Digital Commons Network^™

Shelters In Motion, Erin Nicole Vallido Trasmano

Hospitality Design Graduate Student Capstones

Shelters in Motion is a modular temporary shelter system for festival and outdoor events that replaces disposable tents with reusable, durable design. Overall reducing environmental impact while significantly improving comfort and user experience.

A Descriptive Analysis Of Plant Leaf Disease Detection Using Machine Learning And Deep Learning Models: A Systematic Review, Arzoo Chamoli, Anuj Kumar

Turkish Journal of Electrical Engineering and Computer Sciences

Plant leaf disease detection (PLDD) is a growing active research area with burgeoning practical applications across various sectors such as agricultural monitoring, food security, and environmental conservation. Accurate segmentation and classification of plant leaf diseases remains a key challenge in the field of plant leaf disease prediction. The challenge demands automated methods for the plant disease identification because it needs to develop better crop management systems, which will boost agricultural production. In this article, we provide a systematic review of various machine learning (ML) and deep learning (DL) methods extensively used for PLDD. The review strategy follows a formal protocol, …

Multiobjective Optimization Framework For Renewable Energy Iot Networks Balancing Security, Energy Efficiency, And Communication Reliability, Muhammad Hjouj Btoush, Ashraf S. Mashaleh, Amjad Gawanmeh

Turkish Journal of Electrical Engineering and Computer Sciences

The deployment of Internet of things (IoT) networks powered by renewable energy sources presents unique challenges in balancing security requirements, energy efficiency, and communication reliability. This paper presents a comprehensive multiobjective optimization framework for secure renewable energy IoT nodes that addresses fundamental trade-offs between these competing objectives. We develop a mathematical model incorporating energy harvesting dynamics, security protocols, and communication performance metrics across various environmental scenarios. The proposed framework employs a modified NSGA-II algorithm to identify Pareto-optimal configurations for different deployment contexts. Through extensive simulation analysis, we demonstrate that hybrid energy sources (solar-wind combinations) with lightweight security protocols achieve optimal …

Predictive Current Control Approach For Grid-Integrated Multifunctional Converter Under Source And Load Disturbances, Ravi Kumar Majji, Tirumalasetty Chiranjeevi, Chilukoti Varaha Narasimha Raja, Nagulapati Kiran

Turkish Journal of Electrical Engineering and Computer Sciences

This paper discusses and presents a model predictive control (MPC)-based predictive current control technique for a solar photovoltaic (PV)-integrated grid system during dynamic operation. This control technique employs extension pq (EPQ) theory to estimate reference currents and utilizes an MPC framework for tracking reference currents. Various MATLAB/Simulink simulations were conducted for solar PV generation (source disturbances) and dynamic loading. The results of the OPAL-RT OP4510 real-time simulation are also presented. A multifunctional grid-integrated converter (MFGC) integrates solar active power into the utility grid while achieving unity power factor, reactive power compensation, current balancing, and harmonic suppression. EPQ optimizes mathematical calculations, …

The Origins Of Chondrite Families, Isabelle M. Perron

UNLV Theses, Dissertations, Professional Papers, and Capstones

Chondrites, a type of primitive asteroid, are thought to have formed from dust and gas early in the life of our Solar System. These chondrites have remained unchanged since their formation, avoiding being melted or differentiated like other rocky bodies have. Due to this unique characteristic, chondrites can give us useful insight into how asteroids, planetesimals, and even the terrestrial planets formed. In order to study chondrites and their formation, I used code developed by Li et al. (2020), which utilizes thermodynamic code GRAINS (Petaev 2009), to see what conditions the chondrites may have originated from. GRAINS uses a variety …

Principal Perceptions Of Autonomy And Its Relationship To Job Satisfaction, Kay H. Barlow

UNLV Theses, Dissertations, Professional Papers, and Capstones

This study examined the relationship between principal perceived job autonomy and principal job satisfaction through the lens of Self-Determination Theory. Specifically, it addresses the research question: What is the relationship between principal autonomy and principal job satisfaction? Using bivariate correlations and Ordinary Least Squares regression analyses, the study investigated overall and domain-specific autonomy, including human resources, instructional leadership, facilities, and finance, and their association with overall and subscale measures of job satisfaction including salary, promotion, supervision, benefits, contingent rewards, working conditions, coworkers, the work itself, and communication. Findings indicate a positive and statistically significant correlation between perceived job autonomy and …

Preservation Of Sedimentary Rubidium Isotopic Signatures In Subduction Zones: Insights From The Schistes Lustrés Hp-Uhp Metasediments, Western Alps, Mary Clarich

UNLV Theses, Dissertations, Professional Papers, and Capstones

When oceanic plates bend and sink into the mantle at subduction zones, they carry sediments from the surface down into the mantle. The recycled sediments contain elements, that are depleted in the mantle, thereby influencing the composition of the mantle wedge and the formation of arc magmas, which are the building blocks of juvenile continental crust. However, it remains poorly constrained how much these elements, particularly fluid-mobile elements such as rubidium (Rb), are lost into fluids during metamorphic dehydration, and how much is transported to sub-arc depths where arc magmas are generated. Rubidium in subducting sediments is mainly hosted in …

Harnessing Backcasting To Identify Drivers Of Critical Warming At Hoover Dam Using Hydrodynamic And Machine Learning Models, Eunice Ledres

UNLV Theses, Dissertations, Professional Papers, and Capstones

Elevated water temperatures can pose a significant threat to dam infrastructure, potentially damaging turbines, overheating internal components, and forcing generator shutdowns. This study uses a backcasting framework to evaluate how future scenarios may result in elevated water temperatures. Backcasting defines undesirable outcomes and works backward to identify the conditions that lead to them. The developed backcasting framework integrates 3D physics-based simulations with a Long-Short Term Memory (LSTM)surrogate model and SHapely Additive exPlanations (SHAP) interpretation. The combination captures temporal water temperature dynamics and quantifies the contributions of different drivers to elevated water temperature releases. As proof of concept, these methods are …

Constructing A Laser Stabilization System For Frequency Metrology, Stephanie Letourneau

UNLV Theses, Dissertations, Professional Papers, and Capstones

Frequency metrology and quantum control, which uses light as a tool for measurement and manipulation, relies on spectrally narrow, stable lasers. Often thought of as being perfectly monochromatic and coherent, in reality, the free-running instantaneous linewidth of lasers can be on the order of hundreds of kHz in the millisecond time-frame and drift on the order of tens of MHz over hours. To correct for these instabilities in real-time, a variety of laser stabilization methods have been implemented, including the Pound-Drever-Hall (PDH) method, saturation absorption spectroscopy (SAS), and dichroic atomic vapor laser locking (DAVLL). All of these methods rely on …

Thermal Physiological Ecology Of The Relict Leopard Frog At Hot And Cold Springs, Robert P. Pelletier Iii

UNLV Theses, Dissertations, Professional Papers, and Capstones

The relict leopard frog (Rana onca) once ranged across drainages in southern Nevada, northwestern Arizona, and southwestern Utah. Following a decline, the species only persisted in a few geothermally influenced hot springs, which led to the perspective that hot springs were high-quality habitat. Rana onca has been under intensive, multiagency management and the species has been translocated to establish additional populations, including at cold-water sites. Three research studies are presented into the thermal physiological ecology of R. onca with the aim of informing conservation strategy. The research was focused at a thermally influenced hot spring and a cold-water spring to …

Understanding How Teachers Define And Employ Civics Education In Non-Elective Social Studies Classrooms, Joseph Kyle

Seton Hall University Dissertations and Theses (ETDs)

This study explored the perceptions of civics and civic education held by secondary school teachers of non-elective social studies classes in four schools within the same school district. Using a qualitative approach, this study examined how veteran teachers understood and defined civics, the ways in which they employed civics education in their teaching methodology and daily lessons, and the challenges or obstacles they perceived, if any, incorporating lessons reflecting a progressive experimentalist methodology. The study was undertaken in response to popular and governmental rhetoric reflecting a lack of civil discourse and alleging a lack of civics in public education, as …

Transitioning To The American School System: Insights From Immigrant English Language Learners And Factors Influencing Their Success, Yolanda Gomez

Seton Hall University Dissertations and Theses (ETDs)

This qualitative study examined the factors influencing the post–high school experiences of immigrant English Language Learners (ELLs) in the United States. Fourteen recent high school graduates from the Dominican Republic, Honduras, Ecuador, and Peru participated in semi-structured interviews. Data was analyzed using grounded thematic coding within a social-ecological framework. Four interconnected themes emerged: (1) Language Acquisition, including challenges and empowerment through developing English proficiency; (2) Support Systems, such as family, teachers, peers, and community mentors; (3) Social and Cultural Integration, facilitated by extracurricular involvement and adaptation to new norms; and (4) Resilience and Personal Growth, reflected in persistence, self-efficacy, and …

Does Match Matter? Examining The Role Of Student–College Match And First-Year Stem Retention, Christopher J. Shemanski

Seton Hall University Dissertations and Theses (ETDs)

This quantitative study examines how student–college match relates to first-year STEM retention in a nationally representative cohort of four-year, STEM-intending students from the High School Longitudinal Study of 2009 (HSLS:09) linked with institutional data from the Integrated Postsecondary Education Data System (IPEDS). Building on probabilistic approaches to college match, the study first estimates each student’s predicted probability of admission to institutions in five selectivity tiers using high school GPA, standardized test scores, highest mathematics course completed, AP mathematics credits, gender, and socioeconomic status. It then compares the predicted tier with the selectivity of the institution actually attended to classify students …

Exploring Catholic School Teachers' Perceptions Of Factors That Support Longevity: A Qualitative Study, Kelly A. Koval

Seton Hall University Dissertations and Theses (ETDs)

John Staud, the executive director of Alliance for Catholic Education (2022) stated, “The greatest crisis facing Catholic education, and education in general, is the recruitment and retention of talented and committed teachers and leaders for our schools.” Thus, the purpose of this qualitative study is to illuminate and explore the practical experiences of teachers who have longevity in Catholic schools with respect to their perceived needs, challenges and support in the mid-to-later years of their career path. Through the lens of self-determination theory, this study aims to describe the internal and external factors that teachers describe as “playing a role” …

Principals’ Perceptions On How Artificial Intelligence (Ai) Is Used In Schools By Administrators, Teachers, And Students: A Mixed Method Research Study, Dianna M. Sopala

Seton Hall University Dissertations and Theses (ETDs)

Abstract

In the same way electricity changed how people lived, artificial intelligence (AI) is being hailed as that next transformative technology that will change every aspect of daily living, including education.  As AI is in its infancy and humans are just beginning to make sense of this new technology, educators are trying to integrate it into the educational landscape.  While many technologies are often initiated at the district level, school principals are often charged with leading the adoption and implementation of new technologies within their school.  Additionally, New Jersey has decided to invest $165 million dollars into being the artificial …

Cold Plasma Treatment Of Hydroponically Grown Basil: Effects On Essential Oil Composition For Sustainable Applications (Ocimum Basilicum), Judith Serwaa Marfo

Seton Hall University Dissertations and Theses (ETDs)

Abstract Essential oils are known to have medicinal benefits and pharmaceutical applications. This study investigates the impact of cold plasma treatment on hydroponically cultivated basil (Ocimum basilicum), focusing on physical growth traits, and essential oil composition. Preliminary trials validated our solvent extraction protocol using IPA, hexanes, and methanol without heat on store-bought basil. Rotary evaporation and GC-FID analysis successfully identified key compounds; eugenol, estragole, eucalyptol, and linalool. Plasma-treated hydroponic plants exhibited enhanced physical characteristics, including larger leaves and intensified green pigmentation, compared to untreated controls under identical conditions. The plasma treatment didn't just increase how much oil was extracted, but …

Semi-Rational Strategies For Antifungal Peptide Design, Akilah I. Mateen

Seton Hall University Dissertations and Theses (ETDs)

A recently emerged opportunistic fungi, Candida auris, has been subject to increased scrutiny due to its virulence and rapid geographical spread. Due to the indiscriminate use of antimicrobials as treatments for infectious diseases and as pesticides, the ubiquitous threat of multidrug resistance (MDR) looms large. The lack of progress in antifungal development is of high concern in the treatment of infectious diseases and a rise in fungal resistance highlight the need for updated treatment strategies. This work describes three strategies used to address these concerns:

•  The synthesis of a photosensitizer-membrane-active peptide (PS-MAP) conjugate, Ir-HKII15, that combines the ability of …

Phase Identification Of (La, Sr)Coo3 Solid Oxide Cell Electrode Films Using Dft Based Exafs, Musab A. Siddiqui

Seton Hall University Dissertations and Theses (ETDs)

Perovskite structured mixed ionic electronic conductor (MIEC) materials formed as films by metal-organic precursor deposition have excellent electrochemical performance in solid oxide cell (SOC) air electrode applications due to the large surface area provided by the manufacturing approach. MIEC films created by metal organic precursor deposition are often multi-phased due to low heat treatment temperatures and locally generated low oxygen partial pressures caused by the release of carbonaceous gases during the drying step of the fabrication process. In this work, we use extended x-ray absorption fine structure spectroscopy (EXAFS) to examine the phase contents of La0.8Sr0.2CoO3 (LSC82) and La0.6Sr0.4CoO3 (LSC64) …

Machine Learning Assisted Development Of Al0.2cufemnni High Entropy Alloy Through Selective Laser Melting, Hareharen K Mr

Theses and Dissertations

High Entropy Alloys (HEAs) are an emerging class of advanced materials that have gained significant attention due to their exceptional mechanical strength, thermal stability, and structural performance. Unlike conventional alloys based on a single principal element, HEAs are composed of multiple elements in a near-equiatomic ratio. Despite these advantages, designing HEAs with tailored properties is difficult because of the enormous number of possible combinations and the limitations of traditional trial-and-error methods. To overcome these challenges, this study presents a machine learning (ML) based approach to accelerate the design and development of an HEA.

In this work, a newly designed composition, …

The Role Of Math Anxiety – And Associated Support Mechanisms And Strategies – In The Academic Success Of Stem College Students, William Jacob Tschume

Theses and Dissertations

Math anxiety has been widely documented as a barrier to student success, yet its role within institutional student success frameworks – particularly in gateway STEM courses – remains underexamined. The purpose of this quantitative study was to examine the relationship between math anxiety, academic support mechanisms, and academic performance among undergraduate STEM students enrolled in a gateway mathematics course at a large, public, research-intensive university. Guided by the Debilitating Anxiety Model, student success frameworks, Social Cognitive Career Theory, and equity-oriented perspectives, this study addressed three research questions: (1) how student demographic characteristics relate to levels of math anxiety, (2) the …

Exploring The Effect Of Students' Exclusionary Discipline On Their Academic Achievement In A Rural High School., Danya Turner

Theses and Dissertations

The purpose of this quantitative study was to examine the extent to which exclusionary discipline practices, specifically in-school suspension and out-of-school suspension, are associated with academic achievement among high school students. More specifically, the study investigated the relationship between students’ exclusionary discipline days and performance on Mississippi Academic Assessment Program (MAAP) assessments in Algebra I, Biology, English II, and U.S. History. The study was guided by the Opportunity to Learn (OTL) framework, which emphasizes the importance of access to instructional time for student academic success. Archival discipline and assessment data were collected from a rural public high school in east-central …

Integrating Augmented Reality Visualizations Into Data Science Notebooks Using Microsoft Hololens 2, Derek Willis

Theses and Dissertations

Data-science notebooks support iterative analysis but are limited to two-dimensional (2D) displays. This work presents an approach to extend such environments with rapid augmented reality (AR) visualization while preserving conventional 2D workflows. An opensource R package was developed to convert notebook objects into three-dimensional (3D) models, export them in the Graphics Language Transmission Format (glTF), and transfer directly to a Microsoft HoloLens 2 via a USB connection for viewing in the native 3D Viewer application. The proposed workflow eliminates manual conversion and transfer steps required by earlier methods. A user study employing a post-session questionnaire indicated that participants found the …

Assessing The Relationship Between Subsurface Geology And Surficial Geomorphology: Remotely Predicting Geologic Features And Geohazards Using An Elevation-Trained Machine Learning Algorithm In The Northern Gulf Of Mexico, Allison L. Wing

Theses and Dissertations

This thesis evaluates the capacity to predict subsurface geologic features and submarine landslide susceptibility using surficial geomorphology derived from bathymetric elevation data in the Northern Gulf of Mexico. Quantitative geomorphic variables including slope, curvature, aspect, rugosity, geomorphons, and Bathymetric Position Index were generated from 30-meter digital elevation models and used as explanatory variables in presence-only Maximum Entropy models. Known locations of faults, pockmarks, mud volcanoes, hydrocarbon seeps, and landslides (particularly intact scarps) were used to train and validate predictive models through k-fold cross validation. Model performance was assessed using omission rates and AUC values. Results demonstrate that specific geomorphic signatures, …

Feasibility-Aware Deep Reinforcement Learning For Sustainable Timber Procurement Under Hurricane Demand Uncertainty, Jarod Wright

Theses and Dissertations

The timber supply chain connects landowners and mills to provide wood products but faces challenges from stochastic demand, seasonal variations, and disruptions such as hurricanes. Fur- thermore, sustainability concerns like transportation emissions create trade-offs in procurement. This study proposes a feasibility-aware Deep Reinforcement Learning framework for sustainable timber procurement and inventory control under joint demand–hurricane uncertainty. We develop a stochastic mathematical model capturing mill-landowner interactions, seasonal demand, hurricane- driven pricing, and carbon emissions. The problem is formulated as a constrained Markov decision process and solved using Proximal Policy Optimization with a feasibility-enforcing layer. A Mississippi-based case study with 2,100 landowners …

Wildfire Risk Mitigation Methods To Enhance The Resilience Of Power Systems, Fasiha Zainab

Theses and Dissertations

The resilience of the power system has become increasingly important in recent years and needs to be further enhanced in the future under wildfire conditions. A major concern is the two-way interaction between power systems and wildfires: power systems can ignite wildfires and be disrupted by them. Power system-induced wildfires occur when electrical components, particularly transmission lines, ignite fires due to faults exacerbated by extreme weather conditions. The presence of uncertainties, especially those related to unpredictable weather conditions, makes it difficult to handle and can significantly increase the risk of wildfire. Furthermore, these ignitions not only threaten public safety and …

Beyond English: Auditing And Mitigating Cross-Lingual Data Contamination In Multimodal Large Language Models, Pavan Dharma Adapa

Theses and Dissertations

This thesis extends data contamination auditing for multimodal large language models to multilingual settings. Using LLaVA 1.5 and a high-fidelity French parallel dataset derived from ScienceQA, the study evaluates how performance changes when identical image-question pairs are translated from English to French. The resultsshow a substantial cross-lingual performance decline and frequent flips from correct English predictions to incorrect French predictions, indicating that benchmark performance can depend heavily on memorized English-specific patterns rather than stable multimodal reasoning. To address this weakness, the thesis introduces an inference-time mitigation strategy based on perturbation ensembling and cross-lingual consistency aggregation. The proposed method reduces instance-level …

Enhancing Professional Development Opportunities For Mississippi Early Childhood Education Technology Students, Sheri H. Anders

Theses and Dissertations

This capstone project examined professional development opportunities for early childhood students enrolled in Early Childhood Education Technology (ECET) programs in Mississippi. Grounded in Situated Learning Theory and aligned with the National Association for the Education of Young Children (NAEYC) Professional Standards and Competencies, this study used a desk-based systematic literature review to examine how ECET students engage in professional development during preparation, how standards-aligned experiences support preparedness and professional identity development, and what barriers limit access to meaningful opportunities. Findings indicate that professional development during preparation is often informal and inconsistent; however, participation in professional communities such as conferences, coaching, …

Machine Learning-Based Decision Support Models With Applications In Postsecondary Education, Marco Paolo Anglesio

Theses and Dissertations

This dissertation investigates the deployment of machine learning methodologies in an industrial engineering framework for the development of advanced decision support systems in the context of enrollment management. Drawing on techniques from educational data mining, the research addresses three key phases in the lifecycle of traditional and non-traditional students. First, it analyzes student retention using predictive classification models designed to identify individuals at elevated risk of attrition. Second, it employs temporal convolutional networks for time series forecasting, estimating aggregate enrollment levels over highly variable, finite planning horizons on the basis of partially observed data and using an asymmetric loss function. …

Institutional Predictors Of Overall Retention And Graduation Rates For Black Male Students At Public Two-Year Colleges In The Southeastern United States, Jonathan Larry Armstrong

Theses and Dissertations

This quantitative, cross-sectional, non-experimental study examined institutional characteristics associated with retention and graduation outcomes at public two-year colleges in the Southeastern United States. Using data from the Fall 2023 Integrated Postsecondary Education Data System (IPEDS), the study analyzed institutional student–faculty ratio, core expenditures per full-time equivalent (FTE) student, and academic support expenditures per FTE as predictors of institutional full-time fall-to-fall retention rates for all students and 150% graduation rates for Black male students. Descriptive statistics indicated substantial variation across institutions in spending and student outcomes. Simple linear regression analyses revealed no statistically significant relationships between student–faculty ratio or core expenditures …

On The Bias And Variance Of The Plug-In Estimator For Generalized Shannon’S Entropy, Shirli Salihaj Arndt

Theses and Dissertations

This dissertation analyzes the bias and variance of the plug-in estimator for generalized Shannon’s entropy. The setting is discrete data where observations take categorical values and the statistical object of interest is a probability distribution on an alphabet of possible outcomes. In such problems, entropy provides a relabeling-invariant summary of dispersion, but estimation can be challenging when the number of possible outcomes is large relative to the sample size. In addition, on some countably infinite alphabets, the classical Shannon entropy may diverge, motivating alternative entropy-like targets. Generalized Shannon’s entropy is defined by applying Shannon entropy to an escort reweighting of …