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Evaluation of Anthropogenic Contaminants in Subtropical Aquatic Ecosystems After Catastrophic Events
Get PDFCoastal marine ecosystems are particularly affected by climate change, experiencing rising sea levels and increased erosion, which impact their biodiversity and productivity. Hurricanes, intensified by climate change, bring substantial alterations to marine environments, introducing a variety of anthropogenic contaminants that exacerbate their effects on nearshore ecosystems. Despite these impacts, there is still limited understanding of the specific effects that hurricanes have on coastal marine ecosystems, highlighting the need for further research and monitoring to better comprehend and mitigate these environmental changes. Metal concentrations in surface water, sediment, and seagrass from Abaco after Hurricane Dorian were studied. The results indicate higher contamination in marinas compared to seagrass beds, suggesting the need for remediation strategies. The data demonstrates potential impact of natural disturbances on HM variability in seagrasses and sediments, while bioconcentration factors suggest seagrasses around Abaco primarily uptakes HMs from surface water. Significant correlations between certain metal concentrations in seagrass/sediment and seagrass/surface water suggest similar metal sources. Site-specific risk assessments are necessary to protect seagrasses and associated organisms. Metal concentrations in surface water, sediment, and oyster tissue in Bayou Petit Caillou were analyzed after Hurricane Ida. The results showed that several elements exceeded toxicity thresholds established by NOAA and international regulations. The linear regression analysis revealed complex relationships between environmental factors and heavy metal concentrations, with significant effects observed for pH, temperature, salinity, and dissolved organic matter. The concentration of vanadium in sediments showed significant correlation with resulting hurricane debris present in the bayou. The presence of benzo(a)pyrene in Caribbean spiny lobster and queen conch collected from Abaco Island, The Bahamas, was investigated. An optimized QuEChERS extraction method was employed for the extraction of benzo(a)pyrene, followed by analysis using gas chromatography-mass spectrometry. The results demonstrated the absence of benzo(a)pyrene in the tissue samples. Overall, the results of this dissertation provide insights into the environmental health in coastal marine ecosystems in Abaco Island and Louisiana and the importance of continued monitoring and management strategies to safeguard the marine environments and the organisms they support
Analysis of the Interaction between Distance Relay Protection System and Grid-Connected Inverter-Based Resources
Get PDFThis paper discusses protection challenges posed by the integration of inverter-based resources (IBR), such as large solar farms, into transmission systems due to varying, nonstandard control strategies and fault responses. In particular, this paper investigate the effects of IBR interconnection on distance protection using memory voltage polarization (relay 21). A time-domain electromagnetic transient program (EMTP) is used to perform simulations for analysis. The photovoltaic (PV) park available in the EMTP library is used as the IBR model and is converted into a white-box model in accordance with the latest industry standard, IEEE 2800-2022. The simulations are also experimentally validated to ensure that the simulation models accurately represent the behavior of the protection system under real-world conditions and to verify if the traditional relay settings are sufficient to protect the transmission line due to the low short-circuit current from the IBRs
Choreographing the Rhythms of Observation: Dynamics for Ranged Observer Bipartite-Unipartite SpatioTemporal (ROBUST) Networks
Get PDFExisting network analysis methods struggle to optimize observer placements in dynamic environments with limited visibility. This dissertation introduces the novel ROBUST (Ranged Observer Bipartite-Unipartite SpatioTemporal) framework, offering a significant advancement in modeling, analyzing, and optimizing observer networks within complex spatiotemporal domains. ROBUST leverages a unique bipartite-unipartite approach, distinguishing between observer and observable entities while incorporating spatial constraints and temporal dynamics.
This research extends spatiotemporal network theory by introducing novel graph-based measures, including myopic degree, spatial closeness centrality, and edge length proportion. These measures, coupled with advanced clustering techniques like Proximal Recurrence, provide insights into network structure, resilience, and the effectiveness of observer placements. The ROBUST framework demonstrates superior resource allocation and strategic responsiveness compared to conventional models. Case studies in oceanographic monitoring, urban safety networks, and multi-agent path planning showcases its practical applicability and adaptability. Results demonstrate significant improvements in coverage, response times, and overall network efficiency.
This work paves the way for future research in incorporating imperfect knowledge, refining temporal pathing methodologies, and expanding the scope of applications. By bridging theoretical advancements with practical solutions, ROBUST stands as a significant contribution to the field, promising to inform and inspire ongoing and future endeavors in network optimization and multi-agent system planning
Temple of Familiars
Get PDFMy paintings, etchings, and installation explore encounters with mystery in the natural world, especially through the flora, fauna, light, and water of the swamps around the Atchafalaya Basin. My practice explores kinship, reverence, and awe as an antidote to estrangement from the spirit of the land. I am influenced by artists and scholars engaging with the places that they inhabit and that inhabit them with a reverence and mystery of approach. My work invites viewers to engage with the memory that the water carries of our interconnectedness, and to remember that we are not separate from the natural world
Response of Fish Assemblages to Increased Connectivity and Habitat Restoration in Bayou St. John, a Degraded Waterway in New Orleans, LA
Get PDFBayou St. John is a degraded waterway located within the City of New Orleans. The Bayou, which is disconnected from Lake Pontchartrain by floodgates, has undergone restoration efforts to improve connectivity through frequent floodgate openings and marsh habitat restoration outside of the floodgates. To assess possible responses of local fish assemblages to these restoration efforts, I analyzed long-term fish assemblage data from three sites located inside of the floodgates and one site located at the restored marsh outside of the floodgates. Two sites inside of the floodgates experienced significant changes in assemblages between the pre-opening period (2006-2012) and post-opening period (Allen Toussaint Bridge, R = 0.125, p \u3c 0.01 and Mirabeau Bridge, R = 0.051, p \u3c 0.01). The restored marsh site also experienced significant changes in assemblages over time (BSJ Marsh, R = 0.421, p \u3c 0.01), but these changes appeared due to a 2016 Bonnet Carré Spillway opening
Built Environment Implications on Food Access: A Comparative Study of Two Neighborhoods in New Orleans
Get PDFAccess to fresh, healthy, and nutritious food has been a challenge in certain U.S. neighborhoods, worsening the public health crisis of obesity and diet-related diseases that have persisted since the 1970s. Access to fresh, healthy, nutritious food varies across geographical areas, ethnicities, socioeconomic statuses, and races. People have different levels of resources, resulting in varying access to fresh and healthy food. Also, addressing the issue of obesity can be achieved through ensuring that built environments offer convenient access to fresh and healthy food. Most of the research on food access has focused solely on area-based measures using Geographic Information Systems (GIS). Still, these approaches do not capture individual preferences, decision-making, or education regarding health. This dissertation used a mixed-method approach, grounded theory, and GIS to explore how the built environment impacts residents\u27 quality of life when looking at food access in two neighborhoods in New Orleans. The researcher then gathered the opinions of neighborhood residents about their food journeys by examining their purchasing choices and perceptions of the most efficient ways to increase access to healthy foods in their neighborhoods. The results of this study revealed that cognitive, environmental, and social factors characterize the individual\u27s pursuit of well-being, and there are similarities in the contrasting environments of food and non-food deserts
A Talking Cart
Get PDFThis research investigates the development of a robust AI-powered detection and tracking engine aimed at revolutionizing the retail checkout experience. The foundation of this work is a comprehensive exploration of state-of-the-art Computer Vision methodologies, particularly focusing on object detection, segmentation, and tracking. The study employs a modular pipeline that integrates advanced visual recognition algorithms with a robust data processing framework.
Key to this work is the construction of a synthetic dataset using Unity3D, enabling the generation of high-quality annotated data that mirrors real-world retail scenarios. This approach addresses the challenge of insufficient labeled datasets by simulating diverse and cluttered shopping environments, ensuring models are trained on realistic, high-variance data. The pipeline leverages cutting-edge architectures, including Transformer-based models, which are evaluated extensively against benchmarks such as the COCO dataset. Detailed experimentation was conducted to optimize model performance, encompassing preprocessing, model fine-tuning, and hyperparameter adjustment. Additionally, the research explores strategies for edge deployment, incorporating quantization techniques to ensure computational efficiency without compromising accuracy. The system\u27s performance was validated using metrics such as mAP and latency, showcasing its ability to operate effectively in real-time conditions. Further downstream tasks, such as the integration of recommendation engines powered by LLMs, are outlined as potential future extensions. This methodology-centric study provides a roadmap for developing scalable, accurate, and efficient AI solutions for automated retail systems, offering a significant contribution to the field of Computer Vision and its application in commercial environment