Saudi Cultural Missions Theses & Dissertations

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CONTROLLING SURFACE PLASMON POLARITON ACTIVITY THROUGH CONDUCTION ELECTRON MANIPULATION
(Saudi Digital Library, 2026) ALANAZI, AHMED DHAHER S; McIlroy, David N
Gold mesopyramids (Au MPs) fabricated on silicon substrates exhibit an unusual hysteretic optical reflectivity response when subjected to simultaneous optical excitation and externally applied electrical bias. Unlike bulk metallic systems, where electrically induced reflectivity modulation is typically negligible, Au MPs show large and reproducible changes in reflected optical intensity, indicating that the optical response is strongly influenced by interfacial electronic processes beyond those predicted by the classical free electron model. The complex morphology of the Au MP structure, together with its partially embedded Au–Si interface, suggests that the observed behavior is governed by surface plasmon polariton (SPP) activity coupled to dynamic charge redistribution at the metal-semiconductor boundary. Electrical bias sweeping revealed stable hysteresis loops in the optical reflectivity, demonstrating that the optical response depends not only on the instantaneous applied electrical signal but also on the previous charge state of the system. Measurements performed using silicon substrates with different resistivities showed that the optical response is controlled primarily by electrical current rather than applied voltage, indicating that carrier injection and interfacial charge transport govern the observed behavior. Time dependent measurements further revealed both leading and lagging optical responses relative to the applied voltage, suggesting the presence of fast and slow charge trapping processes that produce an effective RC-like dynamic response at the Au–Si interface. Additional co-illumination and electron-hole pair generation experiments demonstrated that photogenerated carriers in silicon significantly modify the plasmonic optical response of the Au MP, producing measurable shifts in hysteresis behavior and center of mass. These results indicate that conduction electron manipulation near the Au surface can alter the balance between radiative and non-radiative plasmon decay pathways and may influence interaction between multiple plasmonic modes supported by the structure. This work demonstrates a mechanism for actively controlling SPP activity through conduction electron manipulation at a buried Au–Si interface, establishing Au MPs as a promising platform for electrically tunable active plasmonic systems.
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A Framework to Improve Healthcare Services and Wellness Tourism in Saudi Arabia
(Saudi Digital Library, 2026) Hawsawi, Hana; Lazarev, Marija
This study examines how Saudi Arabia can strengthen healthcare services and expand wellness tourism through an integrated framework informed by professional insight. Ten participants from healthcare, digital health, and tourism took part in semi structured interviews. Their perspectives were analysed to explore four central themes. These were service quality, digital health integration, governance and sector alignment, and tourism infrastructure. The findings show that healthcare services have improved through Vision 2030 reforms, yet regional variations, workforce shortages, and inconsistent communication continue to limit service quality. Participants emphasized the value of digital health and described digital platforms as essential for both patients and wellness tourists. They also identified governance gaps that restrict coordination between healthcare and tourism organisations. Tourism infrastructure remains under development, and participants stressed the need for structured wellness programs supported by qualified staff and reliable medical services. The study aligns these findings with current literature and highlights the absence of unified frameworks that connect healthcare transformation with wellness tourism growth. The study proposes a practical framework based on the identified themes. The results show that integrated planning, stronger regulation, digital alignment, and workforce development can support national goals and increase Saudi Arabia’s competitiveness in both healthcare and wellness tourism.
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Automated Motif Indexing On The Arabian Nights
(Saudi Digital Library, 2026) Alyami, Ibrahim; Finlayson, Mark
There is a wealth of information that can be obtained in narratives. Cultural information is an example of what we can find. One form that cultural information can take is motifs. A motif is a recognizable recurring element or concept that frequently aids in the development of other literary or narrative elements like theme or mood. The importance of motifs, according to folklorists, is that they classify and locate stories from different cultures using motifs, and follow the evolution of stories across time. Beyond folklore, motifs also appear in news and social media such as tweets, which can tell us a lot about a culture. Until now, motifs are extracted manually from stories. A folklorist goes through a long and complicated process to identify motifs. Moreover, folklorists must have enough background in a culture to be able to understand motifs and extract them. I propose to develop a system that automatically detects thousands of motifs from text. In this dissertation, I focus on motif indexing: detecting and extracting motif expressions from narratives using an existing motif index as a guide. My thesis has four aims: first, to find, collect, examine, and preprocess the dataset which consists of a list of motifs and folktales. Second, to build a baseline model to automatically detect and understand motifs in text using a handful list of motifs to generate candidate motif expressions for dataset construction. Third, to annotate the dataset, which will be used in training state-of-the-art LLMs. Fourth, to develop a model that automatically detects the entire motif index. The first step of my work is the dataset used to build a culturally aware LLM, including the source of motifs and the narratives that contain them. I showed how we processed both sources and narratives, and my work on motif classification (Simple to Complex motifs). The second step is a large-scale annotation of 58,450 positive and negative motif expressions generated from the baseline system. The third step is fine-tuning state-of-the-art LLMs to automatically index motifs from narratives. The fine-tuned LLM achieved strong performance compared to the baseline models, which highlights the necessity of continued training with larger datasets that contain cultural elements, in order to improve AI systems’ ability to understand culture and its many facets. Finally, I describe using the fine-tuned model to automatically index 5,362 motif expressions from the Arabian Nights. This work demonstrates automatic motif indexing at the scale of an entire motif index. This dissertation contributes resources and methods that support building AI systems that are more culturally aware by enabling large-scale detection and indexing of motifs in narrative text.
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THE MONODROMY GRAPH ASSOCIATED TO THE DYNATOMIC CURVE
(Saudi Digital Library, 2026) Almutarrid, Mohammed Abdulaziz; Doyle, John
This dissertation studies the affine dynatomic modular curves Y_1(n) and Y_0(n), which parametrize unicritical polynomial maps f_c(x) = x^d + c of degree d > 2 together with their periodic orbits of period n, as well as their smooth projective completions X_1(n) and X_0(n). The central objective is to determine for which primes p the curve X_0(n) remains geometrically irreducible despite having bad reduction modulo p. Morton algebraically characterized the primes of bad reduction for these curves using the discriminant of the primitive parabolic factor D_{n,n} = disc_c(Delta_{n,n}(c)). For the quadratic family f_c(x) = x^2+c, Doyle et al. showed that X_0(n) remains geometrically irreducible modulo p whenever an odd prime p divides D_{n,n} exactly once. In this dissertation, we extend this framework to arbitrary degrees d > 2. Specifically, we prove that if p does not divide d and the p-adic valuation satisfies v_p(D_{n,n}) <= (3d - 4)/2, then X_0(n) is geometrically irreducible in characteristic p. Under this condition, up to 3d - 4 finite branch points may collide modulo p, yet geometric irreducibility is nevertheless preserved. The proof combines complex dynamics and the combinatorial structure of the degree-d Multibrot set to establish the required transitivity of the associated monodromy action.
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THE USE OF BEHAVIORAL ECONOMICS FOR STRATEGY EXECUTION
(Saudi Digital Library, 2026) Alkhars, Ahmed; Dhanaraj, Charles
This dissertation addresses the persistent "strategy-execution gap," where approximately 60% to 90% of strategic initiatives fail due to a reliance on "rational actor" assumptions that ignore the psychological realities of human behavior. Adopting a two-essay, mixed-methods approach, the research shifts the execution paradigm from structural compliance to the design of "behavioral choice architectures". The first essay utilizes a qualitative multiple- case study of six organizations in the U.S. and Saudi Arabia to deconstruct organizational resistance. It introduces the Influence vs. Resistance Framework, identifying the "Subversive" archetype—high-influence actors who feign agreement while passively stalling implementation—as the most lethal and frequently misdiagnosed threat to execution. Findings suggest that resistance is often rooted in loss aversion, the endowment effect, and status quo bias. The second essay introduces the Behavioral Economics in Execution (BEX) framework, a five- step prescriptive model. Using a policy-capturing experimental design with 33 senior executives (N=1,056 decisions), the study empirically validates five behavioral interventions: piloting with champions, loss-framed score-boarding, public commitments, rigid review cadences, and milestone celebrations. Results provide robust support for the framework revealing that loss- framed KPIs exert the strongest influence on perceived success. The dissertation culminates in an integrated "Behavioral Operating System," providing a scientifically validated "Behavioral Playbook" for leaders to neutralize resistance and transform strategic intent into operational reality.
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Intelligent Fault Detection for Belt Conveyor Idlers Using Machine Learning
(Saudi Digital Library, 2026) Alharbi, Fahad; Luo, Suhuai; Zhang, Hongyu; Chen, Zhiyong; Wheeler, Craig
Conveyor belt systems are essential components of modern mining, logistics, and manufacturing operations. However, faults in idlers, which are the rollers that support and guide the belt, can reduce system reliability, cause unplanned downtime, increase maintenance costs, and create safety risks. Conventional inspection methods, such as visual assessment and manual listening, are widely used to identify abnormal idler behaviour, but they are labour intensive, subjective, and difficult to apply across large conveyor systems. Acoustic monitoring offers a promising contactless alternative because developing faults often produce changes in sound before complete failure occurs. However, these fault related acoustic signatures can be subtle and may be masked by environmental and operational noise, making them difficult to interpret using traditional signal processing methods alone. Machine learning (ML) has therefore been increasingly applied to acoustic signals to automate feature extraction, fault detection, and classification. Nevertheless, existing ML approaches still face several important challenges, including the limited availability of labelled fault data, inadequate generalisation across recording conditions and sensing platforms, and insufficient modelling of the spatial and temporal characteristics of acoustic signals. This thesis addresses these challenges by developing intelligent fault detection (IFD) models that automatically analyse acoustic signals from belt conveyor idlers. The research follows a progressive methodology comprising supervised transfer learning, semi-supervised anomaly detection, task-specific spatial–temporal deep learning, and cross-domain Convolutional Neural Network (CNN)–Transformer adaptation. To support these investigations, acoustic datasets were collected using handheld microphones and drone-mounted recorders under multiple idler operating and fault conditions. The first study investigated supervised fault classification using the original handheld acoustic dataset, which contained 255 four-second samples representing Normal, Stage 1, Stage 2, and Stage 3 operating conditions. Embeddings were extracted using YAMNet, a pre-trained audio neural network, and processed using Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) networks to model contextual relationships in both forward and backward temporal directions. Attention mechanisms and an Extreme Gradient Boosting (XGBoost) classifier were also evaluated. The best-performing YAMNet–BiLSTM configuration achieved an accuracy of 90.59% and an F1-score of 90.57% for four-class fault-stage classification. Although this study established a strong supervised baseline, it required labelled examples from every operating condition and fault stage. This dependence limits practical application because faulty-idler recordings are relatively rare, costly to collect, and difficult to label in industrial environments. To address this limitation, the second study developed CASSAD (Chroma-Augmented Semi-Supervised Anomaly Detection), which was trained exclusively on normal operating samples during training. Following the first study, additional handheld recordings were collected, increasing the dataset from 255 to 468 acoustic samples. This larger dataset enabled CASSAD to be evaluated using a broader set of normal and abnormal operating recordings. CASSAD combines Chroma-STFT, Chroma-CQT, and Chroma-CENS representations with filtering, statistical aggregation, and a one-class support vector machine (OC-SVM). On the expanded 468-sample handheld dataset, the best-performing CASSAD configuration achieved an accuracy of 90.59%, a positive-class F1-score of 92.42%, and an Area Under the Receiver Operating Characteristic Curve of 96.29%. To enable a direct comparison with the YAMNet-based models, CASSAD was also evaluated on the original 255-sample dataset after the three fault stages had been combined into a single Abnormal class. In this binary evaluation, CASSAD achieved an accuracy of 93.00% and an F1-score of 93.25%, compared with an accuracy of 92.18% and an F1-score of 93.00% for the strongest YAMNet-based configuration. These results demonstrate that competitive anomaly-detection performance can be achieved without labelled abnormal samples during training. However, CASSAD provides only binary Normal–Abnormal decisions and uses temporally aggregated features, limiting its ability to distinguish fault severity and model changes in acoustic behaviour over time. To overcome these limitations, the third study developed TD-CLNet, a Time-Distributed CNN–Long Short-Term Memory (LSTM) architecture designed to perform multi-stage fault classification while learning spatial and temporal representations from the acoustic data. The expanded 468-sample handheld dataset was re-segmented into one-second samples and converted into log-Mel-spectrogram frames. TD-CLNet applies a shared CNN feature extractor to each frame and then uses an LSTM to model the resulting temporal feature sequence. This design enables the model to distinguish among Normal, Stage 1, Stage 2, and Stage 3 conditions while preserving temporal information that was reduced through the statistical aggregation used in CASSAD. Under four-fold cross-validation, TD-CLNet achieved a mean accuracy, precision, recall, and weighted F1-score of 92.1%. It outperformed the evaluated conventional CNN–LSTM configurations and provided a small improvement over the strongest YAMNet model re-evaluated on the same expanded dataset. Nevertheless, the sequential processing used by LSTM networks limits parallel computation and provides less direct access to broader global relationships within the acoustic feature sequence. To address these limitations and investigate cross-domain generalisation, the fourth study developed hybrid CNN–Transformer models using four pre-trained CNN backbones: ResNet-18, DenseNet-121, EfficientNet-B0, and ShuffleNet-V2. Two acoustic feature representations were investigated: Mel-spectrograms, which represent the distribution of signal energy across perceptually scaled frequency bands over time, and Mel-Frequency Cepstral Coefficients (MFCCs), which provide a compact representation of the short-term spectral envelope. The CNN backbones extracted local spectral representations, while Transformer encoders modelled broader contextual relationships within the feature sequences. In the first phase, the models were trained and evaluated using 0.5-second segments derived from the handheld source-domain recordings. The ResNet-18–Transformer models achieved a cross-fold mean accuracy of 96.9%, with a 95% confidence interval of 96.4%–97.6%, while a ResNet-18–Transformer ensemble increased the handheld-domain accuracy to 98.0%. In the second phase, the models trained on the handheld source-domain dataset were adapted to the drone-acquired target-domain dataset. The drone recordings represented a more challenging sensing environment because they were affected by rotor noise, changing recording distances, varying microphone positions, and environmental interference. Multiple fine-tuning strategies were evaluated, included full fine-tuning (Full-FT), freezing the CNN backbone (CNN-Frozen), freezing the Transformer encoder (TR-Frozen), and 𝐿2-SP regularisation. Mean–Covariance Alignment (MCA) was compared with a cross-entropy (CE) baseline and several established domain-adaptation methods. MFCCs produced the strongest CE baseline in several drone-domain experiments, achieving an accuracy of 90.5% under 𝐿2-SP. However, MFCC performance generally decreased when MCA was applied. In contrast, MCA improved the Mel-spectrogram Full-FT configuration, increasing accuracy from 84.7% to 86.0% and the F1-score from 83.4% to 85.6%. These findings demonstrate that domain-adaptation performance depends on the interaction among the acoustic representation, fine-tuning strategy, model architecture, and alignment objective; no single adaptation method was uniformly optimal across all evaluated configurations. In summary, this thesis establishes a connected research pathway for acoustic fault detection in belt conveyor idlers. It provides handheld and drone-acquired acoustic datasets, establishes supervised transfer-learning baselines, introduces CASSAD for anomaly detection without labelled abnormal training samples, develops TD-CLNet for task-specific spatial–temporal fault-stage classification, and proposes a two-phase CNN–Transformer framework for handheld-to-drone domain adaptation. Collectively, these contributions advance acoustic idler monitoring towards more accurate, data-efficient, and adaptable fault detection while identifying the need for further validation across additional industrial sites, conveyor configurations, sensing platforms, and operating conditions before large-scale real-world deployment.
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Becoming A Writer: A Longitudinal Qualitative Study of Saudi Postgraduate Students’ Perceptions of Writer Identity at UK Universities
(Saudi Digital Library, 2026) Alshehri, Batool Mohamad; Jones, Rodney
This study is a qualitative longitudinal study that aims to explore how eight Saudi students perceive their identity as writers while completing their master's degrees at universities in the United Kingdom over the course of one year. Writing involves embodying a specific identity; therefore, it is crucial for Saudi students to develop a strong understanding of both disciplinary and authorial identity, which is essential for an academic writer. The primary focus of this study is on Saudi postgraduate students’ perceptions of disciplinary and authorial identity. To meet the study's objectives, I collected three written texts to elicit responses during three semi-structured interviews with eight Saudi postgraduate students pursuing master's degrees in law, business, science, and linguistics in the UK. The inquiry aimed to explore the shifts in perspective and narrative of each novice writer over one year. The study's findings indicate that writers' perceptions of their writer identity are changing over time, influenced by sociocultural factors, interactions, and the emotions they experience during these interactions. This preliminary investigation enhances our understanding of how Saudi master's students perceive their writer identity as novice writers in their respective disciplines in the UK. It underscores the need for clear and specific instructions to improve their awareness and understanding of writer identity. Additionally, it contributes to our comprehension of theories related to writer identity research and emphasises the importance of contextual and interactional factors in shaping perceptions of writer's identity. Furthermore, it demonstrates how longitudinal qualitative methods could aid in exploring changes in writers' perceptions of their identity over time, highlighting the need for future research to investigate factors influencing these shifts.
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Multiscaling Asymptotic Behaviour of Random Fields Driven by SPDEs
(Saudi Digital Library, 2026) Alghamdi, Maha Mosaad A; Andriy, Olenko; Nikolai, Leonenko
This thesis studies the asymptotic behaviour of random fields which are solutions of stochastic partial differential equations with random initial conditions exhibiting long-range and cyclic long-range dependence. The main focus is on investigating multiscaling limits and their spectral and covariance structures. The cases of solutions to fractional, high-order, and Riesz–Bessel type equations are considered. The results are obtained for the cases of arbitrary multiple spectral singularities, which have not been considered before. First, the thesis investigates multiscaling limits for stochastic fractional equations with random initial conditions possessing cyclic long-memory. The corresponding rescaled solutions are shown to converge to Gaussian limit fields. Their explicit spectral and covariance representations are provided. Next, the thesis studies high-order heat equations with random initial conditions whose spectra have singularities at zero and at non-zero frequencies. Using spectral and scaling techniques, it is proved that the normalised solutions converge to Gaussian random fields determined by the presence or absence of a zerofrequency singularity. Kernel averaging is introduced for odd-order equations to obtain non-degenerate limits. Finally, the thesis analyses fractional Riesz–Bessel equations with initial conditions exhibiting both classical and cyclic long-range dependence. It is proved that the rescaled solutions converge to spatio-temporal Gaussian random fields that are stationary in space and non-stationary in time. In addition, multiscaling limit theorems are derived for the case of regularly varying asymptotics.
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EDUBUDDY: A WEB-BASED AI TOOL FOR DETECTING LEARNING STYLES AND RECOMMENDING PERSONALIZED STUDY STRATEGIES
(Saudi Digital Library, 2025) ALDUWAYS, TAHANI MOHAMMED SH; Dumlao, Menchita F
The ongoing challenge of meeting diverse learning preferences within standardized education highlights the need for more personalized learning support. Although learning styles—preferences for perceiving, processing, and retaining information—are often linked to engagement and academic outcomes, common assessment approaches rely heavily on self-report tools (e.g., VARK, Felder-Silverman ILS) that face major concerns: inconsistent reliability and validity, static profiles that overlook context shifts, and limited usefulness in producing practical study guidance. To address this gap, this study proposes, develops, and evaluates a Web-Based AI Tool for dynamic learning-style detection and personalized study-strategy recommendation. Rather than depending on questionnaires, the tool continuously and non- intrusively infers learning preferences using machine learning on authentic behavioral data within a web learning platform. Indicators include content format choices (text, video, simulations), time-on-task patterns, navigation paths, interactions with learning elements, and performance differences across assessment types. These data generate a probabilistic, continuously updated learning style profile based on observable behavior, reducing self- report bias and accommodating context-dependent changes. A recommendation engine then translates the AI-inferred profile into concrete, actionable study strategies delivered through the platform or a user dashboard. For example, visually oriented learners may receive guidance on concept mapping and color- coded summaries, while active learners may be encouraged to engage in peer discussion and to practice frequently. The system integrates user feedback (explicit ratings or implicit adoption signals) to refine both detection accuracy and recommendation relevance over time. Using a design-based research methodology, the tool is iteratively designed and tested in authentic settings. Mixed-methods evaluation examines detection validity, user acceptance, effects on metacognitive awareness and study habits, and initial links to learning outcomes, alongside ethical safeguards for privacy and bias. Preliminary results suggest the tool is feasible, well-received, and supports more intentional study practices, offering a practical bridge between identifying preferences and improving learning strategies.
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Workplace Bullying in Saudi Arabia: A Mixed Method Research
(Saudi Digital Library, 2026) Alzaidi, Hanaa; Baruch, Yehuda; Roy, Taposh
Workplace bullying is a global issue affecting virtually all organizations and can manifest within different cultural contexts. In Saudi Arabia, the interplay of traditional tribal cultures, state modernization efforts, strict immigration policies, and affirmative action initiatives creates a distinctive environment where bullying may emerge as a strategy for dominance. This study investigates how and why bullying occurs in Saudi organizations and examines its effects on bullied employees. Guided by the assumption that contextual factors are primary drivers of environments in which in-groups may target vulnerable out-groups, this studyemploys a mixed- methods approach. Quantitative data were collected from 681 employees across various sectors in Saudi Arabia. Findings revealed significant evidence that workplace deviance predicts bullying incidents. Workplace bullying is negatively associated with victims' job satisfaction, protean career orientation, work-life conflict, and psychological contract with the organization, contributing to an increased intention to leave. Psychological contract breach was identified as a significant moderator between bullying and its negative consequences, indicating that institutional inaction exacerbates the effects of workplace bullying. Additionally, workplace bullying was found to be more prevalent in family businesses compared to non-family businesses, highlighting challenges associated with tribal culture. Therefore, no significant difference was observed between expatriates and locals regarding bullying experiences, suggesting that groups face bullying for different reasons. Qualitative insights were gathered from interviews with 29 employees who had experienced bullying in various contexts, including family businesses, scenarios involving expatriates and locals, and situations with established groups versus new employees educated abroad. Key i findings indicate that workplace bullying is often a systematic and deliberate process used by family allies, expat groups vs. local groups, well-established groups vs. new employee groups to dominate and protect their positions against vulnerable out-group members. Workplace bullying emerges as a socially constructed experience arising from shifts in power dynamics (e.g., changes in management or leadership, or the introduction of new employees). The perception of job threats among perpetrators may motivate them to bully victims who are perceived as gaining enough power to replace them, suggesting that workplace bullying may be less prevalent in organizational contexts. This thesis contributes to the literature by conceptualizing bullying as a strategic tool for domination employed by employees to safeguard their jobs, fostered by weaknesses in human resources systems and victim vulnerability. Their self-perception influences victims' ability to protect their rights, the perceived power of perpetrators, and the perceived effectiveness of HR systems. Key implications of this study include the importance of the Ministry of Human Resources enacting laws to protect employees' jobs and reconsidering aspects of the Saudization process for employees who have spent their lives in Saudi Arabia. For large organizations, appointing an independent compliance manager could mitigate conflicts of interest within HR departments, enhancing the defence of employees' rights. Additionally, increasing organizational awareness about the importance of inclusiveness and the acceptance of diverse mindsets, races, nationalities, backgrounds, and educational experiences are crucial, as diversity can catalyze progress and growth.
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Saudi Cultural Missions Theses & Dissertations

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