2025 Graduate Student Research Symposium

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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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  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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  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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  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.