Dissertation Defense Announcements

Safety issues of lithium-ion batteries (LIBs) are usually initiated from an internal short circuit (ISC) that can be triggered by external accidental abusive loadings. The generated heat and the increased temperature would lead to several complicated physio-chemical changes of the batteries, e.g., thermal runaway (TR). Thus, investigation of the multiphysics behaviors of lithium-ion batteries becomes a paramount task to understand the battery safety issues. Experimental characterization and numerical simulation are essential ways to understand the underlying nature of the multiphysics behavior of batteries. However, experimental observation may only provide insufficient data due to the limitation of experimental technology. Particularly, in-situ and operando experiment methodologies are limited. Multiphysics modeling is regarded as a critical and insightful tool to unravel the nonlinear and complicated behaviors. Machine learning (ML) model with data-driven methodology is another important tool to realize fast and accurate estimation and classification. Herein, an ML-based ISC risk evaluation model will be first developed based on the training dataset generated by the combination of experimental data and simulation data. A Representative Volume Element (RVE) based mechanical model, which can predict accurate mechanical behaviors at a much lower calculation time cost, will be established to assist the data generation. Next, an ML-based classifier will be developed to classify the cell’s safety levels under various work conditions. A multiphysics model will be developed to assist the generation of training data samples. Finally, two typical safety issues: defect and TR propagation are systematically studied. The safety risk of the defective batteries will be further evaluated. Electrochemical and mechanical characterization tests will be designed and conducted. The multiphysics model will be used to provide necessary auxiliary instructions of the related mechanisms. TR propagation behaviors of battery packs will be experimentally and numerically investigated. The battery pack TR model will be developed based on the single-cell multiphysics model.
This study comprehensively investigates the multiphysics behavior of LIB cells under mechanical abusive loadings, highlights the promise of combining the physical model with a data-driven model, and provides an innovative solution for the recognition of the battery safety risks for battery safety monitoring.

This comparative case study explored the implementation of Education for Sustainable Development (ESD) at two of North Carolina’s Global Ready elementary schools. The following research questions guided the study: 1) How do educators and affiliates of Global Ready elementary schools perceive global education, specifically Education for Sustainable Development (ESD)?; 2) What sustainability topics are covered most by educators at Global Ready elementary schools in North Carolina?; 3) How is ESD incorporated within global education at Global Ready elementary schools in North Carolina (i.e. examination at the curricular, campus, and community levels)?; 4) How do Global Ready elementary schools compare in their conceptualization and implementation of global education, specifically ESD? Bronfenbrenner’s Ecological Systems Theory (1976) and Elser et al. 's (2011) Sustainable Schools Framework served as useful lenses for examining the affordances and constraints of sustainability education at the curriculum, campus, and community levels. A school-wide survey was disseminated to all certified educators at each school to obtain a broad view of ESD implementation within each case. To explore ESD implementation at a granular level, interviews were conducted with select educators, administrators, community partners, and members of a State Education Agency. Further, artifact collection and field visits allowed for the triangulation of data sources. The constant-comparative method (Glaser & Strauss, 1967) was utilized in the analysis of interviews and artifacts. Findings from both cases suggest that sustainability education was often used as a means for deepening global learning. At the curriculum level, survey results and interviews with participants indicate frequent integration of social sustainability topics into the curriculum with lesser attention given to topics of economic sustainability. Additionally, while there are many challenges to sustainability education at the curriculum and campus levels, findings suggest that community partnerships may play a role in mitigating some of these constraints. To conclude, the researcher discusses the need to complicate frameworks related to Education for Sustainable Development to attend to the complexity of ESD implementation within and across the curriculum, campus, and community.

Resilience research within the field of entrepreneurship has increasingly received attention from academia. However, most studies have considered this construct under extreme circumstances such as war, the aftermath of natural disasters, and economic crisis. This dissertation examines resilience from an entrepreneur's perspective by examining the role that culture plays in the consequence of venture performance. Drawing from acculturation theory, this dissertation considers cultural distance, cultural conflict, and perceived discrimination of the entrepreneurs as moderating variables in the interaction between resilience and venture performance. A sample of entrepreneurs (N=158) provides insights into these interactions. Even though this study did not find support to suggest such relationships or moderating effects, it recommends possible improvements and future research agenda in cross-disciplinary studies within the field of entrepreneurship.

In recent years, social media have dramatically improved the dissemination speed of information, which also includes health misinformation. To date, most of the computational approaches to addressing this problem have focused on detecting and flagging misinformation content. However, the majority of these approaches have overlooked many important aspects of health misinformation, such as the behavior of evidence sources and the sharing decisions of social media users. To address the limitations, this dissertation research develops an evidence-based approach to detecting health misinformation and to intervening user sharing intention on social media sites. This work takes on a new perspective regarding health misinformation by understanding user stance (i.e., for, against, neutral) due to their motivation of influencing others. Moreover, this research investigates arguments that combine both stance and evidence for assessing the credibility of health information for the very first time. Our analysis of evidence distribution in health information tweets shows that 70% of tweets contain source-based evidence, which provides the foundation for proposing an evidence-based approach to misinformation detection. Based on these results, we built argument detection models to identify stance positions within arguments. Our results demonstrate the importance of evidence-based features in identifying the stance within arguments on social media sites. Drawing on the evidentiality theory, information credibility heuristics, and consistency heuristics, we propose a research model that seeks to explain health misinformation detection and sharing behavior with evidence-based interventions. To test the research model, we designed and developed eleven types of evidence-based digital nudges and used them to conduct user experiments. The empirical results demonstrate that our nudge design improves credibility assessment of health misinformation. This dissertation makes several research contributions. First, it extends an evidentiality theory and credibility cognitive heuristics provided by health experts to analyze the types of evidence included in health-related user-generated content Second, it presents an evidence-based schema for categorizing evidence in user-generated content. Third, it uses evidentiality theory as the kernel theory to guide the design of digital nudges. In particular, it illustrates how evidence-based design artifacts can be used to support augmented intelligence for mitigating the spread of health-related misinformation on social media sites. Finally, it combines cognitive heuristics to the design of digital nudges. Specifically, it uses information credibility and consistency heuristics to analyze user-generated content on social media sites. The outcomes of this research have significant implications for augmenting users’ assessment of health information credibility and enabling timely intervention of misinformation on social media sites.