Digital Commons Network^™

Factors Influencing Eastern Wild Turkey (Meleagris Gallopavo Silvestris) Productivity And Nesting Chronology In West Virginia, Kirstin Palumbo

Graduate Theses, Dissertations, and Problem Reports (ETD)

Identifying drivers of variation in the timing of breeding behaviors, such as courtship activities and nesting chronology, is important for the effective management of avian species. Likewise, productivity is an important component of game bird population dynamics and can be broken up into multiple components such as the probability of nesting, clutch size, and nest success. One species that experiences a relatively long reproductive season is the Eastern wild turkey (Meleagris gallopavo silvestris). Contemporary research in the southeastern United States has raised concerns regarding declines in both wild turkey populations and overall productivity. West Virginia, however, is unique …

Dataless Neural Networks For Boolean Satisfiability And Network Optimization, Andrew Evan Gautier

Graduate Theses, Dissertations, and Problem Reports (ETD)

Combinatorial optimization problems (COPs) require searching over a finite solution space subject to constraints, with the goal of satisfying an objective function. They arise in operations research, scheduling, resource allocation, circuit design, and many other fields. Many problems in combinatorial optimization (including satisfiability and network design) are NP-hard. Traditionally, researchers have built approximate solvers that return near- optimal solutions efficiently by developing increasingly sophisticated heuristics and meta- heuristics. Deep learning has provided new opportunities for improving combinatorial solvers by leveraging neural guidance to prune the search space. Traditional neural networks have distinct drawbacks in this context: separate training and …

Multi-Timescale Monitoring And Modeling For Resilient Smart Grids: Pmu Anomaly Detection And Battery Digital Twins, Muhammad Imran Hossain

Graduate Theses, Dissertations, and Problem Reports (ETD)

Modern power systems face failures at very different timescales. Cyber-physical disturbances may emerge within seconds, while battery degradation develops over hundreds of operating cycles. Both monitoring problems share the same underlying difficulty: power systems produce measurement data in abundance, but reliably labeled examples of abnormal or degraded operation are scarce. Rare grid events are difficult to label, and battery degradation data are heterogeneous across cells, cycling protocols, and chemistries. This thesis addresses these challenges through two complementary domain-informed learning frameworks that constrain representation learning using information specific to each physical problem.

First, at the system level, T-BiGAN, a Transformer-augmented bidirectional …

Data Augmentation And The Reliability Of Conformal Prediction For Uncertainty Quantification In Medical Imaging, Rizwan Ahamed

Graduate Theses, Dissertations, and Problem Reports (ETD)

The safe clinical deployment of deep learning models for high-stakes medical imaging tasks requires more than high average accuracy; it requires demonstrable, per-case reliability. Uncertainty quantification (UQ) provides the missing signal that tells a clinician when a model prediction can be trusted and when a case should be escalated for expert review. Among UQ approaches, conformal prediction (CP) is especially attractive because it produces prediction sets that are guaranteed, under the assumption of exchangeability, to contain the true label with a user chosen probability, and it does so without assumptions about the model or the data distribution. This report first …

Does Diverse Seeding Adoption Reduce Production Costs? The Case Of Diverse Forage Seeding Strategies In West Virginia., Ivan Andres Wong

Graduate Theses, Dissertations, and Problem Reports (ETD)

Feed and harvested forage represent a major share of production costs in Appalachian cow-calf systems, where a pronounced summer forage gap forces producers to rely on purchased hay. This thesis evaluates whether adopting diverse forage seeding strategies reduces production costs relative to the existing pasture management practice, accounting for the spatial heterogeneity of West Virginia’s production environments. The analysis proceeds in two steps. First, an unsupervised K-means clustering of 14 farms enrolled in the NRCS Conservation Innovation Grant field trial groups the farms into two agroclimatic clusters, Highland-Cool and LowlandWarm, using elevation, growing-season Growing Degree Days, growing-season precipitation, and soil …

Energy Partition And Heating Mechanisms In Asymmetric Antiparallel Magnetic Reconnection, Aleksandra Abova-Volkova

Graduate Theses, Dissertations, and Problem Reports (ETD)

Magnetic reconnection is a fundamental plasma physics process whereby a change in magnetic field connectivity drives the conversion of magnetic energy into energy of the surrounding plasma, often explosively. One of the many settings where it occurs is at the dayside magnetopause of Earth and other planets, where the magnetic field, number density, and/or temperature in the plasmas on the two sides upstream of the reconnection site are asymmetric. How the incoming magnetic energy gets partitioned into outgoing energies during asymmetric reconnection remains an outstanding question; of special interest is the fraction that gets converted into internal energy of ions …

Manipulating Unit Price During Alternative Reinforcement Schedule Thinning In A Concurrent- Schedules Arrangement, Katherine A. Cucinotta

Graduate Theses, Dissertations, and Problem Reports (ETD)

Reinforcement schedule thinning, a critical component when addressing target behavior using differential reinforcement of alternative behavior schedules, aims to decrease alternative reinforcement rates while maintaining suppression of a target response (Davis et al., 2023; Hagopian et al., 2011). When multiple response options are available, manipulating the relative unit price across options alters the relative efficiency with which reinforcement is obtained and can shift responding toward an alternative behavior. Thus, altering the relative unit price can be a beneficial tactic when reinforcement for a target behavior cannot be withheld. Using concurrent-schedule arrangements, two experiments evaluated the conditions under which target response …

The Deposition, Prevalence, And Persistence Of Gunshot Residue In Standard And Less Lethal Ammunition, Liliana Claire Barbosa

Graduate Theses, Dissertations, and Problem Reports (ETD)

During the discharge of a firearm, gunshot residue (GSR) is released and settles into nearby surfaces, leaving traces that can provide valuable information for forensic investigations. This study aims to improve understanding of the deposition, prevalence, and persistence of standard and less-lethal ammunition through an analytical framework that examines collection and analysis protocols, revealing connections among multiple traces for effective use in investigations and courtroom proceedings.

Research into the chemical residues of less lethal ammunition (LLA) is a critical frontier in shooting investigations. While standard leaded ammunition has over a century of established literature, the chemical fingerprints of LLA residues …

Deposition And Diagenesis Of Permo-Triassic Gypsum From Wyoming And Texas: Saline Lake And Groundwater Systems Of Equatorial Pangea, Maya Yamei Bradford

Graduate Theses, Dissertations, and Problem Reports (ETD)

Knowledge of gypsum textures and the processes that yield them is important for interpreting the history of environments, climates, and life on Earth. This is because gypsum textures are products of specific environmental, microbiological, and diagenetic processes, and because gypsum is relatively common in the Earth’s sedimentary record. Despite this significance, gypsum is relatively understudied. The fundamental goal of this dissertation is to advance our understanding of gypsum textures by presenting detailed, petrography-based investigations of Permo-Triassic gypsum from the continental interior of Pangea.

Gypsum is typical in the Permo-Triassic sedimentary record of tropical Pangea, where it is associated with red …

Interpersonal Dark Side Of Impostor Thoughts: Predicting Workplace Ostracism Via Surface Acting And Cognitive Dissonance, And The Moderating Role Of Self-Leadership, Asiye Zeytonli

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation investigates the "interpersonal dark side" of workplace impostor thoughts

(WIT), exploring how the internal experience of feeling like a professional fraud translates into

the social exclusion of colleagues through defensive ostracism. While prior literature has

extensively documented the intrapersonal distress associated with the impostor phenomenon,

such as anxiety and burnout this research addresses a critical gap by examining how these

internal states drive behaviors that harm the organizational social fabric. Drawing upon an

integration of Conservation of Resources (COR) theory and Cognitive Dissonance Theory

(CDT), I propose and test a dual-pathway model. The first pathway (COR) suggests that …

Formation-Based Analysis In Contemporary Saxophone Practice: A Case Study Of Ziwei Shi's The Blue Lotus Bloms, Jiageng Ren

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation examines contemporary saxophone repertoire through a formation-based analytical approach, using Ziwei Shi’s The Blue Lotus Blooms as a case study. It is organized into three parts. Part I establishes the conceptual and methodological framework by introducing the concept of “formation-based analysis,” positioning the “condition of musical formation” as its core. Further situating the study within scholarship on practice-based research, cross-cultural theory, and modernism/postmodernism. Part II provides a detailed analytical and performance-based investigation of the work, including the collaborative commissioning process, the application of Thaat as a pitch resource, the role of extended techniques and embodied endurance, and contrasting …

Assessment Of Countermovement Jump In Athlete Status And Monitoring, Michael David Delong

Graduate Theses, Dissertations, and Problem Reports (ETD)

Utilizing a CMJ to test athlete physical abilities and training program efficacy is popular, as it is simple, noninvasive, and has significant relations with sport qualities. However, assessments often use discrete outcome measures. This research explored strategy-based measures accounting for eccentric power and time and may explain athlete status more effectively. The research objectives are to analyze the use of the mean power rate of the CMJ to compare athletes' playing ability and its usefulness in monitoring during off-season training. Additionally, power-time curve analysis added location and timing of effects due to fatigue or fitness adaptation. Proper testing and monitoring …

Adventures In Black Hole Binary Waveform Modeling, Siddharth Mahesh

Graduate Theses, Dissertations, and Problem Reports (ETD)

The modeling of black hole binaries requires accurate descriptions of both their relativistic vacuum dynamics and their interaction with surrounding astrophysical environments. Although these problems are often treated separately, this dissertation develops a unified view: key observables of black hole binary systems can be understood by identifying the dynamical instabilities that control their evolution. In the vacuum problem, the relevant unstable structure is associated with the remnant spacetime and its role in shaping the merger-ringdown gravitational waveform. In the environmental problem, the relevant instability is the epicyclic instability of orbits in the binary potential, which controls the location and evolution …

High-Resolution Laser Diagnostics For Ion Measurements In Magnetized Plasmas, Katey Stevenson

Graduate Theses, Dissertations, and Problem Reports (ETD)

In this work, we investigate ion heating in two plasma systems (helicon plasmas and flux rope plasmas) using the PHAse Space MApping (PHASMA) experiment. Our studies rely on high-resolution laser diagnostics, specifically laser-induced fluorescence (LIF), to measure ion velocity distribution functions (IVDFs). The temperature and bulk flow of each species are inferred from their respective VDFs. This dissertation is organized into three main parts: the study of ion heating in helicon plasmas, pulsed LIF modeling and diagnostic development, and the study of ion heating in flux rope plasmas. Helicon plasma sources efficiently generate high-density plasmas and are widely used to …

A Multiobjective Framework For Joint Coverage And Motion Planning In Uav Inspection Tasks, Luis Fernando Escobar Carvajal

Graduate Theses, Dissertations, and Problem Reports (ETD)

Unmanned Aerial Vehicles (UAVs) have become essential for data acquisition in complex 3D environments. However, traditional Coverage Path Planning (CPP) methodologies often rely on a sequential pipeline that isolates viewpoint generation from flight path routing. This decoupling fails to account for the interdependence between viewpoint distribution and minimum flight paths, effectively restricting the search space and preventing the identification of a global optimum. This dissertation proposes a unified multi-objective framework for joint coverage and motion planning. The primary contribution of this work is the transition from isolated, sequential steps to a simultaneous optimization of the number and position of viewpoints …

Multi-Grade Deep Learning, Yuesheng Xu

Mathematics & Statistics Faculty Publications

Deep learning requires solving a nonconvex optimization problem of a large size to learn a deep neural network (DNN). The current deep learning model is of a single-grade, that is, it trains a DNN end-to-end, by solving a single nonconvex optimization problem. When the layer number of the neural network is large, it is computationally challenging to carry out such a task efficiently. The complexity of the task comes from learning all weight matrices and bias vectors from one single nonconvex optimization problem of a large size. Inspired by the human education process which arranges learning in grades, we …

Multiple Changepoint Detection For Non-Gaussian Time Series, Robert Lund, Thomas J. Fisher, Norou Diawara, Michael Wehner

Mathematics & Statistics Faculty Publications

This article combines methods from existing techniques to identify multiple changepoints in non‐Gaussian autocorrelated time series. A transformation is used to convert a Gaussian series into a non‐Gaussian series, enabling penalized likelihood methods to handle non‐Gaussian scenarios. When the marginal distribution of the data is continuous, the methods essentially reduce to the change of variables formula for probability densities. When the marginal distribution is count‐oriented, Hermite expansions and particle filtering techniques are used to quantify the scenario. Simulations demonstrating the efficacy of the methods are given and two data sets are analyzed: 1) the proportion of home runs hit by …

Adaptive Multi-Grade Deep Learning For Highly Oscillatory Fredholm Integral Equations Of The Second Kind, Jie Jiang, Yuesheng Xu

Mathematics & Statistics Faculty Publications

This paper studies the use of Multi-Grade Deep Learning (MGDL) for solving highly oscillatory Fredholm integral equations of the second kind. We provide rigorous error analyses of continuous and discrete MGDL models, showing that the discrete model retains the convergence and stability of its continuous counterpart under sufficiently small quadrature error. We identify the DNN training error as the primary source of approximation error, motivating a novel adaptive MGDL algorithm that selects the network grade based on training performance. Numerical experiments with highly oscillatory (including wavenumber 500) and singular solutions confirm the accuracy, effectiveness and robustness of the proposed approach.

A New Functional Setting For Term Structure Modeling Using The Health-Jarrow-Morton Framework, Michael Pokojovy, Ebenezer Nkum, Thomas M. Fullerton Jr.

Mathematics & Statistics Faculty Publications

The well-known Heath–Jarrow–Morton (HJM) framework provides a universal and efficacious instrument for modeling the stochastic evolution of an entire yield curve by explaining the interest rate dynamics in continuous time under no-arbitrage conditions. Existing implementations involve exponentially weighted function spaces as theoretical settings for the former stochastic evolution. While the choice of weight can have a drastic effect on model calibration and subsequent forecasting, it cannot be estimated from market data and does not allow for any objective interpretation. The proposed approach does not have this shortcoming as it adopts a suitably designed unweighted function space. The HJM equation is …

The Adaptive Solution Of High-Frequency Helmholtz Equations Via Multi-Grade Deep Learning, Peiyao Zhao, Rui Wang, Tingting Wu, Yuesheng Xu

Mathematics & Statistics Faculty Publications

The Helmholtz equation is fundamental for modeling wave propagation in acoustics, electromagnetics, and geophysics; however, high-frequency regimes remain notoriously difficult due to the severe numerical “pollution effect.” We propose FD-MGDL, an adaptive framework that synergizes finite difference discretizations with Multi-Grade Deep Learning (MGDL) to efficiently resolve high-frequency wavefields. Unlike standard physics-informed neural networks (PINNs), which frequently suffer from spectral bias and heavy automatic differentiation overhead, FD-MGDL employs a progressive, grade-wise training strategy that incrementally incorporates shallow sub-networks to refine residual errors. By leveraging ReLU activations in refinement grades, the framework reformulates the highly non-convex global optimization problem into a sequence …

The Mercury 2026

The Mercury

No abstract provided.

Reinforce Trustworthiness In Multimodal Emotional Support System, Huy M. Le, Dat Tien Nguyen, Ngan T. T. Vo, Tuan D. Q. Nguyen, Nguyen Le Binh, Duy Minh Ho Nguyen, Daniel Sonntag, Lizi Liao, Binh T. Nguyen

Research Collection School Of Computing and Information Systems

In today's world, emotional support is increasingly essential, yet it remains challenging for both those seeking help and those offering it. Multimodal approaches to emotional support show great promise by integrating diverse data sources to provide empathetic, contextually relevant responses, fostering more effective interactions. However, current methods have notable limitations, often relying solely on text or converting other data types into text, or providing emotion recognition only, thus overlooking the full potential of multimodal inputs. Moreover, many studies prioritize response generation without accurately identifying critical emotional support elements or ensuring the reliability of outputs. To overcome these issues, we introduce …

Cross-Modal Proxy Evolving For Ood Detection With Vision-Language Models, Hao Tang, Yu Liu, Shuanglin Yan, Fei Shen, Shengfeng He, Jing Qin

Research Collection School Of Computing and Information Systems

Reliable zero-shot detection of out-of-distribution (OOD) inputs is critical for deploying vision-language models in open-world settings. However, the lack of labeled negatives in zero-shot OOD detection necessitates proxy signals that remain effective under distribution shift. Existing negative-label methods rely on a fixed set of textual proxies, which (i) sparsely sample the semantic space beyond in-distribution (ID) classes and (ii) remain static while only visual features drift, leading to cross-modal misalignment and unstable predictions. In this paper, we propose CoEvo, a training- and annotation-free test-time framework that performs bidirectional, sample-conditioned adaptation of both textual and visual proxies. Specifically, CoEvo introduces a …

Look, Compare And Draw: Differential Query Transformer For Automatic Oil Painting, Lingyu Liu, Yaxiong Wang, Li Zhu, Lizi Liao, Zhedong Zheng

Research Collection School Of Computing and Information Systems

This work introduces a new approach to automatic oil painting that emphasizes the creation of dynamic and expressive brushstrokes. A pivotal challenge lies in mitigating the duplicate and common-place strokes, which often lead to less aesthetic outcomes. Inspired by the human painting process, i.e., observing, comparing, and drawing, we incorporate differential image analysis into a neural oil painting model, allowing the model to effectively concentrate on the incremental impact of successive brushstrokes. To operationalize this concept, we propose the Differential Query Transformer (DQ-Transformer), a new architecture that leverages differentially derived image representations enriched with positional encoding to guide the stroke …

Compendia: Automated Visual Storytelling Generation From Online Article Collection, Manusha Imesh Karunathilaka Gamage Vidana, Litian Lei, Yiming Gao, Yong Wang, Jiannan Li

Research Collection School Of Computing and Information Systems

In the digital age, readers value quantitative journalism that is clear, concise, analytical, and humancentred. To understand complex topics, they often piece together scattered facts from multiple articles. Visual storytelling can transform fragmented information into clear, engaging narratives, yet its use with unstructured online articles remains largely unexplored. To fill this gap, we present Compendia, an automated system that analyzes online articles in response to a user’s query and generates a coherent data story tailored to the user’s informational needs. Compendia addresses key challenges of storytelling from unstructured text through two modules covering: Online Article Retrieval, which gathers relevant articles; …

Dietary Ecology Of Waterfowl Nest Predators In The Prairie Pothole Region: Evaluating Resource Use From Species To Individual Using Stable Isotope Analysis, Grace Barthelmess

EWU Masters Thesis Collection

Nest predation is a primary factor influencing waterfowl productivity in the Prairie Pothole Region (PPR) of North America. Although this system is intensely managed and studied, research has largely focused on how predators affect waterfowl populations, with comparatively little attention to how predators actually rely on waterfowl as a resource. Understanding predator feeding ecology is therefore critical for informing effective management strategies and interpreting the complexity of predator-prey interactions on this landscape. My objective overall was to elucidate the feeding ecology of nine focal predator species commonly associated with waterfowl nest predation in the Aspen Parkland Ecoregion of the PPR: …

Relationships Between Benthic Macroinvertebrate Communities And Environmental Conditions In Headwater Tributaries Of The Pend Oreille Lake Watershed, Idaho, Devlin Mee

EWU Masters Thesis Collection

Benthic macroinvertebrates are widely used indicators of stream condition, and their communities respond to thermal and land-use conditions in headwater streams. This study characterized macroinvertebrate biomass, community composition, and diversity across forested headwater tributaries of the Pend Oreille lake watershed in northern Idaho, and examined their relationships with summer temperature, land development, and road proximity. Samples from thirteen streams collected in 2024 were identified to genus, measured, and converted to estimated biomass using published length–mass regressions; ten streams had recovered temperature loggers. Because environmental variables were characterized once per stream, all analyses were conducted at the stream level.

Estimated total …

Use Of Thyroid Imaging Reporting And Data Systems (Tirads) Ultrasound In Differential Diagnosis Of Thyroid Nodules, Aya Mohamed Hamed Abdelaal, Ghada M Ahmed Gaballah, Donia Mohamed Sobh

Mansoura Medical Journal

Background: Thyroid nodules refer to masses of abnormally proliferating cells with abnormal tissue stiffness in thyroid. This study aimed to assess diagnostic accuracy of Thyroid Imaging Reporting and Data System in differentiating benign from malignant thyroid nodules.

Methods: This cross-sectional study included forty adult patients with sixty-two thyroid nodules. A 7.5-10 MHz linear array transducer was used to perform imaging protocol. Three-dimensional measurements were done on nodules. All thyroid nodules were  examined using doppler ultrasound. All thyroid nodules were described in terms of composition, echogenicity, margins, shape and echogenic foci.  According to ACR TI-RADS,  the points were given …

Serum Magnesium Level And Carotid Artery Calcification And Stiffness In Dialysis Patients: Single Center Cross-Sectional Study, Kareem Ahmed Elbosaty, Alaa Abd Elaziz Sabry, Gehad Ahmad Saleh, Nehal Atef Elshabrawy

Mansoura Medical Journal

Introduction Vascular calcification is common among end-stage renal disease (ESRD) patients on regular dialysis, either hemodialysis or peritoneal dialysis. Magnesium is suggested to play a role in inhibiting that process. Recently, Mg started to be considered one of the factors that protect against vascular calcification and consequently decrease CVD mortality rate in CKD patients. Many recent studies support that role although the mechanism of that protective effect is not clear. But it is supposed to be a combination of both passive and active mechanisms. The passive one occurs through interference with calcium phosphate deposition and hydroxyapatite inhibition by phosphate binding. …

Dyno : Dynamic Neurosymbolic Orchestrator For Multi-Agent Systems, Ritvik Garimella, Chathurangi Shyalika, Renjith Prasad, Amit Sheth

Publications

Large Language Model (LLM)-based multi-agent systems (LaMAS) represent an emerging paradigm for tackling complex, multi-step reasoning and decision-making problems. As these systems scale, orchestration, which is the ability to coordinate, manage, and evaluate the interactions among diverse agents, becomes central to their success. While recent orchestrators such as AgentFlow have demonstrated promise in managing communication and task delegation, they remain limited in their ability to understand task semantics, coordinate heterogeneous agent types (e.g., reactive vs. cognitive), and adaptively align outputs with human-defined goals. In this position paper, we introduce the DYNO (Dynamic Neurosymbolic Orchestrator), a system developed as part of …