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Measuring Cosmic Expansion From Early To Late Times Using Quasars, Galaxies, & Local Supernovae, Rajeev Vaisakh

Physics Theses and Dissertations

This dissertation investigates the evolution of cosmic expansion across a wide redshift ($z$) range by combining measurements from quasars \& galaxies in the distant universe $(z > 0.8)$ with those obtained from stripped-core collapse supernovae observations from the nearby universe $(z < 0.1)$. Using the DESI spectroscopic survey, we analyze large-scale clustering from quasars over $0.8 < z < 2.1$ and emission line galaxies over $0.8 < z < 1.6$, contributing to DESI DR2 BAO and full-shape measurements of cosmic expansion and structure growth. Combined with BBN in flat $\Lambda$CDM, DESI DR2 BAO gives $H_0 = 68.51 \pm 0.58$ km s$^{-1}$ Mpc$^{-1}$, while combinations of DESI BAO with CMB and supernova data show a preference for evolving dark energy, with representative constraints such as $w_0 = -0.752 \pm 0.057$ and $w_a = -0.86^{+0.23}_{-0.20}$ for DESI+CMB+DESY5. Using the ROTSE-III Supernova Survey and the expanding photosphere method (EPM), we measure distances to a sample of six supernovae for which the thermal phase can be clearly identified. This analysis focuses on testing whether reliable EPM distances can be obtained for this population. The resulting distances will eventually be compared with independent estimates from the literature. Establishing consistency with these external measurements represents an important step toward applying this methodology to larger samples, with the eventual goal of using ROTSE supernova distances to provide an independent measurement of the local Hubble constant.

Toroidal Plasmonic Nanodimers For Enhanced Near-Infrared Emission In Heterostructured Inp Quantum Dots, Arda Gülücü, Emre Ozan Polat

Turkish Journal of Physics

Near-infrared (NIR) emitters operating in the 650--900 nm spectral range are highly attractive for imaging and sensing in turbid media. However, cadmium-free InP-based quantum dots (QDs) often exhibit limited brightness owing to nonradiative recombination pathways and inefficient photon outcoupling. In particular, heterostructured InP QDs may possess band alignments that induce partial spatial separation of charge carriers, thereby reducing the electron-hole wavefunction overlap. This characteristic modifies the intrinsic recombination dynamics and increases the sensitivity of their emission to the surrounding photonic environment. Here, we investigate silver toroidal plasmonic nanoantenna dimers (Ag TPNDs) using finite-difference time-domain (FDTD) simulations as a geometry-tunable platform …

Radiation Pressure And Membrane Dynamics In Cavity Optomechanics: Interaction Hamiltonian And Quantization, Mahmut Can Özuygur

Turkish Journal of Physics

Cavity optomechanics explores the interaction between light confined in optical cavities and mechanical oscillators, primarily driven by radiation pressure forces. These interactions enable significant advances in precision measurements, quantum information processing, and tests of fundamental quantum mechanics. This paper presents a detailed analysis of membrane vibration and radiation pressure force within a cavity optomechanical system. The membrane's equation of motion is derived, the radiation pressure force is evaluated within the Lorentz force formalism, and the corresponding interaction Hamiltonian is formulated. Quantization of the resulting Hamiltonian enables the investigation of quantum phenomena, including entanglement generation and quantum state transfer, and provides …

Ai And The Music Industry: Its Current Status And A Speculative Projection Of Its Evolutionary Trajectory, Rhett D. Morris, Clayton Rosati, Stefan Fritsch

Honors Projects

Music serves as one of society's biggest cultural outlets, allowing millions to share in what used to be a uniquely human form of expression. The commodification of music has built a huge industry full of companies and platforms that have used technology and property laws to shape music's relationship with the public. This study aims to look into the future to see how AI and its implementation could affect the structure of the music industry. To look into the future, this piece establishes two of the most pressing kinds of AI technology for the music industry and looks to contextualize …

Community Detection In Bipartite Networks Using Bipartite Stochastic Block Models With Node-Level Covariates, Geraldine Elaine Percival

Dissertations

In this age, monumental webs of data demands for perpetual cultivation of ways to untangle these webs of information. One of the many curiosities is how to systematically group entities. When clustering, one avenue to take is ascertaining the interconnectedness between the data points, and gauging their influence to each other. This perspective is programmed to model the relationships between the data presented as a network. Many of the methods being used today are algorithm-based, which may pose limitations in understanding and explaining the uncertainty revolving around the data. Hence, it is proposed to steer towards a model-based approach that …

Hidden Symmetries And Higher Dimensional Rotating Black Holes, Luis Fernando Temoche Hurtado

All Graduate Theses and Dissertations, Fall 2023 to Present

A standard approach to probing the dynamics of a physical system is to expose it to an external perturbation—such as an incident wave—and analyze its response after the interaction. Mathematically, this procedure is formulated in terms of differential equations governing the evolution of the perturbation.

In the context of black hole physics, an analogous strategy can be employed by studying the response of the event horizon to external perturbations. The differential equations describing these interactions are often invariant under nontrivial transformations, revealing hidden symmetries that help explain distinctive features of higher-dimensional black holes.

By exploiting these hidden symmetries in the …

Investigation Of Yy Production Methods For Invasive Channel Catfish Ictalurus Punctatus Population Management, Andrew M. Wisniewski

All Graduate Theses and Dissertations, Fall 2023 to Present

Aquatic invasive species are one of the leading causes of native fish declines in the Southwestern United States. Channel Catfish Ictalurus punctatus were introduced into the Colorado River Basin, including the San Juan River, New Mexico, Colorado, and Utah for fishery enhancement during the 20th century. This introduction has contributed to the decline of native species and despite intensive mechanical removal efforts, the Channel Catfish population has persisted in the San Juan River. The Trojan Sex Chromosome approach is a promising eradication strategy that proposes the production and release of Trojan sex chromosome carriers (YY) to skew a targeted population’s …

A Data-Driven Nutrient Density Scoring Framework For Beef Using Principal Component Analysis, Teja Vuppala

All Graduate Theses and Dissertations, Fall 2023 to Present

Beef is one of the most nutrient-rich foods in the human diet, providing high-quality protein, iron, omega-3 fatty acids, B vitamins, and a wide range of other compounds important to health. However, current nutrition scoring systems used on food labels were designed to compare different foods to one another — for example, beef versus broccoli — and do not work well for judging the nutritional quality of different beef samples relative to each other. A grass-fed steak and a conventionally-finished steak can carry nearly identical Nutrition Facts panels while differing substantially in their content of omega-3 fatty acids, vitamins, and …

Integrating Hydrology And Human Water Footprints: A Case Study Of The Great Salt Lake Basin, Rachel Lynne Seeley

All Graduate Theses and Dissertations, Fall 2023 to Present

Global demand for water is outrunning supply, and how we account for water is part of the problem. Many water management frameworks are based solely on how much physical water moves through rivers, aquifers, and other water bodies, missing the water that is embedded in food and other products a region produces and exports. This study developed a new, generalizable water accounting framework that incorporates this “hidden water,” known as virtual water, into water management and applied the framework in the Great Salt Lake Basin.

The Great Salt Lake is shrinking, and an often-overlooked driver of the lake’s decline is …

Modeling Mean And Variability Of Anxiety In Ecological Momentary Assessment Data Using Mixed-Effects Location–Scale Models, Trenzy Odero

Electronic Theses and Dissertations

Ecological Momentary Assessment is a method of collecting repeated measures of people in real time within natural environments. This results in hierarchical data that has a significant amount of variation at the person level. The traditional linear mixedeffects models assume that the residual variance is constant, which might not be true when the residual variance varies among individuals as well as in time. This thesis uses mixed-effects location-scale (MELS) models to model the mean and variance of an EMA outcome together. By introducing the possibility of variability in residual variance within and across individuals and with covariates, the MELS framework …

Federated Learning For Early Medical Diagnosis: Enhanced Diabetic Retinopathy Detection In Smart Healthcare, Mohammad Nasajpour Esfahani

Master's Theses

This thesis investigates the role of federated learning as a privacy-preserving solution for modern healthcare challenges. In traditional machine learning, sensitive medical data must be centralized for model training, raising concerns about privacy, security, and regulatory compliance. Federated learning offers an alternative by allowing hospitals, clinics, and personal health devices to collaboratively train shared models without exchanging raw patient data. The study first explores how federated learning is being used across various healthcare domains, including cancer detection, medical imaging, and disease prediction— highlighting its potential to support secure collaboration across institutions. It addresses key benefits such as data privacy, scalability, …

Llm-As-A-Judge For Software Engineering: Literature Review, Vision, And The Road Ahead, Junda He, Jieke Shi, Terry Yue Zhuo, Christoph Treude, Jiamou Sun, Zhenchang Xing, Xiaoning Du, David Lo

Research Collection School Of Computing and Information Systems

The rapid integration of Large Language Models (LLMs) into software engineering (SE) has revolutionized tasks from code generation to program repair, producing a massive volume of software artifacts. This surge in automated creation has exposed a critical bottleneck: the lack of scalable and reliable methods to evaluate the quality of these outputs. Human evaluation, while effective, is very costly and time-consuming. Traditional automated metrics like BLEU rely on high-quality references and struggle to capture nuanced aspects of software quality, such as readability and usefulness. In response, the LLM-as-a-Judge paradigm, which employs LLMs for automated evaluation, has emerged. This approach leverages …

Continuous Query For Top-K Maximal Sum Intervals Over Streaming Data, Zhongshuai Zhang, Xiaochun Yang, Baihua Zheng, Rui Zhu, Haomin Li, Bin Wang

Research Collection School Of Computing and Information Systems

The continuous identification of top-k maximal sum intervals using a sliding window over a data stream is a critical operation for applications in IoT and beyond. A maximal sum interval is a non-overlapping, contiguous subsequence with the maximal sum in a sequence of signed values. Existing algorithms are ill-suited for streaming contexts: they either exhaustively enumerate all intervals even for small k values, or depend on indexes that require frequent and costly restructuring. We propose a novel partition-based strategy. Our core insight is a partitioning scheme that guarantees that any maximal sum interval is fully contained within a single partition, …

Odyssey To The Extraordinary: Living Our Land Grant Mission And Beyond, Jeffrey P. Gold, Katherine S. Ankerson, Tiffany Heng-Moss

Institute of Agriculture and Natural Resources: Institute Publications

Presents a strategy intended to guide future directions for the University of Nebraska-Lincoln's (UNL) Institute of Agriculture and Natural Resources (IANR) including issues related to governance, funding, organizational structure, and strategic alignment within UNL and the University of Nebraska system.

Summary Report: Second Conference On Attribution Science And Climate Law, Jessica A. Wentz

Sabin Center for Climate Change Law

The Sabin Center for Climate Change Law and the Columbia Climate School hosted the Second Conference on Attribution Science and Climate Law at Columbia University on June 10–11, 2026. This event brought together an interdisciplinary group of researchers and practitioners to discuss recent developments in attribution science and the field’s relevance to climate law and policy. Participants included physical scientists, public health researchers, economists, social scientists, legal scholars, advocates, and policy experts. This report provides a written summary of the conference proceedings.

Drought And Diet Breadth: Does Insect Specialization Influence Herbivory When Plants Are Under Stress?, Jakob Palmer

All Graduate Theses and Dissertations, Fall 2023 to Present

Climate change will alter precipitation worldwide, causing more frequent and severe drought events worldwide. Drought affects many ecological processes, including plant-herbivore interactions, which are critical to biodiversity and ecosystem health. A variety of plant characteristics can influence herbivory – such as how hairy, nutritious, or toxic a leaf is – and drought has been shown to alter many of these traits. Unique characteristics of herbivore species are also important: herbivores can either feed on a variety of plants (generalists) or a group of closely related plants (specialists). Specialists can cope with traits of the plants they eat, while generalists are …

Human-Centered Electric Vehicle Adoption Framework For Smart Mobility: Modeling Perceived Range And Charging Anxiety As A Psychological Barrier, Fatemeh Nazari, Abolfazl (Kouros) Mohammadian, Thomas Stephens

Civil Engineering Faculty Publications

Electric vehicles (EVs) offer a transformative pathway toward reducing the environmental, economic, and health-related externalities of internal combustion engine vehicles in urban settings. Despite substantial advances in battery technology, charging infrastructure expansion, and supportive policy incentives, EV penetration remains limited which poses challenges for smart and sustainable mobility planning. A critical yet insufficiently modeled barrier to adoption lies in the psychological perceptions surrounding electric driving range and charging reliability, which is commonly framed as “range anxiety,” but more broadly reflecting perceived range and charging anxiety. To address this gap, this study introduces a latent psychological construct capturing individuals’ perceived range …

A Deep Reinforcement Learning Framework For Dynamic Order And Rack Scheduling In Robotic Mobile Fulfillment Systems, Lingjun Wang, Bayi Cheng, Zhiguang Cao, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Robotic mobile fulfillment systems play a vital role in modern e-commerce, but their efficiencydepends on overcoming a critical challenge: coordinating orders and racks effectively under dynamicorder arrivals, diverse delivery deadlines, and resource sharing among multiple stations. This papertackles the resulting dynamic order and rack scheduling problem and proposes an intelligentscheduling framework centred on deep reinforcement learning. At its core is TriNet-PPO, whichtrains a scheduling policy using TriNet, a tripartite graph neural network encoder tailored to capturethe many-to-many relationships among orders, racks, and items. Furthermore, we introduce IPCSA,an integer programming-based cooperative scheduling algorithm that maps station-level actionpreferences to conflict-free joint decisions …

Parameter Dependent Chen-Fliess Series And Their Nonrecursive Interconnections, Natalie T. Pham

Electrical & Computer Engineering Theses & Dissertations

In control theory, a Chen-Fliess functional series is a weighted sum of iterated integrals constructed from a given set of input functions. Such series can be used to represent nonlinear input-output systems. In applications, they have been employed to characterize interconnected nonlinear systems, to solve system inversion and tracking problems, and to design predictive and adaptive controllers.

Distributed parameter systems exhibit spatial dependence along with temporal dependence. Such systems are typically represented in terms of partial differential equations. In control theory, there appears to be no existing method for representing the input-output map of a distributed system via a Chen-Fliess …

The Relativity Of Education: Student Perspectives On Artificial Intelligence In Community College, Ashley Greta Magana

Electronic Theses, Projects, and Dissertations

This hermeneutic phenomenological study examined how diverse community college students experience and make meaning of the integration of generative artificial intelligence (AI) into their educational contexts. Although AI is quickly transforming higher education through automated grading, personalized learning systems, and new models of assessment, the discourse surrounding its implementation remains dominated by administrators, faculty, and institutional stakeholders, while the perspectives of students, specifically community college students who are often historically underrepresented and economically marginalized, are systematically excluded. Most existing research is quantitative and centered on universities, leaving a critical gap in qualitative understanding of the most diverse population in higher …

Greenhouse Gas Flux Response In Biochar- And Compost-Amended Lawn Soils Under Simulated Water Saturation Conditions, Angel Salinas, Chu-Lin Cheng, Engil Pereira, Rafael M. Almeida, James Jihoon Kang

School of Earth, Environmental, & Marine Sciences Faculty Publications

Understanding greenhouse gas emission dynamics in lawn soils is important for improving climate change mitigation strategies in urban and suburban landscapes. In this greenhouse mesocosm study, carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) fluxes were measured from 12 turfgrass soil columns arranged in an unreplicated, completely randomized 4 × 3 factorial design. Four amendment treatments (biochar, compost, bio-com [1:1 volume ratio of biochar and compost], and unamended control) were combined with three water-table conditions (full saturation, half saturation, and unsaturated), with a single column representing each treatment combination. Columns were irrigated at 1 cm day−1 for 24 days, …

Leveraging Deep Learning Recurrence And Attention Mechanisms For Flood Forecasting And Assessment, Elnaz Heidari

All Dissertations

Predicting how much water will flow in rivers and streams is important for managing floods, water supply, and the environment. Traditionally, government agencies have used complex models, such as the National Water Model (NWM), which simulate how much water moves through landscapes using physical laws and real-world data. However, recent advances in Artificial Intelligence (AI) have enabled new ways to make these predictions. This research explored whether AI-based models could predict river discharge more accurately. These AI models learn patterns from past data instead of relying only on physical rules. To find out how well they work, the AI models …

When The Best-Fit Model Is Not Best: The Glass Slipper Fallacy And Latent Growth Mixture Modelling, Jonathan L. Chia, Markus Wettstein, Andree Hartanto

Research Collection School of Social Sciences

Despite the use of latent growth mixture modelling (LGMM) to study longitudinal changes, existing practices may inadvertently impede this very investigation. Although subgroup trajectories may theoretically differ in their structure (e.g., some subgroups being linear, some curvilinear), the current convention advocates overreliance on the baseline model to derive subsequent profile trajectories, which may obscure these structural differences. In this article, we provide a brief description of extant LGMM practices, after which we explicate the pitfalls of the current approach. Finally, we provide a principled approach for LGMM research moving forward. Specifically, we recommend specifying a set of theoretically plausible models …

Technique-Level Normalization For Cybersecurity Intelligence: An Empirical Evaluation Of Att&Ck Attribution From Hids Alerts Using Fine-Tuned Transformers And Metadata Re-Ranking, Emad Sherif

International Journal of Cybersecurity Intelligence & Cybercrime

Cybercrime investigations increasingly depend on the ability to interpret large volumes of automated security events. For organizations without dedicated security operations centres, a situation common among small and medium enterprises, the manual translation of raw alerts into structured threat intelligence represents a critical bottleneck that slows investigative triage and limits cross-case comparability. This paper evaluates an automated enrichment pipeline designed to address this bottleneck by mapping security events to standardised adversary behaviour labels drawn from the MITRE ATT&CK framework, supporting both operational response and cybercrime investigation workflows. We compare three pipeline configurations, a general-purpose encoder model, a cybersecurity domain-adapted variant, …

Indigenous Community Research Opportunities, Sharon Hausam, Aaron M. Canter

Faculty Publications

This is a working paper produced as part of National Science Foundation project #2115169, “Transforming Rural-Urban Systems: Trajectories for Sustainability in the Intermountain West,” known as the “Intermountain West Transformation Network” (TN), led by the University of New Mexico with institutional partners at three additional universities in New Mexico, two in Arizona, and one each in Utah, Colorado, and Washington. The TN’s Indigenous and Tribal engagement was supported through a contract with Dr. Sharon Hausam, additional work by Aaron M. Canter, coordination with Dr. Lani Tsinnajinnie, and an Advisory Committee on Indigenous Information Needs primarily comprised of Indigenous representatives in …

Optimizing Urban Forestry Through Green Infrastructure: Tree Health And Water Retention In Ciudad Juarez Rain Gardens, Tomas Rogelio Lizarraga Ruiz

Open Access Theses & Dissertations

Urban green spaces (UGS) in arid regions face significant challenges due to water scarcity and extreme thermal demand. This study evaluates the efficacy of Nature Based Solutions (NBS), rain gardens and median strips, in enhancing the physiological resilience of urban trees in Ciudad Juárez, Mexico. Using a comparative framework, the research monitored a native evergreen (Dermatophyllum secundiflorum) and an introduced deciduous species (Morus sp.) across six planting treatments. Statistical analysis reveals that green infrastructure (GI) acts as a critical physiological accelerant. Trees situated within rain gardens and medians significantly outperformed those in restricted pot and sidewalk settings, maintaining higher stomatal …

A Computational Approach To Electronic Coupling In Molecular Dyads, Irving Alejandro Lopez Ruiz

Open Access Theses & Dissertations

Excitation energy transfer (EET) is a fundamental process in photosynthetic systems and molecular photonic devices, where the efficient transport of electronic excitation between donor and acceptor chromophores determines the overall performance of the system. The electronic coupling V_DA between the transition densities of the two fragments is the central quantity governing this process in the weak-coupling (Förster) regime, and its accurate numerical evaluation is the main objective of this work. Two independent computational implementations of the Coulomb interaction between transition densities were developed and applied to a set of five anthracene-BODIPY donor-acceptor dyads, studied under two geometric configurations: one preserving …

Hfir Antineutrino Flux Mapping With Scale Mesh Tallies, Jacob Adam Mireles

Open Access Theses & Dissertations

This thesis develops and demonstrates a workflow for mapping the spatial distribution of reactor electron antineutrino flux around the High Flux Isotope Reactor (HFIR) using three-dimensional mesh tallies from the SCALE 6.3.1 Monte Carlo transport suite. A detailed HFIR model is executed with KENO, and the mesh-tally output is post-processed to construct a voxelized antineutrino source term. The source field is normalized to reactor power and propagated to sampled spatial points surrounding the reactor to generate three-dimensional source-side flux maps. The main analysis focuses on how the mesh-based HFIR antineutrino flux differs from an ideal point-source approximation. In the point-source …

Physics Model Calibration Via Functional Kernel Regression, Kwesi Appau Ohene-Obeng

Open Access Theses & Dissertations

Physics based computational simulators encode the governing equations of complex physical phenomena through parameters that must be inferred from empirical observations, and the observational record in modern experimental campaigns is increasingly a high dimensional functional response rather than a scalar summary. The canonical treatment of this calibration problem through the additive discrepancy decomposition of Kennedy and O'Hagan incurs O(N^3obs) Gaussian process inversion costs at functional resolution, sensitivity to discrepancy prior specification, and the confounding identifiability between the calibration parameter and the discrepancy function; scalar reduction strategies bypass the cost but discard the morphological information that the functional response carries about …

Towards A Multi-Framework Approach To Explainable Artificial Intelligence, Henry Salgado

Open Access Theses & Dissertations

Artificial Intelligence (AI) and machine learning (ML) models are increasingly being deployed to support decision-making in high-stakes domains such as healthcare, criminal justice, and education, where trust, accountability, and transparency are critical. However, increasing model complexity has made many modern systems insufficiently transparent. Existing approaches to explainable AI (XAI) typically emphasize either intrinsic model simplicity or post-hoc attribution methods that estimate feature importance for predictions. While these approaches provide valuable insights into model behavior, they do not necessarily establish whether the identified importance is grounded in the underlying data patterns or in the structural relationships that generate model behavior. Many …