Digital Commons Network^™

Soil Ethics In Practice: Harvesting Conservation Choices Among Puerto Rican Coffee Farmers, Patricia Marie Cordero-Irizarry

Theses and Dissertations

Coffee farming in Puerto Rico has declined over recent decades, partly due to deteriorating soil quality. However, the decision-making process behind farmers’ adoption of soil conservation practices remains unclear, particularly the overlooked role of soil ethics, which emphasizes the moral principles guiding human relationships with the soil. This mixed-methods study examined coffee farmers’ adoption of soil conservation practices by integrating the Values-Beliefs-Norms (VBN) theory and a newly developed Soil Ethics Survey (SES) instrument for the quantitative component, while using Decision-Making Theory and Social Cognitive Theory to qualitatively explore how farmers’ experiences and reflections shape their soil ethics. Quantitative findings revealed …

Extreme Artifacts Removal With Curriculum Learning, Sushant Gautam

Theses and Dissertations

Restoring severely blurred images remains a significant challenge in computer vision, impacting applications in autonomous driving, medical imaging, and photography. This thesis introduces a novel training strategy based on curriculum learning to improve the robustness of deep learning models for extreme image deblurring. Unlike conventional approaches that train on only low to moderate blur levels, this method progressively increases the difficulty by introducing images with higher blur severity over time, allowing the model to adapt incrementally. Additionally, perceptual loss and hinge loss were integrated during training to enhance fine detail restoration and improve training stability. Various curriculum learning strategies were …

Understanding How Nws Meteorologists Tailor Hazardous Weather Messaging To Core Partners And Identify Local Vulnerabilities, Allison Camille Harvey

Theses and Dissertations

Over the past few decades, the National Weather Service (NWS) has continued to improve impact-based decision support services (IDSS) efforts to strengthen communication with core partners. This study addressed how a new spatially hazard-specific vulnerability tool called the Brief Vulnerability Overview Tool (BVOT) may impact how NWS meteorologists tailor messaging to their core partners. It was found that relationship building between NWS meteorologists and core partners is key to enhancing trust, especially during blue sky days. This was done by learning core partners’ needs, thresholds, and communication styles when presenting weather information. Core partners also noticed cues from NWS meteorologists …

Towards Applying Artificial Intelligence To Solve Np-Complete Problems Using Quantum Computing, Andrew Robert Haverly

Theses and Dissertations

This dissertation explores the methodology for more thoroughly entangling artificial intelligence and quantum computing. This is explored through a background search of problems solved using Grover’s quantum algorithm, a new quantum protein folding and drug discovery algorithm, using Grover’s algorithm to train quantum artificial neural networks, using quantum artificial neural networks for reinforcement learning, mapping classical assembly instructions to quantum circuits to make quantum programming easier, and using prompt engineering to get a classical artificial intelligence agent to solve an NP-Complete problem using a Grover’s algorithm. This method not only simplifies the creation of algorithms but also opens new avenues …

Topnet R1: A Multi-Stage Ai Framework For Topic Discovery In Scientific Abstracts, Md Elias Hossain

Theses and Dissertations

Scientific abstracts are rich sources of knowledge, yet extracting meaningful topics remains challenging due to limitations in existing topic modeling techniques. Traditional methods often struggle with interpretability, scalability, and contextual understanding. To overcome these issues, we introduce TopNet R1, a multi-stage ensemble framework that integrates traditional topic models with contextual embeddings from Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer 4 (GPT-4) large language models (LLMs). Top- Net R1 operates in three phases: (1) topic generation using Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), Latent Semantic Analysis (LSA), and Hierarchical Dirichlet Process (HDP); (2) LLM-assisted pattern recognition …

Spray Characterization And Fuel Property Analysis Of Tier 3 Gasoline In A Gdi System Using Machine Learning, Kyungwon Lee

Theses and Dissertations

This study investigates the spray behavior of Tier 3 gasoline in gasoline direct injection engines using experimental methods. Tier 3 gasoline, formulated with lower sulfur content, enhances aftertreatment system efficiency and supports cleaner combustion. A comparative fuel property analysis between Tier 3 gasoline and PACE-20, surrogate fuel, was conducted to understand compositional effects on spray. A constant volume spray chamber and optical diagnostics, including diffuse back-illumination extinction, Schlieren, microscopic, and 3D CT reconstruction imaging, were used to examine spray morphology under varying injection conditions. Key spray parameters such as liquid volume fraction, projected liquid volume, droplet size, and cone angle …

Exploring The Use Of A Random Forest In Correcting Gfs Tropical Cyclone Landfall Errors, Victoria Grace Maxwell

Theses and Dissertations

While Tropical Cyclone (TC) track forecasting has improved over the years it has been slow. These improvements can be attributed to increased computer capability and better model inputs. Machine learning has been used for forecasting different TC track and intensity. The study here looks to use a Random Forest to predict the TC latitude/longitude “landfall” at 72-hr and quantify improvements to the GFS forecast. Principal Component Analysis was used to reduce the dimensionality of the meteorological variables. Stepwise regression was used to determine the principal components that were important for forecasting landfall. These variables were then tested with a multivariate …

Unboxing The Black Box: Graph-Based Interpretability In Transformer Models, Ramak Nassiri

Theses and Dissertations

This thesis introduces a novel interpretability framework for transformer encoder models by hypothesizing that their internal embedding updates define a state transition system. We propose that transformers implicitly learn token-to-token influence dynamics, which can be analyzed using graph-theoretic methods. To validate this, we construct transition graphs from embedding changes and rank token importance using the PageRank algorithm. We develop two transformer models from scratch: a self-supervised model that incorporates serotype tokens to learn contextualized embeddings, and a supervised model initialized with these embeddings. Cosine similarity analysis of their token influence patterns reveals strong structural alignment, with values exceeding 93%. This …

Augmenting Healthcare Communication: Context-Aware Ai Frameworks For Clinical Decision Support And Automated Summarization, Subash Neupane

Theses and Dissertations

Healthcare communication is plagued by fragmented medical knowledge, patient misunderstanding of care plans, and clinician burnout from documentation burdens. These inefficiencies cost the U.S. healthcare system over $300 billion annually due to preventable nonadherence and administrative waste [47, 19]. While Artificial Intelligence (AI) technology like Large Language Models (LLMs) offer potential solutions, existing systems fail to deliver personalized, context-aware guidance or automate documentation without sacrificing accuracy. This dissertation addresses these gaps through three novel context-aware AI frameworks such as MedInsight, ClinicSum, and ClinicDuo. MedInsight leverages a multi-source context augmentation approach to synthesize patient centric medical responses by integrating Electronic Health …

Decision-Making And Execution Skill Estimation In Continuous Action Space Domains, Delma I. Nieves-Rivera

Theses and Dissertations

In many real-world domains with continuous action spaces, an agent’s performance is driven by two key factors: its decision-making skill, or ability to select high-quality actions, and its execution skill, or precision in carrying out these actions. However, accurately evaluating these skills, particularly when direct information about the agent’s intentions is unavailable, remains a challenging problem. This research addresses this need by developing a theoretical framework and proposing novel estimation methods to assess both decision-making and execution skills based solely on observable data: executed actions and their associated rewards. The work consists of six core studies focused on developing and …

Markov Chain Monte Carlo Techniques– Innovations In Lifetime Data Analysis And Missing Value Imputations, Tobias Omondi Oketch

Theses and Dissertations

This dissertation develops scalable Bayesian solutions for two critical challenges in modern data analysis: modeling failure times and imputing missing biological data. First, we introduce an adaptive semi-parametric MCMC framework for Weibull lifetime modeling, addressing the lack of conjugate priors and multidimensional sufficient statistics. Using hierarchical modeling and the No-U-Turn Sampler (NUTS) in STAN, we evaluate 24 prior combinations across 72 simulated datasets. The method yields robust parameter estimates under increasing and decreasing hazard rates and proves effective in predicting prostate cancer patient survival. Second, we assess imputation strategies for high-throughput proteomics data, where missingness distorts signal integrity. We compare …

A Mathematical Theory Of Epitaxial Growth, Brock C. Price

Theses and Dissertations

In this dissertation we investigate several PDE models of Epitaxial growth. These models are fourth-order PDE’s featuring exponential nonlinearities as well the p-Laplacian and 1-Laplacian. The presence of the exponential nonlinearity is what provides the main mathematical difficulty. Theexponent in particular does not have enough estimates to guarantee any compactness. Because of this, one has to allow the inclusion of a singular portion to the exponent in the sense of the Lebesgue decomposition theorem. In thefirst chapter weinvestigate a related epitaxial growth, with transition rates of the Metropo lis variety and a linear exponent. The metropolis rates induce an extra …

Unraveling Quantum Properties Of Novel Two-Dimensional Systems Using A First-Principles Approach, Binod Regmi

Theses and Dissertations

We used first-principles density functional theory (DFT) to investigate the structural, electronic, and magnetic properties of an oxygen crystal in a copper slab, a ferrimagnetic electride with highly tunable magnetic anisotropy energy (MAE), and the Fermi surface analysis of different kagome metal phases. The Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional was used in the Vienna ab initio simulation package (VASP) for all these calculations. Because of dimensionality reduction, the electronic properties of copper change significantly at the two-dimensional (2D) scale due to whichwe observe more reconstruction in the geometry of the Fermi surface. Dimensionality reduction also influences charge transport. Moreover, we present …

Advancing Rangeland Vegetation Mapping Through Integrated Uav And Satellite Imagery: A Comparative Machine Learning Framework For Fractional Cover Classification, Nishat Shermin

Theses and Dissertations

This study presents a multi-scale remote sensing framework to classify fractional vegetation cover (FVC) across rangelands by integrating UAV multispectral imagery, LiDAR structural data, and multisource satellite inputs. Four machine learning models, ranging from UAV-only to fused UAV-LiDAR-Sentinel models, were developed and evaluated using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms. The research was conducted at Martin Ranch, Texas, using Uncrewed Ariel Vehicle (UAV)-collected multispectral imagery, LiDAR-derived canopy height, and Sentinel-1/2 indices. Model 2 (UAV + LiDAR) emerged as the optimal model for balancing accuracy, processing time, and compatibility with ecological simulation tools. This model captures both spectral …

Thermo-Mechanical Advancements For Very High Strength And Ultra High Performance Concretes, Lana Grayson Brown Sisung

Theses and Dissertations

Interest in the use of high-strength concrete (HSC) has increased in recent years with demand for improved concrete performance. HSCs are widely associated with high heat of hydration and elusive behavior when exposed to high temperatures commonly occurring in mass structures. The optimization of HSC mixture proportioning is difficult due to a wide variety of constituents that influence thermal and mechanical properties. Additionally, determining in-place properties of mass concrete placements is elusive, and there are currently minimal to no test methods available that are both predictive and a direct measurement of mechanical properties. The objective of this dissertation is to …

Mastering Skills Together: Effects Of Video Modeling On Parent-Implemented Academic Intervention, Chelsea S. Thorpe

Theses and Dissertations

Reading proficiency is a fundamental skill for academic success and long-term developmental outcomes. Yet, despite ongoing efforts to strengthen literacy education, a significant number of students continue to face reading difficulties that hinder comprehension, critical thinking, and overall academic achievement. National data demonstrate the urgency of this issue. The 2024 National Assessment of Educational Progress (NAEP) reported that 40% of fourth-grade students and 33% of eighth-grade students scored below the Basic level in reading, marking the highest rate on record for eighth graders. Early reading challenges can often lead to other academic and psychosocial challenges such as low achievement, low …

Estimating Reliability Of Electric Vehicle Charging Ecosystem Using Principle Of Maximum Entropy, Himanshu Tripathi

Theses and Dissertations

This thesis addresses the challenge of estimating electric vehicle (EV) charging system reliability against unpredictable threats like cyberattacks and extreme weather, where traditional methods fail. We utilize the Principle of Maximum Entropy (PME), a statistical tool that provides unbiased risk estimates using limited information. Applied to the EV charging ecosystem, our case study shows how PME models stress factors to predict failures and optimize maintenance. This approach extends beyond EVs to other complex systems with scarce data, such as smart grids or healthcare devices. By linking uncertainty directly to reliability, PME offers a universal method to improve decision-making under unpredictable …

Bridging Physics And Machine Learning: Physics-Informed Neural Networks (Pinn) For Wire-Arc Additive Manufacturing (Waam) Thermal Prediction, Haiye (Justin) Xie

Theses and Dissertations

Over the past decade, additive manufacturing (AM) has gained substantial use in industry and research. It is a promising alternative to traditional subtractive and casting manufacturing. AM not only saves time and materials but can also construct intricate structures within components for specific purposes. Wire-Arc Directed Energy Deposition (WA-DED) is one of the additive manufacturing technologies that offers a high extrusion rate of metal wire materials to fabricate near-net-shape metal components. Accurate temperature control is crucial for controlling the microstructure of metal components fabricated through the AM process. Machine learning has the ability to analyze real-time sensor data, such as …

Reactive Molecular Dynamics And Method Development For Applications In Renewable Energy And Sorption Equilibria, Woodrow Wilson

Theses and Dissertations

It is crucial to harness energy from sustainable sources to combat the dwindling nonrenewable feedstocks used to produce everyday commodities and fuels. Liquid-phase heterogeneous catalysis has demonstrated remarkable product selectivity and yields for reactions relevant to biomass valorization under mild operating conditions. However, active site characterization is challenging due to the complex solvated environment and interface. Ground-state first-principles (FP) molecular simulations have provided insights into reactive systems in catalysis. These calculations, however, scale poorly with the number of electrons. Observing atomic motion with molecular dynamics (MD) or sampling static properties with Monte Carlo (MC) simulations at statistically relevant lengths- and …

The Effect Of Wall Temperature And Transpiration On Görtler Vortices In High-Speed Boundary Layers, Asmaa Ahaggach

Theses and Dissertations

Görtler vortices are counterrotating flow structures generated as a result of the imbalance between the wall radial pressure gradients and the centrifugal forces in a boundary layer developing on a curved surface. Their presence leads to secondary instabilities and increases the wall shear stress as they accelerate the transition from laminar to turbulent flow. These secondary instabilities can be mitigated using control techniques aimed at decreasing the energy and the wall shear stress. In this thesis, the combined effect of wall temperature and transpiration on Görtler vortices developing in supersonic and hypersonic boundary layers is investigated. Görtler vortices are excited …

Development Of A Health Knowledge And Skill-Based Framework For The Digital Age, Evelyn Hovenga, Osama El-Hassan, Manal Almalki, Jawad Ahmed, Paul Barach, Mowafa Househ, Amr Jamal

College of Population Health Faculty Papers

The Gulf Cooperation Council (GCC) region is tackling a significant digital healthcare workforce skills gap by creating a computable unified digital health competency framework and repository. Digital technologies and global competency standards are leveraged to professionalize the digital health knowledge domain and facilitate the development of clear career pathways in digital health. A mixed-methods approach was employed to develop the framework. A standardized categorial structure and information model has been developed to support the creation of this computable framework for workforce evaluation and competency-based career development.

Aristotle, John Herman Randall Jr.

Hannah Arendt Marginalia - All

New York : Columbia University Press, 1962

Call #: B485 .R3 1962

Contains underlining and marginal lining

PDF Information: 92 pages – 70 MB

The Works Of Plato, Plato

Hannah Arendt Marginalia - All

New York: Tudor Publishing Co.,1937

Call #: B358 .J8 1937

PDF Information: 17 pages – 12.7mb

The Lives Of The Noble Grecians And Romans, Plutarch

Hannah Arendt Marginalia - All

New York: The Modern library, 1932

Contains marginal lining and endpaper notes.

Call #: DE7 .P5 1932

PDF Information: 105 pages – 116.3mb

Protagoras, Plato

Hannah Arendt Marginalia - All

New York, Liberal Arts Press, 1956

Call #: B382.A8 E5 1956

Contains marginalia, underlining and marginal lining.

PDF Information: 45 pages – 28.4mb

The Republic Of Plato, Plato

Hannah Arendt Marginalia - All

New York: Oxford University Press, 1945

Contains marginalia.

Call #: JC71 .P35 1945

PDF Information: 132 pages – 116.8mb

The New Science: 3 Complete Works, Max Planck

Hannah Arendt Marginalia - All

[New York] : Meridian Books, 1959

Call#: QC6 .P623

Contains marginalia, underlining and marginal lining. Also contained ephemera: a 1957-1958 Princeton- Penn Station train schedule covered with local business advertisements.

PDF Information: 77 pages – 5.1 MB

Plato’S Phaedo, Plato

Hannah Arendt Marginalia - All

New York : Liberal Arts Press, c1951 (1954 printing)

Call #: B379 .A5 C48 1951

Contains marginalia, underlining, marginal lining and endpaper notes

PDF Information: 42 pages – 19 MB

Entangled In The Design Of A Relational Materiality: Beyond Smart Textiles, Anne Louise Bang, Delia Dumitrescu, Malene Harsaae, Inger Marie Ladekarl, Hanna Landin, Erin Lewis, Mika Latusaari, Alicja Lawrynowicz, Kati Miettunen, Mithila Mohan, Mikael Nyberg, Lena Pedersen, Pedro Silva, Maija Vaara, Jaana Vapaavouri, Linda Worbin, Zahraalsadat Madani, Kristian Rödby

Nordes Conference Series

Nowadays, ethics and care for the environment have shifted paradigms in smart textile design towards responsible energy use and preservation of natural resources. In this research, we aim to relate material science and engineering to textile and interaction design in discovering a vibrant responsive materiality triggered by ultraviolet (UV) energy. The research addresses a bottom-up approach entangling scientific development in material science, textile craftsmanship, and design to the understanding of human use and the capacities for change of this natural phenomena. Compared to a conventional textile design processes where material and form composition connect in a final expression, in this …

Seasonal Design, Svenja Keune, Åsa Ståhl

Nordes Conference Series

Imagine it is a rainy winter day, and you are walking up to a little mustard yellow house on wheels and step up three wooden steps to enter. You notice the pulsing light of candles and the warmth of the fireplace that is heating the indoors. Some people are sitting around the table dreaming of how they would use the house if they could borrow it for a while. You take a step inside to join and have some tea. While stepping in you hear the splash of a puddle and feel your socks soak up water before you even …