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Recent Submissions
ASSESSING NUTRIENT DELIVERY RATIOS AND FLOOD RISKS AMID URBAN GROWTH IN GREENVILLE, NORTH CAROLINA
(East Carolina University, May 2025) Riggs, Amanda Lynn
This study examines ecosystem services and tradeoffs linked to land use changes over twenty years (2001–2021) within five sub-watersheds in Greenville, North Carolina. Rapid urbanization in these areas has altered hydrological processes, such as nutrient export, runoff retention, and flood risk mitigation. Using the Natural Capital Project's InVEST ® modeling suite, specifically the Nutrient Delivery Ratio, Urban Stormwater Retention, and Flood Risk Mitigation models, this research evaluates nutrient export, stormwater retention, and flood risk mitigation, providing insight into land use change.
The study addresses two key questions:
(1) How do urban growth and land use change affect nutrient delivery, stormwater runoff, and flood risk within a watershed?
(2) What trends in these metrics emerge as urban expansion occurs?
The NDR model estimates nutrient loads based on land cover types, while the UFRM model evaluates runoff reduction and economic damage from flood and infrastructure data. The USR model assesses runoff retention, stormwater volumes, and avoided nutrient loads, emphasizing the value of retention services compared to infrastructure replacement costs. Data collected includes land use and elevation rasters, building footprints, road vectors, soil characteristics, curve numbers, runoff coefficients, and precipitation records. These data inputs, formatted to fit the models, are analyzed in ArcGIS Pro and Tableau to assess changes over time. The results indicate a loss in stormwater runoff mitigation by increased impervious surfaces such as roads and housing that also increases the potential for flooding, alongside an increase in nutrient export due to the addition of impervious surfaces to the study sites from 2001 to 2021.
Dynamic Predictors of Posttraumatic Stress in Adolescents and Young Adult Cancer Survivors
(East Carolina University, May 2025) Larsen, Eric R
Adolescent and young adult survivors of cancer encounter unique challenges associated with their health status. These challenges are often lifelong and persistent, including medical and psychosocial late effects, such as posttraumatic stress. Utilizing a model to predict the risk of posttraumatic stress in childhood cancer survivors (Bruce, 2006), the proposed study was designed to analyze the potential dynamic predictors in this broader adolescent and young adult population. Predictors included social support, family stressors, and mental health symptoms related to posttraumatic stress. Using a sample of adolescent and young adult cancer survivors (McGrady et al., 2022), this study (1) examined dynamic predictors of posttraumatic stress symptoms, (2) determined the relative importance of each predictor, and (3) identified which dynamic predictors were consistent with clinically significant levels of posttraumatic stress. Results of statistical analyses suggested that lower social support and higher anxiety significantly predicted higher levels of posttraumatic stress and uniquely predicted the majority of variance in the overall model. Female sex and lower social support demonstrated higher odds of having clinical levels of posttraumatic stress.
Sampling and Selection Methods for Applying 2D Neural Networks to 3D Gaussian Splats
(East Carolina University, May 2025) Dusablon, Raphael
We propose a novel approach for applying interpolation methods to unstructured volumetric data that allows for the operation of 2D neural networks directly on 3D Gaussian splats. Gaussian splatting is at the cutting edge of volume rendering methods, 2D neural networks have achieved a dominant and lasting degree of success and real-life application. We propose leveraging the advantages of both, an approach which is the first of its kind. We extend the method for interpolated convolution on 3D surface meshes with 2D CNNs by Hart et al to the unstructured 3D volumetric data of Gaussian splats and present an end-to-end pipeline for our work. We showcase our results with style transfers on 3D Gaussian splats performed by a 2D convolution model with no retraining. Our results compare favorably with those of current approaches to performing style transfers on 3D Gaussians using purpose-built and purpose-trained 3D models.
FINE SCALE MEASUREMENT OF URBAN SHADE PATTERNS FOR URBAN HEAT RESILIENCE
(East Carolina University, May 2025) Saud, Roshan
Urban Heat Island (UHI) intensifies over time, mounting pressure on social, ecological and economic and infrastructural systems. The combined impact of rapid urbanization and climate change is expected to accelerate the UHI severity, causing immense threat to urban dwellers. Among the implemented solutions, shade has been widely recognized as a one of the most effective solutions. However, methodologies for accurately measuring shade in complex urban environments remain underexplored. This study introduces a novel approach of creating realistic 3D digital representation of buildings, trees and ground by utilizing LiDAR data and advanced modeling techniques for shade detection. The generated vertical and horizontal shade are quantified by subarea and analyzed different dimensions along an urban-to-suburban transect. Combined scenario generates highest amount of shade and follows the pattern of dominant factor, but averages when contributions are similar. Building shade predominates the built environments, occurring mostly on facades, while tree shade prevails in relatively rural landscape primarily on ground, exhibiting contrary pattern. Except Urban Core and Suburban, all transect zone have inconsequential roof shade. The sensitivity examination indicates that shade detection is steady till 5m, minor discrepancies (mostly below 10%) emerge at 10m. Beyond 10m, significant deviations is expected, particularly for ground and roof surfaces, with 30m resolution showing substantial inaccuracies. Roof shade in all scenarios and ground shade
in building scenario are the most volatile to resolution. These findings provide valuable insights for urban planners, geospatial scientists, scholars and policymakers to optimize street level intervention for enhancing human thermal comfort and UHI mitigation strategies.
Exploring the Path to Employee Well-Being: The Role of Inclusivity Policies and Diversity Climate in the Workplace
(East Carolina University, May 2025) Covington, Ciara Lashawn
Within the current study, we aimed to assess the relationship between employees’ perceptions of inclusivity policies and employee well-being (i.e., psychological well-being, job satisfaction, turnover rates, social support, and work-life balance). We also examined diversity climate as a potential mediator of this relationship. Diversity climate pertains to an employee’s perceptions of how well their organization demonstrates support and promotion to diverse employees. The conceptualization of diversity climate is rooted in social identity theory, the interactional model of cultural diversity (IMCD), and organizational justice theory. The study sample consisted of 361 faculty and staff from a large Southeastern university. The findings revealed that positive perceptions of inclusivity policies were associated with a more positive diversity climate which, in turn, influenced certain well-being outcomes such as: psychological well-being, job satisfaction, turnover rates, and work-life balance. However, the relationship between diversity climate and social support was not significant. These results highlight the importance of not only implementing inclusive policies, but also ensuring employees perceive them as fair and effective. The findings also suggest that organizations should implement and engage in workplace interventions to foster an inclusive work environment. Fostering an inclusive work environment can help strengthen the organization’s diversity climate, which can ultimately improve employee health and well-being.