Sea Level Trends Near Critical Levees In The Sacramento-San Joaquin Delta, Reyna Sanchez Gomez

Master's Theses

The Sacramento-San Joaquin Delta (Delta) levee system faces threats from rising sea levels, land subsidence, and deteriorating levees, all of which pose risks to California’s primary surface water supply. The purpose of this thesis is to assess relative sea level rise (RSLR) and estimate the spatially varying factors that influence water levels in the Delta. This thesis extends the work of Baranes et al. (2023) to the period 1975-2022 by appending recently acquired water-level records (1975-1982) to modern datasets (1982-2022) from 10 water-level stations. These water level datasets were digitized, quality-assured, and referenced to a common geodetic datum, as described …

Demystifying Hardware Formal Verification For Undergraduate Education: A Risc-V Processor Case Study With Coursework Implementation, Riley A. Peters

Master's Theses

Hardware verification engineers apply formal methods to prove that a digital device always behaves according to its specification. This differs from traditional functional verification, in which engineers establish correctness by repeatedly sending test inputs to the device and comparing the outputs against a reference model. With the growing complexity of integrated circuits, the demand for digital verification engineers with formal methods experience has continued to increase. However, California Polytechnic State University: San Luis Obispo's current curriculum lacks dedicated material to prepare students for these roles.

This thesis seeks to address the lack of formal methods material through two efforts. First, …

Cmbuilder Introduction Tutorial, Hannah G. Benner

Construction Management

This paper details the process and deliverables for a introductory-level tutorial for the program, cmBuilder. Recognizing the software’s growing relevance in the construction industry and its ease of-use, this student tutorial project aims to provide early exposure to the program for first- and second-year Cal Poly Construction Management students. There is currently no existing course or module at Cal Poly that covers an introduction to the basics of cmBuilder, so the goal of this course module is to bridge that gap. The deliverable is a course module for the Fundamentals of Virtual Design and Construction class, containing two tutorial videos …

Ƒ(Cell): Software For Reproducible Analysis Of Optoretinograms, Robert F. Cooper, Mina Gaffney, Brea D. Brennan, Niko Rios

Biomedical Engineering Faculty Research and Publications

Purpose: There has been a marked increase in use of a noninvasive functional imaging technique called optoretinography (ORG). As more groups use ORGs, it is crucial to have a consistent methodology, and understand what analysis parameters influence repeatability. In this work, we present an open-source software library called ƒ(Cell) designed to facilitate reproducible and repeatable analyses of ORG data.

Methods: We designed ƒ(Cell) as a Python software library that can co-register and analyze ORG datasets, while also enabling process auditing. To validate the software, we used our previously obtained normative optoretinography datasets as well as datasets from six …

Fixel-Based Analysis Of Pretreatment Mri Identifies White Matter Abnormalities In Pediatric Anti-Nmdar Encephalitis, Daniel Ackom, Janine Taitt-Tap, Scott A. Beardsley, Ricardo Vega, Alyssa Jobe, Andrew J. D. Crow, Brian Schmit, Lileth Mondok, Pradeep Javarayee

Biomedical Engineering Faculty Research and Publications

Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is an autoimmune disorder in which conventional MRI often appears normal, leading to clinical–radiologic dissociation and hindering early diagnosis and monitoring. We retrospectively studied five pediatric patients with anti-NMDAR encephalitis and compared their pretreatment diffusion MRI to age- and sex-matched controls. Using fixel-based analysis (FBA), we quantified tract-specific white matter abnormalities at the individual level. All patients showed significantly reduced fiber density and cross-section, with patterns ranging from focal to widespread involvement. In two patients, FBA abnormalities corresponded to seizure lateralization on EEG despite normal MRI, emphasizing FBA's added value in detecting seizure-concordant injury. One patient, …

Improving Interlimb Coordination And Paretic Limb Use After Stroke Using A Novel Robotic Split-Crank Pedaling Device: A Cross-Sectional Study, Tom S. Ruopp, Brian Schmit, Sheila Schindler-Ivens

Biomedical Engineering Faculty Research and Publications

Background. Many stroke survivors cannot walk effectively, even after rehabilitation. Causes include impaired muscle activation, poor interlimb coordination, and limited restorative interventions. To address this, we developed CUped (pronounced “cupid”), a motorized split-crank pedaling device designed to compel use of the paretic limb and retrain interlimb coordination. We examined its within-session effects, comparing three proportional control schemes—assist (A), resist (R), and assist plus resist (A + R)—to identify which best promotes recovery-related movement.

Methods. Nineteen individuals with stroke and eleven controls pedaled in 5-min bouts, one per control scheme. Each bout included pre-test, exposure, and post-test periods. Participants were instructed …

Navigation Beyond Wayfinding: Robots Collaborating With Visually Impaired Users For Environmental Interactions, Shaojun Cai, Nuwan Janaka, Ashwin Ram, Janidu Shehan, Yingjia Wan, Kotaro Hara, David Hsu

Research Collection School Of Computing and Information Systems

Robotic guidance systems have shown promise in supporting blind and visually impaired (BVI) individuals with wayfinding and obstacle avoidance. However, most existing systems assume a clear path and do not support a critical aspect of navigation—environmental interactions that require manipulating objects to enable movement. These interactions are challenging for a human–robot pair because they demand (i) precise localization and manipulation of interaction targets (e.g., pressing elevator buttons) and (ii) dynamic coordination between the user’s and robot’s movements (e.g., pulling out a chair to sit). We present a collaborative human–robot approach that combines our robotic guide dog’s precise sensing and localization …

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 …

Development Of A Framework For Identifying Asphalt Pavement Cracking Distresses Using Machine Learning, Dingxin Cheng

Mineta Transportation Institute

Asphalt pavement cracking is one of the most critical distresses affecting pavement performance and service life. When pavement deteriorates, it can lead to safety hazards, higher vehicle maintenance costs, and expensive repairs for cities and states—making early detection essential for everyone who relies on the roadway system. To address this challenge, the research team developed a prototype cracking identification system that integrates a customized machine learning model with computer vision algorithms. High-resolution images collected from drones or ground-based cameras are processed within the system to automatically detect and classify major cracking types. The core of the framework utilizes the You …

Informing Consumer Product Sound Quality Analysis Using Generative Adversarial Networks, Daniel Jeffery Lesko, Vinh Nguyen

Michigan Tech Publications

Sound quality attributes have been proven important predictors of customer satisfaction with consumer goods and appliances. The results of several studies indicate that level-based, tonal, and temporal aspects of sound influence the perceived quality of consumer products. The current state-of-the-art of inferring consumer satisfaction with sound attributes is based on the jury test methodology. However, product engineers often find the models generated from these studies incomplete, resulting in products that fail to meet consumer expectations. Therefore, this study aims to utilize generative data-driven approaches to create a range of acceptable sound quality attributes given consumer satisfaction requirements. A baseline sound …

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 …

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 …

Optimized Deep Learning Framework With H2o For Lung Cancer Prediction, Walaa Hassan Ibrahim, Mohamed S. Saraya, Sally M. Elghamrawy, Ali I. Eldesouky

Mansoura Engineering Journal

The automatic diagnosis of lung cancer using chest X-ray (CXR) images has significantly advanced with progress in computing, machine learning, and deep learning. However, detecting lesions and nodules remains challenging due to CXR limitations. Early lung cancer detection is critical for successful treatment, but current AI algorithms often rely on large annotated datasets, which are not always available. To address this, a novel multi-classification deep learning framework is proposed that combines CXR and CT images. This approach leverages the detailed feature detection capabilities of CT scans alongside the complementary views from CXRs, improving early-stage lung cancer detection and classification precision. …

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 …

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 …

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 …

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 …

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 …

Physiological And Neurological Impact Of Biophilic Design In Architectural Design Studios: A Case Study Of King Salman International University, Islam Ashraf Mohamed, Alaa El-Eishy, Marwa A. Soliman

Mansoura Engineering Journal

Biophilic design integrates nature into architecture to enrich sensory and cognitive experiences, enhance psychological well-being, and improve performance. This study explores its physiological and neural effects in design studios at King Salman International University through an experimental methodology combining theoretical review and lab-based investigation. Tools included electrocardiography (ECG) for heart rate variability (HRV), eye-tracking for visual attention, and the Self-Assessment Manikin (SAM) for emotional evaluation. Nine participants viewed five digital architectural scenes with varying biophilic integration. Direct nature connections elicited greater visual attention, pleasure, and perceived control, indicating higher engagement and relaxation.  A scene lacking biophilic elements led to reduced …

Balancing Carbon Emissions In Façade Of Residential Buildings Through Bim-Lca: A Comparative Analysis Of Insulation Materials, Mehran Alipour

Journal of Sustainable Construction Materials and Technologies

Thermal insulation materials play a significant role in balancing buildings' total carbon emissions. Balancing involves applying the optimized amount of insulation materials to avoid excessive emissions in either the operational or embodied phases. Due to the wide range of insulation materials and variations of their specifications, it is vital to analyse them individually. The focus of this study is to optimize the amount of insulation materials in the facades of Residential Buildings (RB) to balance the amount of Operational Carbon Emissions (OCE) and Embodied Carbon Emissions (ECE) by applying the Building Information Modelling-Life Cycle Assessment (BIM-LCA) integration method. The Life …

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 …

A Pathfinder Lunar Construction Mission Concept Using Regolith Filled Bags, Cameron S. Dickinson, Fu Nan Shi, Ketan Vasudeva, Rudranarayan M. Mukherjee, Joshua Blanchard, Steve Dubrule, Paul Van Susante, Et Al.

Michigan Tech Publications

Two challenges that have a permanent presence on the Moon are solar and cosmic radiation, as well as the large surface temperature variation between lunar day and night. To address these problems, we propose a lunar pathfinder mission concept that uses robotic systems to investigate whether regolith-filled bags can be used as a versatile construction medium for lunar surface structures and sensors to obtain data on the lunar regolith. The primary objectives of this mission are as follows: evaluation of the surface and subsurface regolith as fill material, lunar excavation using a robotic manipulator equipped with a bucket scoop, bag …

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

Design And Control Of A Multi-Modal Electromagnetic Floor Array For Foot-Based Human Locomotion And Stabilization In Microgravity, Aryan Anand

Electrical Engineering Theses

Long-duration living and working in microgravity creates everyday mobility problems such as drifting, loss of stable footing, higher effort to move, and difficulty doing routine tasks safely. Many solutions have been proposed in literature, including handrails, restraint systems, and concepts for artificial gravity using rotation. Artificial gravity could improve comfort, but it is complex to build and operate for large spacecraft, especially when future missions may include not only trained astronauts but also common people. With companies like SpaceX pushing toward large-scale travel and long-term settlement goals, there is a need for simpler mobility support technologies that can work inside …

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

Mechanical Engineering

No abstract provided.

Nanoscale Opportunities In Extracellular Matrix Mimicry, L. Andrew Lyon, Abbygail Caine, Elif Narbay, E. Daniel Cárdenas-Vásquez

Engineering Faculty Articles and Research

We present an overview of the composition, function, energetics, and dynamics of extracellular matrix and its relation to nanoscale structures and phenomena. These concepts are then related to the development of synthetic and biosynthetic materials that aim to mimic the extracellular matrix for regenerative medicine and tissue engineering technologies. Notable successes and advancements towards the goal of biomimicry are outlined, while remaining challenges and knowledge gaps towards that goal are highlighted. Finally, we frame the remaining challenges in the field in terms of nanoscience-related research opportunities that if solved, might prove to be transformative steps forward in the discipline.

Rapid-Response Reconnaissance Architecture Design For Planetary Defense With Nested Trajectory Optimization, Adam P. Wilmer, Justin A. Atchison, Marcus J. Holzinger, Robert A. Bettinger

Faculty Publications

Reconnaissance is a vital component in a comprehensive planetary defense mitigation strategy—aimed at preventing or reducing the impact threat posed by celestial objects on close-approach trajectories to Earth. It provides decision-makers with critical information on the physical and orbital properties of a near-Earth object (NEO), enabling better assessment of size, composition, and trajectory. This study investigates how to optimally pre-position a fleet of reconnaissance spacecraft prior to the discovery of a specific hazardous NEO. It evaluates response timelines and mission success rates for combinations of spacecraft launched from Earth and those maneuvering from pre-deployed locations within the Sun-Earth system. A …

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 …