Physical Sciences and Mathematics Commons^™

Assessing The Diversity And Composition Of Crayfish Assemblages In The Ogeechee River Basin, Reginald Turner

College of Graduate Studies: Theses & Dissertations

Crayfish assemblage composition in the southeastern United States is understudied relative to other aquatic taxa, such as aquatic insects and fishes, and the Coastal Plain watersheds of that region are particularly underrepresented in the contemporary literature on this topic. For example, although 38% of the crayfish species in Georgia are considered “species of greatest conservation need,” most of the distributional data used to make these designations are outdated, with some dating back over 50 years. This thesis sought to update our understanding of the contemporary distributions of crayfish species within the Ogeechee River Basin (ORB), a watershed in southeastern Georgia …

A Unified Framework For Evaluating Training Efficiency In Deep (Bayesian) Neural Networks: Metrics, Overtraining, Stopping Criteria, And Grokking Computer Science, Eduardo Cueto Mendoza

Doctoral

Measuring training efficiency for artificial neural networks is an open research problem, current literature reports several attempts to define measures or create reporting frameworks. Current methods lack generality as they require measurements of the hardware or software thus, comparing efficiency between different systems can be difficult. Similarly, current metrics or frameworks generally do not propose the use of the metrics to directly improve training efficiency. This thesis presents three main contributions: (1) a novel framework that quantifies the training efficiency of a neural architecture on a learning task as the average ratio of model accuracy to total energy consumption during …

Part Sm: Statistical Mechanics, Konstantin Likharev

Essential Graduate Physics

Includes: Review of Thermodynamics; Principles of Physical Statistics; Ideal and Not-So-Ideal Gases; Phase Transitions; Fluctuations; Elements of Kinetics

Lie-Galois Theory, Giovanni Reed

Honors Undergraduate Theses

Differential equations are a much-studied topic in the field of mathematics, as well as other sciences, such as engineering, economics, and biology. While much is known concerning these, there is still a large gap in our knowledge about such equations. It is important therefore, both to mathematics and other sciences, that we gain a more complete knowledge of differential equations, in particular the nature of their solutions. In this research, we investigate the solution space of linear ordinary differential equations (ODEs) from the standpoint of differential algebra. Differential algebra allows an ODE to be treated similarly to a polynomial, allowing …

Laser Beam Shaping Using A Neural Network, Azeem A. Hakim

Honors Undergraduate Theses

Multiphoton lithography (MPL) is a method of laser-based 3D-printing for fabricating micron-scale structures point-by-point in a photopolymerizable medium. MPL’s high-resolution capabilities have made it a powerful method for the fabrication of many devices, such as microelectromechanical systems (MEMS) and tissue scaffolds.  The throughput of MPL processes can be increased by using spatially shaped laser beams that expose large areas or volumes simultaneously. A laser beam can be reshaped by passing it through a spatial light modulator (SLM) displaying a pre-designed phase mask. One type of laser beam profile used for this purpose is the Bessel beam, a type of structured …

Alexander Duals Of Symmetric Simplicial Complexes And Stanley-Reisner Ideals, Ayah Almousa, Kaitlin Bruegge, Martina Juhnke-Kubitzke, Uwe Nagel, Alexandra Pevzner

Mathematics Faculty Publications

Given an ascending chain (I_n)_n∈N of Sym-invariant squarefree monomial ideals, we study the corresponding chain of Alexander duals (I_n^∨)_n∈N. Using a novel combinatorial tool, which we call avoidance up to symmetry, we provide an explicit description of the minimal generating set up to symmetry in terms of the original generators. Combining this result with methods from discrete geometry, this enables us to show that the number of orbit generators of I_n^∨ is given by a polynomial in n for sufficiently large n. The same is true for …

Volatility Modeling With An Application To Risk Parity Portfolios, Kenneth Hou

CMC Senior Theses

This thesis studies volatility modeling in the context of risk parity portfolio construction. I compare three risk parity portfolios that differ only in their underlying volatility model: a historical covariance baseline, a Bayesian stochastic volatility model, and a GRU–GARCH hybrid neural network. Using daily returns on Kenneth French’s five industry portfolios from January 2016 through December 2025, I construct monthly rebalanced portfolios under each model, with the SV and GRU forecasts embedded in hybrid covariance matrices that combine forecasted volatilities with rolling historical correlations. The results document a divergence between forecast accuracy and portfolio performance: the SV model is the …

Using Ai To Analyze Survey Data, Sara Martucci

Open Educational Resources

This assignment in Methodology in Sociology/Criminology engages students in the full research process by guiding them through variable selection, data analysis, interpretation, and critical reflection on AI-assisted decision-making. Using a class-generated survey dataset (or an existing dataset), students develop a research question, identify independent and dependent variables, and formulate a hypothesis. They then compare their selections with those suggested by an AI tool, analyzing differences in reasoning and variable choice. Through SPSS, students generate frequency tables, charts, and scatterplots to examine relationships between variables, including potential intervening factors. The assignment culminates in a group presentation and reflective analysis on the …

How Candidates Campaign To Sway Minority Voter Bases, Katelyn Karwick

Senior Honors Theses and Projects

Religious groups in the United States are making large partisan switches–decades-long Democrats are questioning their support and often change their vote to another party. This phenomenon contributed to Michigan’s flip to Republican in the 2024 presidential election, as well as divided Jewish support for New York City mayoral candidate Zohran Mamdani. Past research has linked a positive correlation between religious participation and political mobilization, though the tactics used by campaigners to create this connection have gone under research. Looking at Donald Trump’s victory in Dearborn, Michigan in 2024 and Zohran Mamdani’s extensive New York City mayoral campaign, this study focuses …

Nicotine, Bdnf, And Ꞵ-Adrenergic Ligand Receptors Regulate Soluble E-Cadherin Signaling In Non-Small Cell Long Cancer, Stuti Goel

Senior Honors Theses and Projects

E-cadherin is a protein that normally helps cells adhere to each other. Sometimes, part of this protein is cleaved off and released outside the cell as soluble E-cadherin (sE-cad). When this happens, there is less E-cadherin left on the cell surface. Enzymes called matrix metalloproteases, especially MMP9, are responsible for this cleavage. In this study, we investigated E-cadherin in two non-small cell lung cancer cell lines, A549 and H1299. We found that treating these cells with brain-derived neurotrophic factor (BDNF), nicotine, or epinephrine increased the amount of MMP9 released into the surrounding media. When the cells were treated with propranolol, …

Oompa 2025.08: A First Cut Of The Toolkit For Object-Oriented Modeling For Planning And Acting, Mark Roberts, David H. Chan, Dana S. Nau, Jamie C. Macbeth

Computer Science: Faculty Publications

OOMPA is a partially implemented Python 3.13+ toolkit for modeling hierarchical planning domains as annotated Python classes, without writing a separate PDDL or HDDL domain file. State properties, actions, and hierarchical methods attach to domain classes via decorator syntax; OOMPA projects the resulting model into a flat dictionary of dictionaries, like the Pyhop family of planners. We describe OOMPA’s motivation and architecture as we demonstrate its use in a restaurant planning domain. There are many unrealized features, so we end with a discussion of limitations and future work.

Nature-Based Solutions For Urban Resilience And Environmental Justice In Underserved Coastal Communities: A Case Study On Oakleaf Forest In Norfolk, Va, Farzaneh Soflaei, Mujde Erten-Unal, Carol L. Considine, Faeghe Borhani

Civil & Environmental Engineering Faculty Publications

Climate change and sea-level change (SLC) are intensifying flooding in U.S. coastal communities, with disproportionate impacts on Black and minority neighborhoods that face displacement, economic hardship, and heightened health risks. In Norfolk, Virginia, sea levels are projected to rise by at least 0.91 m (3 ft) by 2100, placing underserved neighborhoods such as Oakleaf Forest at particular risk. This study investigates the compounded impacts of flooding at both the building and urban scales, situating the work within the framework of the UN Sustainable Development Goals (UN SDGs). A mixed-method, community-based approach was employed, integrating literature review, field observations, and community …

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 …

Key To Spartina Of The West Coast Based On Vegetative Characteristics, Mary Pfauth, Mark D. Sytsma, Mitchell Kloer, Jennifer Riddle, Jacob Rose, Gabriel E. Campbell

Center for Lakes and Reservoirs Publications and Presentations

In their natural condition, Pacific Coast estuaries have extensive open mudflats in the intertidal area that are important shorebird feeding habitat and are used for oystering and recreational fishing. Several non-native cordgrass species are invading Pacific estuaries and damaging these functions and uses. Early detection is critical to effective management of cordgrass infestation. This key is intended to provide an easy-to-use reference for quick identification of Spartina (synonym: Sporobolus) species on the Pacific coast. We choose to focus on vegetative characteristics here, since flowers are not always visible when it can be advantageous to survey.

This publication is intended to …

Self-Calibrating Uav Navigation: Reinforcement Learning Approaches For Horizontal Trajectory Estimation, Shirin Nasr-Esfahani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Accurate unmanned aerial vehicle (UAV) trajectory estimation is essential for autonomous navigation, particularly in GPS-denied environments. Visualodometry and simultaneous localization and mapping (SLAM) approaches require precise camera intrinsic parameters, which are typically obtained through predefined or offline calibration. Instead, in this work, we propose a reinforcement learning (RL)-based self-calibration framework that estimates camera intrinsic parameters directly from monocular video sequences, without requiring prior knowledge of the camera, environment, or calibration targets. This intrinsic parameter estimation is then leveraged to achieve robust UAV trajectory estimation using only video data. We formulate the problem as a sequential decision-making task, where an RL …

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 …

Differences In Biologic Clinical Trials For Chronic Rhinosinusitis With Nasal Polyps—Are We Comparing Apples With Oranges?, Marjolein Cornet, Peter W. Hellings, Martin Desrosiers, Martin Wagenmann, Richard Follows, Laura Walrave, Luz Adriana Jimenez, Lee Tombs, Dawn Edwards, Peter Howarth, Joseph K. Han

Department of Otolaryngology (ENT) Faculty Publications

In recent years, several biologics targeting Type 2 inflammation have been developed for treating chronic rhinosinusitis with nasal polyps (CRSwNP). These have been studied in registrational randomized controlled trials (RCTs), which vary in their patient populations, trial design, endpoints, geography, timing, or data-handling processes. While (in)direct treatment comparisons and meta-analyses have been carried out to compare efficacy results from RCTs, often these fail to properly account for these between-study differences. Here, we summarize the key between-study differences that can influence trial outcomes and highlight the resulting challenges faced when comparing outcomes from different Phase III RCTs of biologics in CRSwNP.

Rapid Classification And Quality Assessment Of Citrus Essential Oils Via Machine-Learning-Assisted Raman Spectroscopy, Yong Xuan Hong, Jia Wei Tang, Jie Chen, Yun Yun Xie, Zhang Wen Ma, Qing Hua Liu, Liang Wang

Research outputs 2022 to 2026

Citrus essential oils (EOs) require accurate identification and assessment to ensure authenticity and consistency. However, conventional techniques such as gas chromatography (GC) and mass spectrometry (MS) are time-consuming and expensive, highlighting the need for novel analytical methods. This study proposes an approach for EOs detection using Raman spectroscopy (RS) combined with machine learning (ML) algorithms. Six citrus EOs underwent an evaporation experiment, with Raman spectra collected at five time points and GC-MS was used to analyze compositional changes at the starting and ending points of evaporation as a standard reference. Five ML algorithms were developed to identify differences among EOs …

Artificial Intelligence, Fundamental Motives, And Evolutionary Mismatch, Amy J. Lim, Jose. C. Yong, Edison Sora Tan

Research Collection School of Social Sciences

In recent years, the intersection of artificial intelligence (AI) and psychology has garnered unprecedented attention, particularly following the advent of generative AI tools in 2022. These tools, capable of producing human-like text, images, and even deepening our understanding of cognitive processes, have not only captured the public imagination but also sparked new concerns and debates within the psychological community. While AI has been a subject of research for decades, the emergence of its generative capabilities has truly thrust AI into the spotlight. This article explores how these advancements are reshaping our understanding of human cognition and behavior, as well as …

Llm-Driven Weekly Newsletter To Assess Open Source Software Project Github Health, Christian Novalski, Christopher Chavez, Ghalian Fayyadh, Kostadin Damevski

Undergraduate Research Posters

Open Source Software (OSS) projects increasingly depend on a diverse set of contributors, including episodic participants who contribute intermittently. Episodic contributors represent a large portion of OSS communities, yet projects often struggle to retain them, leading to decreased project health and continuity. While dashboards and real-time communication tools support continuously active contributors, they often fail to serve the unique needs of episodic participants, who may struggle to remain informed and re-engage with project activity after periods of absence. In this study, we examine the effect of a weekly, email-based newsletter intervention designed to improve awareness and engagement among episodic OSS …

A Bioinspired Approach For Adaptive Solid-Solid Phase Change Material Coatings With Optimized Surface Features For Passive Thermal Regulation, Rajae Bousselham, Zhiying Xiao, Mingjiang Tao, Sergio Granados-Focil, Adriana Hera, Steven Van Dessel

Chemistry

The necessity to reduce global energy consumption calls for innovative strategies in building thermal management. Passive thermal regulation, particularly through bio-inspired designs, offers a promising avenue by mimicking nature's efficient control of optical properties. This research introduces a novel, climate-responsive coating that integrates optimized bio-inspired surface features with a solid-solid phase change material (SS-PCM) to dynamically manage solar absorptivity without adding additional thickness, enabling both heating and cooling as needed. Drawing on the photonic architectures of the Saharan silver ant and Morpho Didius butterfly, we employed a modeling and multi-objective optimization framework to tailor these surface features. Simulations reveal that …

Machine Learning For Wearable Sensor-Based Human Movement Rehabilitation: A Five-Year Systematic Review, Yassine Benachour, Farid Flitti, Lina Maloukh, Aicha Beya Far, Elhocine Boutellaa, Mohamed Bentoumi, Marwa Chendeb El Rai, Nour Aburaed, Khaled Ali, Moez Rehman, Sultan Mosleh, Rania Dghaim, Sadok Bouamama

All Works

Wearable-sensor-based human movement analysis is an increasingly important component of digital health and rehabilitation, enabling objective monitoring and data-driven personalization of therapy. In parallel, machine learning (ML) methods have rapidly expanded for interpreting multimodal movement signals, yet the evidence base remains heterogeneous and difficult to benchmark. This PRISMA-guided systematic review synthesizes recent ML approaches for wearable human motion analysis in rehabilitation-oriented health applications. We searched IEEE Xplore, PubMed, and Scopus for English-language studies published from 2021 to 2025 and extracted information on sensor modalities, ML task formulations and model families, dataset characteristics, validation protocols, and reported performance metrics, together with …

Edge-Aware Ris-Assisted Dynamic Channel Allocation With Lightweight Llm Decision Agent For Interference Mitigation In Low-Altitude Remote Sensing Networks, Safiya Nasser Al-Jaradi, Mohammad Kamrul Hasan, Nabeel Al-Qirim, Shayla Islam, Muhammad Attique Khan, Rashid A. Saeed, Hashim Elshafie, Bishwajeet Kumar Pandey, Khairul Akram Zainol Ariffin

All Works

Low-altitude remote sensing networks are increasingly important for applications, such as environmental monitoring, disaster response, infrastructure inspection, and real-time sensing services. However, when many sensing nodes share limited spectrum resources, severe cochannel interference can degrade communication reliability and delay sensing-data delivery. This challenge becomes more critical in edge-enabled deployments, where control decisions must be made under strict latency, memory, and computational constraints. To address this issue, this article proposes a large language model (LLM)-enhanced edge-aware lightweight reconfigurable intelligent surface (RIS)-assisted dynamic channel allocation (EL-RIS-DCA) framework for interference mitigation in dense low-altitude remote sensing networks. The novelty of the proposed framework …

Energy-Efficient Mixed-Criticality Multicore Systems, Fayyaz Ali, Saud Wasly, Amjad Ali, Shahid Iqbal, Asad Masood Khattak, Bashir Hayat, Shah Khalid

All Works

Balancing energy efficiency with stringent timing guarantees in real-time mixed-criticality systems (MCS) is a key challenge, especially in multicore architectures. This paper introduces a novel energy-aware scheduling framework that integrates dynamic voltage and frequency scaling (DVFS) with a Decreasing-Criticality-Decreasing-Utilization (DCDU) allocation approach. The optimal operating frequencies are obtained at each criticality level; high-criticality tasks are assigned to cores at full operating frequency to maintain timing guarantees, while low-criticality tasks are allocated using worst-case execution times scaled to their optimal frequency. A fixed-priority response-time analysis is used for schedulability in low mode, high mode, and during mode changes. The extensive simulations …

Lightweight Tinyml-Enhanced Task Offloading In Vanets For Next-Generation Intelligent Transportation Systems, Muhammad Ali, Tariq Qayyum, Asadullah Tariq, Zouheir Trabelsi, Irfan Ud Din, Shabir Ahmed

All Works

Vehicular Ad Hoc Networks (VANETs) face resource constraints, high node mobility, and stringent latency requirements, especially in safety-critical applications such as collision avoidance, path planning, and emergency braking. Task offloading to nearby vehicles or Roadside Units (RSUs) mitigates local computational limits, but dynamic conditions, unreliable nodes, and rapid topology changes complicate dependable node selection. This paper proposes a Tiny Machine Learning (TinyML)-enhanced, credibility-based task offloading framework for real-time decision-making in vehicular networks. RSUs evaluate vehicle reliability through a three-component Credibility Assessment Module: a Task Assignment Component that distributes lightweight test tasks and filters unreliable nodes via TinyML inference; a Verification …

Mapping Multiclass-Targeted Hate Speech In Online Discourse: An Open Dataset, Sanaa Kaddoura, Sumaia Al-Kohlani

All Works

Online social networks have become central spaces for public discourse, where hostile and discriminatory language toward social groups can cause psychological and social consequences for marginalized communities. Although multiple public hate speech datasets are available, many rely on binary categorization practices that obscure linguistic, cultural, and contextual variation across targeted groups. As a result, minority and less visible forms of hate speech remain insufficiently documented and analyzed. This discussion paper examines methodological limitations in existing hate speech annotation schemes and presents a re-annotation framework applied to the HatEval2019 dataset. The proposed framework introduces target-specific multiclass labels that distinguish subcategories of …

A Design Science Research Architecture For Xr-Based Pre-Visit Cultural Heritage Learning Applications, Mousa Al-Kfairy, Omar Alfandi, Saed Alrabaee

All Works

Pre-Visit preparation plays a critical role in shaping visitors’ learning and engagement in cultural heritage sites; however, existing approaches largely rely on static and passive materials that fail to foster meaningful understanding before the physical visit. Extended Reality (XR) technologies offer new opportunities to address this gap by enabling immersive, narrative-driven pre-visit learning experiences. This paper proposes a conceptual architecture for XR-based pre-visit cultural heritage learning applications, grounded in Design Science Research (DSR). Drawing on museum pedagogy, experiential learning, and XR interaction design, the study identifies key educational and technical requirements and translates them into a layered, modular system architecture. …

Aoi-Aware Agentic Federated Mixture-Of-Digital-Twin Experts For 6g Vehicular Edge Intelligence, Asadullah Tariq, Mohamed Adel Serhani, Ikbal Taleb, Shayma Alkobaisi, Tariq Qayyum, Irfan Ud Din

All Works

Digital twin-enabled vehicular edge intelligence is expected to become a fundamental service paradigm for sixth-generation (6G) intelligent transportation systems. However, the performance of such systems depends not only on model accuracy, but also on the freshness of digital twin states, timeliness of inference, privacy-preserving model training, and efficient use of heterogeneous edge resources. Existing DT-assisted federated learning and edge mixture-of-experts solutions optimize digital twin synchronization, distributed learning, and sparse inference largely independently, without allowing digital twin states to actively govern expert specialization, expert refreshing, and distributed orchestration. Nevertheless, the joint problem of how digital twins should guide federated expert specialization, …

Mapping Llm Misuse In Computing Education: A Survey-Based Risk Analysis Of Faculty And Student Contexts, Noura Alzaabi, Mohamed El-Attar, Sarah Kohail, Mahmood Niazi

All Works

Large Language Models (LLMs) have become deeply embedded in computing higher education, yet the misuse risks they introduce for faculty and students remain insufficiently understood from a cybersecurity and data privacy perspective. This paper presents an empirical study in which a structured survey of 105 participants at a computing college was used to identify and systematically risk-score thirteen LLM misuse cases across faculty and student contexts. Using a Likelihood × Impact scoring model, the resulting taxonomy classifies misuse cases as Critical, High, or Medium severity, with over-reliance and skill atrophy, academic integrity violations, and research integrity risks emerging as the …

Evaluating Chatgpt-5 For Misuse Case Diagram Generation: An Empirical Evaluation, Alia Alzarooni, Yasser Khan, Hassan Alsayegh, Mohamed El-Attar, Rima Grati

All Works

Misuse case diagrams are a widely adopted technique in security requirements engineering, enabling analysts to model adversarial threats and derive countermeasures early in the software development lifecycle. However, manual construction of these diagrams is prone to incompleteness and subjectivity, requiring significant security expertise. Large language models (LLMs) such as ChatGPT present a promising opportunity to automate this process, yet their effectiveness for generating structured security modeling artifacts remains largely unexplored. This paper presents an exploratory study evaluating ChatGPT-5's ability to generate misuse case diagrams directly from textual security requirements, using 12 case studies of varying complexity spanning small, medium, and …