Dissertation Defense Announcements

Social disparities and implicit bias are detrimental to patient care and exist among providers. Research has shown that implicit bias hinders rapport between patient and provider, leading to patients becoming resistant to medical advice and treatment protocols and lead to providers to misinterpret or misunderstand patients. Therefore, it is crucial to identify levels of implicit bias among healthcare providers and the ramifications that implicit bias could induce. This quality project aims to assess and establish baseline levels of existing implicit bias among anesthesia providers in a healthcare system located in a large southeastern city.
Anesthesia providers from four hospitals were asked to complete the Harvard Implicit Bias Association test. Three hundred and seventy-four providers and thirty-two student registered nurse anesthetists received the email with instructions to complete the test. In addition, participants provided demographic information about their practice location, age, race, and anesthesia title. The results were scored using the Harvard IAT D-score ‘slight’ (.15), ‘moderate’ (.35), and ‘strong’ (.65). A total of forty-six individuals completed the survey: 26 certified registered nurse anesthetists, 18 students registered anesthetists and two anesthesiologists. There was no statistical significance at 95% confidence that showed the difference in provider bias based on race, location, or title.

Infectious diseases pose a significant threat to public health worldwide as evidenced by the recent coronavirus 2019 (COVID-19) pandemic. Despite significant human losses, the advent of web-accessed, map-based “data dashboards” that can monitor disease outbreaks, proved essential in managing public health responses. In many cases, the backend of these dashboards employs basic mapping functionality, displaying counts or rates. As the pandemic advanced, the identification of elevated rates was increasingly important in the geographical allocation of public health resources. However, such maps miss the opportunity to provide accurate information to policy decision makers such as the rate of disease spread, cyclicity, direction, intensity, and the risk of diffusion to new regions. Space-time geoanalytics, when coupled with rich visualizations, can address these shortcomings. Moreover, when implemented over the web, such functionality can be accessed from virtually anywhere.
This dissertation presents a web-based geographic framework for detecting and visualizing explicit space-time clusters of infectious diseases. First, I conduct a systematic review of the literature around the theme of space-time cluster detection for infectious diseases to identify state-of-the-art techniques that should be included in the proposed web-based framework. Second, I develop a tightly coupled, web-based analytical framework for the detection of clusters of infectious diseases using interactive and animated 3D visualizations to aid epidemiologists in readily and adequately uncovering the characteristics of space-time clusters. As a proof of concept, I populate the framework with COVID-19 county-level data for the 48 contiguous states in the US, and demonstrate data retrieval and storage, space-time cluster detection analysis, and 3D visualization within an open source WebGIS environment. Third, I evaluate the prototype in two steps: 1) present this and two existed COVID-19 systems to a group of infectious diseases experts and solicit feedback, 2) and evaluate functionalities on the prototype by conducting a user study with graduate students in a setting of online surveys.
This tightly coupled approach facilitates the detection of space-time clusters of diseases in a computationally acceptable timeframe. The characteristics of this framework (generic, open source, highly accurate, modifiable) will enable low-cost monitoring of the spatial and temporal trends of diseases causing high risks of infection.

With the evolution of gear design requirements for new applications, classical gear inspection based on a time-consuming line-oriented tactile measurement must be replaced with a more rapid, areal inspection that can capture complex modern gear modifications. Triangulation-based optical instruments provide a promising path to meet new gear metrology demands with respect to access to the gear flanks and having sufficient speed and accuracy. In triangulation sensor measurement, the image position of a laser line strip on the sensor is analyzed to find the measured geometry. This image of the line on the sensor is calculated through a peak detection algorithm that produces a 'ridge line,' which is the line in the x-y sensor domain with the highest light intensity.
The physics of optical measurement dictates that speckles and scattered light exist during an optical inspection. As a result, when a triangulation sensor is used, the deflection of the scattered light may cause inaccurate peak detection and, therefore, large form deviations in the reconstructed (measured) geometry. In addition, multiple light reflections that influence point calculations from an optical measurement must be detected, eliminated, or remedied. This research provides an improved mathematical approach to ridge line detection in each sensor frame, to detect the peak position of that frame even more accurately. This algorithm is used to measure four reference geometries to evaluate its influence on point clouds from surface measurements when compared to the embedded (OEM) algorithm.
This dissertation offers the improved fidelity of triangulation sensor measurements for optical inspection by developing a novel mathematical approach. It can be used in the future closed-loop control process where the new gear production processes require fast-optical measurement and evaluation processes to trace back from the produced gear geometry to the manufacturing process. This can be achieved by equipping the manufacturing machine with suitable optical measuring devices, an appropriate evaluation strategy, and an inline inspection.

All-solid-state batteries (ASSBs) are considered promising candidates for next-generation batteries due to their excellent safety performance guaranteed by inorganic solid electrolytes (SEs) with the non-flammability nature, as well as the greatly increased energy density enabled by the adoption of lithium metal anode. Unlike conventional lithium-ion batteries (LIBs) using liquid electrolytes, all the components within the ASSBs system, including the composite cathode, lithium anode, and solid electrolyte, are solid-state. Solid-solid interfacial contacts within ASSBs, such as the dendrite-electrolyte interface and electrode-electrolyte interface, are the origin of interfacial instability issues. The interfacial instability problems mainly exhibit in the form of lithium dendrite growth-induced short circuits and interfacial debonding failure inside composite cathode, which are the major hurdles on the road towards the large-scale commercialization of ASSBs. Experimental characterizations are limited by the coupling of the solid nature of SE (vision overlap), and ultrasmall length scale. Therefore, versatile and physics-based models to describe the electrochemical behaviors of the ASSBs are in pressing need.

Herein, considering the highly multiphysics nature of ASSB behaviors, fully coupled electrochemo-mechanics models at different scales are developed to investigate the underlying mechanism of dendrite growth and interfacial failure. From the energy conservation perspective, the electrochemical-mechanical phase-field model at the electrolyte scale is firstly established to explore the dendrite growth behavior in polycrystalline SE. The newly formed crack and the grain boundary are found to be the preferential dendrite growth paths, and stacking pressure affects the driving force for both dendrite growth and crack propagation. Next, the cell-scale multiphysics modeling framework integrating the battery model, mechanical model, phase-field model, and short-circuit model is developed to study the entire process from battery charging to dendrite growth and to the final short circuit. The governing effects from various dominant factors are comprehensively discussed. Further on, inspired by the “brick-and-mortar” structure, the strategy of inserting heterogeneous blocks into SEs is proposed to mitigate dendrite penetration-induced short circuit risk, and the overall dendrite mitigation mechanism map is given. Finally, the three-dimensional fully coupled electrochemical-mechanical model is developed to investigate the interfacial failure phenomena, taking into account the electrochemical reaction kinetics, Li diffusion within the particle, mechanical deformation, and interfacial debonding. The randomly distributed LiNi1/3Co1/3Mn1/3O2 primary particles result in the anisotropic Li diffusion and volume variation inside the secondary particle, leading to significant nonuniformity of the Li concentration, strain, and stress distributions. This also serves as a root cause for the internal cracks or particle pulverization. The particle volume shrinkage under the constraint of the surrounding SE triggers the interfacial debonding with increased interfacial impedance to degrade cell capacity. This study explores the dendritic issue and mechanical instability inside ASSBs from the multiphysics perspective at different scales, obtaining an in-depth understanding of the electrochemical-mechanical coupling nature as well as providing insightful mechanistic design guidance maps for robust and safe ASSB cells.