SACM - United Kingdom
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Item Restricted Readiness to expand the role of the nurses working in primary healthcare centres in Saudi Arabia to include prescribing: An exploratory descriptive qualitative study(Cardiff University, 2024) Albariq, Khloud Mohmmed; Albariq KhloudAbstract Introduction: Primary healthcare centres (PHCs) in Saudi Arabia are the first point of contact for patients across the country (Al Saffer et al., 2021). However, these PHCs face various accessibility challenges, including unequal distribution of PHCs and their services (Al-Sheddi et al., 2023), a shortage of medical professionals (Al Saffer et al., 2021), a lack of specialised services (Makeen et al., 2020), and long waiting times (Albarhani et al., 2022). One way to enhance accessibility, reduce waiting times, and alleviate various burdens on the healthcare system in Saudi Arabia is to grant nurses prescribing rights (Hibbert et al., 2017; Almotairy et al., 2023). However, Saudi research into the readiness to incorporate the prescribing role into nursing practice in PHCs is currently lacking. This study aims to investigate readiness to incorporate the prescribing role into nursing practice in Saudi PHCs. Methods: An exploratory and descriptive qualitative (EDQ) research was used as the chosen methodology. Twenty-five individual semi-structured interviews were thus conducted with participants who were divided into three groups: this gave ten nurses at the micro-level, five nursing supervisors and five managers at the meso-level, and five policymakers at the macro-level. The collected data was then analysed using reflexive thematic analysis (RTA). Findings: Six key themes emerged from the data analysis. These were improving primary healthcare services, nurse-doctor partnership prescribing, educational preparation, acceptance of the nurse prescribing role, establishing the legality of the nursing prescribing practice, and readiness of primary healthcare centres. Conclusion: Implementing the nurse prescribing role in PHCs services has the potential to improve PHC services through enhancing accessibility and reducing waiting times. A nurse-doctor partnership prescribing approach may be implemented, in which nurses and physicians share prescribing responsibilities while doctors remain responsible for diagnosis. However, nurses’ general lack of pharmacological knowledge is a major obstacle, requiring additional focus on educational preparation. Another potential barrier could be a lack of acceptance of the nurse prescribing role among patients, doctors, and nurses. Several changes are thus necessary to existing systems, including the introduction of clear legislation and regulations and ensuring the readiness of PHCs to smooth the implementation of the NP role.3 0Item Restricted Design and Development of Organic Electrocatalysts for CO2 Reduction(University of Nottingham, 2024-09) Altemani, Fatin; Newton, Graham; Walsh, DarrenThe electrochemical carbon dioxide reduction reaction (CO2RR) represents a promising method for converting carbon waste streams into valuable chemicals. Current CO2RR electrocatalysts often rely on expensive and rare platinum group metals, highlighting the need for more sustainable and cost-effective alternatives. This thesis explores the use of aromatic nitro group-bearing catalysts for the CO2RR, focusing on their performance in different electrolyte systems. Cyclic voltammetry was employed to evaluate catalytic efficiency. Additionally, the influence of the electrolyte and gas environment (N2 vs. CO2) on the catalytic activity was investigated. Results demonstrated that aromatic nitro group catalysts exhibited improved CO2 reduction activity in both electrolyte systems. This study provides insights into the relationship between catalyst structure, electrolyte composition, and gas environment, suggesting that further optimization of these catalyst-electrolyte systems could enhance the efficiency and selectivity of the CO2RR, advancing sustainable chemical production processes.10 0Item Restricted Interval Analysis and Methods in Software Analysis(Manchester University, 2024) Aldughaim, Mohannad; Cordeiro, LucasThis thesis investigates the application of interval analysis and methods within the domain of software verification, with a particular focus on mitigating the state space explosion problem. State space explosion poses a significant challenge to static and dynamic software verification techniques, such as fuzzing, bounded model checking (BMC), and abstract interpretation. These methods, despite their robustness, struggle to scale when faced with complex programs that generate a vast number of execution paths and states. To address this, the thesis introduces the use of contractors—interval methods that refine the search space by eliminating non-solution regions—across several verification frameworks. By applying interval contractors in fuzzing, BMC, and abstract interpretation, the search space is systematically reduced without compromising the soundness or completeness of the verification process. Contractors are employed to navigate guard conditions, narrow down variable domains, and simplify control structures, leading to a more efficient exploration of execution paths. The thesis presents a detailed implementation of these methods and evaluates their performance through rigorous benchmarking. Results demonstrate that the integration of contractors significantly enhances verification efficiency, reducing both computational resource consumption and time while preserving accuracy in identifying potential software vulnerabilities. This research offers a novel contribution to improving the scalability of software verification methods, making them more practical for real-world applications.6 0Item Restricted Antibiotic Prescribing Practices in Paediatric Hospital-At-Home Care Model A Retrospective Cohort Study(University College London, 2024) Othman, Hassan; Garfield, Sara; Abou Daya, Mohammed; Chambers, PinkieBackground: Paediatric patients at the Royal London Hospital referred to the Hospital-At-Home service mostly receive intravenous antibiotic therapy. However, prescribing practices and the appropriateness of the chosen antibiotics have not been evaluated before. This study evaluated the appropriateness of antibiotics prescribed for those patients. Methods: Patients referred to HAH between 01 August 2023 and 31 January 2024 were screened using a retrospective cohort design. Descriptive analysis was used to analyse the collected data which included biomarkers trends, bio-samples cultured, and evidence of a multi-professional team collaboration. Results: Most patients were neonates aged 28 days or under, 50%. The most common presenting complaint was suspected sepsis (50%). The working diagnosis remained suspected sepsis for 28% of patients while confirmed sepsis in 19%. Also, 17% of patients were diagnosed with viral infections. 79% of participants were reviewed by a clinician before HAH referral, and the advice of an ID consultant was sought by 16%. There was 88% adherence to prescribing guidelines. Only 4.3% of patients were switched to oral antibiotics; however, the switch suitability evaluation suggested that 27.3% of patients were suitable for the switch. Furthermore, the ID consultant recommended stopping or switching to an oral antibiotic for 15% of the chosen patients and potentially discontinuing for 74%. Conclusion: Improvements in antibiotic prescribing practices can be achieved by implementing a robust antimicrobial stewardship program. The active engagement of a multidisciplinary team, comprising a paediatric ID consultant, and pharmacist specialist, is vital in achieving these improvements.11 0Item 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.16 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 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 0