Physical Sciences and Mathematics Commons^™

Groundwater Flooding On Highways In The Nebraska Sandhills: Applying Remote Sensing, Precipitation And Groundwater Modeling To Determine Depth, Cause And Frequency, Aaron R. Mittelstet

Nebraska Department of Transportation: Research Reports

Climate change significantly impacts infrastructure sustainability, particularly through shifts in precipitation patterns, intensity, and duration. These precipitation dynamics may increase the occurrence of surface water and groundwater flooding. Groundwater in the Nebraska Sand Hills (NSH), including the thickest portions of the High Plains Aquifer, extends close to the land surface in interdunal areas, making the NSH vulnerable to groundwater flooding. Recent events involving heavy precipitation, snow melt, and rising groundwater levels have caused prolonged highway flooding, disrupting transportation networks. This study estimates groundwater flood inundation depth, duration and frequency in the highways of NSH using remote sensing techniques and groundwater …

Polarimetric Data: Scenario 01, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

Polarimetric data Scenario 01 collected within the Automated Remote Sensing Solar Simulation Lab at the University of Dayton. The data were collected using a visible monochromatic division-of-time imaging polarimeter. The dataset is parameterized across different sensor, scene, and illumination geometries that mimic outdoor solar irradiance conditions.

Fisheries Science Update - Pilbara Fishing Competition Science 2020 To 2024 - July 2025, Department Of Primary Industries And Regional Development, Western Australia

Fisheries Science Updates

Key Points:

• Pilbara recreational fishing competitions are helping to track the sustainability of pelagic species like Spanish mackerel.
• Over 500 pelagic fish have been sampled at Pilbara fishing competitions over 5 years.
• This information goes towards DPIRD’s assessment of Spanish mackerel and monitoring the large pelagic resource in the North Coast Bioregion.

Data Annotations, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

Pixel-wise object masks for each polarimetric scene in ASL file format for the Model Desert Terrain Monochromatic DoT data.

Geophysical Tools For Agricultural Management: Trends, Challenges, And Opportunities, Sophia Becker, Trenton Franz, Yufeng Ge, Joe Luck, Derek M. Heeren

School of Natural Resources: Faculty Publications

Agrogeophysics is a convergence of proximal soil sensing and hydrogeophysics in which geophysical methods are used to describe soil patterns and dynamics that shape agronomic management (including precision agriculture). To fully realize its potential for optimizing agricultural production, current barriers and targets for future agrogeophysics research must be identified. In this review, we compare the trajectory of agrogeophysics within peer-reviewed literature, patents, and the US university extension literature and examine the current challenges and opportunities for supporting agricultural production and sustainability with agrogeophysical tools. We conducted a repeatable search of Web of Science and Scopus for seven geophysical methods (electromagnetic …

Exploring The Exceptional Extreme Rays Of The Copositive Cone, Trent Holmgren

All NMU Master's Theses

In this paper we will look at the exceptional extreme rays of COP^5 and COP^6 and identify if they are exposed or nonexposed. We start with some necessary background material on copositive matrices and cones. Then we construct two algorithms to see if a matrix is exposed or nonexposed.

Intervention Or Interventionist? Importance Of Treatment Fidelity, Abigayle Clontz, Lauren Lewis, Rita A. Jablonski

Expo Student Presentations

2025 Summer Expo Poster Presentation

• Physical & Applied Sciences

Patient Activation Measure (Pam) And Perceptions Of Bariatric Surgery, Jaiden Lee, Adrian Harsono, Gina Kim, Nathan C. English, Gurudatta A. Naik, Dinakar Sai Velagala, Kristen Wong, Richard D. Stahl, Jayleen M. Grams, Daniel I. Chu, Margaux N. Mustian

Expo Student Presentations

2025 Summer Expo Oral Presentation

• Physical & Applied Sciences

Analyzing Racial And Geographic Distribution In Clinical Trial Enrollment Among Women With Advanced Stage Ovarian Cancer, Paxton Gillispie, Dhruva Dave, Gerald Mcgwin, Ashwini Katre, Ashley Anderson, Teresa Boitano, Nneka Ijeli, Purva Pawar, Rebecca Arend

Expo Student Presentations

2025 Summer Expo Poster Presentation

• Physical & Applied Sciences
• Health Sciences & Health Professions

Serum Analysis For Hiv-1 Envelope Specific And Autoantigen Reactive Igg And Igm Antibody Response In Hvtn 702 Trial Participants, Tatyana Mompoint, Rashi Kalra, James Kobie

Expo Student Presentations

2025 Summer Expo Poster Presentation

• Physical & Applied Sciences

Monte Carlo Block Methods For Quantum Spin Chains, Demi Butler, Shannon Starr

Expo Student Presentations

2025 Summer Expo Poster Presentation

• Physical & Applied Sciences

Caregiver Versus Parenting Stress: Evaluation Of Psychometric Instruments, Stormie Bryant, Abigail Viola, Alexandra Armstrong, Leslie Pitts

Expo Student Presentations

2025 Summer Expo Poster Presentation

• Physical & Applied Sciences

Synthesis Of The Refractory High-Entropy Alloy (Monbtavw) From Pure Metal Precursors Using Microwave Plasma, Erin Wojcik, Agrima Shrestha, Aaron Catledge

Expo Student Presentations

2025 Summer Expo Poster Presentation

• Physical & Applied Sciences

Heterogeneity In Forms Of Distributed Machine Learning, Ian Lape

Expo Student Presentations

2025 Summer Expo Poster Presentation

• Physical & Applied Sciences

Substance Use Disorder Risk Factors For Women In A Residential Recovery Program, Jillian Vickers, Deborah Bowers

Expo Student Presentations

2025 Summer Expo Poster Presentation

• Physical & Applied Sciences

Innate Immune Dysregulation In Cystic Fibrosis-Related Diabetes (Cfrd) Evidenced By Cell Clustering And Marker Expression, Amelia Wanqin Liu, Delores A. Stacks, Camilla Margaroli Bell

Expo Student Presentations

2025 Summer Expo Poster Presentation

• Physical & Applied Sciences

Adaptation Planning To Mitigate Flood Risk: Bellevue, Nebraska As A Case Study, Michael Nti Ababio

Community and Regional Planning Program: Theses

Flooding remains one of the most pressing natural hazards in the United States, with increasing frequency and intensity driven by climate change. This thesis explores the adaptation planning strategies employed to mitigate flood risks in Bellevue, Nebraska, a city significantly impacted by the 2019 floods in eastern Nebraska. Using a mixed-methods approach, the study combines quantitative analysis of flood vulnerability with a critical review of local planning policies using documents, zoning ordinances and institutional frameworks. Spatial analytical tools such as Global Moran’s I and Hot Spot Analysis (Getis-Ord Gi*) were used to detect clustering of inundated buildings during the 2019 …

Assessment Of Spatial Autocorrelation And Scalability In Fine-Scale Wildfire Random Forest Prediction Models, Madeleine Pascolini-Campbell, Joshua B. Fisher, Kerry Cawse-Nicholson, Christine M. Lee, Natasha Stavros

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Wildfire prediction models that can be applied across diverse regions at fine scales (<  100 m) are critical for wildfire management. Remote sensing offers a path forward by providing heterogeneous and dynamic measurements of fuel load, type, and flammability. Machine learning methods such as random forests provide an empirical framework that are high-accuracy, computationally efficient, interpretable and able to model complex ecological relationships. Here we use high resolution (70 m, every 3–5 days) remote sensing observations of evapotranspiration and evaporative stress index, which represent plant water stress, from Ecosystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS), as well as topography and weather data, to predict burn severity and occurrence for 8 large wildfires that burned 3715 km² from 2021 and 2022 in New Mexico, USA. These fires ranged from low to high burn intensity, and covered a diverse range of ecoregions (deserts, grasslands, forests), plant species, and topographies. We used a single model to predict the burn severity of all wildfires one week before occurrence. The prediction accuracy was greatest when using all predictors (ECOSTRESS, weather, topography) (R² = 0.77). We assessed the role of spatial autocorrelation in driving model performance by: (1) increasing the sample spacing of our dataset, (2) …

Dataset Description, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

Polarimetric dataset containing data collected within the Automated Remote Sensing Solar Simulation Lab at the University of Dayton. The data were collected using a visible monochromatic division-of-time imaging polarimeter. A model desert terrain model was constructed and imaged for eight different scenarios consisting of different model panel and vehicle targets. The dataset is parameterized across different sensor, scene, and illumination geometries that mimic outdoor solar irradiance conditions.

Machine Learning For Digital Biomarker-Based Detection Of Cognitive Decline, Seng Khoon The

Dissertations and Theses Collection (Open Access)

Dementia is a neurodegenerative disease with a prevalence rate expected to triple by 2050, posing a significant challenge for health services. To impede the increasing prevalence, medical professionals and scientists are actively investigating technology to detect cognitive decline at a reversible stage known as Mild Cognitive Impairment (MCI). Digital biomarker technology is an emerging pragmatic approach to permit objective, ecologically valid, and long-term continuous measurement of cognitive health status, rendering it as one of the promising technologies for early MCI detection. Despite its potential, it is nontrivial to encode, extract and combine predictive information from these digital biomarker technologies; advanced …

Standard Operating Procedures For The Laser Particle Size Analysis Of Terrigenous Sediments And Soils, Sandra Passchier

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

This report details the preferred standard operating procedures for use of the Malvern Mastersizer 3000 (MS 3000) laser particle sizer in the analysis of terrigenous sediments and soils. Materials with median grain size < 500 µm can be measured on the MS3000. Preparation methods of sediment and soil samples before placing them in the laser particle sizer are described and the details of and rational behind the instrument standard operating protocols.

West Antarctic Ice Retreat And Paleoceanography In The Amundsen Sea In The Warm Early Pliocene, Sandra Passchier, Claus-Dieter Hillenbrand, Sidney R. Hemming, Werner Ehrmann, Thomas Frederichs, Steven M. Bohaty, Ronald Leon, Olga Libman-Roshal, Lisbeth Mino-Moreira, Karsten Gohl, Julia S. Wellner

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

Mass loss from polar ice sheets is poorly constrained in estimates of future global sea-level rise. Today, the marine-based West Antarctic Ice Sheet is losing mass at an accelerating rate, most notably in the Thwaites and Pine Island glacier drainage basins. Early Pliocene surface temperatures were about 4 °C warmer than preindustrial and maximum sea level stood ~20 m above present. Using data from a sediment archive on the Amundsen Sea continental rise, we investigate the impact of prolonged Pliocene ocean warmth on the ice-sheet−ocean system. We show that, in contrast to today, during peak ocean warming ~4.6 − 4.5 …

Modeling The Impact Of Explosive Volcanism On Biogeochemical Cycling At The Peak Of The Late Paleozoic Icehouse, Lily S. Pfeifer, Qingting Wu, Ying Cui, Gerilyn S. Soreghan

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

The peak of the Late Paleozoic Ice Age (LPIA) coincided with atmospheric carbon dioxide (pCO2) lows, and large-magnitude fluctuations in glacial-interglacial pCO2, but the driver(s) for pCO2 drawdown at this time remain debated. Backed by parameters for the frequency and magnitude of Late Carboniferous volcanism derived from the rock record, we apply an intermediate complexity Earth system model to evaluate the biogeochemical impacts of ash-borne nutrients from frequent (decadal) and explosive silicic volcanism on nutrient cycling ca. 310–300 Ma. Results show that volcanic perturbations result in negligible changes in marine particulate organic carbon export concurrent with sustained increases in pCO2 …

Urbanization In Resource-Based County-Level Cities In China: A Case Study Of New Urbanization In Wuan City, Hebei Province, Jianguang Hou, Danlin Yu, Hao Song, Zhiguo Zhang

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

This study investigates the complex dynamics of new-type urbanization in resource-based county-level cities, using Wuan City in Hebei Province, China, as a representative case. As China pursues a high-quality development agenda, cities historically dependent on resource extraction face profound challenges in achieving sustainable and inclusive urban growth. This research employs a multi-method approach—including Theil index analysis, industrial shift-share analysis, a Cobb–Douglas production function model, and a composite urbanization index—to quantitatively diagnose the constraints on Wuan’s development and assess its transformation efforts. Our empirical results reveal a multifaceted situation: while the urban–rural income gap has narrowed, rural income streams remain fragile. …

Enhancing Graph Representation Learning Through Self-Supervision: An Augmentation Perspective, Jianyuan Bo

Dissertations and Theses Collection (Open Access)

Graph representation learning has become fundamental in various domains, from social networks to molecular structures, enabling extraction of meaningful patterns from graph-structured data. While deep learning approaches, particularly graph neural networks, have shown promising results, their effectiveness is often limited by the scarcity of labeled data. This challenge is particularly acute in graph domains where annotation requires specialized expertise and is prohibitively expensive. Self-supervised learning has emerged as a promising direction to address this limitation by creating auxiliary tasks from unlabeled data, with augmentation strategies playing a crucial role in their success.

Current graph self-supervised learning methods face several critical …

From Sparse Feedback To Sequential Decision-Making: Learning Safety Constraints With Weak Supervision, Siow Meng Low

Dissertations and Theses Collection (Open Access)

Real-world decision-making often involves safety constraints that are implicit, non-Markovian, or difficult to specify directly. Standard reinforcement learning (RL) approaches typically assume access to fully specified cost functions and constraint budgets—assumptions that limit their applicability in domains where such structure must instead be inferred from data. This dissertation develops a sequence of methods for learning safety-relevant structure from weak supervision, such as sparse binary feedback on trajectory segments, and using these signals to guide planning and policy optimization.

The first part of the dissertation introduces a sample-efficient method for planning in continuous Markov Decision Processes (MDPs) using deep reactive policies. …

Scaler Rates From The Pierre Auger Observatory: A New Proxy Of Solar Activity, S. Mancuso, A. Abdul Halim, P. Abreu, M. Aglietta, I. Allekotte, K. Almeida Cheminant, B. Fick, K. Nguyen, D. Nitz, Et Al.

Michigan Tech Publications

The modulation of low-energy galactic cosmic rays reflects interplanetary magnetic field variations and can provide useful information on solar activity. An array of ground-surface detectors can reveal the secondary particles, which originate from the interaction of cosmic rays with the atmosphere. In this work, we present an investigation of the low-threshold rate (scaler) time series recorded in 16 yr of operation by the Pierre Auger Observatory surface detectors in Malargüe, Argentina. Through an advanced spectral analysis, we detected highly statistically significant variations in the time series with periods ranging from the decadal to the daily scale. We investigate their origin, …

Synthesis Of Inorganic Actinide Compounds With Neptunium, Plutonium, And Americium, Travis Kelly Deason

Theses and Dissertations

There is a rush of countries around the world making the switch to greener energy sources. Nuclear energy power plants are an integral part of the shift from fossil fuels, but there are concerns over the best way to handle nuclear waste. Most of the nuclear waste is currently disposed of by incorporating the waste into a glass form through vitrification, but research into alternative waste forms, such as promising crystalline materials, could provide a more robust method of containment. Research on actinide materials is lacking due to the dangers of working with radioactive material and there are few labs …

Research On The Capacity Development Of National Science And Technology Intelligence System, Hui Li, Shu Chen, Xiao Tan

Journal of Scientific Information Research

[Purpose/significance] The unique form and systematic characteristics of great power competition, such as system-establishment and whole of society confrontation, multi-domain and multi-depth high-tech containment and counter-containment, and multi-means and multi-element mixed deterrence, determine that national scientific and technological intelligence must have considerable systemic capabilities to match and follow up.

[Method/process] This article focuses on scientific and technological intelligence institutions within the traditional system, defines the connotation of national scientific and technological intelligence system capabilities, constructs a six-dimensional capability cognition model of ""institutional improvement capability-system coordination capability-resource guarantee capability-technological innovation capability-intelligence shaping capability-safe operation capability"", and expounds its main contents.

[Result/conclusion] …

Syntax-Enhanced Boundary-Aware Named Entity Recognition Model, Chuanming Yu, Bin Deng, Zhengang Zhang

Journal of Scientific Information Research

[Purpose/significance] This study addresses the issue of inadequate perception of entity boundaries in traditional character-level modeling-based named entity recognition models by integrating syntax information containing entity boundary features into the task using a multi-head graph attention network with dense connections. This integration enhances the effectiveness of named entity recognition.

[Method/process] This study proposes a Syntax-enhanced Boundary-aware Named Entity Recognition Model (SynBNER), which utilizes BERT for text semantic representation and integrates syntax information using a dense-connected graph attention network. This integration incorporates implicit entity boundary information from syntax information into word representations, thereby enhancing the model's entity boundary perception capability.

[Result/conclusion] …