management in chronic bladder pain
Tense and tendencies: a linguistic analysis of verbal data from people with low mood
Full text linkMy psychologically grounded linguistic analysis of verbal data investigates the link between language use and low mood. The focus of this study is low mood such as that experienced as part of mood disorders including depression. I address research question one: What linguistic markers are prevalent in the verbal language of people experiencing low mood? And research question two: Do people with low mood focus on the past?
Existing studies into language and mood disorders frequently rely on written language without demographic metadata. This obfuscates the potential for intersectionality, whilst popular analytical software LIWC is methodologically obtuse.
In this thesis, I cover the distinctive process of data generation including informant recruitment, interviews and transcription alongside categorisation. The focus of this study is the researcher elicited verbal data from informants with low mood, which is compared to the data from the remaining interviews as a reference.
I analysed the language data for patterns potentially associated with low mood, including stylistic, thematic, syntactic and temporal preponderances. I primarily used corpus linguistic methodologies including keyword and part of speech analysis. I also combined the psychological framework of attribution theory with linguistic appraisal theory.
The findings for research question one include an increased focus on the self and a reduced sense of agency for people experiencing low mood when compared to the reference group.
Further to this, low mood informants use a less varied vocabulary when compared to the reference group. The low mood informants are specific about negative things and talk generally about positive things, whilst the reference informants are the opposite. In response to research question two, low mood informants consider their current experience: important events, people and effects to be constant and unchanging. These findings have applications for linguistic approaches to mood data and for health professionals’ usage in outreach and amelioration
VeriPy and ChatGPT-Aided VeriPy: A Compatible, Extensible, and Comprehensive Python-Based High-Level Synthesis Framework for Readable and Optimisable Hardware Generation
No full textField-Programmable Gate Arrays (FPGAs) are widely adopted in domains such as Artificial Intelligence (AI) and Software-Defined Radio (SDR) due to their reconfigurability and inherent parallelism. However, existing High-Level Synthesis (HLS) tools still suffer from major limitations, including rigid coding styles, limited transparency of generated hardware, restricted low-level control, and suboptimal performance caused by conservative compiler-driven scheduling and optimisation. These issues contribute to a persistent gap between software-oriented development and efficient hardware implementation.
This thesis addresses these challenges through the development of VeriPy and ChatGPT-Aided VeriPy, two Python-based HLS frameworks that prioritise explicit hardware intent, structural transparency, controllable optimisation, and stable use of generative AI. Unlike conventional HLS tools that rely heavily on automated scheduling and opaque intermediate representations, VeriPy adopts a deterministic Python-to-Verilog translation methodology that preserves algorithmic structure and exposes parallelism explicitly. Performance improvements are achieved through user-controlled unrolling and pipelining, fine-grained mapping of operations to hardware primitives, and the generation of lean, modular Verilog that enables effective downstream synthesis and timing optimisation. An extensible hardware library further promotes modular reuse of verified building blocks, reducing over-generalisation and inefficiencies common in traditional HLS flows.
ChatGPT-Aided VeriPy extends this approach by automatically restructuring standard Python code into VeriPy-compatible, hardware-aware representations, significantly reducing coding effort without changing the underlying hardware mapping strategy. By constraining generative AI to front-end code transformation and retaining deterministic backend generation, the framework ensures stability and repeatability.
Experimental results show that VeriPy achieves up to 147% higher maximum frequency and 5700% higher throughput than Vivado HLS 2024 on selected benchmarks, with competitive resource usage. ChatGPT-Aided VeriPy reduces code length by up to 88% while maintaining equivalent performance. Overall, this work bridges the software–hardware gap without sacrificing hardware efficiency
Singular Control, Ambiguity and Real Options
Full text linkUncertainty in decision making is a core challenge in economics, finance, and operations research. While classical models address risk with known probabilities, real-world environments often involve ambiguity, where probabilities are unknown or misspecified. Ambiguity arises in corporate cash holdings, climate-driven water management, and inventory control, each structured as an inventory-type problem in which resources accumulate or deplete and interventions occur once thresholds are reached. This thesis develops singular stochastic control models that explicitly incorporate ambiguity, extending the scope of real options theory beyond risk-based formulations.
The thesis progresses from restrictive to more flexible ambiguity frameworks. The first study analyzes cash management under maxmin utility with κ-ignorance, showing via Dynkin games that extreme ambiguity aversion narrows inaction regions and raises expected costs, providing a tractable benchmark. The second study considers reservoir management under smooth ambiguity with a finite-state hidden variable, capturing flood-drought dynamics under climate uncertainty. Using forward-backward stochastic differential equations (FBSDEs), Hamilton-Jacobi-Bellman variational inequalities, and an efficient Markov chain approximation scheme, it shows how ambiguity aversion accelerates interventions while learning gradually mitigates this conservatism. The third study advances to smooth ambiguity with Gaussian hidden variables in a finite-horizon inventory setting, where the problem is formulated through an FBSDE with quadratic growth. This analysis uncovers novel patterns: while higher risk typically delays action, under deep ambiguity, it can instead prompt earlier and more cautious interventions.
Together, these contributions enrich the theory of singular control by embedding ambiguity preferences within nonlinear expected utility, advance analytical and numerical techniques of independent interest, and offer practical insights for managing cash, water, and inventory under deep uncertainty. Conceptually, all models act as forms of ambiguity insurance, quantifying the additional strategic costs required to safeguard decisions in environments where probabilities are themselves uncertain
The nowhere place: an autofictional approach towards understanding the unspeakable dimensions of queer trauma.
No full textTraumatic experiences are often marked by unspeakability. The impact of violence and its enduring effects on a survivor’s emotional landscape frequently resists articulation, eluding both language and traditional narrative forms. As the work of trauma writers and survivors such as Christina Sharpe, Cathy Caruth, and Dorothy Allison demonstrates, language often falls apart when called upon to represent the embodied, lived realities of individual and collective suffering, of violence and its reverberating aftermaths. As traumatic memory is often characterised by fragmentation, absence, and the persistent return of the past, trauma narratives are shaped by silence, disruption, and non-linearity. The limits of language mean that it lacks the capacity to fully bear witness to the complex and multifaceted dimensions of trauma.
This thesis employs a creative-critical autofictive methodology, comprising playwriting, literary close reading, creative freewriting, and critical as well as (auto)theoretical analysis to explore the creative, conceptual, and political potentialities of trauma’s fraught relationship with language. The play, titled The Nowhere Place, tells the story of four women who materialise in a sentient realm beyond this earth. The characters learn to navigate this land through engaging with the traumatic memories of their past and sharing these experiences with one another.
This thesis demonstrates how creative approaches can open new avenues for expressing and understanding trauma. The analysis chapters adopt a reflexive methodology, drawing on the voices of trauma theorists alongside writers of fiction and memoir. Grounded in my own lived experiences, I engage with autofiction as a visceral and empathetic means of navigating and representing trauma, blurring the boundaries between truth and fiction to explore the complexities of queer identity, traumatic memory, and survival. Framing trauma as a response to the lived experience of violence, this project places emphasis on the survivor’s embodied reality and the enduring impact of violation
The Effect of Darkness on the Number of Cyclists
Full text linkCycling remains a marginal mode of transportation in many countries despite its known benefits and the efforts made by those countries to promote it. Darkness may be one barrier to cycling, as without sufficient light cyclists may experience a reduced sense of safety because it is more difficult to see obstacles in their path and to be seen by other road users like motorists to avoid collisions. The main aim of this thesis is to investigate the effect of darkness on cycling rates.
Previous studies have used a comparison of cyclist counts in case hours (that change between daylight and darkness) and control hours (that remain in the same ambient light condition) to assess the effect of darkness on cycling. These studies tended to use only one to two case and control hour combinations in their analysis, when up to 88 other combinations are possible across their datasets. This thesis shows that the choice of case and control hours significantly impacts the measured effect of darkness. An alternative method is therefore proposed to combine all possible case and control hour combinations into a single measure of the effect of darkness on cycling.
The improved method is then applied to cycling data from 15 cities around the world to investigate the effect of darkness on their cyclists. The findings from this analysis confirm a reduction in cyclist numbers after dark, with the size of this effect varying across cities. When exploring this variance at a city scale, the effect of darkness tended to decrease in cities higher in latitude and stronger in cycling culture; when exploring this variance at the scale of cycling sites within the city, the effect of darkness tended to decrease in sites dominated by utilitarian cycling and closer to the city centre
Exploring the Hardware Design Space for Practical Lattice-Based Post-Quantum Cryptography
No full textAs quantum computing advances threaten to undermine classical encryption schemes, cryptography must evolve to maintain secure communication. Modern cryptographic standards will soon be replaced by recent Post-Quantum Cryptography (PQC) standards developed to mitigate the risks posed by quantum computing. However, this transition introduces increased computational overhead, creating a heightened demand for efficient hardware accelerators to achieve practical performance. Among PQC propositions, lattice-based schemes are considered leading contenders due to their robust mathematical security foundations. Nevertheless, their practical deployment is hindered by performance bottlenecks, notably the computational cost of polynomial multiplication, which drives key generation, encryption, and decryption.
This thesis addresses this challenge by investigating the optimisation of polynomial multiplication in lattice-based schemes through hardware acceleration. It reviews both time-domain (e.g., schoolbook, Karatsuba, Toom–Cook) and frequency-domain (e.g., Number Theoretic Transform (NTT)) methods, identifying modular arithmetic as the primary bottleneck.
To tackle this, the thesis presents two constant Barrett modular multiplication algorithms: the constant Barrett and a novel Truncated Modulus-Size Constant Barrett (TMSCB) variant. Complexity analysis and FPGA implementations demonstrate that the proposed TMSCB algorithm achieves up to a 2.8 reduction in area--time product compared to classical Barrett and up to a 1.4 reduction compared to constant Barrett at larger operand sizes, while reducing register usage by approximately 16.7\%. These algorithms are then integrated into scalable NTT hardware accelerators for ML-DSA and Falcon schemes. The designs exploit DSP slice efficiency, achieving execution-time reductions of up to 46.7\% and hardware area savings of up to 35.4\%, while improveing speed and resource utilisation. In addition, a parametric and scalable schoolbook-based polynomial multiplier is proposed for time-domain multiplication, exploiting coefficient splitting and truncation for power-of-two moduli and achieving execution-time reductions of 36--51\%.
Overall, this research enhances the practicality of post-quantum cryptographic hardware by optimising polynomial multiplication, enabling high-performance and deployable implementations
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