Digital Commons Network^™

Leveraging Information Theory And Ecological Network Analysis To Monitor Communication Networks In A Student Aerospace Team, Christine Sessions

Doctoral Dissertations and Master's Theses

Effective communication is a critical component of successful collaboration in group projects and team settings. However, systematically tracking and analyzing team communications can be challenging, especially in complex, multi-member teams such as those found in the aerospace industry. This paper explores an information-theoretic approach that leverages encoding techniques and graph theory to analyze communication networks within a university’s multi-year, student-led cubesat design project (Project COMET). By utilizing Shannon Entropy as a measure of information flow, encoding communication patterns, and analyzing Ecological Network Analysis parameters, this research aims to understand how an Embry-Riddle Aeronautical University student project team evolves over the …

Security Assessment Of A Machine Learning Approach To Generate And Validate Digital Signatures, Juan Ortiz Couder

Doctoral Dissertations and Master's Theses

Cybersecurity has become a global concern as cyber-attacks have become more common, and the cost of the damage caused by them continues to increase. There are several approaches to improve the cyber security of systems such as Digital Signatures, hashing, watermarking, and encryption among others. Digital Signatures are a cryptographic technique used to verify the authenticity and integrity of digital messages or documents. Digital Signatures use a combination of hashing and public-private key encryption to verify the authenticity and integrity of videos, just as they are used for documents and messages. As a result of using a combination of other …

Do Dreams Reflect Our Culture? A Statistical Analysis On Dream Narratives, Michal Kuderski

Honors Capstones

Dreams are often viewed as personal experiences, but they may also reflect cultural influences. This project investigates whether dream content varies across cultures by analyzing written dream reports from American, Japanese, and Peruvian college students using data from DreamBank.net. The study applies text analysis techniques to identify common themes and compares language patterns, including the use of ‘I’ and 'We,' to examine differences in self-focus. Statistical methods for count data are used to evaluate these patterns, along with resampling to address differences in sample size. Preliminary findings suggest that both dream themes and language use may vary by cultural …

Do National Parks Significantly Improve Air Quality?, Xinyi Liu

Honors Capstones

Healthy living has long been a goal pursued by individuals. In an era where PM2.5 concentrations and greenhouse gas levels are steadily increasing, many people prefer to live in areas with relatively lower levels of air pollution. National parks, as well-known protected areas, typically contain more vegetation and water resources, along with lower levels of human activity. This suggests two potential mechanisms: first, pollutant emissions are expected to be lower; second, vegetation, water bodies, and non-urban land may contribute to the removal or deposition of airborne pollutants, including particulate matter. In everyday experience, people often perceive the air to be …

Should I Stay Or Should I Go?: Evaluating The Role Of Climate And Weather Stressors On Moving Decisions In Southeast Florida, Arielle Perry

Electronic Theses and Dissertations

As climate and weather hazards intensify, Southeast Florida communities face mounting pressures that increasingly influence patterns of migration and immobility. This research examines evidence of climate migration in high flood-risk neighborhoods in Fort Lauderdale by exploring why some populations choose to stay, while others leave, in the context of a changing climate. Using focus group discussions, I investigate how climate and non-climate related factors shape mobility outcomes among residents in the city. Migration outcomes were primarily influenced by negative climate and weather hazard experiences, discontent with existing public in-situ adaptations, and non-climate disamenities, which eroded participants’ sense of place. Voluntary …

Phenological Overlap In Obligate Plant-Pollinator Mutualism, Austin J. Carlson

2026 Spring Honors Capstones Projects

Plant-pollinator mutualisms require temporal overlap between flowering and pollinator activity, so climate-driven timing shifts can weaken the interaction and, in severe cases, destabilize the system. This work investigates how reduced overlap affects persistence in an obligate plant-pollinator pair using a coupled differential equation model in which a phenological overlap factor scales the saturating mutualistic benefit. Simplification with a constant overlap enables closed-form equilibrium and stability analysis, revealing that below a critical overlap threshold, coexistence is no longer maintained. Rescaling reduces the parameter space from ten quantities to seven dimensionless groups, and sensitivity analysis identifies the degree of species dependence and …

Fodder Maize Variety Trial: Broome 2021, Carla Milazzo, Samuel Crouch, Christopher Ham, Geoff Moore

Grain and other field crops research reports

• High yielding, highly digestible maize can be produced under irrigation on Pindan sands in northern Western Australia.
• Using high inputs, the biomass and digestibility of all trialled varieties equalled or exceeded varieties used in well-established maize growing regions of Australia.
• Eleven hybrid maize varieties were trialled near Broome. The average dry matter yield of all trialled varieties was 24t/ha at 10.5MJ/kg of dry matter.
• The impact of fall armyworm on yield was successfully reduced by using integrated pest management combining NPV virus (nucleopolyhedrovirus) with strategic insecticide applications.

Faircarenlp: An Ai-Driven Patient Review Analyzer For Healthcare, Sayyed Mohammad Pourya Momtaz Esfahani, Davey Seeman, Christoffer Dharma, Mohammad Noaeen, Shion Guha, Zahra Shakeri

Health Services and Informatics Research

Objective

To develop and evaluate an automatic patient review analyzer that applies advanced Natural Language Processing (NLP) and machine learning methods to improve the efficiency, fairness, and accuracy of healthcare feedback analysis.

Materials and methods

We designed a multi-component pipeline incorporating sentiment analysis, key theme extraction, clinical Named Entity Recognition (NER), and fairness modules. Bias mitigation was addressed through the integration of three complementary approaches: adversarial debiasing, Hard Debiasing, and Iterative Null-space Projection (INLP). Multiple BERT-based models (DistilBERT, BioBERT, RoBERTa-base, BERT-base-uncased) were trained and evaluated under varying hyperparameters and fairness/adversarial loss configurations. Model performance was assessed using accuracy, F1, recall, …

Red Lines, Green Blooms: Assessing The Effects Of Historical Environmental Disparity On Algal Communities In The Twin Cities, Mn, Georgia C. Akins

Environmental Studies Honors Projects

Redlining, a discriminatory housing policy from the 1930s, shaped lastng urban inequalites that may extend to environmental conditons. This study examines links between redlining and urban water quality in the Twin Cites using algal communites as indicators. Across 28 water bodies sampled in 2022 and 2024, physical, chemical, and biological metrics were compared by redlining grade. Results show limited relatonships between redlining and most variables, with only surface water temperature differing significantly. Nutrients and algal diversity were unrelated to redlining, instead reflectng present-day conditons. These findings suggest contemporary factors play a stronger role than redlining in shaping urban freshwater ecosystems.

Fossil-Fuels In A Decarbonized Country? Modeling The Drivers Of Icelandic Oil Sales, Inbal Armony

Environmental Studies Honors Projects

Although 100% of Iceland’s electricity comes from renewable energy sources, it still relies on fossil fuels for land transportation, marine transportation, aviation, and some industry. Understanding geographic nuances in oil use is critical to achieving Iceland’s goals of carbon neutrality by 2040. As the island has one primary urban center with two thirds of the population, information is lacking about oil use in non-Capital areas and a gap between state and municipal climate plans. Using newly available data of oil sales at the municipality-level in a Small Area Estimation model, we analyze drivers of oil sales across Icelandic municipalities. We …

Ais26s: Ai In Biomedicine, Shiqian Shen

Paul English Applied Artificial Intelligence (AI) Institute Publications

This presentation explores the role of artificial intelligence in advancing biomedical research and clinical practice from the perspective of a physician-scientist. Dr. Shiqian Shen discusses current challenges in neuroscience and medicine, including limitations in human observation, data analysis, and decision-making across complex biological systems. The talk highlights how AI-driven approaches such as computer vision, neural signal processing, and large-scale data modeling can improve the understanding of disease mechanisms, enhance experimental workflows, and enable more precise, patient-centered care. Specific applications include automated classification of neural activity, behavioral analysis in animal models, and multimodal data integration. A key concept introduced is “shadow …

Tissue-Specific Heavy Metal Accumulation And Sediment–Mangrove Dynamics In Port Everglades, Florida, Dimitrios Giarikos, Amy Hirons, Jason Gershman, Sophie Welch

SECLER Data

Coastal wetlands near industrialized waterways face increasing heavy metal contamination, yet the role of mangroves as biogeochemical buffers remains poorly understood. This study quantified 16 heavy metals in sediments and red mangrove (Rhizophora mangle) tissues (roots, branches, and leaves) across four sites near Port Everglades, Florida, examining tissue-specific partitioning, contamination patterns, and sediment-to-plant transfer. The South Turning Basin (STB) was the principal contamination hotspot, with geometric mean (GM) concentrations of arsenic (As; 27.8 µg/g) and zinc (Zn; 203 µg/g) exceeding threshold effect levels, while Cu exceeded its probable effect level (PEL; 193 µg/g). Mercury (Hg) exceeded its PEL …

Re: Conditional Approval Letter For Draft Final Silver Bow Creek Conservation Area Materials Management Plan Revision 1 (Dated March 24, 2026), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Modeling Of Polar Cap Ionospheric Patches And Attendant Plasma Instabilities: Initialization From Data And Parametric Analysis, Mark Redden

Doctoral Dissertations and Master's Theses

There is a robust history of theoretical work on ionospheric plasma patches and their attendant plasma fluid irregularities, particularly within the polar cap region.  Within this region though, plasma theory does not always yield tractable problems with succinct analytic solutions that are conceptually illuminating. Observational research methods and techniques, using a multitude of ground-based and in-situ systems, provide insights into this complex environment not always readily achieved through theory alone.  Limitations in observational insights exist; however, due to two inherent shortcomings of such systems; sensor observational persistence (in-situ sounding rocket and satellite plasma sensors) and/or sensor spatial resolution (terrestrial systems …

Re: Conditional Approval Letter For Final Silver Bow Creek Conservation Area (Sbcca) Construction Quality Assurance Plan And Draft Final Silver Bow Creek Conservation Area (Sbcca) Construction Monitoring Quality Assurance Project Plan (Dated April 7, 2026), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Evaluating Improvements In Landslide Susceptibility Mapping Using Newly Derived Slope Units, Xinyi Zhang

Honors Capstones

Landslides frequently occur in mountainous regions and remain one of the major natural hazards threatening human safety, infrastructure, and regional development. In areas with steep terrain and intense rainfall, landslide disasters often cause serious economic losses and environmental damage. Landslide susceptibility mapping (LSM) has been widely used to identify potential landslide prone areas and to support disaster prevention, land management, and hazard mitigation. The accuracy of LSM is strongly influenced by the selection of mapping units.

This study focuses on I-lan County, Taiwan, and applies a newly derived slope unit dataset to evaluate its influence on landslide susceptibility mapping. Using …

Machine Learning For Handwritten Character Recognition, Hannah Freitag

Honors Capstones

Handwritten character recognition remains a challenging problem in machine learning due to the high variability of handwriting across individuals and the visual similarity between certain character classes. This project explores whether Singular Value Decomposition (SVD)-based dimensionality reduction can serve as an effective preprocessing step for a fully connected neural network trained on the EMNIST Balanced dataset, a 47-class benchmark of handwritten digits and letters. By projecting 784- dimensional pixel inputs onto the top 70 principal components, approximately 90% of the total variance is preserved while reducing input dimensionality by 91%. The resulting SVD-based model achieves approximately 94% test accuracy, outperforming …

Chemometric Analysis Of Dissolved Organic Matter Using Fluorescence, Dani Fellner, Ken Overway

Honors Projects

Univariate spectroscopic techniques have been utilized to measure dissolved organic matter (DOM), but challenges arise in the separation and characterization due to similarities in chemical structures and spectral overlap. Multivariate techniques utilize a combination of known analytes with their unique excitation and emission spectra from fluorescence measurements to quantify amount of each analyte present in the mixture using parallel factor analysis. Tryptophan, tyrosine, and phenylalanine are common amino acids found in environmental samples and can be quantified due to their intrinsic fluorescence qualities. These amino acids can be utilized to estimate the amount of DOM in a sample since proteins …

Limit Of Detection Variations In Polyphenol Mixtures Using Parafac-Feem Spectroscopy, Ken Overway, Brockton Zorn

Honors Projects

Polyphenols are chemical compounds commonly found in food products like olive oil and red wine. Research suggests that their presence in such foods conveys positive cardiovascular and cognitive health benefits. This group of compounds is often difficult to quantify in such samples due to an overlap in the chemical signals and the presence of interferences, such as coloring, found naturally in the food. Use of 3D Fluorescence Excitation Emission Matrix (EEM) measurements combined with pH sensitivity could establish a method in which polyphenols are identified more easily in mixtures. Binary and ternary mixtures of polyphenols were analyzed without the additional …

Propasafe-Hybrid: A Text-Based Hybrid Propaganda Detection Tool (Ila 2026 Presentation), Thomas Kimmeth, Avijit Roy, Vivek Sharma

Publications and Research

This presentation introduces Propasafe-Hybrid, a hybrid system for sentence-level propaganda detection that combines offline transformer-based classification with selective large language model (LLM) explainability. The system employs a two-stage pipeline in which a local BERT-based classifier evaluates all input text and filters non-propagandistic content, while only high-confidence candidates are forwarded to an LLM for rhetorical technique labeling and explanation. This design enables cost-aware, privacy-conscious, and scalable analysis by reducing unnecessary reliance on external models.

Propasafe-Hybrid identifies propagandistic techniques such as loaded language, obfuscation, and appeal to fear, and generates concise natural language rationales that make these techniques interpretable to users. By …

Ai Institute Summer Camp Academic Preview Webinar: Curriculum, Research, And Outcomes, Paul English Applied Artificial Intelligence Institute

Paul English Applied Artificial Intelligence (AI) Institute Publications

This webinar presents an academic preview of the AI Institute Summer Camp hosted by the Paul English Applied Artificial Intelligence Institute at the University of Massachusetts Boston. The session introduces the program’s curriculum, structure, and student outcomes, providing insight into a hybrid learning model that combines faculty-led lectures, hands-on labs, and guided project development. The webinar highlights the program’s five-week structure, covering topics such as machine learning, neural networks, computer vision, speech and language processing, and generative AI. Participants learn how students engage in real-world AI applications, complete portfolio-ready projects, and develop research and presentation skills. This session is designed …

Progress, Characteristics And Prospects Of Interdisciplinary Research Collaboration At Home And Abroad:Research Based On Literature Published In The Past Five Years, Yueliang Zeng, Ying Yu, Ruirui Gao

Journal of Scientific Information Research

[Purpose/significance] Interdisciplinary research collaboration is an important way to promote knowledge innovation and solve complex social problems in the new era. Reviewing the research progress of interdisciplinary research collaboration at home and abroad in the past five years can help grasp the research frontiers in this field and provide direction for future research. [Method/process] This paper adopts a systematic review method, selecting literature closely related to the theme of interdisciplinary research collaboration from inWeb of Science and CNKI from 2020 to 2024, condensing the research topic, analyzing the main research viewpoints, and summarizing the changing characteristics, then proposing future research …

A Survey On Knowledge-Enhanced Healthcare Question Answering Systems, Junnan Su, Pu Han, Jianxiang Wei

Journal of Scientific Information Research

[Purpose/significance] This paper aims to review the research progress and applications of knowledge enhancement techniques in healthcare question answering systems, in response to the limitations of traditional systems in knowledge representation and reasoning, as well as challenges faced by current large language model-based systems, such as insufficient domain knowledge, privacy concerns, and hallucination. The review provides a systematic reference for improving the precision and knowledge reliability of such systems. [Process/method] Focusing on knowledge enhancement strategies, this paper firstly outlines their fundamental concepts and overall framework. The strategies are then categorized into explicit and implicit types, with an analysis of their …

Research On Academic Journal Evaluation Based On Factor Dimensionality Reduction, Data Weighting, And Journal Characteristics Orientation, Liping Yu, Jing Zhang

Journal of Scientific Information Research

[Purpose/significance] The characteristics of academic journals have important value, and current evaluation systems lack assesment for this dimension. [Method/process] This paper proposes a variable weight method for factor reduction data, firstly uses factor analysis combined with manual classification to determine the characteristics of the journal, and uses Sigmoid function to standardize the public factors to determine the characteristic journals, and then uses the variable weight function to modify the original data for the characteristic indicators of the characteristic journals based on the data of forestry journals in CNKI. Three representative methods including linear weighted aggregation, weighted TOPSIS, and factor analysis, …

A Meta-Analysis Of Corporate Innovation Intention And Its Driving Mechanisms, Yaping Hu, Xuerong Shen, Yi Zheng

Journal of Scientific Information Research

[Purpose/significance] Stimulating corporate innovation intention is a pivotal issue for promoting innovation. However, the academic discourse on its key drivers presents significantly divergent and even contradictory conclusions, leading to theoretical ambiguity and practical guidance challenges. This study aims to systematically and quantitatively integrate empirical research in this field to clarify the true effects of core driving factors and their operational boundaries. [Method/process] Adopting a meta-analysis approach, this study systematically retrieves and screens literature, ultimately analyzes 29 empirical studies and examine four potential moderating variables which include location, data type, industry, and culture. [Result/conclusion] The study finds that government support, internal …

Research On Smart Intelligence Service System Of National Defense Science And Technology In Complex Information Environment, Yang Yang, Keping Wang, Huawei Sun

Journal of Scientific Information Research

[Purpose/significance] National defense science and technology intelligence serves as a powerful guarantee for promoting national defense science and techndogy innovation and development. Exploring optimization paths for defense science and technology intelligence service systems holds practical significance in developing new quality combat capabilities and safeguarding national security and stability. [Method/process] Through literature review and summarization, this study clarifies the impact of complex information environments on intelligent defense science and technology intelligence services, as well as the core tasks of intelligent intelligence services. Based on activity theory, the study deconstructs system elements and employs systems engineering principles to construct an intelligent defense …

Research On Text Translation Model Based On Large Language Model And Knowledge Enhancement Framework, Chuanming Yu, Haoxuan Li

Journal of Scientific Information Research

[Purpose/significance] This paper aims to improve the translation quality of large language models and effectively alleviate the translation illusion problem, thereby enhancing cross-linguistic information retrieval capabilities. [Method/process] A translation generation method based on a knowledge enhancement framework is proposed. This framework optimizes the translation process from multiple dimensions, such as style, focus, and cultural adaptability, by combining external knowledge provided by the translation context building module and the knowledge base building and retrieval module, and then utilizing the guidance of the text attention module. [Result/conclusion] Experimental results show that the proposed method effectively enhances model performance. Specifically, on the WikiLingua, …

Ai Need Not Make One Slothful, Roy A. Kaelin Jr

Faculty Publications

As National Louis University seeks to plan and advance its way amid the potential perils and pitfalls of so-named artificial intelligence, one finds that with reasonable workflows and guard rails, the use of AI proposes both potential promise and prospect, to assist and augment the natural intelligence that students and teachers bring to the classroom, encouraging them to pursue STEM-related activities with the preparation and deployment of focused and directed AI-bots. This paper presents examples and a case study of that directed and focused approach, the incremental lessons learned to date, and a proposed path to advance worthwhile AI …

Designing Enhanced Nonlinearity In Plasmonic Devices With Epsilon-Near-Zero Films, Kevin Tran Le

Electrical Engineering and Computer Science (MS) Theses

The growing demand for energy-efficient optical information processing motivates compact nonlinear photonic devices that can operate at low power. Silicon photonics is a mature platform for linear optical functions, but nonlinear operation remains challenging because of its weak Kerr response, two-photon absorption at telecommunication wavelengths, and limited compatibility with deeply subwavelength plasmonic confinement. This thesis computationally investigates epsilon-near-zero thin films integrated into plasmonic waveguide architectures as a route toward stronger light–matter interaction in compact nonlinear devices.

Two waveguide geometries are examined: a hybrid metal-insulator-metal plasmonic slab waveguide incorporating an ultrathin indium tin oxide epsilon-near-zero layer (5–50 nm), and a dielectric-loaded …

Sequential Robustness In Adversarial Reinforcement Learning, Roman Lok-Ming Belaire

Dissertations and Theses Collection (Open Access)

My goal is to build autonomous systems that expand the reach of human capability in challenging domains such as undersea and space exploration, disaster response, and large-scale infrastructure. In everyday settings, these systems will increasingly appear in safety-critical applications such as autonomous driving, robotics, and industrial manufacturing. A central requirement for these systems is the ability to operate reliably under uncertainty, particularly when the environment behaves in unanticipated ways.

The robust handling of unforeseen environment dynamics is therefore a technical cornerstone of autonomous decision-making; Adversarial attacks provide a useful and principled lens through which to study this problem. Adversarial \textit{robustness}, …