Digital Commons Network^™

Efficiently Stowed Flashers With Uniform-Thickness Panels Based On Non-Developable Origami, Larry L. Howell, Robert J. Lang, Specer P. Magleby, Davis Wing

Faculty Publications

We present the mathematical construction of a deployable flasher pattern with near-uniform nonzero thickness in the deployed form and efficient face-to-face packing in the stowed form. We demonstrate its fabrication, deployment, and stowage. The results facilitate the design and manufacture of deployable systems with panels whose substantial thickness is dictated by the end application, with particular utility for space systems.

¡Salud! Sacrificing The Sacred Sip: A Critical Analysis Of The Impact Of The Spiritual Conquest On Alcohol Traditions In Central México, Elizabeth Vilchis

Graduate Theses/Dissertations

By thoroughly investigating the available primary sources, this thesis will convey the importance of the Mesoamerican inebriating drink pulque in both the ethos and mythos of ancient Nahua society, examining how the ritual use of pulque became problematic in the eyes of the Spaniards, by investigating the cultural and ideological contradictions or double standards that enabled the tensions, struggles, and violence with which the Spanish colonial regime repressed the ritual use of pulque. This thesis compares the Nahua ethos and mythos of pulque to the Christian ethos and mythos of wine, highlighting the parallels perceived by Spaniards in Central México …

Scalar Source Localization Using Multi-Sensor Domains Of Dependence In Turbulent Channel Fow, Zejian You, Xiaowei Zhu, Qi Wang

Mechanical and Materials Engineering Faculty Publications and Presentations

Tracking hazardous events such as wildfire smoke, coastal oil spills, or chemical releases in natural environments is complicated by turbulent dispersion and molecular diffusion. This study presents a method to localize pollutant sources rapidly and accurately using infinite time-averaged measurements from a multi-sensor network, leveraging the duality between mean forward and adjoint scalar fields. The forward-adjoint duality states that measurements at the sensor (forward feld) are equal to the adjoint field at the source, providing crucial spatial information about the source. Consequently, the adjoint scalar fields can be interpreted as the domain of dependence for the sensor observations [1, 2]. …

Spatial Patterns And Morphometric Evolution Of Sinkholes In Response To Watershed Hydrogeomorphology: Examples From A Karst Landscape In The Missouri Ozarks, Usa, Marcell Tóth

Graduate Theses/Dissertations

Sinkholes are characteristic enclosed depressions with internal drainage connecting the surface and subsurface karst hydrologic system. Their morphological evolution involves gradual subsidence of the ground surface and hazardous collapses. Therefore, understanding sinkhole morphometry and linked spatial distribution is critical for managing geohazards in karst watersheds. Sinkhole occurrence shows spatial variability based on watershed geomorphological attributes, surficial hydrological patterns, hydrogeological and geological settings. The aim of this research is to investigate morphometric properties and distribution of sinkholes in the carbonate karst-dominated lands of Mark Twain National Forest in Missouri, USA. The specific objectives were to (1) identify sinkhole clusters based on …

Exploring Factors Mediating White-Nose Syndrome In Little Brown Bats, Carly R. Trujillo

Graduate Theses/Dissertations

The little brown bat (Myotis lucifugus) has experienced drastic population declines across North America due to the recent disease white-nose syndrome (WNS). Previous studies have identified putatively adaptive alleles (PAAs) that may give individuals a greater likelihood of survival. However, there could be geographic-specific adaptation, creating different adaptive landscapes. Additionally, there may be energetic trade-offs between disease survival and reproductive output, and it is not clear what the long-term survival outcomes for the species may be as a result. Long-term evolutionary outcomes are unclear because this species has a lifespan exceeding the arrival of WNS and is difficult …

Remediation Impacts Metal Concentrations And Metal Tolerant Bacteria In Soils Of The Missouri Tri-State Mining District, Ophelia R. Pettington

Graduate Theses/Dissertations

After over 100 years of Zn and Pb mining in the Tri-State mining district (TSMD), former mines continue to be sources of metals. Metal contaminated soils can be remediated using plant-microbe interactions. Plants manipulate their microbiome and recruit microbes to increase metal tolerance. Remediating bacteria are site-specific and identifying native microbes can accelerate remediation efforts. Studies relating microbes and metal concentrations in remediated areas in the TSMD are scarce. I collected root and bulk zone soils associated with Andropogon virginicus from remediated and non-remediated sites in Webb City, MO. I used 16S rRNA gene amplicon sequencing to evaluate the bacterial …

Flood-Regime Shifts Across The Lower Midwest, Usa: Identifying Patterns Through Flood Magnitude–Frequency Analysis And Clustering, Kaiser Mostafiz

Graduate Theses/Dissertations

Flood regimes describe how flood behavior changes in magnitude, recurrence, and occurrence through time. Changes in these flood characteristics can affect flood hazards, river systems, infrastructure, and floodplain management. This thesis examines flood-regime change across the Lower Midwest, focusing on Nebraska, Iowa, Kansas, and Missouri from 1961 to 2020. Records from 1,452 U.S. Geological Survey gaging stations were initially compiled, and 208 stations were retained after screening for record availability and analytical consistency. Flood-regime change was evaluated between two 30-year study periods: Period 1 (1961–1990) and Period 2 (1991–2020). The analysis combined Log-Pearson Type III flood-frequency analysis using L-moments in …

Synthesis And Characterization Of The New G3 Family Of Organoantimony(V) Cyanoximates, Thao Trang Phan

Graduate Theses/Dissertations

Bacterial infections pose a serious threat to humans by developing resistance, rendering antibiotics ineffective. Of the 12 antibiotics recently approved since 2017, 10 belong to an already existing class. Various resistance mechanisms, such as reduced drug uptake, active efflux, drug inactivation, and target modification, can significantly reduce the efficacy of entire classes of antibiotics. Discovering novel strategies to overcome diverse mechanisms of resistance and improve therapeutic efficacy is in high demand. Inorganic compounds with organic ligands (both complexes and organometallics) represent a new class of compounds against which antimicrobial resistance has not yet been established. Recently, we discovered two series …

The Composition Of Gases From A Diffusion Flame Above Longleaf Pine Needle Fuel Beds, David R. Weise, Thomas H. Fletcher, Timothy J. Johnson, Weimin Hao, Russell G. Tonkyn, Catherine A. Banach, Javier Palarea-Albaladejo, Mahsa Alizadeh, Stephen Baker

Faculty Publications

The gas and tar composition of a wildland fire diffusion flame from longleaf pine needles is currently relatively unmeasured and more data are needed to fill in the gap between pyrolysis data and smoke plume data, thus improving physical and chemical modeling of wildland smoke formation. A pilot experiment to measure light gas and tar composition of such a flame is described for three flame zones: persistent flame (flame base), intermittent flame, and smoke plume. Flame gases from 24 experimental fires were collected in canisters and analyzed for CO2, CO, H2, CH4, and C2 to C7 hydrocarbon gases. Other light …

Ai-Powered Lawyering: Ai Reasoning Models, Retrieval Augmented Generation, And The Future Of Legal Practice, Daniel Schwarcz, Sam Manning, J. J. Prescott, Patrick Barry, David R. Cleveland, Beverly Rich

Articles

Generative AI is set to transform the legal profession, though its most promising uses and ultimate effects are still unclear. While AI models like GPT-4 improve efficiency, they can also “hallucinate” and may undermine legal judgment, particularly in complex tasks typically handled by skilled lawyers. This article examines two emerging AI innovations that may mitigate these concerns: Retrieval Augmented Generation (RAG), which grounds AI-powered analysis in legal sources, and AI reasoning models, which structure complex reasoning before generating output. We conduct the first randomized controlled trial assessing these technologies, assigning upper-level law students to complete legal tasks using a RAG-powered …

State Climate Superfunds, Rachel Rothschild

Articles

The harmful effects of climate change have already arrived in cities and states across America, with disasters increasing markedly in recent years along with more gradual environmental changes like sea-level rise and drought. To protect populations and natural resources, significant funding will be necessary for preventative measures as well as disaster response.

At present, it is states and ordinary taxpayers who must shoulder the enormous costs and planning for climate adaptation. A number of state legislators, however, have recently proposed enacting new laws that would require the companies who have most profited from fossil fuel usage to assist in funding …

An Incidental Standard For Medical Ai, Nicholson W. Price Ii

Articles

Medical AI is poised to make a major difference in the provision of health. It brings major challenges, though: how can developers and implementers ensure that it will work safely and effectively—especially within the context of complex and highly variable health-care systems? Standards provide one key tool, potentially providing guidelines for everything from privacy to accuracy to how AI interacts with human clinicians. This Essay considers the last of these, describing a powerful quasi-standard from a surprising source: an FDA guidance document that tells developers when certain AI systems are not considered medical devices, and are therefore not regulated by …

Beyond Numbers: Bridging Statistical Significance And Clinical Relevance In Healthcare Research, Maharaj Singh, Waheeda Khan, Mehreen Fatima

College of Nursing Faculty Research and Publications

Objective: This study aims to bridge the gap between statistical significance and clinical relevance using rigorous statistical methodologies applied to simulated healthcare data, thereby enhancing the translational value of research outcomes for patient‑centered care. Methods: A synthetic dataset representing 10,000 patients was generated, and descriptive statistics were applied to compare two treatment groups. Binary outcomes were predicted using both a logistic regression model and a neural network. Two‑tailed tests with a significant level of 0.05 were employed, utilizing SAS version 9.4 and IBM SPSS Statistics, Version 29.0 (IBM Corp., Armonk, New York, United States of America). Results: The simulated dataset …

Resolving Heterogeneity In Lymph Node Stromal Cells Using High-Dimensional Analysis Of Non-Optimized Flow Cytometry Data, Mikala E Heon, Eduardo Rosa-Molinar

2020-Current year OA Pubs

Lymph Node Stromal Cells (LNSCs) are a diverse population of cells responsible for maintaining the lymph node environment and regulating the immune response. Given these roles, they have the potential to help replicate lymph node functions

Associations Of Blood Pressure Level And Variability With Cortical Thickness: A Cross-Sectional Analysis From The Maracaibo Aging Study, Romeo De Leon, Shana Garza, Silvia Mejia-Arango, Kristina Vatcheva, Sokratis Charisis, Claudia L. Satizabal, Luis J. Mena, Joseph H. Lee, Joseph D. Terwilliger, Eron Manusov

School of Medicine Publications

Background: Although high blood pressure (BP) level and variability are associated with Alzheimer's Disease (AD), their relationship with cortical thickness in brain regions that are associated with AD is unclear. Furthermore, the role of 24-hour BP has not been examined. We investigated the associations of office and ambulatory BP measures with cortical thickness in brain regions implicated in AD.

Methods: We performed a cross sectional analysis of 304 participants without dementia from a population-based study with office and 24-hour BP and magnetic resonance imaging (MRI) data. We considered cortical thickness values derived from 10 regions throughout the frontal, parietal, and …

Memory Effects In Many-Body Systems, Jeffrey Beckstrand

Master's Projects

This thesis investigates memory effects in many-body systems through the MoriZwanzig Formalism for projected dynamics of a Hamiltonian System which yields the Generalized Langevin Equation (GLE). The GLE is a stochastic differential equation (SDE) that studies the dynamics of observables under the effects of many other observables in the system. Although satisfying, the GLE has a term called the Memory Kernel that encodes the past of the system and introduces a computational challenge by introducing a non-Markovian property to the equation. The kernel is often approximated by introducing a delta function, which simplifies the computation, but at the loss of …

Safe-R2r: A Safety-Aware And Budget-Optimized Retrieval Controller For Rag Systems, Mandar Sunil Gondane

Master's Projects

Retrieval-Augmented Generation (RAG) improves factual grounding in large language models by incorporating external evidence during inference. However, most RAG systems rely on fixed retrieval strategies that ignore query difficulty, computational cost, and prediction uncertainty. This project introduces SAFE-R2R (Safety-Aware and Budget-Optimized Reason-to-Retrieve), a retrieval controller that treats retrieval as a query-dependent decision problem. SAFE-R2R organizes retrieval into a multi-rung ladder ranging from no retrieval to deeper retrieval with reranking. At each rung, the system generates an answer, computes reliability signals, and combines them into a risk score used within a conformal calibration framework to decide whether to accept the answer …

Multi-Agent Path Planning And Optimization Using Q-Learning, Chirag Rudrish

Master's Projects

Robot navigation in a multi-agent setting requires a balance between safety and efficiency, especially in dense environments. In these two-dimensional spaces, the scope for geometric errors is much less and could lead to collisions or immobility. This project proposes to address the navigation task using a two-phase path planning pipeline that combines reinforcement learning and convex optimization in a scalable and robust manner. The first phase consists of generating diverse collision-free paths using a Q-learning agent that is trained on a visibility graph representation of the environment. The discretization of the environment using waypoint-based graphs allows the agent to train …

Cost-Aware Predictive Routing For Vision-Language Models, Sahil Sait Naveed

Master's Projects

Vision-language models (VLMs) are increasingly used for multimodal tasks, but their inference costs vary widely across model tiers. This work presents a predictive routing framework that assigns each query to the most cost-effective VLM by using multimodal embeddings, clustering, and per-cluster model error estimates. Evaluated on six commercial VLMs, the selected router reduces average cost by about 49 percent relative to the quality-first setting on both validation and test. This comes with accuracy losses of 0.4 percentage points on validation and 0.9 on held-out test data. It also outperforms K-NN and ZeroRouter routing baselines while providing lowercost operating points than …

Stepwise Sudoku Reasoning Training Using Transformers, Lok Man Chu

Master's Projects

Sudoku is a constraint satisfaction problem that serves as a testbed for studying reasoning and stepwise deduction. While many solvers can produce correct solutions, they often fail to generate human interpretable sequences of logically consistent steps. This study investigates whether a transformer trained on stepwise deduction traces can learn to solve Sudoku puzzles through sequential, logically deducible moves. Results show that solve accuracy improves significantly with more training data, reaching approximately 78% at 1500k samples. Stepwise analysis indicates that the model effectively learns simple strategies, achieving near-perfect performance. However, performance on more complex strategies remains limited. Overall, while the model …

Adaptive Real-Time Fraud Detection Using Online Learning And Explicit Concept-Drift Detection, Purva Govind Tugaonkar

Master's Projects

Real-time credit card fraud detection faces challenges such as extreme class imbalance, delayed feedback, and concept drift in transaction streams. This project implements and evaluates an adaptive streaming fraud detection framework based on three methodologies: (1) online learning with incremental updates, (2) explicit conceptdrift detection using statistical monitoring, and (3) separate models for immediate and delayed supervision, combined with cost-sensitive learning and anomaly detection. The system processes the credit card fraud dataset in a batched streaming fashion, uses multiple online learners and ensembles. Experiments show that online, driftaware models maintain high recall on frauds while controlling false positives under imbalanced …

Multi-Agent Path Planning And Optimization Using Q-Learning, Chirag Rudresh

Master's Projects

Robot navigation in a multi-agent setting requires a balance between safety and efficiency, especially in dense environments. In these two-dimensional spaces, the scope for geometric errors is much less and could lead to collisions or immobility. This project proposes to address the navigation task using a two-phase path planning pipeline that combines reinforcement learning and convex optimization in a scalable and robust manner. The first phase consists of generating diverse collision-free paths using a Q-learning agent that is trained on a visibility graph representation of the environment. The discretization of the environment using waypoint-based graphs allows the agent to train …

Una Nueva Herramienta Para Estimar El Volumen Muscular Independientemente De La Edad, El Sexo Y El Nivel De Actividad Física, José J. Rivera-Sánchez, Laura P. Burgos-Morelos, Daniel Castillo-Castillo, Gerardo Méndez-Suárez, Juan C. Lopez-Alvarenga, Damián Carrillo-Ruíz

School of Medicine Publications

Antecedentes: Actualmente, la evaluación del volumen muscular requiere técnicas invasivas o de imagen, lo que aumenta los costos y retrasa la evaluación, y las mediciones antropométricas con frecuencia son inexactas.

Objetivo: Desarrollar una fórmula matemática para estimar el volumen del cuádriceps.

Material y métodos: Se evaluaron 127 sujetos: 40 adultos sedentarios de 20 a 35 años, 47 atletas adultos de 20 a 45 años y 40 adultos sedentarios de 70 a 94 años. Se dividieron en grupos de desarrollo y de validación. Se realizaron mediciones de variables antropométricas y fuerza de agarre, y se desarrolló la fórmula de predicción del …

News From The States, Blake Denton

The Southeastern Librarian

No abstract provided.