Digital Commons Network^™

Recirculated Submarine Groundwater Discharge Dominates Nutrient Inputs And Enhances Eutrophication Risk In A Coastal Lagoon, Júlia Rodriguez-Puig, Clara Ruiz-González, Marc Diego-Feliu, Irene Alorda-Montiel, Aaron Alorda-Kleinglass, Daniel Romano-Gude, Andrea G. Bravo, Júlia Dordal-Soriano, Javier Gilabert, Sophia Bergeler, Celine Lavergne, Gemma Casas, Marisol Manzano, Jordi Garcia-Orellana, Valentí Rodellas

OES Faculty Publications

Submarine groundwater discharge (SGD) is a widely recognized pathway for nutrient transport to coastal systems. Prior studies overlook the unique biogeochemical signatures of different SGD pathways, neglecting their differences in biogeochemical transformations and spatiotemporal factors. Here, we present an integrated assessment of nutrient delivery through three SGD pathways (fresh SGD, long‐scale recirculated SGD, and short‐scale porewater exchange), alongside surface water inputs, to evaluate their seasonality and impact on nutrient dynamics in a coastal lagoon. We conducted five field surveys in March, July, and November 2021, July 2024, and March 2025 in the Mar Menor lagoon, an ecosystem facing severe ecological …

Efficient Preservation Of Old Methane-Derived Organic Carbon In Deep-Sea Surface Sediments, Rui Bao, Yangli Che, Mingzhi Liu, Naiyu Zhang, Yang Zhou, Lihua Dong, Shen Li, Owen A. Sherwood, Min Yu, Zongheng Chen, Shengxiong Yang, Nan Wang, Longhai Zhu, Jiawang Chen, Mutai Bao, Haoshuai Li, Luonan Chen, Weifeng Wu, Haiyue Ma, David J. Burdige

OES Faculty Publications

Methane released from the subseafloor is significantly attenuated during upward migration, yet the preservation of methane-derived organic carbon (OC) in global deep-sea sediments remains poorly understood. Here, we measured carbon isotopes coupled with temperature-ramped analyses on various methane seepage sediments. Our results reveal a correlation between the radiocarbon (¹⁴)C) content in deep-sea surface sediments and sulfate-methane transition depth. Notably, our findings suggest that a substantial amount of (¹⁴)C-depleted OC may originate from deep-seated methane and is efficiently preserved in surface seepage sediments. We propose that the efficient OC preservation is related to microbial-mediated aggregate formation at the seawater-sediment interface, where …

Regional Oceanographic Controls On Water Column Nitrogen Fixation In Northern Australian Waters, Douglas G. Capone, Ajit Subramaniam, Yubin Raut, Joseph P. Montoya, Margaret R. Mulholland, Rachel Ann Foster, Miles Furnas, Edward J. Carpenter

OES Faculty Publications

Large blooms of the diazotrophic cyanobacteria, Trichodesmium, have been re-ported along the north coast of Australia and are readily evident in remote sensing images. During a research cruise in November 1999, we sampled from Townsville to Broome, examined Trichodesmium population densities and their rates of carbon and N₂ fixation alongside microscopy-based cell counts of them and other cyanobacte-rial diazotrophs. Additionally, we also enumerated the picophytoplankton community using flow-cytometry, measured bulk chlorophyll concentrations, carbon and N₂ fixation rates, and water column hydrography, enabling comparison of diazotrophic and picophytoplankton functional groups across the system. Agglomerative hierarchical clustering analysis of physicochemical oceanographic …

Differences In Sideerophore Production During The Growth Of Phytoplankton In Southern Ocean Incubation Experiments Under High- And Low-Iron Conditions, Randelle M. Bundy, Kristen N. Buck, Bethany D. Jenkins, P. Dreux Chappell, Daniel J. Repeta, Alexa Sterling, Shannon M. Yang, Laura Holland, Léo Mahieu, Laura E. Moore

OES Faculty Publications

Iron is an important micronutrient required by marine microorganisms. The Southern Ocean is an ecosystem where primary production is limited by the availability of iron, but how microorganisms in this region adapt to low-iron conditions or respond to episodic iron inputs is understudied. We tested how external iron additions would impact bacterial production of siderophores during phytoplankton growth using shipboard incubation experiments under a range of iron conditions. Similar heterotrophic bacteria communities and phytoplankton biomass were observed whether iron was added from the continental margin or as inorganic iron. Under both high- and low-iron conditions, the heterotrophic bacteria community remained …

Gradient-Based, Post-Optimality Sensitivity Analysis With Respect To Parameters Of State Equations, Gene Hou, Jonathan Degroff

Mechanical & Aerospace Engineering Faculty Publications

Design optimization is a computational tool that can enable a designer to investigate the effectiveness of a design concept in an organized format. However, this design process requires the design variables, constraints, and objective function to be properly defined and expressed in mathematical forms. Post-optimality analysis thus becomes a necessary step to investigate different variations in the problem formulation and parameters to ensure that optimization produces a stable and trustworthy outcome. One efficient way to achieve this aim is to compute the local derivative of the optimized objective function with respect to the optimization problem parameters, such as bounds on …

Uncertainty-Aware Estimation, Planning, And Control For Tracking Multiple Drifting Patches In Flow Fields, Daniel O. Akanji, Krishnanand N. Kaipa, Cong Wei

Mechanical & Aerospace Engineering Faculty Publications

In this study, we present a replay-based framework for uncertainty-aware persistent tracking of multiple advected surface patches using an autonomous marine vehicle operating in spatiotemporal-varying currents. The method combines three components: local flow estimation, covariance-aware patch-boundary propagation with intermittent boundary fusion, and mission-level scheduling over multiple patches. Each patch is represented by a polygonal boundary, whose vertices are propagated through the estimated flow field while carrying per-vertex covariance, thereby quantifying uncertainty growth during advection. A flow-aware gain-scheduled linear quadratic regulator (LQR) was designed to shape the desired surge speed to take advantage of favorable currents. When the vehicle services a …

Systematic Approaches To Characterizing Vulnerabilities And Enhancing Robustness Of Text And Vision-Language Models, Poojitha Thota

Computer Science and Engineering Dissertations

The proliferation of artificial intelligence (AI) across critical domains, including news summarization, privacy-policy analysis, and medical decision support, has raised growing concerns about the security and robustness of these systems against adversarial manipulation. This dissertation investigates adversarial robustness in generative AI by addressing three key research goals: (1) characterizing adversarial vulnerabilities across generative models, (2) developing systematic defenses to improve the robustness of generative models, and (3) designing deployment-time safeguards for securing LLM interactions.

Towards the first goal, we characterize adversarial vulnerabilities across text-based and multimodal systems. In abstractive text summarization, we show that inference-time perturbations can exploit lead bias …

Soil Respiration Drivers And Challenges In A Changing Environment, Olivia Rae Haas

Theses, Dissertations and Culminating Projects

This study aims to understand post agricultural forested soils (sandy loam, silt loam, and loam) and the influences that drive carbon sequestration and soil respiration flux. Duke Farms in Hillsborough NJ, has historically been utilized as an agricultural zone for farming a variety of crops. Currently the area is protected with a focus on environmental conservation and restoration of the land. This includes getting a better understanding of the forested portions of the property. Understanding the quality of forested areas is vital as they have the potential to act as a carbon sink which can mitigate the effects of climate …

Mapping Coastal Vegetation To Evaluate Salt Marsh Decline From 2016 To 2025 In South Carolina, Usa, Zak Henry Bartholomew

Theses, Dissertations and Capstones

Accelerating sea-level rise and storm surge events pose a substantial threat to salt marsh ecosystems, which provide critical ecosystem services. Classification and mapping of coastal vegetation through remote sensing can identify marsh dieback events and provide spatial context for patterns of marsh vulnerability. We used a multi-sensor approach that incorporated LiDAR data, multispectral imagery, and field-derived cover estimates to classify Spartina alterniflora (smooth cordgrass) dominated marsh types on Marine Corps Recruit Depot Parris Island (MCRDPI), a sea island in South Carolina, USA. In summer 2025, surveys were conducted within marsh classes to collect training and validation data. We used random …

Superelectrophile Promoted Synthesis Of Heterospirocycles, Mariam A. Adegbenro

Graduate Research Theses & Dissertations

Spirocycles are important scaffolds with a wide range of applications in material science as well as in medicine. The introduction of a heteroatom into the spirocyclic framework can improve its electronic, photophysical, and structural properties. Various reports have been published on the incorporation of atoms other than carbon into the spirocycle. The work outlined in this dissertation involves the development of synthetic methodologies for the synthesis of hetero-spirocycles via superacid-promoted cyclization reactions. These concepts are summarized in Chapter 1.

Chapter 2: Aromatic imines were prepared from 9-fluorenone and azafluorenones, and these substrates were reacted in the Brønsted superacid, triflic acid, …

Enhanced Photostability Through Rapid Exciton Decay In Desymmetrized Cyclopentannulated Acenes With Strong Face-To-Face Pi Stacking, Chad D. Cruz, Karl J. Thorley, Zachary J. Knepp, Jared Wahlstrand, Gil M. Repa, John C. Stephenson, Sean Parkin, Lisa A. Fredin, John E. Anthony, Emily G. Bittle

Chemistry Faculty Publications

The photophysics of organic semiconductors impacts their efficiency in optoelectronic devices where exciton transitions, including singlet fission, intersystem crossing and the formation of charge transfer states influence the ability to convert between bright and dark states and to dissociate into free charges. Unfortunately, photodegradation and spurious signals often confound the results of optical studies, especially of important triplet states. Here four asymmetric cyclopentannulated acenes are synthesized and studied. This system represents an extreme in photophysics achieved via molecular design to fully quench the photoluminescence and bypass triplet formation allowing for comparative studies with other highly absorbing acenes. Rapid molecular exciton …

From Authorization To Loss: A Blockchain Forensic Analysis Of Transaction-Level Mechanisms In Cryptocurrency Airdrop Scams, Chanwoo Shin, Kyung-Shick Choi

International Journal of Cybersecurity Intelligence & Cybercrime

Cryptocurrency airdrop scams have emerged as a rapidly growing form of cyber-enabled financial crime, yet remain underexplored in empirical research. This study examines how transaction-level mechanisms and offender strategies influence variation in monetary loss in airdrop scam incidents. Grounded in Cyber-Routine Activities Theory (Cyber-RAT), the study conceptualizes financial harm as occurring within decentralized environments where users’ online behaviors, particularly transaction authorization, intersect with limited digital capable guardianship. Data were drawn from 112 validated airdrop scam cases reported on Chainabuse.com between January and December 2025. Blockchain forensic analysis using Breadcrumbs was conducted to reconstruct transaction pathways, identify exchange interactions, and detect …

High-Resolution Infrared Spectroscopy Of Jet-Cooled 1-Butene, Vladyslav Filatov

Chemistry Honors Papers

A high-resolution absorbance spectrum of 1-butene was obtained in the 994–1000 cm⁻¹ region using a quantum cascade laser-based spectrometer coupled with a supersonic jet expansion source. To mitigate the low number density of the molecular jet, a multi-pass optical configuration was employed to achieve an effective path length of approximately six passes. Experimental spectra were calibrated using a methanol reference and etalon. Supporting calculations for vibrational frequencies and rotational constants were performed at the MP2/cc-pVTZ level of theory. The observed spectral features were assigned to the vibrational band ν25 (centered at 993.67 cm⁻¹), which was identified as a primarily a-type …

Discovery Of Tidal Disruption Event Candidates In The Hubble Catalog Of Variables, Elliot M. Schweitzer

Pitzer Senior Theses

A tidal disruption event (TDE) occurs when a star passes within the tidal radius of the supermassive black hole (SMBH) likely to reside at the center of most galaxies. Within this radius, the star is torn apart as tidal forces overcome the star's binding energy, and roughly half of the stellar debris spirals back towards the SMBH. The formation of an accretion disk produces bright emissions for months to years, modeled to peak in the ultraviolet and extend well into x-ray and optical wavelengths. TDEs occur only about once every 10⁴-10⁵ years per galaxy. As one of …

Mathematics And Political Ideology: A Historical Argument Against The Cultural Understanding Of Mathematics As An Apolitical Field, And A Journalistic Report On The Impact Of The Trump Administration On American Mathematics In 2026, Philo Judson

Pitzer Senior Theses

The interplay between political ideology and mathematics is a recurring theme throughout the history of the academic field. Mathematics has been used as a tool of empire, while mathematicians have been elevated by states as symbols of national genius for political prestige. Political ideologies have also shaped the field from within, both through the suppression of mathematical thought and the enforcement of mathematical authority. Since 2025, the Trump administration has launched large-scale political attacks on American institutions of higher education, which include lawsuits, discrimination investigations, and the removal of federal funding from certain institutions that don’t adhere to the administration’s …

Beyond Fixed Thresholds: Per-Label Calibration For Fine-Grained Emotion Detection On The Goemotions Dataset, Sai Puneet Naga Venkata Subramanyam Patchipulusu

Selected Full-Text Master Theses 2021-

This study investigates the effectiveness of five community fine-tuned transformer models for fine-grained emotion detection on the GoEmotions dataset: SamLowe/roberta- base-go_emotions (RoBERTa-base), cirimus/modernbert-base-go-emotions (ModernBERT), mrm8488/deberta-v3-base-goemotions (DeBERTa-v3-base), bhadresh-savani/bert-base-go- emotion (BERT-base-cased),and tasinhoque/distilbert-go-emotions (DistilBERT) . While the original GoEmotions research by Demszky et al. (2020) established a BERT-base baseline with a macro-F1 of 0.46, this thesis extends that work through independent empirical evaluation of five derivative models, systematic per-label threshold optimization, and comparative analysis of architectural trade-offs across the full transformer model family. Using the GoEmotions simplified test split (5,427 examples across 28 categories), all five models were evaluated at a fixed 0.5 …

Vision-Language System Using Open-Source Llms For Consent And Instruction Gestures In Medical Interpreter Robots, Tung Ngo, Emma Murphy, Robert Ross

Conference papers

Effective communication is vital in healthcare, especially across language barriers, where non-verbal cues and gestures are critical. This paper presents a privacy-preserving vision-language framework for medical interpreter robots that detects specific speech acts (consent and instruction) and generates corresponding robotic gestures. Built on locally deployed open-source models, the system utilizes a Large Language Model (LLM) with few-shot prompting for intent detection. We also introduce a novel dataset of clinical conversations annotated for speech acts and paired with gesture clips. Our identification module achieved 0.90 accuracy, 0.93 weighted precision, and a 0.91 weighted F1-Score. Our approach significantly improves computational efficiency and, …

Regulating Ai Beyond Product Liability, Shruti Trikanad

Michigan Technology Law Review

Artificial Intelligence (AI) is being used by governments across the world to enforce regulatory mandates, adjudicate benefits and privileges, predict and analyze risks, and much more. Although this has significant potential to increase efficiency and responsiveness, it also comes with several risks of transparency, government accountability, and the amplification of discrimination and bias. It is crucial we oversee and regulate these AI systems effectively. This essay argues against the models that current regulatory frameworks are adopting to govern AI use: those resembling product liability.

Through the lens of the European Union's AI Act and Liability Directive, it highlights the unsuitability …

Identification And Characterization Of Tornado Seismic Signals In The Central U.S., Seth Thompson

Theses and Dissertations--Earth and Environmental Sciences

Tornadoes cause extensive property damage and endanger lives. While Doppler radar can indicate the potential for tornado development, it cannot confirm that a tornado is in contact with the ground unless a debris signature is present. While on the ground, a significant amount of a tornado’s energy may be transferred into the earth as seismic vibrations. Thus, seismometers have the potential to detect on-the-ground tornadoes. Since 1960, only five papers have published seismic recordings from tornadoes. These studies have mostly reported on seismic waves generated by tornadoes at a relatively large range, from 0.01-2.5 Hz, representing uncertainty in the dominant …

Trustworthy Multimodal Ai For Medical Imaging: Enhancing Diagnosis, Reasoning, And Human-Agent Interaction In Extended Reality, Jai Prakash Veerla

Computer Science and Engineering Dissertations

The transition from traditional microscopy to digital pathology has digitized diagnostic data, yet clinical workflows remain constrained by two-dimensional screens and passive, opaque analysis tools that fail to capture the spatial complexity of biological systems. While Foundation Models now promise to reason across histology and genomics, a critical disconnect persists between the richness of this data and the limited cognitive bandwidth of clinicians, who currently lack the immersive interfaces and trustworthy agents necessary to utilize it effectively. This dissertation presents a unified framework for "Embodied Agentic AI," establishing a pipeline that augments physician capabilities through immersive visualization, robust security, and …

Evaluating Sampling Strategies To Monitor Southern Fox Squirrel (Sciurus Niger Niger) Populations, Casey Robin Hitchens

Theses, Dissertations and Capstones

Reliable population monitoring is essential for evaluating wildlife translocations and informing conservation management, yet traditional monitoring approaches are often labor intensive and require prolonged sampling. This study evaluated marking techniques, survey methods, and population estimation approaches for monitoring a translocated population of southern fox squirrels (Sciurus niger niger) at Marine Corps Recruit Depot Parris Island, South Carolina, USA (MCRDPI). From January-June 2023, I conducted six live trapping and camera trapping sessions across five survey sites. I compared the performance of color-coded collars and tail shaves for individual identification and the recapture probability between live traps and camera traps. Logistic mixed-effects …

Impact Of Cross-Client Heterogeneity In Federated Learning For Real-World Plant Disease Classification, Ritesh Janga

All Graduate Theses, Dissertations, and Other Capstone Projects

Deep learning applications are being adopted in agricultural image analysis that include challenges of data privacy and limited institutional data and heterogeneity of different types of architectures. However, Federated Learning is a model that allows collaborative training on data that does not have to be shared among parties. Therefore, Federated Learning is an effective method of collaborative training; however, its comparative effectiveness as compared to individual (local) training on diverse architectures has never been examined in an agricultural context. The objective of this study was to examine Federated Learning for the purpose of crop disease classification on extreme non-IID distributed …

Artificial Intelligence Adoption In The Workplace. An Exploration Of Augmentation, Oyinkansola O. Sodiya

Management Dissertations

As collaborative work with artificial intelligence (AI augmentation) gains interest, it is crucial to investigate factors that affect how employees perceive and use AI tools at work. Drawing on task-technology fit and technology adoption theories, this dissertation examines the ways in which task dimensions, organizational contexts, and individual differences affect the perceived usefulness of working with AI tools. This dissertation demonstrates that task-technology fit is fundamental. Employees in jobs with high information processing demands are likely to positively perceive the usefulness of AI augmentation relative to employees in jobs with high interpersonal demands. Employees with more proactive personalities perceive greater …

Toward Interpretable Multi-Omics Multimodal Biomedical Artificial Intelligence, Yanjun Lyu

Computer Science and Engineering Dissertations

The complexity of human disease arises from biological processes that unfold across multiple scales, from molecular variation through cellular function, tissue organisation, brain phenotypes, each of which is associated with distinct measurement modalities, regularities, and characteristic. Contemporary biomedical artificial intelligence has brought the opportunity to reveal the complexity with in; however, its methodological default, in which models are trained on most readily available modality, does not adequately engage with the multi-scale connected structure by which biological meaning is constituted. The research area of multi-omics and multi-modal AI for biomedicine remains at an early exploratory stage, and the work presented in …

Liutex - A Fluid Vortex, Oscar Alvarez

Mathematics Dissertations

Fluid vortices are found everywhere in our universe. A vortex can take the form of almost anything - from the classical spiral vortex to chaotic plumes. Defining a vortex physically and mathematically is absolutely necessary if we desire to study vortices and their interactions with each other as well as our physical world. Fluid vortices are incredibly important in the study of turbulent flows. From determining wear, optimizing design for better flow, efficiency, etc., to even predicting the weather on Earth or other planets, having the ability to measure vortices in fluid flow is invaluable. In this study, I investigate …

Adaptive Control For A Robotic Bipedal Device Using A Hybrid Discrete-Continuous Reinforcement Learning Strategy, Karla Rincon-Martinez, Wen Yu, Isaac Chairez

Mathematics Faculty Publications

This research develops and implements a novel reinforcement learning (RL) architecture to address the trajectory-tracking problem in bipedal robotic systems under articulated-joint constraints. The proposed RL framework extends previously designed adaptive controllers characterized by state-dependent gain structures. The learning mechanism comprises two hierarchical adaptation layers: the first employs an adaptive dynamic programming (ADP) formulation to approximate the Bellman value function using a class of continuous-time dynamic neural networks. In contrast, the second uses an iterative optimization scheme based on the deep deterministic policy gradient (DDPG) algorithm. The resulting control strategy minimizes a robust performance index defined over the tracking trajectories …

Ablative Study Of Large Language Model-Based Gesture Inference For Autonomous Navigation, Neil Loftus

Theses, Dissertations and Capstones

Human gesture inference has broad applications ranging from sign language interpretation to device control. Traditional methods often rely on extensive manually labeled hand datasets for deep learning. Furthermore, they are typically limited to a discrete set of gestures existing in these datasets. Large Language Models (LLMs) created by enterprise companies such as OpenAI have demonstrated positive results in many artificial intelligence tasks, with a notable strength being their adaptability. Existing literature has shown that LLM based systems can not only perform gesture inference but can propose user intent provided with a context and list of possible actions. We propose an …

Neuroevolution Of Compact Search Heuristics In Sokoban, Yeonghun Lee

Computer Science Honors Papers

This thesis investigates whether neuroevolution can produce heuristic functions for A* search on Sokoban that are drastically more parameter-efficient than a conventional gradient-trained baseline. Sokoban is a combinatorial planning puzzle that is both NPhard and PSPACE-complete, and solving it at scale requires an accurate cost-to-go heuristic to guide search. The standard approach fixes a neural network architecture and trains it by gradient descent; this thesis investigates whether evolutionary search over network topology can find heuristics that are competitive in accuracy while using orders of magnitude fewer parameters.

Four heuristic approaches are implemented and compared on the medium-difficulty split of the …

Design Principles For Customer-Engaging Digital Service Systems: An Action Research Study, Keng Leng Siau, Xiaofeng Chen, Xin Tan

Research Collection School Of Computing and Information Systems

Digital services represent a business approach employed by organizations to operate in the digital environment. However, systematic development guidelines for developing quality digital service systems are lacking in the literature. The authors identified four general challenges for developing and implementing customer-engaging digital service systems (CEDSS). By employing the method of canonical action research in a digital service system project, they derived 10 design principles for developing high-quality CEDSS. They empirically evaluated the design principles in the development project and through follow-up focus group sessions. The design principles provide applicable and actionable guidelines for the development of CEDSS.

Investigating Ui Based And Ai Based Interactions In Vr Environments For Human Anatomy Education, Siddharth Sondhi

Master's Projects

Traditional approaches to learning and simulation often struggle to convey the complexity of three dimensional structures. In anatomy education, methods such as textbooks, lectures, and cadaver based learning are used, but can make it difficult to understand the complex structure of organs within the human body. Virtual Reality (VR) offers a promising alternative by providing immersive and interactive 3D environments that allow users to explore and manipulate anatomical structures more intuitively. These environments can be further enhanced by integrating large language models (LLMs) as intelligent agents capable of guiding users through natural language interaction. This project presents a Unity-based VR …