Physical Sciences and Mathematics Commons^™

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

University of Kentucky Doctoral Dissertations

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

Modeling Rank Distribution And The Relative Importance Factor Index In Discrete Power-Law Models: Application To Social Resilience Using The Scopus Database, Brian Llinas, Jose Padilla, Humberto Llinas, Erika Frydenlund, Katherine Palacio

VMASC Publications

Prior research on power-law distributions has primarily focused on modeling frequency patterns, with less attention given to rank distributions and how ranked positions reflect relative importance among elements. In discrete power-law distributions, frequency-based metrics often provide limited discrimination in the tail, where elements may exhibit similar counts but differ in relative dominance. These patterns are especially evident, for instance, in academic publishing, where keywords, affiliations, and citations commonly exhibit power-law behavior. To address this limitation, we introduce the Relative Importance Factor (RIF) Index, a statistical measure derived from the estimated discrete power-law rank distribution rather than an additional independent parameter. …

Standardization Of Neuromuscular Reflex Analysis—Role Of Fine-Tuned Vision-Language Model Consortium And Openai Gpt-Oss Reasoning Llm-Enabled Decision Support System, Eranga Bandara, Ross Gore, Sachin Shetty, Ravi Mukkamala, Christopher K. Rhea, Brittany S. Samulski, Amin Hass, Atmaram Yarlagadda, Shaifali Kaushik, Malith De Silva, Andriy Maznychenko, Inna Sokolowska, Kasun De Zoysa

VMASC Publications

Background/Objectives: Accurate assessment of neuromuscular reflexes, such as the Hoffmann reflex (H-reflex), plays a critical role in sports science, rehabilitation, and clinical neurology. Conventional interpretation of H-reflex electromyography (EMG) waveforms is subject to inter-rater variability and interpretive bias, limiting reliability and standardization. This study aims to develop an automated, interpretable, and robust agentic AI–driven framework for H-reflex waveform analysis. Methods: We propose a fine-tuned Vision–Language Model (VLM) consortium combined with a reasoning Large Language Model (LLM)–enabled decision support system for automated H-reflex interpretation. Multiple VLMs were fine-tuned on curated datasets of H-reflex EMG waveform images annotated with expert clinical observations, …

Distilling The Complexity Of Agent-Based Simulations Into Textual Explanations Via Large Language Models, Noé Y. Flandre, Philippe J. Giabbanelli

VMASC Publications

Communicating the design and results of agent-based models (ABMs) to subject matter experts is challenging, which hinders participation and limits trust in simulation-based decision support. Large language models (LLMs) can communicate ABMs as textual summaries, thus complementing traditional disclosure through statistical and visualization techniques. While prior work translated the structure of conceptual models into narratives via LLMs, our extension covers the dynamics of simulation models via an automated simulation-to-text method that extracts contextual information from NetLogo ABMs, performs repeated simulations, and generates narrative descriptions (including the model’s purpose, parameters, and simulation dynamics) using mutimodal LLMs. Furthermore, four summarization algorithms spanning …

Machine Learning Classification Of Prostate Cancer Genomic Sequences Using K-Mer And Sequence-Derived Features, Kuldeep Rawat, Hirendra Nath Banerjee, Jamie Noble, Saa Naudia Deloatch, Satyendra Banerjee, Sachin Shetty, Soumya Banerjee

VMASC Publications

Prostate cancer disproportionately impacts African American men, who experience significantly higher mortality rates and earlier disease onset than other populations. Current diagnostic approaches, including prostate-specific antigen testing and biopsy, lack sufficient specificity and sensitivity, underscoring the need for accurate, molecular-level classification tools. This paper presents a machine learning framework for binary classification of genomic DNA sequences as cancerous or healthy. A dataset of 1684 FASTA-formatted sequences obtained from the National Library of Medicine - GenBank was analyzed, with 1662 sequences retained after quality control filtering. Feature engineering yielded 67 attributes, including GC content, Shannon entropy, sequence length, and trinucleotide k-mer …

Hierarchy And Ideology Antagonism: Artificial Intelligence In Ridley Scott's Alien, Grace Anastasia Pula

Theses, Dissertations and Culminating Projects

This thesis examines the objectively threatening structure of artificial intelligence (AI) in the narrative plot of Alien and how it exerts control over the humans. Using a structuralist approach with Louis Althusser's Ideological State Apparatuses (ISAs), I will examine the character relationships and how an android, Ash, enforces a patriarchal, hierarchical system. Drawing on Mark Coeckelbergh’s AI Ethics and Jacques Ellul’s The Technological Society, I will outline broader fears that technology will surpass human intellect and serve a destructive function within an oppressive system. Analyzing two examples of AI characters, the film showcases capitalist ambitions through technological identities and their …

Dyno : Dynamic Neurosymbolic Orchestrator For Multi-Agent Systems, Ritvik Garimella, Chathurangi Shyalika, Renjith Prasad, Amit Sheth

Publications

Large Language Model (LLM)-based multi-agent systems (LaMAS) represent an emerging paradigm for tackling complex, multi-step reasoning and decision-making problems. As these systems scale, orchestration, which is the ability to coordinate, manage, and evaluate the interactions among diverse agents, becomes central to their success. While recent orchestrators such as AgentFlow have demonstrated promise in managing communication and task delegation, they remain limited in their ability to understand task semantics, coordinate heterogeneous agent types (e.g., reactive vs. cognitive), and adaptively align outputs with human-defined goals. In this position paper, we introduce the DYNO (Dynamic Neurosymbolic Orchestrator), a system developed as part of …

Using Nmr Spectroscopy And Linear Discriminant Analysis To Molecular Profile Varietal Honey, Taylor Mac

Master's Theses and Doctoral Dissertations

In recent years, varietal honey has been a massive target of adulteration through mislabeling and the addition of other sugars. Unethical companies do this to cut production costs while still charging the consumer full price. Previous studies have used nuclear magnetic resonance (NMR) to unravel possible adulteration in honey, but currently, there are no standard rapid methods to authenticate varietal honey. In our research, we collected NMR signatures (“fingerprints”) and combined them with linear discriminant analysis (LDA) to predict the varietal, country, and region of varietal honey. As a part of the project, we investigated whether an adjustment to the …

Does Fishery Management For Groupers (Teleostei: Epinephelidae) Protect Them Effectively? Context From The Iucn's Red List Of Threatened Species, Sean T. Fennessy, Christi Linardich, Kevin Rhodes, Joao P. Barreiros, David Pollard, Eloy Sosa-Cordero, Felicia Coleman, Alfonso Aguilar-Perera, Christopher R. Malinowski, Thierry Brulé, Pedro Afonso, Kayan Ma, Min Liu, Muktha Menon, Colin Wen, Stanley K. H. Shea, Sean N. Porter, Matthew Craig, Yvonne Sadovy De Mitcheson

Biological Sciences Faculty Publications

Worldwide, groupers (Epinephelidae) are commercially valued fishes, which also play key ecological roles on tropical and subtropical reefs. In 2007 and 2016, the IUCN's Groupers and Wrasses Specialist Group assessed all 160+ grouper species, with 17 of these being identified as threatened in 2016 and the major threat factor being overexploitation. Our present study aimed to identify whether management measures (MMs) for previously assessed groupers were established, whether these measures aligned with IUCN's Red List categories, and whether they effectively protect grouper populations. Experts in grouper biology and management assigned scores per grouper species based on the extent to which …

The Coral Microbiome Under Climate Change: Shifting Symbiotic Interactions, Katherine E. Parker, Kate M. Quigley

Biological Sciences Faculty Publications

Host–microbe symbioses are central to organismal health, yet these complex partnerships are often strongly shaped by the environment. Climate change is increasingly altering environmental conditions, disrupting the balance of host–microbe interactions and pushing them along a continuum from mutualism (both partners benefit) toward parasitism (one partner gains at the other’s expense). Reef-building corals provide a clear example of this vulnerability, as the coral holobiont, an ecological unit comprising diverse photosynthetic algal symbionts (Symbiodiniaceae) and other microbial partners, depends largely on these mutualistic interactions for nutrition, immunity, and stress tolerance. Increasingly, evidence suggests that under heat stress, the algal symbionts retain …

The Shoreline Protection Potential Of Oyster Reefs: A Systematic Review And Meta-Analysis, Jessica R. Fergel, Robert E. Isdell, Gabriella Dipetto, Karinna Nunnez, Sean T. Gregory, Evan Hill, Donna Marie Bilkovic

Biological Sciences Faculty Publications

Nature-based solutions for erosion control that incorporate oyster reefs, alone or in combination with other habitats, are an increasingly popular approach due to their potential to protect shorelines and enhance oyster production. However, the extent to which natural or constructed oyster reefs provide shoreline protection remains unclear. We conducted a global systematic literature review and meta-analysis to summarize and evaluate the potential of oyster reefs in attenuating waves, promoting sediment accretion, and/or reducing shoreline erosion. Factors extracted from studies included shoreline protective measures examined, oyster reef structure type, and oyster reef tidal location. The results of the meta-analysis showed generally …

Mitochondrial Genome Of The Indo-Pacific Mesophotic Coral Leptoseris Columna (Scleractinia: Agariciidae) Assembled Using Pacbio Long-Read Sequencing, Nomita Rani Adhikary, Daniel J. Barshis, J. Antonio Baeza

Biological Sciences Faculty Publications

Leptoseris columna, a mesophotic coral species belonging to the family Agariciidae, is distributed throughout the Indo-Pacific region. This species is considered as of "Least concern" by the IUCN, yet, faces multiple local and global stressors. To support conservation plans, this study sequenced and characterized the complete mitochondrial genome of L. columna. The complete mitochondrial genome of Leptoseris columna was assembled using PacBio long-reads with a coverage of 459× per bp. The AT-rich mitochondrial genome of Leptoseris columna is 18,546 bp long and comprises 13 protein-coding genes (PCGs), 2 transfer RNA genes (trnM and trnW), and 2 ribosomal RNA …

Topic Modeling The Cuny Graduate Center's Dissertations And Theses, Michael Mandiberg

Open Educational Resources

This 4 week module is designed for Data Analysis, Data Visualization, and Digital Humanities courses at the MA/MS or advanced 400-level undergraduate level. It introduces students to textual analysis with topic modeling and requires a solid foundation in Python. The module uses Gensim and a Colab notebook to introduce a standard text analysis workflow used in Digital Humanities, archival research, and exploratory data analysis.

Students build a topic model describing 19,000 CUNY Graduate Center dissertations and theses. They work with an unexplored dataset to load and explore the data, prepare the corpus, train and evaluate a topic model, and interpret, …

Time Series Decomposition And Forecasting Of Alzheimer’S Disease Mortality Using A Kolmogorov-Zurbenko Filter, Jack D. Farrell

Electronic Theses & Dissertations (2024 - present)

Alzheimer’s disease mortality has substantially risen in recent history, placing a significant burden on public health infrastructure and highlighting the need for improved analytical methods to better understand mortality data patterns and offer reliable predictions. Time series methods often struggle to balance both accuracy and interpretability, which hinders the ability to gather meaningful insights from time series data. To address these limitations, this study applies the Kolmogorov-Zurbenko (KZ) filter to monthly U.S. Alzheimer’s mortality data spanning from 1999 to 2023 and decomposes the series into long-term trend, seasonal, and noise components on a logarithmic scale. Long-term trend accounts for 86.49% …

A Dataset-Centric Analysis Of Evaluation Protocols In Face Morph Attack Detection, Sheerko R. Hmasalah, Abdulbasit K. Al-Talabani, Aras T. Asaad

Polytechnic Journal

Face morph attack detection (MAD) is often evaluated using datasets collected from diverse sources, created for different purposes, and acquired under varying conditions. This heterogeneity raises questions about how evaluation protocols and dataset characteristics influence reported performance. This work adopts a dataset-centric perspective to analyze the effects of evaluation protocol and dataset composition, including visual quality and morph-to-genuine imbalance, on MAD performance. Experiments are conducted on eight datasets and four morphing techniques using a fixed pipeline evaluated across six backbones, including Vision Transformer (ViT) and five CNN architectures, and ten evaluation scenarios covering homogeneous and heterogeneous cross-morph generalization. Performance is …

Managing Multi-Drug Resistance: An Evolutionary Game Theory And Optimal Control Approach, Shukhrat Nasrulloev

Theses and Dissertations

Multi-drug resistance is an evolutionary process in which treatment eliminates sensitive cells, allowing resistant clones to dominate. This thesis investigates this process using a framework integrating population dynamics, evolutionary game theory, and optimal control theory. We develop a two-population logistic growth model describing competition between drug-sensitive and drug-resistant cells under treatment, construct dose-dependent payoff matrices and replicator dynamics to characterize evolutionary competition, and derive a critical drug level D_crit = (r_S - rR)/(dS - d_R) at which resistant cells gain a fitness advantage. An optimal control problem is formulated via Pontryagin's Maximum Principle to identify schedules …

Fuchs' Problem For Quasi-Cyclic Groups, Dalen F. Elliott

Theses and Dissertations

Fuchs’ problem asks which groups can arise as the group of units of a ring. Although the finite cyclic case has been completely classified, much less is known in the infinite setting. This thesis contributes to this problem by investigating quasi-cyclic. (Pr¨ufer) groups and their finite direct products. We show that for every odd prime p, there is no commutative ring R such that R×∼= Cp∞. This obstruction arises from characteristic restrictions and the algebraic structure of finite fields. More generally, we prove that any group in which every element has order a power of an odd prime p and …

Data Defines Success: Algorithm For Dataset Quality Assessment In Deep Learning For Malware Detection, Matei Ionescu

Theses and Dissertations

The field of artificial intelligence is based upon the premise of constructing architectures through which to propagate training data. However, the majority of existing research literature is focused on architecture. While necessary, the attention devoted to the architecture should not so precipitously exceed that of the data. It should be noted that this disparity is not without reasonable cause. Data quality is often exceedingly difficult to verify due to particularities of the field or subfield; LLM repositories of text are distinct from image recognition pictures of dog breeds which are distinct from EEG waveforms of human brains which are distinct …

Data-Driven Partitioning In Distributed Optimization For Networked Systems, Prosper Azameti

Theses and Dissertations

The convergence behavior of distributed optimal power flow (OPF) depends strongly on how the power network is partitioned into regions. Classical graph-based methods such as METIS are widely used, but they rely mainly on static topological criteria and do not explicitly incorporate operating-point-dependent information that may affect distributed optimization performance. This thesis develops a data-driven partitioning framework for distributed OPF using graph neural networks (GNNs). Each OPF scenario is represented as a graph in which buses are nodes and transmission lines are edges. Node and edge features capture both structural and operational characteristics of the network. Partition prediction is formulated …

Symmetry In Latin Hypercubes, Levi Neiburger

Theses and Dissertations

Let [n] = {1, ..., n}. A hypercube H of order n and dimension d is a d-dimensional array whose nᵈ cells are indexed by [n]ᵈ. A hyperplane in H is obtained by fixing one coordinate, while allowing the remaining d–1 coordinates to vary. We wish to color each cell of H from a palette of n^d-1 colors such that each hyperplane is polychromatic.

Our main result is the following. Let n be sufficiently large. There exists a symmetric coloring of the d-dimensional hypercubes of order n whose all hyperplanes are polychromatic if and only if: …

Fostering A Growth Mindset In Mathematics: Faculty And Student Experiences, Yolanda G. Rush

Theses and Dissertations

According to the Center for Community College Student Engagement (2019), many students attending two-year institutions need productive persistence strategies, including the development of a growth mindset. Although some growth mindset interventions have been effective in improving academic achievement among students (Boaler, 2016; Canning et al., 2024) and persistence (Lewis, 2019) among students, especially those with developmental needs (Suh et al., 2019) and those in mathematics, little is known about the experiences of students and teachers (i.e., students’ perceptions of teachers’ intentions and implementation) as teachers work to foster a growth mindset culture (Murphy et al., 2021). In this dissertation, I …

Classifying Mathieu-Zhao Subspaces In Products Of Cyclic Rings, Sarah A. Huber

Theses and Dissertations

Mathieu-Zhao subspaces are a generalization of ideals in an algebra and were introduced by Wenhua Zhao in connection to the Jacobian conjecture and its variants. These subspaces have interesting properties, and often the problem of classification is hard. In this thesis, we investigate the structure of Mathieu-Zhao subspaces of the cartesian product of integers modulo powers of a prime p, Zpr × Zps . We will give a complete classification of the subgroups, maximal subgroups, Mathieu-Zhao subspaces, and maximal Mathieu-Zhao subspaces in these rings.

Theoretical Analysis Of Filtering And Smoothing Techniques In Stochastic Time Series, Kaosarat Bola Azeez

Theses and Dissertations

Polynomial-preserving linear filters are fundamental tools for smoothing and trend extraction in noisy time series, particularly when the objective is to reduce short-run variation without distorting underlying low-degree structure. This thesis develops a general framework for such filters based on local polynomial approximation and least-squares estimation. In particular, we establish that the resulting estimators can be represented as symmetric linear filters whose weights satisfy moment conditions that ensure exact polynomial reproduction up to a specified degree. Building on this framework, we derive explicit coefficient vectors for several classical smoothing filters, including the Savitzky-Golay filters SG(5,2) and SG(7,3), as well as …

Schiff Bases As Versatile Platforms: From Multinuclear Dysprosium Complexes To Halogen-Mediated Quasiracemic Crystal Engineering, Macy M. Michorczyk

Theses and Dissertations

Understanding the factors that govern molecular assembly is fundamental in both coordination chemistry and crystal engineering. This thesis investigates these processes through two studies that employ single-crystal X-ray diffraction to elucidate the structural principles underlying the formation of multinuclear metal complexes and the crystal packing of quasiracemates.

The first study examines the unexpected formation of multinuclear metal complexes. Structural characterization of a series of dysprosium complexes was used to compare molecular architectures and identify structural relationships that support a proposed pathway of formation. These findings shed insight into the assembly process that may have lead to their formation.

The second …

Does The Powder River Pass Shear Zone Exist? Geologic Map And Geochronology Of The Bighorn Mountains Caribou Creek 7.5-Minute Quadrangle, Lula G. Staley

Theses and Dissertations

This study produced a 1:24000-scale geologic map of the Caribou Creek 7.5-minute quadrangle in the Bighorn Mountains, Wyoming, by integrating field observation and U-Pb zircon age data. The results of this study will contribute to the USGS EDMAP program by subdividing the southern gneiss terrain. The quadrangle occurs almost entirely in the Bighorn National Forest and is in the drainage of Clear and Crazy Woman creeks. Elevations range from 6,800-9,400 ft. The quadrangle is dominated by Archean crystalline rocks of the Wyoming Province Beartooth-Bighorn Magmatic terrane, mainly granodiorite and tonalite gneiss with lesser migmatite. Several orientations and ages of mafic …

Investigating Phosphorus Load Trends And Driving Factors In Illinois Watersheds Using Machine Learning Algorithms, Hannah Lartey

Theses and Dissertations

Phosphorus (P) pollution in Illinois watersheds has become a significant water quality concern. Recent studies have shown increasing concentrations in streams and rivers, leading to eutrophication, which damages aquatic ecosystems and threatens human health. This study examines temporal trends in total phosphorus loads across 79 monitoring stations in Illinois from 1979 to 2020 and identifies the main environmental factors that influence P load variability. Trend analyses were conducted using the nonparametric Mann-Kendall and Sen’s slope methods. GIS-based climate, hydrology, and soil property datasets were assembled for each monitoring station. These datasets were used to build machine learning models using the …

How And Why Preservice Mathematics Teachers’ Respond To Students’ Statistical Thinking, Radhika Pathak Kharel

Theses and Dissertations

Researchers have found that how preservice teachers (PSTs) respond to students' thinking is crucial to their students' learning and to PSTs' own teaching effectiveness. Although there are some research studies on how teachers respond, there is relatively scarce research on why PSTs respond to students’ thinking the way they do. Such understanding is crucial for designing mathematics methods courses and other professional development opportunities that enable PSTs to create more thoughtful and effective teaching plans. I examined how and why PSTs respond in-the-moment to students’ statistical thinking. The study involved 16 PSTs who enrolled in a middle school mathematics methods …

Concerted Proton Transfer In Homogeneous And Heterogeneous Cyclic Hydrogen-Bonded Clusters Of H2o, Hf, And Hcl, Max R. Tucker, Yuan Xue, Nathan R. Speake, Eden Nickolson, Jeremy M. Carr, Gregory S. Tschumper

Chemistry Faculty Research & Creative Works

This work examines various homogeneous and heterogeneous clusters of hydrogen-bonded (HF)x(H₂O)y(HCl)z complexes, where x + y + z = 3 or 4. For each unique cyclic structure associated with the xyz permutations, the geometries of the reactant, product, and transition state (TS) for concerted proton transfer (CPT) were fully optimized using second-order Møller-Plesset perturbation theory (MP2) with a mixed basis set consisting of cc-pVTZ for H atoms and aug-cc-pVTZ for O, Cl, and F atoms (denoted haTZ). Harmonic vibrational frequencies were also computed at the same level of theory to verify the nature of the stationary points. Intrinsic …

Multidimensional Analysis Of Human Outdoor Comfort: Integrating Just-In-Time Adaptive Interventions (Jitais) In Urban Digital Twins, Binyu Lei, Pengyuan Liu, Kunihiko Fujiwara, Mario Frei, Clayton Miller, Xuan Yun Chua, Filip Biljecki

Research Collection College of Integrative Studies

How can bidirectional information exchange be enhanced in urban digital twins, and support human-centric data and processes? Their key characteristic is the nearly real-time exchange of information, allowing adjustments to physical environments based on simulations and analytics within virtual models. Yet, achieving such interaction remains challenging, particularly regarding device deployment and infrastructure development. Embracing the concept of humans as sensors, this work develops a two-way framework based on the emerging concept of just-in-time adaptive interventions (JITAIs), exploring how urban digital twins can play a role in understanding and enhancing human comfort outdoors. Human comfort outdoors is inherently spatio-temporal and personalised, …

Solving High-Dimensional Differential Equations Using Recurrent And Residual Neural Network Architectures, Hind Khaled Kolaib

Knowledge Engineering and Data Science

High-dimensional Partial Differential Equations (PDEs) form the foundation of complex process modeling in various scientific and engineering applications, including finance, physics, and optimal control. However, classical numerical methods are adversely affected by the curse of dimensionality, making them inapplicable for large-scale problems. Recently, however, deep learning-based approaches have provided a new toolbox for these high-dimensional PDEs, including methods such as the Deep Backward Stochastic Differential Equation (Deep BSDE) method. Our approach draws on a more sophisticated deep learning backbone, using neural networks (in our case, a Residual Neural Network and a Long Short-Term Memory network (LSTM) integrated into the Deep …