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Predicting and Optimizing the Fair Allocation of Donations in Hunger Relief Supply Chains
No full textNon-profit hunger relief organizations rely on donors’ benevolence to combat food insecurity. However, fluctuations in donation quantity and frequency create challenges in ensuring equitable distribution. A hierarchical forecasting methodology is developed to predict monthly food donations across a multi warehouse food aid network. These forecasts are integrated into an optimization framework to guide fair allocation of supplies while accounting for supply chain coordination and flexibility. The approach highlights under- served regions within the network and provides actionable insights to reduce disparities between overserved and underserved counties, ultimately improving food access equity.https://digital.library.ncat.edu/gradresearchsymposium25/1101/thumbnail.jp
Gold Nanoparticles Conjugated with Iodinated Copolymers as a Potential Dual X-ray Imaging Contrast Agent
No full textGold nanoparticles and radiopaque iodinated polymers have great potential in the field of bioimaging due to their high-contrast X-ray imaging properties. Here, we report the synthesis of iodinated copolymers, poly(2-[2’,3’,5’-triiodobenzoyl]oxoethyl methacrylate-co2- hydroxyethyl methacrylate) using reversible addition-fragmentation chain transfer polymerization and the preparation of gold nanoparticles conjugated with the iodinated copolymers. GPC confirmed the copolymer with an Mn of 14,259 g/mol and a polydispersity index of 1.53 while the EDX analysis showed that the copolymer contains about 27% iodine. Gold nanoparticles conjugated with the copolymer were prepared via in situ approach, which produced nanoparticles with average sizes of 28 nm as measured by DLS. SEM visually confirmed the formation of spherical gold nanoparticles whose sizes agreed with that of the DLS measurement. The UV-Vis spectrum of the gold nanoparticles showed a strong absorption at λ 530 nm, which was attributed to the nanoparticle surface plasmon resonance. The work presented here illustrates a new platform for developing a potential dual X-ray imaging contrast agent that could be used in bioimaging and allied fieldshttps://digital.library.ncat.edu/gradresearchsymposium25/1104/thumbnail.jp
Ag-decorated copper microsphere for electrochemical reduction of CO2 into methane
No full textWe synthesized the copper microsphere decorated with silver nanoparticles using a hydrothermal process followed by photoreduction of Ag ions into Ag nanoparticles. Sub-100 nm Ag nanoparticles anchored on the surface of Cu microspheres improve the electrochemical performance and selectivity of the CO2 reduction into CH4. By enhancing the conductivity and active site of the catalyst and lowering the charge transfer resistance, Ag nanoparticles on Cu accelerate the rate of CO2 reduction. The faradaic efficiency of methane in a copper microsphere coated with silver nanoparticles was 70.94%, about twice as high as that of a copper microsphere (44%). Higher catalytic performance, stability, and faradaic efficiency of silver decorated copper microspheres were noted in the electrochemical reduction of CO2.https://digital.library.ncat.edu/gradresearchsymposium25/1110/thumbnail.jp
Towards Autonomous Network Management: AI-Driven Framework for Intelligent Log Analysis, Troubleshooting and Documentation
No full textModern network management is increasingly complex, requiring administrators to handle vast amounts of log data from diverse sources, leading to inefficiencies, errors, and operational challenges. In this work, we propose a novel AI-driven framework that integrates Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) and human- in-the-loop process to automate network management tasks such as log analysis, troubleshooting recommendations, and documentation generation. This study aims to enhance network reliability, reduce operational complexity, and move forward to autonomous network management.https://digital.library.ncat.edu/gradresearchsymposium25/1126/thumbnail.jp
Role of CD163+ Macrophages in Organic Dust Exposure: A Study on Porcine Blood and Lungs
No full textCD163, a scavenger surface receptor of alveolar macrophages (AMs), facilitates pathogen recognition, however, its specific role under organic dust (OD) exposure in swine confinement remains unclear. We hypothesized that continuous OD exposure within swine barns activates CD163+ AMs and promotes their proinflammatory responses. To investigate our hypothesis, macrophages were isolated from porcine bronchoalveolar lavage fluid (BALF) and blood, exposed to fluorescent-FITC-beads and stained with CD163 and CD14 markers. Supernatants and cells were analyzed for oxidative stress, inflammation, and phagocytic activity via immunoassays and flow cytometry. Data were analyzed using Two- way ANOVA (p-value \u3c0.05) Results elevated CD14+CD163+ expression with increasing phagocytic activity in indoor AMs. However, CD14+CD163+ expression was higher in blood macrophages of outdoor versus indoor pigs. Bead uptake by BALF AMs of indoor pigs produced higher nitric oxide and TNF-α levels. Conversely, interleukin (IL)-1β and IL-6 production was higher in BALF AMs of outdoor pigs. This study suggests exposure to OD in indoor environments may increase phagocytic activity by CD163+ macrophages, oxidative stress, and inflammatory markers, which potentially contributes to the pathogenesis of chronic bronchitis among agricultural workers. Future studies could focus on these implications for humans.https://digital.library.ncat.edu/gradresearchsymposium25/1131/thumbnail.jp
Advances in Plastics Upcycling: Catalytic Pyrolysis of Polypropylene Using Co- and Zn- Impregnated H - Mordernite to yield Value Added Chemicals
No full textPolypropylene (PP) is one of the major plastic waste contributors produced globally for its wide range of applications, high chemical resistance and mechanical strength. In this study, cobalt (Co) and zinc (Zn) impregnated H-mordenite (HM) zeolite-supported catalysts were used for pyrolysis of polypropylene to produce lighter hydrocarbons and hydrogen. This study explores the catalytic pyrolysis of PP over Co- and Zn-impregnated Hmordenite (HM) zeolite catalysts to produce valuable light hydrocarbons and hydrogen. The catalysts were characterized by N₂ adsorptiondesorption, NH₃-TPD, H₂-TPR, XRD, XPS, and TGA to investigate their structural and chemical characteristics. Pyrolysis experiments were conducted at various temperatures (up to 450°C) and catalyst-polymer weight ratios (2:1 and 1:1), and gas products were characterized by GCMS. Results showed that Co-HM exhibited \u3e90% PP conversion and 65% selectivity towards light olefins at 450°C, qualifying it for petrochemical application. Zn-HM achieved 90.51% conversion with \u3e35% paraffin selectivity, leaning towards fuel production. The findings confirm that catalyst design is significant in hydrocarbon selectivity, offering a sustainable pathway for plastic waste valorization and complementing clean energy and circular economy initiatives.https://digital.library.ncat.edu/gradresearchsymposium25/1147/thumbnail.jp
Using Topic Modeling and LLMs to Recommend CAPEC Attack Patterns: A Comparative Study
No full textAs technology becomes more prominent today, the need for cybersecurity increases. Software developers must develop secure software systems. Common Attack Pattern Enumeration and Classification (CAPEC) is a community resource developed by the U.S. Department of Homeland Security as part of the Software Assurance strategic initiative of the Office of Cybersecurity and Communications. The CAPEC repository provides a collection of over 500 attack patterns, which contains information on software vulnerabilities and how they can be exploited using the given attack pattern. With the repository containing so much information, it can be challenging for software developers to identify which attack pattern is most relevant to their project. This project compares three methodologies for recommending relevant attack patterns: topic modeling, text embedding with OpenAI\u27s GPT-4o-mini model, and prompting with the same model. These methods are evaluated based on the relevance of the recommended attack patterns to the software requirement specification project being tested. The CAPEC description and the prerequisites for each attack as criteria. A publicly available SRS will be used to evaluate these three methods. The results showed that the prompting method was the best-performing method for recommending attack patterns.https://digital.library.ncat.edu/gradresearchsymposium25/1149/thumbnail.jp
Turbine Based Combined Cycle Exoskeletal Engine (TBCC-ESE) Compressor Blade Design, FEA, & Material Selection
No full textThe Turbine Based Combined Cycle Exoskeletal Engine concept presents a creative approach to achieving high Mach number flight, starting from ground speed. Key design aspects that move away from traditional jet engine designs is having all the rotating turbomachinery mounted on rotating drums / discs, thereby removing the need for a central shaft. This research addressed the initial compressor blade design, FEA and material selection. These areas play a critical role in developing the low-speed engine of the TBCC ESE.https://digital.library.ncat.edu/gradresearchsymposium25/1151/thumbnail.jp
Generative-AI for Air Traffic Control Enhancement: Advancing Safety and Communication Efficiency for Autonomous & Manned Vehicles
No full textWith air travel expected to surpass 4 billion passengers post 2025, the demand for efficient air traffic management is growing. Air Traffic Control (ATC) coordinates flights, manages air and ground traffic, and ensures optimal scheduling, but controllers often handle up to thirty aircraft at once, creating significant workload challenges. Generative AI integration in ATC offers a solution by enhancing scheduling, optimizing routing, and improving crew management. By automating key processes, AI can reduce cognitive load, increase safety, and make airspace management more adaptive and scalable, ultimately ensuring safer and more efficient operations.https://digital.library.ncat.edu/gradresearchsymposium25/1152/thumbnail.jp
Exploring the Use of Generative Artificial Intelligence for Bias Mitigation
No full textArtificial intelligence (AI) and related topics are growing in popularity across various industries as there is a desire to improve the accuracy and efficiency of decision-making processes. The algorithms at the foundation of such technologies are initially developed and trained on datasets, then implemented in real-world applications that can directly affect humans, such as in healthcare, criminal justice, and finance. However, with this power comes the potential for a lack of fairness, an issue that is becoming a prominent concern in the realm of AI research. While the development of AI technology is on the rise, there is a need to ensure that the algorithms are constructed in a way that eradicates the influence of unfairness and bias, especially when considering the potential of outcomes that negatively impact marginalized communities. One approach that has been considered is the usage of generative AI, such as generative adversarial networks (GANs). The tools can not only be employed to generate data for computer vision problems, especially when data is limited and/or difficult to obtain, but also address concerns surrounding fairness and bias in AI. Additionally, the tools can potentially be implemented in COVID-19 research to better understand the virus’s impact in marginalized communitieshttps://digital.library.ncat.edu/gradresearchsymposium25/1157/thumbnail.jp