Digital Commons Network^™

Piezoelectric Energy Harvesting From Roadways: Challenges, Advances, And Future Directions, Heba Gaber, Mohamed Abdelraheem

Faculty Publications

As the global demand for renewable energy intensifies, piezoelectric energy harvesting from roadways has emerged as a promising avenue for sustainable power generation. This systematic literature review analyzes 61 peer-reviewed studies to assess the feasibility, performance, and potential of integrating piezoelectric systems into roadway infrastructure. While technology faces challenges, such as high installation costs, limited energy output, and a scarcity of thorough economic evaluations, findings suggest it holds considerable promise as a supplementary renewable energy source. The review analyzes the operational characteristics and efficiencies of various piezoelectric transducers, identifies key factors influencing system performance, and evaluates recent technological advances. It …

Is Complexity Virtuous?, Ryan Elmore, Jack Strauss

Business Information and Analytics: Faculty Scholarship

(Kelly et al., 2024) show that increasing complexity in linear models, with potentially thousands of predictors, is "virtuous". Their work contradicts the dogma of model selection, including the Principles of Parsimony and Occam's Razor. They find that when the number of predictors far exceeds the number of observations, the bias-variance trade-off breaks down, the variance declines, and the Sharpe ratio increases. In the context of ridge regression, we find that very high complexity coupled with large penalty terms (excessive shrinkage) generate forecasts that converge to a rolling window of past returns. For example, we show the past twelve-month moving average …

Introduction To Machine Learning And Machine Learning Systems, Raffi T. Khatchadourian Ph.D.

Open Educational Resources

Lecture slides introducing machine learning and machine learning systems for an undergraduate software engineering course. Topics include what machine learning is and how it differs from traditional programming, foundation models, the major types of learning (supervised, unsupervised, reinforcement, and others), and applications across domains. Using a food-delivery time-prediction case study, the deck walks through a typical ML pipeline—data collection and cleaning, feature engineering, model training, and evaluation—and covers evaluation methods (precision and recall, confusion matrices, error measures) along with underfitting versus overfitting and the realities of learning and evaluation in production. Based on "Machine Learning in Production/AI Engineering" by Christian …

Phase Equilibria Modeling Constraints On Channel Flow Of Metasedimentary Crust Beneath The Southeastern Tibetan Plateau, Govind Joshi, Gregory Dumond

Geosciences Faculty Publications and Presentations

Lower crust of the southeastern Tibetan Plateau includes 100 s of km2-scale zones of low resistivity at 20-40 km-depths indicating the presence of melt +/- fluid +/- graphite. These zones coincide with interconnected zones of low shear wave velocity. Data from central and southeastern Tibet support the occurrence of meta-sedimentary felsic granulites in the lower crust along a backarc-like geothermal gradient. Phase equilibria modeling of average metasedimentary pelite and greywacke bulk compositions constrained by these geophysical data explored the impact of partial melting on shear wave velocity for a range of fluid content. Low shear wave velocities beneath southeastern Tibet …

A Feature-Free Deep Learning Approach For Midair Hand Gesture Recognition From Surface Electromyogram (Semg) Data, Yasir Altaf, Abdul Wahid, Mudasir Manzoor Kirmani

Turkish Journal of Electrical Engineering and Computer Sciences

Midair hand gesture recognition plays a crucial role in applications such as sign language recognition and human-computer interaction, particularly for supporting individuals with partial or complete hearing loss. However, recognizing gestures in midair remains challenging due to the rapid and complex nature of hand movements. To address this, noninvasive techniques like surface electromyography (sEMG)—which captures muscle activity through sensors placed on the skin—have gained attention. sEMG provides rich time-series data that reflect both spatial and temporal muscle dynamics. In this study, we propose a deep learning architecture that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to classify …

Integrated Log Spectrogram Convolutional Neural Network (Ils-Cnn) For Robust Spoken Digit Recognition, Awais Ahmed

Turkish Journal of Electrical Engineering and Computer Sciences

Spoken digit recognition (SDR), a type of supervised automatic speech recognition, is essential for various human-machine interaction applications, including banking operations, dialing systems, price extraction, and airline reservation systems. However, designing an effective SDR system presents several challenges, such as developing labeled audio data, selecting appropriate feature extraction methods, and creating high-performance models. To overcome these challenges, a novel approach for robust spoken digit recognition using an integrated log spectrogram convolutional neural network (ILS-CNN) has been proposed. The proposed work presents an efficient SDR method by taking advantage of a log spectrogram layer directly within the neural network to enhance …

Field Validation Of Multiple Species Distribution Models Shows Variation In Performance For Predicting Aedes Albopictus Distributions At The Invasion Edge, Anna V. Shattuck, Brandon D. Hollingsworth Ph.D., Jared Skrotzki, Scott R. Campbell, Christopher L. Romano, Courtney C. Murdock

Faculty Publications

Background

Climate and land use changes have resulted in range expansion of many species. In this shifting disease landscape, it is important to leverage tools that can predict the distributions of invading vectors to target surveillance and control efforts and identify at-risk populations. Species distribution models (SDMs) are used to predict ranges of invasive species; however, invasive species often violate assumptions of equilibrium and niche conservatism. Moreover, these studies are rarely validated using independent data.

Methods 

We use long-term surveillance data for Aedes albopictus, a highly invasive mosquito capable of transmitting several arboviruses, at its range edge to evaluate a …

Intro To Ai Literacies Discussion, Miranda Rectenwald

Generative AI Teaching Activities

Students consider and dicuss key literacies when using generative AI tools.

Simulating Realistic Lyman-𝛼 Emitters Including The Effect Of Radiative Transfer, Hasti Khoraminezhad, Shun Saito, Max Gronke, Chris Byrohl

Research Data

We present an empirical yet physically motivated simulation of realistic Lyman-𝛼 emitters (LAEs) at 𝑧 ∼ 2 − 3, crucial for ongoing and forthcoming cosmological LAE surveys. We combine an empirical UniverseMachine galaxy-halo model with a simple spherical expanding shell model for the Lyman-𝛼 radiative transfer, calibrating only three free parameters to simultaneously reproduce the observed Lyman-𝛼 luminosity function and the angular clustering. Our LAE model is further supported by its consistency with other observables such as the Lyman-𝛼 equivalent width distribution, the Lyman-𝛼 escape fraction as a function of stellar mass and dust reddening, and the systemic velocity offsets. …

Uc-1207 Ai Driven Resident Inquiry Processing, Ben Moran, Sahil Sachwani, Thomas Ashe, Sean Johnson

C-Day Computing Showcase

The AI Driven Resident Inquiry Processing System is designed to enhance the National Housing Compliance (NHC) ability to process resident inquiries using artificial intelligence(AI). NHC is a 501(c)(4) not-for-profit corporation who provides training and compliance services to the affordable housing industry. Each month NHC receives over 200 inquiries from residents via phone and email. These inquiries range from general questions to urgent, life-threatening concerns. Efficiently processing and responding to these inquiries is often critical to resident safety and well being. This project uses AI to automate resident inquiries as they are received, extract and classify key information, and display this …

Uc-1226 Iknowit: Multilingual Smartphone Tutorial Platform, Jacqueline Juarez, David Bazan, Julissa Rivera

C-Day Computing Showcase

Digital literacy challenges affect millions of adults who struggle with basic smartphone use due to rapidly changing technology and limited support. iKnowIT is a dynamic, web-based learning platform designed to provide clear, visual, and multilingual tutorials that guide users through essential device functions. The goal of iKnowIT is to bridge the digital divide and empower users to engage confidently with modern technology

Mobile Computer Vision Application For Agricultural Disease Detection Of Pepper Diseases Using Two-Stage Deep Learning System, Carlos Jose Estevez, Mai Dang, Ryan Bass

SMU Data Science Review

Plant diseases pose a significant threat to food security, particularly in developing countries where farmers often lack the resources and infrastructure for early detection. In nations like Mexico and the Dominican Republic, the spread of harmful plant diseases impacts key agricultural commodities, such as habanero peppers, leading to substantial yield losses. This study presents a computer vision system based on Convolutional Neural Networks (CNNs) and an object detection model (YOLO) to help farmers detect pepper diseases efficiently. The system uses a two-stage approach: YOLOv11n first detects pepper leaves in images, then a lightweight MobileNetV3Small model classifies whether the detected leaves …

A Decolonial Approach To International Education: Insights From A Cal Poly Global Program In Palermo, Sicily., Elvira Pulitano, Iyad Jamaly

csuglobaljournal

This article presents a critical intervention in the current debates about decolonizing international education. It is based on a Global Program in Palermo, Sicily, offered by Cal Poly San Luis Obispo in summer 2023. As both program creator/director and student participant in the program, the authors rely on their personal experience and insights along with expertise in coloniality and decolonial theories. The article focuses on a series of pedagogical activities led by a young group of migrants and refugees who, in the city of Palermo, have come together to form two associations designed and structured to offer new contemporary models …

When Scripts Diverge: Strengthening Low-Resource Neural Machine Translation Through Phonetic Cross-Lingual Transfer, Ammon Shurtz

Theses and Dissertations

Multilingual Neural Machine Translation (MNMT) models enhance translation quality for low-resource languages by exploiting cross-lingual similarities during training--a process known as knowledge transfer. This transfer is particularly effective between languages that share lexical or structural features, often enabled by a common orthography. However, languages with strong phonetic and lexical similarities but distinct writing systems experience limited benefits, as the absence of a shared orthography hinders knowledge transfer. To address this limitation, we propose an approach based on phonetic information that enhances token-level alignment across scripts by leveraging transliterations. We systematically evaluate several phonetic transcription techniques and strategies for incorporating phonetic …

Privacy In Flux: A 35-Year Review Of Trends, Legal Evolution, And Emerging Challenges, Kong Phang, Jihene Kaabi

Research & Publications

Privacy harms have expanded alongside rapid technological change, challenging the adequacy of existing regulatory frameworks. This systematic review (1990–2025) systematically maps documented privacy harms to specific legal mechanisms and observed enforcement outcomes across jurisdictions, using PRISMA-guided methods and ROBIS risk-of-bias assessment. We synthesize evidence on major regimes (e.g., GDPR, COPPA, CCPA, HIPAA, GLBA) and conduct comparative legal analysis across the U.S., E.U., and underexplored regions in Asia, Latin America, and Africa. Key findings indicate increased recognition of data subject rights, persistent gaps in cross-border data governance, and emerging risks from AI/ML/LLMs, IoT, and blockchain, including data breaches, algorithmic discrimination, and …

Stars And Shapes: An Investigation Of Stripped Envelope Supernovae Structures Using Spectropolarimetry Observations And Hydrodynamic Models, Sabrina Desoto

Electronic Theses and Dissertations

Supernovae (SNe) are the energetic deaths of massive stars, dispersing heavy elements throughout the galaxies and shaping cosmic evolution across the universe. However, understanding the connections between the evolution of massive stars, their catastrophic demises, and the myriad of observed SNe, remains a fundamental challenge in astrophysics. My work aims to narrow this gap by integrating extensive observational data with advanced hydrodynamical simulations to better interpret SNe observations and understand the origin of their geometry. The observational component utilizes the largest spectropolarimetry dataset of stripped-envelope (SE) core-collapse supernovae (CCSNe) gathered by the SNSPOL project. Analyzing nine Type Ib and eleven …

Principal Quandles, Jesse Parrish

Electronic Theses and Dissertations

This thesis concerns principal quandles and, as a special case, Alexander quandles. A principal quandle is a coset quandle, Q(G,H, f), in which the subgroup H is trivial. If in addition the group G is abelian, then the coset quandle is called an Alexander quandle. The isomorphism types of Alexander quandles were classified [16], and some progress was made on describing the isomorphism types of principal quandles in [14] and [13]. The first part of this thesis applies the ideas used in Holmes’ paper to give a classification of the isomorphism types of principal quandles. The second …

Exploring The Impact Of Statistics Course Modality On Student Learning: A Mixed Methods Approach, Becky Kelleman

Electronic Theses and Dissertations

The importance of statistical literacy has become increasingly evident, as individuals grapple with interpreting statistics to inform critical decisions. Despite the recognized significance of statistical literacy, challenges persist in both educators’ and students’ efforts to navigate the complexities of statistics education. The purpose of this study is to explore the impact of different course modalities on undergraduate students’ academic performance in introductory statistics courses. Using Social Cognitive Theory (SCT) as a theoretical framework, this research aims to shed light on the complex dynamics influencing statistical literacy attainment. By addressing gaps and inconsistencies in the current literature, this study seeks to …

High Frequency Dc-Dc Converter Design Optimization, Modeling And Control Based-Wbg Technology., Salah Ahmed Abdullah Eltief

Electronic Theses and Dissertations

The performance of DC-DC power converters is a cornerstone of modern electric vehicle (EV) powertrains, directly governing overall system efficiency, size, cost, and reliability. This dissertation presents a comprehensive performance analysis and optimization of DC-DC converter topologies to determine the most suitable design for high voltage EV applications. The evaluation rigorously compares efficiency, power losses, and physical size under a range of harsh operating conditions. A primary objective is to leverage Wide Bandgap (WBG) semiconductors, specifically Silicon Carbide (SiC), to push the performance boundaries of power conversion. While SiC devices are known for their superior material properties, a clear understanding …

Controls On Speleothem Carbon Isotopes, Paloma Marcos

Undergraduate Research Symposium Posters

The Sistema Huautla cave system is the deepest known in the Western Hemisphere. It is located in the Sierra Mazateca Mountain Ranges near Jiménez de Huautla, Oaxaca. There are over 29 separately discovered entrances into this karst environment making it the 28th longest cave system in the world. It can be considered by some as one of the most complex vertical drainage systems on Earth. This extensive study site has been an area of interest among cavers and scientific researchers for nearly 6 decades. Speleothem samples from the study sites have been found to be over 433,000 years old. The …

Simulation Of Fluid Flow Based On Navier Stokes Equations, Ricardo Marquez, Javier Perez

Undergraduate Research Symposium Posters

This work presents a computational study of incompressible flow based on the Navier-Stokes equations using finite element method. The Crank-Nicolson approximation is used for discretizing the time derivative. The study consists of investigating the effect of the choice of finite elements and the size of the time step in obtaining the numerical velocity and pressure. This is illustrated with FreeFEM++ simulations of the von Karman benchmark flow problem. Profiles of drag, lift and pressure drop will be presented in time.

The Mathematics Of Motion: Applying Derivatives To Fluid Systems, Sayla Daily

Undergraduate Research Symposium Posters

Mathematical modeling through differential equations provides a basis for understanding change in physical and engineering systems. My project explores the transition from single-variable derivatives to multivariable partial derivatives and their applications in describing engineering applications. Beginning with the Fundamental Theorem of Calculus, derivatives are introduced as measures of change, quantifying relationships such as velocity and acceleration. The study then extends to partial derivatives, which represents systems influenced by multiple variables, including spatial and temporal factors. These principles are applied to model fluid motion, and pressure gradients, including the Navier–Stokes equations governing fluid dynamics. By linking mathematics with engineering applications, this …

Non-Invasive Infection Detection Framework: Smartphone-Based Hematologic And Thermal Biomarker Monitoring, Nafi Us Sabbir Sabith

Dissertations (1934 -)

Infectious diseases remain one of the leading global causes of illness and death in both community and hospital settings. Infections lead to lasting health issues, such as life-threatening chronic conditions or even cancer. Early detection is crucial for timely intervention, such as isolation and treatment. Hematology originated from the Greek word haima, meaning blood. It is a unique discipline in medicine that involves the study of blood and its contents. White blood cells (WBC) are at the forefront of the body’s immune system. WBCs are categorized into five main subtypes: neutrophils, lymphocytes, monocytes, eosinophils, and basophils. Monitoring the total WBC …

Doughnut Economics And Strong Sustainability: Rethinking The Role Of Financial Investment, Isabelle Pierotti

Electronic Theses and Dissertations

This thesis investigates how private investment could align doughnut economic framework. Doughnut economics is a strong sustainability economic framework where the economy operates within planetary boundaries while ensuring social equity. Theories of sustainable development, weak sustainability, and strong sustainability are discussed to provide theoretical context for doughnut economics and mainstream economics. The analysis concludes that conventional, profit maximizing private investment does not support a doughnut economics system by supporting the depletion of natural resources and exacerbating social issues. Sustainable mainstream investment also fails to support a doughnut framework. Lastly, while impact investment is theoretically committed to social and financial returns, …

Archaeological Predictive Meta-Modeling In Pre-Columbian Mexico, Peter Stamm

Electronic Theses and Dissertations

Archaeological Predictive Modeling stands firmly as an important tool for Archaeologists to predict undiscovered sites from civilizations all across the globe. While powerful, this methodology is not without its own set of qualms. Striking a balance between pure a data-driven approach while also observing leading expert theories can be a complicated task. Going further, deciding on the specific domain of features to emphasize or overlook can be a challenge within itself, as one misstep can drastically change the output of model, sometimes for the worst. In addition, creating models that can expose their reasoning process can be rather difficult to …

Exploring Inorganic And Organometallic Chemistry, W. Christopher Boyd Phd

MSL Academic Endeavors eBooks

This textbook, Exploring Inorganic and Organometallic Chemistry, covers the fundamentals of inorganic and organometallic chemistry for undergraduate and graduate students. Both theories of structure and bonding and reaction chemistry are covered, with applications to the catalysis of organic reactions, biochemistry, and drug design. A modular approach allows for basic materials of each chapter to be covered in an undergraduate course, with more advanced material suitable for M.S. and Ph.D. students."

Enhancing Recommendation Performance Via Stacking Ensemble And Synthetic Data Augmentation, Zahraa Yaareb Hani, Mohsin Hasan Hussein

Karbala International Journal of Modern Science

Recommendation systems are essential tools that primarily aim to help users navigate through a large volume of information. They simplify the decision-making process by suggesting relevant items based on users’ historical behaviour. However, their performance is often affected by common problems such as data sparsity. This work proposes a stacking-based ensemble recommendation system that integrates multiple machine learning models to enhance the model’s predictive performance. A new synthetic data augmentation technique is introduced to address the sparsity issue in the user–item rating matrix. This method uses the Naïve Bayes algorithm to predict additional ratings for each user. These are then …

Safety, Security, And… Beavers? An Engineer's Low-Tech Climate Strategy, Matt Mullins

University College: Environmental Policy and Management Capstones

Colorado’s headwaters are the source of drinking water to millions and are a key pillar of the state’s tourism industry. Consequently, they are some of the most vulnerable areas to natural disasters including wildfires, droughts and flooding. Climate change will increase the severity and frequency of these natural disasters. This project focuses on some of the most vulnerable areas from an economic and climate change perspective of Colorado’s headwaters including the Big Thompson River, and the Colorado River. These headwaters located in and around Estes Park, Grand Lake and Rocky Mountain National Park are currently severely impaired. Low-tech process-based restoration …

Creating An Iot User Centric Security And Privacy Label, Luke Joseph Gleba

Theses and Dissertations

Concerns for cybersecurity awareness and training in the public have been rising at astronomical rates over the past few years. People globally have entered a critical intersection with computers. Computers are being used in everyday life, and users do not have an easy way to understand their own cyber risk. Consumers deserve to know how secure their new Internet of Things (IoT) system is in a quick, efficient way before they purchase the device and while they own it. The following research looks to improve on ideas for the criteria and the development of a security label. Few prototypes currently …

Unraveling Hydration- And Temperature-Induced Structural Transformations Of Linear Poly(Ethylenimine) Using Raman Microscopy And Numerical Unmixing, Miharu Koh, Jay P. Kitt, Andrew D. Pendergast, Joel M. Harris, Shelley D. Minteer, Carol Korzeniewski

Chemistry Faculty Research & Creative Works

Linear poly(ethylenimine) (LPEI) is a hydration-sensitive polymer of interest for its breadth of application in areas that include medicine, energy, and materials science. To facilitate advances, this work demonstrates in situ Raman microscopy and self-modeling curve resolution (SMCR) analysis as an approach for investigating changes in LPEI structure during melting and crystallization under controlled heating and cooling conditions. With focus on neutral (freebase) LPEI, SMCR analysis of Raman data sets showed that the melting transition of anhydrous LPEI was well described by a pair of spectral vectors representing anhydrous crystalline and amorphous states. However, the transition from the melted to …