SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted HYBRID MACHINE LEARNING APPROACHES FOR SOC AND RUL ESTIMATION IN BATTERY MANAGEMENT SYSTEMS(Oakland University, 2024) Hawsawi, Tarik Abdullah; Zohdy, MohamedWith the fast development of electric vehicles (EVs), new technologies are needed to manage batteries more efficiently to optimize performance and more profound and longer battery use. A significant problem that must be solved successfully is accurate estimation of the State-of-Charge (SoC) to avoid fully discharging a battery. It shortens battery life and prolongs the time it takes to charge the battery. This dissertation introduces a new approach that uses Edge Computing and real-time predictive analytics to assess the status of EV batteries and send alerts when necessary, thus facilitating energy efficiency. The Edge Impulse platform is used to predict the Remain Useable Life RUL of batteries with enhanced accuracy using EON-Tuner and DSP processing blocks, enhancing computational capability and making it feasible for edge devices. Since traditional SoC estimations include tools like Kalman filters and Extended Kalman filters, which are effective but have a considerable drawback in estimating the SoC with changing battery parameters, this study proposes a multi-variable optimization method. The method enhances performance prediction after key parameters are iteratively adjusted, thus resolving the emergence hypotheses of most existing techniques. The system was designed and tested on Jupyter Notebook, and performance indicators of accuracy, MSE, and efficiency further validated the design. This study helps ensure proper energy use and long battery life for e-vehicles, which promotes clean energy use.5 0Item Restricted Towards Understanding and Improving Software Professionals' Security and Privacy Practices(University of California (Berkeley), 2024-12-20) Alomar, Noura Nassir; Egelman, Serge; Wagner, DavidOrganizations operating in various industries are continuously experiencing the negative consequences of not sufficiently addressing security and privacy issues in their software development processes. Exploitation of such issues is increasingly leading to breaches of sensitive user data and exposing organizations to legal liabilities. Depending on organizations' sizes and the maturity levels of their development processes, effective handling of these issues might fall under the responsibilities of technical and managerial roles that have different specializations. Throughout the process of developing or maintaining software, the practices adopted by these roles can lead to introducing preventable security and privacy issues that have serious implications to their organizations. Despite the prevalence of these issues, we are yet to obtain sufficient understanding of why they persist, and how the characteristics of organizational software development or maintenance processes are influencing the privacy or security practices adopted software professionals who contribute to these processes. This dissertation presents the results of a series of holistic process-oriented investigations of the factors that are hindering timely detection and remediation of security and privacy issues by organizations. We start by qualitatively examining how process-related factors and interactions with managerial roles were shaping the engineering practices of software developers and testers tasked with handling these issues in organizations operating in the United States and several other countries. We then present the results of in-depth longitudinal measurements of the prevalence of privacy and security issues in thousands of Android apps that were published on the Google Play Store and which were leading their developers to potentially be in violation of applicable privacy regulations. To obtain insights into why these issues existed in the tested apps, we supplement the results of our technical measurements with qualitative data collected through semi-structured interviews and surveys that we targeted to professionals who were involved in the development of the same apps. Our results identify a range of non-technical factors that hindered informed decision-making on how security and privacy issues should be handled, which include the lack of organizational processes that facilitated information sharing between the various roles whose effective cooperation was needed (e.g., software developers and security engineers). However, the results of our technical and qualitative investigations consistently identified enforcement of regulatory and industry compliance requirements as one of the main factors that was driving organizations' efforts to detect or remediate security or privacy issues. Notably, our large-scale longitudinal measurements of the behaviors of Android apps provide evidence of overall improvement in organizational security and privacy practices, which likely resulted from Google Play's ongoing enforcement efforts of privacy compliance requirements over the past few years (2018 to 2023). However, we also show that use of third-party code is introducing complexities in software development processes and continuing to lead software developers to introduce privacy issues (e.g., exfiltration of personal data). The discussions we had with developers of these apps showed that most of them lacked awareness of the behaviors of their own apps, did not have sufficient understanding of their privacy compliance obligations, and did not follow systematic approaches to vetting third-party Software Development Kits (SDKs) for inclusion in their apps. These challenges call for the need of reducing the burden of privacy compliance on software developers by providing them with usable guidance that can help them address security and privacy issues early in their software development processes. We hope that this dissertation will inform regulatory debates about the challenges that are hindering effective handling of security or privacy issues in practice, and the types of interventions that can be incorporated in software development processes to guide software professionals through how to address these issues in a timely fashion.10 0Item Restricted FINDING PATTERNS IN COMPLEX SYSTEMS AT THE SCIENCE-SOCIETY INTERFACE: A MULTIDISCIPLINARY STUDY(University of Rhode Island, 2024) Bankhah, Hanan; Cardace, DawnThis dissertation offers a comprehensive overview of how complex interactions between environmental, social, and individual factors shape patterns in diverse systems at the science- society interface. The first study explores the spatial- temporal distribution of Aedes japonicus in Rhode Island, identifying urban areas as significant habitats influenced by climatic factors and socioeconomic factors. This work employs machine learning techniques to identify the critical variables affecting mosquito abundance, offering insights into mosquito management for enhanced public health. The second study investigates the spatiotemporal dynamics of dengue in South Asia, emphasizing the roles of urbanization, climatic factors, and water insecurity in disease proliferation. By analyzing the interaction between these elements, the research advocates for integrated early warning systems and community education to mitigate dengue outbreaks. Lastly, the dissertation examines the academic experiences of Saudi female students in the U.S., revealing a strong link between a sense of belonging and academic success. Together, these studies underscore the necessity of multidisciplinary approaches in addressing complex world challenges at the intersection of science and society.20 0Item Restricted Assessing the Performance of Model Fit Indices in Multilevel Structural Equation Modeling: A Comparison of the Standard Approach, Level-Specific Evaluation, and Equivalence Testing(University of Denver, 2024-11) Alibrahim, Noor; Thomas Pitts, RobynMultilevel Structural Equation Modeling (MSEM) is a method suitable for analyzing data with multi-level structure. This method is particularly useful for exploring relationships across different levels of analysis. The objective of this dissertation was to assess the performance of model fit indices in MSEM using a standard approach (SA), level-specific (LS) evaluation, and equivalence testing (ET) methods. Monte Carlo Simulations were implemented to contrast the performance of common fit indices in each method in MSEM under three design factors: sample size (SS), intraclass correlation coefficient (ICC), and specification model (SM). Additionally, the effectiveness of SA, LS, and ET approaches were evaluated using real-world data, utilizing emotional intelligence as a personality state dataset. The results demonstrated that the SA model fits, assessed using CFI and RMSEA, effectively identified the correct specification model (CSM) and rejected the measurement misspecification model (MMM) across all SSs and ICCs. However, these fit indices failed to detect the structure misspecification model (SMM). Furthermore, the LS model fits, including CFILSW, CFILSB, RMSEALSW, and RMSEALSB, successfully retained the CSM and rejected the MMM. Only the CFILSW was sensitive to detecting the misfit of SMM when the ICC was 0.3. In the examination of ET for the CSM, the T-size (CFIETWt, RMSEAETWt, and RMSEAETBt) indicated excellent fit across all SSs and ICCs, while the CFIETBt showed excellent fit with an ICC of 0.1 and close fit with an ICC of 0.3. All T-sizes measures except RMSEAETBt, rejected MMM, while only CFIETBt consistently detected SMM across all SSs and ICCs. ANOVA results indicated that specification model (SM) had the most significant effect on the performance of fit indices, followed by ICC and SS. Real-world data analysis supported these findings, highlighting the limitations of traditional fit indices and emphasizing the need for comprehensive evaluation methods to detect model misspecifications accurately. Although no single approach performed successfully in all scenarios, a combined approach, especially LS and ET, using multiple indices and methodologies is recommended for a more robust and accurate assessment of model fit in MSEM.23 0Item Restricted Effects of free carriers and acceptor-bound excitons on the exciton dephasing in strained GaAs films: A study with spectrally- and time-resolved four-wave mixing(University of Cincinnati, 2024) Alyami, Samia; Wagner, Hans-PeterThis research aims to improve the knowledge of coherent exciton dynamics in strained bulk semiconductors, paving the way for more efficient applications in optoelectronics and quantum technologies. We studied the coherent exciton dynamics in a 470-nm thick gallium arsenide (GaAs) film using non-degenerate three-beam Four Wave Mixing (FWM) at 14 Kelvin with ~150 fs laser pulses. The film exhibits energy splitting between heavy-hole (Xh) and light-hole (Xl) excitons, attributed to the presence of biaxial tensile strain, which varies across the film. Strain calculations and spectrally resolved FWM measurements at three different regions are used to examine how strain levels impact the dephasing times of Xh and Xl excitons. The strain in the film also causes splitting in the acceptor-bound exciton energy transition (A0X), which -when aligned with the Xl exciton energy- prolongs the Xl dephasing time. Heterodyne FWM (HFWM) measurements in photon echo (PE) configuration demonstrate that the exciton transitions are predominantly homogeneously broadened. They also reveal distinct beat structures for Xh - Xl excitons under different polarization conditions, resulting from a combined effect of excitation-induced dephasing (EID), phase space filling (PSF), and biexciton formation (BIF). The experimental studies are reproduced and supported by numerical calculations with the optical Bloch equations (OBEs) for a 10-level model. This work contributes to a deeper understanding of the exciton dynamics in strained-bulk semiconductors.9 0Item Restricted Cosimulation Approach for High-Frequency Magnetic Component Modeling in DC-DC Converters(Western Michigan University, 2024-12) Alyami, Faraj; Gomez, Pablo; Asumadu, Johnson; T Meyer, RichardThis dissertation aims to evaluate the efficacy of a novel methodology to support the transient analysis and electromagnetic design of high-frequency transformers and inductors in power converters. By integrating finite element method (FEM)-based tools with dynamic analysis techniques, the proposed methodology accurately reflects the physical characteristics of high- frequency magnetic components under both steady-state and transient conditions in power converters. This approach addresses the stresses generated by the extensive integration of power electronic-interfaced sources, loads, and storage units in various power electronic topologies. High-frequency transformers and inductors are highlighted as crucial elements for the next generation of energy systems, driven by advancements in distributed power generation, DC power grids, energy storage, and sensitive electronic loads. High-frequency transformers offer benefits such as galvanic isolation, high power density, small size, low cost, high efficiency, output regulation, and improved electromagnetic compatibility performance, making them vital for modern energy applications. High-frequency inductors, on the other hand, enhance the efficiency of energy transfer, stabilize voltage regulation, and minimize switching losses in power converters, significantly improving the performance of photovoltaic power systems, electric drives, and adjustable power supplies. The proposed methodology is implemented through cosimulation between COMSOL Multiphysics® and MATLAB/Simulink®, demonstrating its potential to advance the design and analysis of high-frequency magnetic components.17 0Item Restricted Leveraging Deep Learning for Change Detection in Bi-Temporal Remote Sensing Imagery(University of Missouri-Columbia, 2024) Alshehri, Mariam; Hurt, J. AlexDeforestation in the Brazilian Amazon poses significant threats to global climate stability, biodiversity, and local communities. This dissertation presents advanced deep learning approaches to improve deforestation detection using bi-temporal Sentinel-2 satellite imagery. We developed a specialized dataset capturing deforestation events between 2020 and 2021 in key conservation units of the Amazon. We first adapted transformer-based change detection models to the deforestation context, leveraging attention mechanisms to analyze spatial and temporal patterns. While these models showed high accuracy, limitations remained in effectively capturing subtle environmental changes. To address this, we introduce DeforestNet, a novel deep learning framework that integrates advanced semantic segmentation encoders within a siamese architecture. DeforestNet employs cross-temporal interaction mechanisms and temporal fusion strategies to enhance the discrimination of true deforestation events from background noise. Experimental results demonstrate that DeforestNet outperforms existing models, achieving higher precision, recall, and F1-scores in deforestation detection. Additionally, it generalizes well to other change detection tasks, as evidenced by its performance on the LEVIR-CD urban building change detection dataset. This research contributes a robust and efficient framework for accurate change detection in remote sensing imagery, offering valuable tools for environmental monitoring and aiding global efforts in sustainable forest management and conservation.10 0Item Restricted Survival Rates, Technical Complications and Dimensions of Monolithic Zirconia Fixed Complete Arch Dental Prostheses(Tufts University School of Dental Medicine, 2024) Malluh, Ahmad; Papaspyridakos, Panos; Vazouras, Konstantinos; Finkelman,Matthew; Kudara, Yukio; Papaspyridakos,PanosOBJECTIVE: The primary objective of this study was to assess the survival and complication rates of monolithic zirconia implant-supported fixed full dental prostheses (IFCDPs) in completely edentulous patients following a minimum 1-year clinical follow-up. Additionally, the study sought to evaluate associations between risk indicators, structural dimensions, and quality-of-life outcomes. MATERIALS AND METHODS: This observational, single-center retrospective cohort study included a convenience sample of 44 participants who received 61 zirconia IFCDPs at Tufts University School of Dental Medicine (TUSDM). Data on demographics, clinical variables, technical complications, and prosthesis dimensions were collected. Descriptive statistics were calculated, and associations between independent variables and complications were analyzed using generalized estimating equations (GEE). Statistical analyses were performed using SPSS v.28 (IBM Corp., Armonk, NY, USA) and SAS 9.4 (SAS Institute Inc., Cary, NC, USA). RESULTS: The study included 44 participants (45.5% female, 54.5% male; mean age 67.07 (SD=12.1) years) with a mean prosthesis use duration of 28.67 (SD=18.32) months. The majority (72.7%) did not use a nightguard, and 25% reported bruxism. Opposing dentition included natural teeth (13.1%), implant-supported prostheses (50.8%), removable prostheses (14.8%), a combination of teeth and implants (19.7%), and implant overdentures (1.6%). Of the 61 prostheses, 52.5% were maxillary, and 47.5% were mandibular. Minor technical complications included Ti-base decementation (13%), chipping (12%), loss of access hole material (13%), and wear of prosthetic screws (11.5%). Major complications were infrequent, with fracture of screws (2%) and fracture of frameworks (3%). The overall prosthesis survival rate was 93.44%, with a mean total complication rate of 0.8 (SD=1.15)events per prosthesis. Structural analysis identified reduced dimensions at critical cross-sections as potential contributors to fractures. CONCLUSION: This study demonstrated a 93.44% survival rate for monolithic zirconia IFCDPs over an average follow- up period of 28.67 months. While the prostheses showed high reliability, minor technical complications were relatively common, highlighting the need for routine maintenance. The findings underscore the importance of prosthesis design and risk factor consideration in treatment planning. Larger, multicenter studies with longer follow-up periods are recommended to validate these findings and provide greater insights into the long-term performance of zirconia prostheses.15 0Item Restricted CODIFYING INHUMANITY: LEGAL FOUNDATIONS, RACIALIZATION, AND DEHUMANIZATION IN THE CONSTRUCTION AND PERPETUATION OF AMERICAN AND NORTH CAROLINA CHATTEL SLAVERY(Wake Forest University, 2024-06-12) Salah, Yuossof; H. Knox, John; J. Morath, SarahThis dissertation argues that American chattel slavery, from its earliest inception, was developed through a deliberate legal framework that systematically transformed people of African descent into property, institutionalizing mechanisms of systematic dehumanization and violence to maintain the institution of racial bondage. By examining British, international, colonial, and constitutional laws, this study demonstrates how multiple legal systems actively and collectively constructed and perpetuated slavery in America rather than merely tolerating it. Furthermore, this analysis traces the legal interconnections between these systems across continents and time periods to reveal their role in codifying and legitimizing the forced commodification of human beings. Through analysis of key legal documents, including British court rulings, colonial slave codes, international treaties, and the U.S. Constitution, this dissertation challenges perspectives that position slavery as “a private matter,” underdeveloped, or merely a local matter outside or in contrast to the established legal order. Instead, the research argues that law and legal institutions were central to normalizing racial enslavement and embedding it within American jurisprudence. This study also argues that racial enslavement was enforced and sustained through systemic racial violence, institutionalizing mechanisms of “collective cruelty.” This system of "Collective Cruelty" served to suppress both individual and collective resistance among the enslaved population. The analysis contends that African enslavement and state-sanctioned violence were inextricably linked throughout both the mainland colonies and American constitutional eras. This dissertation finally explores North Carolina's unique legal foundations, where “absolute slavery” was instituted from its inception, demonstrating how legal frameworks did not merely accommodate slavery, but actively created, legitimized, and sustained a system of racial bondage through explicit mechanisms of dehumanization and commodification.6 0Item Restricted Early Detection of Pleuropulmonary Blastoma Using Transformers Models(Bowie State University, 2024) Almenwer, Sahar; El-Sayed, HodaChildhood cancer is the second leading cause of death among children under the age of fifteen, according to the American Cancer Society. The number of diagnosed cancer cases in children continues to rise each year, leading to many tragic fatalities. One specific type of cancer, pleuropulmonary blastoma (PPB), affects children from newborns to those as old as six years. The most common way to diagnose PPB is through imaging; this method is quick, cost-effective, and does not require specialized equipment or laboratory tests. However, relying solely on imaging for early detection of PPB can be challenging because of lower accuracy and sensitivity. It is time consuming and susceptible to errors because of the numerous potential differential diagnoses. A more accurate diagnosis of PPB depends on identifying mutations in the DICER1 gene. Recent advancements in biological analysis and computer learning are transforming cancer treatment. Deep learning (DL) methods for diagnosing PPB are becoming increasingly popular. Despite facing some challenges, DL shows a significant promise in supporting oncologists. However, some advanced models possess a limited local receptive field, which may restrict their ability to comprehend the overall context. This research employs the vision transformer (ViT) model to address these limitations. ViT reduces computation time and yields better results than existing models. It utilizes self-attention among image patches to process visual information effectively. The experiments in this study are conducted using two types of datasets, medical images and genomic datasets, employing two different methodologies. One approach uses the ViT model combined with an explainability framework on large medical image datasets with various modalities. The other involves developing a new hybrid model that integrates the vision transformer with bidirectional long short-term memory (ViT-BiLSTM) for genomic datasets. The results demonstrate that the ViT model and the new hybrid model, ViT-BiLSTM, significantly outperform established models, as validated by multiple performance metrics. Consequently, this research holds great promise for the early diagnosis of PPB, reducing misdiagnosis occurrences, and facilitating timely intervention and treatment. These findings could revolutionize medical diagnosis and shape the future of healthcare.9 0