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Mathematics With|In Conocimientos: A Mathematical Embodiment And Conscious-Raising Experience, Ricardo Martinez, Gabrielle Elizabeth Bernal, Larissa Peru

Education and Leadership Faculty Publications and Presentations

Mathematics interwoven with Youth Participatory Action Research (YPAR) EntreMunods is an ontological playground for youth liberation, where mathematical learning helped to create an experience where youth empower themselves by engaging in critical social science research. In this study, seven stages of conocimientos are expanded to help understand how consciousness shifts while engaged in mathematics. Thematic analysis of student reflections on the YPAR mathematics activities revealed how students developed a mathematical critical consciousness. This manuscript is a theatrical conceptualizing of the Math YPAR experience, leading to development of the Mathematics with|in Conocimientos framework; seven mathematical embodiments or stages that once experienced, …

A Conversation On Fundamental Data Literacy Concepts For Undergraduate Education, Anna Bargagliotti, Wendy Pothier, David Homa, Arshia Mathus, Keith Mccormick, Paula Payton, Brian Wright

Mathematics, Statistics and Data Science Faculty Works

As the demand for data literacy grows, integrating foundational data literacy skills into undergraduate education becomes increasingly essential. This panel presents six core themes for cultivating data literacy among undergraduate students, addressing its interdisciplinary nature. Drawing upon the collective expertise of the group, literature research, case studies, and interviews, the panel explores the need for data literacy across various disciplines and the challenges of integrating it into higher education curricula. The proposed fundamental concepts include understanding data’s role in daily life, distinguishing between inference and prediction, recognizing the potential for misleading data, exploring ethical considerations, and honing communication and storytelling …

Bounded Compactness From G(E)Up, Jonas Mureika, Roberto Casadio, Ilim Irfan Çimdiker, Octavian Micu

Physics Faculty Works

We analyse how different Generalised Uncertainty Principles could place bounds on the compactness of self-gravitating systems. By considering existing experimental bounds on the relevant parameters, we conclude that the compactness of large astrophysical objects is bounded above by the inverse of the GUP parameter, which would naturally be of order one. Conversely, the existence of black holes imposes stronger bounds on those parameters.

Multitec: A Data-Driven Multimodal Short Video Detection Framework For Healthcare Misinformation On Tiktok, Lanyu Shang, Yang Zhang, Yawen Deng, Dong Wang

Computer Science Faculty Works

With the prevalence of social media and short video sharing platforms (e.g., TikTok, YouTube Shorts), the proliferation of healthcare misinformation has become a widespread and concerning issue that threatens public health and undermines trust in mass media. This paper focuses on an important problem of detecting multimodal healthcare misinformation in short videos on TikTok. Our objective is to accurately identify misleading healthcare information that is jointly conveyed by the visual, audio, and textual content within the TikTok short videos. Three critical challenges exist in solving our problem: i) how to effectively extract information from distractive and manipulated visual content in …

Minding The Gap: How Do New Lcap Requirements Address Equity For English Learners?, Magaly Lavadenz, Ph.D., Raquel Gonzalez, Ph.D., Elvira G. Armas, Ed.D., Gisela O'Brien, Ph.D., Hannah Catron, M.A., Jeimee Estrada-Miller, Ph.D., M.P.P.

Reports

California’s Local Control Funding Formula (LCFF), signed into law in 2013, centers equity as a key to increased and improved services for three targeted student subgroups, including English Learners (ELs), low-income students, and foster youth. As a component of LCFF, districts develop Local Control and Accountability Plans (LCAPs) to specify their goals and strategies for using LCFF funds for equity and continuous improvement purposes. In this report, Lavadenz and colleagues use a sample of 26 California public school districts with high numbers/percentages of ELs, finding that although some progress has been made, public schools continue to miss the mark in …

Hunting And Fishing Ceos: Environmental Plunderers Or Saviors?, Thomas Covington, Steve Widler, Keven Yost

Finance Faculty Works

CEOs who participate in hunting and fishing benefit by appreciating natural environments and permanently consuming natural resources. We examine whether CEOs who hunt and fish make different environmental decisions and find that firms led by CEOs who obtain the most hunting and fishing licenses have lower environmental performance as measured by MSCI-KLD. This effect is strongest in the environmental category of climate change but also extends to pollution, waste, and the protection of natural capital. Furthermore, firms led by CEOs with the most hunting and fishing licenses are significantly more likely to pay a regulatory settlement for an environmental regulatory …

Development And Evaluation Of Machine Learning Models For Early Pediatric Sepsis Prediction, Ancita M. Andrade

Browse all Theses and Dissertations

Sepsis is a leading cause of pediatric mortality, claiming more lives in the United States annually than all childhood cancers combined. Early identification in Emergency Departments (EDs) remains challenging, as the current Phoenix criteria establishes an updated international consensus definition for sepsis, however is not designed for use as a screening tool. This study aimed to develop predictive models identifying pediatric patients at risk of sepsis within 24 hours of admission. Multiple tree-based and deep learning models were trained utilizing clinical and laboratory data from the initial four hours of presentation. Both the LightGBM and LSTM architectures demonstrated superior performance, …

Cellular Mechanisms Of Spinal Motoneuron Hypoexcitability Underlying Dynapenia In Aging, Ibrahim Abdul Halim

Browse all Theses and Dissertations

Age-related weakness remains poorly understood as the underlying mechanisms remain unclear. While synaptic input and muscular changes have been investigated with age, intrinsic motoneuron excitability alterations are often overlooked. This thesis provides the first direct assessment of intrinsic excitability and ion channel properties of spinal α-MNs from male and female mice across three ages: young, middle aged, and old. Our findings reveal a decline in intrinsic excitability of motoneurons with age in both sexes. Mechanistic analysis shows sex specific differences: female motoneurons exhibit increased dendritic size, hyperpolarized RMP, and SK channel overactivation, whereas males show only SK overactivation with age. …

Wearable Sensor Data Analysis For Machine Learning-Based Detection Of Posture And Autonomic Responses, Chaitanya Vardhini Anumula

Browse all Theses and Dissertations

This study investigates how Iyengar yoga postures influence autonomic nervous system (ANS) activity by analyzing multimodal physiological signals collected via wearable sensors. The physiological mechanisms underlying Iyengar yoga’s therapeutic effects remain under-explored at the granular, pose-level. Using data collected from 16 participants, this research evaluates whether machine learning models can distinguish between baseline, parasympathetic-dominant, and sympathetic-dominant states based on wrist-worn sensor data. The goals were to explore whether subtle postural variations elicit measurable autonomic responses and to identify which sensor features most effectively capture these changes. Participants performed a sequence of yoga poses while wearing synchronized sensors measuring electrodermal activity …

Impact Of Graph Structures For Rag Outcomes In Llms, Chris Davis Jaldi

Browse all Theses and Dissertations

Explainability, interpretability and adaptability (EIA) remain three central motivations for next-generation Artificial Intelligence (AI), especially as Large Language Models (LLMs) continue to engage with ever-increasing knowledge bodies. As the landscape pushes toward controllable agentic Retrieval-Augmented Generation (RAG) systems where AI agents engage in iterative, guided reasoning, a critical question arises as to the extent to which the knowledge design itself shapes these models' reasoning behavior. This work conducts a systematic evaluation of how different conceptualizations and representation of the identical knowledge affect an LLM's path-based reasoning capabilities. Through the introduction of controlled variations along graph structural complexity, linguistic and semantic …

Re-Parameterizing Adversarial Reprogramming In Low-Dimensional Subspace For Efficient Software Vulnerability Detection, Hootan Alavizadeh

Browse all Theses and Dissertations

Software vulnerabilities are a major cause of security breaches, making effective detection critical. Traditional learning-based methods require large datasets and significant computational resources, which are often impractical due to high annotation costs and data scarcity. To address this, we propose an innovative system, RearVul, which Re-parameterizes adversarial reprogramming in a low-dimensional subspace for software vulnerability detection. Unlike conventional approaches, RearVul repurposes a pre-trained classification model using adversarial reprogramming, enabling detection with minimal modifications. It learns a universal perturbation applied to program representations, preserving the original model’s feature extraction capabilities while adapting it to a new domain. Furthermore, we introduce a …

Subjective Readiness Forecasting Using Supervised Machine Learning And Wearable Device Data, Nathaniel Michael Weiland

Browse all Theses and Dissertations

Recent advances in wearable technology allow continuous monitoring of physiological and behavioral data, opening new opportunities for real-time assessments of readiness and well-being. However, creating predictive models that generalize across diverse users remains challenging, especially in high-stakes settings like the military, where preventable injuries, illnesses, and stress-related performance declines are frequent. This research assesses the feasibility of using supervised machine learning models trained on wearable device data to predict subjective readiness indicators—recovery, stress, injury, and illness. Data from over 10,000 users in the OHWS (Optimizing the Human Weapons System) program combined daily check ins with physiological metrics from Garmin, Polar, …

Computational Assessment Of Vitrimers Self-Healing For Renewable Energy And Aerospace Structures, Walaaeldin Mohamed Ahmed Derbala

Browse all Theses and Dissertations

Self-healing polymers, particularly vitrimers, are emerging as promising candidates in the development of advanced materials for renewable energy and aerospace structures. These materials exhibit dynamic covalent bond exchange mechanisms that enable reprocess ability, damage repair, and extended operational lifetime under harsh conditions. This study presents a density functional theory (DFT)-based computational investigation of the mechanistic pathways and energetics of bond exchange reactions in model vitrimer systems. We explore transition states, energy barriers, and thermodynamic features corresponding to associative and dissociative self-healing reactions in vitrimers. The study focuses on Diaminodiphenyl disulfide (AFD), a bifunctional molecule composed of two para-substituted aminophenyl rings …

Ab Initio Simulations For Oxidation Of An Ultra-High Temperature Ceramic (Hfb2), Wesley I. Black

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Ultra-High Temperature Ceramics are a class of ceramics that possess high strength and melting points in excess of 3000°C. These ceramics are promising for aerospace applications, where materials need to endure high-temperature and high-stress environments. Utilizing ab initio simulations, this thesis research focuses on the atomistic details of oxidation for a typical ultra-high temperature ceramic material, namely hafnium diboride. The simulations provide energy barriers for transition from the initial to final structures via transition states on the (0 0 0 1) surface. These result in estimates for reaction rates and other thermodynamic features that are essential for assessing applicability under …

Reinforcement Learning For Adversarial Environments: Multi-Agent Hide And Seek With Multi-Modal Sensing, Christian Alejandro Carrizales

Browse all Theses and Dissertations

The development of intelligent and competitive agents in AI versus AI adversarial environments was explored through the utilization of reinforcement learning techniques with sensing modalities. A Hide-and-Seek simulation environment was developed using the Unity game engine along with the ML-Agents Toolkit. An engagement test campaign with a set of performance metrics was designed. Four AI versus AI adversarial scenarios were considered using the Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) multi-agent reinforcement learning algorithms. Furthermore, the impact of sensing modalities on competing agents’ learning performance was investigated by varying the sensing capabilities of the hider and seeker, respectively. Experiments …

Dataset Generation For Routing Policy Study In Ad Hoc Wireless Networks, Vishnu Vishnu Priya

Browse all Theses and Dissertations

Ad Hoc wireless networks, with their decentralized architecture and dynamic topology, present challenges in reliable and energy-efficient routing. While machine learning (ML) and reinforcement learning (RL) offer promising solutions, progress is limited by the lack of realistic, high-fidelity datasets. This research introduces a simulation-based framework for generating four diverse datasets representing combinations of node mobility (mobile vs. static) and spatial distribution (random vs. clustered). Each dataset captures critical metrics such as Signal-to-Interference-plus-Noise Ratio (SINR), bottleneck rate, and power consumption across multi-hop paths. A lookahead-based greedy routing algorithm with scenario-aware power control is implemented to emulate practical behavior. Supervised ML models, …

Isometric Centroid Encoder (Ice) And Synthetic Data Generation Approaches For Biological Datasets, Prathyusha Kanakamalla

Browse all Theses and Dissertations

This thesis addressed two main challenges in biological data analysis: structure-preserving dimensionality reduction and synthetic data generation for small sample datasets. I proposed the Isometric Centroid Encoder (ICE), a supervised dimensionality reduction method that preserves pairwise distances between class centroids during dimension reduction. Unlike existing methods like Centroid Encoder and Super Encoder, ICE explicitly maintains geometric relationships between biological classes, achieving nearly perfect structure preservation at C dimensions (where C equals the number of classes) with strong performance even in 2D and 3D spaces. Additionally, I compared three generative models (VAE, LSH-GAN, and scDiffusion) for synthetic data generation on small …

Hydrogen Storage Density And Adsorption Energy Barriers On Li-Decorated Bc3 Nanosheet, Sri Venkat Pavan Upasi

Browse all Theses and Dissertations

Hydrogen is considered an emerging carrier of clean energy with renewable capabilities. Widespread hydrogen energy utilization necessitates efficient storage strategies. Functionalized nanomaterials, such as Li-decorated BC3 nanosheets, are among the primary candidate materials for hydrogen storage. This research investigates the feasibility of hydrogen storage by estimating energy barriers and their dependence on storage density on Li-decorated BC3 nanosheet. Density functional theory (DFT) simulations provide estimates of adsorption energies and saddle points for hydrogen storage and compare corresponding reaction rates. The results are expected to help us understand the advantages and possible shortcomings of hydrogen storage on such nanomaterials.

Biosensing Applications Of Thz Rotational Spectroscopy: Sensing And Analysis Of Exogenous And Endogenous Compounds In Exhaled Breath, Daniel J. Tyree

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Exhaled human breath contains a wealth of volatile molecular species which bear an imprint of compounds dissolved in blood. Assessing trace amounts of these species in exhaled breath requires a highly sensitive and selective method. Terahertz (THz) rotational spectroscopy satisfies these needs by detecting numerous and narrow, molecule specific spectral features with feature intensity dependent on the quantity of absorbing molecules, molecular structure, and experimental parameters. Expanding the applicability of THz sensing to biological gases requires systems to measure this spectral data, reliable analysis of the spectra, and demonstration of the utility of biological data extracted from the spectra. To …

Disengaged And Drowsy: Using An Adaptive System To Detect Suboptimal Learning, William Andrew Stalker

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Instruction is increasingly asynchronous, providing new opportunities but at the cost of losing some guard rails of in-person classes. One such guard rail is an instructor’s ability to detect boredom and learner disengagement. This dissertation explores the development and evaluation of an adaptive training simulator designed to respond to user engagement levels in real-time. The simulator monitors eyeblinks, comparing metrics to individual baselines to detect signs of fatigue and boredom. Participants are exposed to novel information in an audiobook recording of a textbook chapter and later quizzed to assess retention. An adaptive intervention offering periodic brief respite was tested against …

Ultrasonic Sensor-Based Sound Synthesis Using Raspberry Pi Pico W, Niraj Jaishwal

Williams Honors College, Honors Research Projects

At the intersection of Human Computer Interaction and digital art, this project transforms simple motion into musical expression. It explores an interactive real-time sound synthesis system using ultrasonic sensors to generate continuous audio. The objective is to design a system that maps physical distances into musical parameters such as pitch and amplitude, which will create a responsive audio environment. Two ultrasonic sensors are used in combination with the Raspberry Pi Pico W microcontroller running CircuitPython and Adafruit Audio Hat for real-time sound output. One sensor controls the pitch of the generated tone, while the other controls volume. This enables expressive …

Pca Text Sentiment Analysis Tool, Luke Gegick

Williams Honors College, Honors Research Projects

This project applies principal component analysis (PCA) to sentiment analysis of text to identify complex emotional responses from plain text. Existing sentiment analysis tools often rely on large language models or struggle to achieve high accuracy when processing large collections of short inputs, such as social media comments. By contrast, this project uses PCA as a lightweight, mathematically grounded alternative that can scale efficiently while still capturing meaningful emotional structure in text data.

PCA has shown strong effectiveness in text analysis, particularly when supported by a sufficiently large dataset and a robust preprocessing pipeline. To create consistent, information-rich input vectors, …

Improving The Accuracy Of Neighborhood Median Pixel Method (Nmpm) In Classifying Landsat-8 Oli Images By Optimizing The Scoring System’S Point Values, Abraham T. Magpantay, Proceso L. Fernandez Jr

Department of Information Systems & Computer Science Faculty Publications

The Neighborhood Median Pixel Method has previously been introduced as an image processing technique in remote sensing, developed to classify Landsat-8 OLI satellite image pixels into categories of vegetation, water, and built-up areas. This method relies on a lookup table based on the median pixel values within a pixel’s neighborhood and a scoring system that assigns point values for classification. While a 9x9 neighborhood size was originally proposed, a succeeding study suggested a 13x13 neighborhood for better classification accuracy. This study focuses on refining the scoring system used in the Neighborhood Median Pixel Method, particularly the original set of arbitrary …

Electronic Health Records In Non-Hospital Settings Of Developing Economies: A Systematic Review On Enablers And Barriers, Bejie Rodriguez, Joenelyn Kaye Demoral, Jan Jacob Carpio, Alan Napoleon Gultia, Gloria Shiela Coyoca, Cecilio Garciano, Lemuel Clark Velasco

Institute of Philippine Culture (IPC)

In recent years, rapid advancements in Information and Communications Technology (ICT) have greatly transformed the healthcare landscape by streamlining health data management and providing decision-makers with secure and convenient access to health records. In developing economies, limited resources hinder healthcare access. Implementing EHRs in non-hospital settings is essential for enhancing healthcare quality and accessibility. While existing literature supports EHR use, further research is needed to pinpoint specific barriers and enablers. Using PRISMA guidelines, 18 relevant articles were systematically analyzed with the Human, Organization, and Technology Fit (HOT-fit) framework to examine these factors in non-hospital settings within developing economies. This study …

Fabrication And Characterization Of Chalcogenide And Oxide Thin Films, Shuhang Pan

Yale Graduate School of Arts and Sciences Dissertations

This dissertation explores the fabrication, structural characterization, and quantum transport properties of chalcogenide and oxide thin films. High-quality SnTe films were synthesized via molecular beam epitaxy (MBE), demonstrating tunable topological surface states through nanoscale patterning. Magnetoconductivity measurements revealed weak antilocalization (WAL) effects across continuous and patterned films, with coherence lengths governed by boundary scattering in hole-patterned geometries. Advanced focused ion beam (FIB) protocols enabled atomic-resolution transmission electron microscopy (TEM) of monolayer ZrO2 on silicon, defect distribution in off-stoichiometric CaWO4 films and orientation analysis in YVO4 thin films. Josephson junctions fabricated on superconducting Nd1−xEuxNiO2 thin films exhibited …

Tennessee’S Education Savings Account Pilot Program: An Equal Protection Violation Under The Guise Of “School Choice”, Haylie N. Lovelace

University of Memphis Law Review

No abstract provided.

Algorithmic Lawmaking, Moral Judging, And Free Will, Jacob Eisler

Case Western Reserve Law Review

No abstract provided.

Across The Bitter Divide: A Proposal To Improve Certainty In Patent Eligibility, Ian M. Adams

Case Western Reserve Law Review

No abstract provided.

Directed Assembly Of Magnetic Binary Suspensions, David H. Harris

Dissertations

Colloids are particle suspensions in a continuous medium. The particle interactions promote the assembly of structures under the influence of a magnetic field, known as directed-assembly. The magnetic properties and shape anisotropy are relevant features of the particle building blocks to the assembled structures. The dipolar interaction between colloidal particles for directed assembly has been extensively modeled considering spherical particle potentials, which is inaccurate when anisotropic particles are at close range. This research aims to address the problem of quantifying the dipolar interaction between arbitrarily positioned and oriented ellipsoids with different aspect ratios and material properties. We combine the recently …

Correlating Optical Phase And Atmospheric Coherence Width In Characterizing Turbulence, Scott Alexander Kaiser

Dissertations

This dissertation presents the development, validation, and optimization of a novel Hartmann Turbulence Sensor (HTS) for real-time, high-speed characterization of atmospheric optical turbulence. The HTS captures and processes live data at 1 kHz and reports turbulence parameters at 22 Hz. A key innovation is the integration of a dominant spectral component selection method into the Difference in Differential Tilt Variance (DDTV) algorithm. By using spectral analysis to identify areas of interest (AOIs) based on their dominant spatial frequency content, this method reduces computational load by up to 75% while maintaining a low RMS error. It also offers a visual representation …