Digital Commons Network^™

On The Vibrational Dynamics Of Wind Turbine Blades: The Oscillatory Response To Gust Pulses, And The Physical Mechanisms Behind Them, North A. Yates

Dissertations, Master's Theses and Master's Reports

An easily noticed trend in the utility-scale wind turbine industry is that the size of the machines has continued to increase over time. While this may have benefits in terms of increasing the power generation for a single turbine, a major drawback is the rate with which the weight of the turbine blades grow. To ensure these larger machines can be utilized, efforts are being put forth to create lighter blades. This may fix the weight issue, but brings on a new one; these lighter blades can also be more flexible than previously studied ones. It is, therefore, vitally important …

Designing A Research Study: Controlling For A Variable And Using Chatgpt, Fritz Umbach

Open Educational Resources

This assignment uses a hypothetical society to help students understand the idea of “controlling for a variable” and practice applying it to a real-world research question. Students design a research approach to examine whether income differences are better explained by family background or by systemic prejudice, working within clear data limits. ChatGPT is used as a research guide to help students brainstorm, test, and revise their methods, while encouraging them to critically evaluate the usefulness and limits of AI-generated suggestions. The assignment emphasizes careful reasoning, clear research design, and thoughtful use of AI as a support for learning rather than …

Are Judges Influenced By Dollar Amounts? Evidence From Tax Law, Jonathan Farrar, Thomas Farrar, Harjot Mehmi

Dalhousie Law Journal

We investigate whether idiosyncratic features of numbers in dollar amounts unconsciously bias judges. If so, appellants’ likelihood of success could depend on the dollar amount under dispute. Specifically, we examine the following: whether the first digit in a dollar amount follows the frequency distribution predicted by Benford’s Law, and, accordingly, whether the likelihood of appellants’ success varies with the first digit of their disputed dollar amount; round number bias, which is the psychological tendency to prefer round numbers (ending in 0) over other numbers; and even-odd number bias, which is the psychological tendency to ascribe different characteristics to even numbers …

Review Of Bones: Structure And Mechanics, Alexander Curry

Nebraska Anthropologist

Review of Bones: Structure and Mechanics (2002) by John D. Currey.

Unsettling The West: Migration Routes And Homesteading Through Oral Histories, Sidney Wickham

Nebraska Anthropologist

Westward expansion in the nineteenth-century United States has long been framed through settler-centered historical narratives that emphasize progress and national development. This article employs oral history as an ethnographic method to examine migration routes and homesteading experiences in Nebraska and the broader Midwest, foregrounding how memory, storytelling, and lived experience function as cultural practices. Drawing on interviews with descendants of homesteaders, community members, and museum professionals, alongside diaries, newspapers, and archival materials, this study analyzes how migration was remembered, narrated, and transmitted across generations. Rather than treating overland trails and homesteads as singular pathways of movement, this research situates them …

Af-Xray: Visual Explanation And Resolution Of Ambiguity In Legal Argumentation Frameworks, Yilin Xia, Heng Zheng, Shaun Bowers, Bertram Ludäscher

Computer Science Faculty Scholarship

Argumentation frameworks (AFs) provide formal approaches for legal reasoning, but identifying sources of ambiguity and explaining argument acceptance remains challenging for non-experts. We present AF-XRAY, an open-source toolkit for exploring, analyzing, and visualizing abstract AFs in legal reasoning. AF-XRAY introduces: (i) layered visualizations based on game-theoretic argument length revealing well-founded derivation structures; (ii) classification of attack edges by semantic roles (primary, secondary, blunders); (iii) overlay visualizations of alternative 2-valued solutions on ambiguous 3-valued grounded semantics; and (iv) identification of critical attack sets whose suspension resolves undecided arguments. Through systematic generation of critical attack sets, AF-XRAY transforms ambiguous scenarios into grounded …

Typology And Spatiotemporal Patterns Of Illegal Fishing Activities In The Philippines: Implications For Fisheries Management, Marlowe O. Acevedo, Tzu-Yun Ching, Chih-Shin Chen

Journal of Marine Science and Technology–Taiwan

Illegal, unreported, and unregulated (IUU) fishing activity is a global threat and continues to undermine the effectiveness of fisheries regulations and management measures. However, the extent of illegal fishing in Philippine waters remains unclear. This study aims to identify the types of illegal fishing in Philippine waters and analyzed their temporal and spatial patterns. Data were obtained from the records of the Philippine Coast Guards (PCG) and Maritime Law Enforcement Agencies from 2015 to 2020. A total of 602 apprehensions were recorded, resulting in 813 violations, of which 630 cases were fishery violations. Most of the watercrafts involved in the …

Soft-Constrained Variants Of T-Distributed Stochastic Neighbor Embedding For Global Structure Preservation, Joseph A. Balderas

Mathematics Dissertations

Dimensionality reduction (DR) is a fundamental tool in data science and machine learning that transforms high-dimensional data into a low-dimensional representation while preserving important structural properties of the original data. Among modern DR methods, t-distributed stochastic neighbor embedding (t-SNE) has become one of the most widely used techniques for visualization due to its strong ability to preserve local neighborhood structure and produce visually separated clusters. However, despite its popularity, t-SNE is well known to struggle with preserving global structure of data, often producing embeddings in which distances between clusters and neighborhoods do not accurately reflect relationships in the high-dimensional space. …

Court-Appointed Neutrals And Legal Education, F. Peter Phillips

NYLS Law Review

No abstract provided.

Machine-Learning- And Iot-Based Approach For Predicting Water Quality Using Data Classification And Explainable Ai Technique, M. Samir Abou El-Seoud, Omar H. Karam 2, Hosam El-Sofany 3

Computer Science

Maintaining water quality is crucial for both public health and environmental sustainability. This study proposes a machine-learning- (ML-) and Internet-of-Things- (IoT-) based approach to water quality prediction that utilizes explainable artificial intelligence (XAI) and data classification techniques. The proposed approach integrates IoT devices to enhance real-time data collection, facilitating continuous monitoring and early anomaly detection. Ten different ML classifiers—Decision Trees, K-Nearest Neighbors, XGBoost, Naïve Bayes, Logistic Regression, AdaBoost, Random Forests, Support Vector Machines, Voting, and Multi-Layer Perceptron—were evaluated to find out which one works best for predicting water quality. We used three distinct approaches for feature selection: analysis of variance …

Eeg And Imu Gait Signal Processing: A Comparative Assessment Of The "Reza" Exponential Filter And Classic Filters, Reza Pousti, Daniel M. Russell, Derek C. Monroe, Christopher K. Rhea

Rehabilitation Sciences Faculty Publications

Noise degrades both EEG and gait signals, and classical IIR filters (Butterworth, Chebyshev, elliptic) involve trade-offs between passband flatness, ripple, and roll-off. This study compared a novel exponential "Reza" filter with these designs for neural and locomotor data. We analyzed an open-source mobile brain-body imaging dataset with EEG and gait data from 49 healthy adults (EEG: 256-channel, 512 Hz; IMUs: six APDM Opals, 128 Hz). EEG channels were grand-averaged and band-pass filtered at 0.5-50 Hz, while IMU axes were averaged and band-pass filtered at 0.5-5 Hz. The outcomes were signal-to-noise ratio SNR (dB) and band-integrated Welch PSD (EEG:0.5-50 Hz; IMU:0.5-5 …

The Sequence (S) Index As A Marker Of Diminished Step-To-Step Transition Efficiency In Older Adults, Elham Alijanpour, Ashwini Kulkarni, Peter G. Adamczyk, Daniel M. Russell

Rehabilitation Sciences Faculty Publications

Introduction: Age-related declines in walking efficiency are often attributed to musculoskeletal and neuromuscular changes, yet the mechanisms underlying these inefficiencies remain poorly understood. One key contributor is the step-to-step transition (STST), during which the center of mass (CoM) is redirected between limbs. The Sequence (S) index quantifies the temporal overlap between push-off and collision forces during STST, with higher values indicating greater mechanical energy loss. This study investigated whether aging affects STST efficiency, as quantified by the S index, and examined the timing and impulse characteristics underlying observed differences.

Methods: Thirteen young and eleven older healthy adults walked at five …

Balancing Inquiry Based Learning And Direct Instruction, Sharon Liu , '26

Senior Theses, Projects, and Awards

No abstract provided.

Enlem: Ensemble Learning-Based Model To Detect Phishing Websites, Most Nilufa Yeasmin, Md Abu Rumman Refat, Bikash Chandra Singh, Zulfikar Alom, Zeyar Aung, Mohammad Azim

School of Cybersecurity Faculty Publications

Phishing involves manipulating individuals into revealing private data, e.g., user IDs, bank details, and passwords. The observed surge in fraud is related to increased deception, impersonation, and advanced online attacks. Thus, effective phishing detection methods are required to mitigate escalating global phishing threats. Existing methods (e.g., heuristics-based, signature-based, and visual similarity-based methods) attempt to detect phishing sites, and machine learning (ML) and deep learning (DL) methods are effective in the cybersecurity context in terms of learning from data, offering insights, and forecasting. However, independent ML algorithms are limited when handling complex data, and DL techniques surpass traditional ML methods in …

Toward Secure And Practical Machine Learning-Based Access Control: A Framework With Real-World Constraints And Adversarial Analysis, Olusesi Balogun, Mohammad Ghasemigol, Zhipeng Cai, Daniel Takabi

School of Cybersecurity Faculty Publications

Attribute-Based Access Control (ABAC) frameworks coordinate access requests based on subject, object, and environment attributes, as well as policy rules, and are widely used in corporate security systems. Recently, machine learning has been applied to ABAC to address policy-generation imbalances, misassigned privileges, and attribute leakages. However, existing MLBAC techniques do not consider the structural constraints and attribute interdependencies present in traditional ABAC systems. Moreover, these frameworks have not been extensively evaluated under black-box attack scenarios. To address these gaps, we propose extensions to MLBAC that integrate structural constraints, attribute dynamism, and attribute weighting into the MLBAC objective function. Additionally, we …

Exploring The Synergy Between Very Large Transformer And Lstm Models For Effective Medical Captioning From Videos To Text: The Impact Of Captioning In Healthcare, R. V. Aswiga, Moin Ahmed Zahir

Data Science Faculty Publications

In today’s rapidly evolving digital landscape, the demand for accurate and contextually relevant subtitles for image and video content, particularly in the medical domain, is increasingly critical. Despite the proliferation of visual data across various platforms, existing captioning systems often struggle due to variations in visual settings, complex temporal relationships, and nuanced semantics. Additionally, challenges such as limited datasets, privacy issues, and specialized annotation requirements make medical image captioning particularly difficult. To tackle these challenges, we investigate cutting-edge deep learning methodologies, specifically Transfer Learning and Transformer models, through a comparative analysis. Specifically, we focus on Transfer Learning through the MedVisionCapturer …

A Hybrid Cnn-Lstm Surrogate Model For Hyper-Resolution Spatiotemporal Flood Forecasting In Norfolk, Virginia, Yidi Wang, Jonathan L. Goodall, Chetan Kumar, Diana Mcspadden, Sergio A. Barbosa, Binata Roy, Ali Shahabi, Navid Tahvildari

Data Science Faculty Publications

Study region

Norfolk, Virginia, United States

Study focus

Accurate and timely flood forecasting is essential for enhancing resilience in coastal urban areas in the context of increasing frequency and intensity of rainfall, sea level rise and urbanization. This study presents a hybrid deep learning-based surrogate model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enable real-time spatiotemporal flood forecasting. The model leverages CNN to capture spatial features from inputs such as elevation and Topographic Wetness Index (TWI), while LSTM processes time-series inputs of rainfall and tide data to capture temporal features.

New hydrologic insights for …

Blue Spring (2001):, George Dibble

BYU Asian Studies Student Journal

“Congratulations on your graduation,” broadcasts on the upper secondary school’s PA system—the first sound in the film Blue Spring (2001), directed by Toshiaki Toyoda—yet none of the students we follow for the next 83 minutes have anything to graduate for. Like a chick denied egg-shell breakage, stuck in its hard world, the film-focused boys are told to live. But where, and how? Blue Spring exists between the graffiti laden walls of high school where classes are secondary to student politics and fights are as common as words.

Annoyances lead to murders. Money is extorted. Teachers are harassed. The first image …

Teachers’ Perceptions Of Artificial Intelligence And Its Impact On Student Learning, Ranging From Understanding, Originality, Motivation, Problem-Solving, And Resilience, Joseph Whelan

Selected Full Text Dissertations, 2011-

Artificial intelligence (AI) is rapidly transforming education, challenging traditional notions of how learning should be structured in schools. Although research on educational AI remains limited, its influence on student learning is already evident. On a daily basis, educators observe the impact artificial intelligence has on five specific areas of student learning: being, understanding, originality, motivation, problem-solving. and resilience. Using a mixed-methods approach, with descriptive research using surveys with free-response questions, the study aimed to examine how the five student characteristics are affected in the classroom daily through the integration of artificial intelligence into student learning. Teachers participated in a survey …

Front Matter, Natural Resources Journal Nrj

Natural Resources Journal

No abstract provided.

Visual Aids And Numeracy In Healthcare: Leveraging The Drawing Effect, Zephyr J. Markley

Capstone Showcase

Healthcare decisions often require patients to interpret numerical risk information under stress, yet many struggle with low numeracy, which can distort comprehension and undermine informed consent. Well-designed visual aids can improve diagnostic inference, calibrate confidence, and support better choices, especially for patients with limited numeracy, but their impact depends on whether users can actually interpret the format accurately. Drawing may further enhance these tools by adding a tactile, generative layer that engages visual, semantic, and motoric processing while helping patients organize information into concrete models.

Passive visual aids appear useful but limited because they often position patients as observers rather …

Narrowing The K-12 Excellence Gap In Kentucky Schools: Lessons On High Achievement From High Achievers, Jimmy M. W. Bartlett, Johnathan K. Hurley

MPA/MPP/MPFM Capstone Projects

Disparities in high-level academic achievement – known as excellence gaps – remain a persistent equity challenge in K–12 education, particularly in access to advanced mathematics learning. This study examines school-level practices associated with narrower excellence gaps in Kentucky public schools. Principals of schools demonstrating higher-than-expected advanced achievement and relatively small gaps by gender and economic status were invited to semi-structured interviews. The interviews cover seven schools, spanning grade levels and other characteristics. Guided by the Excellence Gap Intervention Model, a hybrid inductive–deductive thematic analysis reveals that narrower excellence gaps are not driven by isolated programs or policy mandates, but by …

Why Physical Environment Matters: The Cognitive Effects In Elementary Classrooms, Amanda Cavender

Integrated Studies

All physical elements, such as color, lights, acoustics, and furniture must work cohesively with the layout and space to create a welcoming, calm, and inclusive classroom.  This paper reviews the data surrounding these physical elements and their correlation with academic success.  Academic success in this case is defined as scores students achieve, the speed at which they can complete work, the accuracy of completed work, and attention spans.  The quality of school building plays a crucial role here; Temperature control, adequate space, and insulation are crucial to be able to properly control the environment.  Regarding lighting, studies have shown that …

Supporting Students With Learning Differences: A Dyslexia Educator’S Perspective, Hailey S. Mason

Integrated Studies

Dyslexia is a common neurobiological learning difference that affects reading, writing, and language processing skills, often despite average or above-average intelligence. This project examines dyslexia from an educator’s perspective, focusing on its characteristics, impact on student learning, and effective strategies for support. A review of current scholarly literature highlights the importance of early identification and intervention in improving academic outcomes and reducing negative emotional effects such as low self-esteem and anxiety. The findings emphasize that dyslexia extends beyond reading difficulties, influencing writing, comprehension, and overall academic performance. Evidence-based instructional approaches, particularly structured literacy and multisensory teaching strategies, are identified as …

Building Number Sense: Setting The Foundation For A Working Knowledge Of Mathematical Concepts With Practical Life Application, Judith E. Rakestraw

Integrated Studies

Abstract

This paper examines the importance of number sense in early mathematical development and how it is affected by classroom environments and instructional methods.  Building a strong number sense is crucial in developing a working knowledge of number concepts that can be applied to real-life experiences.  A creative learning environment, discovery style instruction, mathematical representations, interactive lessons, and technology-enhanced experiences can help students internalize learning and transform taught skills into applied skills.  Even though everyone is born with a degree of natural mathematical awareness, most understanding and ability develops as a result of experiences that take place in the classroom. …

"As A Woman I Have No Country": Patriarchy And Patriotism In Virginia Woolf's Interwar Fiction, Katherine S. Cole

Scripps Senior Theses

This paper examines the evolving ways in which the ideological framework of Virginia Woolf's political essay Three Guineas manifests in her fiction, as well as how it is shaped by both world wars. In analyses of Mrs. Dalloway, Orlando, The Years, and Between the Acts, I examine Woolf’s critiques of the masculine-patriotic-imperial value system that structures English society, its interdependence with the patriarchal tyranny of the domestic sphere, and how these critiques are complicated by a fraught relationship to Englishness that often balances sentimentality, personal attachment, and the fear of identity loss with the fiercely critical feminist-pacifist perspective articulated in …

Climate Change In An Educational Context: A Model Addressing Psychological Distance, Hope, And Concern, Brooke E. Kasl-Godley

Scripps Senior Theses

As the threat of climate change becomes more urgent, educators should focus on fostering climate concern and action instead of simply providing climate information. These studies present a novel model of climate education where locally-framed and action-oriented curriculum increases students’ environmental concern through the mechanisms of psychological distance and hope. This curriculum was taught over two years to sixth-grade students at a school in the Los Angeles Metropolitan Area. Study 1 examines students’ environmental concern and sense of responsibility before and after delivery of the curriculum. Study 2 is a proposed follow-up study, that will address limitations of Study 1 …

Doikayt And The Ethics Of Belonging: Lessons In Place-Making From The Jewish Labor Bund, Zoë L. Jacobs

Scripps Senior Theses

This thesis traces the evolution of doikayt—the Bundist principle of hereness—from its origins in the Jewish Labor Bund to contemporary applications in North America. Rooted in a refusal of both assimilation and nationalist escape, doikayt emphasizes cultivating safety, solidarity, and belonging through collective responsibility rather than territorial claims.

Building on this foundation, the exploration situates hereness within a settler-colonial context, where ethical dwelling is complicated by Indigenous dispossession. Drawing on Alexis Shotwell alongside Indigenous thinkers Vanessa Watts and Kyle Whyte, I examine how inhabiting place requires confronting historical injustices and ongoing relational responsibilities. Chapter 3 analyzes Linke Fligl (2016–2022) as …

"Chatcbt”: Understanding The Implications Of Artificial Intelligence In Cognitive Behavioral Therapy, Esther R. Goldberg

Scripps Senior Theses

The proposed study is intended to explore the impacts of artificial intelligence (AI) tools when used in cognitive behavioral therapy (CBT) to treat anxiety in American adults. The research is designed to measure whether AI systems such as large language models (LLMs) provide sufficient therapy through the factors of empathy, coping skills, and anxiety symptom reduction. Human therapists are hypothesized to surpass chatbots in all three. Through an experimental design, participants will be placed in either an AI therapy condition or human therapy condition. Participants will undergo a 12-week CBT program, after which they will measure anxiety reduction, perceived empathy …

Spectral Decimation On The Schreier Graphs Of The Basilica Group: A Thesis In Fractal Analysis, Anne D. Bannon

Scripps Senior Theses

This thesis is intended to provide a comprehensive overview of the literature required to fully understand research conducted during the University of Connecticut's Fractals & Stochastics REU in the summer of 2025. The literature review includes a description of Robert Strichartz's seminal work pertaining to the Laplacian spectrum of the Sierpiński Gasket, which provides a framework for how we approach studying the spectrum of the basilica Julia set. Defining the basilica Julia set and the closely-related Basilica group involves graph theory, automata theory, iterated monodromy group theory, and amenable group theory. Further time is dedicated to defining the graph Laplacian …