SACM - Australia
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Item Restricted Strategic sport communication and the making of place(Saudi Digital Library, 2026) Alghamdi, Mohammed Mesfer; Franzisca, WederThis thesis examines the communicative role of sport mega-events in Saudi Arabia from a place-centred strategic sport communication perspective, with particular attention to their role in nation branding and reputation formation. Moving beyond media-centric and outcome-focused approaches, the study conceptualises sport mega-events as strategically organised communicative environments through which place, identity, and reputation are articulated, negotiated, and contested. Addressing key gaps in sport communication and strategic communication scholarship, the research shifts the focus from visibility and exposure to communicative processes, professional expertise, and meaning-making at the national level. Adopting an interpretivist qualitative design, the study draws on semi-structured interviews with 32 communication experts operating across governmental and non-governmental institutions in Saudi Arabia and applies reflexive thematic analysis to examine how SMEs are embedded within broader governmental communication strategies. The analysis explores three interconnected dimensions: the role of strategic communication in nation branding, the role of sport communication within nation- branding processes, and the perceived impact of nation branding on Saudi Arabia’s reputation. The findings demonstrate that strategic communication in Saudi Arabia is understood as a long- term, place-centred process characterised by coordination, narrative governance, and institutional negotiation, rather than by isolated messages or short-term campaigns. Sport communication emerges as a strategically central yet contested communicative practice through which nation-branding efforts are organised and enacted. Empirically, the study extends Anholt’s Competitive Identity framework by identifying sport itself as a distinct and contextually influential nation-branding dimension that amplifies tourism, culture, policy, investment, people, and brands, while producing differentiated effects across these domains. In terms of reputation, the findings reveal a dual dynamic of transformation and contestation. While sport communication accelerates reputational change by projecting narratives of reform, ambition, and openness, it simultaneously intensifies exposure to critique, including sportswashing accusations. Reputation is therefore understood not as a stable outcome but as an ongoing process of negotiation shaped by cumulative impressions over time. The thesis contributes to sport communication scholarship by advancing a strategic sport communication conceptualisation and repositioning sport communication as a central analytical lens for understanding how states communicate place, identity, and reputation in global contexts.7 0Item Restricted Blockchain-Based Platform for Managing Joint Ownership in Real Estate (ReJOM)(Saudi Digital Library, 2026) Almalki, Mohanad; Hussain, FarookhReal estate joint ownership allows multiple investors to pool capital, share risks and access properties that may be difficult to purchase individually. However, managing these arrangements transparently and consistently across key stages of the joint ownership lifecycle remains challenging. Joint ownership arrangements rely on personal relationships and centralised record-keeping. Blockchain technology provides a decentralised, tamper-evident ledger that can record time-stamped actions. However, existing work provides limited and fragmented support for lifecycle management of joint ownership groups across these stages. The main objective of this research is to design, implement and evaluate a blockchain-based platform that supports lifecycle management of real estate joint ownership groups using smart contracts. The platform uses a document-weighted Trust Score to assess participant reliability before group entry. Smart contracts are used to govern joint ownership groups and support automated enforcement of predefined rules with key actions recorded on the blockchain to provide an auditable and tamper-evident history. The platform is implemented as a proof-of-concept prototype that uses Ethereum smart contracts, a web-based application and MetaMask integration to manage user wallets and transactions. The scope is limited to on-chain governance and auditability and does not include off-chain legal transfer processes or physical asset liquidation.11 0Item Restricted Statistical Methods Used to Predict Progression in Parkinson’s Disease: A Critical Review(Saudi Digital Library, 2026) Almohammadi, Abdulrahman Ghuwayzi; Stelios, GeorgiouThis thesis critically reviews the statistical methods used to predict progression in Parkinson’s disease, with emphasis on how different model families match the mathematical structure of the outcomes they are de- signed to predict. The review is organised as a structured narrative synthesis of a curated corpus of 50 references spanning Parkinson’s disease prognostic studies, methodological reviews, and reporting or ap- praisal guidance. Across the literature, progression is represented through continuous symptom trajectories, fixed-horizon binary outcomes, milestone-based time-to-event endpoints, multidomain deterioration, latent severity processes, and multimodal subtype or risk-classification tasks. These outcome structures motivate different statistical approaches, including regression and mixed-effects models, Cox and time-dependent sur- vival models, joint longitudinal-survival models, multivariate and latent-variable frameworks, and machine- learning systems. The central conclusion is that no single modelling family is universally best. Rather, methodological appro- priateness depends on the structure of the response variable, the role of repeated measures, the presence of censoring, dropout, or missingness, the required level of interpretability, and the intended clinical or research use of the prediction. The review also shows that many published prognostic models remain limited by weak handling of missing data, incomplete calibration assessment, inconsistent external validation, and inadequate reporting transparency. Its main contribution is therefore not simply to catalogue existing methods, but to clarify how model choice in Parkinson’s disease progression prediction should be driven by statistical structure and methodological maturity rather than by algorithmic novelty alone. The thesis therefore con- tributes a decision-oriented statistical framework for matching Parkinson’s disease progression outcomes to appropriate model families, with explicit attention to assumptions, repeated-measures structure, censoring, missingness, validation, calibration, interpretability, and intended use.25 0Item Restricted Mathematical Model for Simulating Ice Shelf Breakup(Saudi Digital Library, 2026) Alshahrani, Faraj; Mike, Meylan; Ben, WilksOcean waves strongly influence the mechanical stability, fragmentation, and even- tual disintegration of floating ice shelves, which are floating extensions of continental ice sheets that form where glacial ice moves from land into the ocean. Understanding these hydroelastic processes is crucial for improving models of ice-shelf breakup and their contribution to global sea-level rise. This thesis develops analytical and nu- merical mathematical models to study wave-ice interactions and sequential ice-shelf breakup. This thesis makes three novel contributions to the hydroelastic modelling of ice shelves. The first novel contribution analyses the interaction between water waves and multiple ice-shelf fragments in front of a semi-infinite ice sheet. Hydrodynamics are described by shallow-water theory and ice elasticity by Kirchhoff–Love plate theory, resulting in a multiple-scattering problem solved in the frequency domain with the transfer matrix method. An energy conservation identity validates the numerics, and transient responses to wave packets are built from vs. This provides a basic framework for modelling wave-induced ice-shelf fragmentation. The second novel contribution presents a time-domain model for an ice shelf interacting with waves in a finite domain containing open water and ice-covered regions. The Shallow-water equations are coupled with the Kirchhoff-Love plate theory, leading to a nonlinear matrix eigenvalue problem for the natural modes and frequencies of the coupled system. These modes are used in a spectral method to reconstruct transient responses to wave forcing. Simulations show how incident wave packets excite multiple structural modes and create complex interference patterns through internal reflections. The third novel contribution introduces a framework to simulate the sequential breakup of a wave-forced ice shelf, again coupling shallow-water hydrodynamics with Kirchhoff–Love plate theory. Fragmentation is triggered by a critical threshold criterion that allows progressive cracking into multiple fragments. The formulation leads to a nonlinear eigenvalue problem whose complexity grows as new cracks form. Simulations illustrate the breakup sequence and examine the effects of clamped, free- edge, and simply supported boundary conditions. The results are checked using energy conservation and time-step convergence. Overall, the thesis establishes mathematical and numerical frameworks for wave- induced ice-shelf vibrations and fragmentation, progressively incorporating wave scattering, transient hydroelastic response, and sequential breakup. This work sup- ports future extensions to unbounded domains and higher-dimensional geometries to improve predictive models of ice-shelf disintegration.11 0Item Restricted A Distributed System Framework for Smart E-waste Inventory and Forecasting(Saudi Digital Library, 2026) Madkhali, Hatim; Prasad, MukeshElectronic waste (E-waste) is one of the fastest-growing waste streams globally, driven by rapid technological innovation, shortened product lifecycles, and increased consumption of electronic devices. This growth creates urgent environmental and public health risks due to hazardous material leakage, while also undermining circular-economy ambitions by diverting valuable resources away from formal recovery pathways. The background and motivation for this research were established in the Introduction chapter, which highlights the increasing pressure on national infrastructures to measure, track, and forecast E-waste flows through transparent and scalable systems that support operational planning and evidence-based policymaking. Despite this need, E-waste inventory and monitoring practices remain fragmented in many contexts, limiting the availability of audit-ready data required for capacity planning, optimised resource allocation, and effective national interventions. Saudi Arabia is selected as the primary case study because it is among the highest E-waste-generating countries in the Arab region, yet formal recycling and collection systems capture only a small fraction of these flows, leaving planners with limited visibility over E-waste generation, movement, and disposal outcomes. This lack of a unified national inventory system makes it difficult to establish reliable baselines, forecast future volumes, and translate policy targets into operational requirements. In response, this thesis aims to contribute to the advancement of waste management strategies in Saudi Arabia by designing and developing a distributed system framework for smart E-waste inventory and forecasting, leveraging cloud computing, Internet of Things (IoT)-enabled data capture, and predictive analytics. To achieve this aim, three research questions and corresponding objectives were formulated to guide the research design, structure the empirical investigation, and validate the proposed framework across technical, predictive, and stakeholder-driven dimensions. The thesis first conducts a systematic literature review of distributed approaches to E-waste inventory and management, focusing on cloud, IoT, and blockchain-enabled solutions. The review identifies persistent gaps, including the lack of holistic architectures that integrate real-time sensing with scalable data pipelines, traceability, and forecasting-driven decision support. Based on these gaps, the thesis proposes and validates an integrated Hybrid Cloud–IoT framework that formalises stakeholder roles, IoT-enabled smart collection points, and cloud-based processing and storage layers to support scalable, near-real-time E-waste inventory monitoring and decision-making in Saudi Arabia. To ground the proposed framework in real-world behavioural and operational conditions, the thesis empirically examines E-waste practices from three stakeholder perspectives in Saudi Arabia: individuals, corporate organisations, and licensed E-waste collector firms. Survey findings reveal a critical systems mismatch: E-waste producers demonstrate low awareness but high willingness to engage in sustainable disposal behaviours, while collector firms operate with ineffective and outdated monitoring, collection, and recovery mechanisms that do not adequately accommodate producer needs. Cluster analysis further identifies distinct behavioural typologies among individuals and organisations, reflecting variation in disposal practices, barriers to participation, and engagement readiness. These results indicate that smart inventory frameworks must be complemented by consumer-specific strategies, targeted awareness interventions, and digitally enabled collection channels to translate willingness into measurable recovery outcomes. To strengthen the forecasting component of the distributed framework, ML model implemented on a secondary E-waste dataset from 32 European countries to evaluate and make any changes in the framework. Using hierarchical mixed-effects modelling, advanced ensemble learning, and data augmentation techniques on the data from 2005 to 2018 of these countries, it was found that ensemble models beat individual models when predicting two units, which are total E-waste in ton (T) and E-waste per person (KG-HAB). Meanwhile, CatBoost and Gradient Boosting worked better for the collection rate calculations unit (AVG 3Y). The hierarchical modelling layer allows us to better understand how E-waste generation differs by country, allowing policies to adapt to local needs. The system is built to scale, stay reliable, and be clear, making it ready to use for national tracking, regional planning and building sustainability policies that can handle E-waste challenges beyond 2025. The findings demonstrate that conservative feature design and ensemble learning can yield robust, uncertainty-aware forecasts suitable for operational and policy planning even when data availability is limited. Finally, to validate the framework, the thesis evaluates national-scale feasibility and planning utility through a nation-wide simulation-based digital twin developed using next-event discrete-event simulation in AnyLogic. The simulation represents 1-kg E-waste agents originating from four sectoral sources (education, government, IT, and consumers), flowing through capacitated smart-bin queues and periodic cloud processing cycles, with all events logged to an SQL datastore enforcing mass conservation to enable transparent auditing. A 7-day validation run processed 8,068,473 kg (mean 1,152,639 kg/day), and a material-balance moving-average forecast achieved 3.2% absolute error relative to observed throughput. Scenario analysis demonstrates that increasing recycling from 2% to 10% raises daily recovery from 23.053 t/day to 115.264 t/day (+33.66 kt/year), while a 25% target yields 288.160 t/day (+105.18 kt/year), translating diversion targets directly into staged recycling-capacity requirements and indicative investment planning. Overall, this thesis contributes a distributed system framework that operationalises smart E-waste inventory through IoT-enabled data acquisition and cloud-based audit-ready logging and enables forecasting through validated predictive modelling and simulation-based scenario planning. Collectively, these contributions advance scalable, transparent, and data-driven E-waste strategies aligned with Saudi Arabia’s sustainability and circular-economy objectives.10 0Item Restricted The Architecture of Opportunity: An AI-Powered Approach for Large-Scale Discovery and Recommendation(Saudi Digital Library, 2026) Alotaibi, Naif Nasser N; Saberi, Morteza; Hussain, Farookh Khadeer; Bandara, MadhushiResearch organisations often struggle to plan proactively because signals about opportunities are dispersed across heterogeneous and largely unstructured web sources (e.g., researcher profiles, grant portals, and public announcements). Traditional tools such as SWOT support reflection, but they rarely operationalise these signals into measurable factors that enable continuous, data-driven opportunity discovery and recommendation. This thesis develops an end-to-end framework that transforms web-derived unstructured information into high-quality stakeholder opportunity datasets and recommendation outputs. The thesis is organised into three stages. Stage 1 (Data Collection and Data Quality) defines a unified stakeholder and opportunity data model and develops an extraction pipeline that integrates prompt engineering and few-shot GenAI extraction with traditional ML and rule-based validation to populate feature values from unstructured sources. It further improves dataset reliability through data cleaning, normalisation, and ML-based imputation of missing stakeholder and opportunity features. Stage 2 (Recommendation for a Known Opportunity) investigates the setting where the opportunity is given (e.g., a specific grant). It designs and evaluates models that rank and recommend the most relevant stakeholders using evidence derived from both structured fields and unstructured text, leveraging pre-trained language models to represent and retrieve matching signals. Stage 3 (Recommendation for an Unknown Opportunity) addresses the discovery of opportunities that are not provided in advance. It develops and validates methods to identify previously unseen opportunities from external signals and recommend them to suitable stakeholders. Overall, the thesis contributes a practical pipeline from web data to opportunity matching and opportunity discovery for research and educational institutions.9 0Item Restricted Decarbonising the aluminium value chain through cross-regional corridor optimisation: a techno-economic and emissions framework for Australia-Middle East-Europe pathways(Saudi Digital Library, 2026) Alsayed, Mohammed; Rahman, DaiyanAluminium is a structural input to the energy transition, yet its production remains emissions-intensive because of the heat demand of alumina refining, the electricity intensity of smelting and the carbon consumed at the anode. This study develops a corridor-level techno-economic and emissions framework for aluminium value-chain configurations linking Australia, Saudi Arabia and the United Arab Emirates to destination markets in Europe and China. The framework integrates bauxite extraction, inland logistics, alumina refining, primary smelting and casting, secondary aluminium production and marine delivery within a consistent mass, cost and emissions accounting structure. Stage-level process assumptions are combined with location-specific electricity-system outputs and a set of scenario pathways spanning natural-gas baseline operation, gas with abatement, electrification, hydrogen-supported refining, inert-anode deployment, PPA-based electricity procurement and secondary recycling. The framework is used to examine whether the lowest-cost corridor is also the lowest-emissions corridor, to quantify switching behaviour under electricity, hydrogen and carbon-policy sensitivities, and to evaluate how CBAM alters the relative competitiveness of Australian and Middle Eastern routes. The results show that no single corridor dominates across all objectives. Under the five-criterion multi-criteria assessment with balanced weighting, the Australian full chain ranks first, while the United Arab Emirates full chain ranks second. When the analysis is extended in the final synthesis to include subsidy reliance, CBAM 2034 exposure and pathway flexibility, the ranking shifts: UAE-anchored full-chain configurations emerge as the highest-ranked balanced option, reflecting their combination of low delivered cost, limited CBAM exposure and minimal reliance on direct subsidies, while hybrid Australia-to-UAE smelt corridors follow as a competitive intermediate option. Australia-only full-chain routes dominate under carbon-priority weighting and score strongly on regulatory stability and chokepoint resilience, supporting their inclusion in a diversified procurement portfolio. Applied to the Australian baseline, the framework estimates a delivered cost of USD 2,906/t Al and an embedded footprint of approximately 11.0 t CO₂-e/t Al under business-as-usual conditions. Under the 2050 S2 gas-with-abatement pathway, delivered cost rises to USD 3,835/t Al while embedded emissions fall to 4.3 t CO₂-e/t Al, remaining below the 2050 LME reference of USD 4,200/t Al. The corresponding 2050 S2 full-chain cases for Saudi Arabia and the United Arab Emirates reach USD 3,174/t Al and USD 3,261/t Al, with emissions of 4.5 and 4.1 t CO₂-e/t Al, respectively. Secondary aluminium delivers the lowest per-tonne emissions but is bounded by scrap availability and alloy quality, and therefore acts as a complement rather than a substitute. Taken together, the findings indicate that aluminium decarbonisation is best approached as a corridor and portfolio problem, and that long-term industrial PPAs, supported by time- limited Contract-for-Difference mechanisms, can help bridge the near-term renewable-electricity competitiveness gap during the transition.12 0Item Restricted Designing Privacy-Preserving IoT Smart Speakers: A Human-Centered Wearable Privacy Approach in Saudi Arabia(Saudi Digital Library, 2026) Alorini, Abdulrhman; Baki, KocaballiPrivacy concerns surrounding Internet of Things (IoT) smart speakers have become increasingly prominent, yet users struggle to understand and manage risks associated with continuous voice monitoring, cloud-based data processing, and third-party access. Existing privacy research predominantly focuses on Western contexts, creating a critical gap in understanding how cultural factors influence privacy protection behaviours in non-Western settings. This thesis investigates privacy protection behaviours among smart speaker users in Saudi Arabia and develops wearable technology-based interventions to enhance privacy awareness through three interconnected studies. Study One employed cultural probes and semi-structured interviews with 16 Saudi users, revealing that privacy protection behaviours are shaped by collectivist values, Islamic principles, and multi-generational household structures. Study Two developed a smartwatch application through co-design workshops with nine participants. Field evaluation with ten participants demonstrated sustained improvements in privacy awareness, with participants preferring the wearable interface over conventional privacy controls and the majority expressing intention to continue use beyond the study period. The thesis contributes three primary findings: privacy protection behaviours in Saudi Arabia are culturally constituted rather than individually motivated; wearable interfaces can bridge the awareness-action gap in IoT privacy management; and effective interventions must encode local cultural protocols rather than assuming universal user needs.19 0Item Restricted Monitoring vegetation change associated with the Riyadh Green Initiative using satellite data(Saudi Digital Library, 2026) ALSULTAN, SULTAN ABDULAZIZ S; Robinson, Todd; Dewan, AshrafUrban greening is central to improving liveability in hot-arid cities. In Riyadh, daily maximum temperatures can reach 52°C during summer, increasing the importance of urban greening for heat-stress reduction. In this context, the Green Riyadh initiative has become an important programme for improving the urban environment. However, there remains limited neighbourhood-scale evidence showing where vegetation cover has increased or declined across different parts of Riyadh. This study provides neighbourhood-scale evidence of vegetation change in selected districts of Riyadh by comparing vegetation conditions between 2017 and 2025. Landsat 8 surface reflectance imagery from 2017 and 2025 was used to compare baseline and recent vegetation conditions.8 0Item Restricted Limitations of Current Solar Magnetic Field Modeling Techniques Across Spatial and Temporal Scales(Saudi Digital Library, 2025) Alanazi, Abdulhadi; Shelyag, SergiyMagneticfieldsgovernmanyofthephysicalprocessesobservedinthesolaratmosphere, includingenergytransport,wavepropagation,andtheformationofstructuredfeatures suchasfluxtubesandactiveregions. Accuratemodellingofthesemagneticconfigurations is essential for understanding their behaviour and for validating numerical magnetohy- drodynamic(MHD)simulations. Thisthesisdevelopsaunifiedanalyticalandnumericalframeworkforstudyingsolar magneticfieldsandlinearMHDwaves. Magnetohydrostatic(MHS)equilibriaarederived usingflux-functionandGrad–ShafranovformulationsinbothcylindricalandCartesian geometries. Theseequilibrianaturallysatisfythesolenoidalconditionandprovideclean, controlledmagneticstructuressuitableforbenchmarking. Numericalevaluationconfirms that divergence is preserved to machine precision, and visualisations in two and three dimensionsreproducetheexpectedexpansionandtopologyofsolar-likemagneticfields. LinearMHDwavesareanalysedwithintheseequilibriabycomputingthephasespeeds oftheAlfv´en,fast,slow,andacousticmodes. Theresultsagreecloselywiththeoretical dispersion relations, demonstrating the suitability of the equilibria as backgrounds for wavepropagationstudies. Takentogether, theanalyticalconstructionsandwavediag- nostics establish a consistent and flexible framework for understanding solar magnetic structuresandsupportongoingdevelopmentandvalidationofnumericalMHDmodels.5 0