SACM - United Kingdom

Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667

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Partial Schauder estimates for second-order elliptic systems
(Wollongong University, 0018-07-17) Alharthi, Saleh; Du, Kai
Second order elliptic equations and systems are among the most important types of partial differential equation(PDEs). The classical Schauder’s theory for this type of equations has played an essential role in the study of linear and non-linear elliptic equations, which reveals that if all the coefficients and data are Holder continuous in all variables, then the solutions and all their derivatives up to second order will also be Holder continuous in all the variables. The main objective of this research is to obtain a class of pointwise estimates, called partial Schauder estimates, for second-order elliptic systems. The desired result will show that if the inhomogeneous term f ^a is Holder continuous in the x_n direction, then the u_x , ...,u_x_n derivatives are also Holder continuous.
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A SYSTEMATIC REVIEW OF LEFT ATRIAL APPENDAGE OCCLUSION FOR STROKE PREVENTION IN PATIENTS WITH NON-VALVULAR ATRIAL FIBRILLATION: EFFICACY AND SAFETY COMPARED TO ORAL ANTICOAGULANT THERAPY.
(University of Brighton, University of Sussex, 0022) Alshahrani, Ali; O'Nunain, Sean
Background: Atrial Fibrillation AF is the most common sustained arrhythmia and is associated with significant morbidity and mortality. While anticoagulation is generally an effective therapy to reduce the incidence of stroke, one in 10 individuals has a contraindication to anticoagulants. Given that in non-valvular AF (NVAF), 90% of thrombi originate from the left atrial appendage (LAA), closing the LAA using a percutaneous device has been developed to prevent AF-related stroke in high-risk patients. Nevertheless, there is limited data about the efficacy and safety of LAA occlusion (LAAO) compared to anticoagulant therapy. Methods: In this thesis, a comprehensive systematic review was done to compare LAA occlusion and anticoagulant therapy (Warfarin or NOACs) in stroke prevention for patients with NVAF. Meta-analysis was conducted to obtain a single summary estimate of stroke prevention (haemorrhagic stroke versus ischemic stroke). Results: Six studies (3 randomized control trials and 3 observational studies) were eligible which involved a total of 4891 participants with a follow-up time of 18-36 months. Compared to anticoagulant therapy, LAAO showed no significant difference in preventing ischemic stroke or systemic embolism. Procedure and device-related complications in the LAAO arm drove these outcomes. However, significant trends were seen favouring LAAO in reducing haemorrhagic stroke or major bleeding. Moreover, CV mortality showed better outcomes with LAAO in some studies. Conclusion: This thesis showed that LAAO can be effective replacement therapy for anticoagulation in preventing stroke. It may be practically useful in minimising haemorrhagic stroke and in patients with a clear contraindication to anticoagulant therapy. Physicians should consider late device-related complications including device-related thrombosis and peridevice leaks when planning for LAAO, as this can increase the risk of stroke.
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EVALUATION OF NON-ADHERENCE IN CLINICAL TRIALS OF ANTIDEPRESSANTS FOR MAJOR DEPRESSION DISORDER: A SYSTEMATIC REVIEW AND METAANALYSIS.
(Saudi Digital Library, 0022-10-03) Alrasheedi, Maram; Williams, Sian
Major depression disorder is a serious mental issue which affects the physical and mental well-being of affected persons. Hence, there is a need to develop antidepressants to treat major depression disorder. However, medicine non-adherence is a major challenge in major depression disorder drug development. Similarly, medication adherence is an essential aspect of the success of antidepressant development. Adherence/non-adherence data informs the interpretation of antidepressant clinical results, reducing clinical risks and realising antidepressant benefits in clinical trials. However, adherence prevalence is a crucial aspect of clinical research, existing evidence points to underreporting adherence assessment methods and outcomes in published clinical trials. Also, RCTs that report adherence measures and outcomes fail to provide adequate methodology regarding adherence measures and outcomes. To improve the completeness and quality of the information in antidepressant clinical trials, this research examined medication adherence prevalence in RCTs, measures of adherence/non-adherence, and clinical and cost impacts of non-adherence. A systematic review and meta-analysis methodology was adopted. Meta-analysis findings affirm that medication adherence prevalence in clinical trials for antidepressant development is 72.2%, translating to a 27.8% non-adherence level. Qualitative findings from the studies established direct and indirect non-adherence measures of adherence, including self-report measures and the use of questionnaires. Impacts of non-adherence include disease relapse and recurrence (clinical impact) and increased clinical trial costs. Keywords: Major depression disorder, Antidepressant clinical trials, Non-adherence, Adherence, Adherence prevalence.
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The potential and challenges of recycling metals swarf from machining processes
(Saudi Digital Library, 0023) Abdulelah, Asiri; McLaren, Andrew
The dissertation discusses the challenges and opportunities for efficient and sustainable recycling of metal swarf, which is a hazardous waste generated during metal processing. The study investigates the effectiveness of different recycling methods for aluminum, steel, and titanium, including hot extrusion, equal channel angular pressing, forging, and spark plasma sintering. The dissertation also highlights the importance of addressing the particle size distribution of swarf and the presence of cutting fluids in the recycling process. The findings suggest that while direct conversion recycling methods of swarf shows promise, further research is needed to optimise recycling practices and reduce energy consumption associated with metal swarf recycling.
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Accuracy of ultrasonography and computed tomography in the diagnosis of acute appendicitis
(0023-03-09) Alqahtani, Yahya; Street, Angela
Background: Acute appendicitis (AA) is a common reason for surgical intervention. Considering the high burden of AA, early and correct diagnosis is crucial. Two imaging modalities (i.e., ultrasonography and computed tomography) are frequently used for the diagnostic evaluation of AA. However, there is lack of clarity regarding their diagnostic accuracy. Choosing the most valid intervention to diagnose AA would have important clinical implications. Aim of Study: To assess the diagnostic accuracy of ultrasonography (US) and computed tomography (CT) in the diagnosis of AA in adolescents and adults. Methods: This structured literature review (SLR) was carried out using the components of the PICO framework. The literature search was conducted using Cochrane, Embase and PubMed databases. All steps in the SLR were followed, from searching for relevant studies to selecting them, extracting their data, assessing their quality, and synthesising their results. Results of the search process were recorded according to the Preferred Reporting Items for Systematic reviews and Meta-analyses (PRISMA) flow diagram. This SLR included studies on adolescents and adult patients (aged >14 years) with confirmed AA, that were diagnosed using both US and CT. Articles published before 2012, those published in languages other than English, or with no available full text were excluded. The quality assessment of the selected articles was done using the Critical Appraisal Skills Programme (CASP) checklist. Results: Five articles were included in this SLR. The highest reported sensitivity for US was 98.5%, while the lowest was 58.2%. The highest specificity of US was 97.3%, while the lowest was 54.2%. High diagnostic accuracy rates of US were reported by the included studies ranging from 83% and 93.14%. All included studies reported that CT had very high sensitivity for the diagnosis of AA, reaching 100% in one study, while slightly lower sensitivity rates were reported by other studies ranging from 96% to 98.9%. The specificity of CT was reported to be 100% by two studies, while other high specificity rates were reported by three studies (i.e., 97.2%, 89% and 88.9%). The diagnostic accuracy for CT was consistently higher than that of US. Conclusions and implications: The diagnostic accuracy of CT is higher than that of US. However, CT cannot be routinely used because of its high cost and the associated potentially harmful effects of ionizing radiation. Therefore, it is recommended that in cases clinically suspected to be AA, US can be used as the first-line diagnostic modality, followed by CT when the results of US are not conclusive. Keyword: Ultrasonography, Computed tomography, Acute appendicitis, Diagnostic Accuracy, Sensitivity, Specificity, Predictive Value, Validity.
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DIGITAL COLLABORATION IN THE COVID-19 CRISIS: TRANSFORMING SAUDI ARABIA'S DISASTER RESPONSE
(Saudi Digital Library, 0023-09-21) Almalki, Raed; Aryal, Komal Raj
The COVID-19 pandemic has had a significant impact on a worldwide scale, which has led governments to develop a variety of preventative and response measures in an effort to lessen the severity of its consequences. The education system, particularly that of Saudi Arabia, faced tremendous problems and was required to fast adjust to the new conditions brought about by the epidemic. This dissertation analyzes the transformational influence of digital cooperation in Saudi Arabia's reaction to the COVID-19 crisis, with a special emphasis on education, health, safety, and security. The topic of this research is COVID-19. During the epidemic, Saudi Arabia, like with many other nations, understood the significance of digital technology, particularly in the fields of public health, healthcare services, education, telecommunication, commerce, and risk communication. Analysis of publicly available official announcements, press briefings, published data, research, and professional discussions were performed with the goal of gaining a thorough understanding of Saudi Arabia's digital response. According to the findings of the research, the pandemic prompted a faster adoption of digital solutions across a variety of industries. The education system in Saudi Arabia has shown resiliency by adopting online education platforms to guarantee that students always have the opportunity to learn new things. The cooperation provided by telecommunications firms was essential to the continuing efforts, and those working to disseminate information about potential dangers made good use of websites, social media platforms, and SMS text messaging. This study comes to a close by investigating the role that digital cooperation had in changing Saudi Arabia's emergency reaction to the COVID-19 issue. The research adds vital insights to continuing worldwide efforts in harnessing digital collaboration to lessen the effect of crises and influence future disaster response tactics by studying its impact on education, health, safety, and security. These are only few of the areas that were investigated in the study.
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Perception of residents about sustainability impacts of mega events: A case study of Qatar World Cup 2022
(Saudi Digital Library, 0023-09-29) Alyami, Hamad; Terzidou, Matina
The objective of this dissertation is to evaluate the perception of the residence of the Doha city about the sustainability impact of the World Cup 2022. Using stakeholder and triple bottom line theories, this study argues that degree of involvement (independent variable) of the residents of host city in a mega event depends upon their perception about event’s economic, social, and environmental (dependent variables) impact on the host city. Following quantitative methodology, this dissertation collected 246 responses through conducting an online survey using convenience sampling technique. Findings subjected to descriptive and correlation analysis to test research hypothesis. Analysis of data reveals that despite of having economic, social, and environmental concerns, residents of the Doha city showed their commitment towards involving in the World Cup 2022. This is proved by the results of correlation analysis that reveals positive and significant relation between economic and social perception of Doha residents about World Cup 2022 and their involvement in the event. However, moderate yet significant relationship is found between the perception of Doha city residents about the environmental impact of the World Cup 2022 and their involvement in the event. It is found that reason behind trade-offs between economic, social, and environmental perception of the World Cup 2022 and their involvement in the event comes from their emotional attachment to the event as it created their social identity, development tourism brand image of the Doha city, and led to developing their national pride. These findings implicate that residence of the Doha forgo economic, social, and environmental concerns because of their emotional attachment with the country, however organizers could have done more to take locals in confidence through more direct communication. Therefore, this study recommends authorities to develop clear communication plan to improve awareness of the people about sustainability issues while holding future mega events. Exclusion of demographic variables from the analysis and small research population are two key limitations of this study.
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Gulf Cooperation Council Countries’ Electricity Sector Forecasting: Consumption Growth Issue and Renewable Energy Penetration Progress Challenges
(Lancaster University, 0023-10-18) Alharbi, Fahad Radhi; Csala, Denes; Wang, Ziwei; Campobasso, M.S
The Gulf Cooperation Council (GCC) countries depend on substantial fossil fuel consumption to generate electricity which has resulted in significant environmental harm. Fossil fuels also represent the principal source of economic income in the region. Climate change is closely associated with the use of fossil fuels and has thus become the main motivation to search for alternative solutions, including solar and wind energy technologies, to eliminate their reliance on fossil fuels and the associated impacts upon climate. This research provides a comprehensive investigation of the consumption growth issue, together with an exploration of the potential of solar and wind energy resources, a strict follow-up to shed light on the renewable energy projects, as currently implemented in the GCC region, and a critical discussion of their prospects. The projects foreshadow the GCC countries’ ability to comply with future requirements and spearhead the renewable energy transition toward a more sustainable and equitable future. In addition, four forecasting models were developed to analyse the future performance of GCC power sectors, including solar and wind energy resources along with the ambient temperatures, based on 40 years of historical data. These were Monte Carlo Simulation (MCS), Brownian Motion (BM), and a seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model model-based time series, and bidirectional long short-term memory (BI-LSTM) and gated recurrent unit (GRU) model-based neural networks.
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Factors contributing to hate crimes and racial discrimination against Arab Muslims in the UK
(Saudi Digital Library, 0023-11-23) Alanazi, Maryam; Alanazi, Maryam
In recent years, a considerable body of research and surveys has been undertaken, notably in the aftermath of the United Kingdom's withdrawal from the European Union. These investigations have shed light on a noticeable increase in hate crimes directed explicitly towards the Muslim community within the UK, notwithstanding the existence of specific surveys conducted among Muslims residing in the United Kingdom, which have produced positive outcomes, indicating a positive trend in the degree of acknowledgement granted to Muslims within the nation (Ghani & Nagdee, 2019). According to the 2021 report published by the UK Home Office, it was determined that around 50% of individuals targeted in hate crimes are affiliated with the Muslim community in England and Wales. What determinants contribute to hate crimes and racial discrimination towards Arab Muslims in the United Kingdom? Is the United Kingdom considered to have the lowest incidence of racial discrimination against Muslims compared to other European nations? What factors contribute to the increase in hate crimes? Do Arab Muslims perceive a sense of societal failure? This ongoing crisis of racism has prompted numerous inquiries, including those about Arab Muslims, which are but a small subset of the broader range of questions being posed. And lastly, what do Arab Muslims anticipate from the government in combating prejudice and hate crimes? This research examines the variables causing hate crimes and racial prejudice against Arab Muslims in the UK in response to this problem and the abovementioned points. To do this, the research will first depend on secondary data from administratively released national-level statistics, which will be analysed in the literature review last section. After that, a varied sample of Arab Muslims from throughout the UK will be used for semi-structured interviews and a questionnaire-based survey to gather primary data using quantitative and qualitative methodologies. This study attempts to provide a thorough investigation of the views and perceptions of Arab Muslims about the causes influencing hate crime and racial prejudice by integrating primary and secondary data sources.
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Evaluation of the Digital Transformation in Engineering Management in Saudi Arabia's Ministry of Transport and Logistics Service
(University of the West of England, 0024-02-07) Alrehaili, Ahmad Zaben; Algharaibeh, Sana
This thesis examines the digital transformation initiatives within the Ministry of Transport and Logistics Service in Saudi Arabia, focusing on engineering management practices. The study aims to assess the impact of digital transformation on engineering management processes, evaluating the efficiency and transparency of technological advancements specifically in the Ministry's engineering department. A quantitative methodology is used, using online questionnaire surveys to gather insights from stakeholders, government officials, engineers, and IT professionals involved in the digital transformation initiatives within engineering department of Ministry of Transport and Logistics. Hence, the quantitative measures were employed to assess key performance indicators, such as project timelines, cost efficiency, and resource allocation, before and after the implementation of digital tools. The findings of this research contribute to existing literature on digital transformation in the public sector, offering insights into challenges and successes in engineering management. Recommendations will be provided, guiding future strategies for digital transformation in government engineering projects and shedding light on best practices for similar initiatives in the broader context of public administration
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Development of a ZnO-based aptamer system for biosensing applications
(University of Liverpool, 0024-03-05) Alshammari, Adeem; Sandall, Ian
Biosensors are analytical devices that have the capability to convert some information about biological interaction into physical signals. The significance of biosensors in various disciplines, such as medical diagnosis, environmental monitoring, facility security, and food safety, is growing. A significant effort is directed towards developing the recognition element and the transducer component of biosensors to improve their sensitivity and selectivity. One of the elements that has attracted attention in recent years is an aptamer system. Aptamers are single-stranded nucleic acid molecules (DNA or RNA) that bind to specific target molecules, enabling high affinity and selectivity in binding. The transducer is the primary component that transforms and amplifies recognition events. The transducer can be based on a variety of technologies, including optical, electrical, or electrochemical technologies. However, the performance of biosensors can be improved by exploiting specific properties of the transducer’s component material for a better detection technique. In this thesis, it is proposed to utilise zinc oxide (ZnO) as the main material with the aim of developing aptamer-based biosensors. The excellent properties of ZnO including biocompatibility, high electron mobility, ease of fabrication, and labe-free detection, are demonstrated to be an effective approach for biosensing. These features are useful for providing highly selective and sensitive detection, which can accurately and specifically identify a target analyte while maintaining a low detection limit for trace amounts of the substance (down to few femtomolar concentrations). In addition, among semiconductor materials available in the market such as silicon (Si), gallium nitride (GaN), titanium dioxide ( TiO2), ZnO is generally considered a cost-effective regarding synthesis and fabrication. The ZnO-aptamer biosensors system is characterised for its optical, electrical and transducing properties for the detection of the SARS-CoV-19 spike protein and the VEGF-165 protein. Initially, ZnO-aptamer devices have been built using microfabrication and surface functionalisation. ZnO thin films have been deposited using radio-frequency (RF) sputtering, shadow mask evaporation to deposit aluminum (Al) electrodes, and photolithography processes to shape ZnO waveguide configurations. This was followed by functionalisation process of the ZnO surface using different approaches, including amino functional groups of 3-Aminopropyl triethoxysilane (APTES), triethoxysilylpropyl succinic anhydride (TESPSA), a thiol functional group of 3-mercaptopropyl trimethoxysilane (MPTMS), and phosphorothioate oligonucleotide linkage (PS linkage), to facilitate the aptamer bond. Optical and electrical characterisation techniques are utilised to investigate ZnO-aptamer devices responses in various parameters including protein-aptamer dynamic binding, electrical field effect, and transmitted spectra. Spectroscopic ellipsometry (SE) in biosensors is used to measure the thickness and refractive index of adsorbed layers on a sensor surface. SE can provide insights into adsorption kinetics by monitoring these properties over time. The adsorption kinetics of targeted proteins is real-time monitored on the functionalized ZnO surface in an aqueous environment. Current-voltage (I-V) characterisation is used to study the ZnO-FET behaviour upon attaching different protein concentrations. These attached proteins are serving as gate charges. When the target biomolecule binds to the receptor on the functionalised surface, it induces a change in the electrical properties due to charge redistribution. As a result, the conductance of the Bio-FET channel is modulated. A monochromator system is used to optically characterize a ZnO waveguide and its responsiveness to different proteins in concentrations from femtomolar to nanomolar level. SE technique has been employed to examine the thickness and optical characteristics of the ZnO thin film during the chemical reaction of surface functionalisation. This process effectively verifies the stability of ZnO thin film, preventing dissolution and aggregation effects, so ensuring its dependable utilisation. Furthermore, the extraction of optical characteristics before and after functionalization is necessary to verify the stability of ZnO. The validation of the later techniques involved doing I-V characterization to assess the functionalisation roles in achieving ZnO-aptamer specificity to proteins. The APTES coupling agent exhibited strong binding, while the TESPSA, PS linkage, and MPTMS coupling agents showed comparatively weaker binding. In addition, SE in-situ measurements were used to determine the thicknesses and optical characteristics of the adsorbed spike protein and VEGF over time. This helped gain insights into the dynamic binding process to ZnO-aptamers that were modified using optical models based on polarised light reflection over thin layers. The surface mass density (SMD) was determined by considering the thicknesses and refractive indices, yielding a limit of detection (LOD) of around 1 nanomolar (nM) for the spike protein and 100 picomolar (pM) for the VEGF. Moreover, I-V measurements have verified the ZnO-aptamer sensing mechanism through the detection of current responses resulting from the presence of the connected biolayer. The present shift ratio was determined based on concentration, revealing a limit of detection as low as a few picomolars. Furthermore, the same amount of specific proteins exhibited reactions to the ZnO waveguide surface, as evidenced by alterations in the spectral response for various concentrations. These changes were observed at a LOD of 1 nanomolar for spike proteins and 1 pM for VEGF. Ultimately, the obtained optical measurements of the proteins were then employed to construct a model of a micro ring resonator (MRR) for the purpose of creating a small-scale sensing platform at the micro-nano level. The MRR provided extremely sensitive biosensors with a resolution of a few nanometers per refractive index unit (nm/RIU) and a high-quality factor ∼7.66 ×103. This thesis presents a novel approach by conducting experiments to showcase the application of ZnO characteristics in creating a biosensing system. The system is built using aptamer receptors and may be utilised with various detection techniques. Additionally, this study demonstrates several techniques to modify the surface of ZnO with the ability to enhance its sensitivity, while also enhancing the selectivity of ZnO-aptamers towards specific proteins of interest. In addition, a design and model of a ZnO biosensor based on MRR (Micro-Ring Resonator) technology has been introduced to exhibit sensitive biosensor.
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Analysing Scientific Collaboration Networks Using Network Science Techniques to Study Covid-19 preprint papers/articles in UK and USA
(University of Exeter, 0024-03-11) Asiri, Najat; Saunders, Owen
The COVID-19 pandemic has instigated an unparalleled surge in scientific research, necessitating extensive collaboration among researchers, institutions, and nations. In this study, we employ advanced network science techniques, including centrality and betweenness measures, to conduct a comprehensive analysis of scientific collaboration networks within the context of COVID-19 research. The focus is specifically on preprint papers and articles sourced from Kaggle databases in the UK and USA. The research methodology involves collecting and analyzing over 50 million preprint papers and articles to provide a robust foundation for the analysis. Building upon existing research, we utilize bibliometric analyses and network science methodologies to unveil the intricate dynamics of collaboration. By mapping and quantifying evolving collaboration patterns, this research aims to identify key players and uncover research hotspots. The inclusion of network science methods such as centrality and betweenness enriches the analysis, providing a nuanced understanding of the collaborative landscape. The findings not only present a comprehensive view of collaborative dynamics within the scientific community but also shed light on key network metrics, including centrality and betweenness, highlighting pivotal contributors and facilitating efficient information flow. The paper contributes to the ongoing discourse on global health crises, offering valuable insights into the collaborative responses to the pandemic. The study, encompassing 32 papers over the period 2020 to 2023, represents a significant and timely addition to the existing body of knowledge. As the global scientific community continues to grapple with the complexities of COVID-19, this research serves as an essential guide for informed decision-making by policymakers, researchers, and institutions involved in shaping strategies for both current and future pandemics. The incorporation of new results further enhances the relevance and applicability of the findings, positioning this study as a crucial contribution to the understanding of collaborative networks in the face of global health challenges.
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Kalman Filtering and Bayesian Inference in Enhancing Pandemic State Estimation
(University of Exeter, 0024-03-13) Alyami, Lamia; Das, Saptarshi; Townley, Stuart
The COVID-19 pandemic is a contemporary challenge that requires long-term prediction and sustainable management strategies to effectively deal with its consequences. Developing mathematical models for the COVID-19 pandemic is subject to uncertainties and limitations due to inherently inaccurate phenomena. In this context, this thesis has the following aims: Firstly, an epidemiological model is proposed as a nonlinear ordinary differential equation (ODE), named the SEIQRD model (Susceptible-Exposed-Infected-Quarantined-Recovered-Deceased). This model extends the simple SIR (Susceptible-Infected-Recovered) model to predict the transmission dynamics of COVID-19. The recursive estimator Kalman filters help to accurately extract information for the states or quantities of interest from noisy measurements. Subsequently, the extended Kalman filter (EKF), designed to handle nonlinear dynamical systems, is integrated with the proposed SEIQRD model. This integration enhances estimation accuracy, minimising uncertainties for underlying dynamic systems and providing estimates for unmeasurable hidden states. Additionally, improvement is made to the proposed model by incorporating further parameters, resulting in the improved-SEIQRD model. Moving forward, the aim is to enhance the accuracy of the EKF algorithm by introducing the extended skew Kalman filter (ESKF) algorithm based on a skewness distribution within the improved-SEIQRD model. This is crucial as COVID-19 data may include outliers that could result in inaccurate estimations using traditional Gaussian Kalman filtering approximation. The necessity arises from the asymmetry in the posterior distribution, where the effectiveness of the ESKF algorithm has been proven in capturing skewness in both states and noise distributions. Secondly, the generalised Bayesian inference method, known as the nested sampling algorithm, is employed to conduct realistic parameter estimation, especially in complex and high-dimensional parameter spaces or when the posterior distribution exhibits irregular shapes. This enhancement in estimation is achieved compared to standard Markov Chain Monte Carlo (MCMC) methods by utilising the mean posterior distribution of the quantity of interest and estimating uncertainties as well. Then, these probabilities are used to fit the epidemiological models presented in this thesis. This thesis evaluates the proposed method using numerical simulation results for active cases and death cases in Saudi Arabia. Additionally, within the context of the EKF algorithm, an open question in the Kalman filter framework is explored by tuning its coefficients. A method is proposed for estimating the measurement noise covariance matrix in the EKF algorithm. This is achieved by fitting the error between the reported data and the mean SEIQRD model, demonstrating improvement over arbitrarily chosen values. Thirdly, an effective Bayesian model comparison approach is employed, utilising Bayesian evidence approximated by nested sampling, to compare the SEIQRD models proposed in this thesis with the traditional SIRD model. This comparison is supported by evaluating the EKF performance for each chosen model. It is demonstrated that the proposed technique can mitigate variations between the models' predictions and assess the required complexity levels. Overall, the main contribution is developing a generalised approach encompassing deterministic/stochastic model alterations, parameter estimation, noise distribution, and model comparison in a single pipeline. Finally, relevant conclusions and future development trends are provided for addressing unknown pandemics, along with the potential utilisation of non-Gaussian Kalman filters.
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Enhancing Chemical Adherence Testing through Pharmacokinetics and Pharmacogenetics Insights and Mass Spectrometry Advancements.
(University of Leicester, 0024-05-18) Alghamdi, Randah; Gupta, Pankaj
This thesis addresses the pressing issue of medication non-adherence with a focus on hypertension. Non-adherence is common and significantly elevates the risk of hospitalisation and mortality. The study investigates chemical adherence tests to assess medication adherence, employing liquid chromatography with tandem mass spectrometry (LC-MS/MS) as a robust method. The introduction highlights the complexities of adherence measurement and outlines potential limitations, including the influence of pharmacokinetics (PK) and pharmacogenetics on medication detection and the time-consuming nature of chemical adherence testing (CAT) processes. The central hypothesis underpinning this research is that the pharmacokinetics and pharmacogenetics of antihypertensive medications do not significantly affect medication detection or, consequently, the results of CAT by LC-MS/MS. This hypothesis is explored through a series of specific aims, including establishing PK parameters for the 20 most commonly prescribed antihypertensive medications through a comprehensive literature review. Additionally, the study aims to determine whether these PK parameters have any bearing on the outcomes of CAT using LC-MS/MS. A systematic review is conducted to identify genetic polymorphisms related to the effects of cardiovascular medications within the Biology Study to Tailored Treatment in Chronic Heart Failure (BIOSTAT-CHF) cohort, and the subsequent investigation focuses on the association between genetic polymorphisms and medication detection rates in the same cohort. Furthermore, the study strives to develop and partial validate an improved and more efficient CAT method for quantitating various cardiometabolic medications using LC-QQTO MS, a crucial step in ensuring accurate adherence assessments. The findings of this research reveal several critical insights. Chapter 3, which reviews the PK parameters of commonly prescribed antihypertensive medications, demonstrates no significant correlation between these parameters and adherence scores. This observation holds for multiple parameters, including bioavailability, urine excretion, clearance, volume of distribution (VD), half-life, peak time, and peak concentrations. Logistic regression analysis confirms that PK parameters do not predict non-adherence, even when considering additional factors such as age, sex, the number of medications, and creatinine levels. In Chapter 4, the systematic review uncovers various genetic polymorphisms associated with cardiovascular medication effects in the BIOSTAT-CHF cohort. However, these genetic variations do not exhibit a substantial correlation with non-adherence to prescribed cardiovascular drugs and encompass a wide range of effects, including PK influences, adverse drug reactions, metabolic responses, therapeutic outcomes, and risk-related impacts. Additionally, a non-directed genome wide association study (GWAS) showed weak associations with some potential polymorphisms, but none met the usual threshold of significance. Chapter 5 focuses on the development and partial validation of LC-QTOF-MS methods for the quantitation of cardiometabolic medications. Due to the distinct challenges posed by the COVID-19 pandemic, the objective was modified to conduct a partial validation assessment. The analysis time was reduced by 10 times from the previous method. The optimization of conditions for both positive and negative modes of LC-QTOF-MS is detailed, covering parameters such as capillary voltage, energy settings, and mobile phase selection. The validation results underscore the importance of tailored approaches for different pharmaceutical compounds, emphasising the significance of meticulous method development and validation in pharmaceutical analysis. In conclusion, this thesis proves the central hypothesis that the pharmacokinetics and pharmacogenetics of antihypertensive medications do not significantly affect medication detection and, therefore, do not influence the outcomes of CAT by LC-MS/MS. These findings offer valuable insights into improving medication adherence assessment and management in cardiometabolic diseases, highlighting the need for a multifaceted approach that considers pharmacokinetics and pharmacogenetics. Notably, my thesis had to adapt to the challenges posed by the COVID-19 crisis. The research shifted its focus from the initial plan of conducting PK profiles of antihypertensive medications in healthy volunteers to the plan of undertaking a systematic review of genetic polymorphisms associated with various effects of cardiovascular medications within the BIOSTAT-CHF cohort study.
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THE IMPACT OF THE DEVELOPMENT OF PUBLIC TRANSPORTATION ON THE TOURISM SECTOR IN THE KINGDOM OF SAUDI ARABIA
(Bournemouth University, 0024-05-24) Alrashdi, Mohammed Sulaiman; Shiva
The research is based on the importance of public transportation development and its impact on the tourism industry of Saudi Arabia (SA). It highlights the impact of developing the transport facility that can help in attracting the potential tourist from the UK. SA has potential tourist destination in the country that makes it one of the major tourist spot for the visitors around the globe. The research questions have identified in the research that highlights various aspects of the research topic. In the theoretical framework, the innovation and future development process of SA’s public transportation system highlights the adoption of new technologies and scientific techniques. The developed strategies of public transportation in tourism contribute to enhance its profitability. The strategies of Tourism marketing follow and understand the needs and expectations of customers for online bookings, focusing on mobile, as well as optimized market. Sustainable tourism widely focuses on schemes to create a low carbon as well as encourage travel. Scientific Management Theory and Diffusion of Innovation Theory develop new ideas and practices of tourism key concepts. The research methodology introduces the concept and benefits of positivism research philosophy, deductive research approach, and descriptive research design. The data collection method is done through primary quantitative method from survey. Reliability and validity are used in research study for maintain accuracy and consistency of research study. Random sampling is used for collecting responses from 155 people working in Saudi Arabian tourism sector. Ethical considerations are used in research paper to protect it from any type of harm. Findings and discussion of the research topic is by conducting an online survey among 155 participants regarding the 11 questionnaires. Public Transport helps in reducing carbon emissions and traffic congestion in the country for tourist’s attraction that are environment conscious. The development of Saudi vision 2030 strategy focuses on developing the sector of tourists. The outcome of the research has been identified. It identifies that development and innovation of the PT system improves tourism comfort ability and safety. Some recommendations on the development of PT have also been given; the future scope and research limitation have also been identified here.
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A Survey Assessing the Perceptions and Interpretations of Saudi Therapists on the Use of Virtual Reality Therapy for Patients with Spinal Cord Injury
(Cardiff University, 0024-06-13) Alqarni, Rawabi; Williams, Alison
Title: A Survey Assessing the Perceptions and Interpretations of Saudi Therapists on the Use of Virtual Reality Therapy for Patients with Spinal Cord Injury. Background: Virtual Reality (VR) has emerged as a promising tool for enhancing rehabilitation, particularly for individuals with spinal cord injuries (SCI) and neurological conditions. This study offered a comprehensive examination of VR-based interventions, encompassing the user experience (UX), game mechanics (GM), in-game assistance (IGA), and VR-induced symptoms and effects (VRISE). Methods: A descriptive survey with a self-developed questionnaire investigating the 31 Saudi therapist's perspectives that were based on patient feedback who were recruited via WhatsApp then filled up the questionnaire that contained 22 questions (both closed& opened-ended), providing a well-rounded assessment of the VR rehabilitation experience. Statistical outcomes, therapist involvement, and specific context were considered to identify areas for improvement and opportunities for further research. Results: The study highlighted the significance of enjoyment in fostering patient engagement and the need for customized VR setups aligned with patient preferences. Graphics and sound quality, while important, are not the sole determinants of engagement, emphasizing the importance of a holistic VR experience. GM and IGA show promise but require customization. Addressing VRISE through technological advancements is essential. Conclusion: This research underscored the continuous evolution of VR technology and design to better serve individuals with SCI and neurological conditions, emphasizing the importance of tailored approaches and ongoing advancements in VR-based rehabilitation.
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Application Placement Approaches to Improve Quality of Service in Fog Computing
(University of Manchester, 0024-06-25) Aljohani, Aisha; Sakellariou, Rizos
Fog Computing (FC) addresses Cloud Computing's (CC) limitations by utilizing distributed computational devices, known as fog devices, near the Internet of Things (IoT) environment to support a wide range of IoT applications. In FC, to ensure Quality of Service (QoS), users need to specify a placement plan for distributing IoT applications among fog devices for processing; this is known as the application placement problem (APP). With a potentially huge number of fog devices and applications, solving the APP can be decentralized, i.e., independent optimization can be performed in parallel for different clusters of fog devices, thus mitigating the networking and computing overhead and enhancing the QoS consequently. In this approach, clusters lacking sufficient fog devices may propagate undeployed applications to other clusters, potentially leading to uncertain fulfillment of QoS constraints, i.e., delay bounds on response time. Moreover, deploying applications based on available resources at the placement decision time might result in an increased number of propagated applications among clusters. Additionally, the heterogeneity in fog devices' capabilities and the variations in IoT application characteristics, such as computing and networking intensity, and delay sensitivity, pose challenges in choosing competent applications for powerful fog devices. Assigning specific applications to these powerful devices may result in delay violations for other applications on less powerful ones, potentially leading to propagating the latter ones to other clusters. A raise in the number of propagated applications, especially those with data streams, might lead to increased networking congestion, resulting in extended response time and potential violation in delays, particularly for delay-sensitive applications. This thesis proposes three approaches aiming to improve the QoS of IoT applications, i.e., delays. First, an improved application placement approach through parallel collaboration (ParColl) is proposed to increase the probability of placing propagated applications within their delays, incorporating algorithms to enable parallel searching and manage the searching process. Second, an improved application placement approach through postponement (PostP) is proposed to maximize the number of non-propagated applications meeting their delays, employing algorithms to postpone placement of undeployed applications, instead of propagating them, if such postponement ensures their delays. Third, an application placement approach maximizing response times for applications while meeting delays through cluster-wide resource selection CWRS) is proposed. CWRS ensures that powerful fog devices are reserved for applications needing them to meet their delays, minimizing violations on other devices whenever possible. Experimental results of implementing the proposed approaches in iFogSim show an improvement in the percentage of applications processed within their QoS constraints and a reduction in average delay violation times compared to existing approaches.
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EXTRACTION OF TEMPORAL RELATIONSHIPS BETWEEN EVENTS FROM NEWS ARTICLES FOR TIMELINE GENERATION
(University of Manchester, 0024-06-27) Alsayyahi, Sarah; Batista- Navarro, Riza
Extracting temporal information from natural language texts is crucial for understanding the sequence and context of events, enhancing the accuracy of timeline generation and event analysis in various applications. However, within the NLP community, determining the temporal ordering of events has been recognised as a challenging task. This difficulty arises from the inherent vagueness of temporal information found in natural language texts like news articles. In Temporal Information Extraction (TIE), different datasets and methods have been proposed to extract various types of temporal entities, including events, temporal expressions, temporal relations, and the relative order of events. Some of these tasks have been considered easier than others in the field. For instance, extracting the temporal expressions or events is easier than determining the optimal order of a set of events. The complexity of determining the event order arises due to the requirement of commonsense and external knowledge, which is not readily accessible to computers. In contrast, humans can effortlessly identify this chronological order by relying on their external knowledge and understanding to establish the most appropriate sequence. In this thesis, our goal was to improve the performance of state-of-the-art methods for determining the temporal order of events in news articles. Accordingly, we present the following contributions: 1. We reviewed the literature by conducting a systematic survey, categorising tasks and datasets relevant to extracting the order of events mentioned in the news articles. We also identified existing findings and highlighted some research directions worth further investigation. 2. We proposed a novel annotation scheme with an unambiguous definition of the types of events and temporal relations of interest. Adopting this scheme, we developed a TIMELINE dataset, which annotates both verb and nominal events and considers the long-distance temporal relations between events separated by more than one sentence. 3. We integrated problem-related features with a neural-based method to improve the model's ability to extract temporal relations that involved nominal events and the temporal relations with small classes (e.g., EQUAL class). We found that integrating these features has significantly improved the performance of the neural baseline model and could achieve state-of-the-art results in two datasets in the literature. 4. We proposed a framework that uses local search algorithms (e.g., Hill Climbing and Simulated Annealing) to generate document-level timelines from a set of temporal relations. These algorithms have improved the performance of the current models and resolved the problem in less time than the state-of-the-art models.
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The Knowledge of Autism Spectrum Disorder Among Male and Female Public Education Teachers in Jeddah, Saudi Arabia.
(Nottingham Trent University, 0024-07) Alobaidi, Batool; Dillon, Gayle
This study investigated teachers' knowledge of autism spectrum disorder (ASD) in Jeddah, Saudi Arabia, and examined what factors influenced the knowledge that teachers had. In Saudi Arabia, ASD is common but often diagnosed late (Hayat et al., 2019). This highlights the need to assess teachers' ASD knowledge, as they are well-positioned to notice signs of ASD. Understanding teachers' awareness can guide targeted training programs, improving early detection and support for students with ASD. Participants included 197 male and female teachers from public schools who completed the Autism Spectrum Knowledge Scale-General Population (ASKSG, 2019) and the Knowledge about Childhood Autism among Health Workers (KCAHW, 2008) scale. The findings revealed that participants demonstrated an average level of knowledge across both scales, scoring below average on the ASKSG but above average on the KCAHW, suggesting a potential gap in understanding ASD. Teachers with prior contact with individuals with ASD spectrum exhibited significantly higher ASD knowledge compared to those without such exposure, underscoring the impact of firsthand experience. Contrary to expectations, no significant differences in ASD knowledge were found based on gender, teaching experience, or school level taught between all educational levels, be it primary, secondary, or high school. The results aligned with previous regional studies (Alharbi et al.,2021; Otaif et al.,2019) documenting weak to moderate ASD knowledge among Saudi Arabian educators. Due to the findings from previous studies in Saudi Arabia until this study, which find that teachers' ASD knowledge has not improved, the study accentuates the pressing need for intensified and reinvigorated ASD training initiatives tailored to teachers, emphasising immersive, experiential learning modalities. By addressing the identified knowledge gaps and recognized limitations, further research efforts can help to provide a comprehensive understanding of teachers' knowledge of ASD. These studies will help to develop comprehensive and tailored programs to provide teachers with the knowledge needed to support children with ASD best.
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MEASURING THE IMPACT OF MEGA-EVENTS ON TOURISM AND ECONOMIC GROWTH IN SAUDI ARABIA
(Leeds Beckett University, 0024-07-02) Alasseri, Reem; Cox, Perter
The Kingdom of Saudi Arabia (KSA) recently announced its intention to expand its tourism sector as part of its economic diversification plans; a deliberate attempt to mitigate reliance on the petroleum industry. The KSA seeks to leverage the potential of the growing and expanding global tourism industry. The KSA has stated its clear intention to fully pursue this diversification, through its Vision 2030 programme, investments in developing tourism infrastructure, and deliberate efforts to host significant sporting events (mega-events). This paper evaluates whether the KSA’s aggressive approach of pouring billions of dollars into mega events and infrastructure helps the country’s bid to become one of the most favored tourist destinations in the world. It compares current efforts and previous approaches towards economic diversification used by different countries in the recent past, both those that succeeded and those that failed. This deliberate, systematic review predicts the KSA’s success if it leverages its greatest assets – the enormous wealth and investment capital provided by the sovereign wealth fund, its unique geopolitical position, a unifying branding strategy, and its budding young population. In doing so, however, the KSA needs to consider the cultural impacts of its tourist expansion efforts.
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