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Ontology of Informal Settlements in Riyadh, Saudi Arabia with Geospatial Intelligence
(Curtin University, 2024-05) Alrasheedi, Khlood Ghalib; Dewan, Ashraf; El-Mowafy, Ahmed
Any management policies developed for managing the urban growth of a city, and to ensure the sustainability of that growth into the future, must understand the spatial distribution of informal settlements found within the city boundaries. These settlement types can be found in a large number of metropolitan areas around the world. Accurate identification requires an understanding of the various characteristics which tend to be associated with these settlement areas, including the types of materials used in building construction and the unique street patterns found within the settlement neighbourhoods. Due to the dynamic nature of these settlements, however, the existence of a universally-accepted framework which can be used to define and map these areas is lacking. This study aims to integrate local knowledge, remote sensing data, and machine learning to investigate and develop an informal settlements ontology for use within the Arabian Peninsular region. Information used included very high to medium resolution satellite images, field surveys, expert opinion regarding local conditions, and a wide range of geographic data. Object-based image analysis (OBIA), machine learning methods such as random forest (RF), and support vector machine (SVM), expert knowledge, and various geographic datasets were employed to identify the distribution of informal settlements over time and space within Riyadh, the capital city of Saudia Arabia. Many variations in settlement character can be found, so the development of a local ontology of informal settlements (LOIS) has been proposed. The major findings of the current work are: (i) the inclusion of local expert knowledge (EK) about the various spatial, spectral and textural image attributes identified, can enhance the identification of informal settlements over time and space; (ii) a combination of OBIA-RF and OBIA-SVM, augmented by local knowledge, can improve the image-based classification of informal settlements, and; (iii) the approach taken, involving the selection of thirty unique geospatial indicators, was found to be very useful in the study of informal settlements over time, particularly when processing the very high and medium resolution satellite images in tandem with the Landtrend tool. The efficacy of the proposed approach, in regards detailing the spatiotemporal growth of identified areas, was shown by the accuracy of the mapping result outputs.
The findings of this work may prove to be of value to urban planners and decision-makers when designing and constructing new infrastructure (roads, water, and energy plants), and when assessing the potential for any adverse environmental, social, or financial impacts associated with informal settlements - specifically within the Arabian Peninsula, but also in other regions of the world with similar characteristics.
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Machine Learning to Understand Solar Cell Performance
(University of Glasgow, 2021-11) Alsulami, Bashayer N.; Kettle, Jeff
This study contributes to the understanding of a perovskite solar cell (PSC) performance
database which contains 7,026 data points gathered from research papers published between
2012 and 2020. The aim is to capture and analyse historical main patterns through machine
learning technologies to generate models and heuristics for predicting cell performance. The
dataset utilized has a total of 16 attributes, each of which contains several categories. The
attribute for each device includes the structural parts (such as the electrodes, absorber layers,
substrate, transport layers), cell architecture, type of module and band gap of the perovskite
solar cell. By visualizing the data with Python, several factors for increasing the efficiency of
PSC were identified. For instance, it was found that maximum device efficiency can be achieved
by using polyimide or PSG as substrate, indium tin oxide (ITO) or `fluorine-doped tin oxide
(FTO) as electrodes, and copper as electrode2. For the analysis, different machine learning
models were constructed and tested, with random forest providing the best results in predicting
perovskite solar cell efficiency. To identify the most important factors that influence solar
power conversion efficiency (PCE), the sequential minimal optimization regression (SMOreg)
model was also used. This is accomplished by looking at the attribute weights in the generated
SMOreg model. The analysis given significantly enables the identification of variables and
layers that may lead to improvements in PCE through device design, as well as emphasizing
the involvement of various factors in the degradation of PSC.
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RANDOMISATION METHODS FOR ADAPTIVE AND MULTI-ARM CLINICAL TRIALS
(NEWCASTLE UNIVERSITY, 2024-05) Azher, Ruqayya Asaad; Wason, James; Grayling, Michael
Randomised control trials (RCTs) typically compare one experimental treatment to
a control. However, over recent years, with the growing availability of many treatments
for evaluation and the increasing complexity of determining which are promising, a
growing need has emerged for more efficient trial designs. Accordingly, adaptive designs
have gained popularity in clinical research, including multi-arm multi-stage designs and
platform trial designs. Such designs aim to improve the clinical trial process by allowing
the removal and/or addition of treatment arms during the course of the trial.
In almost all clinical trial designs, randomisation remains a fundamental principle
to ensure unbiased treatment comparisons. This is no less true of adaptive designs,
yet randomisation routines have received less attention for such studies compared to
historical fixed sample designs. Therefore, throughout this thesis, the key considerations
discussed include the importance of proper randomisation approaches and allocation
ratios to achieve clinical trial objectives in adaptive trials.
First, I compare different randomisation approaches in the context of multi-arm trials
to achieve various trial objectives, such as group size balance, covariate balance, effect
precision, low allocation predictability, and high power. Next, two adaptive clinical trial
designs are considered: multi-arm multi-stage designs build on multi-arm designs by
incorporating interim analyses with stopping rules. In particular, if an experimental
treatment shows poor performance, it can be dropped early. In this design, allocation
ratios can be fixed throughout the trial, flexible (adjusted based on observed interim
data), or pre-specified but variable across the study stages.
Finally, attention shifts to randomisation methods in a platform trial design, to which
new treatments can be added or dropped over time, and allocation ratios might need
to be modified to achieve the study objective(s). Here, the focus is on randomisation
approaches with different allocation ratios, aiming to achieve high covariate balance,
especially for a newly added arm in the trial.
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An Investigation into How Anti-Fibrotic Agents Rescue Cardiac Insulin Resistance and Improve Cardiac Function in a High Fat Diet-Induced Obesity Murine Model
(University of Dundee, 2024-10) Banah, Ayman Kamal; Kang, Li; Lang, Chim
In recent decades, there has been significant advancement in our comprehension of the extracellular matrix (ECM) in terms of its composition, structure, and physiological functions. Moreover, mounting evidence suggests that increased ECM deposition contributes to the pathogenesis of various diseases. Current research endeavours targeting the ECM hold promise for the development of novel therapeutic strategies to address challenging medical conditions. Diabetes and heart failure often coexist, exerting reciprocal influences on each other and thereby affecting disease progression and outcomes. Notably, insulin resistance emerges as a pivotal mediator in this bidirectional relationship between diabetes and heart failure, posing a significant risk factor for heart failure development through compromised cardiac insulin signalling pathways. However, the underlying mechanisms of insulin resistance in heart failure remain incompletely elucidated.
A growing body of evidence implicates cardiac ECM remodelling in the pathogenesis of insulin resistance, necessitating the identification of novel therapeutic targets to mitigate or reverse cardiac IR and its associated dysfunction. In this thesis, I specifically tested the hypothesis that reducing heart ECM constituents using clinical and pre-clinical anti-fibrotic agents may alleviate cardiac insulin resistance and improve cardiac function. Results from my PhD study have revealed that pharmacological inhibition of ECM receptor integrin α5β1 enhances insulin signalling in H9C2 cells. Additionally, my studies with the mineralocorticoid receptor antagonist Eplerenone have demonstrated its effectiveness in regulating body weight gain and enhancing cardiac function in obese mice.
My findings also establish a novel association between increased ECM deposition, cardiac insulin resistance, and cardiac dysfunction in obesity. Notably, pharmacological reduction of hyaluronan, a key ECM component, using pegylated human recombinant hyaluronidase PH20 (PEGPH20) has demonstrated its potential to ameliorate cardiac insulin resistance and associated functional impairments in obese mice. Further evidence was attained through genetic and pharmacological inhibition of the hyaluronan receptor RHAMM. Specifically, my results indicate that mice deficient of RHAMM (KO) exhibit improved glucose tolerance and lower aortic pressures compared to littermate wildtype (WT) controls, particularly in males fed a high fat diet (HFD). Cardiac ECM remodelling and functional alterations induced by HFD were attenuated in KO males. Conversely, such protective effects were not evident in female mice.
The present findings underscore the therapeutic promise of early interventions aimed at cardiac ECM remodelling to alleviate cardiac insulin resistance and dysfunction associated with obesity. It is proposed that strategies intended to prevent pathological ECM expansion may confer protection against the progression to severe cardiovascular complications. As such, the implications of these findings are of considerable importance to the development of effective interventions targeting cardiac ECM remodelling in the context of obesity-associated cardiovascular disease.
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Mary Stewart's Arthurian Fiction: A Comparative Study
(University of Exeter, 2024-05) Al Thubaiti, Ashwag AbdulHakeem M.; Parker, Joanne; Kendall, Elliot
This thesis examines Mary Stewart’s Arthurian novels; The Crystal Cave (1970), The Hollow Hills (1973), The Last Enchantment (1979), The Wicked Day (1983), and The Prince and the Pilgrim (1995). These five novels are compared with Stewart’s thrillers, written before the publication of her first Arthurian novel, and three other modern Arthurian novels. The comparative Arthurian works are T. H. White’s The Sword in the Stone (1958), Marion Zimmer Bradley’s The Mists of Avalon (1982) and Rosemary Sutcliff’s Sword at Sunset (1963). These texts have been selected to facilitate an original study of Stewart’s treatment of three major themes in her Arthurian fiction: (i) natural history and the philosophy of royal education, (ii) religion and gender, and (iii) post-Roman Britain and archaeology. In doing so, the thesis casts a new light on Stewart’s Arthurian novels where each chapter brings to the thesis germane cross-disciplinary readings drawing from British history, educational theories, theology, gender studies and archaeology.
To develop this argument, four chapters are devoted to the analysis of the suggested texts and themes. Chapter One shows how Stewart’s Arthurian fiction shares many underlying structural features and themes with her thrillers, regardless of genre differences, and reads them in dialogue with one another as a coherent oeuvre. Chapter Two discusses White’s and Stewart’s different views on royal education while paying attention to the educational theory of Jean-Jacques Rousseau’s Emile (1762). Chapter Three examines the complex interplay between religion and gender in Stewart’s Arthurian novels and Bradley’s The Mists of Avalon besides second-wave feminism and the changing religious atmosphere in both Britain and America. Chapter Four compares Stewart’s Arthurian novels with Sutcliff’s Sword at Sunset alongside archaeological texts and discoveries from the mid to late twentieth century and analyses the extent to which they are informed by recent archaeological thought. Together, the chapters bridge the gap in previous scholarship by providing the first full study of Stewart’s Arthurian novels from three main perspectives integral to the modern Arthurian canon.