2025 Graduate Student Research Symposium

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  A Pipeline for Immersive Data Visualization for CAVE System

  Abhinav Pendem and Koundinya Challa Ph.D.

  Virtual Reality (VR) has transformed data visualization by enabling immersive and interactive experiences. CAVE (Cave Automatic Virtual Environment) systems provide a unique platform for exploring complex datasets in an intuitive, spatially immersive manner. However, a standardized pipeline for developing VR scripts tailored to CAVE-based data visualization remains underexplored. This paper presents a structured pipeline for creating and deploying immersive data visualization in CAVE systems, specifically using the WorldViz Prism CAVE environment. Our approach facilitates seamless integration of multi- dimensional datasets, enabling real-time interaction with 27 visual graphs within a single VR scene. The system allows dynamic graph manipulation, interactive exploration, and enhanced user engagement. By streamlining VR script development, this pipeline improves accessibility for researchers and practitioners, fostering a more intuitive understanding of complex datasets. Experimental results demonstrate the effectiveness of this approach in enhancing data interpretability and decision-making.

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  Engineering Quantum Point Defects in 2D Transition Metal Dichalcogenides for Next Generation Quantum Devices

  Sean Persaud

  Quantum Information Science and Engineering (QISE) seeks to develop next generation quantum technologies by leveraging superposition, entanglement, and coherence. This research aims to engineer quantum point defects in wideband gap monolayer transition metal dichalcogenides (TMDs), particularly Molybdenum Disulfide (MoS₂), to create stable, optically active spin defects for quantum sensing. The primary research question explores how atomic-scale defects in MoS₂ can be designed to function as coherent quantum emitters. We hypothesize that specific substitutional and vacancy-based defects will exhibit long spin coherence times and strong optical transitions, making them suitable for solid state quantum technologies. The objectives include computational modeling of defect states, controlled fabrication of defects, and advanced characterization to validate their quantum properties. Density functional theory (DFT) and quantum defect embedding theory (QDET) will be employed to predict defect stability, charge transition levels, and spin coherence. Experimentally, monolayer MoS₂ will be synthesized via chemical vapor deposition (CVD), with defects introduced using scanning tunneling microscopy (STM). Optical and spin properties will be assessed using photoluminescence spectroscopy, magnetic resonance techniques, and scanning tunneling spectroscopy. This research will provide critical insights into defect-host interactions in 2D materials, advancing scalable quantum sensing platforms

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  Safe Remote Operation of Connected Automated Vehicles

  Tienake Phuapaiboon and Ali Karimoddini

  Over the past few years, automated shared ride services have been increasingly tested on public roads, with several pilot projects taking place in North Carolina. Although these ride- sharing services can navigate structured traffic environments effectively, the autonomous systems remain prone to challenges in complex or atypical scenarios. Should the autonomous driving system fail, it may lead the vehicles to crash or stall, creating potential hazards and congestion on the roadways. This study introduces a human-in-the-loop fallback operation through the establishment of a remote-operated driving system. The proposed approach develops a reliable software and communication architecture to facilitate connectivity between Connected Autonomous Vehicles (CAVs) and the remote-operated driving station. The ROS2 framework and WebSocket protocol grant access to vehicle perception and control, ensuring robust communication with the remote driving control system. Additionally, the research outlines guidelines for the fallback procedure and safety measures to enhance the overall reliability of remotely operated CAVs.

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  Integrating Personalized Incentives for Enhanced Transportation System Management

  David Quansah, Ridwan Tiamiyu, and Kayla Stevens

  As the issue of urbanization accelerates, transportation networks face growing congestion challenges, leading to increased travel delays, environmental impacts, and infrastructure strain. Traditional Transportation System Management (TSM) strategies focus on optimizing traffic flow and enhancing infrastructure efficiency, but demand-side approaches—such as behavior incentives—offer promising alternatives. This research explores the integration of personalized incentives (such as monetary rewards, tokens, and gamification) within TSM to encourage off-peak travel and multimodal transportation choices. Preliminary hypotheses suggest that tailored incentives will significantly improve compliance and reduce peak-hour congestion. Furthermore, we hypothesize that integrating dynamic, user-specific incentives with an existing multimodal trip planner can significantly improve traffic distribution. To test this, we are designing behavioral experiments and developing simulation models to assess user responses. The experiments take into consideration both rational and non- rational user behaviors in decision making. Additionally, we present a survey of existing congestion mitigation strategies to inform our approach. The findings aim to inform the development of adaptive, user-centered solutions for reducing urban congestion while enhancing mobility and sustainability.

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  Role of CD163+ Macrophages in Organic Dust Exposure: A Study on Porcine Blood and Lungs

  Jenny Pakhrin Rana, Kristen L. Foust, Rohit S. Ranabhat, and Jenora T. Waterman

  CD163, 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 <0.05) 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.

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  Towards Autonomous Network Management: AI-Driven Framework for Intelligent Log Analysis, Troubleshooting and Documentation

  Shaghayegh Shajarian

  Modern 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.

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  Predicting and Optimizing the Fair Allocation of Donations in Hunger Relief Supply Chains

  Nowshin Sharmile, Isaac A. Nuamah, Lauren Davis, and Funda Samanlioglu

  Non-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.

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  Effect of Different Irrigation Levels on the Growth and Yield of Tomatoes

  Olabisi Somefun

  Effective water management practices are essential for maximizing tomato yield while mitigating the risks associated with weather extremes and ensuring environmental integrity. The need for climate-smart irrigation management techniques in agriculture has increased to optimize water use efficiency and enhance crop productivity. Irrigation scheduling using precision agriculture technologies like soil moisture sensors is an effective and efficient water management strategy in crop production. Therefore, this study evaluated the effects of different irrigation levels on the performance of tomatoes. Irrigation levels were based on initiating irrigation when soil matric potential was at 15 kPa and 45 kPa. A control treatment was included based on the crop’s condition. The experiment was laid out in a randomized complete block design (RCBD) in raised beds on campus during the 2024 summer growing season, with data collected on agronomic and soil moisture content. The data generated in this study will be used to develop best management practices and guidelines on climate-smart irrigation management in North Carolina and similar regions. These guidelines will serve as recommendations for farmers to enhance irrigation and nutrient use efficiencies in vegetable cropping systems. Additionally, the study will increase the adoption of scientific irrigation scheduling using plant and soil moisture sensors

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  A Narrative Analysis of Black Women C-suite Leader's Career Success in STEM

  Tiffani Teachey

  This qualitative narrative study investigates the experiences of Black women who have successfully advanced to C-suite leadership positions in STEM organizations. Grounded in Social Cognitive Career Theory and Intersectionality Theory, it explores how intersecting identities, environmental factors, and personal strategies shape their career trajectories. The research aims to identify effective strategies, support systems, and organizational practices that facilitate their career advancement. By examining these narratives, this study seeks to inform organizational practices and policies that promote more inclusive leadership pathways for Black women in STEM, ultimately enhancing representation in these fields.

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  Enhancing Cargo Movement Automation: ROSBased Design and Control of an Autonomous Overhead Crane with Depth Camera Object Detection

  Amanuel Tereda

  The automation of cargo movement is crucial in industries like nuclear power plants, where precision, safety, and efficiency are essential. This research focuses on developing an autonomous overhead crane system integrated with the Robot Operating System (ROS) to enhance material handling in such high-risk environments. A comprehensive geometric analysis using SolidWorks optimizes the crane’s structure, while an advanced control mechanism ensures seamless coordination of its X, Y, and Z-axis movements along with the gripper operation. A depth camera is incorporated to detect and locate objects, allowing the system to dynamically adjust its gripping mechanism based on the object's position. The crane’s pulley system is modeled as a mass-damped second-order system, with a PID controller ensuring stable and efficient performance. Unlike existing studies that focus primarily on hardware improvements or basic automation, this research introduces a unified ROS-based control framework for real-time motion planning, precise motor actuation, and adaptive object handling. Additionally, a digital twin of the overhead crane is developed to simulate real-world dynamics accurately. The proposed system is validated through extensive simulations in SolidWorks and ROS, demonstrating improved accuracy, reliability, and scalability. This work significantly contributes to advancing autonomous material handling solutions for nuclear facility crane automation

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  Predicting Effects of Conservation Practices on Runoff, Sediment, and Nutrient Loads from a Commercial Cotton Field Using Machine Learning and Deep Learning

  Arjun Thapa, Niroj Aryal, and Michele L. Reba

  Estimating real-time sources of pollutants and evaluating the effectiveness of conservation practices in agriculture are crucial for prevention of water resources contamination. Pollutant load released from agricultural fields can be estimated using process-based model or data driven models. This study utilized machine learning (ML) models to predict water pollutant loads since they need fewer input features than process-based models. Hydro-meteorological data (e.g., temperature, rainfall, runoff) were collected from control and treatment fields (2016–2022), where cover crops and filter strips were used for pollution mitigation. Pollutant loads, including sediment, total phosphorus (TP), and total nitrogen (TN), were measured and used to train nine ML models: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF), Extreme Gradient Boosting (XGB), Histogram Gradient Boosting (HGBt), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and a hybrid CNN-LSTM model. Results showed that the hybrid model best-predicted runoff in the control field (R²=0.87) and KNN in the treatment field (R²=0.82). LSTM excelled in sediment prediction for both fields, while RF and ANN were superior for TP and TN predictions, respectively. Model performance declined from runoff to sediment to nutrient loads due to error propagation. Advanced models (e.g., LSTM, CNN, hybrid) outperformed conventional ML models, showing robustness.

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  High-Performance Journal Shaft Design for Extreme Altitudes

  Yashitha Thuraka and John Kizito

  Developing robust mechanical components for high-stress environments is a significant challenge in the aerospace industry. Traditional journal shafts struggle with structural integrity and lubrication at elevated rotational speeds and altitudes. Our solution introduces a self-pumping journal shaft with a taper, designed to enhance lubrication retention and minimize friction during highspeed operations. The taper ensures a precise fit, improving alignment and reducing slippage, crucial at altitudes around 50,000 feet. By incorporating fluid physics principles, the design optimizes hydrodynamic flow within the shaft, ensuring continuous lubrication and efficient bearing performance at high RPMs. The self-pumping feature leverages fluid dynamics to maintain lubrication, reduce wear, extend the lifespan of mechanical systems, and minimize downtime. Our research emphasizes the importance of lubrication and alignment for enhancing durability and efficiency in journal shafts. This innovation addresses the specific challenges of high altitudes and high rotational speeds, making it especially impactful for aerospace applications. Enhancing lubrication and bearing efficiency, our advanced journal shaft design improves aircraft system reliability and efficiency, sets new industry standards, and offers significant economic benefits to the multibillion-dollar aerospace sector.

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  Turbine Based Combined Cycle Exoskeletal Engine (TBCC-ESE) Compressor Blade Design, FEA, & Material Selection

  Joshua Tucker, Mookesh Dhanasar Ph.D, Ajit Kelkar, and Kenroy Stewart

  The 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.

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  Bridging Physical Activity Gaps in Low-Income Older Adults for Healthy Aging in place (Based on Tentative Findings)

  Christiana Christiana Ugbem, Sung-Jin Lee, Minyong Lee, and Elizabeth N. Hopfer

  Older adults' engagement in physical activity supports health, delays functional decline, and promotes independent living (aging in place). This qualitative study explored the experiences of low-income older adults in maintaining physical function and independence, examining their physical activity habits, barriers, and motivations. In-home interviews were conducted with 20 low-income older adults (M = 75 years) in North Carolina. Thematic analysis of transcribed interviews and notes revealed key patterns. Among participants, 16 engaged in regular physical activity, with walking as the most common form. 12 utilized health clubs or wellness programs, citing social connections and accessibility as motivators. However, health concerns, time constraints, transportation, safety, and lack of motivation were major barriers. While 13 participants had exercise equipment at home, only seven used it consistently. Positive experiences, such as social engagement at fitness centers, feeling energized, and maintaining health, motivated participation. Findings highlight inconsistencies in physical activity among low-income older adults. Addressing barriers such as health limitations and transportation, while providing affordable, accessible wellness programs and virtual fitness options can improve participation. Enhancing community support can help older adults sustain physical activity, promoting their ability to age in place healthily. Ongoing Research: Preliminary results

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  Adapting NIST Cybersecurity Framework (CSF) 2.0 for Lightweight Security in Assistive Devices.

  Imonkhae Ugboya

  Assistive devices, including smart glasses, wearable navigation aids, and voice-controlled assistants, enhance accessibility for visually impaired individuals but remain highly vulnerable to cybersecurity threats due to limited hardware capabilities. Risks such as unauthorized access, spyware, keylogging, and firmware exploits necessitate lightweight security solutions that balance protection with low-resource constraints. This research adapts NIST Cybersecurity Framework (CSF) 2.0 to assess and mitigate risks in assistive technologies. The study integrates ASCON, Elliptic Curve Cryptography (ECC), and ChaCha20 encryption to secure communication with minimal computational overhead. Additionally, voicebased multi-factor authentication (MFA), behavioral access control, and secure boot mechanisms are implemented to enhance device security. To validate the approach, cybersecurity datasets including IoT attack data, intrusion detection, and malware detection will be analyzed, with penetration testing and risk scoring models used for evaluation. By aligning NIST CSF 2.0 with lightweight security techniques, this research ensures assistive devices remain low-power, privacy-preserving, and cyber-resilient, supporting broader adoption in healthcare, mobility assistance, and smart environments

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  Development and characterization of plant-based surimi: A sustainable protein source for next-generation seafood

  Shahriyar Valizadeh

  The growing demand for sustainable protein sources has led to advancements in plant-based seafood alternatives, particularly surimi. This study developed and characterized plant-based surimi (PBS) using soy, pea, and mung bean protein isolates combined with konjac glucomannan and oleogel. Seven formulations were analyzed for their physicochemical, textural, rheological, and morphological properties. Pea protein-based surimi (PBS2) exhibited the highest hardness (3781.11 g) and chewiness (2106.37 g), attributed to its compact microstructure and strong gel network. In contrast, PBS6 (mung bean and pea protein) had the lowest hardness (1319.60 g) and chewiness (564.82 g), indicating weak protein cross-linking and an unstable gel structure. Differential scanning calorimetry showed that PBS7 (soy-pea-mung bean blend) had the highest thermal stability (denaturation at 150.56°C), suggesting enhanced protein interactions and gelation properties. Scanning electron microscopy revealed significant microstructural variations, with PBS2 forming a compact fibrous network similar to traditional fish-based surimi, whereas mung bean-based formulations displayed porous, discontinuous matrices. The findings highlight the impact of protein selection and blending strategies on PBS functionality. This study demonstrates that optimizing protein composition and processing conditions can lead to high-quality plant- based surimi, offering manufacturers a viable alternative to conventional seafood product.

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  Biomimetic Robotic Hand Controlled via Deep Reinforcement Learning with Digital Twin

  Lowell Welburn

  Robotic arms are frequently deployed in many manufacturing architectures, such as automotive, aerospace, semiconductor, and pharmaceutical industries. Most of these arms are limited to 6 or less degrees of freedom, restricting the fine control needed to manipulate delicate parts adequately. The purpose of this work is to present a physical biomimetic hand with a digital twin that are both trained and controlled via deep reinforcement learning. The expectation of this research is a hand that can perform a delicate operation such as picking up a bolt and screwing it into a threaded hole without the use of another tool. This hand could be implemented in any manufacturing industry where high dexterity is needed.

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  AI Fairness Investigating and Developing AI Models to Mitigate Bias and Enhance Fairness

  Kemani White

  Artificial Intelligence (AI) is increasingly being deployed in sensitive environments where malfunctioning electronic devices or chemical leaks can lead to catastrophic consequences. AI models are trained to process and analyze images and data from various detectors and sensors, playing a critical role in threat detection and safety assessments. However, ensuring these models remain fair and unbiased is a significant challenge. Bias in AI systems can stem from historical data, flawed algorithms, or systemic design issues, potentially leading to inaccurate assessments that compromise security and decision-making. The objective of this ongoing research is to identify and develop methodologies for ensuring AI fairness in high- risk environments. This involves examining bias detection techniques, implementing fairness-aware AI architectures, and proposing best practices for mitigating algorithmic bias. By addressing these challenges, AI can be leveraged more effectively in sensitive facility environments, reducing the risks associated with biased decision-making and enhancing overall security and operational integrity.

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  Turbine Based Combine Cycle Exoskeleton Engine Architecture (TBCC ESE)

  Andre Williams and Nadia Linett

  The concept of combined cycle jet engines is not new. It represents the idea of having an aircraft travelling from ground speed to the high Mach numbers using a single propulsion system that can navigate the various flow realms. Combined cycle jet engines can fall into two broad categories, rocket based combined cycle engines (RBCC) and turbine based combined cycle (TBCC) engines. This research effort presents an innovative approach to the design of a TBCC engine. In a move away from the design of traditional jet engines where all of the rotating turbomachinery are mounted on a central rotating shaft; the TBCC ESE architecture has all of its rotating turbomachinery mounted on rotating drums. This approach presents a jet engine architecture with a hollow central core, in which the high-speed engine can be designed.

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  Advancing Liver Models: Exploring Immortalized Hepatocytes for Improved Pharmacokinetic Studies

  Camryn Woolfolk

  The liver serves as a primary role to the understanding of pharmacokinetics, driving the need for effective and reproducible liver models. While animal liver models and HepG2 cell lines offer accessibility and replicability, they lack functional accuracy in comparison to human livers. Primary human hepatocytes, often considered the “golden standard” for in vitro modeling due to high metabolic function and capabilities, face challenges in scalability and long-term function. These limitations hinder the scalability of successful primary hepatocyte models, impairing their use as a rapid response tool to chemical threats. Immortalized hepatocytes are liver cells that have been genetically modified to indefinitely divide while still maintaining normal liver cell characteristics and function. Cell modeling with immortalized hepatocytes has been relatively unexplored but shows promising potential to bridge the gap between reproducibility and quality cell function. Our study aims to provide an effective liver organoid model within a microfluidic system to build upon the underlying potential of these hepatocytes. Preliminary findings serve as characterization of the hepatocytes to be used in our model and further exploration of their capabilities for essential cell function.

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  Utilizing Hybrid Deep Learning Models for Streamflow Prediction

  Habtamu Workneh and Manojk. Jha

  Predicting streamflow with process-based models is challenging due to parameter uncertainties and streamflow complexity. Data-driven approaches provide viable alternatives without requiring physical process representation. This study introduces a hybrid deep learning framework, CNN-GRU-BiLSTM, for daily streamflow prediction. This model integrates Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Bidirectional Long ShortTerm Memory (BiLSTM) networks to leverage their complementary strengths. When applied to the Neuse River Basin (NRB) (North Carolina, USA), the proposed model achieved strong predictive performance, yielding a root mean square (RMSE) of 11.8 m³/s (compared to an average streamflow of 132.7 m³/s), and a mean absolute error (MAE) of 8.7 m³/s, and a Nush-Sutcliffe efficiency (NSE) of 0.994 for the testing dataset. Similar performance trends were observed in the training and validation phases. A comparative analysis against seven other deep learning and hybrid models of similar complexity highlighted the outstanding performance of the CNN-GRU-BiLSTM model across all flood events. Furthermore, its stability, robustness, and transferability were evaluated in a seasonal dataset, as well as peak floods and two locations along the river. These findings underscore the potential of hybrid deep learning models and reinforce the effectiveness of integrating multiple data-driven techniques for streamflow prediction.