Saudi Cultural Missions Theses & Dissertations
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Item Restricted ASSESSING THE IMPACT OF THE INTRODUCTION OF A RESEARCH-LED COMPUTER SCIENCE FRAMEWORK FOR PRIMARY EDUCATION IN THE KINGDOM OF SAUDI ARABIA(UNIVERSITY OF GLASGOW, 2024-02) Alharbi, Noha Abdulkhalig; Cutts, QuintinPrimary teachers around the world are being asked to teach computational thinking with little or no prior knowledge and limited support. This dissertation starts with analysing the challenges primary teachers face when teaching a subject new to them, identifying a lack of content and pedagogical content knowledge as a critical hurdle among many. Related situations in other subject areas are identified, where a new perspective on those subject areas has become central, and their approaches explored: the introduction of inquiry science and a more problem-based focus for mathematics both made use of a high-level framework that helped teachers to connect the top-level outcomes with low-level classroom materials provided to them. Computer science education is considered as a tool to develop computational thinking skills among learners, and there are worldwide efforts to implement it at the K-12 education level. However, being a relatively new subject, the teachers face similar challenges as the Mathematics and Science teachers mentioned above. Therefore, drawing on the mathematics/science experience, and on existing frameworks and research findings in CS, a Research-Led Computer Science Framework (RLCSF) that has three major components is presented. These components include the problem domain, computing domain, and problem-solving process, and computational thinking is presented as a modelling activity. The effectiveness of the framework was evaluated in the Kingdom of Saudi Arabia (KSA). To ensure that KSA is an appropriate evaluation context, data was collected from 114 teachers using the METRECC survey tool. This survey work provided detailed insight into the state of the intended and enacted curriculum at the K-12 level in KSA and the challenges the teachers face while teaching CS. The survey reports that the teachers in KSA have a limited understanding of computational thinking and problem-solving, and hence KSA is an appropriate context. For the evaluation, a Professional Development Program (PDP) was developed for teachers in which a teacher training guide was created, and a Professional Development Course (PDC) was conducted to educate the teachers about the way the RLCSF works and can solve the Content Knowledge (CK) and Pedagogical Content Knowledge (PCK) issues. As part of the PDP, other teacher training sessions were conducted during teaching. The researcher not only trained the teachers but also recorded their use of the framework and feedback using mixed-method approaches such as focus group studies, interviews and learners evaluation. To ensure the effectiveness of the results, another training session and interview were conducted with the teachers. The researcher involved one of the experienced group teachers, who is a non-CS background teacher from KSA, in the process of training and presentation. The objective is to investigate if the RLCSF can be transferred by a teacher who can teach the training processes and has recently experienced the framework. The second training guide is text-based, along with a video presentation explaining the examples given in the text guide. The teacher continued to assist teachers during their teaching. In the first semester, the CS teachers were able to use the materials more effectively than the non-CS teachers, but in the second semester, non-CS teachers were also at the same level. Teachers were able to start creating lessons using the material and framework. The results also reflect that the performance of learners from the experimental group was better than that of the control group learners. In the end, the non-CS teachers developed an understanding of why and how to develop computational thinking. The CS teachers had earlier focused only on teaching tools, but they developed an understanding of the importance of computational thinking. The teachers understand that modelling is a critical process in problem-solving. The results are promising and show that teachers are better able to understand different examples in the given curriculum and are able to deliver the contents more effectively.13 0Item Restricted Reducing Type 1 Childhood Diabetes in Saudi Arabia by Identifying and Modelling Its Key Performance Indicators(Royal Melbourne Institute of Technology, 2024-06) Alazwari, Ahood; Johnstone, Alice; Abdollahain, Mali; Tafakori, LalehThe increasing incidence of type 1 diabetes (T1D) in children is a growing global health concern. Reducing the incidence of diabetes generally is one of the goals in the World Health Organisation’s (WHO) 2030 Agenda for Sustainable Development Goals. With an incidence rate of 31.4 cases per 100,000 children and an estimated 3,800 new cases per year, Saudi Arabia is ranked 8th in the world for number of T1D cases and 5th for incidence rate. Despite the remarkable increase in the incidence of childhood T1D in Saudi Arabia, there is a lack of meticulously carried out research on T1D in children when compared with developed countries. In addition, it is crucial to recognise the critical gaps in current understanding of diabetes in children, adolescents, and young adults, with recent research indicates significant global and sub-national variations in disease incidence. Better knowledge of the development of T1D in children and its associated factors would aid medical practitioners in developing intervention plans to prevent complications and address the incidence of T1D. This study employed statistical, machine learning and classification approaches to analyse and model different aspects of childhood T1D using local case and control data. In this study, secondary data from 1,142 individual medical records (359-377 cases and 765 controls) collected from three cities located in different regions of Saudi Arabia have been used in the analysis to represent the country’s diverse population. Case and control data matched by birth year, gender and location were used to control confounders and create a more robust and clinically relevant model. It is well documented that genetic and environmental factors contribute to childhood T1D so a wide range of potential key performance indicators (KPIs) from the literature were included in this study. The collected data included information on socioeconomic status, potential genetic and environmental factors, and demographic data such as city of residence, gender and birth year. Several techniques, such as cross-validation, hyperparameter tuning and bootstrapping, were used in this study to develop models. Common statistical metrics (coefficient of determination, R-squared, root mean squared error, mean absolute error) were used to evaluate performance for the regression models while for the classification models accuracy, sensitivity, precision, F score and area under the curve were utilised as performance measures. Multiple linear regression (MLR), artificial neural network (ANN) and random forest (RF) models were developed to predict the age at onset of T1D for all children 0-14 years old, as well as for the most common age group for onset, the 5-9 year olds. To improve the performance of the MLR models, interactions between variables were considered. Additionally, risk factors associated with the age at onset of T1D were identified. The results showed that MLR and RF outperformed ANN. The logarithm of age at onset was the most suitable dependent variable. RF outperformed the others for the 5-9 years age group. Birth weight, current weight and current height influenced the age at onset in both age groups. However, preterm birth was significant only in the 0-14 years cohort, while consanguineous parents and gender were significant in the 5-9 age group. Logistic regression (LR), random forest (RF), support vector machine (SVM), Naive Bayes (NB) and artificial neural network (ANN) models were utilised with case and control data to model the development of childhood T1D and to identify its key performance indicators. Full and reduced models were developed to determine the best model. The reduced models were built using the significant factors identified by the individual full model. The study found that full LR had the highest accuracy. Full RF and SVM with a linear kernel also performed well. Significant risk factors identified as being associated with developing childhood T1D include early exposure to cow’s milk, high birth weight, positive family history of T1D and maternal age over 25 years. Poisson regression (PR), RF, SVM and K-nearest neighbor (KNN) were then used to model the incidence of childhood T1D, taking in the identified significant risk factors. The interactions between variables were also considered to enhance the performance of the models. Both full and reduced models were created and compared to find the best models with the minimum number of variables. The full Poisson regression and machine learning models outperformed all other models, but reduced models with a combination of only two out of three independent variables (early exposure to cow’s milk, high birth weight and maternal age over 25 years) also performed relatively well. This study also deployed optimisation procedures with the reduced incidence models to develop upper and lower yearly profile limits for childhood T1D incidence to achieve the United Nations (UN) and Saudi recommended levels of 264 and 339 cases by 2030. The profile limits for childhood T1D then allowed us to model optimal yearly values for the number of children weighing more than 3.5kg at birth, the number of deliveries by older mothers and the number of children introduced early to cow’s milk. The results presented in this thesis will guide healthcare providers to collect data to monitor the most influential KPIs. This would enable the initiation of suitable intervention strategies to reduce the disease burden and potentially slow the incidence rate of childhood T1D in Saudi Arabia. The research outcomes lead to recommendations to establish early intervention strategies, such as educational campaigns and healthy lifestyle programs for mothers along with child health mentoring during and after pregnancy to reduce the incidence of childhood T1D. This thesis has contributed to new knowledge on childhood T1D in Saudi Arabia by: * developing a predictive model for age at onset of childhood T1D using statistical and machine learning models. * predicting the development of T1D in children using matched case-control data and identifying its KPIs using statistical and machine learning approaches. * modeling the incidence of childhood T1D using its associated significant KPIs. * developing three optimal profile limits for monitoring the yearly incidence of childhood T1D and its associated significant KPIs. * providing a list of recommendations to establish early intervention strategies to reduce the incidence of childhood T1D.24 0Item Restricted Modelling the stability of ternary solid dispersions(University of Galway, 2024-08) Albaqami, Modhi; Meere, MartinThis thesis provides a mathematical analysis of the stability of some ternary solid dispersions using thermodynamic theory. Solid dispersions have been developed to improve the solubility of poorly soluble drugs. Traditional solid dispersions typically consist of two components - a drug and a hydrophilic polymer, the purpose of the polymer being to improve the solubility of the drug when administered orally. More recently, ternary dispersions (that is, systems with three components) have been developed. Ternary dispersions provide an extra degree of freedom to enable system designers to optimize both the stability and solubility properties of the dispersion. Unstable dispersions may phase separate, thereby limiting their shelf life. In this thesis, I mathematically model ternary solid dispersions with a view to identifying parameter regimes that lead to favourable and unfavourable stability properties for the dispersion. A systematic study of this kind has hitherto been lacking in the literature. The methodology used is as follows. The solid dispersions are modelled using Flory- Huggins solution theory, a well-established thermodynamic model in polymer science for polymer blends. The stability properties of the dispersions are determined by constructing phase diagrams using the Gibbs free energy of mixing. The phase diagrams determine whether a particular ternary composition is stable or unstable or metastable. The requisite numerical calculations to construct the diagrams are carried out by programs that I wrote using the mathematical packages MAPLE and MATLAB. Thermodynamic theory determines the broad character of the ultimate state of the mixture, but yields no information on how the mixture evolves to that state, or what the detailed character of the final state is for unstable compositions. To address these issues, a partial differential equations model is developed to describe the dispersion mixture evolution, and some simulations of this model are also presented. A few notable results are as follows. For polymer-polymer-drug ternary systems, it is found that for desirable stability properties to be possible, the two polymers need to be compatible (in a sense that has been described quantitatively). Also, the stability behaviour can be finely dependent on the asymmetry between how the two polymers interact with the drug. Closed loops of immiscibility are shown to be theoretically possible. For polymer-surfactant-drug ternary systems, it is shown that the stability behaviour can be sensitive to the character of the interaction between the polymer and the surfactant, and the molecular weight of the surfactant. Numerous other predictions are made.18 0Item Restricted A Simulation Framework for Evaluating the Performance of Blockchain-based IoT Ecosystems(Newcastle University, 2024-09-05) Albshri, Adel; Solaiman, EllisRecently, it has been appealing to integrate Blockchain with IoT in several domains, such as healthcare and smart cities. This integration facilitates the decentralized processing of IoT data, enhancing cybersecurity by ensuring data integrity, preventing tampering, and strengthening privacy through decentralized trust mechanisms and resilient security measures. These features create a secure and reliable environment, mitigating potential cyber threats while ensuring non-repudiation and higher availability. However, Blockchain performance is questionable when handling massive data sets generated by complex and heterogeneous IoT applications. Thus, whether the Blockchain performance meets expectations will significantly influence the overall viability of integration. Therefore, it is crucial to evaluate the feasibility of integrating IoT and Blockchain and examine the technology readiness level before the production stage. This thesis addresses this matter by extensively investigating approaches to the performance evaluation of Blockchain-based IoT solutions. Firstly, it systematically reviews existing Blockchain simulators and identifies their strengths and limitations. Secondly, due to the lack of existing blockchain simulators specifically tailored for IoT, this thesis contributes a novel blockchain-based IoT simulator which enables investigation of blockchain performance based on adaptable design configuration choices of IoT infrastructure. The simulator benefits from lessons learnt about the strengths and limitations of existing works and considers various design requirements and views collected through questioners and focus groups of domain experts. Third, the thesis recognises the shortcomings of blockchain simulators, such as support for smart contracts. Therefore, it contributes a middleware that leverages IoT simulators to benchmark real blockchain platforms' performance, namely Hyperledger Fabric. It resolves challenges related to integrating distinctive environments: simulated IoT models with real Blockchain ecosystems. Lastly, this thesis employs Machine Learning (ML) techniques for predicting blockchain performance based on predetermined configurations. Contrariwise, it also utilises ML techniques to recommend the optimal configurations for achieving the desired level of blockchain performance.57 0Item Restricted Visualising of cyber crime data by Communication Structured Acyclic Nets(Newcastle University, 2024-09-02) Alahmadi, Mohammed Saud; Koutny, MaciejCommunication Structured Acyclic Nets (CSA-nets) are a Petri net-based formalism used to represent the behaviour of Complex Evolving Systems (CES). CSA-nets, comprising sets of acyclic nets, are suitable tools for modelling and visualising the behaviour of event-based systems. Each subsystem is represented using a separate acyclic net, linked to others through a set of buffer places depicting their interactions. However, CSA-nets suffer from challenges especially in analysing and visualising CESs that have a large number of subsystems resulting from alternative and concurrent execution scenarios. Moreover, CSA-nets currently lack the capability to represent multiple or coloured tokens, thereby limiting their ability to represent several similar processes simultaneously. This thesis introduces extensions for CSA-nets to capture compactly the relationships between interacting systems’ components represented by sets of acyclic nets. Specifically, it introduces a way of folding buffer places to address the issue of a large number of buffer places. Then it introduces a new class of CSA-nets, called Parameterised Communication Structured Acyclic Nets (PCSA-nets), using multi-coloured tokens and allowing places to accept multiple tokens distinguished by parameters. The thesis also aims at improving the visualisation of csa-nets by rearranging their component acyclic nets to minimise the number of crossing arcs by taking inspiration from the main ideas behind three well-known sorting algorithms (bubble sort, insertion sort, and selection sort). Furthermore, this thesis presents a novel approach that combines TCP protocol anomaly detection with visual analysis through CSA-nets. The strategy provides a clear visualisation of cyber attack behaviours, leading a deeper understanding of Distributed Denial of Service (DDoS) patterns and their underlying causes. A new concept of Timed-Coloured Communication Structured Acyclic Nets (TCCSA-nets) is introduced, which allows elaboration of the system’s performance and emphasising the system’s operations in real-time. This approach allows for the classification of messages as abnormal if their duration exceeds a predetermined time limit.39 0Item Restricted Beyond Bonds: Unlocking Sukuk's Potential in Hedge Fund Portfolios Amidst Market Volatility, an ARCH and GARCH Models Analysis(King's College London, 2023-09-14) Ajaj, Mohammed; Papailias, FotisThe global financial ecosystem, which is fraught with economic uncertainty and turbulent bear markets, drives hedge funds to seek for alternative investment opportunities that promise stability and substantial returns. In this context, the potential of Sukuk, an Islamic alternative to conventional Bonds, is examined in comparison to Bonds, particularly during bear markets. Despite abundant research on Sukuk and conventional Bonds, a quantitative comparison analysis, particularly one focusing on GCC Sukuk, remains relatively unexplored. This work fills this need by modelling the financial time series of these instruments using the Capital Asset Pricing Model (CAPM) and Autoregressive Conditional Heteroskedasticity (ARCH) and its generalized counterparts (GARCH & EGARCH). Preliminary findings indicate that Sukuk, due to their asset-backed nature, demonstrate exceptional resilience during economic downturns. Because of their low market sensitivity, Sukuk have the ability to diversify hedge fund portfolios, according to the CAPM model research. In addition, the GARCH and EGARCH models revealed a divergence in volatility patterns between Sukuk and Bonds between 2020 and 2023, emphasizing Sukuk's resilience to negative shocks. To summarize, while Sukuk looks to offer various advantages over conventional Bonds, particularly in bear markets, financial practitioners are recommended to take a balanced approach, always re-evaluating their investment strategies in the ever-changing finance landscape.10 0Item Restricted Modelling Heat diffusivity(Saudi Digital Library, 2023-10-27) Alenzi, Fawzah; Meylan, MikeMany nations are confronted with the issue of coal combustion during extraction and transportation, which is not desired. Self-heating is the main source of this problem. There- fore, it is essential to analyse this phenomenon in order to understand and control it. Not- ably, the study of heat-transfer behaviour is essential to comprehend self-heating. Thermal diffusivity, thermal conductivity, and heat capacity are essential thermal properties that in- fluence the heat transfer process. In particular, knowing the thermal diffusivity is essential to calculate the speed of heat propagation in an object. Additionally, knowing thermal dif- fusivity is the most important requirement for modelling many other temperature-related issues. Heat-transfer equations are essential for modelling heat measurements, and their struc- ture is determined by the geometry of the sample and the time period of heat transfer. The variables of these equations can be determined on the basis of the shape of the sample. In mathematical analysis, two essential parameters appear, the thermal diffusivity and the Biot number. To obtain precise measurements, it is necessary to calculate the Biot number as well as the thermal diffusivity values. Although the Biot number itself is not so funda- mental, it must be determined accurately to find the diffusivity. The aim of this thesis is to create a mathematical model for heat transfer in both cyl- indrical and cubic samples, as well as to investigate the techniques used to calculate the Biot number and thermal diffusivity. We develop a numerical solution for the heat transfer model to determine the Biot number and the thermal diffusivity from the measurements. Our research findings demonstrate that the matching method we developed is an effective approach for analysing heat measurement when compared to other methods. Furthermore, we examine the differences in the heat transfer model for cylinder and cube samples. xiii56 0Item Restricted Modelling & Simulation the Performance of User Behaviour in Serious Contexts(Saudi Digital Library, 2023-09-27) Alkoradees, Ali Fayez; Thomas, Nigel; Harrison, Michael; Colquhoun, JohnReal-time experiments on healthcare procedural improvement can be infeasible due to the domain’s criticality and sensitivity. For instance, high morbidity rates and escalated patient treatment duration can, in some circumstances, be associated with medical resources exhaustion. Thus, formal methods can be an answer to lower the effects of experimentation within these healthcare domains as such an approach may be effective in deriving new insights and proposing further recommendations to the investigated domain. Specifically, performance modelling formalisms provide a rich theoretical foundation for dynamic systems, which are affected by an extensive collection of interventions, and supported by the existing formalisms toolsets. Hence, investigating healthcare system contexts involves several complex challenges. These challenges range from data collection methods and data analysis formalisms to optimising medical outcomes. This optimisation is beneficial to behaviour analysts and medical administrators. The current thesis contributes to addressing these challenges in many different ways: (i) By presenting an improved web-based version of a sketch simulation that collects the clinician behaviour during massive bleeding scenarios. This unconventional data collection method is proposed to minimise the need to observe the interventions in person where such treatment of these medical cases are performed; (ii) The modelling of two medical scenarios using different modelling formalisms for analysis and evaluation purposes, these modelling formalisms are Performance Evaluation Process Algebra (PEPA), Collective Adaptive Resource-sharing Markovian Agents (CARMA), and Stochastic Petri nets (SPN); (iii) A proposed tool to enhance the log analysis process. Doing so required the implementation of a trace-driven simulation tool. The tool simulates a clinical behaviour that has been recorded using a sketch simulation version. (iv) Proposing different suggestions to improve medical outcomes and to effectively reduce the cost of health resources.5 0Item Restricted Modelling Health Process and System Requirements Engineering for Better E-health Services: Focus on Diabetes in Saudi Arabia(Saudi Digital Library, 2023-08-28) Alanazi, Fuhid; Valerie, GayThis research is aimed to identify design and process engineering requirements for the implementation of an efficient and effective e-health-based personalised diabetes management in Saudi Arabia. The incidences of obesity and diabetes are high and increasing in Saudi Arabia. The steadily increasing cost burden to the government and patients is a major worry for the Ministry of Health. Research has shown that e-health-based personalised diabetes management can reduce the cost burden to both the government and the patients without any reduction in the effectiveness of healthcare. However, there was no work on e-health models for personalised diabetes management in the Saudi context. This research addresses this need and thus justifies this endeavour. A review of the literature showed high potential for such applications. Thematic analysis was done on the focus group responses. One of the themes identified was directly related to the modelling requirements and provided insights into the variables required for a comprehensive e-health self-care system. Survey responses from 210 patients near King Abdulaziz Medical City, Jeddah, were statistically analysed. Findings revealed a higher likelihood of diabetes among women aged 25-50 with a bachelor's degree or higher. Most patients monitored blood glucose levels and adhered to medical guidance but didn't seek hospital advice. Diet control and exercise were also prescribed, but not followed by a majority. Abnormal conditions were rarely reported. Limited e-health facility availability and patient registration were noted, possibly due to inadequate awareness. Survey results on the effect of interventions on the reduction of blood sugar levels showed the highest reduction for the intervention of medication, followed by diet, and then exercise. The reduction was higher in the case of females and patients who were older than 50 years of age. Regression analysis resulted in significant predictive power only for exercise. Based on the above results and literature, a list of design requirements for an e-health system for the self-management of Saudi diabetic patients was made. Four design options were also identified. What remains is designing these four options, pilot testing with small samples, and finalisation of the most desirable model from pilot test results with any improvements required. The selected model can be taken for validation. After any further improvements required, the finalised model can be transferred to the Ministry of Health for gradual implementation across the country.21 0