Dissertation Defense Announcements

The mental health and well-being of college students has become a growing concern for colleges and universities. Research has shown that prevalence rates of mental health in colleges students is growing and there are multiple complicating impacts of mental health on student well-being and academic success. Resident advisors play a crucial role in campus life as student employees that live amongst students and have a high level of responsibility as first responders to emergencies and rule enforcers. Despite their importance, there is little research on the mental health of resident advisors and how different aspects of the resident advisor role impact their mental health.
The purpose of this study was to examine the differences between resident advisors and undergraduate residential students on measures of mental health, burnout, and perceived stress. The study also examined how different aspects of the resident advisor position may impact resident advisor mental health, burnout, and stress and if resilience had a mediating impact on these variables. A total of 551 college students (including 84 resident advisors) were included in this research study. Participants completed an online survey, which included the Mental Health Inventory, Copenhagen Burnout Inventory, Perceived Stress Scale, Scale of Protective Factors, and a demographic questionnaire. The multivariate analysis indicated that there were no statistically significant differences between resident advisors and undergraduate residential college students on mental health, burnout, and perceived stress. A multivariate analysis of covariance indicated that there was not a significant difference between the groups on the combined dependent variables after controlling for resilience. The findings of the present study suggest that resident advisors and residential college students are experiencing symptoms of mental health, burnout, and perceived stress. Results suggest that university staff who work with students should be aware of the mental health challenges of students and work to find ways to support them.

The availability of OpenCL for FPGAs along with High-Level Synthesis tools have made FPGAs an attractive platform for realizing massively parallel compute-intensive applications. FPGAs with their customizable data-path, deep pipelining abilities and enhanced power efficiency features are the most viable solutions for programming and integrating them with heterogeneous platforms. Furthermore, OpenCL for FPGAs raises many challenges which require in-depth understanding to better utilize their enormous capabilities. In this work we identify, analyze and categorize the semantic differences between the OpenCL parallelism and the execution model on FPGAs. As an end result we propose a generic taxonomy for classifying FPGA parallelism potential.

At the same time, new design challenges continue to emerge for massive thread-level parallelism on FPGAs. One major execution bottleneck is the high number of memory stalls exposed to data-path which overshadows the benefits of data-path customization. We introduce a unique approach for hiding the memory stalls on FPGAs when running massively parallel applications and present a novel LLVM-based tool for decoupling memory access from computations. To enable systematic decoupling, we use the idea of kernel parallelism and implement a new parallelism granularity that breaks down kernels to separate data-path and memory-path (memory read/write) which work concurrently to overlap the computation of current threads with the memory access of future threads (memory pre-fetching at large scale).

We next move to the Xilinx based AWS cloud platform and conduct an exhaustive study on the scalability of OpenCL coarse-grain parallelism, Compute Unit(CU) replication on cloud FPGAs. In addition we present a generic template and a front-end design exploration tool to explore and identify the optimum CU number for a given application, while hiding the programming and exploration difficulties from programmers.

Family business leaders often include preserving socioemotional wealth (SEW) and successful intergenerational succession efforts in their list of primary non-economic goals, yet most next-generation family members seek careers outside of the family business.
This research aims to understand how internal family dynamics affect the development of psychological ownership feelings towards the family business in next-generation family members. The moderating effect of gender, as socially constructed, was also explored.
Data was collected using a snowball technique and an anonymous online survey (n=161) and was analyzed using regression analysis. Next-generation family members were encouraged to participate regardless of their ownership of, or employee status within, the business. The family dynamics measured were cognitive cohesion, emotional cohesion, adaptability, communication, intergenerational attention to needs, and authority. Findings are included, followed by discussion, limitations, and future directions for research. Currently, family business scholars have a limited understanding of how and when psychologically related micro-factors manifest in next-generation family members; however, relationships have been identified between a business family's internal dynamics and the development of certain attitudes, beliefs, and feelings held by their next-generation family members. Despite this research's similarities to past examples in the SEW and related literature streams, no support was found for this study's hypothesized relationships. Additional empirical research is necessary to understand when and how a business family's internal dynamics influence the manifestation of psychological ownership feelings in their next-generation family members.

Si/C composite materials have attracted enormous research interest as the most promising candidates for the anodes of next-generation lithium-ion batteries, owing to their high energy density and mechanical buffering property. However, the major disadvantage of materials with ultra-high capacities, such as Si-based materials, is the significant volume change during cycling, which further leads to mechanical and electrochemical degradation. A comprehensive computational model is indispensable in the developing process of the excellent performance of anode material due to the low realizability, inconvenience, and high cost of experiments, which also provides powerful tools for fabrication guidance of novel Si/C composites designs. Hence, this study explores the multiphysics behavior of Si/C anodes material from the atomic level to cell level using DFT modeling and FEA methodology, systematically revealing the coupling mechanism among various physical fields, as well as providing efficient and powerful tools in the design, development, and evaluation of high energy density lithium-ion batteries.