SACM - United Kingdom

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    Robust Multi-Layer Calibration of the Heston Stochastic Volatility Model: The Balanced Premium Calibration Method
    (Saudi Digital Library, 2026) Aldossari, Mohammad; Malham, Simon
    This thesis presents the balanced premium calibration method (BPCM), a three-layer framework for robustly fitting the Heston stochastic volatility model to large option datasets. The BPCM method involves three layers. Layer 1 ensures market consistency by filtering and structurally repairing raw quotes via put-call parity and bid-ask bounds, separating stable observations from noise. Layer 2 performs daily least-squares calibration of the Heston parameters using closed-form characteristic function pricing and derived analytic gradients and Hessians, thereby achieving rapid convergence without finite-difference approximations. Layer 3 redistributes errors and allows for controlled adjustments to model inputs and outputs, absorbing residual pricing errors and restoring arbitrage-free consistency. Working with 1.5 million call and put quotes on a major equity from 2018 to 2024, BPCM ensured that model prices closely adhere to market bid-ask spreads (91.58% adherence) for stable regimes while maintaining realistic spot price behaviour. The calibrated model achieves high consistency with observed prices and reconstructs the underlying spot price trajectory with minimal deviation even during market crises. In addition to BPCM, this thesis derives explicit closed-form expressions for the Heston model's Hessians.
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    Prevention of Gestational Diabetes Mellitus (GDM); Targeting Early Pregnancy Intervention to Women at Risk
    (Saudi Digital Library, 2025) Quotah, Ola; Poston, Lucilla
    Background: Gestational diabetes mellitus (GDM) is a common metabolic disorder of pregnancy, particularly among women living with obesity. GDM is associated with increased risk of short- and long-term adverse consequences for both the mother and her infant. Identification of women at high risk of developing GDM would facilitate targeted intervention in those who would benefit the most. This thesis comprises different studies, each aimed at enhancing the understanding, prediction and prevention of GDM in women who are at increased risk of the condition. Overarching aim: To identify pregnant women at higher risk of developing GDM and test the feasibility and efficacy of diet and/or metformin to improve glucose homeostasis and pregnancy outcomes. Methods: This thesis consists of a systematic review and meta-analysis in addition to three distinct studies. These are outlined below: i) ii) iii) Study (1): studies evaluating behavioural/pharmacological interventions or dietary supplements in the preconception period and in pregnant women with greater risk of developing GDM were assessed and evidence synthesised using a systematic review and meta-analysis. Study (2): Using a prospective cohort (the GDM validation study), the performance of two pragmatic GDM prediction tools (model 1 and model 2) was compared in women with obesity. Study (3): A secondary analysis was performed using data collected from women who took part in the UPBEAT study (UK Pregnancy Better Eating and Activity Trial). This analysis compared metabolic profiles in women identified as high-risk of developing GDM using the prediction tool, stratified by GDM diagnosis. Analyte 13 iv) data, measured using a nuclear magnetic resonance platform and conventional assays, were compared between women who developed GDM and those who did not in early (15+0 –18+6 weeks’ gestation) and mid-pregnancy (23+2 –30+0 weeks’ gestation). Study (4): A preliminary feasibility and Patient-Centred outcomes analysis of UPBEAT- Taking it Forward (TIF) was carried out. UPBEAT-TIF is an ongoing randomised controlled trial (RCT) of lifestyle advice with or without metformin compared to standard antenatal care in individuals identified as high-risk of developing GDM using the prediction tool. Results: i) Study (1): The systematic review and meta-analysis identified eighty-four RCTs (preconception=2, pregnancy=82). The review provided evidence that interventions targeted at women at risk of GDM may be an effective strategy for GDM prevention and highlighted the need for further studies using validated prediction tools or multiple risk factors to target high-risk women for intervention before and during pregnancy (Quotah et al., 2024). ii) Study (2): Model 2 incorporating non-biochemical factors (age/mid-arm circumference/systolic-blood pressure), haemoglobin A1c (HbA1c), glucose and triglycerides demonstrated a higher performance in predicting GDM compared to model 1 with non-biochemical factors, HbA1c and adiponectin (AUC 0.755 vs AUC 0.637; p=0.006). Model 2 could facilitate early detection among women with obesity and increased risk, enabling timely implementation of targeted lifestyle or pharmacological interventions for those who are most likely to benefit. However, external validation of the models' performance is underway. 14 iii) Study (3): The secondary analysis revealed biochemical differences between women with obesity identified as high-risk using the predictive tool who went on to develop GDM compared to those identified as high risk who did not. These differences were found both early in gestation and at the time of GDM diagnosis. The identified differences may be related to defective β-cell secretory function (Quotah et al., 2022). iv) Study (4): The preliminary feasibility analysis of UPBEAT-TIF RCT demonstrated that a targeted intervention in women at increased risk of GDM is a feasible approach. Preliminary analysis of outcomes did not support the use of lifestyle interventions with or without metformin to improve glucose concentrations among women who are at increased risk of GDM. The unexpectedly low rates of GDM across all groups, including the controls, warrant additional explorations. At the time of writing, recruitment to the study had not yet reached the predefined sample size. Conclusion: Overall, this thesis makes innovative contributions to understanding GDM pathophysiology and identifying high-risk women in order to assess the potential benefit of interventions. Intervention studies in the preconception period and in early pregnancy that target women at increased risk of GDM using multiple risk factors or validated prediction tools are warranted, as a strategy towards prevention of GDM and improved maternal and infant health among those who would benefit the most.
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    IPO Governance in Saudi Arabia's Energy Sector: Legal Structures, Compliance, and Reform under Vision 2030
    (Saudi Digital Library, 2025) Aljohani, Mohammed; Nigro, Casimiro
    This thesis examines the legal and institutional dimensions of initial public offerings (IPOs) in Saudi Arabia’s energy sector, with a focus on how corporate governance, compliance, and ESG mechanisms are formalized within state-owned enterprises (SOEs) undergoing partial market exposure. IPOs in this context are not solely financial transactions but are also instruments of institutional transformation—tools that enable alignment with international standards while accommodating sovereign policy priorities. Employing a doctrinal legal methodology supported by a case study of Saudi Aramco, the research investigates how statutory provisions, Capital Market Authority regulations, and corporate governance codes shape the IPO process, particularly in relation to board independence, disclosure obligations, and sustainability oversight. The findings reveal that although the Saudi regulatory framework demonstrates formal convergence with global governance norms, its implementation reflects selective adaptation, shaped by the continued presence of state ownership through entities such as the Public Investment Fund. The thesis further explores how IPO governance structures are designed to advance transparency, investor confidence, and ESG integration, while retaining flexibility for national development strategies under Vision 2030. In doing so, it contributes to corporate governance literature by offering a nuanced understanding of hybrid regulatory models, where public-sector priorities and market-facing reforms coexist. Ultimately, the study argues that IPOs in Saudi Arabia’s energy sector function as calibrated mechanisms of governance modernization, balancing legal compliance, institutional credibility, and sovereign interests, rather than serving as vehicles of full privatization or market liberalization.
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    Motion Events Conceptualisation in Arabic and English: A Study on Monolingual and Bilingual Speakers.
    (Saudi Digital Library, 2026) Aljohani, Eman; Aveledo، Fraibet; Kasprowicz, Rowena
    This thesis investigates the typological characteristics and cross-linguistic influences in the encoding of motion events among monolingual Arabic speakers (MA) and bilingual Arabic–English speakers (BAE). Grounded in Talmy’s (1985, 2000) and Slobin’s (1996, 2003) typological frameworks, the research addresses two primary questions: (1) What are the typological patterns of motion event structures preferred by Arabic speakers, and how do these align with verb-framed languages? (2) How does cross-linguistic influence manifest in bilingual Arabic–English speakers when encoding motion events in their second language (English) and their first language (Arabic)? The study particularly emphasises the restructuring of conceptualisation patterns arising from bilingualism, focusing on Arabic, a typologically distinct and under-researched language. The research adopts a quasi-experimental, quantitative approach using two elicitation tasks: a controlled video description task featuring dynamic motion events, and an elicited narrative task using the picture book Frog, Where Are You? (Mayer, 1969). Participants included 30 monolingual Arabic speakers, 30 monolingual English speakers, and 90 bilingual Arabic–English speakers with varied proficiency levels and ages of second language acquisition. Results indicate that MA speakers adhere closely to verb-framed typological patterns by prioritising path verbs, minimally using clausal packaging, and exhibiting relatively detailed ground elaboration. BAE speakers displayed hybrid lexicalisation patterns with task-sensitive cross-linguistic influences in L2 English. Selective and context-sensitive reverse transfer was evident in their L1 Arabic, particularly influenced by L2 proficiency. The findings significantly enhance understanding of bilingual restructuring of conceptualisation patterns, underscoring the nuanced, selective, and proficiency-driven nature of cross-linguistic influences. Practically, these insights can inform targeted second language teaching methodologies, effective translation strategies, and improved intercultural communication between Arabic and English speakers.
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    Linguistic and Multimodal Character Construction in Arabic Dubbing A Comparative Analysis of Original dubbing with Egyptian Arabic and Modern Standard Arabic in Disney Animation.
    (Saudi Digital Library, 2025) alhanaya, amjad; pinto, sara ramos
    This study examines how linguistic variety in Arabic dubbing-specifically the use of Egyptian Arabic (EA) and Modern Standard Arabic (MSA)-affects character construction, multimodal cohesion, and audience engagement in animated films. By comparing the English source text (ST) with its Arabic-dubbed versions, this research investigates the interplay between linguistic and non-linguistic elements, such as gestures, visual representation, and character behaviour, in shaping audience perception. The study employs a mixed-methods approach, integrating qualitative multimodal discourse analysis with quantitative corpus-based examination. Three Disney films-Cinderella (1950), The Lion King (1994), and Pocahontas (1995)-serve as the core corpus, providing a diachronic perspective on the shifts in dubbing strategies. A structured dataset documents character-specific linguistic varieties, intermodal relationships, and translation choices across the English, EA, and MSA versions. The analysis applies the framework of Ramos Pinto & Mubaraki (2020) to assess whether linguistic choices align with, contradict, or exist independently of non-linguistic elements. Findings reveal that EA dubbing enhances character expressivity, humor, and audience relatability, particularly for comedic and emotionally driven roles. In contrast, MSA is often associated with formality and narrative authority but may create communicative distance, especially in character-driven interactions. The study also highlights how industry-driven language policies have shaped the transition between EA and MSA dubbing, reflecting broader ideological and sociopolitical considerations. By bridging sociolinguistics, audiovisual translation, and multimodal analysis, this research contributes to the understanding of how language choice in dubbing influences character portrayal, cultural representation, and audience reception. The findings have broader implications for dubbing practices in Arabic media localization and global audiovisual translation strategies.
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    Advancing Translation Learning with AI: Dataset, Educational Exploration, and Multi-Agent Chatbot Design
    (Saudi Digital Library, 2025) Aleedy, Moneerh Mohammad A; Atwell, Eric; Meshoul, Souham
    This research presents the design, development, and evaluation of an AI-powered chatbot to support English–Arabic translation learning in higher education. It is motivated by three key factors: (1) the heavy instructional workload faced by faculty members at the College of Languages, Princess Nourah bint Abdulrahman University, particularly in supervising and assessing student translation work across various stages of the learning process; (2) the availability of the Saudi Learner Translator Corpus, a rich bilingual dataset offering authentic translation material; and (3) recent advancements in AI and natural language processing, which enable the creation of intelligent, context-aware learning tools. The study contributes to AI-assisted translation education by introducing a structured methodology for generating high-quality English–Arabic parallel sentences from SauLTC and leveraging them to build a modular chatbot system. The chatbot integrates machine learning, deep learning, and NLP techniques, and performs a range of translation tasks, including translation generation, contextual example retrieval, learner translation evaluation, and competence testing. These tasks are implemented through multi-agent architecture, in which each agent is responsible for a specific function to deliver personalized, real-time feedback. The system combines retrieval-based and generative models to provide real-time, personalized support. For instance, one agent handles translation generation, another retrieves relevant examples from a bilingual corpus, a third evaluates user-provided translation, and a fourth tests overall translation competence using similarity scoring. This architecture design enables flexibility, scalability, and pedagogical support, enhancing the learner’s experience and translation accuracy. The system was rigorously evaluated through a mixed-methods approach: corpus quality was assessed using cosine similarity and expert review, generative and retrieval models were evaluated via embedding-based similarity, and the overall system performance was tested in a user study where participants interacted with the chatbot and completed a structured survey. The findings demonstrate the chatbot’s effectiveness in facilitating translation learning while reducing instructor workload, highlighting its potential as an innovative educational tool.
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    Water Management Strategies in Date Palm Farming: A Path toward Sustainable Agriculture in Al-Ula, Saudi Arabia
    (Saudi Digital Library, 2025) Alanazi, Omar Ghaleb; Ainslie, Andrew
    Abstract This dissertation examines water management strategies among date-palm farmers in Al-Ula, Saudi Arabia. The qualitative study, grounded in agroecology and the Water-Energy-Food (WEF) nexus, explores how practices such as drip irrigation, mulching, crop diversification and alternative water sources are implemented and perceived. Semi-structured telephone interviews were conducted with 18 farmers (5-7 ha each), and the data were analysed using thematic coding. Moreover, two-thirds of participants use drip irrigation (12/18 = 67%), most monitor water visually rather than with sensors (72%), and cost and affordability were the dominant barriers (83%). Furthermore, 72% reported having attended some form of training, and 44% expressed concerns about salinity. These local findings resonate with national and regional pressures: agriculture accounts for approximately 72% of freshwater withdrawals globally and stresses renewable budgets in MENA states (Buchholz, 2023), while Saudi investment in desalination is substantial $14.6 billion in projects, and desalination provided roughly 60% of water in 2019 (Fleck, 2023). On the one hand, agroecological measures, mulching, composting and residue reuse, offer low-cost, soil-building benefits (Gliessman et al., 2022; Kavvadias et al., 2024), and hydrogels from date residues can improve retention (Alsubaie et al., 2025). On the other hand, WEF trade-offs are clear: saving water via smart systems may increase energy demand and require policy alignment (Mabhaudhi et al., 2022; Sušnik et al., 2023). Farmers’ pragmatic openness to technology, yet they request targeted subsidies, technician networks and training. Therefore, the thesis argues for locally tailored packages combining agroecological practices, affordable drip retrofits, treated wastewater trials (publicly supported) and WEF-aware planning to sustain production in Al-Ula, with implications for policy and oasis biodiversity stewardship (MEWA; Abd El-Ghani et al., 2025). Moreover, projected climate trends threaten date productivity, with temperature and precipitation negatively affecting yields and slowing growth rates, emphasising the urgency for adaptive water strategies (Emam, 2025).
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    The Universe of Discourse in a Virtual World: Triangulating Discourse Syntax with VR-Mediated Corpora, Sensory Schemata, and Cognitive Simulation
    (Saudi Digital Library, 2026) Alsayed, Abdulrahman Abdullatif A; Horesh, Uri; Bergman, Ted
    In the universe of discourse and its text-internal and text-external worlds, information flows in and out of consciousness, shaping linguistic information structure. This thesis directs its scope to the central roles of cognition and text-external context in the syntactic construction of information structure. Following a comprehensive review of rationalist and empiricist research that identifies a range of context-sensitive constructional phenomena, the thesis presents an innovative simulationist exploration of these phenomena in discourse syntax. A two-phase mixed-method triangulation pipeline is designed and implemented to seek novel insights supported by the parallelism of language, cognition, and computation. In the pipeline’s documentary phase, a virtual reality staging paradigm is applied to collect audiovisual data from thirty native speakers of Peninsular Arabic through utilizing virtual-reality-mediated fieldwork, immersive stimulus content, and participant psychological priming. The data is compiled into an egocentric embodiment corpus, systematically annotated by processing visual captures from virtual reality into spatiotemporal units and visual schema domains, which categorize text-external sensory-modality input based on patterns of visibility and actuation. In the pipeline’s analytical phase, the quantitative component examines occurrence probabilities in the context of visual schema domains through a text-external abstraction linkage. The probabilistic results indicate that exposure to domain patterns triggers statistically significant effects of large magnitude associated with discourse-syntactic marking. A continuum mapping informed by these findings estimates trajectories of cognitive activation, reactivation, and deactivation. The results further guide the qualitative component, enabling an empirical-theoretical integration into models of discourse and common ground in functional linguistics; attentive/preattentive processing and mental simulation in cognitive science; mental spaces in cognitive linguistics; and optimality in formal linguistics. The empirical findings of this work offer an explanatory account of the domain-general cognitive basis of information structure and support new theoretical proposals on sensory schemata, common ground simulation, activation equilibrium, ecosystemic marking inventory, and compensatory constructional optimization.
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    Optimising care for acute ischaemic stroke: Early detection, treatment and outcomes
    (Saudi Digital Library, 2025) Alobaida, Muath Mubarak M; Lane, Deirdre; Lip, Gregory; Harrison, Stephanie; Rowe, Fiona
    Abstract Background and Aim: Stroke management in pre-hospital and the early acute phase is crucial for timely and effective treatment to optimise care and outcomes. This thesis investigates the efficacy of machine learning (ML) models and traditional stroke scales for early detection of large vessel occlusion (LVO), evaluates novel visual impairment screening tools in emergency departments, and analyses outcomes of endovascular thrombectomy (EVT) in stroke patients with atrial fibrillation (AF). Methods: A systematic review and meta-analysis compared pre-hospital stroke scales and ML models for detecting LVO, where the ML models were based on clinical data (e.g., neurological examination findings and demographic characteristics). The effectiveness of Vision-Face-Arm-Speech-Time (V-FAST) checklist in detecting visual impairments was assessed against National Institutes of Health Stroke Scale (NIHSS) and orthoptist assessments in a hyperacute emergency setting. Outcomes of EVT in patients with AF were analysed, focusing on the impact of bridging thrombolysis (BT) and comparing sex and age differences in functional recovery and reperfusion success in a federated network and a nationwide cohort. Results: ML models showed higher discriminative performance than traditional stroke scales but faced challenges in real-world application due to variability and potential biases. The V-FAST checklist improved detection of several key visual impairments (visual field deficits, eye movement abnormalities, reading difficulties and visual extinction) in hyperacute settings. Its purpose is to serve as a screening tool to identify patients requiring comprehensive orthoptic assessment, although it was less effective in identifying complex eye movements disorders. AF status did not significantly impact haemorrhagic complications or mortality following EVT, and bridging thrombolysis (IV thrombolysis prior to EVT) offered a survival benefit in anticoagulated patients with AF. Females with AF had higher odds of good functional outcomes at 90 days compared to those without, and males with AF had higher successful reperfusion rates, especially in older groups. Conclusions: ML models can enhance early detection capabilities for LVO in pre-hospital settings, although their real-world application is limited by methodological and sample heterogeneity. The V-FAST checklist, evaluated within emergency department settings, shows improved detection of visual impairments in acute stroke care. AF status does not significantly impact EVT outcomes, supporting its safe use. Furthermore, the observed sex and age differences in EVT outcomes call for personalised stroke management approaches. Together, these findings present a cohesive research focus on improving acute stroke care across the entire patient journey, from early recognition and pre-hospital triage (via ML and stroke scales) to targeted symptom screening in emergency settings (V-FAST for visual deficits) and evaluation of outcomes in high-risk populations (AF patients undergoing EVT, including those on oral anticoagulants, and across demographic groups). This comprehensive approach supports the refinement of diagnostic and therapeutic protocols to enhance stroke care across diverse clinical environments and patient demographics.
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    The effect of Granulocyte-Macrophage colony- stimulating factor (GM-CSF) on the immune response against Candida albicans using human inflammatory monocytes.
    (Saudi Digital Library, 2026) Munshi, Khawalah Abid; Martinez-Pomares, Luisa
    Granulocyte-macrophage colony-stimulating factor (GM-CSF) plays a pivotal role in antimicrobial immunity by enhancing survival, adhesion, and migration of innate immune cells, including neutrophils, macrophages, and monocytes. Monocytes and macrophages are central for host defence against fungal pathogens such as Candida albicans (C. albicans), acting independently or synergistically with neutrophils. C. albicans is an opportunistic fungal pathogen that lives typically as a commensal. Infection with C. albicans occurs when it shifts from innocuous to pathogenic, often leading to mucosal or systemic diseases. This study investigates how GM-CSF modulates the activity of human inflammatory monocytes during C. albicans infection, aiming to uncover protective mechanisms and determine the therapeutic potential of GM-CSF in the context of fungal infection. The work is presented in three results chapters. Chapter 3 centres on the characterisation of monocytes treated with GM-CSF in comparison to M-CSF-treated monocytes, used as negative controls. GM- CSF and M-CSF monocytes display similar viability (~95%), but GM-CSF monocytes had enhanced expression of CD11b, and HLA-DR compared to M- CSF monocytes. This indicates a significant effect of GM-CSF on monocyte activation. In addition, we observed a substantial increased expression of the mannose receptor MR (CD206), Dectin-1 and CLEC5A, all of which are C-type lectin receptors that specialise in carbohydrate recognition and play a crucial role in detecting infections. Levels of Dectin-2 and the production of extracellular vesicles (EVs), were not affected by GM-CSF. Chapter 4 centres on responses of monocytes to infection with C. albicans and how are affected by GM-CSF. We exposed M-CSF and GM-CSF-monocytes to C. albicans at MOI 1 (cytokine secretion, CFU) or MOI 10 (phagocytosis), at 37 °C, 5% CO2, in the presence of human serum for 1 and 3 h. In response to infection with C. albicans, GM-CSF-treated monocytes, compared to M-CSF- treated monocytes, increased TNF-α, IL-6, IL-1β and CCL22 secretion despite similar phagocytic activity and fungal viability (CFU). Interestingly, GM-CSF- monocytes more effectively control hypha growth, indicating enhanced anti- fungal activity and differential phagosome maturation. These results were linked to a drastic reduction of surface MR after C. albicans uptake in GM-CSF monocytes, which would be consistent with enhanced MR shedding. Chapter 5 considers the role of MR in the response of GM-CSF- monocytes to C. albicans. MR surface expression was inhibited using specific sulfated glycopolymers (sGP). sGP treatment reduced MR expression, which resulted in the reduction of TNF-α production during infection, indicating that MR plays an essential role in modulating inflammation during C. albicans infection. Chapter 6 extended the investigation to induced pluripotent stem cell–derived macrophages (iMacs). The expression of CD45 and CD14 confirmed their macrophage identity. Although the study was limited in both replicates and experimental diversity, we were able to show that iMacs treated with GM-CSF expressed MR and Dectin-1, as determined by qPCR analysis (N=3). In contrast, cytokine secretion was not detected in these cells under the conditions tested. We also performed a CFU assay following the same experimental design used with primary monocytes; although conducted only once, the outcome was consistent with the results obtained from primary cells. Overall, ongoing work aims to clarify the mechanisms underlying hyphal growth restriction and MR-mediated responses, thereby improving our understanding of how GM-CSF confers antifungal protection. Further research will be required to elucidate the effector functions of iMacs and to explore their potential as a model system for studying fungal infections.
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