Physical Sciences and Mathematics Commons^™

A Microgenetic Learning Analysis Of Contextuality In Reasoning About Exponential Modeling, Elahe Allahyari

Dissertations

This work explores the complex cognitive processes students engage in when addressing contextual tasks requiring linear and exponential models. Grounded within Piagetian constructivism and the Knowledge in Pieces (KiP) epistemological perspective (diSessa, 1993, 2018), this empirical study in a clinical setting develops a Microgenetic Learning Analysis (MLA) of the reasoning of 14 students from an Algebra II course. It reveals the critical role of cognitive disequilibrium as an essential cognitive state for conceptual development and the process of reorganizing knowledge systems. The study uncovers the fluctuations in students’ reasoning patterns and the significant impact on students’ reasoning patterns of task-specific …

An Experimental Study Of Supervised Machine Learning Techniques For Minor Class Prediction Utilizing Kernel Density Estimation: Factors Impacting Model Performance, Abdullah Mana Alfarwan

Dissertations

This dissertation examined classification outcome differences among four popular individual supervised machine learning (ISML) models (logistic regression, decision tree, support vector machine, and multilayer perceptron) when predicting minor class membership within imbalanced datasets. The study context and the theoretical population sampled focus on one aspect of the larger problem of student retention and dropout prediction in higher education (HE): identification.

This study differs from current literature by implementing an experimental design approach with simulated student data that closely mirrors HE situational and student data. Specifically, this study tested the predictive ability of the four ISML classification models (CLS) under experimentally …

Multivalued Variational Inequalities With Generalized Fractional Φ-Laplacians, Vy Khoi Le

Mathematics and Statistics Faculty Research & Creative Works

In this article, we examine variational inequalities of the form (Formula presented.), where (Formula presented.) is a generalized fractional (Formula presented.) -Laplace operator, K is a closed convex set in a fractional Musielak–Orlicz–Sobolev space, and (Formula presented.) is a multivalued integral operator. We consider a functional analytic framework for the above problem, including conditions on the multivalued lower order term (Formula presented.) such that the problem can be properly formulated in a fractional Musielak–Orlicz–Sobolev space, and the involved mappings have certain useful monotonicity–continuity properties. Furthermore, we investigate the existence of solutions contingent upon certain coercivity conditions.

Differential Methylation Region Detection Via An Array-Adaptive Normalized Kernelweighted Model, Daniel Alhassan, Gayla R. Olbricht, Akim Adekpedjou

Mathematics and Statistics Faculty Research & Creative Works

A differentially methylated region (DMR) is a genomic region that has significantly different methylation patterns between biological conditions. Identifying DMRs between different biological conditions is critical for developing disease biomarkers. Although methods for detecting DMRs in microarray data have been introduced, developing methods with high precision, recall, and accuracy in determining the true length of DMRs remains a challenge. In this study, we propose a normalized kernel-weighted model to account for similar methylation profiles using the relative probe distance from "nearby" CpG sites. We also extend this model by proposing an array-adaptive version in attempt to account for the differences …

Maximizing Network Throughput In Heterogeneous Uav Networks, Shuyue Li, Jing Li, Chaocan Xiang, Wenzheng Xu, Jian Peng, Ziming Wang, Weifa Liang, Xinwei Yao, Xiaohua Jia, Sajal K. Das

Computer Science Faculty Research & Creative Works

In this paper we study the deployment of an Unmanned Aerial Vehicle (UAV) network that consists of multiple UAVs to provide emergent communication service for people who are trapped in a disaster area, where each UAV is equipped with a base station that has limited computing capacity and power supply, and thus can only serve a limited number of people. Unlike most existing studies that focused on homogeneous UAVs, we consider the deployment of heterogeneous UAVs where different UAVs have different computing capacities. We study a problem of deploying K heterogeneous UAVs in the air to form a temporarily connected …

A Sentiment Analysis Approach For Understanding Users’ Perception Of Metaverse Marketplace, Ahmed Al-Adaileh, Mousa Al-Kfairy, Mohammad Tubishat, Omar Alfandi

All Works

This research explores the user perceptions of the Metaverse Marketplace, analyzing a substantial dataset of over 860,000 Twitter posts through sentiment analysis and topic modeling techniques. The study aims to uncover the driving factors behind user engagement and sentiment in this novel digital trading space. Key findings highlight a predominantly positive user sentiment, with significant enthusiasm for the marketplace's revenue generation and entertainment potential, particularly within the gaming sector. Users express appreciation for the innovative opportunities the Metaverse Marketplace offers for artists, designers, and traders in handling and trading digital assets. This positive outlook is tempered by notable concerns regarding …

Remediating History: A Review Of Restoration For Creeks Polluted From Historical Mining Sites, With The Red Boy Mine As A Primary Case Study, Kara Atiyeh

University Honors Theses

I conducted a literature review to examine the key aspects of restoring watersheds affected by pollution from historical mining. This review is then applied to a case study discussion of the Red Boy Mine and Clear Creek remediation project in Granite, Oregon. The goal of this discussion is to explore how an analysis of site conditions along with current literature on management practices can help guide these projects. Thousands of abandoned hard rock mines remain throughout the country, and many pose serious environmental health effects. Heavy metals like cadmium, nickel, and copper are brought to the surface from mining activity, …

The Cascading Effects Of Database Design, Liam Mccracken

University Honors Theses

This paper details how a relational database influenced the design of the rest of a software project. The software project in question is WonderTix, an open-source ticketing and donation platform for the use of Portland Playhouse under continuous development by teams of Portland State students as their computer science capstone project. The paper, a capstone review thesis, examines WonderTix as an instantiation of the Model-View-Controller design pattern, noting how the model, a relational database based on the SQL standard, influenced the design of the view and controller components. This influence is explored from three angles. First, when the implementation of …

Comparison Between Galfit And Lenstronomy To Analyze The Host Galaxies Of Reverberation Mapped Active Galactic Nuclei, Samantha Kay Allen

Physics

Active Galactic Nuclei (AGN) are some of the brightest objects within our Universe. Most galaxies contain a supermassive black hole (SMBH) at their center, but not all are powered by active accretion which can form an AGN. Due to the conservation of angular momentum, dust and gas rotate around the SMBH to form a disk. The large gravitational potential energy from the SMBH is converted into heat through friction, which produces a hot and luminous source of light. As a result, the SMBH is unresolved. Through a technique known as reverberation mapping (RM), the mass of the SMBH has been …

Investigating The Relationship Between Single And Multiple Sersic Models Of Active Galaxies Using Galfit, Ellie H. Johnson

Physics

Supermassive Black Holes (SMBHs) can be found in the center of almost every galaxy, and in some cases, can form Active Galactic Nuclei (AGNs). AGNs are some of the brightest objects in our observable Universe and are distinguished from quiescent galaxies by accretion onto the central SMBH, which forms a disk where the luminosity is produced. Reverberation mapping (RM) of broad-line AGNs determines the mass of the SMBH by resolving the gravitational sphere of influence of the BH ``in time". In this study, GALFIT is used to fit 2D analytic functions to existing Hubble Space Telescope (HST) images for 23 …

Identifying Ion-Scale Waves In Parker Solar Probe Sub-Alfvénic Intervals, Nicholas Henry Androski, Kristoff Paulson

Physics

Identification of coherent quasi-parallel ion-scale (1-32 Hz) wave activity in sub-Alfvénic regions of the solar wind observed by NASA's Parker Solar Probe during perihelion encounters 8 to 16. Wave activity is filtered computationally by coherency, circular polarization and propagation angle with respect to the mean magnetic field. Initial statistical results are presented with suggestions for future improvements and studies. A general overview of the heliosphere and the context of ion-cyclotron waves (an ion-scale wave) in the coronal heating problem. Along the way to identify ion-scale wave activity, tables of sub-Alfvénic intervals and current sheet crossings for encounters 8 to 16 …

Matching The Scales Of Planning And Environmental Risk: An Evaluation Of Community Wildfire Protection Plans In The Western Us, Matthew Hamilton, Cody Evers, Max Nielsen-Pincus, Alan Ager

Environmental Science and Management Faculty Publications and Presentations

Theory predicts that effective environmental governance requires that the scales of management account for the scales of environmental processes. A good example is community wildfire protection planning. Plan boundaries that are too narrowly defined may miss sources of wildfire risk originating at larger geographic scales whereas boundaries that are too broadly defined dilute resources. Although the concept of scale (mis)matches is widely discussed in literature on risk mitigation as well as environmental governance more generally, rarely has the concept been rigorously quantified. We introduce methods to address this limitation, and we apply our approach to assess scale matching among Community …

A Pilot Study On Particulate Matter Concentrations From Cooking And Its Effects On Indoor Air Pollution In A Mexican American Household In Mission, South Texas, Usa, Sai Deepak Pinakana, Carlos Garcia Patlan, Esmeralda Mendez, Amit U. Raysoni

School of Earth, Environmental, and Marine Sciences Faculty Publications and Presentations

This pilot study focuses on particulate matter (PM) while cooking in a South Texan household. Dishes such as Beef, Burger, Fish, Chicken, Egg Sandwich, and Hotdog were prepared. Indoor PM levels were compared with outdoor PM levels. A DustTrak DRX was used to monitor the PM released during the cooking process. PM2.5 levels were highest while cooking beef, 162.79 + 209.62 μg m−3. Hot Dog preparation resulted in the lowest PM2.5 concentration of 27.72 + 5.58 μg m−3. Indoor PM2.5 levels were observed to be greater in contrast to outdoor levels when compared to the outdoor levels (96 words).

Impact Of Similarities In Gender And Physical Appearance Between User And Embodied Conversational Agents On Trustworthiness, Empathy, And Service Evaluation, Sookyoung Park

Dartmouth College Master’s Theses

Embodied conversational agents (ECAs) have significantly enhanced human-machine interactions and show considerable potential in various industries such as customer service, education, healthcare, entertainment, and finance [1, 2]. This study explores the impact of similarities in gender and physical appearance between ECAs and users on the perceptions of trustworthiness, empathy, and service evaluation within the context of counselor ECAs. We conducted a within-subject experiment (n=50), using a 2x2 factorial arrangement, that varied the gender and the physical appearance of four distinct AI avatars. Participants interacted with each avatar, completing a post-experiment survey and participating in semi-structured interviews. Our findings indicate that …

On Coresets For Fair Clustering In Metric And Euclidean Spaces And Their Applications, Sayan Bandyapadhyay, Fedor V. Fomin, Kirill Simonov

Computer Science Faculty Publications and Presentations

Fair clustering is a constrained clustering problem where we need to partition a set of colored points. The fraction of points of each color in every cluster should be more or less equal to the fraction of points of this color in the dataset. The problem was recently introduced by Chierichetti et al. (2017) [1]. We propose a new construction of coresets for fair clustering for Euclidean and general metrics based on random sampling. For the Euclidean space Rd, we provide the first coreset whose size does not depend exponentially on the dimension d. The question of whether such constructions …

Auditory Ace Mobile Application Capstone Review, Layla Smith

University Honors Theses

This paper describes the development process and outcomes of my 2023-2024 Capstone Project, Auditory Ace, a self-directed auditory training mobile application for individuals with cochlear implants. Recognizing the limitations of current market offerings, Dr. Timothy Anderson created a Capstone project proposal to develop an accessible auditory training mobile application. The Capstone team that took on this proposal consisted of Darya Haines, Dustin Huynh, Jordan Nguyen, Nihar Koppolu, Scott Thorkelson, Sienna Day, and myself, Layla Smith. This paper is structured to follow the Agile software development methodology, which we used to develop Auditory Ace, reviewing in detail every major choice we …

Development Of A Two-Photon Imaging System, Jesseca Hollenbaugh

University Honors Theses

The objective of this project was to convert a Sarastro 2000 confocal laser scanning microscope (CLSM) into a system capable of far-field two-photon excitation (TPE) imaging for the use of the PSU Biology department. TPE microscopy operates on the ability of fluorophores to accept two photons each with half the energy of a desired transition in a single quantum event via a virtual energy state and then emit a higher energy photon upon relaxation. This is preferable to single-photon excitation (SPE) imaging due to lower photon imaging, causing less damage to delicate biological samples, as well as the inherent localization …

Virtual Field Environments Capstone Software Review, Ashton Sawyer

University Honors Theses

This is a review of the Virtual Field Environments computer science capstone project, sponsored by geology professor Rick Hugo. The tool aims to create and render VFEs, interactable 360° environments hosted on the web that are used as virtual field trips for K-12 students. This essay discusses the development process, including understanding requirements, tool and technology selection, problem-solving, and decision-making strategies. It also highlights the differences between the capstone and the other core computer science courses, and how those differences help to prepare students for the workforce. The project was completed over the course of twenty weeks by a team …

Friendly Sharpness-Aware Minimization, Tao Li, Pan Zhou, Zhengbao He, Xinwen Cheng, Xiaolin Huang

Research Collection School Of Computing and Information Systems

Sharpness-Aware Minimization (SAM) has been instrumental in improving deep neural network training by minimizing both training loss and loss sharpness. Despite the practical success, the mechanisms behind SAM’s generalization enhancements remain elusive, limiting its progress in deep learning optimization. In this work, we investigate SAM’s core components for generalization improvement and introduce “Friendly-SAM” (F-SAM) to further enhance SAM’s generalization. Our investigation reveals the key role of batch-specific stochastic gradient noise within the adversarial perturbation, i.e., the current minibatch gradient, which significantly influences SAM’s generalization performance. By decomposing the adversarial perturbation in SAM into full gradient and stochastic gradient noise components, …

Consistent3d: Towards Consistent High-Fidelity Text-To-3d Generation With Deterministic Sampling Prior, Zike Wu, Pan Zhou, Xuanyu Yi, Xiaoding Yuan, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Score distillation sampling (SDS) and its variants have greatly boosted the development of text-to-3D generation, but are vulnerable to geometry collapse and poor textures yet. To solve this issue, we first deeply analyze the SDS and find that its distillation sampling process indeed corresponds to the trajectory sampling of a stochastic differential equation (SDE): SDS samples along an SDE trajectory to yield a less noisy sample which then serves as a guidance to optimize a 3D model. However, the randomness in SDE sampling often leads to a diverse and unpredictable sample which is not always less noisy, and thus is …

Improving Interpretable Embeddings For Ad-Hoc Video Search With Generative Captions And Multi-Word Concept Bank, Jiaxin Wu, Chong-Wah Ngo, Wing-Kwong Chan

Research Collection School Of Computing and Information Systems

Aligning a user query and video clips in cross-modal latent space and that with semantic concepts are two mainstream approaches for ad-hoc video search (AVS). However, the effectiveness of existing approaches is bottlenecked by the small sizes of available video-text datasets and the low quality of concept banks, which results in the failures of unseen queries and the out-of-vocabulary problem. This paper addresses these two problems by constructing a new dataset and developing a multi-word concept bank. Specifically, capitalizing on a generative model, we construct a new dataset consisting of 7 million generated text and video pairs for pre-training. To …

Predictive Power Of Machine Learning Models On Degree Completion Among Adult Learners, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

The integration of machine learning (ML) into higher education has been recognized as a transformative force for adult learners, a growing demographic facing unique educational challenges. This study evaluates the predictive power of three ML models—Random Forest, Gradient-Boosting Machine, and Decision Trees—in forecasting degree completion among this group. Utilizing a dataset from the academic years 2013-14 to 2021-22, which includes demographic and academic performance metrics, the study employs accuracy, precision, recall, and F1 score to assess the efficacy of these models. The results indicate that the Gradient-Boosting Machine model outperforms others in predicting degree completion, suggesting that ML can significantly …

Present Case Studies Highlighting Practical Implications Of Architectural Design Choices, Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of deep neural networks (DNNs) has become a crucial focus within artificial intelligence and machine learning, particularly as these models are increasingly used in high-stakes applications such as healthcare, finance, and autonomous driving. This article explores the impact of architectural design choices on the interpretability of DNNs, emphasizing the importance of transparency, trust, and accountability in AI systems. By presenting case studies and experimental results, the article highlights how different architectural elements—such as layer types, network depth, connectivity patterns, and attention mechanisms—affect model interpretability and performance. The discussion is structured into three main sections: real-world applications, architectural trade-offs, …

Architectural Elements Contributing To Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of Deep Neural Networks (DNNs) has become a critical focus in artificial intelligence and machine learning, particularly as DNNs are increasingly used in high-stakes applications like healthcare, finance, and autonomous driving. Interpretability refers to the extent to which humans can understand the reasons behind a model's decisions, which is essential for trust, accountability, and transparency. However, the complexity and depth of DNN architectures often compromise interpretability as these models function as "black boxes." This article reviews key architectural elements of DNNs that affect their interpretability, aiming to guide the design of more transparent and trustworthy models. The primary …

Draft Final Quarterly Operations And Maintenance Report Butte Treatment Lagoon System – First Quarter 2024, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Explicit Composition Identities For Higher Composition Laws In The Quadratic Case, Ajith A. Nair

Dissertations, Theses, and Capstone Projects

The theory of Gauss composition of integer binary quadratic forms provides a very useful way to compute the structure of ideal class groups in quadratic number fields. In addition to that, Gauss composition is also important in the problem of representations of integers by binary quadratic forms. In 2001, Bhargava discovered a new approach to Gauss composition which uses 2x2x2 integer cubes, and he proved a composition law for such cubes. Furthermore, from the higher composition law on cubes, he derived four new higher composition laws on the following spaces - 1) binary cubic forms, 2) pairs of binary quadratic …

Using Gaussian Processes To Measure M-Dwarf Rotation Periods From Ground-Based Light Curves, Ryan J. Lebron

Dissertations, Theses, and Capstone Projects

Stellar rotation is a readily observable characteristic that plays a crucial role in the generation and activity of magnetic fields through a process known as a magnetic dynamo. For low mass main sequence stars, they exhibit fully convective interiors, giving rise to a distinct dynamo mechanism compared to solar-type stars. Examining the rotational speeds of these stars can offer valuable insights into the workings of these mechanisms. To measure these rotation periods, we developed a pipeline to analyze 192 archival light curves of low mass stars observed by the Zwicky Transient Facility (ZTF) by utilizing a combination of Lomb-Scargle and …

Illustris-Tng Simulated Central Black Mass(Mbh) And Galaxy Properties Correlations With A Machine Learning Approach, Imani L. Dindy

Dissertations, Theses, and Capstone Projects

Observationaly it is well established that the masses of central black holes are tightly correlated with galaxy properties, most notably the bulge’s velocity dispersion. Cosmolog- ical hydrodynamical simulations can capture most of these correlations, but it is yet not understood why this occurs. To gain greater insight into central black hole growth we use machine learning algorithms to study the relationship between central black hole mass(MBH) and other galaxy properties at z=0 in the TNG simulations. We find that the central black hole mass can be accurately predicted with just a few galaxy properties only if the central black hole …

Assessing Job Vulnerability And Employment Growth In The Era Of Large Language Models (Llms), Prudence P. Brou

Dissertations, Theses, and Capstone Projects

This paper explores the impact of Large Language Models (LLMs) and artificial intelligence (AI) on white-collar occupations in the context of job vulnerability and employment growth. Utilizing the Kaggle dataset "Occupation Salary and Likelihood of Automation," the study employs a data-driven approach to analyze trends across states. Through interactive data visualization, the project aims to provide actionable insights for affected workers, businesses, and policymakers navigating the changing dynamics of the workforce amidst technological advancements.

Using A Novel Chain Of Chemical, Crystallographic, And Isotopic Analytical Techniques To Examine The Formation Histories Of Features In Chondritic Meteorites And Orbicular Granites, Samuel P. Alpert

Dissertations, Theses, and Capstone Projects

Understanding the chemistry, crystallography, and isotopic variability of minerals allows us to place significant constraints on the formation history of their host rocks. These constraints provide insight into everything from the distribution of water in the solar system to the onset of plate tectonics. Electron beam instruments and ion probes are important tools used by modern geologists to obtain crystallographic, chemical, and isotopic data. Here I present a new workflow, using these instruments, combining wavelength dispersive spectrometry (WDS), backscattered electron (BSE) imaging, machine learning algorithms, electron backscatter diffraction (EBSD), and secondary ion mass spectroscopy (SIMS), to place quantitative constraints on …