Digital Commons Network^™

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 …

2026 January, Morehead State University. Office Of Communications & Marketing.

Morehead State Press Release Archive, 1961 to the Present

Press releases for January of 2026.

Regulating Galaxy Growth: Ionized Gas Conditions, Star Formation Histories, And Environmental Effects, Shweta Jain

University of Kentucky Doctoral Dissertations

Understanding galaxy formation and evolution requires tracing how baryons cycle between stars, gas, and the interstellar medium (ISM) across cosmic time. The efficiency of star formation, the buildup of metals, and the regulation of gas inflows and outflows are governed by evolving physical conditions within galaxies and their environments. My thesis investigates how the ionized ISM, star formation histories, and environmental processes have shaped galaxy evolution over the past ∼ 12 Gyr. The primary focus of this work is the evolution of the physical conditions of ionized gas in galaxies, including gas-phase metallicity and ionization state. Using new and archival …

Multiscale Modeling Of The Thermomechanical Behavior Of Polymeric And Molecular Organic Semiconductors, Kehinde H. Fagbohungbe

University of Kentucky Doctoral Dissertations

Organic semiconductors, derived from π-conjugated polymers and molecules, enable the development of deformable, stretchable and flexible electronics due to their tunable redox, optical, electronic and mechanical properties. However, an informed understanding of how multi-scale morphological characteristics of the polymeric and molecular semiconductors influence bulk properties that contribute to electronic and optical performance, especially under operational thermal and mechanical stresses, remains incomplete. This lack of understanding poses a challenge to scalability and commercialization of organic electronics. This dissertation develops and deploys computational modeling approaches, particularly atomistic molecular dynamics (MD) simulations, to investigate the multiscale morphological behavior of these synthetic semiconducting materials …

Promptable Segmentation For Adaptive And Data-Efficient Medical Image Analysis, Tyler Ward

University of Kentucky Doctoral Dissertations

Image segmentation is a fundamental task in computer vision. While segmentation models have traditionally been trained in a fully-supervised manner, recent approaches have leveraged large-scale pre-training and prompting mechanisms to great effect. However, the performance of such approaches often degrades when applied to domain-specific tasks like medical image analysis. A major reason for this lies is that these models are trained on large, labeled datasets of natural images, which have drastically different characteristics compared to medical images, limiting the generalizability of the methods when applied to medical data. This dissertation presents several data-efficient, adaptive, and promptable medical image segmentation models. …

Personality Exceptions: Enhancing Character In Narrative Planners With Internal Memory, Elinor Rubin-Mcgregor

University of Kentucky Doctoral Dissertations

Stories and narratives are powerful tools for forging connections between people, as they induce emotions shared through their audience and help build empathy and understanding through sharing unique perspectives. Interactive stories can do all this and more, as they can be used to better educate and engage their audience. But in such stories, the characters must have consistent personality and human-like emotions in order for the audience to relate to them. Current implementations of personality in interactive narratives either portrays characters with static personality that feel less realistic, or else require a great deal of additional work from the author …

Coexistence Between Ci And Feminization Inducing Symbionts: A Theoretical Perspective, Dylan Thorp

University of Kentucky Master's Theses

Endosymbiotic bacteria that manipulate the reproduction of their host are abundant in arthropods. Theoretical efforts to model these bacteria have seldom included symbionts that feminize their hosts, and those that do have assumed an environmental sex determination system. Here, I expand upon other theoretical works to model the dynamics of a host population with an XX/X0 sex determination system that contains separate CI and feminization inducing symbionts. I found that coexistence cannot occur without double infection, consistent with previous models. However, I also found that without double infection, the presence of the feminizing symbiont can facilitate the spread of the …

Development Of An Atmospheric Particle Imaging System (Apis), James W. Hebert

University of Kentucky Master's Theses

The study of atmospheric particulates plays a crucial role in both climate change studies and the improvement of hypersonic travel. The current issue at hand is a lack of knowledge of stratospheric particulate content and the high cost that is associated with conducting research at these altitudes. This project focuses on the development of an Atmospheric Particle Imaging System (APIS) to be a low-weight, low-cost optical payload. The goal is to make a device that can be mounted onto an uncrewed aerial system that has the capability of imaging particulates moving at steady flow velocities, and thereby characterize the individual …

2026 Guide To Planned Giving, Morehead State University. Office Of Communications & Marketing., Morehead State University Foundation.

Communications and Marketing Publications Archive

Theh 2026 Guide to Planned Giving from the Morehead State University Foundation.

Child Of My Heart: One Girl's Story Of Adoption, Danielle Bussolini

Graduate Student Independent Studies

This Integrative Masters Project explores the author's personal experience with adoption, written from a child's perspective. Unlike most published books on adoption, which are typically framed through the lens of an educator or parent, this work centers the emotional and psychological experience of the child at the heart of the story.

Temperature-Dependent Dielectric Function Of Solids From Coupled Oscillators With Radiation Reaction: Application To Atom–Surface Interactions, Tuhin Kanti Das

Doctoral Dissertations

In this dissertation, we propose a uniform functional form of the dielectric function of solids that is applicable over a wide range of frequencies. We apply our model to describe the dielectric function of two technologically important materials: silicon and calcium fluoride. The temperature dependence of their dielectric functions is also described using simple analytic forms. We found that a generalized Sellmeier-type model with complex denominators (“damped oscillators”) does not lead to a satisfactory fit of experimental data for the dielectric function. In contrast, our model, which is analytically only slightly more involved (“complex oscillator strengths”, complex numerators), allows us …

Architecting A Complex Adaptive System Model For Selecting Policies To Reduce Kidney Discard, Lirim Ashiku

Doctoral Dissertations

"The kidney allocation system is a complex, evolving system involving multiple heterogeneous agents. Each agent exhibits emergent behavior that may not fully align with the complex system goals. Therefore, there is a need for a transdisciplinary systems approach to visualize the interdependency among agents and understand the dominant patterns that shape the kidney allocation system.

First, this research presented an incremental hierarchical system engineering approach in identifying the agents’ needs and behaviors toward the complex systems’ goal of maximizing deceased donor kidney utilization and reducing kidney discard. The hierarchical systems approach linked with model-based system engineering aided in eliciting agents’ …