Institutional Repository
The UDSpace Institutional Repository collects and disseminates research material from the University of Delaware.
- Faculty, staff, and graduate students can deposit their research material directly into UDSpace. Faculty may use UDSpace to fulfill the University of Delaware Faculty Senate Open Access Resolution, and in many cases may use it to fulfill open access requirements from grant funding agencies.
- Departments can use UDSpace to publish or distribute their working papers, technical reports, or other research material.
- UDSpace also includes all doctoral dissertations from winter 2014 forward, and all master's theses from fall 2009 forward.
To learn more about UDSpace, and how you can make your research openly accessible to the public, visit our UDSpace Policies website.
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Recent Submissions
Predictive habitat occupancy models for North American river otters along inland streams in New Jersey
(Wildlife Biology: A journal for wildlife science, 2025-01-23) Williams, Christopher K.; Bennett, Curtis; Schley, Hannah; Castelli, Paul
The North American river otter Lontra canadensis is a semi-aquatic furbearer species that historically ranged throughout North America. Starting in the mid-1800s and continuing through the early 1900s, the negative effects associated with anthropogenic disturbances (i.e. overharvest, development and ultimately habitat alternation) led to local extinctions. Researchers debate whether current land use patterns are affecting river otter occupancy. New Jersey is the most densely populated state in the United States, thus it provides a perfect study area to test potential anthropogenic effects on river otters. Using occupancy modeling to examine river otter habitat preferences, we measured presence/absence at 244 low order streams from January–April 2011–2012 along with 19 corresponding site/landscape covariates in both northern and southern New Jersey. In southern New Jersey, we detected otters at 83/141 sites (58.9%) with a detection probability of 97.7% across repeat visits and a predicted occupancy of 59.4 ± 0.04%. In northern New Jersey we detected otters at 31/103 sites (30.1%) with a detection probability of 44.5% across repeat visits and a predicted occupancy of 58.8 ± 0.04%. We determined the influence of habitat covariates on otter occupancy and found that water depth, water quality, stream width and mink presence were positively correlated with otter occupancy. The % commercial, industrial, transportation and recreational habitat, % low intensity development, bank slope, and distance to lake were negatively correlated with otter occupancy. Knowing the location of occupied stream and latrine sites will assist biologists in their efforts to monitor river otter populations and help estimate river otter density for harvest and conservation efforts.
Bridging theory and practice in peer-to-peer energy trading: market mechanisms and technological innovations
(Environmental Research: Infrastructure and Sustainability, 2025-01-30) Raghoo, Pravesh; Shah, Kalim
The article provides a synthesis of the literature on the peer-to-peer (P2P) energy trading paradigm. P2P energy trading is a prosumer business model and a transformative concept that allows prosumers to sell surplus generation to other prosumers and consumers within an energy community or microgrid. P2P energy trading is a novel concept to promote decentralization, decarbonization, democratization, digitalization, and enhancing energy resilience of the energy sector. The article covers different facets of P2P energy trading, including market designs, changing actor roles, pricing mechanisms, enabling technologies, and challenges. The article thus addresses emerging and complementary aspects not covered in prior literature reviews. As such, three market designs are discussed: centralized, decentralized, and distributed, and four pricing mechanisms, which are optimization, game theory, auction-based, and reinforcement learning. Enabling technologies discussed are Energy Internet, Internet of Things, Artificial intelligence, Blockchain, Communication networks, and battery flexibility. The paper discusses the challenges that the development and commercialization of the P2P energy trading faces—especially focusing on the social ontology of the concept—and provides research directions to amplify the scaling up of the technology.
Improving Foundation Models on Electronic Health Records
Poulain, Raphael
Recent advances in foundation models have opened up new possibilities for
healthcare applications, particularly by utilizing transformer-based models to take advantage of the longitudinal nature of both natural language and electronic health
records (EHRs). While these models have shown promise, existing approaches face
challenges related to multi-task learning; knowledge transfer from pre-training to finetuning stages; simultaneous representation of medical codes and visits; and potential
social biases in predictions.
The primary goal of this dissertation is to tackle these issues by presenting multiple transformer-based models while investigating and mitigating their issues related to
fairness. Our proposed solutions have been evaluated on a multitude of popular medical
predictive tasks. We first propose a transformer-based model tailored for multi-task
learning, used for the primordial prevention of cardiovascular disease. Second, we
tackle the issue of decreasing performance on small datasets with a semi-supervised
transformer model that leverages both in- and out-of-cohort patients in the context of
few-shot learning. Third, we propose a hybrid model that leverages graph neural networks to extract the structure of medical visits, and a transformer encoder to extract
the temporal relationships of visits. Fourth, we investigate the fairness implications
of our models and propose a bias mitigation technique based on federated learning
principles. Lastly, we investigate the specific challenges of fairness in medical large
language models (LLMs), conducting a comprehensive evaluation of the bias patterns.
We then present a novel bias mitigation technique for medical LLMs based on model
alignment ideas within a knowledge distillation framework.
DEVELOPMENT AND APPLICATIONS OF THE 19F MAGIC ANGLE SPINNING NMR SPECTROSCOPY FOR STUDYING PROTEIN-LIGAND INTERACTIONS
Kalabekova, Roza
Protein-ligand interactions underpin a wide range of biological processes, including enzymatic catalysis, signal transduction, immune modulation, and transcriptional regulation. A comprehensive understanding of these interactions is fundamental to drug discovery and development, as they dictate the specificity, binding affinity, and efficacy of therapeutic agents. Numerous models and methodologies have been developed to elucidate protein-ligand interactions, with ongoing advancements in high-resolution experimental techniques such as X-ray crystallography, cryo-electron microscopy, and NMR, alongside increasingly efficient computational approaches including molecular docking, molecular dynamics, and quantum mechanics-based simulations. The expanding repository of structural data on protein inhibition targets has catalyzed the integration of artificial intelligence and machine learning frameworks in drug discovery efforts, accelerating lead identification, optimization, and rational drug design. These synergistic efforts aim to decode the molecular mechanisms of protein-ligand interactions, providing valuable insights into ligand selectivity, binding kinetics, and conformational dynamics. To provide a more comprehensive review of the current state of protein-ligand research, this dissertation begins with an overview of experimental and computational techniques for studying protein-ligand interactions. It evaluates their strengths and limitations to establish the rationale for developing high-resolution methods such as 19F MAS NMR. Chapter 3 provides the theoretical foundations of MAS NMR experiments, while Chapter 4 presents an original study employing 19F MAS NMR to elucidate protein-ligand interfaces using fluorinated small molecules as reporters. A key emphasis of the study is the application of small fluorinated molecules as highly sensitive reporters in solid-state NMR, highlighting the technique's capacity to probe protein-ligand interfaces with high precision in the solid state. The research focuses on Galectin-3, a biologically significant protein involved in cell adhesion, immune regulation, and cancer progression. By developing optimized experimental protocols and advanced data processing workflows, the study demonstrates how 19F MAS NMR captures ligand-induced perturbations, maps binding interfaces, and quantifies chemical shift anisotropy parameters associated with ligand dynamics. Furthermore, two-dimensional heteronuclear correlation experiments yield high-resolution spectra, identifying key residues and revealing stereospecific binding in the complexes. These findings underscore 19F MAS NMR's unique ability to resolve dynamic and structural details, serving as a complementary method to conventional techniques for studying complex systems. In summary, this dissertation presents 19F MAS NMR as a versatile tool for investigating protein-ligand interactions, underscoring its potential for widespread adoption in structural biology, biophysics, and medicinal chemistry.
INVESTIGATING DAILY ASSOCIATIONS BETWEEN NEGATIVE AFFECT AND IMPULSIVE URGES AMONG INDIVIDUALS WITH AND WITHOUT A LIFETIME HISTORY OF SELF-DIRECTED AND OTHER-DIRECTED VIOLENCE
Sheehan, Ana Elena
Objectives: Impulsive urges and negative affect are transdiagnostic factors theorized to underlie risk for engagement in self- (SDV) and other-directed violence (ODV). Yet, the dynamic interplay between these factors at the within-person daily level remains poorly understood. Therefore, the present study aims to test within-person bidirectional associations between daily impulsive urges and negative affective states both within and between-days (Aim 1), and further evaluate the extent to which these daily associations are moderated by a history of engagement in SDV and ODV (Aim 2).
Methods: A community sample of 84 adults (ages 18-55; 52.39% female) with and without histories of SDV and ODV participated in a 7-day daily diary survey during which they reported on their momentary impulsive urges and negative affective states in the morning and evenings. Hierarchical linear models were estimated to examine 1) within-person bidirectional associations between impulsive urges and negative affective states and 2) the moderating effect of SDV or ODV lifetime histories on these bidirectional associations. These relationships were examined across two-timescales: 1) within and 2) between days.
Results: Impulsive urges and negative affective states were bidirectionally associated at the within-person level both within- and between- days (with the exception of negative affect on impulsive urges between-days). Further, we found SDV history moderated the within-person association between self-reported urges to act impulsively and next-day negative affectivity (β = 0.28, SE = 0.08, p = <0.001). An individual’s increase in impulsive urges, relative to their average levels, was associated with an increase in their negative affect the next day only for individuals with a history of SDV. In addition, ODV history moderated the within-person association between negative affective states and subsequent impulsive urges within-days (β = 0.26, SE = 0.11, p = 0.01). Specifically, an individual’s increase in negative affective states, relative to their average levels, was associated with increases in their impulsive urges within-days only for individuals with a history of ODV.
Conclusion: Ultimately, findings from the present work reveal that, compared to those without a history of past violence perpetration, individuals with a history of SDV and ODV may experience amplified within-person associations between impulsive urges and negative affective states within- and between-days. These findings provide insight into the daily risk processes that may maintain vulnerability towards engagement in future violence in at-risk groups. A more thorough understanding of daily patterns of risk has the potential to inform violence prevention efforts targeting proximal risk factors, particularly among high-risk individuals.