Saudi Cultural Missions Theses & Dissertations
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Item Restricted CLIMATE VARIABILITY STUDY FOR ARID REGIONS(South Dakota State University, 2025-05) Almutairi, Faisal; Burckhard, SuzetteWater is the primary source for all living species to thrive, and water scarcity has been a primary concern for biological species and plants in arid regions due to urban planning, population growth, poor water management, and overgrazing. The objective of this research was to study the climate variability in precipitation and temperature for an arid region. This research encompasses two distinct studies. The first study examined the impact of climate variability on precipitation in Phoenix. Precipitation data were acquired from NOAA from 1948 to 2023 and the study was broken down into three time scales: annually, seasonally, and monthly. The accumulated annual precipitation was used to classify specific years as very wet year, wet year, average year, dry year, and very dry year, corresponding to their rainfall depth. Annual precipitation showed stability in the trendline with some fluctuations over the 76-year period of study. In addition, the seasonal analysis showed more precipitation during the summer season more frequently than in other seasons. The monthly analysis showed that months within the non-monsoon season had the driest months as expected. February had the highest precipitation in very dry years and January had the highest precipitation in the very wet years. Also, the prediction of the accumulated yearly precipitation for the years 2030,2040, and 2050 is based on the correlation. The second study was the impact of climate variability on temperature in an arid region. Temperature was obtained from NOAA for the period of 1948 to 2023. The years were classified based on the average annual temperature dataset as hot years, warm years, moderate years, cool years, and cold years. The average annual temperature showed an increase in the trendline. The results showed minor CV for the average annual temperature over the 76-year period. The seasonal temperature showed more variability during the winter season, with a CV of 5%, compared to other seasons. The monthly analysis showed that months within the winter season had more variability at a CV ranging from 4% to 6% than months in the summer season which were around 2%. In addition, the prediction for the average annual temperature for the years 2030,2040, and 2050. In conclusion, these two studies are comprehensive studies on climate variables applying historical data to gain a cognitive comprehension of climate weather behavior. This case study was conducted on arid regions that have high temperatures during summer and relatively low precipitation. These studies hold future awareness of the climate for more practical purposes in engineering. This will be an effective application for future water scarcity, urban planning, agriculture, and food supply.15 0Item Restricted A Critical Analysis of Cyber Threats and Vulnerabilities in Satellite Ground Systems(University of the West of England, 2024) Almutairi, Faisal; Mills, AlanThe growing dependence on satellite ground systems for critical applications such as telecommunications, navigation, and weather forecasting has underscored the importance of cybersecurity in these systems. This paper critically analyses the current state of cybersecurity threats and vulnerabilities in satellite ground systems. Utilising a comprehensive literature review and critical analysis of existing scholarly works, technical papers, and industry reports, this study identifies key cyber threats, including unauthorised access, jamming, spoofing, Advanced Persistent Threats (APTs), man-in-the-middle attacks, eavesdropping, and hijacking. The analysis reveals vulnerabilities in encryption protocols and communication channels. The study evaluates existing security measures and highlights gaps in empirical validation and practical implementation. It emphasises the need for robust encryption methods, advanced cryptographic techniques, and adaptive security strategies. We also discuss the crucial step of enhancing the resilience of satellite ground systems by incorporating developing technologies like Artificial Intelligence (AI) and quantum cryptography. This paper concludes with practical recommendations, emphasising empirical validation of security measures and comprehensive risk management frameworks. The research aims to improve the security and reliability of satellite ground systems, ensuring their protection against evolving cyber threats and contributing to the overall enhancement of cybersecurity in this infrastructure29 0Item Restricted COMPARING AND ASSESSING THE POLYGENIC PREDICTORS ACCURACY ACROSS EUROPEAN, AFRICAN AND SOUTH ASIAN ANCESTRY FOR COMMON DISEASES AND TRAITS(The university of Exeter, 2023) Almutairi, Faisal; Wood, AndrewAn evaluation of the polygenic score's predictive accuracy and portability for common diseases (CAD and breast cancer) and traits (height and BMI) across European, African, and South Asian ancestries was conducted. Using effect sizes estimated in European individuals, and data from the UK Biobank (which includes imputed genotypes and phenotypes for 500,000 individuals). The findings revealed that the polygenic score's predictive accuracy was inadequate for African and South Asian ancestries, indicating that the score performs optimally when derived from the same ancestry it is applied to.13 0Item Restricted Latent Factorization for Hierarchical and Explainable Embeddings and Data Disaggregation(University of Minnesota Digital Conservancy, 2021) Almutairi, Faisal; Sidiropoulos, NicholasA tremendous growth in data collection has been an important enabler of the recent upsurge in Machine Learning (ML) models. ML techniques involve processing, analyzing, and discovering patterns from real user generated data. These data are usually high-dimensional, sparse, incomplete, and, in many applications, are only available at coarse granularity. For instance, a location mode can be at a state-level rather than county, or a time mode can be on a monthly basis instead of weekly. These (dis)aggregation challenges in real world data raise some intriguing questions and bring some challenging tasks. Given coarse-granular/aggregated data (e.g., monthly summaries), can we recover the fine-granular data (e.g., the daily counts)? Aggregated data enjoy concise representations and thus can be stored and transferred efficiently, which is critical in the era of data deluge. On the other hand, recent ML models are data hungry and benefit from detailed data for personalized analysis and prediction. Thus, data disaggregation algorithms are becoming increasingly important in various domains. In this thesis, we provide data disaggregation frameworks for one-dimensional time series data and multidimensional (tensor) data. The developed models recognize the structure of the data and exploit it to reduce the number of unknown parameters. In a related setting, multidimensional data are often partially observed, e.g., recommender systems data are usually extremely sparse as a user interacts with only a small subset of the available items. Can we reconstruct/complete the missing data? This question is central in many recommendation and more general prediction tasks in various applications such as healthcare, learning and business analytics. A major challenge stems from the fact that the number of unknown parameters is usually much larger than the number of observed samples, which has motivated using prior information. Imposing the appropriate regularization prior limits the solution search to the ‘right’ space. In addition to sparsity, high-dimensionality also creates the challenge of ‘hiding’ the underlying structures and causes that can explain the data. In order to tackle this ‘dimensionality curse’, many dimensionality reduction (DR) methods such as principal component analysis (PCA) have been proposed. The reduced-dimension data usually yields better performance in downstream tasks, such as clustering. This suggests that the underlying structure (e.g., clustering) is more pronounced in some low-dimensional space compared to the original data domain. In this thesis, we present principled approaches that bridge incorporating prior information and DR techniques. We rely on low-rank (nonnegative) matrix factorization for DR and incorporate two different types of priors: i) hierarchical tree clustering, and ii) user-item embedding relationships. Imposing these regularization priors not only improves the quality of latent representations, but it also helps reveal more of the underlying structure in latent space. The tree prior provides a meaningful hierarchical clustering in an unsupervised data-driven fashion, while the user-item relationships underpin the latent factors and explain how the resulting recommendations are formed.4 0