Digital Commons Network^™

Isolation Of Essential Oils From Oregano Leaves Via Steam Distillation And Extraction, Alyssa Brasko, Madison Fauntleroy, Sophia Bordone

Discovery Day - Daytona Beach

Organum vulgare, also known as Oregano, is a fragrant herb in the mint family widely used in culinary and medical applications. The essential oil of oregano contains various compounds, with carvacrol and thymol being the primary constituents responsible for the herb’s distinctive aroma and antimicrobial properties. Carvacrol, a chemical compound found in oregano oils, has a molecular formula of C₁₀H₁₄O, with carvacrol presenting a phenolic structure that contributes to its biological activity. Additionally, the compound has three double bonds giving the compound four units of unsaturation. Due to its properties, oregano oil has historically been used …

Enhanced No₂ Gas Sensing Using Silver-Doped Cadmium Telluride Nanocrystalline Thin Films, Tunis Balasim Hassan

Karbala International Journal of Modern Science

Nitrogen dioxide (NO₂) is a toxic pollutant that necessitates sensitive and reliable monitoring systems. Conventional gas sensors often lack adequate responsiveness and fast recovery under changing conditions and therefore create a need for semiconductors with enhanced performance, especially at high industrial temperatures (around 250 °C). The study therefore aims to synthesis and evaluate silver-doped cadmium telluride (Ag:CdTe) thin films as NO₂ gas sensors. Pure CdTe and Ag:CdTe with silver concentrations of 5, 10, and 15 wt% were prepared by a co-precipitation process. XRD verified cubic symmetry with a progressive fall in crystallite size (6.67 nm to 5.46 nm at 15 …

An Integrated Framework For Memory-Centric Analysis: From Trace Collection To Co-Design, Dhruv Gajaria, Prajwal Challa, Yasodha Suriyakumar, Joseph Manzano, Nathan Tallent, Andrés Márquez

Computer Science Faculty Publications and Presentations

IntroductionThe memory wall phenomenon—where advances in processor performance significantly outpace those in memory subsystems-poses a fundamental challenge for contemporary computing systems. In memory-bound applications, memory subsystem behavior dominates performance, yet existing analysis approaches present significant limitations: detailed microarchitectural simulators require days to weeks to simulate modest workloads; hardware performance counters provide only aggregate statistics that obscure temporal and spatial access patterns; and scaled simulation approaches face challenges in capturing contention effects, bandwidth saturation, and interference patterns that emerge at larger scales. These limitations reflect a processor-centric design philosophy—in both performance analysis tools and system co-design methodologies—that is increasingly misaligned with …

Design Considerations For Hypertension Chronotherapy Trials: Insights From Experience And Modelling, Olivia Walch, Amy Rogers, Yitong P. Huang, Marc D. Ruben, Kenneth A. Dyar, Robert W. V. Flynn, Isla S. Mackenzie, Roberto Manfredini, Francesco P. Cappuccio, Filippo Pigazzani

Mathematics Sciences: Faculty Publications

Chronotherapy aims to maximise treatment efficacy while minimising side effects by scheduling treatment according to personal biological rhythms. In recent years, randomised clinical trials (RCTs) have been conducted to evaluate whether scheduled blood pressure interventions can improve patient outcomes. However, reports of time-of-day effects have attracted rebuttals and engendered methodological debate. A perfectly controlled chronotherapy trial (i.e., a trial that assesses the effect of assigning time of intervention) will never be feasible in the real world; yet some factors may be more critical to consider and control for than others. To advance the conversation about how best to evaluate the …

Storyteller: Training-Free Narrative Grounding And Forseebench: Evaluation For Long Form Audio Description, Seung Hyun Hahm

Dartmouth College Master’s Theses

Understanding long-form video requires tracking events, motivations, and relationships across time rather than describing isolated frames. However, existing video--language models (VLMs) and audio description (AD) systems often generate short-horizon descriptions that omit narrative context, causal intent, and story continuity, limiting accessibility for blind and low-vision (BLV) audiences. This thesis investigates how long-form AD can be grounded in narrative memory without relying on expensive supervised training pipelines or heavily curated annotations.

We propose StoryTeller, a training-free retrieval-augmented framework for long-form audio description. Instead of depending solely on frame-level perception, StoryTeller summarizes observations into structured narrative facts that capture who did what …

Approval Motivations In Sharing Humorous Tiktok's, Mariam Al-Areedy

InnovateHER Meeting 2026

TikTok is a short-form video platform where users create and share content that is often centered around humor, trends, and everyday social experiences. In face-to-face interactions, people typically rely on immediate feedback to navigate conversations, often using approval seeking behaviors to gain positive reactions and rejection-avoidant behaviors to reduce the risk of negative judgement. While these motivations are well-established in in-person settings, less is known about how they function in digital environments like TikTok, where teens privately share humorous content without immediate social cues to guide their interactions. My general hypothesis was that both rejection avoidance and approval-seeking behaviors will …

Parameter Estimation In Ode Models Using Least-Squares Regression, Ulrich A. Hoensch

CODEE Journal

We present a method of estimating model parameters for non-linear ODEs using least-squares regression. The coefficient of determination can be used as a measure of model fit. The method is demonstrated using US population data to fit a logistic growth model. Also, a competing species model is used to describe the interaction of two different species of yeast.

Butte Priority Soils Operable Unit Butte Reduction Works Smelter Area Intermediate 60% Remedial Design Submittal, Pioneer Technical Services, Inc., Atlantic Richfield Company

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Machine Learning For Modeling In An Elementary Differential Equations Class, Nathan Albin, Andrew G. Bennett, Abhinav Chand

CODEE Journal

Mixing machine learning with modeling is an area of increasing importance. This paper presents a lesson where students model a spring-mass system both using traditional analysis with linear damping and using machine learning to learn the damping from real data. The machine learning is implemented in a Jupyter notebook hosted on Google Colab, allowing students to train the neural network without requiring the students to carry out coding. Students get experience with how machine learning can fail, how it can work, and the time and data requirements for machine learning to succeed, and are asked to apply this knowledge to …

Exploring Resource-Efficient Deep Learning For Medical Image Segmentation, Pallabi Dutta

Doctoral Theses

Automated medical image segmentation improves diagnostic accuracy by au tomating the precise delineation of target anatomical structures in the input images. Artificial Intelligence (AI), and specifically, Deep Learning (DL), has emerged as a state-of-the-art approach for this task. However, the significant computational demands of DL approaches often hinders their deployment. Ad vanced models, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), require substantial processing power and a large memory footprint, limiting their use in resource-constrained settings. This thesis aims to address this challenge by developing a series of novel, resource-efficient DL models that achieve high segmentation accuracy with reduced …

3d Puzzle Generation Beyond Voxelized Parts, Iris Xia

Computer Science Theses

Burr puzzles are interlocking assemblies whose pieces must be inserted and removed through tightly constrained motions. Designing them is difficult because geometric fit, interlocking behavior, and disassembly order are tightly coupled, while existing computational methods remain largely limited to voxelized or template-based constructions.

This work presents a framework for 3D puzzle generation beyond voxelized parts. The method replaces local mobility heuristics with a certified search over geometry edits. Starting from a topological contact specification, it constructs signed distance fields for individual parts, applies complementary local edits, and validates each candidate using exact geometric checks and a kernel disassembly graph. The …

Node Differentially Private Algorithms For Survivable Networks And Graphs Analysis, Jinghua Sun

Computer Science Theses

This thesis studies two graph algorithmic settings where additional structure gives stronger guarantees than worst-case black-box methods. The paper considers higher order edge connectivity under node differential privacy. We study the minimum k-edge-connected spanning subgraph problem (k-ECSS) and the minimum k-edge-connected component problem (k-ECC). These objectives have large global sensitivity under node privacy, since adding or deleting one vertex and its incident edges can significantly change robust connectivity structure. To address this, we use Propose-Test-Release for locally stable k-ECC instances and a Lipschitz extension framework for k-ECSS, based on bounded-degree complement objectives and the Generalized Exponential Mechanism.

The second part …

Multi-Level Variable Selection Using A Bart-Enhanced Mixed-Effects Framework, Keming Zhang, Yaoyao Li, Jungang Zou, Sijian Wang, Bernadette A. Fausto, Liangyuan Hu

College of Health Professions Faculty Papers

Selecting important individual- and cluster-level predictors has become increasingly critical in healthcare research, where data often exhibit hierarchical structures due to collection from multiple clusters. Mixed-effects models, which account for within-cluster correlation and between-cluster heterogeneity, are a natural approach for multilevel variable selection. However, currently available variable selection methods for multilevel data are predominantly based on mixed-effects models that impose restrictive parametric assumptions, potentially limiting their utility when the underlying relationships are nonlinear or involve interactions. While nonparametric methods have shown promise for variable selection in non-clustered data, they have been much less studied in the multilevel setting. Moreover, nonparametric …

Robert, Powers; Unl Chemistry; Nmr-Assisted Drug Discovery, Mark Griep, Robert Powers

Department of Chemistry: Faculty Interviews

Dr. Robert Powers became a chemistry professor at the University of Nebraska-Lincoln in 2003 and is about to retire after 23 years. Prior to UNL, Bob was a drug discovery researcher for 11 years at American Cyanamid, which eventually became Wyeth and is now part of Pfizer. Bob was born in Jersey City, New Jersey. Something in his youth must have sparked an interest in chemistry because he earned a bachelor's in that subject from Rutgers University in New Brunswick. Then he traveled 1200 miles west to Purdue University in Indiana where he earned his doctorate. Next, he did postdoctoral …

A Formal Ontology Of Combat Feel, Grayson Julian Von Goetz Und Schwanenfliess

LMU Theses and Dissertations

Combat feel, the moment-to-moment subjective character of real-time melee combat in ac- tion games, is a central concern of game design and a recurring subject in design literature, but practitioners currently navigate it through intuition and reference to admired prior work, with no shared formal vocabulary for the design trade-o!s being made. This thesis presents a decision-theoretic framework that formalizes combat feel as a Bayesian network in which designer decisions act as interventions on measurable system variables, those variables drive latent perceptual states whose conditional distributions are grounded in the psychophysics literature on input-lag detection, duration discrimination, and audiovisual temporal …

Real-Time Instruction Internalization For Large Language Models, Brenden Smith

Theses and Dissertations

For the end user, Large Language Models (LLMs) are programs that process natural language inputs into natural language outputs. In popular usage, this tends to take the form of conversation: a user asks a question, provides information, or gives instructions, and the LLM (hopefully) replies in a manner we would expect of an informed and compliant person. While convenient and intuitive for users, this natural conversational format encourages the misconception that LLMs are learning from conversations, when they do not. This work presents the benefits and practicality of a language model paradigm that meets this user expectation -- that is, …

Formation Mechanism Of High-Quality Reservoirs In The Shanxi Formation Of The Qingyang Gas Field In A Meandering River Delta Setting, Xingming Duan, Shu Liu, Meng Wang, Yecan Fan, Xiyu Wang, Xinan Yu, Zubing Li

Turkish Journal of Earth Sciences

As global energy demand rises, unconventional gas resources, particularly tight gas reservoirs, have become increasingly important for future energy supply. Located in the southwestern Ordos Basin, the Qingyang gas field is a newly discovered deep tightgas field with proven geological reserves exceeding 31.8 × 109 m3 and has become a strategic focus for deep-gas exploration in China. Despite rapid appraisal that delineated several stable gas-bearing zones, a comprehensive understanding of the sedimentary architecture, diagenetic transformation, and enrichment mechanisms of high-quality reservoirs in the Permian Shanxi Formation remains incomplete. Focusing on the Shan 1 Member (hereafter referred to as Shan 1), …

Spectral Classification Of Diverse Lithologies Using Multi- And Hyperspectral Satellite Data: A Comparative Study, Önder Gürsoy, Emre Özelkan, Rutkay Atun, Ayşe Betül Çalişkan, Ahmet Efe

Turkish Journal of Earth Sciences

Accurate lithological mapping requires selecting the appropriate remote sensing data and classification methods. This study evaluates the performance of four satellite datasets—Landsat 8 OLI, Sentinel-2A, ASTER, and Hyperion EO-1—using three spectral classification techniques: Matched Filtering (MF), Spectral Angle Mapper (SAM), and Spectral Information Divergence (SID). The study area is located between the Zara and Koyulhisar districts in eastern Türkiye and comprises diverse lithological units. A total of 49 rock samples collected in the field were used for validation. The results indicate that MF consistently outperformed the other methods, achieving the highest accuracy with Landsat 8 (Kappa = 94.2%). ASTER data …

Geometric Structure In High-Dimensional Representations: Theory And Applications To Language, Jiayi Chen

Dartmouth College Ph.D Dissertations

This thesis develops a geometric perspective on high-dimensional representations, motivated by applications to language. Rather than treating representations solely as inputs to predictive models, we view them as structured objects whose geometry encodes meaningful information. In particular, we argue that such representations exhibit organization at multiple scales: at a global level, metric and clustering structure capture relationships such as genre, authorship, and discourse; at a local level, geometric quantities such as intrinsic dimension and curvature describe how these relationships vary across the space.

To study these phenomena, we combine empirical analysis with theoretical development. On the empirical side, we examine …

Draft Final 2024 Unreclaimed Sites Sampling: Ur-21 Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Improving Explainability And Interpretability Of Neural Networks Via Hyperparameter-Extended Influence Functions, William Breslin

Dissertations and Theses

Understanding how model predictions and training outcomes vary with changes in data, features, and modeling choices is central to explainable artificial intelligence. This dissertation introduces a unified framework for explainability by generalizing classical influence functions to encompass user-defined hyperparameters embedded in the training loss, model architecture, or data representation. By extending influence functions in this way, the framework broadens their applicability and integrates multiple explainability techniques into a single, coherent approach. It provides a common mathematical foundation linking data impact, feature importance, and model design analysis, and supports a broad class of additional explainability analyses beyond these settings. The demonstrated …

Hyperspectral Image Classification Using Novel 1-D And 2-D Deep Neural Networks, Özlem Polat, Zümray Dokur, Tamer Ölmez

Turkish Journal of Earth Sciences

Hyperspectral image (HSI) classification is of critical importance in many fields including agriculture, geology, environmental monitoring, and urban planning. In recent years, many researchers have utilized deep neural networks (DNNs), known for their high performance in the classification of HSIs. When 2-D/3-D convolutional neural networks are used in HSI classification, filters are applied using input patches typically larger than 11 × 11. This allows spectral and spatial features to be evaluated together. However, this combination creates several problems. Because HSIs have low spatial resolution, they often do not contain strong texture details. Furthermore, features with little relevance to classification make …

Applicability Of Machine Learning For Shear-Wave Velocity Prediction From Conventional Well Logs: The Lsboost Approach, Rahmat Catur Wibowo, Fadsyah Muhammad Arby, Bagus Sapto Mulyatno, Ordas Dewanto, Isti Nur Kumalasari, Muh Sarkowi

Turkish Journal of Earth Sciences

Shear-wave velocity (Vs) is one of the most critical parameters for determining geomechanical properties and basin overpressure. However, assessing Vs via techniques like core analysis requires considerable effort and expense. This study predicts Vs using several approaches and compares the accuracy levels of all models. For this objective, the multiple linear regression, multiple linear stepwise regression, support vector machine, and least-squares boost (LSBoost) methodologies were selected. The six well-logging data inputs of density (RHOB), gamma-ray (GR), deep resistivity (ILD), acoustic wave velocity (Vp), shale volume (VCL), and water saturation (SW) were selected as effective variables, whereas Vs was regarded as …

A Shallow Landslide Hazard Assessment Using A Probabilistic Approach: An Example From Northeastern Türkiye (Beşikdüzü, Trabzon), Kübra Tezel, Aykut Akgün

Turkish Journal of Earth Sciences

The objective of this study was to identify and assess shallow landslide hazard in both spatial and temporal terms within the boundaries of Beşikdüzü District in northeastern Türkiye. The workflow was initiated with the development of a detailed multitemporal mass‑movement inventory map derived from satellite imagery provided on the Google Earth platform,1 covering the period between 2000 and 2018. Inventory mapping was complemented by extensive field verification campaigns to identify discrepancies, confirm spatial accuracy, and document additional morphological details that could not be detected from imagery alone. A 10-m spatial resolution digital elevation model (DEM) was generated from 1:25,000-scale digital …

Geoelectrical Signatures Of The Giant Karaburun Pelitic-Mafic-Type Volcanogenic Massive Sulfide Mineralization In The Central Pontides (Türkiye), Kurtuluş Günay, Türker Yas, Buğra Çavdar, Ertan Pekşen

Turkish Journal of Earth Sciences

The Karaburun deposit, hosted in greenschist facies metamorphic rocks, is a newly discovered giant volcanogenic massive sulfide (VMS) deposit in Anatolia and provides an exceptional natural laboratory for geophysical monitoring. The main ore body is less affected by metamorphism than the surrounding wall-rocks, where metamorphic and metasomatic processes formed pyritemagnetite- sericite-quartz assemblages that significantly influence geoelectrical properties. In this study, the geometry and extent of mineralization within geologically defined target zones were investigated using direct current resistivity and two-dimensional timedomain induced polarization (2D-TDIP) methods and the results were compared with the geology. A pole–dipole electrode array was employed in the …

Design And Implementation Of Error Estimators For Finite Element Eigenvalue Problems, Gabriel Esteban Pinochet Soto

Dissertations and Theses

We present three publications, all encompassed under the umbrella of a posteriori error estimation theory for eigenvalue problems for finite element discretizations. The central objective of the research is the development of a general framework for the study of reliable estimation of eigenvalues and eigenspaces. We introduce applications to problems of theoretical interest as well as problems arising in real-life scenarios, such as optical fibers. The first paper focuses on the implementation of a dual-weighted residual error estimator for a nonselfadjoint eigenvalue problems arising from the study of leaky modes in optical fibers---Maxwell's equations, Perfectly Matched Layers, and a conforming …

A Quantum Phase Space Description Of Local Noise In Atomic Ensembles, Andrew Kolmer Forbes

Physics & Astronomy ETDs

Nonclassicality in quantum sensors can improve sensitivity, but often increases susceptibility to noise. Thus, modeling physically relevant noise sources and analyzing their effect on quantum metrology are both of importance to the field of quantum sensing. In this dissertation, I demonstrate that local noise sources, which are present in almost all many-spin systems, can be tractably modeled when assuming permutation symmetry of the noise, and we show that many common local noise sources can be mapped to a Fokker-Planck equation on quantum phase space. We apply this description of noise to study quantum sensing using noisy probe states and establish …

Optical Nuclear Spin Detection In Diamond And Varifocal Metasurface Optics, Maxwell D. Aiello

Physics & Astronomy ETDs

This dissertation presents two experimental investigations at the intersection of quantum sensing and precision optical instrumentation. The primary project demonstrates optically detected nuclear magnetic resonance (NMR) of ¹³C nuclear spins in diamond, using state-selective Landau-Zener transitions under microwave frequency sweeping to bidirectionally transfer spin polarization between nitrogen-vacancy (NV) electron spins and remote ¹³C nuclear spins. This enables optical polarization and readout of large ensembles of polarized nuclear spins at low magnetic fields and room temperature, with spin dephasing times limited by longitudinal relaxation of nearby NV electron spins. The secondary project reports the design, fabrication, and characterization of …

Quantifying Co2 And Ch4 Fluxes In A Semi-Arid Floodplain: Assessing Spatial And Temporal Drivers, Miles E. Kelsey

Earth and Planetary Sciences ETDs

Rivers transport sediment and carbon across Earth’s surface, and their floodplains can store carbon over decades to millennia, making them important to terrestrial carbon management. While soil carbon can persist long term, it may be released as greenhouse gases through processes like methanogenesis and heterotrophic respiration. Environmental controls on these fluxes remain poorly constrained across floodplains in different climate and geomorphic setting, but especially in semi-arid systems where measurements are limited. To address this gap, we quantified CO₂ and CH₄ fluxes along the Middle Rio Grande (New Mexico, USA) using 227 chamber measurements collected May to November 2025 at three …

An Analytical Framework For Quantifying Urban And Community Resilience To Natural Hazards From Cell-Phone Gps-Location And Traffic-Flow Data, Georgios Chatzikyriakidis

Civil and Environmental Engineering Theses and Dissertations

Urban areas are increasingly exposed to natural hazards while accommodating a growing share of the global population, yet a consistent science-based framework for quantifying urban and community resilience remains lacking. This dissertation develops a physics-based analytical framework grounded in statistical mechanics and the quantitative theory of Brownian motion. A city is conceptualized as a complex medium in which citizens move analogously to Brownian particles within a viscoelastic environment, influenced by socioeconomic interactions and infrastructure functionality.

A central premise is that urban resilience, interpreted as engineering resilience (an outcome), can be quantified through a single metric: the mean-square displacement MSD=⟨r²(t)⟩, of …