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Mapping Sedimentary and Crustal Thicknesses in the Northern Atlantic Ocean from Public Domain Geophysical Data
No full textThe Northern Atlantic Ocean is a complex region that contains many inactive and active tectonic features. These features include the Jan Mayen Microcontinent, Jan Mayen Fracture Zone, Aegir Ridge, Kolbeinsey Ridge, the Greenland-Iceland-Faroe Ridge, the Voring Plateau, the Voring Spur, and the Iceland Hotspot. This project is motivated by the region’s complex geology and its highly debated tectonic history and disputed crustal affinity. This study aims to evaluate tectonic elements of the Northern Atlantic Ocean with three major objectives. The first objective is to develop a map of crustal thickness from publicly available seismic refraction data. For the second objective, publicly available seismic reflection data were used to develop a map of sedimentary thickness for the region. The third objective is to evaluate the boundaries of tectonic features in the study area from the developed crustal and sedimentary thickness maps.
The composed crustal thickness map showed the crust as thick as 42 km over the Greenland-Iceland-Faroe Ridge and as thin as ~2 km over the active Kolbeinsey Ridge. The sedimentary thickness map revealed that sediments varied greatly in the study area. The thickest sediments were mapped over the Voring Plateau, although our data does not allow us to uniquely map the basement, leaving room for some uncertainty. The thinnest sediments are located over the actively spreading centers, Kolbeinsey and Mohns Ridges. The results of this study show how crust and sediments differ throughout the region and how these variations are associated with tectonic features. The boundaries of tectonic features were interpreted from the developed maps. Comparisons with published crustal and sedimentary thickness datasets outlined several regions of discrepancies. Seismic data in those regions were carefully analyzed, revealing that these discrepancies relate to differences in interpretations of the basement. The thickness maps composed in this study are considered to be the most plausible.
Faculty mentor: Irina Filin
Environmental Controls of Hail in Quasi-linear Convective Systems
No full textA study of hail occurrence in quasi-linear convective systems (QLCS), as well as severe weather parameters contributing to the likelihood and intensity of hail, is presented. Rapid Refresh (RAP) proximity soundings for 218 QLCS events from 2012 to 2022 are selected to sample QLCS environments near peak intensity. Study objectives are to assess common environmental parameter spaces that favor hail in QLCSs while connecting these findings to results for supercells. Hail occurrence is also examined in terms of synoptic regime.
Six synoptic regimes were identified to support QLCSs with hail, from high shear, low cape (HSLC) to low shear, high CAPE (LSHC), deep trough, closed low, open trough, zonal flow, ring of fire, and weak flow. Hail was favored in cases with a dry boundary layer, steep lapse rate, and weak low-level shear. Hail count was evenly distributed across synoptic regimes, except for ring of fire and weak flow regimes, which had notably lower median hail counts. Cases with a large hail count predominately occurred over the Great Plains. This distribution is hypothesized to be heavily influenced by the geographic variability of mean United States severe weather environments.
Advisor: Matthew S. Van Den Broek
Group and State-specific Estimation in Animal Movement Models Using a Bayesian Approach
No full textThis research study investigates statistical approaches for modeling the movement patterns of white-tailed deer in Louisiana using GPS tracking data. We start by classifying the latent behavioral states using a hidden Markov model (HMM) and then integrate those inferred states into a state-dependent step selection framework to evaluate the land cover preferences. Standard HMMs, however, assume the same movement patterns for all animals, overlooking the differences due to characteristics such as sex, age, breeding season, etc.
To address this limitation, we extend the modeling framework to incorporate the group-level structure defined by similar characteristics or conditions to assess whether such unobserved attributes, combined with the behavioral states, affect the animal movement. Unlike other studies that incorporate such characteristics in transition probabilities and state-dependent distributions, our model provides a direct estimation of the state-dependent parameters governing the animal movement for the separate groups. We use a Bayesian inference framework with Markov chain Monte Carlo (MCMC) methods to estimate such group and state-specific parameters.
To examine the performance of our model in parameter estimation, we conduct simulation studies to evaluate true parameter recovery and compare our Bayesian estimates to the traditional maximum likelihood estimates (MLEs). The results show that our model yields estimates with lower bias and smaller standard errors than MLEs. We then apply the group and state-specific model to the white-tailed deer data and carry out posterior predictive checks to evaluate model fit. The results show that our model effectively captures the central tendency of the observed data, although some refinement may be needed to allow greater flexibility in capturing the variability and distributional characteristics of step lengths, particularly at the lower end.
Ultimately, our work provides a robust framework in ecology for studying the latent characteristics defined as groups, along with the behavioral states. We believe that this study not only deepens our understanding of animal movement but also holds potential applications across other domains beyond ecology.
Advisor: Stephen Kachma
Simulations of Differential Reflectivity Columns in Quasi-linear Convective Systems
No full textMuch research over the past decade has revealed that dual-polarization radar is a powerful tool in furthering our understanding of severe storm dynamics and subsequently improving warning strategies for these hazardous events. However, there are currently very few studies examining a particularly prolific severe convective storm mode with dual-polarization radar: quasi-linear convective systems (QLCSs). These storms can occur in any season of the year throughout most of the contiguous United States and can have immense societal impacts. Warning for the hazards produced by QLCSs is currently a significant operational challenge, therefore, research is needed to see if applying dual-polarization radar more in warning strategies for QLCSs will help.
Two novel and complementary modeling studies are presented in this dissertation that examine a particular dual-polarization signature in QLCSs: the differential reflectivity (ZDR) column. The ZDR column has been shown to likely have the most potential in anticipating tornadic potential in supercells. Additionally, the ZDR column is an updraft-associated feature and updrafts are critical for severe weather production in QLCSs. In the first study, the ZDR column is compared between two microphysics parameterization schemes and found to be more realistic in the National Severe Storms Laboratory-Double Moment scheme than the Morrison scheme. Additionally, ZDR columns are often found collocated with a strong mid-level updraft and often found at the same time with a strong low-level updraft in the vicinity. In the second study, the sensitivity of the ZDR column to low-level hodograph shape and size is examined as well as the connection between ZDR columns and low-level mesovortices. ZDR columns are largest when the low-level shear is strongest, yet there is no systematic decrease of column size with decreasing shear. ZDR columns are more likely to be found immediately rearward of a near-surface mesovortex when the low-level hodograph is straight and has large shear. However, ZDR columns away from mesovortices are often more pronounced than when in the vicinity of a mesovortex.
Advisor: Matthew S. Van Den Broek
Fire History, Ecological History, and Historical Human-Environment Interactions in the Northern Great Plains
No full textThis dissertation explores the spatiotemporal signatures of fire in North American grassland ecosystems. In three chapters, I explore different aspects of fire history in the northern Great Plains. Chapter 1 is an introduction. Chapter 2 focuses on the Euro-American Settlement Period (ca. 1850–1950 C.E.). Fire histories from six different lakes in the Nebraska Sandhills are compared with two periods of historic drought and the establishment of United States Post Offices. Five of the six records show increased levels of biomass burning during the initial phases of Euro-American Settlement. Chapter 3 is a long-term (ca. 15,000 years), high-resolution fire history and paleoecological reconstruction from Dewey Marsh in the north-central Nebraska Sandhills. Charcoal counts, grain size and organic content analysis reveals temporal trends in landscape disturbance around the marsh, primarily occurring when the adjoining dunes were mobilized during prolonged droughts. Charcoal was deposited on the surrounding dunes during wetter periods and was blown into the marsh during sand dune mobilization. Chapter 4 is a fire history reconstruction from Blyburg Lake, located on the floodplain of the Missouri River in northeastern Nebraska. The Blyburg Lake fire record is compared with trends in grain size analysis and reveals connections of fire occurrence with changes in silt and sand flux, both proxies for flooding on the Missouri River. Unstable winter conditions and increased flooding in the Upper Missouri basin are correlated with increased charcoal deposition in Blyburg Lake. Chapter 5 concludes the work of this dissertation. These chapters illustrate how charcoal accumulates in three different environmental settings and during different time periods over the last ~15,000 years. Knowing more about how fire fluctuates during changing environmental conditions will help inform a concise knowledge of the ecological operating range for future climatic changes in the northern Great Plains.
Advisor: Paul R. Hanso
Privy AI: Automated Note Taking for Physical Therapists
No full textThis paper presents the 2024–2025 Design Studio project for PrivyAI, a medical technology startup focused on automating physical therapy documentation. The team developed a minimum viable product: a web application that transcribes clinician-patient interactions and generates AI-assisted electronic health record (EHR) notes from customizable templates. The platform includes features for session recording, note management, and template creation, aiming to reduce administrative burden and improve documentation accuracy. Future development areas include patient management dashboards, automated onboarding, and dynamic multi-template note generation. The project delivers a scalable solution aligned with clinical workflows and regulatory expectations
\u3cem\u3eChoses lumineuses et mortes\u3c/em\u3e: A Translation of Ada Limón\u27s \u3cem\u3eBright Dead Things\u3c/em\u3e
No full textThis thesis, Choses lumineuses et mortes, is the first full-length translation of United States Poet Laureate Ada Limón’s collection Bright Dead Things into French. The poems themselves explore themes of loss, motherhood, and identity, and such themes are carried over into the French translations. Choses lumineuses et mortes provides a translation that is both uniquely American and French; the poetry, including its style and structure, heavily relies on traditional themes and movements in the contemporary American poetry canon, while the language itself reflects the standards of French expression. The critical introduction provides French and Francophone readers with important historical and cultural contexts that allow them to interact more deeply with Limón’s poetry.
Advisor: Jordan Stum
Traffic Prediction for Research and Education Networks: Anomaly-aware Deep Learning and Benchmarking
No full textResearch and Education Networks (RENs) and High-Performance Computing (HPC) environments are critical infrastructures for modern scientific discovery, demanding sustained high-throughput and low-latency data transfers. Unlike commercial networks, RENs exhibit unique traffic characteristics, including predominant “elephant flows,” inherent burstiness, and complex temporal-spatial dynamics often decoupled from human-driven cycles. Traditional traffic forecasting methods, tailored for commercial Wide Area Networks (WANs), consistently fail to capture these distinct REN dynamics, leading to inefficient resource management and potential impediments to scientific progress.
This thesis addresses this critical gap by developing and validating a robust, scalable, and anomaly-aware traffic forecasting framework specifically tailored for REN/HPC networks. Our contributions are threefold: (1) We developed and analyzed a two-month, multi-billion packet traffic corpus from ten Internet2 core routers, overcoming data scarcity challenges and providing a unique foundation for large-scale empirical analysis. (2) We designed and empirically validated a novel hybrid GRU-LSTM model that effectively captures both long-term dependencies and short-term fluctuations in REN traffic, demonstrating the critical impact of prediction lead time on operational utility. (3) We integrated Isolation Forest anomaly detection into forecasting models, significantly enhancing robustness against unexpected traffic surges, and conducted comprehensive benchmarking of various state-of-the-art deep learning models (N-BEATS, TiDE, PatchTST) to assess their performance with anomaly awareness.
Our findings demonstrate that tailored hybrid deep learning models, augmented with anomaly detection and optimized for lead time, achieve superior forecasting accuracy and robustness in REN environments. This work provides valuable tools for proactive resource management, congestion prevention, and optimized network operations, thereby accelerating scientific discovery and ensuring the efficient utilization of critical digital infrastructures.
Advisor: Byrav Ramamurth
Bone Biomechanics and Fractography: Observing Trauma Characteristics in Cases from Past American Conflicts
No full textThis thesis explores the potential of bone fractography for use in examining skeletal trauma in burial recovery, such as in the frequent casework of the Defense Prisoner of War/Missing in Action Accounting Agency. Many current methods for analyzing skeletal trauma rely on broader weapon-concentrated categories. This poses a risk resulting in trauma misinterpretations, limiting the accuracy of a forensic expert’s reconstruction of death events and, further, legal testimony. Fractography, an approach historically rooted in analyzing the fracture surfaces and composition of a material, can glean information about the ‘why and how’ of material failure by understanding its biomechanical properties and fracture surface features. This research hopes to provide the evidence to support the visibility trends of several fractographic surface features/characteristics in remains that have stood up to the harsh test of time and burial.
Advisor: William R. Belche
Identification of Single Nucleotide Polymorphisms that Contribute to the Bovine High Androstenedione Phenotype
No full textA population of females in the University of Nebraska-Lincoln research herd exhibit naturally occurring androgen excess (androstenedione; A4; High A4) in the follicular fluid of dominant follicles. Previously, a novel single nucleotide polymorphism (SNP) in the Follicle Stimulating Hormone Receptor (FSHR) gene was associated with pubertal classifications and initially with the High A4 phenotype in our herd. We designated this SNP, FSHR SNP2 ((chr11:31404255G\u3eC (rs21971504)). We hypothesized FSHR SNP2 could be used as a marker to identify High A4 cows. To test this hypothesis, we conducted Kompetative Allele Specific PCR (KASP) genotyping on cows with their A4 status classified in 2008-2024 (n = 206) to determine if FSHR SNP2 was associated with the High A4 phenotype. A Chi-square test determined that the alternate FSHR SNP2 did not segregate to the High A4 classification. We then identified heifers born in 2024 (n = 10) which represent all three genotypes (homozygous for the reference allele, heterozygous, and homozygous for the alternate allele) to determine A4 classification and ovarian phenotypic characteristics different between the three genotypes. A Fisher’s test determined that FSHR SNP2 did not segregate to High A4 animals. The androstenedione concentrations in cortex media, nor ovarian phenotypic measures between the three genotypes were not different. There was a tendency for both homozygous groups to have more follicles \u3e 10mm. To determine if we could identify other SNPs contributing to the High A4 phenotype, we utilized a Genome Wide Association Study (GWAS) using SNP chip data in animals with an existing A4 classification (n = 248). There were no SNPs that reached the statistically significant threshold (p \u3c 5×10-8). While FSHR SNP2 was not found to be a marker for the A4 classification, we demonstrated that High A4 cows tended and High A4 heifers had more surface antral ovarian follicles compared to controls. Furthermore, High A4 heifers had greater numbers of follicles \u3c 7mm on their ovaries compared to controls. No significant SNPs were found in our GWAS analysis to contribute to the High A4 phenotype, however, we have a foundational data set for determining potential SNPs that contribute to the excess A4 phenotype in future studies.
Advisor: Andrea S. Cup