Physical Sciences and Mathematics Commons^™

The Space In Between, Cailyn Dawson

Theses and Dissertations

This paper asks the question: is it possible for our bodies to hold contradicting identities at the same time?  Through monochromatic self- portraits, the artist creates a link between quantum mechanics and painting, using the principle of superposition to depict these multiple unformed states of being.

Efficient Compression Framework For Time Series Self-Supervised Learning, Brooklyn Berry

Theses and Dissertations

Time series data is perhaps one of the most broad data types that exist and is studied by diverse research fields. Recently, Self-Supervised Learning (SSL) training frameworks, the training frameworks to pre-train deep learning models without human annotations, have been proposed. Because human annotation for time series is typically associated with being costly, there is a growing interest in developing effective SSL for time series data. In SSL, the pre-trained model will often produce a time series embedding series summarized from the original time series to ensure temporal information is preserved. Although such representation can effectively capture the semantic information, …

Data Driven Monitoring And Control Of Laser Powder Bed Fusion Process, Jose De Jesus Galarza

Theses and Dissertations

The Laser Powder Bed Fusion Process (LPBF) has been one of the main processes of additive manufacturing, enabling the manufacturing of complex geometries, customization, and lightweight parts. Modern LPBF processes have integrated monitoring systems that capture the light emissions per layer for quality assurance. However, standard defect detection algorithms have not yet achieved the high precision required due to the inherently variable nature of the signal, insufficient data for model training, and the confounding effects of the print.

The processes still have some challenges, such as characterizing the roughness from the build parameters alone, improving the pore detection using the …

A New Approach To Generate Combinatorial Patterns In Logical Analysis Of Data And Its Application To Predict College Retention, Salihah Ahmed E. Jaafari

Theses and Dissertations

Student retention and degree completion remain central challenges for higher-education institutions, with significant implications for student success, institutional effectiveness, and public accountability. While advances in predictive analytics have enabled earlier identification of students at risk of withdrawal, many commonly used machine learning approaches suffer from limited interpretability, constraining their practical usefulness for advising, intervention, and policy decision making. This dissertation addresses the problem of predicting student persistence by developing and evaluating optimization based, interpretable classification models within the Logical Analysis of Data (LAD) framework. Building on existing LAD formulations, this research introduces two novel pattern generation models, the Best Term …

Aircraft Fault Detection Via Weight And Bias Analysis Of A Custom First Neural Network Layer, George Harrison Chen

Theses and Dissertations

Fault detection in aircraft is traditionally handled through redundant hardware and comparison algorithms to detect failures. Alternatives like model-based residual generation and data-driven approaches such as supervised fault classification and unsupervised anomaly detection have been explored, but they suffer from practical limitations; model-based methods require accurate system models, and data-driven methods have large constraints on the data limiting scalability and adaptability. This work presents a purely data-driven neural network architecture featuring a custom first layer designed for real-time fault detection where the weights and biases of this layer are used to detect faults. The network requires zero supervision and complements …

Design And Simulation Of Anti-Resonant Hollow-Core Fiber For Sensing Applications, Pravallika Kante

Theses and Dissertations

This thesis presents the design and simulation of an 8-tube single-ring anti-resonant hollow-core fiber for sensing applications, with particular emphasis on methane gas detection at the fundamental absorption wavelength of 3.3 µm. Conventional solid-core silica optical fibers exhibit strong multi-phonon material absorption beyond 2.5 µm, rendering them fundamentally unsuitable for efficient light guidance and direct gas sensing at mid-infrared wavelengths. Anti-resonant hollow-core fibers overcome this limitation by guiding light predominantly through an air-filled hollow core via the anti-resonant reflecting optical waveguide mechanism, in which the thin silica glass walls of the cladding tubes act as Fabry-Pérot etalons that confine the …

Draft Final 2024 Reclamation Improvement Sampling: Bres No. 78 – Original Mine Yard Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Final Annual Operations And Maintenance (O&M) Report: Butte Treatment Lagoon (Btl) System – 2025, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Func: Reducing The Impact Of Android Framework Evolution On Malware Detection, Hailong Yu, Tiantian Wang, Lwin Khin Shar, Hanmeng Li, David Lo

Research Collection School Of Computing and Information Systems

Android malware detection approaches commonly use APIs and permissions as features for classifying malware. However, since the release of the first Android operating system in 2008, the Android framework has undergone numerous version updates. The evolution of the Android framework over time has led to changes in APIs and permissions, including deprecations and replacements. These changes can result in inaccurate characterization of Android malware, thereby affecting performance of malware detectors. There is a lack of methods to mitigate the impact of Android framework evolution on malware detection. To fill this gap, we conduct a systematic study of the impact of …

Enhancing Action And Ingredient Modeling For Semantically Grounded Recipe Generation, Guoshan Liu, Bin Zhu, Yian Li, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Recent advances in Multimodal Large Language Models (MLMMs) have enabled recipe generation from food images, yet outputs often contain semantically incorrect actions or ingredients despite high lexical scores (e.g., BLEU, ROUGE). To address this gap, we propose a semantically grounded framework that predicts and validates actions and ingredients as internal context for instruction generation. Our two-stage pipeline combines supervised fine-tuning (SFT) with reinforcement fine-tuning (RFT): SFT builds foundational accuracy using an Action-Reasoning dataset and ingredient corpus, while RFT employs frequency-aware rewards to improve long-tail action prediction and ingredient generalization. A Semantic Confidence Scoring and Rectification (SCSR) module further filters and …

Advances In Computational Methods For Sparsity-Promoting Linear Inverse Problems, Jonathan Lindbloom

Dartmouth College Ph.D Dissertations

Inverse problems arise throughout science and engineering, where indirect, incomplete, and noisy observations are used to recover unknown parameters of interest. In these applications, the corresponding forward or measurement models are often ill-conditioned or underdetermined, so direct inversion is unstable and regularization is required. This thesis develops computational methods for linear inverse problems in which the unknown is assumed to be approximately sparse in a transformed domain defined by a linear, possibly rank-deficient operator, such as a finite-difference matrix, with particular emphasis on large-scale problems.

The thesis makes three main contributions. First, it generalizes hierarchical Bayesian maximum a posteriori estimation …

Insights Into Androgen Receptor Allosteric Modulation By P,P'-Dichlorodiphenyldichloroethylene Binding At The Binding Function-3 Site And Site-Specific Binding Function-3 Mutations, Emanuel Aggeo Flores

Theses and Dissertations

One of DDT’s metabolites, p,p’-dichlorodiphenyldichloroethylene (DDE), has been classified as an endocrine disrupting chemical (EDC) due to its ability to interfere with hormone signaling by binding to nuclear receptors (NR) such as the androgen receptor (AR).

Structurally, the AR contains a ligand-binding domain (LBD) where endogenous steroids bind. Furthermore, the LBD contains a surface binding site known as binding function-3 (BF-3), which studies suggest exerts an allosteric effect on bound DHT when small, hydrophobic molecules bind.

Here, we present a study that examines how specific BF-3 site mutations and DDE binding affect steroid stability within the LBP utilizing computational methods. …

I Am Still Learning, Too!: Pre-Service Teachers' Experiences Of Mathematics Anxiety In An Inquiry-Based Mathematics Education Course At Utrgv, Jeremiah James Adriano Dela Rosa

Theses and Dissertations

Mathematics anxiety is a prevalent issue in mathematics education that negatively impacts students’ learning, performance, and engagement in mathematics. Prior research suggests that mathematics anxiety is often shaped by students’ experiences and emotional responses within the classroom environment.

The purpose of this study is to explore how Pre-Service Teachers experience mathematics anxiety in an Inquiry-Based Mathematics Education (IBME) classroom. This study employed a qualitative research design supported by descriptive survey data collected through the Abbreviated Mathematics Anxiety Scale (AMAS), selected components of the Fennema-Sherman Mathematics Anxiety Scale (FSMAS), and semi-structured interviews. The survey instruments were used to provide descriptive background …

Prism: Priming Relationships In Syntax And Mathematics, Michelle J. Zhu

Honors Scholar Theses

The implementation of sheltered education programs has become increasingly prevalent in schools as new research in second language acquisition emerges. Yet despite this growing attention to ESL instructional practice, far less is known about the cognitive processes that underlie how bilingual students engage with academic content. This study investigates cross-domain structural priming between mathematical and linguistic processing in monolingual and bilingual individuals, focusing on how bilingual experience influences syntactic attachment preferences. Prior research suggests that mathematical and linguistic structures share underlying cognitive representations, and that exposure to structures in one domain can influence processing in another. Additionally, bilingualism has been …

Csbsju Ash Tree Maps, Trevor Barton

Celebrating Scholarship and Creativity Day (2018-)

Ash trees in MN are recently susceptible to the invasive species of Emerald Ash Borer. These insects burrow into native MN ash trees, lay their larvae, and significantly harm or kill the trees. MN native ash trees are not equipped to deal with this invasive species and rather need to be chemically treated to save the trees. These maps depict the GPS locations of the ash trees on CSBSJU campus proper. Trees were mapped by hand with a Garmin GPS device while size, health, and value to campus assessments were recorded to pair along with the maps in a separate …

Know Thy Enemy: Building A Command-And-Control Solution For Adversarial Emulation, Caleb J. Chen

Senior Honors Theses

Command and Control (C2) is a critical part of any cyberattack. It serves many purposes, including Distributed Denial of Service (DDoS) attacks, data exfiltration, and malware deployment. Consequently, C2 frameworks play an important part in red team engagements and adversary emulation. However, many adversary emulation solutions focus on comprehensive testing through sequential technique execution instead of realistic chained and automated attacks. The proposed solution is Centurion, an open-source C2 framework that integrates MITRE's ATT&CK framework and several cybersecurity tools into modular playbooks for effective threat emulation. This paper provides background by defining key terms and concepts before delving into a …

You Can’T Spell Audit Without Ai: The Current Uses Of Artificial Intelligence In Audit, Jena Perkins

Senior Honors Theses

The accounting profession continuously adapts to the innovations provided by the broader context in which it exists. Artificial intelligence (AI) is a forerunner among tools used to enhance and optimize auditing services within the accounting profession. The realm of AI offers advancements to procedures used within an audit to detect misstatements. Based on the proprietary platforms developed by Big 4 accounting firms, AI is a key component in maintaining an advanced approach towards auditing.

Rhodium On Alumina Catalyzed Hydrodechlorination Of Trichlorethylene, Amber Krueger, Gage Mueller

Celebrating Scholarship and Creativity Day (2018-)

Chlorinated ethylenes are common groundwater contaminants that persist in the environment and pose significant risks to human health such as damage to the nervous system, liver, kidneys, and even increased risk of cancer. Catalytic dehydrochlorination has emerged as a promising strategy for detoxifying compounds such as trichloroethylene (TCE). This study investigates whether rhodium on alumina (Rh/Al₂O₃) in the presence of hydrogen gas can completely degrade TCE to non-toxic products without the accumulation of harmful chlorinated intermediates. While palladium catalysts have been widely studied for dehydrochlorination, the activity and mechanism of rhodium supported on alumina in aqueous systems remains less characterized. …

Deep Learning For Predicting Impact Energy And Compression After Impact Strength Of Composite Materials Using C-Scan Images, K. T. Tan, Jason P. Mack, Faizan Mirza, Zhong-Hui Duan

University Research

Traditional assessment of post-impact performance in carbon fiber reinforced polymer (CFRP) composites often relies on simplified scalar metrics that fail to capture the complex spatial interactions driving failure. This study addresses this limitation by developing an automated, end-to-end deep learning framework that shifts from manual feature extraction to the direct interpretation of raw damage morphology from ultrasonic C-scans. Using a ResNet18-based convolutional neural network (CNN) trained on 1,428 augmented images, the model achieved coefficients of determination (R2) of 0.7948 ± 0.0847 for compression after impact (CAI) strength and 0.9436 ± 0.0098 for impact energy. Beyond prediction, this dual-purpose methodology serves …

Sumset Lower Bounds In Abelian Groups, Van T. Huynh

Honors Theses

This thesis investigates sumset lower bounds across discrete and continuous settings. We begin with general inequalities in torsion-free abelian groups and then specialize to the integers modulo prime p, where we present the Cauchy–Davenport Theorem, which establishes the bound ∣A+B∣≥min(p,∣A∣+∣B∣−1). The equality case is further examined via Vosper's Theorem, which characterizes subsets attaining this bound as arithmetic progressions under suitable conditions. The continuous analogue in Euclidean spaces is then considered, where cardinality is replaced by Lebesgue measure. In this setting, the Brunn–Minkowski Inequality provides a sharp lower bound for the Lebesgue measure of A+B and serves as a geometric counterpart …

A Climatology Of Sudden Stratospheric Warmings And Their Tropospheric Response, Joe Lovelien, Mark Sinclair

Publications

Sudden Stratospheric Warmings (SSWs) are major disruptions of the wintertime polar vortex that can significantly influence surface weather, including cold air outbreaks (CAOs) across the midlatitudes. This study presents a climatology of SSWs from 1948-2024 using NCEP-NCAR reanalysis data and examines their temporal distribution, relationships with large-scale climate variability, and impacts on surface temperatures in the United States. SSWs are identified using previously published definitions based on the reversal of zonal-mean winds at 60°N and 10 hPa. A total of 44 SSW events were identified, with a strong seasonal preference for January through March. Some decadal variability is observed, though …

Clear-Air Turbulence Climatology And Trends, Liam Rodgers, Mark Sinclair

Publications

Clear‑air turbulence (CAT) is a major aviation hazard that occurs near airline cruising altitudes in both cloud and cloud‑free environments. Its lack of a distinct visual signature makes detection and avoidance difficult. CAT is associated with wind shear near jet streams, gravity waves, and Kelvin–Helmholtz instability and may be further enhanced by climate change. This study examines the climatology, spatial distribution, seasonal variability, and trends of CAT over the contiguous United States.

Pilot Reports (PIREPs) from 2001–2025 between 100 and 400 hPa are analyzed alongside jet stream, shear, and stability diagnostics derived from NCEP–NCAR Reanalysis data. Proxies such as inverse …

An Analysis Of The Rapid Intensification Of Hurricane Milton, Ayden Rodriguez, Mark Sinclair

Publications

Hurricane Milton (October 2024) provides a compelling case study of a multiscale pathway to rapid intensification (RI), highlighting the alignment of synoptic preconditioning, favorable environmental conditions, and rapid inner-core structural evolution. The storm originated from the interaction of multiple tropical waves embedded within a low-level trough associated with the Central American Gyre, which enhanced low-level vorticity and moisture convergence prior to development. Upon entering the Gulf of Mexico, Milton encountered an environment characterized by anomalously warm sea surface temperatures, high ocean heat content, and weak vertical wind shear, enabling steady intensification and the onset of explosive RI that exceeded conventional …

Geospatial Science And The Changing Environment: Applied Methods For Sustainable Agriculture And Landscape Management, Harrison Wakefield Smith

Graduate Theses and Dissertations

Rapid environmental change is disrupting agricultural productivity, ecological function, and the long-term resilience of managed landscapes. At the same time, the rapid growth of geospatial data science has improved our understanding of environmental change and increasingly is being used to improve sustainability in agricultural and environmental management. However, critical gaps remain that limit the applicability of data-driven insights in landscape management. This dissertation investigates the potential of geospatial analytics for sustainable agriculture and landscape management, with a focus on applied methods that operate across spatial and temporal scales. Using field, landscape, regional, and national datasets, it explores the capabilities and …

Atmospheric Remote Sensing Using X-Band Radar In The Marine Atmospheric Surface Layer, Daniel P. Greenway

Electronic Theses and Dissertations

Atmospheric remote sensors have always offered promising technological advancements for measuring atmospheric properties over large spatial areas at high spatiotemporal resolution – a feat not currently possible with present atmospheric measurement technologies. However, remote sensors do not measure these atmospheric properties directly; they rely on robust calibrations or conversions, complex mathematical inversion methods, and/or machine learning to retrieve these properties. These inverse methods rely on the selection of an objective function, a machine learning technique, and an accurate parameterization for atmospheric property estimation. By leveraging inverse methods to improve the characterization of properties measured by remote sensors, this work enables …

Turbulent Plenum Jet-Crossflow Validation Via Subgrid Scale Model Variation And Upstream Forcing Under Dynamic Hybrid Rans-Les, Cole W. Mccallum

Mechanical Engineering Undergraduate Honors Theses

In modern gas turbine design, film cooling has become ubiquitous as a method for limiting heat transfer between high temperature gases post-combustion and the surface of downstream blades. This paper validates the use of various computational fluid dynamics techniques in recreating an experiment measuring adiabatic effectiveness over a surface downstream of a compound-angle N2 plenum jet incident on a turbulent-air boundary layer [1]. To do this, both RANS and Dynamic Hybrid RANS-LES (DHRL) methods are implemented and compared to previous research [2]. The latter method is then modified through implementation of a different subgrid scale (SGS) model and through addition …

Genre Prediction Using Rnns And Llm-Enhanced Video Game Review Data, Gabriel Young

Graduate Theses and Dissertations

LLMs (Large Language Models) are powerful tools for engaging with textual data, carrying many advantages over classical NLP (Natural Language Processing) and ML (Machine Learning) approaches. However, a classical ML model can still be faster, more efficient to run, and accessible than an LLM. We seek to gain the benefits of LLM text comprehension and preserve them in a classical ML model, a hybrid approach. The LLM operates on text to surface relevant information and associations in our problem space, then the ML model trains on the LLM output. The model may learn from the LLM and provide a more …

Efficient Deep Neural Networks For Autonomous Perception, Reeshad Khan

Graduate Theses and Dissertations

Autonomous perception systems must operate reliably under uncertainty arising from noisy observations, incomplete supervision, and hardware constraints. This dissertation investigates the design of efficient deep neural networks for autonomous perception through a unified perspective that treats uncertainty, efficiency, and sensing as interconnected challenges. The first contribution develops adaptive extensions of unbiased risk estimators, including eSURE and ePURE, enabling unsupervised training of deep neural networks for magnetic resonance image denoising under Gaussian and Poisson noise. However, these methods rely on known noise assumptions, which motivates the second contribution: a unified diffusion and Bayesian risk framework that estimates and adapts to unknown …

Fisheries Management Paper No. 313: Western Australian Statewide Small Pelagic Scalefish Resource Harvest Strategy, Department Of Primary Industries And Regional Development, Western Australia

Fisheries Management Papers

Harvest strategies for Western Australia’s (WA) aquatic resources are formal documents developed by the Department of Primary Industries and Regional Development (DPIRD, the Department) to support decision-making processes that ensure the outcomes are consistent with the principles of Ecologically Sustainable Development (ESD; Fletcher 2002a) and Ecosystem Based Fisheries Management (EBFM; Fletcher et al. 2012).

Harvest strategies are a key component of all contemporary fishery management systems and a requirement for certification under the Marine Stewardship Council (MSC; Marine Stewardship Council 2018). The objectives of ESD are reflected in the objects of the Fish Resources Management Act 1994 (FRMA).

This Statewide …

Engineering Silicone Magnetic Fluids For Localized Radiation Attenuation During Ocular Melanoma Brachytherapy, Zachary L. Caprow

All Dissertations

Ocular melanoma is commonly treated using plaque brachytherapy; however, radiation-induced damage to healthy ocular tissues frequently results in partial or complete vision loss. This work investigates the development of an injectable, magnetically responsive silicone magnetic fluid designed to localize adjacent to the tumor and attenuate low-energy gamma radiation during treatment.

The material system consists of iron oxide nanoparticles surface-functionalized with a siloxane polymer, in which the nanoparticles provide magnetic responsiveness and radiation attenuation, while the polymer coating ensures colloidal stability, injectability, and biocompatibility. A one-pot synthetic approach was developed wherein a functionalized siloxane polymer containing iron-affinitive groups was reacted with …