Saudi Cultural Missions Theses & Dissertations
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Item Restricted Uncertainty Assessment of Sediment Contribution in Tributaries to the Upper Esopus Creek, New York(Catholic University of America, 2025) Alsulaimani, Mahmoud; Massoudieh, ArashThis study highlights the interplay between spatial scale, sub-catchment characteristics, and sediment dynamics in order to address the significant challenge of quantifying sediment source uncertainties across various spatial scales in the Upper Esopus Creek watershed, New York State, where sediment reduces water quality for more than nine million residents of New York City. The sediment samples collected from the Stony Clove and Woodland Creek sub-catchments, from 2017 to 2020, were analyzed with the Bayesian chemical mass balance (CMB) approach to measure source contributions and related uncertainties; discriminant power analysis and analysis of variance (ANOVA) tests helped identify key tracers with low p-values to distinguish sources clearly and lower uncertainty. The four primary sediment sources previously identified were forest, glacial till, lower and upper lacustrine, and terrace and bank alluvium. Findings indicate that analyzing each sub-catchment separately uncovered distinct source patterns and greatly reduced uncertainty compared to using combined methods. In contrast to Woodland Creek, which showed stable dominance of terrace and bank alluvium (60–95%), Stony Clove Creek was dominated by lower and upper lacustrine sources (60–80% contribution), with contributions changing dynamically by storm event magnitude and shifting across early versus later hydrograph stages. Storm event analysis in Stony Clove revealed flow-triggered source changes, with clear spikes in lower and upper lacustrine contributions. This research assists watershed managers by demonstrating how storm timing, scale-focused analysis, and careful tracer choices enhance sediment source tracking and reduce uncertainty in fingerprinting studies, supporting precise erosion control strategies and effective resource use.9 0Item Restricted Assessing artificial intelligence MRI autocontouring in Raystation and the AutoConfidence uncertainty model for brain radiotherapy(The University of Leeds, 2024-10) Alzahrani, Nouf; Henry, Ann; Nix, Michael; Murray, Louise; Al-qaisieh, BasharAbstract: Background: In radiotherapy, deep learning autosegmentation (DL-AS) and automation of quality assurance (QA) have the potential to efficiently standardize and enhance the quality of contours. Aim: To assess the performance of DL-AS in delineating organs-at-risk (OARs) in brain RT using the RayStation Treatment Planning System. Secondly, to build and test a novel artificial intelligence QA model called AutoConfidence (ACo). Methods: Retrospective MRI and CT cases were randomly selected for training and testing. DL-AS models were evaluated from geometric and dosimetric perspectives, focusing on the impact of pre-training editing. The ACo model was evaluated using two sources of autosegmentation: internal autosegmentations (IAS) produced from the ACo generator and two external DL-AS with different qualities (high and low quality) produced from RayStation models. Results: The edited DL-AS models generated more segmentations than the unedited models. Editing pituitary, orbits, optic nerves, lenses, and optic chiasm on MRI before training significantly improved at least one geometry metric. MRI-based DL-AS performed worse than CT-based in delineating the lacrimal gland, whereas the CT-based performed worse in delineating the optic chiasm. Except for the right orbit, when delineated using MRI models, the dosimetric statistical analysis revealed no superior model in terms of the dosimetric accuracy between the MR and CT DL-AS models. The number of patients where the clinical significance threshold was exceeded was higher for the optic chiasm D1% than for other OARs, for all models. ACo had excellent performance on both internal and external segmentations across all OARs (except lenses). Mathews Correlation Coefficient was higher on IAS and low-quality external segmentations than high-quality ones. Conclusion: MRI DL-AS in RT may improve consistency, quality, and efficiency but requires careful editing of training contours. ACo was a reliable predictor of uncertainty and errors on DL-AS, demonstrating its potential as an independent, reference-free QA tool.13 0Item Restricted The impact of risk and uncertainty on healthcare project delivery and its effect on intra-group conflict in the context of Saudi Arabia(The University of Manchester, 2024-07-25) Kheel, Metib Khalifa; Kirkham, RichardHealthcare construction projects are characterised by temporal risks and uncertainties, which may give rise to conflict. Decision-making within project teams is therefore an important focus of academic enquiry, particularly in a post-COVID-19 era, where healthcare projects play a crucial role in supporting pandemic recovery. Understanding the landscape that characterises projects in the healthcare setting could provide useful insights into broader project delivery issues and the effects of decision-making in situations of risk and uncertainty. This thesis describes a qualitative exploratory single case study situated within the King Faisal Specialist Hospital and Research Centre (KFSHRC) in the Kingdom of Saudi Arabia; semi-structured interviews provide an evidence base by which to understand the presence of risk, uncertainty, and intra-group conflict. The findings of this study identify the prevalence of conflict within project teams, exposing the detrimental effects of inconsistent decision-making. Moreover, the study uncovers how risk and uncertainty may directly impact decision-making processes and project delivery schedules, and thus contributing to the existing body of knowledge in the broader field of project studies. The findings illustrate the impacts of regulatory changes, supply chain disruptions, unexpected delays, and cost overruns. In order to moderate the effects of these impacts, the thesis offers recommendations for healthcare construction stakeholders, emphasising the development of robust mitigation strategies and contingency plans. These recommendations advocate for targeted training in project management, risk assessment, and crisis management. By embracing these measures, stakeholders may proactively manage the multifaceted challenges, thus enabling the successful completion of healthcare construction projects in the dynamic landscape of risk and uncertainty.17 0Item Restricted Creating value using Big Data applications in complex projects: a systematic review of the construction sector in a risk management perspective(Saudi Digital Library, 2023-09-04) Yamour, Jenaideb S; Qazi, KamalThe study delineates several key objectives: a comprehensive exploration of Big Data's integration in the sector; an assessment of its merits and challenges; a historical mapping of its evolution; and proffering trajectories for future scholarly and practical endeavours. It underscores the inherent inadequacy of conventional risk assessment tools, particularly for contemporary construction undertakings characterized by intricate designs and stringent timelines, emphasizing the revolutionary potential of Big Data in bolstering industry resilience and predictive prowess. The methodology underpinning this research is anchored in a systemic literature review, aiming to holistically encapsulate the extant body of knowledge on the subject. Pivotal inquiries driving this investigation include the value-addition of Big Data in construction risk management, and its interplay with project complexity. A methodological flowchart shows the research's steps. Key revelations from this investigation points up the reputation of Big Data-centric technologies in risk detection and mitigation throughout construction phases. Techniques like Monte Carlo simulations using Big Data, employing probabilistic assessments for diverse scenarios, have gained prominence. Furthermore, Building Information Modelling (BIM) leverages Big Data for enhanced design fidelity, minimizing design-associated risks. The research also highlights the potency of the MapReduce Hadoop programming paradigm in fortifying risk identification and management. The study also sheds light on Big Data's instrumental role in improving the occupational environment for construction personnel. Conclusively, the paper clarifies the expanding potential of Big Data in refining construction processes, risk mitigating, and bolstering the efficacy and foresight of project management. In essence, this review offers a holistic perspective on Big Data's role in the construction sector's risk management, enhancing existing literature through the discussion of contemporary frameworks. The insights garnered will undoubtedly prove invaluable to researchers and industry practitioners keen on refining risk management strategies through Big Data integrations29 0Item Restricted Algorithm And Design Tool Development Of Sizing A Stand-Alone PV/WT/Electrolyzer/Hydrogen/Fuel-cell Power Generation System For Electricity Supply Under Weather related Uncertainty Considerations And Thermo-Fluid Effects(PreQuest, 2023-09-22) Alfulayyih, Yasir Mohammed; Li, PeiwenToday, reducing the greenhouse gas emissions and decentralizing the electrical power plants has become a worldwide target, which, in turns, helps in avoiding causing any damage to the environment and increasing and easing the accessibility to the network, respectively, especially at remote areas. One of the most promising ways for achieving this target is the utilization of green and renewable energy resources (RER) such as solar energy (SE) and wind energy (WE). However, in nature, SE and WE suffer from the intermittence and variability (IV) (also called volatility), which have made the task of forecasting the power outputs from and designing/sizing a RE-based power plant (REPP) very challenging and have made the reliability to become a big source of concern for the decisions makers. Nonetheless, researchers have been attempting to develop different approaches for the purpose of utilizing these RER and mitigating this natural issue as much as possible, simultaneously. One of the most promising and appreciated mitigator for the IV effect is by hybridizing the power plants of SE and WE (H-REPP), with not or equal penetration levels, by integrating the H-REPP with an energy storage system (ESS) (e.g., compressed air, pumped hydro (PHS), regenerative fuel cell (RFC), etc.), and by using a method that can forecast the historical weather data (called WYGM from now onward) for the sake of obtaining any statistical data that can map the potential at any site of interests. Moreover, since it is impractical to request unlimited surface area and energy storage capacity (ESC) for harvesting and storage SE/WE, respectively, but to be reasonable via developing sizing algorithms, instead; this has been introduced in the literature under the topic of sizing REPP. However, usually, in the literature, these three tasks (modeling H-REPP, formulating WYGMs, and developing sizing algorithms) are separately studied/adopted, which should not be the case due to the strong connections among these tasks; additionally, the literature is lacking from modeling and sizing algorithms for H-REPP where the above-mentioned complexity is addressed. Therefore, in this work, the core goal is to estimate the least required size of a H-REPP that can operate for a year-round, according to different weather conditions, and consists of: solar photovoltaic power generation system (SPVPGS) (thus, the concentrated solar panel (CSP) are excluded) and wind turbine power generation system (WTPGS) for direct power supply and RFC for indirect power supply and/or hydrogen production (called green hydrogen production system). This core goal has been divided into a set of objectives as follows: 1) building an advanced modeling of SPVPGS and WTGPS, 2) designing a novel WYGM that efficiently involve the IV effect and fulfills the requirements of such H-REPP at any particular site of interest and along with a new spatial-temporal weighting (STW) approach, 3) developing an algorithm for sizing such kind of H-REPP along with a RFC as function of the supply-demand period, 4). Optimizing the penetration levels of SE-WE according to the estimated potential of the location; also, improve the possible sub-optimal results by decreasing the size and the updating step. Additionally, this conducted research aims at: studying the effect of the initial conditions for the sake of selecting the “best day of start energy harvest and storage (BDHS), proposing a new method of determining the most-frequent data point for a data set that has a strongly varied standard deviation from time to time, considering the need for updating the surface roughness with wind direction inside the simulation of WTPGS, and implementing the entire work into a software. These objectives have been planned to be achieved in four phases: I. simple SPVPGS-RFC and WYGM, II. advanced SPVPGS-WTPGS and simple RFC and semi-advanced WYGM, III. advanced SPVPGS-WTPGS-RFC (and other forms of ESS as possible) and advanced WYGM. IV. releasing of the software (called renewable energy power system sizing software (REPPS)). The obtained results have showed the following. First, the hybridization of multiple resources (i.e., PV-WT) have reduced the required size of power plant, which has proven that such solution can minimize the intermittency effect and then, eventually, increase the reliability. Second, investigating and considering more than one statical type of weather years (i.e., average, most-frequent, worst, and best) has led to a major and significant finding, but not limited to, which is that Considering only one type is not recommended due to the huge variation on the harvested energy among all the types. Moreover, the novel developed generation methodology for weather year data set, in general, and the most-frequent data point selection method, in particular, have showed a very promising solution along with an acceptable range of errors. Also, it has been validated and showed that its performance is capable of predicting the limits of the highest and lowest possible of energy harvesting and supply. Additionally, the effect of different miscellaneous factors (i.e., tracking systems, round-trip efficiency of different energy storage systems, model-related uncertainty, etc.) have been investigated. Among the major and unique results that have been attained using the developed algorithm (REPPS) and weather year data set generator (ABTMY(SITY)) is the estimated range of uncertainty for the four modes; and, based on the validation, the tested day for a year-round operation and based on a real-new data set, the required area and energy storage capacity have both fallen within the estimated range. Future work can be conducted to study the effect of the environment on the performance of the RFC, consider the cooling and shading effects on the performance of the SPVPGS, apply the STW to the developed WYGM, and optimize the levels of penetration by the SE and WE at the studied site and selected conditions.34 0Item Restricted High Reliability Organizing in Digital Platforms: Managing Uncertainties in Negative Events(2023-08) Alsahli, Amal; Lyytinen, KalleDigital platforms, such as Facebook, Amazon, and Uber, are becoming crucial components of modern societies' infrastructure. In addition to driving innovation and economic growth, they shape political opinion and facilitate social change. Despite their pervasiveness, digital platforms are increasingly challenged with emerging uncertainty that stems from a variety of sources and affects a wide range of platform actors. Without a proper and prompt approach to navigate such uncertainty, digital platforms are susceptible to potential failures and business discontinuity. This dissertation provides a preliminary understanding of the emerging uncertainty in digital platforms. It focuses on uncertainty associated with negative events that range from incidents in the interactions between the platform’s external users to major exogenous shocks that have a system-wide impact on the digital platform. Drawing on qualitative methods and interdisciplinary research, the dissertation is comprised of three independent studies. The first study utilizes a grounded theorizing approach to understand how users of digital platforms attribute blame for negative incidents. It follows media coverage of extreme incidents in two major platforms: YouTube and Airbnb. Findings show that the initial attribution of blame is transformed into a collectively distributed attribution through a retrospective sensemaking process. Study 2 seeks to understand how digital platforms organize for high reliability to manage uncertainty in negative incidents. An in-depth case study of the support function in a marketplace platform demonstrates evolving routine dynamics in the upstream (preventing incidents) and the downstream (resolving incidents) processes. Study 3 adopts a macro perspective on negative incidents by studying how digital platforms maintain operational resilience against major shocks. A longitudinal case study follows the response of a marketplace platform to the disruptions caused by the recent COVID-19 pandemic. The study identifies digital capabilities for absorbing, adapting, and ultimately transforming to a new stable state following a disruption. Taken together, the three studies contribute to research and practice by: (1) understanding the multidimensional and emergent nature of uncertainty in the context of digital platforms; (2) providing a rich description of digitally-enabled capabilities that a digital platform develops and uses to navigate uncertainty; and (3) providing suggestions for platforms seeking to organize for high reliability and resilience.18 0