Creation Of The 2-Way Cross Laminated Timber Subroutine For Computing Dynamic Analysis Parameters, Joseph Freitas

Architectural Engineering

This document will describe the inputs, methodology, and outputs for the CLT 2-way subroutine for cases with and without openings recently added to a proprietary structural analysis software. For the sake of simplicity, most of the discussion will be focused on cases without openings from here on out, as the verification process for a novel method for cases with openings is still in progress. The CLT 2-way module calculates multi-point resistance functions for panels without openings by automating the procedure outlined in an industry-standard structural design manual. The subroutine also finds the appropriate dynamic response factors, kinetic load multipliers, mass, …

Cvj Torque Loss Dynamometer, Malaika Sriram, Coleman Brown, Zachary Norris, Logan Mast, Chris Mattis

Mechanical Engineering

This report details the design and results of a dynamometer testing rig built to measure torque losses in constant velocity joints (CVJs) for Cal Poly Racing's Baja SAE team. The rig spins a flywheel connected to articulable CVJs up to 800 RPM using a motor, then decouples it and measures the spin-down rate from 800 to 100 RPM to calculate angular acceleration and torque loss. The data collected from this dyno will be used to inform freewheel packaging for future Baja car development.

Elevating The Underrepresented And Marginalized Using Experiences In Stem (Lumens): A Stem Diversity And Inclusion Initiative, Robert Cobb Jr, Paula E. Faulkner, Deiadra Modlin, Obinna Chiekezi, Victoria Cobbold, Madison Beaudoin

Journal of Research Initiatives

The study offered a 5-week summer immersion program to address the shortage of students from underrepresented populations enrolling in degree programs and seeking careers in science, technology, engineering, and mathematics (STEM). The study also addressed the importance of offering summer immersion programs to close the achievement gap. A quantitative descriptive design was used to gather data from participants related to STEM lessons during the program. Gender, grade level, and race served as the demographic variables. The study included 21 secondary students in grades 9–12. Pre- and post-tests assessed participants' knowledge gain regarding STEM lessons. Data analyzed with SPSS version 29 …

A Unified Methodological Framework For Generating Digital Twins Of Multi Class Uncrewed Systems (Uxs), Sai Raghava Pathuri

Shelby Hall Graduate Research Forum Presentations

No abstract provided.

A Scalable Iteration Of The Horizon Simulation Framework Using Multithreading Techniques, Jason E. Beals

Master's Theses

The Horizon Simulation Framework (HSF) occupies a unique space in the modern aerospace modeling landscape, enabling flexible, modular modeling of mission-level agent behavior through an object-oriented, hierarchical design. HSF's hallmark breadth-first search scheduling algorithm explores a "multiverse" of possible mission execution pathways, enabling exhaustive evaluation of schedule combinations against user-defined heuristics.

As aerospace systems become increasingly complex, HSF faces critical challenges in establishing verifiable, deterministic behavior. The framework's core scheduling algorithm had not undergone systematic validation, leaving questions about temporal consistency, state management correctness, and reproducibility across different program executions. Furthermore, the exponential growth of schedule combinations creates computational bottlenecks …

Impact Of Femalized Kinesthetic Learning Model On African American Middle School Girls’ Career Interest, Mercy F. Fash, Andrea Ofori-Boadu

Journal of Global Education and Research

Engaging African American middle school girls in out-of-school-time (OST) Architecture, Engineering, and Construction (AEC) programs can boost interest in male-dominated AEC careers. This study adopts Lent’s Social Cognitive Career Theory to evaluate the impact of an OST feminized AEC kinesthetic learning model (fAEC-KLM) on the career interests of this demographic. Through a qualitative research approach, fourteen (14) African American middle school girls from Guilford County, NC, completed pre- and post-interviews on how fAEC-KLM interactions influenced their AEC career interests. Inductive thematic analysis was carried out on transcribed interviews. Results show that effective components, such as lectures on AEC, bridge construction …

Tourniquet Tester, Tyler Offerman, Thuy Nguyen, Ryan Ly, Cody Hoeptner

Biomedical Engineering

The Tourniquet Tester is a device intended to be used as a standardized testing device for tourniquets. Due to the lack of a standard testing device for testing tourniquets that can produce accurate and reproducible results, it is difficult to test the efficacy of tourniquets currently being sold on the market. A standard testing device that can produce consistent and reproducible results would allow tourniquet manufacturing companies to properly test the efficacy of their product and develop proper guidelines and directions for users of the product. Medical professionals and military personnel who may use tourniquets in their line of work …

Cpap Belly Prevent, Alek Corpuz, Emma Hoffman, Danielle Tran

Biomedical Engineering

Neonates with low gestation periods often face complications such as respiratory distress syndrome, or RDS, which increases risk for complications such as necrotizing enterocolitis. Use of bCPAP helps facilitate regular breathing, but can also force air into the stomach and consequently cause abdominal distention. Clinicians currently use an already inserted feeding tube to vent the accumulated air, however this interrupts the feeding process.

The PreVent device was developed to allow for simultaneous feeding and venting. The dual function minimizes interruptions to NICU nurse workflow and improves capability to relieve abdominal distention before the onset of adverse complications.

Newborn Spinal Needle, Allison Inouye, Kyle Morton, Evan Russell

Biomedical Engineering

Spinal punctures, or lumbar punctures, are important diagnostic procedures used to collect cerebrospinal fluid (CSF) to diagnose issues in the central nervous system (CNS) such as meningitis. While these procedures are routinely performed on adult and neonatal patients, the current tools are not optimized for use on newborns. Existing spinal needles are often modified versions of adult devices, leading to challenges in fluid collection efficiency, user ergonomics, and patient safety when used in neonatal care.

The Newborn Spinal Needle Project aims to design and develop a spinal needle specifically optimized for neonatal anatomy and clinical workflow. The goal is to …

State Of Charge Estimation Of Ev Secondary Battery Pack Using Hybrid Hedge Feedforward Feedback-Based Gated Recurrent Unit To Extend Lifespan, Md Ohirul Qays, Iftekhar Ahmad, Daryoush Habibi, Mohammad A.S. Masoum, Paul Moses

Research outputs 2022 to 2026

Accurate estimation of state of charge (SoC) and maintaining balanced charge levels across secondary battery cells are crucial in battery management systems (BMSs) to extend battery life while improving the performance and thermal stability of Li-ion batteries (LIBs) in electric vehicles (EVs). However, there are still underexplored challenges associated with circulating currents in electrochemical cells during continuous operation which can overheat battery packs, reducing their life span or result in dangerous thermal runaways. This paper investigates SoC estimation using various real-world charging and discharging profiles, along with charge-balancing strategies to enhance the longevity of parallel-connected Li-ion battery cells. A newly …

Emerging Design Paradigms And Microstructural Innovations In Refractory High-Entropy Alloys: A Critical Review, Deyu A. Jiang, Lai Chang Zhang, Kuaishe Wang, Mahmoud Ebrahimi, Wen Wang, Liqiang Wang, Weijie Lu, Di Zhang

Research outputs 2022 to 2026

Refractory high-entropy alloys (RHEAs) are being developed to meet mechanical, thermal and chemical requirements that exceed what current super-alloys can withstand. This review explains how composition design, processing routes and the resulting microstructures now combine to realize that potential. We first link phase selection in BCC-, FCC- and dual-phase RHEAs to atomic-size mismatch, mixing enthalpy and valence-electron concentration, and compare manufacturing paths ranging from arc melting to powder metallurgy, additive manufacturing and vapor deposition, showing how each reshapes grain structure and defect chemistry to improve high-temperature strength, corrosion resistance and irradiation tolerance. Computation-led tools—density-functional theory, calculation of phase diagrams and …

Microstructural Evolution Of Zirconium Diboride Irradiated With 5–10 Mev Au Ions At Room Temperature And 570 °C, Narrie Loftus, José Olivares, Miguel Crespillo, Esther Enríquez Pérez, Jeremy Watts, Eric W. Bohannan, Gregory Hilmas, William Fahrenholtz, Joseph Graham

Materials Science and Engineering Faculty Research & Creative Works

Zirconium diboride was irradiated with 5 MeV Au^2 + , 7 MeV Au⁴⁺ and 10 MeV Au³⁺ ions at room temperature and 570 °C to doses from 1 to 8 displacements per atom (dpa). Grazing incidence X-ray diffraction (GIXRD) analysis revealed no secondary phase formation. Rietveld analysis of the GIXRD data indicated an accumulation of microstrain at low dpa and room temperature. Dislocations observed in transmission electron microscopy (TEM) cross-sections are likely the main contributor to the microstrain. High dpa and high-temperature samples exhibit lower lattice distortion than lower dpa samples, suggesting the presence of enhanced defect …

Efficient Active Training For Deep Lidar Odometry, Beibei Zhou, Zhiyuan Zhang, Zhenbo Song, Jianhui Guo, Hui Kong

Research Collection School Of Computing and Information Systems

Robust and efficient deep LiDAR odometry models are crucial for accurate localization and 3D reconstruction, but typically require extensive and diverse training data to adapt to diverse environments, leading to inefficiencies. To tackle this, we introduce an active training framework designed to selectively extract training data from diverse environments, thereby reducing the training load and enhancing model generalization. Our framework is based on two key strategies: Initial Training Set Selection (ITSS) and Active Incremental Selection (AIS). ITSS begins by breaking down motion sequences from general weather into nodes and edges for detailed trajectory analysis, prioritizing diverse sequences to form a …

A Knowledge Transfer-Based Membrane Evolutionary Algorithm For Solving Large-Scale Sorted Waste Collection Problem With Timeliness, Wenxue Zhang, Boquan Gao, Aldy Gunawan, Yunyun Niu, Jianhua Xiao

Research Collection School Of Computing and Information Systems

The sorted collection of municipal solid waste has emerged as an effective waste management strategy due to varying timeliness requirements across different waste types, giving rise to the critical research challenge of timeliness-based waste collection. While existing algorithms primarily focus on small-scale versions of this problem, solving large-scale timeliness-based waste collection problems remains particularly challenging. To tackle this issue, this paper proposes a knowledge transfer-based membrane evolutionary algorithm. Specifically, the original problem and simplified problem are constructed in different membranes respectively, and the knowledge transfer learning mechanism is incorporated into the membrane evolutionary algorithm, enabling effective information exchange between the …

Single-Valued, Double-Valued, Triple-Valued, Quadruple-Valued, And Quintuple-Valued Neutrosophic Graph, Takaaki Fujita, Arif Mehmood, Arkan A. Ghaib

Neutrosophic Systems with Applications

Concepts such as fuzzy sets, neutrosophic sets, rough sets, and plithogenic sets have been extensively studied as formal tools for modeling uncertainty, and they have found broad applications across many disciplines. A Double-Valued Neutrosophic Set (DVNS) extends the classical neutrosophic framework by splitting indeterminacy into two distinct components: one leaning toward truth and the other leaning toward falsity. In recent years, further refinements—namely Triple-Valued, Quadruple-Valued, and Quintuple-Valued Neutrosophic Sets—have also been introduced and investigated. These uncertainty models have naturally been lifted to graph-theoretic settings, where vertices and edges represent entities and relationships under ambiguity. Although fuzzy graphs and neutrosophic graphs …

N-Dqn: Neutrosophic Deep Q-Network For Uncertainty-Aware Forecasting And Decision Optimization, Rania Lutfi

Neutrosophic Systems with Applications

Uncertainty remains a critical challenge in dynamic spatiotemporal forecasting. This study proposes the Neutrosophic Deep Q-Network (N-DQN), a framework that integrates neutrosophic logic with deep reinforcement learning to enhance decision optimization under uncertainty. Features are modeled through truth, indeterminacy, and falsity membership functions, enabling robust handling of ambiguous data. The framework incorporates attention-guided preprocessing and horizon-aware optimization to adapt predictions across short- and long-term intervals. Experiments on benchmark traffic datasets (METR-LA and PEMS-BAY) demonstrate improved forecasting accuracy and reduced error rates compared with established baselines. The results highlight the scalability and resilience of N-DQN, positioning it as a promising approach …

Evaluating Domains' Trustworthiness Based On Uncertainty-Driven Methodologies In The Era Of Sixth Generation, Zekra Sakr, Mona Mohamed

Neutrosophic Systems with Applications

The onset of today's innovations pledges to have a beneficial influence on contemporary civilization in an era of intelligent revolutions, setting a precedent for unrivaled efficiency, creativity, and connectedness. The integration between these technologies contributes to the mutual benefit of each one, wherein this relation is a so-called ``reciprocal partnership''. For instance, the sixth generation (6G) wireless networks permit blockchain nodes to coordinate huge volumes of transaction data in real-time. On the other hand, blockchain is considered a secure valve because spectrum sharing can be automated with blockchain and smart contracts. Accordingly, analyzing and evaluating the contribution of these technologies …

Neutrosophic Finsler–Cohomological Framework For Engineering Systems Under Uncertainty, Mona Gharib, Ghulam Muhammad, Muhammad Idrees, Zeeshan Gul

Neutrosophic Systems with Applications

This paper introduces a novel mathematical framework that combines Neutrosophic Finsler Geometry with Neutrosophic Cohomology for evaluating the performance of Brushless Direct Current (BLDC) motors under uncertain and indeterminate operating conditions. Classical motor performance models typically assume precise measurements of torque, current, and efficiency; however, in real-world settings, these parameters are often affected by noise, incomplete information, and conflicting observations. By embedding motor operating states into a neutrosophic Finsler space, the proposed approach captures variations not only in magnitude but also in direction, uncertainty, and conflict of performance metrics. In addition, neutrosophic Cohomology is employed to characterize global invariants of …

An Uncertainty-Aware Entropy-Oreste Framework For Big Data Platform Selection In Complex Multi-Sector Environments, Ahmed M. Ali, Ibrahim Alrashdi, Karam M. Sallam

Neutrosophic Systems with Applications

The increasing reliance on Big Data platforms across various industries has necessitated the development of systematic decision-support frameworks to guide their evaluation and selection. Given the diversity of available platforms, each offering different capabilities, scalability, and computational efficiency, choosing the optimal solution remains a complex challenge. This research proposes a novel analytical framework that integrates Spherical Fuzzy Sets (SFS) with the Entropy and ORESTE methods to address uncertainty and enhance the accuracy and robustness of Big Data platform evaluation. This hybrid integration, not previously applied to Big Data platform selection, enables objective criteria weighting through the Entropy method and comprehensive …

An Electrochemiluminescence-Based Arsenic (Iii) Sensor Using Luminol On Screen-Printed Gold Electrodes, Harmesa Harmesa, Isnaini Rahmawati, Andrea Fiorani, Yasuaki Einaga, Eny Kusrini, A’An Johan Wahyudi, Asep Saefumillah, Tribidasari A Ivandini

Journal of Electrochemistry

Electrochemiluminescence (ECL) of luminol has been studied on a screen-printed gold electrode for a simple and sensitive detection of arsenic ions (As(III)). Cyclic voltammetry (CV) was applied as the proposed technique to study luminol’s electrochemical behavior and to evaluate the arsenic’s effect in the ECL system, while hydrogen peroxide (H₂O₂) served as a co-reactant to enhance luminol’s light emission under alkaline conditions. To achieve optimal electrode performance, key parameters including pH, scan rate, and the concentrations of H₂O₂ and luminol were carefully optimized. The presence of As(III) induced a quenching effect on the …

Current Research Progress On Electrode Materials For All-Vanadium Redox Flow Batteries, Wen-Qi Wang, Jie Jin, Li-Min Wang, Xin-Yue Liu, Tao Cheng, Yong Hou, Han Xue, Zhi-Yu Wang, Bo Liu, Jia-Bao Liu, Xu-Bin Lu

Journal of Electrochemistry

The redox active species in all-vanadium redox flow batteries (VRFBs) reside in the electrolyte, while the heterogeneous reactions occur on the electrode surface; the electrode is therefore the decisive platform for dynamic adsorption, electron transfer, and ion conversion, especially for the VO²⁺/VO₂⁺ and V²⁺/V³⁺ couples. One of the major challenges for VRFBs is the slow charge transfer in VO²⁺/VO₂⁺ and V²⁺/V³⁺ reactions, mainly caused by poor catalytic performance of electrodes and weak adhesion of catalysts to electrodes. This review focuses on the key challenges and recent …

Triphenylmethane-Derived Levelers For High-Speed Redistribution Layer Copper Electroplating Of Tailored Surface Morphologies, Zi-Hao Song, Wei-Bin Wang, Xiao-Hui Liu, Xiao-Min Han, Yi Zhou, Rui Huang, Yan-Xia Jiang, Zhe Li, Xiao-Wei Liu, Mei-Ling Xiao, Hong-Gang Liao, Wei-Lin Xu, Rong Sun

Journal of Electrochemistry

Redistribution Layer (RDL), composed of layered dielectrics and electroplated copper materials, is a basic structure to rearrange numerous I/O pads on the chip surface in wafer-level advanced packaging. As the key chemicals in electrolyte baths, electroplating additives have undergone continuous development to meet the industrial needs for high-speed and fine-line/fine-pitch applications. Meanwhile, the intricate relationships between additive chemical structures and electroplated copper properties are yet to be well understood. In this work, a pair of triphenylmethane-based dye molecules, i.e., gentian violet (GV) and methyl green (MG), was comparatively investigated as levelers for high-speed RDL copper electroplating. Compared to GV, significantly …

Control Of Pore Nucleation And Rearrangement Kinetics During Aluminium Anodizing In Phosphoric Acid Electrolyte, Ilya V. Roslyakov, Nikita A. Shirin, Dmitry M. Tsymbarenko, Sergei N. Pavlov, Sergey E. Kushnir, Nikolay V. Lyskov, Kirill S. Napolskii

Journal of Electrochemistry

Anodic aluminium oxide (AAO) porous films with an interpore distance of several hundred nanometers are of great interest due to their unique interaction with visible and near-infrared light, and high thermal stability up to 1500 °C. These porous films are prepared by aluminium anodizing at high voltages in weak acids, leading to a slow kinetics of initial stages of porous structure formation. Here, we propose an approach to accelerate AAO formation in electrolytes based on weak acids such as phosphoric acid. Aluminium foils, pre-patterned using first anodizing under different conditions and subsequent selective dissolution of a sacrificial AAO layer, were …

Evaluating Regularized Logistic Regression And K-Nn On Mnist Under Increasing Random Missingness, Daniel Markwei

Data Science and Data Mining

This paper investigates the effect of random missingness on the performance of regularized multinomial logistic regression and the k-nearest neighbors (k-NN) classifier for handwritten digit recognition on the MNIST dataset. In particular, we study L1-regularized (LASSO) logistic regression and L2-regularized (Ridge) logistic regression alongside k-NN. Varying percentages of random missingness were introduced into the original dataset, and each model was evaluated in terms of its classification performance. The results show that random missingness degrades the performance of all three classifiers. Overall, k-NN consistently achieves higher accuracy than both L1- and L2-regularized logistic regression across all missingness levels; however, its performance …

Chapter 6 - Robust Stabilization Ellipsoidal Design For Normal And Contingency Operated Power Systems Using Markov Jump, Ehab Bayoumi

Mechanical Engineering

No abstract provided.

Renewable Microgrid Frequency Regulation Using Active Disturbance Rejection Control And Elephant Herding Optimization, Ehab Bayoumi

Mechanical Engineering

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Renewable Microgrid Frequency Regulation Using Active Disturbance Rejection Control and Elephant Herding Optimization

Machine Learning–Driven Prediction Of Dementia From Mri And Clinical Features: A Comparative Analysis Of Ensemble And Baseline Models, Sarah Raad Hameed, Zainab Muhannad Nahid, Rawan Ahmed Abdulmahdi

AUIQ Technical Engineering Science

Early detection of dementia remains a pressing challenge in clinical neuroscience, as delayed diagnosis limits therapeutic impact and healthcare planning. Leveraging the Open Access Series of Imaging Studies (OASIS) cross-sectional dataset of 436 participants, this study developed a robust machine learning pipeline integrating sociodemographic, clinical, and neuroimaging-derived features. Preprocessing included removal of highly sparse variables (Delay), median imputation of partially missing but clinically essential measures (SES, MMSE, CDR, Educ), Winsorization of extreme values, and skewness correction. The target Clinical Dementia Rating (CDR) was binarized (0 = no dementia, ≥ 0.5 = dementia) to align with clinically actionable screening. Categorical features …

Improving The Uv Resistance And Service Life Of Polystyrene-Based Building Materials Via Low-Concentration Metal Complex Modifiers, Dina S. Ahmed, Marwa Fadhil, Raghda Alsayed, Mohammed Kadhom, Muna Bufaroosha, Amani Husain, Thura Alyasiri, Hanan Ibraheem, Alaa A. Rashad, Bassam M. Abood, Riyadh Noaman, Ahmed Ahmed, Ruqaya Raad, Ala'a Shaker, Abeer Erfan, Hassan Hashim, Saba Hamza, Rahimi M. Yusop, Emad Yousif

AUIQ Technical Engineering Science

PS is highly susceptible to UV-induced photodegradation, which results in deterioration of its mechanical and aesthetic properties. In the present study, four novel and highly aromatic levofloxacin metal complexes, synthesized from Zn, Sn, Cu, and Ni, were investigated as low-concentration (0.5% w/w) photostabilizer additives for PS. Composite films were prepared by solution casting and subjected to accelerated UV irradiation up to 300 hours. Photostabilizing performance was systematically followed by monitoring changes in the carbonyl index via FT-IR spectroscopy, mass loss measurements, and surface morphology analyses using SEM and AFM. Results showed that all levofloxacin metal complexes had a pronounced inhibition …

Advances In Real-Time American Sign Language Recognition System Using Deep Learning Techniques For Enhanced Accessibility, Bader Alsharif

Electronic Theses and Dissertations 2020 - Present

Advancements in technology have significantly contributed to the development of innovative tools aimed at improving communication and accessibility for individuals with hearing impairments. This dissertation explores various machine learning and deep learning techniques for recognizing American Sign Language (ASL) gestures, focusing on enhancing accessibility and bridging the communication gap between hearing-impaired and hearing individuals. Traditional machine learning models, such as Random Forest, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN), alongside deep learning architectures like AlexNet, ResNet-50, EfficientNet, ConvNeXt, and VisionTransformer, were investigated for their effectiveness. Experiments conducted on an extensive dataset of 87,000 ASL gesture images revealed exceptional recognition …

Rapid Single-Cell Measurement Of Transient Transmembrane Water Flow Under Osmotic Gradient, Hong Jiang, Jinnawat Jongkhumkrong, Y. J. Chao, Qian Wang, Guiren Wang

Faculty Publications

Aquaporins (AQPs) are critical for transmembrane water transport in response to osmotic gradients, but their gating and regulatory mechanisms remain poorly understood. A central challenge is the lack of methods to measure water flow across AQPs from individual cells with the spatiotemporal resolution and sensitivity equivalent to patch-clamp recordings of ion fluxes—a limitation stemming from the electrically silent nature of water flow. Here, we present a novel optical technique—Flow-Induced Fluorescence Increase Velocimetry (FIFIV) based on Laser-Induced Fluorescence Photobleaching Anemometry (LIFPA)—that enables direct, real-time monitoring of cytoplasmic flow induced by transmembrane water transport under osmotic pressure gradients. Using small molecular fluorescent …