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Minutes of the Formal Session of the Oakland University Board of Trustees, May 7, 2025
No full textA. Call to Order | B. Roll Call | C. Consent Agenda for Consideration/Action: DTMB Phase 200/300 Programming and Schematic Design for Science Complex - Dodge Hall Renovation Project; Increased Costs of Science Complex – Dodge Hall Renovation Project and Authorization to Complete Renovation; Increased Construction Manager Fee for Science Complex – Dodge Hall Renovation | D. Adjournmen
Exploring Spirituality Rounds: A Reflective Inquiry Among Chaplains and Medical Students
No full textMulti-Objective Optimal Routing Schemes for High Mobility Vehicular Networks: A Path to Efficiency
No full textTechnological advancements in wireless communication networks have enabled futuristic applications that support massive device access and pervasive communications. Moreover, vehicular networks in Intelligent Transportation Systems (ITS) require efficient communication and routing schemes to accommodate Electric and Flying Vehicles (EnFVs). A centralized approach is often flawed due to the high mobility and dynamic nature of device movement. Therefore, efficient and novel solutions are required to provide connectivity to EnFVs without any centrally connected unit. Our main focus in this study is to enable a faster, better, and improved communication platform for EnFVs, support a wide range of applications. This thesis provides an in-depth examination of EnFVs within ITS, emphasizing the necessity for a unified approach to take the unique challenges they pose. Moreover, this study thoroughly analyzes the role of Artificial Intelligence (AI), specifically Genetic Algorithms (GAs), in optimizing communication decision-making for high-mobility vehicles. This comprehensive work extensively reviews existing solutions and the background of GAs, highlighting the relevance of multi-objective optimization algorithms. Communication and routing issues in EnFVs are examined from various angles. A novel multi-objective routing scheme addresses the diverse constraints and goals of EnFV networks, aiming to improve packet routing performance and efficiency. Our novel scheme prioritizes energy and transmission rate for routing decisions while focusing on vehicle connectivity time. The Genetic Algorithms employed identify the optimal solution for multi-objective routing problems. Significant findings include an optimized routing scheme that outperforms current solutions, achieving over 90% packet delivery ratio, extended connectivity time, reduced average hop distance, and efficient energy use. The research also explores the potential of Genetic Algorithms in solving complex optimization problems in EnFVs, demonstrating their effectiveness in dynamic routing scenarios. Further enhancements to the solution improve route discovery methods, making the process lighter and more suitable for high-mobility UAV networks. The thesis concludes with recommendations for future research to further improve the efficiency and effectiveness of routing algorithms in EnFV networks, aim for seamless integration into modern transportation systems and advance the field of EnFV
Disturbance Accommodating Control of an Automotive Transmission Torque Converter Clutch System
No full textModern automotive drivelines are facing increasing pressure to improve overall efficiency while maintaining vehicle comfort and performance. This thesis presents several new ways to control the torque converter clutch in an automotive automatic transmission in such a way they improve driveline system efficiency while maintaining its comfort. This efficiency improvement is accomplished by extending the clutch’s application conditions to a lower speed where the environment is harsher than is possible with the current class of controllers. Three new model-based methods are evaluated on their ability to control the simplified model of a torque converter clutch. These controllers include one-step-ahead and complex observe based controller methods. These controllers are applied in the presence of large disturbances that include parameter uncertainties and unmeasured state errors to evaluate their robustness in these environments. The lack of clean sensor measurements highlights the limitations of the one-step-ahead method and are demonstrated by the controller's inability to maintain system regulation in this high noise environment. Because it is not practical to accurately measure the torques and internal speeds of the torque converter system, this research addresses this by estimating them using observers. Using these new observer-based-control methods, this dissertation presents the simulation results that demonstrate this method provides a significant increase in robustness to disturbances. The precise and smooth tracking of the regulator's setpoints highlights the system's ability to meet the powertrain's comfort and economy targets, while being practical enough to be implemented in current automotive system
Nutrition for health promotion in nursing education: knowledge, attitudes, beliefs, and instructional practices of nursing faculty
No full textNutrition's role in health is undisputed, yet nursing education programs do not consistently integrate essential nutrition content into curricula. Nurses are well-positioned throughout the healthcare system to provide health-promoting nutritional guidance but must have the requisite knowledge to do so. The purpose of this study was to describe the knowledge, attitudes, beliefs, and instructional practices of nursing faculty regarding teaching nutrition for health promotion. The theory of planned behavior with Wilkins' KABP model was employed as the theoretical framework for the descriptive study. A pre-study validity evaluation of the attitude and belief measures for sufficiency, relevance, clarity, and coherence was completed by a group of experts. Data were collected via a nationwide survey of full-time faculty members (n = 266) teaching in prelicensure baccalaureate nursing programs. The instrument comprised five sections measuring knowledge (General Nutrition Knowledge Questionnaire-Revised), attitudes, beliefs, instructional practices, and demographics. The scores on the knowledge measure ranged from 59 to 95 (M = 80%, SD = 6.6, 95% CI (69.77 [79.2%], 71.37 [81.1%]). Attitude scores ranged from -7 to 20, with the average being 13.1 (SD = 5.10, 95% CI [12.47, 13.70]), and 80% of respondents' attitude scores fell in the top quarter (> 11). Belief scores ranged from -11 to 20, with the average being 7.08 (SD = 5.93, 95% CI [6.36, 7.79]. Only 154 (57.9%) of respondents have had previous experience teaching nutrition for health promotion. The findings highlight the importance of continuing education and resources for nursing faculty members in promoting the inclusion of nutrition for health promotion in prelicensure nursing education. Academic nursing must work to ensure that nursing program graduates are well-equipped with the required competencies to deliver health-promoting nutritional guidance and impact the growing chronic disease burden caused by poor die
Detecting and refactoring technical debt for software containers
No full textIn today’s fast-paced software development environment, containerization has emerged as a cornerstone of modern infrastructure, enabling consistent and scalable deployments across varied platforms. Docker, as the leading containerization platform, plays a critical role in this landscape, but the management of Dockerfiles - scripts that automate the creation of container images - presents significant challenges. These challenges include the accumulation of technical debt, inefficient image sizes, prolonged build durations, and the emergence of anti-patterns, all of which can undermine the efficiency, maintainability, and quality of Docker-based projects. This dissertation presents and advances a suite of techniques and tools to enhance the quality of Docker projects through carefully targeted refactoring strategies, anti-pattern detection, and the mitigation of technical debt. It begins with an empirical study of open-source Docker projects, identifying 38 Docker-specific refactoring techniques and nine distinct categories of technical debt. These findings illuminate the unique challenges inherent in Dockerfile management, which differ fundamentally from those in traditional software development due to Docker's Infrastructure as Code (IaC) nature. Building on these insights, the research introduces DRMiner, a tool designed to detect and analyze refactorings within Dockerfiles. Utilizing an Enhanced Abstact Syntax Tree (E-AST) approach, DRMiner addresses the specific complexities of Docker artifacts, automating the identification of refactoring opportunities and significantly reducing the manual effort required for Dockerfile maintenance. The dissertation also proposes a novel method for the specification and detection of Docker-specific anti-patterns, expanding the scope beyond traditional code smells to address broader design flaws. This method defines five new anti-patterns and develops a metric-based framework for their automated detection. Finally, this research delves into automating Dockerfile refactoring through large language models (LLMs). The findings reveal that LLM-driven refactoring reduces image sizes and builds durations and enhances maintainability and understandability, outperforming manual refactoring methods. Together, these contributions provide a robust, empirically grounded framework for enhancing the quality and sustainability of Docker projects, offering valuable tools and insights for both practitioners and researchers in containerizatio
Molecular mechanisms of β-iii-spectrin in dendritic arborization and spinocerebellar ataxia type 5
No full textβ-III-spectrin is a membrane-associated cytoskeletal protein predominantly expressed in the cerebellum, and is essential for the maintenance and development of the complex dendritic arbors extended by Purkinje neurons. Mutations in various functional domains of β-III-spectrin cause the neurodegenerative disease spinocerebellar ataxia type 5 (SCA5). My dissertation has focused on understanding the molecular mechanisms by which β-III-spectrin supports neuronal structure and function and how SCA5 mutations disrupt this function. Specifically, my work has shown that the β-III-spectrin N-terminal domain, preceding the actin-binding domain, is required for SCA5-induced high-affinity actin binding and arborization defects in vivo. Furthermore, through characterizing the molecular consequences of SCA5 mutations in the spectrin-repeat domains (SRDs) of β-III-spectrin, I have shown that SRD2- and SRD3-localized mutations disrupt the physical interaction of β-III-spectrin with actin and α-II-spectrin. My dissertation work has provided significant insights into the role of β-III-spectrin in the actin cytoskeleton and the mechanisms underlying SCA5, laying the groundwork for developing a drug compound to treat this currently untreatable disease