SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
Browse
1749 results
Search Results
Item Restricted HOW DO RISK MANAGEMENT PRACTICES MEDIATE THE RELATIONSHIP BETWEEN CYBERSECURITY STRATEGY IMPLEMENTATION AND ORGANIZATIONAL PERFORMANCE?(Marymount University, 2025) Aldawsari, Najla; Mbaziira, ALexIn the era of rapid digital transformation and increasing interconnectivity, healthcare organizations face an alarming rise in sophisticated cyber threats. Despite considerable global investment in cybersecurity, healthcare institutions continue to experience costly ransomware attacks, exposing persistent vulnerabilities in cyber risk governance. This study empirically examines how risk management practices mediate the relationship between cybersecurity strategy implementation and organizational performance. Grounded in General Deterrence Theory, the research utilizes a quantitative methodology to analyze data collected from 269 senior cybersecurity professionals in Saudi Arabia. Findings reveal that risk management practices significantly enhance the effectiveness of cybersecurity strategies. Organizations with fully integrated risk management frameworks reported higher perceived effectiveness and better alignment with business outcomes. Mediation analysis confirmed that integration, not the frequency of risk assessments, plays a critical role in translating cybersecurity initiatives into improved organizational performance. Furthermore, respondents overwhelmingly affirmed the financial and strategic benefits of cybersecurity investments, particularly through mechanisms such as multi-factor authentication, continuous employee training, and cultivating a cybersecurity-aware culture. Widely used frameworks like the NIST Cybersecurity Framework and HIPAA were associated with stronger organizational resilience. This research fills a critical gap in the existing literature by providing empirical insights into how strategic risk management influences the impact of cybersecurity on performance. The findings underscore the importance of embedding cybersecurity into broader risk governance structures and offer practical guidance to healthcare organizations seeking to strengthen their cybersecurity posture.8 0Item Restricted Nonlinear Kalman Filtering for Systems under the Influence of State-Dependent Noises(The Pennsylvania State University, 2025) Alsaggaf, Abdulrahman; Ebeigbe, DonaldKalman Filtering (KF) theory stands as a cornerstone in the field of dynamic state estimation, but it continues to encounter persistent challenges, particularly with respect to nonlinear systems and state-dependent noise. While established variants such as the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) have achieved considerable prominence for their utility in complex estimation problems, their foundational assumption of zero-mean, Gaussian noise is often at odds with some physical systems and their observations. Indeed, practical engineering applications — ranging from autonomous vehicles navigating uncertain environments to financial models tracking volatile markets, and robotic sensors operating under fluctuating conditions — reveal the prevalence of noise that is not only non-Gaussian and biased, but also intricately linked to the system state. Such conditions can significantly undermine estimation accuracy and may even precipitate filter divergence. Accordingly, there is a pressing need for filtering methodologies that are both more resilient and more attuned to the nuances of real-world systems that are influenced by state-dependent noises. This dissertation seeks to address these gaps through several key contributions. First, it introduces a novel nonlinear Kalman Filtering approach that explicitly accommodates non-zero-mean and state-dependent noise within both process and measurement models. Second, it introduces a structured framework for noise modeling, seamlessly integrating these characteristics into a revised prediction-correction paradigm. Third, the methodology is extended to encompass systems lacking direct measurement-to-state correspondences and is shown to be compatible with arbitrary nonlinear transformations, thereby broadening its practical scope. Fourth, rigorous theoretical guarantees are established, demonstrating that the proposed filter achieves unbiasedness and minimum variance under well-defined conditions. Fifth, the principle underlying state-dependent noise Kalman filtering is extended to improve performance when stronger nonlinearities exist. The proposed Kalman filtering schemes preserve the well-known recursive structure of Kalman filters while maintaining computational tractability. A comprehensive suite of empirical evaluations attests to the efficacy of the proposed approach. Across a spectrum of test scenarios, the proposed filters demonstrate the ability to give reliable state estimates by reducing estimation errors and improving robustness. These empirical findings not only reinforce the theoretical developments presented herein but also illustrate the filter's capacity to adapt to nonlinear systems characterized by intricate, state-dependent noise. Furthermore, this work draws attention to enduring limitations in current Kalman Filtering methodologies, including the need for more comprehensive convergence analyses and the development of robust strategies for handling systems influenced by state-dependent noise. Opportunities for future research emerge in several promising directions, including the design of adaptive filters leveraging machine learning for dynamic noise model adaptation, and the incorporation of supplementary sensing modalities to enhance error detection and mitigation. The formulation of a unified theoretical framework capable of accommodating a wide array of noise structures would represent a significant advancement for real-time state estimation. Addressing these open challenges promises not only to advance the field of nonlinear filtering theory but also to broaden its applicability to areas such as autonomous systems, sensor networks, economics, and healthcare. This dissertation strengthens the foundational theory of Kalman filtering and creates a path forward for sustained scholarly innovation in the modeling and estimation of systems that do not typically satisfy the assumptions required for the implementation of traditional Kalman filtering.20 0Item Restricted Kinematic Synthesis and Analysis for Soft Robots with Compliant Mechanisms Using Fuzzy Logic and Neural Networks(Saudi Digital Library, 2025) الهندي, أحمد; Meng-Sang, ChewThis dissertation presents a novel framework for the kinematic synthesis and analysis of Compliant Mechanisms (CMs) that leverages fuzzy logic and neural networks to address inherent uncertainties in their design and behavior. Traditional deterministic and probabilistic methods often fail to capture the full spectrum of CM performance or are computationally prohibitive. The core contribution is the development of a Fuzzy-Kinematic Synthesis Framework that reformulates mechanism design using fuzzy arithmetic. The classic Freudenstein's equation is transformed into a parametric fuzzy form, treating input and output angles as Triangular Fuzzy Numbers (TFNs) to enable region-based synthesis. Solving these equations yields fuzzy link lengths—defined regions encompassing all viable mechanism configurations. This framework quantifies performance uncertainty through a "Function Spread" metric, derived from the fuzzy output. Building on this, an Envelope-Driven Control Methodology is developed. Forward and Inverse Kinematic Envelopes define the achievable workspace and input-output relationships. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are applied to serve as efficient surrogates for kinematic problems, drastically reducing computational cost. A Mamdani-type Fuzzy Inference System enables generative design within the performance boundaries, creating adaptable systems from a single mechanism. The methodology demonstrates closed-loop control by synthesizing inputs to trace arbitrary paths within the positional envelope. The framework is validated through detailed case studies, including a compliant surgical grasper. Results show efficient handling of kinematic complexity, design optimization via performance envelopes, and robust prediction for mechanisms with complex sensitivity profiles.35 0Item Restricted Capital Structure, ETF Flows, and Performance(Saudi Digital Library, 2025) Alkabbaa, Nayef; Kabir, MohammadThe aim of the dissertation is to study the impact of distinct financial variables such as ETF flows and capital structure on the performance of ETFs and firms. We investigate the heterogeneous effects of decomposed ETF flows, demand-driven, arbitrage-driven, and unexpected on abnormal returns across six ETF categories. Using a sample of 424 U.S. equity ETFs from 2000 to 2023 we run panel regressions, quantile models, and two-stage least squares (2SLS) estimations. The findings of this paper are in line with return-chasing behavior and crowding dynamics, demand flows are significantly associated with underperformance in index and Smart Beta ETFs, while active ETFs show less consistent effects. These effects persist across lag structures and are most pronounced in higher-performing quantiles. Under high volatility conditions, arbitrage flows improve alpha in most ETF classes. Unexpected flows generally lack predictive power, underscoring their idiosyncratic nature but their impact is more pronounced in sector active ETFs. Our findings challenge the one-size-fits-all approach of ETF flow analysis, suggesting the importance of ETF classification when evaluating flow-performance relationships We also study the relationship between capital structure and firm performance in the U.S. information technology (IT) sector during 2010–2022. Using panel data from 32 publicly listed IT firms (401 firm-year observations), we apply a comprehensive econometric framework including pooled OLS, fixed and random effects, quantile regression, 2SLS, and Pooled Mean Group ARDL. Return on equity and market capitalization are the two main performance measures in this paper, while capital structure is captured through total liabilities to total assets (debt ratio), cost of debt, and cost of capital. Results show a consistently negative impact of the cost of debt on both accounting- and market-based performance. At high leverage levels, debt ratio has a nonlinear and distribution-sensitive effect and is positive in long-run models. The capital structure outcomes confirm heterogeneity across firm types by using threshold and quantile regressions. Robustness checks validate the findings. Both papers highlight the distinct link between the impact of financial variables such as ETF flows and capital structure to the variation in performance level at both fund and firm level.7 0Item Restricted Evaluating the Impact of Orthodontic Treatment on Discrepancy Index Scores(jacksonville university, 2025) Alshahrani, sami; Sawsan, TabbaaIntroduction: The American Board of Orthodontics (ABO) Discrepancy Index (DI) is widely used to assess the complexity of orthodontic cases. However, its scoring system assigns significant weight to skeletal parameters such as ANB and SN-MP angles—components that are often unmodifiable in adult patients receiving non-surgical treatment. This raises concerns about whether the DI accurately reflects true treatment complexity and outcomes in adult orthodontic care. This study aimed to evaluate changes in DI scores before and after comprehensive orthodontic treatment in adult patients. It also investigated the influence of gender, premolar extraction status, and treatment duration on DI score reduction. Methods: A total of 102 adult patients (≥18 years) treated at Jacksonville University were included. Pre- and post-treatment DI scores were assessed using ABO scoring criteria. Statistical analyses included paired t-tests, independent t-tests, ANOVA, and Pearson correlation. Results: The mean DI score decreased significantly from 19.39 ± 15.97 to 5.59 ± 7.60 (p < 0.001). Patients treated with premolar extractions showed greater DI reductions than those without (18.24 vs. 10.21 points; p = 0.002). No significant gender-based differences were observed. A weak inverse correlation was found between treatment duration and DI change (r = –0.209, p = 0.035). Conclusions: Orthodontic treatment in adults leads to a significant reduction in DI scores. However, the persistent influence of unchanging skeletal components highlights limitations in the current DI model. These findings support the need for potential modification of the DI—such as soft-capping skeletal weights—in adult orthodontic evaluations to ensure accurate reflection of treatment complexity and outcome.32 0Item Restricted Malignant Transformation of Oral Epithelial Dysplasia: Precision Diagnostics Utilizing a Deep Learning and Spatial Transcriptomics Predictive Modeling Approach.(University of Maryland Baltimore, 2025) Alajaji, Shahd Abdullah; Sultan, AhmedOral squamous cell carcinoma (OSCC) remains a major global health burden with limited improvements in overall survival over recent decades. Most OSCCs arise from oral potentially malignant disorders (OPMDs), including oral epithelial dysplasia (OED), which is currently graded subjectively by histopathological examination. The urgent need for objective, biologically informed risk stratification tools has driven the integration of artificial intelligence (AI), spatial transcriptomics, and functional genomics in oral cancer research. This thesis tests the central hypothesis that deep learning and spatial transcriptomic approaches can objectively predict the malignant transformation of OED by identifying histomorphological patterns and immune-epithelial gene signatures associated with malignant transformation zones and cancer progression. To evaluate this, three specific aims were pursued: 1. Develop and compare AI models for predicting malignant transformation of OED based on lymphocyte distribution and tissue morphology. 2. Identify spatially informed predictive biomarkers in proliferative leukoplakia (PL) using spatial transcriptomic profiling. 3. Functionally assess the role of mEAK-7, a novel regulator of non-canonical mTOR signaling, in OSCC initiation using a gene knockout mouse model. In Aim 1, we trained and evaluated multiple machine learning and deep learning models, including classical regressors, state-of-the-art neural networks, and weakly supervised pattern-recognition networks using a multi-institutional dataset of annotated whole slide images (WSIs) of OPMD cases with known transformation status. In Aim 2, spatial transcriptomics (10x Genomics Visium HD) was performed on PL samples to identify gene signatures predictive of transformation, with a focus on immune–epithelial interactions. In Aim 3, a 4NQO-induced oral carcinogenesis model was applied to mEAK- 7 knockout mice to assess its functional role in OSCC development. AI models demonstrated that lymphocyte infiltration patterns can predict malignant transformation, with deep learning models achieving accuracies up to 83.4% in distinguishing transformed from non-transformed cases. Spatial transcriptomics revealed downregulation of epithelial barrier genes (FLG, CASP14) and immune activation signatures (S100A8, S100A9, CD74) in transformation zones, supporting a model of barrier disruption and neoantigen-driven immune remodeling. The mEAK-7 knockout study showed significantly reduced OSCC incidence, implicating alternative mTOR signaling in OSCC initiation and validating spatial findings through in vivo functional evidence. In conclusion, this thesis presents an integrated, multi-modal investigation into the malignant transformation of OED, providing evidence that AI and spatial biology can complement conventional pathology in predicting cancer risk. The combined findings offer a foundation for future precision diagnostics in oral cancer prevention and identify novel molecular targets for early intervention.31 0Item Restricted Secondary ELA Mentor Teachers’ Feedback on Preservice Teachers’ Video-Recorded Lessons(University of South Florida, Tampa, FL, 2025) Alharbi, Homood; Sherry, MichaelThis dissertation explores the feedback practices of secondary English Language Arts (ELA) mentor teachers and the reasoning behind their feedback to preservice teachers (PSETs) during early field experiences. Motivated by my own experience as a preservice teacher, where the absence of timely, meaningful feedback often left me without needed guidance, I was drawn to study how mentor teachers notice, interpret, and respond to novice teaching. This study focuses not on the mentoring relationship itself, but on the content and rationale of feedback provided by mentors. Three secondary ELA mentor teachers participated in this qualitative study. Each responded to two video-recorded lessons of preservice teachers and took part in two sets of interviews. Using a Teacher Noticing framework, I analyzed what mentors noticed, why they chose to respond, how they delivered feedback, and how their prior experiences shaped those decisions. Findings indicate that mentors’ own experiences with receiving and giving feedback significantly influenced what they prioritized and how they responded to preservice teaching moments. Across participants, feedback focused on seven key areas of teaching and was driven by three consistent reasons. While the study focuses on a small group in a specific context, it raises important questions about broader trends in ELA mentor feedback. I recommend further research across diverse contexts to examine the consistency of feedback practices among mentor teachers.12 0Item Restricted The Relationship Between Vertical Skeletal Patterns and Acoustic Distortion in Fricative Sound Production(Jacksonville University, 2025) Bin Homran, Faris; Tabbaa, SawsanObjective: This pilot study aimed to investigate whether vertical skeletal patterns, as measured through cephalometric analysis, are associated with acoustic distortion in the production of the /s/ fricative sound among orthodontic patients. Methods: Thirty-three unselected orthodontic patients aged 13 to 26 were recruited from Jacksonville University School of Orthodontics. Each subject underwent cephalometric radiography to measure vertical skeletal variables, including PP-GoGn angle, Upper Anterior Facial Height (UAFH), Lower Anterior Facial Height (LAFH), and overbite (OB). Speech recordings were captured using an iPhone TrueDepth camera during the articulation of words containing /s/ and /sh/ fricatives. Acoustic data were analyzed in Praat software to extract spectral moment features: center of gravity (COG), standard deviation (SD), skewness, kurtosis, and the COG difference between /s/ and /sh/. Pearson-product moment correlations were used to evaluate linear associations between skeletal dimensions and acoustic measures. Results: A significant positive correlation was found between PP-GoGn and COG of /s/, suggesting steeper mandibular inclination is associated with higher acoustic frequencies. UAFH was negatively correlated with both COG and SD of /s/, indicating that reduced maxillary height may produce sharper, more distorted fricative acoustics. LAFH correlated negatively with skewness of /s/, and OB was significantly associated with kurtosis, reflecting energy dispersion changes. These structure-function correlations provide evidence of a measurable relationship between craniofacial form and speech acoustics. Conclusion: Vertical skeletal morphology significantly influences fricative sound production. Specifically, mandibular plane angle and anterior facial height appear to affect articulatory posture and oral cavity resonance, altering spectral properties of the /s/ sound. These findings highlight the importance of interdisciplinary collaboration between orthodontists and speech-language pathologists in evaluating and managing patients with malocclusion-related speech distortion.22 0Item Restricted ENDOCRINE DISRUPTING PROPERTIES OF NANOPLASTICS AND PHTHALATES IN THE FEMALE REPRODUCTIVE SYSTEM(New Jersey Institute of Technology, 2025) Alahmadi, Hanin; Warner, GenoaThe female reproductive system serves as a crucial component for continuation of life on Planet Earth. It is one of the most susceptible systems to disruption by environmental contaminants. The ovary is an endocrine tissue that is responsible for hormonal balance essential for reproduction and development. Many chemicals are endocrine disruptors, agents that cause hormone disruption by mimicking hormone function or blocking hormone signaling. Phthalates are notorious endocrine disrupting chemicals widely used in consumer and industrial products. In this dissertation, phthalate mixtures are studied rather than single phthalates to represent environmental exposure. Nanoplastics are considered emerging endocrine disruptors that may lead to similar outcomes as phthalates. Nanoplastics pose significant risk due to their small size, which enables them to enter living things. Nanoplastics have been detected in human organs and tissue, but the health implications are yet unknown. This dissertation aims to address the knowledge gap of the impact of ubiquitous endocrine disruptors such as nanoplastics and phthalates on the female reproductive system utilizing both in vitro and in vivo rodent experimental models. Understanding toxicological effects on the female reproductive system is crucial to maintain healthy reproductive systems and produce healthy offspring. The female reproductive system and endocrine signaling within it are especially vulnerable to disruption by environmental contaminants. In chapter one, we introduce the ovary and the placenta, and their role in the endocrine system. In chapter two, we introduce well-known endocrine disruptors, bisphenols and phthalates, which have been proven throughout the years to cause adverse female reproductive effects, including infertility and pregnancy complications such as miscarriages and fetal growth restriction. In chapter three, we assess the ovarian impacts of exposure to phthalate mixtures at environmentally relevant doses. Using our in vitro model, phthalates were found to interfere with vital cellular functions such as the cell cycle and can impact ovarian steroid hormone levels, which poses a risk for fertility. In chapter four, we assess the effects of nanoplastics on ovarian follicles. Both polystyrene (PS) and polyethylene (PET) were investigated to observe their effects on hormone levels and cell growth. We measured gene expression and hormone levels and found that exposure causes hormonal disruption and impacts hormone synthesis processes. In chapter five, we tested the hypothesis that nanoplastic exposure can lead to placental disruption by crossing the placenta and accumulating in tissue. The placenta is the sole organ responsible for fetal health and development. Any disruption could pose a risk to fetal health. An in vivo mouse model was utilized to study the effects of nanoplastic exposure. Our findings show that exposure to nanoplastics causes disruption of expression of genes that are essential during pregnancy and disruption of placental morphology. This dissertation demonstrates the hazards that environmental pollutants, particularly nanoplastics and phthalate mixtures, pose for the female reproductive system.4 0Item Restricted POLYPHENOLS FOR THE MITIGATION OF VASCULAR CALCIFICATION; IN VITRO AND IN VIVO STUDIES(Clemson University, 2025) Altuhami, Abdullah; Simionescu, Dan; Simionescu, Agneta; Sierad, LeslieCardiovascular diseases remain the leading cause of mortality worldwide, and one contributing factor is vascular calcification—the abnormal accumulation of calcium within blood vessels. This process stiffens the vessel walls, disrupts normal blood flow, and significantly increases the risk of heart attacks, strokes, and other life-threatening complications. In severe cases, organ transplantation may be the only viable treatment. However, this approach is limited by donor shortages and the risks associated with long-term immunosuppressive therapy, including infection and rejection. To address these limitations, the field of tissue engineering is exploring alternatives such as tissue-engineered vascular grafts (TEVGs), which aim to replace or restore damaged blood vessels. Despite its promise, this approach presents several scientific challenges: identifying appropriate cell types that mimic natural vascular behavior, ensuring those cells receive the necessary biological signals, and developing a scaffold that can support the graft structurally while remaining biocompatible. In this study, we propose using decellularized porcine arteries as scaffolds, owing to their mechanical similarity to human vessels. We exposed these acellular tissues to calcifying conditions both in laboratory settings (in vitro) and in live animal models (in vivo) to investigate how vascular calcification develops. We also evaluated the therapeutic potential of Plant-derived antioxidant (PDA-1), for its ability to inhibit or reduce calcification. Our experiments included a dynamic in vitro bioreactor that mimicked physiological flow, as well as subdermal implantation in juvenile rats to assess immune response, biocompatibility, and calcium deposition. Across both models, PDA-1 treatment was associated with a noticeable reduction in calcification, suggesting its promise as a preventative or therapeutic agent. These findings support the potential of PDA-1 to improve outcomes in vascular grafts—either by direct application to calcified vessels or as a pre-treatment for off-the-shelf grafts. Future studies will focus on refining drug delivery methods, scaling the model, and progressing toward clinical translation.47 0