SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
Browse
4017 results
Search Results
Item Restricted Observer- based numerical schemes(Exeter University, 2024) AlHayzea, Aisha Mousa; Twonley, StuartNumerical analysis and control theory are fundamental areas in engineering and applied mathematics. This thesis explores three concepts from control theory used to enhance classical numerical solvers. The proposed improvement integrates a sampled-data Luenberger observer with a conventional numerical solver in a switched system framework. The method employs the numerical solver when its updates are sufficiently accurate and switches to using process samples to drive an observer when they are not. The switching mechanism is governed by an energy inequality based on a Lyapunov function, potentially triggering sampling as needed. Stability proofs and error estimates utilize input-to-state style stability techniques. This new numerical scheme can handle step sizes significantly larger than those required for the stability of the traditional numerical solver. Additionally, the hybrid approach of switching between the sampled-data observer and the numerical solver can reduce the frequency of sampling needed for accurate observer-based state estimation of the process. In this context, this thesis has the following aims. Firstly, it combined any numerical scheme with sampled-data Luenberger observer in a new hybrid scheme based on switching conditions. The scheme uses the numerical scheme when scheme updates are good enough but switches to an observer driven by process samples when not. A Lyapunov function-based energy inequality determines switching. Thus, the switching condition is central to the hybrid observer-based numerical scheme. The idea underpinning this switching condition is to use a Lyapunov function for the observer as an energy function for the Euler scheme. Loosely speaking, energy for the observer’s solutions will decrease, and we only use the Euler scheme when its energy also decreases. In this sense, the Lyapunov function for the observer becomes a Lyapunov function for the overall hybrid scheme. The switching condition partitions the state space into sections or regions where we use Euler scheme and where we use the observer. Depending on the system and the scheme’s parameters, the region where Euler method is used may be large, small, or even null. Secondly, the aim is to extend the generalized hybrid scheme with a higher-order approximation of the Taylor exponential. We generalize the switched system by using Runge-Kutta. After that, this hybrid ODE solver is constructed by combining the Euler and Luenberger observer to switch from the numerical scheme to the observer when the numerical scheme produces inadequate results. Underpinning our approach is a λ-tracking-based sampled-data observer that invokes a λ dead zone. The resulting hybrid algorithm is a time-stepping numerical scheme. The gains and sampling periods in the sampled data observer are tuned using a λ-tracking approach. Using a sampled-data observer allows process measurements to be only available at some discrete times, while adaptive tuning allows the gains and sampling times to adjust automatically to each other rather than being subject to design. Finally, an alternative switching approach is considered: switching from observer to Euler based on λ and µ strips.14 0Item Restricted Deep Reinforcement Learning for Real-Time Energy Management in Community Microgrids(Lancaster University, 2025) Aldahmashi, Jamal; Ma, XiandongThe integration of renewable energy sources (RESs), energy storage systems (ESSs), and the electrification of transportation are driving a rapid transformation of modern power systems. These changes not only provide great potential to reduce carbon emissions, increase sustainability and improve reliability, but also present complex challenges. Traditional and centralized power systems were originally designed for one-way power flow, from large power plants to consumers, and are becoming increasingly inadequate in the face of intermittent renewable generation, distributed and variable loads, and heightened risks of severe weather disturbances. Thus, intelligent, adaptive and resilient methods for energy management have become a critical priority. In this thesis, I address the need for advanced, real-time control in modern power systems through the use of deep reinforcement learning (DRL) to optimize active and reactive power flows under uncertainty. First, a model-free framework for a single home energy management system (HEMS) that integrates photovoltaic (PV) panels, ESSs, electric vehicles (EVs), and multiple types of residential loads. In contrast to existing methods that focus on active power flows alone, the proposed method optimizes reactive power to improve power factor and avoid possible financial penalties. This framework adapts to fluctuating renewable generation, uncertain EV charging profiles, and the unpredictable behavior of loads by using DRL algorithms that can learn directly from interactions with the environment without explicit mathematical models. Real world data tests show over 30% electricity cost savings and substantial power factor improvements. To extend this concept from individual homes to larger communities, a community energy management system (CEMS) is proposed. Multiple smart homes, each equipped with a HEMS, are interconnected through a point of common coupling to form a community microgrid (CMG). Each home acts as a local agent making autonomous decisions, and a multi-agent DRL (MADRL) architecture is employed to coordinate their actions in a decentralized yet cooperative manner. Further, electricity price forecasting is integrated with a Long Short Term Memory (LSTM) network for proactive scheduling of flexible loads. Simulation results show that this data driven, cooperative control approach can reduce overall community electricity costs by up to 29.66% and keep community voltages more stable than conventional centralized and model-based methods. Also, the proposed MADRL strategy retains decision making at the household level, which provides benefits in terms of privacy, scalability, and adaptability to various grid conditions. The thesis then incorporates optimal power flow (OPF) constraints into the energy management system (EMS) for CMG with high penetration of renewables, ESSs and EVs, recognizing that even larger scale distribution networks require advanced coordination. The work reformulates the OPF problem as a Markov decision process (MDP) and uses a dual-layer DRL structure. The objectives of the first layer controls for continuous control of active power using a twin delayed deep deterministic policy gradient (TD3) algorithm with cost minimization, load shedding prevention and efficient use of DERs. The second layer, which uses a double deep Q-network (DDQN), controls discrete reactive power to maintain voltage stability. This dual-layer approach addresses the challenges of high-dimensional, non-linear, and stochastic power systems. The experiments on a modified IEEE-15 bus system demonstrate up to 10.41% cost savings versus no EMS, with less voltage violations and less load shedding. The dual-layer DRL framework is resilient to stochastic variations in renewable output and load demand, and is a practical candidate for real-time distribution network operations. Overall, the research presented demonstrates that DRL-based solutions, whether applied to individual homes, local communities or larger distribution networks, can successfully deal with the uncertainty and variability of modern power systems. By integrating cutting-edge neural network architectures for price forecasting, multi-agent coordination, and dual-layer control, the proposed methods outperform traditional optimization and control approaches in terms of cost efficiency, voltage stability, and scalability. As a result, these techniques offer great potential for enabling flexible, economically viable, and robust power grid operations. With increasing proportion of RESs, ESSs and EVs, the demand for such intelligent, adaptive and decentralized energy management solutions will increase, leading to a more sustainable and resilient electricity infrastructure.15 0Item Restricted Translation Norms and Euphemisms: Analyzing Code-Switching and Dialect Translation in Outlander Novel(The University of Edinburgh, 2025) Bakhet, Renad; Mouzen, MarwaThis dissertation looks at the intricate task of translating selected chapters from the award-winning novel, Outlander, by Diana Gabaldon (Gabaldon, 1991). The novel led to a best-selling series of books by the same author, which were then translated into several languages worldwide. However, there has hitherto been no published translation of the novel in Arabic, leaving a gap that interested me to explore under various translation approaches. This study aims to explore how cultural references and cultural differences may be translated into Arabic, while applying euphemisms and the translation norms of Arabic language literature without compromising the essence of the source text. The significance of this dissertation lies in its application of a model comprised of appropriate theories and strategies for solving translation problems. Moreover, it sheds light on the translational norms that apply in Saudi Arabia5 0Item Restricted Exploring Factors Important for Clinical Application of Cortical Responses to Continuous Speech(University of Southampton, 2025) AlJarboa, Ghadah Salem; Bell, Steve; Simpson, DavidThere is considerable interest in neural responses to continuous speech. Techniques for analysing these responses typically involve tracking EEG change due to stimulus features, such as the amplitude envelope. However, the clinical utility of these measurements, especially for challenging to test subjects such as infants with hearing aids, remains under-explored. This thesis aimed to investigate the clinical feasibility of neural tracking as an objective test for aided speech detection in infants. This aim was tackled through four studies designed to test factors essential for future application in infant testing in clinical environments. These factors included the feasibility of detecting responses in single-channel EEG recordings, detection time, effects of stimulus intelligibility, and attention. The two approaches used to analyse EEG signals were the temporal response function (TRF) and cross-correlation. The first study assessed the effectiveness of single-channel EEG testing, achieving a 100% detection rate using cross-correlation within a detection time appropriate for clinical application. The second study focused on speech intelligibility effects during passive listening in recordings of cortical responses via single-channel EEG. The responses to speech-modulated noise demonstrated greater robustness regarding detectability and detection times than natural speech, indicating the potential utility of non-language-specific stimuli. Nevertheless, detection rates fell below 100%, potentially due to passive listening or shorter recording durations compared to the first study. The third study evaluated the envelope distortion induced by hearing aids using various stimuli. It found that the envelope distortion from the International Speech Test Signal (ISTS) was similar to that of natural speech, in contrast to speech-modulated noise, which exhibited significantly lower envelope distortion. The fourth study investigated the impact of different distortion levels on response detection using ISTS recordings from the third study. Higher levels of envelope distortion significantly lowered detectability and increased detection times, though using the envelope measured at the hearing aid output for detection analysis significantly improved these metrics. Additionally, no impact of attention on response detectability was observed. In conclusion, single-channel EEG analysis showed variable detectability across different stimuli and conditions, suggesting that signal processing methods and recording times may still need to be optimised. The ISTS stimulus produced results comparable to natural speech, supporting its potential clinical use as a non-language-specific option. However, detectability was compromised in aided condition with high levels of envelope distortion. Using the speech stimulus from the output of a hearing aid (as opposed to the input signal to the aid) shows potential for improving response detectability. Additionally, the study demonstrated that neural tracking can be recorded under passive listening conditions, which could be important when testing infants.6 0Item Restricted Corneal Expansion for Blindness Prevention(Imperial College London, 2020-09) Banaama, Saeed; Stevens, MollyCorneal shortage is a major issue, which limits the number of corneal transplant procedures performed worldwide. It is estimated that only one cornea is available per every seventy needed. Approximately 200,000 vision-saving CTs take place annually worldwide, but there are another 12 million people waiting for corneas. The majority of these patients are located in India and China. The number of people in need of corneas is expected to rise due to the aging population. Corniplex has developed a novel approach for addressing this shortage using stem cell and regenerative medicine technologies. There are currently no biosynthetic corneas on the market. The product is currently in the development stages and the preclinical trials are expected to be completed in 2024. The device is classified as a combination product under FDA guidelines and is anticipated to enter the market in 2030, following the successful completion of an international phase III clinical trials. Corniplex is targeting India as its initial market. We anticipate annual peak sales in 2040, with revenues reaching up to $7 million dollars annually. To achieve this objective, we attempted to optimise the printing conditions of Gelatin Methacryloyl (GelMA) , one of the most widely used materials in bio-applications due their biocompatibility and biodegradability profile. In the technical part of this thesis, the photo-rheology of GelMA is investigated to determine its suitability to be used as a bioink in Stereolithography based 3D printing. The impact of changing the degree of functionalisation (DOF, 42 vs. 93%), photoinitiator concentration (0.2-1% wt), and GelMA concentrations (50, 100, and 200 mg/mL) on growth rate and time to halfway point (thwp) is examined and compared semi-empirically using a Gompertz function. The results show a higher growth rate (0.007 vs. 0.01) and a decrease in thwp with higher photoinitiator concentration (394 vs. 240 s) – indicating a more rapid polymerisation. Higher GelMA concentrations showed a higher growth rate for lower concentrations (0.025 at 50 mg/mL vs. 0.01 for 100 and 200 mg/mL). On the other hand, the increase in GelMA concentration resulted in decrease to thwp (315 vs. 196 s, for 50 and 200 mg/mL, respectively). The greatest effect of the DOF was observed at 200 mg/mL with regards to thwp (210 vs. 280 s, the former being the sample with 93% DOF). The growth rate was slightly higher in the samples with higher DOF in the 100 mg/mL GelMA. However, this effect seems to subside with the 200 mg/mL GelMA, indicating the possibility that DOF is might be less relevant with higher GelMA concentrations.14 0Item Restricted Human Action Recognition Based on Convolutional Neural Networks and Vision Transformers(University of Southampton, 2025-05) Alomar, Khaled Abdulaziz; Xiaohao, CaiThis thesis explores the impact of deep learning on human action recognition (HAR), addressing challenges in feature extraction and model optimization through three interconnected studies. The second chapter surveys data augmentation techniques in classification and segmentation, emphasizing their role in improving HAR by mitigating dataset limitations and class imbalance. The third chapter introduces TransNet, a transfer learning-based model, and its enhanced version, TransNet+, which utilizes autoencoders for improved feature extraction, demonstrating superior performance over existing models. The fourth chapter reviews CNNs, RNNs, and Vision Transformers, proposing a novel CNN-ViT hybrid model and comparing its effectiveness against state-of-the-art HAR methods, while also discussing future research directions.20 0Item Restricted Addressing risk, challenges, and solutions in Megaprojects: A case study of Neom Smart City in Saudi Arabia(leeds beckett university, 2024) Alluqmani, Waleed Salem; Omotayo, TemitopeNeom Smart City is designed as a high-tech city with sustainable living and renewable energy sources such as solar power and autonomous transportation systems. The City aims to become an attractive destination for talents and investment Mega Projects are more common in the 21st century due to global population growth, urbanisation, and technological innovation that requires the establishment of big projects. The aim of this research is to focus on risks, challenges, and solutions that may occur at the Neom Smart City project in KSA, and provide insights for the effective management of megaprojects. A positivist philosophy and a deductive research approach was used in this research. The overarching methodology was quantitative. The data-gathering procedure involved questionnaire instruments. The sample involved twenty participants including project managers, while the data analysis technique used was descriptive and inferential statistics using SPSS and crucial ethical considerations were confidentiality and informed consent. Descriptive analysis of the findings have revealed that the participants perceived financial risks to be the most important concerns, followed by legal and construction risks. The result from the study also shows poor planning, political failures, and the lack of high-performing teams were the most significant contributor to the failure of megaprojects. The inferential statistics have revealed that there is a significant positive correlation between design risks and legal risks, contractual risks, construction risks and operational management risks. Financial risks are linked to construction risks, political risks, and leadership risks. Empirically, financial risks are influenced by contractual risks, poor leadership, and poor planning. The outcomes also suggest that stakeholder collaboration has a statistically significant impact on construction risks.7 0Item Restricted Investigating the Implementation of Governance Through the Adoption of the Universities Law in Saudi Higher Education(ASTON UNIVERSITY, 2025) Alowaid, Othman; Hall, MatthewThis research investigates the implementation of the new Universities Law in Saudi Arabia and its implications for higher education governance. The primary aim is to develop a comprehensive governance framework tailored to Saudi universities' unique sociocultural, political, and economic context. This study provides a robust theoretical foundation for understanding governance dynamics within Saudi higher education by synthesising agency, stewardship, and stakeholder theories. There has previously been an in-depth exploration of adapting governance in Saudi higher education through the new Universities Law; hence, this research examines the two universities that first implemented the new law. The study explores the governance framework of Saudi higher education, the adaptation processes, and the challenges encountered. A qualitative case study approach allowed participants to describe their experiences. Data collection involved two main methods: document analysis and semi-structured interviews. The key documents analysed were the previous Higher Education and Universities Council Law and the new Universities Law. Fifteen semi-structured interviews were conducted with participants from the two universities and the Universities Affairs Council, considered the body supervising universities. This study contributes to the theoretical discourse on higher education governance by demonstrating the novel combination of agency, stewardship, and stakeholder theories and revealing the limitations of existing governance frameworks when applied in isolation. This study addresses practical challenges universities face during the transition, providing insights crucial for successfully implementing governance reforms. By bridging the gap between theory and practice, this research supports ongoing efforts to improve governance in Saudi higher education, aligning with the broader goals of the Vision 2030 initiative. Additionally, it addresses practical challenges universities face during the transition, providing insights crucial for successfully implementing governance reforms. By bridging the gap between theory and practice, this research supports ongoing efforts to improve governance in Saudi higher education, aligning with the broader goals of the Vision 2030 initiative.9 0Item Restricted Heat shock protein 90 is a master regulator of HIV-1 latency(University College London, 2025) Noor Saeed, Somaya; Fassati, AribertoAn estimated 39.9 million people live with HIV-1 globally. While combined antiretroviral therapy has significantly reduce the mortality rate of HIV-1 patients by controlling the virus and preventing its spreading, interrupting the treatment causes the virus to rebound from a latent reservoir that is mostly present in memory CD4+ T cells. Therefore, treatment is not curative but rather lifelong. Alternative treatment strategies involve the use of pharmacological agents to Induce deep latency or stimulation of latently infected cells to facilitate immune-mediated clearance. The multifactorial nature of HIV-1 latency is associated with the infected CD4+ T cell's activation status. Hence to perturb latency, it is necessary to target several pathways simultaneously without compromising CD4+ T cell activity and function. HIV-1 latency has been demonstrated to be regulated by Hsp90, although knowledge on the pathways is limited. However, Hsp90 known to enhance the proper folding of numerous cellular proteins required for HIV-1 gene expression, for this reason, we hypothesized that Hsp90 might be a master regulator of latency. We tested this hypothesis using a polyclonal Jurkat cell model of latency and ex-vivo latently infected primary CD4+ T cells. Here we showed that Hsp90 is necessary for HIV-1 reactivation in the Jurkat model, which is mediated via the T-cell receptor, agonists of TLR-7 and TLR-8, phorbol esters, TNF-α, and FOXO-1 suppression. Additionally, in primary cells, targeting Hsp90 reduced HIV-1 gene expression induced by stimulation the TCR or in the presence of IL7/IL15 or a FOXO-1 inhibitor. The activation of the NF-kB, NFAT, and AP-1 signal transduction pathways was inhibited by chemically inhibiting Hsp90. We showed that Hsp90 inhibition for HIV-1 was mostly significant within the CD4+ T cell population, CDRA45+ CCR7+ “naïve” and CD45RA- CCR7- “effector memory” which did not perturb their phenotype or activation state. Our results indicate that Hsp90 is a master regulator of HIV-1 latency that can potentially be targeted in cure strategies.5 0Item Restricted Heterogeneous Catalysis Using Noble and Non-noble Metals nanoparticles supported on metal-oxide catalysts(Cardiff University, 2025) Aleyadah, Layla; Davies, PhilipThe main objective of this work is to use investigate non-noble metals with the aim of reducing the cost of noble metals catalysts while preserving effective performance across a range of applications.2 0