Physical Sciences and Mathematics Commons^™

Revised Draft Final Bpsou Unreclaimed Sites: Ur-05 Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Pm2.5 Forecasting At U.S. Embassies And Consulates Worldwide Using Nasa Model Powered By Machine Learning, Junhyeon Seo, Alqamah Sayeed, Seohui Park, John Kerekes, Stephanie Christel, Mary Tran, Pawan Gupta

Articles

Air quality forecasting is crucial for public health, especially in rural, suburban, and developing areas lacking reliable monitoring data. Hybrid monitoring (surface, satellite, and models) offers a scalable, cost‐ effective solution for tracking pollution and trends. This work presents a machine learning model that integrates ground measurements with global model outputs assimilating satellite observations to forecast air quality. Ground measurements of fine particulate matter (PM2.5) from over 60 U.S. embassies and consulates were used to calibrate global model outputs for local air quality forecasting. Multi‐channel input data was prepared using the Goddard Earth Observing System forward processing for meteorology and …

A Neutrosophic And Q-Rung Orthopair Fuzzy Sets Approach For Desertification Susceptibility Mapping: A Case Study In Matrouh, Egypt, Nabil M. Abdelaziz, Khalid A. Eldrandaly, Amira M. Fawzy, Gehan A. Fouad, Safa Al-Saeed

Neutrosophic Systems with Applications

This study introduces an innovative approach to desertification susceptibility mapping by integrating q-rung orthopair fuzzy sets (Q-ROFS) with a neutrosophic environment. Conducted in Matrouh, Egypt, the research quantifies desertification risk through advanced modeling techniques that address uncertainty and non-linearity in environmental data. The Q-ROFS framework enhances risk prediction by capturing complex relationships among desertification indicators. Neutrosophic logic, meanwhile, effectively addresses imprecision and ambiguity. The resulting susceptibility map clearly distinguishes between vulnerable and non-vulnerable regions, offering valuable guidance for policymakers and planners. The analysis revealed that approximately 79.98% of the study area falls under moderate susceptibility, 14.27% under high susceptibility, and …

Fused-Silica Microelectromechanical Systems For Relative Gravimetry, Ethan Doerstling

Theses and Dissertations

Gravimeters are devices that measure gravitational acceleration which can be used by the United States Air Force (USAF) in the areas of navigation and remote sensing. Fused-silica microelectromechanical systems (MEMS) devices offer capabilities to make inexpensive relative gravimeters with higher thermal stability than common silicon devices while maintaining good gravitational sensitivity. The fused-silica devices in this research were designed, simulated, fabricated, and tested to observe their performance as gravimeters. The devices exhibit properties of highly sensitive accelerometers but the current designs do not qualify as gravimeters. This study provides information to improve the sensitivity and stability of these fused-silica MEMS …

Enabling Automatic Solar Pv Array Identification Using Big Satellite Imagery, Qi Li, Keyang Yu, Carson Snow, Dong Chen

Computer Science Faculty Research and Publications

Recently, there has been a growing interest in automatically collecting distributed solar photovoltaic (PV) installation information in smart grid systems, including the quantity and locations of solar PV deployments, as well as their profiling information across a given geospatial region. Most recent approaches are still suffering low detection accuracy due to insufficient sample and principal feature learning when building their models and also separation of rooftop object segmentation and identification during their detection processes. In addition, they cannot report accurate multi-deployment results. To address these problems, we design a new system-SolarDetector+, which can automatically and accurately detect and profile distributed …

Impact Of Seed Moisture And Temperature On Hemp Seed Germination, Paul Cockson, Andrea Webb, Natalia Martinez-Ochoa, Lindsey Moffitt, Robert Pearce, Manohar Chakrabarti

School of Integrative Biological & Chemical Sciences Faculty Publications

Germination rates of commercial lots of hemp have been highly variable, resulting in poor stand establishment. Germination rates in some seed lots have decreased by 50% after only 1 year of storage. The objective of this trial was to investigate the impact of seed storage conditions on seed germination over time. Industrial hemp (IH) seeds (cv. NWG2730) were harvested from the field. The seeds were cleaned, sorted, and dried to specific moisture contents (MC) 6%, 8%, 10%, and 14%. Seeds were subdivided, placed in hermetically sealed packets, and stored at temperatures of −20°C, 4°C, 10°C, or 21°C for 3, 6, …

City & Town Perspectives On Water Management In Utah: Descriptive Report Of Survey Findings, Bailey M. Holdaway, Courtney G. Flint

Environment and Society Student Research

In an effort to better understand the water management practices of municipalities in Utah, surveys were conducted with city and town representatives. These surveys aimed to capture insights across a variety of key topics related to water resource management. A brief overview of survey methods is provided followed by findings organized by question topic.

Nonlinear Physics Of Parity-Broken Fluids, Sudheesh Srivastava

Dissertations, Theses, and Capstone Projects

This thesis explores parity-breaking mechanisms, nonlinear wave phenomena, and localization transitions across different physical contexts. First, we examine modulation instability in parity-breaking systems, deriving modified nonlinear Schr¨odinger equations that reveal direction dependent instabilities. Next, we investigate wave turbulence, demonstrating numerically that parity-breaking dispersion significantly alters turbulent cascades and modifies their statistical properties. Lastly, we analyze localization in quasiperiodic tight- binding model based on polariton condensate lattice. Collectively, these studies illustrate the universal role of symmetries and nonlinear interactions in shaping macroscopic behaviors, facilitating interdisciplinary insights.

A Digital Dive: Redesigning The Cabrillo High School Aquarium Website, Jacob V. Cacho

Graphic Communication

Tucked away on the Central Coast in Lompoc, you’ll find the Cabrillo High School (CHS) Aquarium. Started in 1986, the CHS Aquarium is the only high school aquarium of its kind in the nation run entirely by high school students. This 10,000+ square foot aquarium serves an underserved community at a Title I school, where students manage all aspects of animal care, nutrition, breeding, educational curriculum development, and visitor tours.

This program is truly one-of-a-kind and deserves the spotlight for just how unique it is. As a CHS graduate, I felt the current website lacked in many areas and could …

Surface Temperature Analysis Report: Kellogg Creek Restoration & Community Enhancement Project, Dalton Palin

University Honors Theses

Kellogg Creek is located in the Kellogg-Mt Scott watershed which flows through downtown Milwaukie, Oregon into the Willamette River. At the confluence of Kellogg Creek and the Willamette River is the Kellogg Dam. This dam impedes salmon, steelhead, and lamprey movement up Kellogg Creek, which is known historically as a salmon and steelhead rearing and migrating habitat. A plan to remove Kellogg Dam and restore 14 acres of Kellogg Creek is in progress by North Clackamas Watersheds Council (NCWC) and partners. Water temperature studies in Kellogg Creek have been conducted for the past several years, this report analyzed thermal surface …

An Exposition Of "Probabilistic Polynomials And Hamming Nearest Neighbors", Vivek Srirama

University Honors Theses

This paper is an exposition of the paper Probabilistic Polynomials and Hamming Nearest Neighbors by Josh Alman and Ryan Williams. It presents the findings of this paper in a more accessible format for Computer Science students earlier in their career who may not be as familiar with Computational Theory and its concepts as their PhD counterparts are. The paper assumes that the reader has a basic understanding of Algorithms and Complexity, typically obtained in an introductory level Algorithms course.

The paper by Alman and Williams analyzes a specific problem known as the Hamming Nearest Neighbor problem. All known solutions for …

Raising The Roof For All: Integrating Companion Planting Ecology, Policy And Community In Portland's Green Roof Future, Zoe Edelman

University Honors Theses

Green roofs, or ecoroofs, provide environmental and social benefits in urban areas. Ecoroofs manage stormwater by absorbing rainfall, reducing rooftop temperatures, supporting pollinators, and offering green space in densely developed cities. Portland, Oregon has adopted progressive policies that encourage green roof installation through incentives and building requirements. However, many ecoroofs in the city are underperforming due to poor maintenance, low public awareness, and limited access to rooftop spaces. This thesis explores whether companion planting can improve the ecological performance and long- term viability of extensive green roofs. A rooftop experiment at Portland State University tested the growth of radishes, with …

Rebuilding Consent: Fear, Trust, And The Future Of Nuclear Energy, Austin Dunham

University Honors Theses

This thesis traces the cultural and emotional roots of nuclear fear in the United States and explores how those inherited anxieties continue to shape public resistance to nuclear energy in the face of a worsening climate crisis. Drawing on personal narrative, risk perception theory, and the history of nuclear discourse - from Hiroshima to Chernobyl to today - the research examines how trust, rather than technology, remains the largest barrier to progress. By analyzing the stories we tell about energy, disaster, control, and overall perceived risk, this work asks what it would take for Americans to imagine nuclear energy not …

Early-Stage Detection Of Copper Ion Release From Bronze Corrosion Using Uv/Vis Spectroscopy And Hydrogel Sensing, Hibah Khan

University Honors Theses

Detecting copper(I) (Cu⁺) release at early corrosion stages is important for preserving bronze heritage materials. This study investigated the use of neocuproine (NC), a Cu⁺-specific ligand, for simple and selective colorimetric detection of Cu⁺ in solution. NC reacts with Cu⁺ to form a Cu(NC)₂ complex, which produces a distinct orange-red color with peak absorbance at 455 nm. Solutions were prepared with varying concentrations of Cu⁺ generated by reducing Cu²⁺ with excess ascorbic acid. Ultraviolet/Visible (UV/Vis) spectroscopy was used to quantify absorbance changes. The absorbance at 455 nm increased linearly across the tested Cu⁺ concentration range (6.25–200 µM) with an R² …

Indigenous Science And The Legacy Of Fire: Resilience, Fuels, Mosaics, And Eco-Cultural Landscapes In Oregon’S Coastal And Cascade Range, Julia Alcalá

University Honors Theses

Most of Oregon's western Cascade and Coastal range is a landscape that has historically been considered devoid of mid to low-intensity fires by Western science. However, for time immemorial, Indigenous peoples have been stewarding the forests through the use of "good fire," creating diverse and heterogenous landscapes with mixed fire regimes, ranging from less than a decade to several centuries since the last fire, resulting in a mosaic of patchy forest, oak savannas, prairies, and wetlands. Yet centuries of Indigenous displacement, cultural assimilation, and suppression of cultural burning contributed to this prevailing belief. Despite that, in recent decades, Western scientists …

Predictors Of Nursing Students' Stress, Anxiety, And Depression During The Covid-19 Pandemic In A Hispanic-Serving University In South Texas: A Cross-Sectional Study, Maria I. Diaz, Eleftherios Gkioulekas, Nancy Nadeau

School of Mathematical & Statistical Sciences Faculty Publications

Background: In nursing education, there have been several studies on the impact of the COVID-19 pandemic on the ability of nursing students to cope while in nursing school.

Purpose statement: The goal of this study is to assess undergraduate nursing students' support mechanisms as predictors of stress, anxiety, and depression during the COVID-19 pandemic within a Hispanic-serving institution in South Texas.

Methods: Across-sectional design was used in this study. An online survey using self-reported questionnaires was used to gather data from an undergraduate nursing student cohort during the Fall 2021 semester. Linear regression was used to identify the predictors of …

“What Makes It Eigen-Esque-Ish?”: A Form-Function Analysis Of The Development Of Eigentheory Concepts In A Quantum Mechanics Course, Megan Wawro, Kaitlyn Stephens Serbin

School of Mathematical & Statistical Sciences Faculty Publications

Eigentheory concepts are central in mathematics and physics; they serve multiple functions, such as symbolizing physical phenomena and facilitating mathematical computations. Words and meanings associated with eigentheory develop and vary over time, as do their associated symbols. In this study, we investigate how “eigen” develops over time in one quantum mechanics course by analyzing form-function relations (Saxe, 1999) for eigentheory concepts over 22 class sessions. We share results concerning our microgenetic and ontogenetic analyses of the creation of form-function relations and their shifts over time by characterizing the continuity and discontinuity of the various functions and forms associated with concepts …

Banach Algebras And The Gelfand Theory Of Group Algebras On Locally Compact Abelian Groups, James Gabriel Bonvanie

Master's Theses

A Banach algebra is a complex algebra that is simultaneously a Banach space in which the norm is submultiplicative. Notably, $L^1(\mathbb{R})$ with the convolutional product is an Abelian, non-unital Banach algebra that admits an approximate identity. We rectify $L^1(\mathbb{R})$ lacking a unit via the unitization $L^1(\mathbb{R})\times\mathbb{C}$ with identity $(0,1)$. Unitization opens the discussion to the spectrum $\sigma(x)$ of a Banach algebra element, in which the spectrum is a nonempty, compact subset of the complex plane. The spectrum of an Abelian Banach algebra is fully characterized with multiplicative linear functionals, and we prove that the Fourier transform is the unique multiplicative …

Data Driven Analysis Of Samara Seed Kinematics And Dynamics, Shashwat Sparsh

Master's Theses

Samara Seeds are a class of fruit most famously belonging to the Acer species and are characterized by their single-bladed geometry and their auto-rotation response during descent. This steady-state auto-rotation response is the subject of aerodynamic analysis which aim to quantify the performance. The period prior to the beginning of steady-state auto-rotation is classified as the transition regime and has not been the subject of intense scrutiny.

This thesis employs a data-driven approach to analyzing the kinematic and dynamic response of these seeds during both the transition and auto-rotation stages of flight to quantify the performance with respect to the …

On Diffeomorphism Groups Of Surfaces, Madeleine Goertz

Master's Theses

Let $M$ be a closed, connected, smooth manifold. What are the symmetries of $M$? From a geometric viewpoint, the symmetries of $M$ are precisely its isometries, the self maps which preserve lengths and angles. In the smooth category, the symmetries of $M$ are its diffeomorphisms, the self maps which are smooth and have a smooth inverse. Does expanding our notion of symmetries to include diffeomorphisms result in ``more'' symmetries in a meaningful sense? As a formal conjecture, the claim is that the isometry group of $M$ is a deformation retract of the diffeomorphism group of $M$. If $M$ is the …

Adaptive Volatility Forecasting Models, Jeffrey K. Tan

Master's Theses

In finance, risk is often quantified by volatility, and computing accurate volatility forecasts — while vital to financial decision making — remains one of the most challenging tasks in financial modeling. This thesis, motivated in part by the Black-Scholes-Merton Model and its limitations, adopts a statistical approach to volatility forecasting. The two main models of interest are the Exponential Weighted Moving Average (EWMA) model and the GARCH(1,1) model. Specifically, this work expands upon a potential adaptive lambda algorithm for EWMA models first proposed by Bernard Bollen (2014), and this work also utilizes the Momentum of Predictability (MoP) to generate adaptive …

Community Detection In Heterogeneous Information Networks Without Materialization, Jiaxin Jiang, Siyuan Yao, Yuhang Chen, Bingsheng He, Yudong Niu, Yuchen Li, Shixuan Sun, Yongchao Liu

Research Collection School Of Computing and Information Systems

Community detection in heterogeneous information networks (HINs) poses significant challenges due to the diversity of entity types and the complexity of their interrelations. While traditional algorithms may perform adequately in some scenarios, many struggle with the high memory usage and computational demands of large-scale HINs. To address these challenges, we introduce a novel framework, SCAR, which efficiently uncovers community structures in HINs without requiring network materialization. SCAR leverages insights from meta-paths to interpret multi-relational data through compact vertex-based sketches, significantly reducing computational overhead and materialization overhead. We propose a sketch-based technique for estimating changes in modularity, improving both the precision …

Keep The Balance: A Parameter-Efficient Symmetrical Framework For Rgb+X Semantic Segmentation, Jiaxin Cai, Jingze Su, Qi Li, Wenjie Yang, Shu Wang, Tiesong Zhao, Shengfeng He, Wenxi Liu

Research Collection School Of Computing and Information Systems

Multimodal semantic segmentation is a critical challenge in computer vision, with early methods suffering from high computational costs and limited transferability due to full fine-tuning of RGB-based pre-trained parameters. Recent studies, while leveraging additional modalities as supplementary prompts to RGB, still predominantly rely on RGB, which restricts the full potential of other modalities. To address these issues, we propose a novel symmetric parameter-efficient fine-tuning framework for multimodal segmentation, featuring with a modality-aware prompting and adaptation scheme, to simultaneously adapt the capabilities of a powerful pre-trained model to both RGB and X modalities. Furthermore, prevalent approaches use the global cross-modality correlations …

A Knowledge Enhanced Large Language Model For Bug Localization, Yue Li, Bohan Liu, Ting Zhang, Zhiqi Wang, David Lo, Lanxin Yang, Jun Lyu, He Zhang

Research Collection School Of Computing and Information Systems

A significant number of bug reports are generated every day as software systems continue to develop. Large Language Models (LLMs) have been used to correlate bug reports with source code to locate bugs automatically. The existing research has shown that LLMs are effective for bug localization and can increase software development efficiency. However, these studies still have two limitations. First, these models fail to capture context information about bug reports and source code. Second, these models are unable to understand the domain-specific expertise inherent to particular projects, such as version information in projects that are composed of alphanumeric characters without …

Human-Computer Interaction And Artificial Intelligence For Ageing Population, Keng Siau, Hailiang Wang, Fiona Fui-Hoon Nah, Runyu Wang, Ruitong Che, Can Liu

Research Collection School Of Computing and Information Systems

As the global population ages rapidly, the field of human-computer interaction (HCI) is in urgent need of innovation, redesign, and reengineering to meet the evolving needs of older adults. The older demographic faces a range of challenges—including physical limitations, cognitive decline, reduced social in-tegration, and varying levels of technological literacy—that can hinder effective engagement with digital technologies. In response to these challenges, research-ers and designers are using inclusive and adaptive approaches to enhance acces-sibility, usability, and emotional well-being. This paper reviews key design prin-ciples in HCI for the ageing population and discusses how artificial intelligence (AI) tools, such as voice …

Ef21 With Bells & Whistles: Six Algorithmic Extensions Of Modern Error Feedback, Ilyas Fatkhullin, Igor Sokolov, Eduard Gorbunov, Zhize Li, Peter Richtarik

Research Collection School Of Computing and Information Systems

First proposed by Seide (2014) as a heuristic, error feedback (EF) is a very popular mechanism for enforcing convergence of distributed gradient-based optimization methods enhanced with communication compression strategies based on the application of contractive compression operators. However, existing theory of EF relies on very strong assumptions (e.g., bounded gradients), and provides pessimistic convergence rates (e.g., while the best known rate for EF in the smooth nonconvex regime, and when full gradients are compressed, is O(1/T2/3), the rate of gradient descent in the same regime is O(1/T)). Recently, Richtàrik et al. (2021) proposed a new error feedback mechanism, EF21, based …

Less Is More: On The Importance Of Data Quality For Unit Test Generation, Junwei Zhang, Xing Hu, Shan Gao, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Unit testing is crucial for software development and maintenance. Effective unit testing ensures and improves software quality, but writing unit tests is time-consuming and labor-intensive. Recent studies have proposed deep learning (DL) techniques or large language models (LLMs) to automate unit test generation. These models are usually trained or fine-tuned on large-scale datasets. Despite growing awareness of the importance of data quality, there has been limited research on the quality of datasets used for test generation. To bridge this gap, we systematically examine the impact of noise on the performance of learning-based test generation models. We first apply the open …

Group-And-Match Vs. Route-Then-Insert: Order Dispatching In Vehicle-Based Dual Services (Vedus), Yue Lin, Hai Yang, Hai Wang

Research Collection School Of Computing and Information Systems

Rapid urban transportation and delivery demand and relevant resource constraints have driven the need for more efficient vehicle utilization. An innovative concept, “Vehicle-based MultiServices” (VeMuS), is a service model in which a single vehicle offers multiple services simultaneously in an urban mobility system. Similarly, “Vehicle-based Dual Services” (VeDuS) refers to a vehicle that provides two services simultaneously (Sun et al., 2023).

A Multimodal Fusion Model Leveraging Mlp Mixer And Handcrafted Features-Based Deep Learning Networks For Facial Palsy Detection, Heng Yim Nicole Oo, Min Hun Lee, Jeong Hoon Lim

Research Collection School Of Computing and Information Systems

Algorithmic detection of facial palsy offers the potential to improve current practices, which usually involve labor-intensive and subjective assessments by clinicians. In this paper, we present a multimodal fusion-based deep learning model that utilizes an MLP mixer-based model to process unstructured data (i.e. RGB images or images with facial line segments) and a feed-forward neural network to process structured data (i.e. facial landmark coordinates, features of facial expressions, or handcrafted features) for detecting facial palsy. We then contribute to a study to analyze the effect of different data modalities and the benefits of a multimodal fusion-based approach using videos of …

Modfinity: Unsupervised Domain Adaptation With Multimodal Information Flow Intertwining, Shanglin Liu, Jianming Lv, Jingdan Kang, Huaidong Zhang, Zequan Liang, Shengfeng He

Research Collection School Of Computing and Information Systems

Multimodal unsupervised domain adaptation leverages unlabeled data in the target domain to enhance multimodal systems continuously. While current state-of-the-art methods encourage interaction between sub-models of different modalities through pseudo-labeling and feature-level exchange, varying sample quality across modalities can lead to the propagation of inaccurate information, resulting in error accumulation. To address this, we propose Modal-Affinity Multimodal Domain Adaptation (MODfinity), a method that dynamically manages multimodal information flow through fine-grained control over teacher model selection, guiding information intertwining at both feature and label levels. By treating labels as an independent modality, MODfinity enables balanced performance assessment across modalities, employing a novel …