Dissertation Defense Announcements

Candidate Name: Anthony J. Roux
Title: An examination of the impact of urbanization on stream biodiversity and ecosystem function
 November 08, 2022  10:00 AM
Location: McEniry 329; Zoom Meeting https://charlotte-edu.zoom.us/j/93576370640?pwd=WG5LcEpyT1kvdDBDbU1XV2hvcWNldz09 Meeting ID: 935 7637 0640 Passcode: 986769
Abstract:

The “Urban Stream Syndrome” is a term that refers to a group of predictable negative impacts to stream ecosystems due to the alteration of the natural hydrologic regime associated with urbanization including increases in the volume and intensity of storm water inputs to streams, channel erosion, streambed sedimentation, and nutrient and pollutant concentrations. These negative impacts of urbanization degrade the habitat available to the aquatic biota in streams. The decline in aquatic insect taxa richness due to urbanization has been well documented. However, the impact of the stressors associated with the increased stormwater flashiness to the composition of the aquatic insect assemblages’ taxa and trait richness and diversity is not well known.

For my dissertation, I proposed three research studies designed to improve the understanding of how the increased stormwater from urban areas impacts the aquatic insect assemblages’ taxa and trait richness and diversity. To do this, I first examined a 26-year data set to study the impact of land use changes on biodiversity and ecosystem function in stream ecosystems in watersheds that span a gradient of impervious cover and stream habitat conditions. Next, to better understand the impact of urbanization on biodiversity and ecosystem function, I examined the relationship between aquatic insect taxa and trait richness and diversity and stream habitat diversity at the watershed scale and the importance of microhabitats at the reach scale. Finally, to better understand stormwater impacts on aquatic insect assemblages, I compared macroinvertebrate taxa and trait richness and diversity in 2 adjacent headwater tributaries that received stormwater runoff through different processes (stormwater infrastructure verse natural overland and subsurface processes).



Candidate Name: Jonathan Flinchum
Title: A New Approach to Promote Employee Engagement: One-on-one Meetings Between Managers and Direct Reports
 November 14, 2022  12:30 PM
Location: Cone 211
Abstract:

Organizations often struggle to engage their workforces despite various known benefits and predictors of employee engagement. The current study examined a new approach to promote employee engagement—1:1 meetings—which are commonly occurring, theoretically grounded, and understudied. Leveraging job-demands resources and self-determination theories, it was hypothesized that the quantity (i.e., frequency) and quality (i.e., presence of manager task- and relations-oriented behaviors) of 1:1 meetings promote direct report engagement by satisfying direct reports’ basic psychological needs. The proposed moderated mediation model was tested with data collected from two time-separated online surveys (N = 303). Results suggest that 1:1 meeting quality—particularly manager relations-oriented behaviors—plays a stronger role in promoting direct report engagement as compared to 1:1 meeting quantity—with the important caveat that these meetings happen at least monthly. Results also suggest that 1:1 meetings are conceptually distinct from and can promote direct report engagement above and beyond other manager-direct report meetings and interactions by better supporting direct reports in a synchronous and individualized manner. Together, the current study supports 1:1 meetings as a critical tool managers can leverage to promote their direct reports’ engagement, while also contributing to both the meeting science and engagement literatures.



Candidate Name: Milad Hosseinpour
Title: Improved fidelity of triangulation sensor measurements in optical inspection
 November 11, 2022  1:00 PM
Location: Duke 106 Conference Room
Abstract:

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.



Candidate Name: Chunhao Yuan
Title: Mechanical Instability of the Interfaces in Solid-State Batteries
 November 11, 2022  11:30 AM
Location: Duke 276
Abstract:

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.



Candidate Name: Chunhao Yuan
Title: Mechanical Instability of the Interfaces in Solid-State Batteries
 November 11, 2022  11:30 AM
Location: Duke 276
Abstract:

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.



Candidate Name: Md Imrul Reza Shishir
Title: Fracture and mechanical properties of graphene-like two-dimensional materials using molecular dynamics (MD) simulations
 November 09, 2022  11:00 AM
Location: Duke - 106A
Abstract:

Graphene is a monoatomic thick sheet of sp2-hybridized carbon atoms tightly packed in a honeycomb lattice structure. Since its discovery, it has drawn extensive attention to the science community for its unique 2D structure and has been studied for both basic science and commercial applications due to its extraordinary thermal, optical, and mechanical properties. In this research, we employed molecular dynamics simulations and machine learning methods to study mechanical and fracture properties of graphene-like two-dimensional materials (i.e.; C3N, bicrystalline graphene, and polycrystalline graphene). Molecular dynamics (MD) simulations are used to extract the traction-separation laws (TSLs) of symmetric grain boundaries of bicrystalline graphene. Grain boundaries with realistic atomic structures are constructed using different types of dislocations. The TSLs of grain boundaries are extracted by using cohesive zone volume elements (CZVEs) ahead of the crack tip. The areas under the traction-separation curves are used to calculate the separation energy of the grain boundaries. The results show that as the grain boundary misorientation angle increases the separation energy of the grain boundaries decreases. The impact of temperature on the traction separation laws is studied. The results show that, with an increase of the temperature from 0.1 K to 300 K, the separation energy first increases to reach its peak at around 25 K and then slightly decreases. Finally, a deep convolutional neural network model has been developed to predict the mechanical and grain properties of polycrystalline graphene. The data required for training our machine learning model is generated using molecular dynamics simulations by modeling the behavior of polycrystalline graphene under uniaxial tensile loading. More than 2000 data points are generated for graphene sheets of different grain sizes and grain orientations. The goal is to train the network such that it can predict the Young's modulus and fracture stress of graphene sheets by analyzing an image of the polycrystalline sheet.
Molecular dynamics simulations are also used to study the mechanical and fracture properties of C3N, a graphene-like two-dimensional material. The impact of initial crack orientation on the crack path is studied by applying tensile strain to C3N sheets containing initial cracks in the armchair and zigzag directions. The results show that the cracks grow by creating new surfaces in the zigzag direction. The impact of temperature and strain rate on Young's modulus and fracture stress of C3N are studied. The capability of Griffith theory, and quantized fracture mechanics (QFM) in predicting the fracture strength of C3N is studied. The molecular dynamic results indicate that Griffith theory cannot predict the fracture strength of C3N if the crack length is shorter than 9 nm. The notch effects on the fracture strength of C3N is studied and it is shown that notch effects are important in predicting the fracture strength of C3N. Using the Rivling-Thomas method, the molecular dynamics simulations predict a critical energy release rate of 10.982 Jm-2 for C3N.



Candidate Name: Mr. Ali Ihsan Aygun
Title: Optimal centralized and decentralized management strategies for electric vehicles considering customer demand, road and electric grid infrastructure
 November 04, 2022  9:00 AM
Location: EPIC 2354
Abstract:

The electrical grid is a complex network becoming increasingly linked with smart devices and energy sources, such as electric vehicles, appliances, grid support systems, and renewable energy resources. However, the power delivery systems in use today are antiquated and have been in operation for over a hundred years. In this dissertation, several methodologies for optimal management of electric vehicle (EV) fleets connected to the power grid are discussed. First, a hybrid methodology is suggested for determining the quickest way for a vehicle to reach the charging station, taking into account both the distance and the current traffic conditions developed based on graph theory. The strategy is accurate, more efficient, and scalable. Second, a technique that considers the shortest distance to the charging station considering the impact and optimal use of the electric grid is developed. The method takes advantage of distance and simultaneously considers the influence on the grid, such as variations in voltage or power. The procedure is tested and quantitative and qualitative analysis is conducted. Also, with the help of a convex optimization methodology, a speed optimization framework is developed that mitigates range anxiety. Next, an optimization methodology is developed that addresses real-time electric car charging congestion as well as centralized and decentralized charging scheduling of electric vehicles. The charging of plug-in electric vehicles (PEVs) has to be handled through the use of "smart" charging processes to lessen the demand that PEVs have on the electrical grid. These studies examine the impact that the actual implementation of four distinct smart charging architectures has on the electric grid, including a centralized and decentralized design. The capabilities of each method are summarized.
Further, a methodology for demand-side management and distributed load management is developed, considering customer comfort with the help of an electric vehicle fleet. A new mathematical model of household loads such as air conditioners, water heaters, clothes dryers, and dishwashers considering the weather conditions is developed. It was identified that during high temperatures, the system's operational architecture may derive a significant advantage from these massive demand-responsive loads. Further, a robust energy optimization framework is proposed that suggests healthy results to keep the grid stable and sustained after optimizing household loads avoiding customer comfort violation. The proposed methodologies are scalable, field implementable, and have a significant advantage in collectively managing electric vehicle fleets, customer comfort, and energy usage considering road and grid conditions.



Candidate Name: Ali Almadan
Title: A User-Based Stance Analysis for Gauging Public Opinion with Stance Detection in Twitter Data
 November 10, 2022  1:00 PM
Location: Zoom link: https://charlotte-edu.zoom.us/j/92706165257
Abstract:

Stance detection in social media data has received attention in recent years as an approach to determine the standpoint of users towards a target of interest, such as a person or a topic included in Twitter data. Although interviewing, surveying, and polling representative populations have long proven reliable methods for analyzing public opinion, these methods suffer from various limitations, including high costs and an inability to be collected retrospectively. On the other hand, detecting and analyzing social media trends through natural language processing approaches, such as text classification, offers a valuable alternative or complementary approach to gathering, analyzing, monitoring, and understanding public opinion on emerging issues.

Existing stance detection and analysis studies use multiple methodologies and strategies to determine and analyze the standpoint of Twitter users toward a target. These techniques feature strengths and weaknesses, and the literature lacks studies investigating the broad implications of using such methods for public stance measurements. Understanding these implications is crucial to the validity, interpretation, and replicability of research findings.

In this dissertation, we first introduce the concept of user-based stance analysis and highlight the difference between user-based and tweet-based stance analyses. We describe the relevance of user-based stance analysis to the measurement of public opinion. We suggest that the stance of Twitter users, instead of a stance presented in a tweet's content, must be the core aspect of stance analysis for measuring public opinion. Therefore, we claim that a user-based stance analysis is more aligned with the concept of public opinion than a tweet-based stance analysis. Second, we compare the results of measuring public opinion with tweet- and user-based stance analyses from Twitter data and demonstrate that each produces statistically different results. Third, we present findings that while a tweet-based stance analysis is sensitive to the presence of social bots, a user-based stance analysis provides a more robust measure of public opinion with minimal impact from social bots. Fourth, we describe the design and evaluation of StanceDash, a web-based dashboard that assists end users measure, analyze, and monitor public opinion through a user-based stance analysis of Twitter data.



Candidate Name: Allyson R Cochran
Title: PREDICTING COMPLIANCE, BARRIERS, AND OUTCOMES TO SURGICAL CARE GUIDELINES
 October 28, 2022  2:00 PM
Location: College of Health and Human Services, Room 426 for committee members. Zoom link for non-committee members: https://charlotte-edu.zoom.us/my/acochra6
Abstract:

Clinical care guidelines optimize patient care, including Enhanced Recovery after Surgery (ERAS) guidelines specific to surgery. However despite their efficacy, compliance to guidelines by providers remains a challenge. Understanding ways to predict, and thus prevent, non-compliance can aid in improving uptake by providers and post-surgical recovery for patients.

Four approaches were taken to understand the issue. A novel method coined Vertical Compliance for measuring ERAS compliance in real-time can predict and prevent adverse surgical outcomes before they occur. Next, a multi-institutional, multi-surgical specialty retrospective data analysis revealed specific ERAS recommendations that - if not performed - predict adverse patient outcomes such as increased length of stay (LOS) and clinically-relevant complications. To understand the barriers to compliance, a meta-analysis was conducted for all medical literature and regression models developed to understand which barriers predict non-compliance to guidelines. Finally, to understand barriers to compliance specific to surgery and ERAS, a survey was developed and analyzed using a mixed-methods approach to understand which barriers to compliance predict reduces feelings of compliance assurance amongst ERAS professionals.

While conceptually different, vertical compliance and multi-institutional data analysis revealed similarities in which specific recommendations predict adverse outcomes, including oral carbohydrate loading, early removal of Foley catheter, and limited use of nasogastric tubes affected LOS. The two studies examining barriers to compliance revealed lack of familiarity and acceptance, and presence of external barriers were drivers of non-compliance.

Taken both individually and collectively, these four studies reveal why predicting adverse surgical outcomes due to non-compliance to evidence-based care is important, yet, predicting barriers may prove a critical element to preventing that non-compliance before it occurs.



Candidate Name: April D. Thomas
Title: Do you see yourself in multicultural literature? Seeking self-reflections from Black students
 November 09, 2022  11:00 AM
Location: Cato COED 362
Abstract:

APRIL DENIECE THOMAS: Do you see yourself in multicultural literature? Seeking self-reflections from Black students

(Under the Supervision of Dr. ERIK BYKER)

In the United States, Muhammad (2020) explains how Black students who attend schools have a greater potential success when they see themselves represented in the curriculum and when their cultural, gender, and racial identities are affirmed. This dissertation study examined the ways in which third grade Black girls and boys (n=5) see themselves when they read African American multicultural literature. The study also investigated the literary elements in African American multicultural text that encourage self-reflection. The study’s methodology was based on a qualitative phenomenological research design, which included a pilot study (n=4) of the interview protocol. The interview protocol was revised for suitability based on the findings from the pilot study. Both the pilot study and the main dissertation research study were conducted using semi-structured interviews. The participants chose a text from a collection of African American multicultural literature and shared their responses to that text based on the interview protocol. The following research questions guided the study: 1) How do Black children respond to African American multicultural literature?; 2) How do Black children describe their cultural and racial identity within multicultural literature?; and 3) What literary features facilitate Black children’s ability to self-reflect? The findings of the study were organized based on these research questions. The study utilized two frameworks, Reader Response Theory (Rosenblatt, 2004) and Black Identity Theory (Jackson III, 2012), to unpack and discuss the findings. A new theory emerged from the study’s findings, which is called Multicultural Self-Reflection Theory. This theory explains and provides insights into how Black children self-reflect when reading African American multicultural literature. Multicultural Self-Reflection Theory provides a lens for understanding how Black children engage in what the dissertation coins, “multicultural self-reflection" when responding to African American multicultural text.

Key Words: African American, Black children, Black Identity Theory, interview study, multicultural literature, Multicultural Self-Reflection Theory, phenomenology

Reader Response Theory, self-reflect,




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