Digital Commons Network^™

Synergizing Ground, Satellite And Numerical Weather Prediction Datasets To Derive Cloud Base Properties As Used In The Kain-Fritsch Convective Parameterization, David T. Haliczer

Dissertations

Cloud base height (CBH) and cloud base vertical velocity (CBVV) are essential parameters for understanding cloud formation, boundary layer evolution, and air exchange between the atmosphere and the boundary layer. CBH is linked to aviation safety and impacting the amount of solar radiation reaching the surface, while CBVV influences cloud development, condensation (droplet formation) and precipitation potential. High CBVV can erode capping inversions and trigger convection. These two parameters are the focus of this dissertation. CBH and maximum CBVV values were derived using a multi-linear regression equation (i.e., generalized linear models (GLM)) for CBH and a random forest model for …

Radar Resolution Improvements Using Reference Target Sliding Method In Range Doppler Matrix, Trent Shelton

Dissertations

A novel reference target sliding method (RTSM) in the frequency domain of post-match-filtered radar data is presented, and it is shown that target resolution can be improved to levels much finer than classical bandwidth-limited resolution metrics. The RTSM uses a priori knowledge of the radar system to test returned signals against baseline references to provide expected target fitting. The methods developed have broad application and can be used to provide crossing target resolution and estimation parameter refinement in the presence of overlapping targets. Additionally, the reduced requirement for bandwidth in target resolution will allow for low-bandwidth systems to perform on …

Spatial And Temporal Variability Of Dayside Magnetopause Reconnection, Krishna Khanal

Dissertations

The dissertation investigates the spatial and temporal variability of magnetopause reconnection, a central tenet of the solar wind-magnetosphere-ionosphere coupling. In the spatial sense, magnetopause reconnection occurs along X-lines of variable extent, which can be determined through remote sensing using ionospheric observations. Using in situ observations from Time History of Events and Macroscale Interactions during Substorms (THEMIS) in conjunction with ionospheric observations from the Super Dual Auroral Radar Network (SuperDARN), we conducted a statistical study to examine the variability and controlling factors of the spatial extent of magnetopause reconnection. Our findings indicate that the magnetopause X-lines can span from a few …

Directed Assembly Of Magnetic Binary Suspensions, David H. Harris

Dissertations

Colloids are particle suspensions in a continuous medium. The particle interactions promote the assembly of structures under the influence of a magnetic field, known as directed-assembly. The magnetic properties and shape anisotropy are relevant features of the particle building blocks to the assembled structures. The dipolar interaction between colloidal particles for directed assembly has been extensively modeled considering spherical particle potentials, which is inaccurate when anisotropic particles are at close range. This research aims to address the problem of quantifying the dipolar interaction between arbitrarily positioned and oriented ellipsoids with different aspect ratios and material properties. We combine the recently …

The Impact Of Electrode Design On Lithium-Ion Battery Fast Charging, Prehit Patel

Dissertations

The growing demand for lithium-ion batteries in electric vehicles, grid storage, and consumer electronics drives the need for advancements in energy density and power capability. Achieving higher energy density requires improved electrode architectures to enhance ionic transport while minimizing polarization and resistive losses. Although increasing electrode thickness can boost energy density, it also introduces transport limitations and raises the risk of lithium plating. A promising solution to this challenge is the integration of electrolyte channels within multi-layer electrodes, which can enhance lithium-ion mobility, particularly under high-rate cycling conditions. This study explores potential improvements to conventional lithium-ion cells that enable high …

Understanding The Behaviors Of Internal Short Circuit And Thermal Runaway Of Li-Ion Batteries Through In Situ Diagnosis And Modeling, Siyi Liu

Dissertations

Internal short circuit (ISC) caused thermal runaway is a critical challenge for Li-ion batteries that are widely used in various applications. However, currently, there is still a lack of understanding on how exactly an ISC triggers thermal runaway. ISC is a highly localized and transient phenomenon, hence, in situ diagnosis is important in understanding the development of ISC. In this work, experimental methods were developed for simultaneous in situ measurement of ISC temperature, ISC current, and ISC resistance during slow nail penetration of small single-layer Li-ion cells and large-format multiple-layer Li-ion cells. Furthermore, the experimental results of multiple-layer Li-ion cells …

Learning Paradigms For Rhythm Detection And Generation Using Mathematical Models, Biophysical And Artificial Neural Networks, Prianka Bose

Dissertations

Humans possess an inherent ability to recognize evenly-spaced rhythms, known as isochronous rhythms, owing to the brain's predisposition to entrain to external auditory stimuli with regular temporal intervals. The central focus of this research is to understand how the brain learns and retains rhythmic time intervals in the context of music. This dissertation studies rhythm detection and generation through mathematical models, biophysical networks, and artificial neural networks, addressing both isochronous and non-isochronous patterns.

A primary focus of the thesis is on isochronous rhythms. In particular, given a perturbation to an isochronous rhythm such as a tempo change or phase shift …

Pushing The Boundaries Of Large Language Models: Innovations And Limitations In Nlp, Finance, And Mathematics, A M Muntasir Rahman

Dissertations

Large Language Models (LLMs) have emerged as transformative tools across a spectrum of domains, yet their practical deployment reveals a blend of remarkable potential and notable limitations. This research explores innovative methodologies to extend the capabilities of LLMs while addressing critical challenges in their evaluation and application. By leveraging rule-based approaches, the in-context learning capabilities of LLMs, and human-in-the-loop validation across three focused studies, this research introduces robust strategies for dataset synthesis, model enhancement, and model assessment in three distinct domains: natural language processing, financial sentiment analysis, and mathematical reasoning

The first study proposes an efficient data augmentation framework, EASE, …

Game-Based Learning With Augmented Reality, Kantida Nanon

Dissertations

Augmented Reality (AR) technology provides an effective and controllable learning environment, especially for complicated, potentially dangerous, or critical content. Effective implementation of interaction and platforms for educational tools can maximize learner engagement and optimize the learning experience. This dissertation explores the potential of AR technology to enhance learning experiences across multiple platforms and subject areas, particularly within chemistry and mathematics. The dissertation is grounded in the field of Human-Computer Interaction (HCI) and explores the potential of AR to increase learner engagement, improve learning outcomes, and transform traditional pedagogical practices.

The dissertation is divided into two parts. The first part examines …

First-Principles Study Of Ferroelectric Properties And Co2 Reduction Reaction Capabilities In Two-Dimensional Monolayers And Heterostructures, Mo Li

Dissertations

Two-dimensional (2D) materials hold significant potential for CO₂ reduction reactions (CO₂RR) due to their high surface-to-volume ratio. However, achieving high selectivity for desired products and overcoming limitations posed by scaling relationships remain challenging. Recent studies suggest that ferroelectric (FE) materials with switchable out-of-plane polarization (OOP) can effectively tune the adsorption behavior, thermodynamics, and kinetics of CO₂RR, offering promising solutions to these challenges. Using density functional theory (DFT) and the Berry phase approach, this work expands the family of 2D ferroelectrics by theoretically identifying Y₂CO₂, Y₂CS₂, and Sc …

Determination Of Electrochemical Parameters For Predicting Reaction Mechanism And Algorithmic Approaches To Pain Assessment, Huize Xue

Dissertations

This dissertation introduces novel advancements in electrochemical kinetics and pain assessment, structured into two main parts. The first part focuses on the comprehensive analysis of the kinetic and mechanistic aspects of electrochemical reactions, utilizing a combination of experimental techniques and simulation methods. A new software tool, Envismetrics, was developed using Python to facilitate the analysis of complex electrochemical data, including cyclic voltammetry (CV), chronoamperometry (CA), and hydrodynamic voltammetry (HDV). The software was rigorously tested and validated with well-characterized redox systems such as the ferricyanide/ferrocyanide couple, dimethylamine borane (DMAB), and Per- and Polyfluoroalkyl Substances (PFAS). It was successfully used to determine …

Ensemble Learning Models For Large-Scale Time Series Forecasting In Supply Chain, Minjuan Zhang

Dissertations

Machine learning and AI techniques are transforming supply chain forecasting, driven by the expanding availability of data assets. These advanced methods offer powerful opportunities to optimize management processes, reduce operational costs, and enhance strategic decision-making, which is crucial for enterprise success. However, conventional statistical approaches, such as Autoregressive Integrated Moving Average Models (ARIMA), dynamic regression, and Unobserved Component Models (UCMs)—which have long dominated time series forecasting—often fall short in accuracy and scalability. These traditional models face limitations in batch processing, handling large-scale data, addressing uncertainty-induced disruptions, and synchronizing demand-supply scenarios.

To address these challenges, a novel class of AI-powered ensemble …

Machine Learning Methods For Pattern Recognition Analysis Of Genomic And Molecular Data, Kuang Du

Dissertations

While immune therapies achieve remarkable success in treating various cancers, only a subset of patients achieves a durable clinical response, and many exhibit innate or acquired resistance. Precision medicine aims to tailor treatments to individual patients based on specific biological markers, ensuring that each patient receives the therapy most likely to be effective. Predictive biomarkers and gene signatures offer potential for more personalized treatment strategies by identifying patients likely to benefit. Recent studies suggest that gene signatures, comprising sets of genes, hold predictive value for certain clinical variables. Typically derived from biological expert knowledge, these signatures demonstrate substantial predictive potential, …

Knowledge Diffusion In Networks Of Artificial Learners, Ehsan Beikihassan

Dissertations

The dissertation draws inspiration from the topic of peer learning in the social sciences and the study of information dissemination and knowledge diffusion in network science. In particular, it introduces and studies a setting involving a population or network of artificial learners, with the objective of optimizing aggregate performance measures under constraints on training resources. In this context, natural knowledge diffusion processes in networks of interacting artificial learners are studied. The term "natural" refers to processes that emulate human peer learning, where the internal state and learning processes of students remain largely opaque, and the main degree of freedom lies …

Interactive Visualization Workflows For Mitigating Analytical Uncertainty, Kaustav Bhattacharjee

Dissertations

This dissertation takes a process-centric and stakeholder-first perspective for handling analytical uncertainty: the form of uncertainty that confronts data analysts' insight-generation processes in high-consequence decision-making scenarios. The cost of an incorrect decision when data is used for movie recommendations as opposed to when personal data is used to drive insights or when data-driven modeling is used to drive real-time decisions for maintaining the health of a grid are vastly different in terms of consequences. This dissertation looks at analytical uncertainty in two real-world scenarios: i) how sensitive information leakage can be prevented during the open data release process with data …

Decomposition Of Diisopropyl Methylphosphonate (Dimp) Exposed To Elevated Temperatures And Combustion Products Of Reactive Materials, Elif Irem Senyurt

Dissertations

The safe and efficient destruction of chemical weapon agents (CWAs) stockpiles remains a critical global challenge. Understanding the thermophysical properties, decomposition mechanisms, and interactions of CWAs with their environment is crucial for developing effective strategies to mitigate their threats. Diisopropyl methylphosphonate (DIMP) and Dimethyl methylphosphonate are commonly used nerve agent surrogates. This dissertation focuses on DIMP and DMMP, investigates their properties and behavior under conditions relevant to prompt defeat scenarios.

The thermophysical properties of DIMP and DMMP were experimentally characterized, including vapor-liquid surface tension (3-60 °C) and viscosities of neat and aqueous solutions. Results revealed significant non-ideal behavior in aqueous …

Visual Analytic Techniques For Interpretable Algorithmic Ranking Systems, Jun Yuan

Dissertations

Rankings have a profound impact on the increasingly data-driven society. From leisurely activities like the movies to watch, the restaurants to patronize; to highly consequential decisions, like making educational and occupational choices or getting hired by companies— these are all driven by sophisticated yet mostly opaque algorithmic rankers. A small change in how these rankers order the data items can have profound consequences, like deterioration of the prestige of a university or a job applicant missing out on being on the list of the top candidates for an organization. These scenarios necessitate data-driven and human-centered innovation to make rankers accessible, …

Crowd-Sourced Learning For Computer Graphics Applications, Yunhao Zhang

Dissertations

Computer Graphics (CG) revolves around virtual content creation using computational methods, spanning applications from games to visual effects. Typically, the creation of CG content is led by expert practitioners who guide computational algorithms towards satisfactory results. Thus, creating CG content often requires manual iterations encompassing algorithm design, parameter tuning, and aesthetic feedback. This work investigates how to leverage crowd-sourcing to streamline such creation processes, focusing on animation and simulation. In animation, a novel crowd-sourcing framework is proposed for combat animation, enabling users to analyze motion similarities, and retrieve matching motions using novel crowd-sourced motion features. Such features enable quantifying previously …

Representation Of Alaskan Native People In Nonprofit Leadership, Vanessa Smith

Dissertations

The nonprofit sector faces a significant issue: the underrepresentation of the communities it aims to serve, including Alaska Native communities. This research investigates the likely causes of this underrepresentation and explores potential solutions. It includes nine qualitative interviews with a majority of Alaskan Native leaders and consultations with other non-Native nonprofit leaders who have extensive experience in Alaska's nonprofit organizations.

This study arose from firsthand experience in an Anchorage, Alaska nonprofit, where the lack of Alaska Native representation in leadership roles within an organization meant to serve the Alaska Native community was evident. The research aimed to understand how well …

Beyond Average Effects In Education Research Syntheses: Explaining Heterogeneity Of Concept Mapping Research In Science Education Through Meta-Regression Modeling, Aaron Wenger

Dissertations

Contemporary education research seeks causal inferences in order to guide policy and practice. Often well-controlled quasi-experimental and experimental designs are used as they are very useful for supporting and contextualizing causal inferences. Nevertheless, individual studies, even high-quality, resource-intensive randomized controlled trials, are not able to establish generalized claims. Because primary studies vary over dozens of variables (e.g., grade, subject, duration) simply conducting additional primary studies cannot change this. Furthermore, the informal assessments of primary studies common in literature reviews are unable to resolve certain challenges (e.g., publication bias) that are described in the emerging field of meta-research. These challenges require …

Rethinking Safe Nurse Staffing In The Post-Covid Icu: A Comparative Analysis Of The Nurse-To-Patient Ratio And Mixed Model Approach For State Policies, Emilie A. Maxie

Dissertations

The coronavirus pandemic (COVID-19) led to an unprecedented surge in ICU admission, increasing the nurse-to-patient ratios and intensifying stress on ICU nurses. This prompted many nurses to leave bedside care, exacerbating the ongoing nursing shortage and creating unsafe working conditions. Fourteen states have enacted safe nurse staffing policies. California implemented a mandated nurse-to-patient ratio, while Massachusetts adopted a mixed model for ICUs that involves a ratio, nurse committees, and workload tools. In post-COVID, this raises the question of which state policy approach is more effective in retaining ICU nursing staff. Literature suggests a mixed policy may be more effective.

A Phenomenological Study To Determine How Expert Avid Teachers Use Collaboration To Strengthen Student Metacognition And Student Self-Efficacy And Determine When It Occurs, Megan Hernandez

Dissertations

Purpose: The purpose of this phenomenological study was to determine how expert AVID teachers from the California Central Valley describe the use of collaboration to strengthen student metacognition and self-efficacy and how they describe the evidence that indicates metacognition and self-efficacy are occurring.

Methodology: Five Central California school districts were chosen to conduct the phenomenological study on expert AVID teachers. One to two AVID teachers from each of the five school districts were chosen in the study for eight participants. The expert AVID teachers were chosen for having at least five years of experience teaching AVID and being up to …

Discovering The Process Of Reasonable Academic Accommodations For Pre-Licensure Nursing Students With Learning Disabilities, Amy Yarbrough

Dissertations

The purpose of this study was to explore the process by which nursing faculty make reasonable academic accommodations for students with learning disabilities in the classroom setting. Nursing students with identified learning disabilities require academic accommodations to facilitate their success. The process of reasonable academic accommodations for nursing students with learning disabilities has not been examined within nursing education literature. Clarification is necessary regarding this process to promote student success and inclusive learning environments within nursing education. Constructivist grounded theory methods as described by Charmaz (2014) guided data collection and analysis. The accommodation processes with categories were identified. The emergent …

Examining Teachers’ Experiences In Fostering Social-Emotional Learning After Returning To In-Person Instruction In The Midst Of The Covid-19 Pandemic, Rosario Grajeda

Dissertations

Purpose: This qualitative phenomenological study examines how middle school teachers in Riverside County, California, support the social-emotional learning (SEL) needs of students as students returned to in-person instruction after the COVID-19 pandemic. Methodology: This qualitative study explores how Riverside County middle school teachers addressed students' social and emotional needs upon returning to in-person learning after COVID-19. Twelve teachers from local public middle schools (grades 6-8) participated in semi-structured interviews, answering ten questions. Data from interviews and artifacts were transcribed and analyzed using NVIVO software to identify recurring themes related to the study questions. Findings: The shared experiences of participants and …

A Latent Profile Analysis Of High-Risk Children's Problematic Sexual Behavior, Claudia Campbell

Dissertations

As research and intervention regarding children’s problematic sexual behavior (PSB) have broadly defined PSB, including all forms of developmentally atypical sexual behavior and all potential etiological factors, limited work to date has identified subtypes or unique patterns of PSB. The aim of the current study was to examine the presence of unique patterns of PSB and etiological factors among high-risk children utilizing latent profile analysis (LPA) based on children’s self-focused PSB, interpersonal PSB (IPSB), maltreatment factors, co-occurring symptoms, adaptive functioning, and family functioning. The present study included 689 8-year-old children (50.1% male, 50.2% Black) with potential PSB and their caregivers …

Subsyndromal Delirium And Postoperative Pain In Older Adults, Dawn Lujean Denny

Dissertations

The purpose of this study was to determine the relationship between subsyndromal delirium and pain in older adults following major elective orthopedic surgery. The design of this correlational study was based on Inouye and Charpentier's (1996) multifactorial model of delirium.

Prostate Cancer Screening Survey Among African-American And Their Partners, Angela James

Dissertations

The purpose of this translational research project was to survey adult African-Americans and their partners to explore inhibitors and facilitators to PC screening.

Circadian Body Temperature And Activation Rhythms And The Well-Being Of Independent Older Women, Diana J. Mason

Dissertations

This study examined changes in the circadian parameters of body temperature and activation and their relationship to the well-being of the older woman.

Nurse Educators' Perceptions Regarding Faculty Participation In University Governance, Janet J. Gross

Dissertations

The purpose of this descriptive study was to identify nurse educators' perceptions of faculty participation in university governance

Evaluating The Impact Of Post Discharge Callback On Patients With Copd Exacerbation, Stephanie L. Staten

Dissertations

The purpose of this project was to evaluate the impact of a post-discharge callback within 72 hours of discharge on medication adherence, attendance to post discharge follow up clinic visit, and 30-day readmissions in COPD patients. A secondary purpose was to explore reasons why patients who are discharged with a diagnosis of COPD exacerbation, did or did not adhere to their medications and keep their post-discharge follow up clinic visit during the 30-days following hospital discharge.