Physical Sciences and Mathematics Commons^™

Improving The Accuracy Of Neighborhood Median Pixel Method (Nmpm) In Classifying Landsat-8 Oli Images By Optimizing The Scoring System’S Point Values, Abraham T. Magpantay, Proceso L. Fernandez Jr

Department of Information Systems & Computer Science Faculty Publications

The Neighborhood Median Pixel Method has previously been introduced as an image processing technique in remote sensing, developed to classify Landsat-8 OLI satellite image pixels into categories of vegetation, water, and built-up areas. This method relies on a lookup table based on the median pixel values within a pixel’s neighborhood and a scoring system that assigns point values for classification. While a 9x9 neighborhood size was originally proposed, a succeeding study suggested a 13x13 neighborhood for better classification accuracy. This study focuses on refining the scoring system used in the Neighborhood Median Pixel Method, particularly the original set of arbitrary …

Sampling Balanced High-Quality Data To Train An Automatic Mesh Generator, Jie Pan, Jingwei Huang, Gengdong Cheng, Yong Zeng

Engineering Management & Systems Engineering Faculty Publications

In real-world scenarios, high-quality data are often scarce and imbalanced, yet it is essential for the optimal performance of data-driven algorithmic models. Data synthesis methods are commonly used to address this issue; however, they typically rely heavily on the original dataset, which limits their ability to significantly improve performance. This article presents a quality function-based method for directly generating high-quality data and applies it to a mesh generation algorithm to demonstrate its efficiency and effectiveness. The proposed approach samples input-output pairs of the algorithm based on their feature spaces, selects high-quality samples using a defined quality function that evaluates the …

A Governance-Centric Framework For Strengthening Healthcare Cybersecurity: A Systems Perspective, Sujatha Alla, Sai Gireesh Komaragiri, Teresa Duvall, Satluk Karahan, Nagesh Bheesetty, Vijay Kumar Chattu

Engineering Management & Systems Engineering Faculty Publications

Healthcare systems face unprecedented security and privacy challenges due to increasing digitization and interconnectedness. This paper provides a comprehensive analysis of these challenges by examining various cyberattacks, defensive mechanisms, and governance frameworks within modern healthcare infrastructure. The research systematically categorizes prevalent security threats, such as ransomware, insider threats, and data breaches, identifying vulnerabilities specific to healthcare systems. Furthermore, the study evaluates current defensive strategies, including encryption techniques, access control systems, and intrusion detection tools, assessing their effectiveness against complex cyber threats. A key focus is placed on governance structures and their role in cybersecurity resilience. The research explores how regulatory …

Dynamics Of Water Mass Exchange Across The Central Ross Sea Slope, Yuanjie Chen, Zhaoru Zhang, Pasquale Castagno, Xianxian Han, Michael S. Dinniman, Zhiqiang Liu, Chuning Wang

OES Faculty Publications

The central Ross Sea slope (CRSS) is a critical region for water mass exchange, significantly influencing the physical and biological processes of the Ross Sea shelf and the global overturning circulation. This study utilizes a high-resolution ocean-ice shelf-sea ice coupled model to investigate the mechanisms driving this exchange, involving the onshore transport of circumpolar deep water (CDW) and the offshore transport of dense shelf water (DSW). Combining numerical simulations and mooring observations, this work reveals high-frequency oscillations with a periodicity of ∼32 hr in CDW transport induced by Topographic Rossby Waves and DSW transport induced by cyclonic eddies. These findings …

A Unionid Mussel Biodiversity Hotspot Experiencing Unexplained Declines: Evaluating The Influence Of Chemical Stressors Using Caged Juveniles, W. Aaron Wilson, Christine Bergeron, Jennifer Archambault, Jason M. Unrine, Jess Jones, Braven Beaty, Damian Shea, Peter R. Lazaro, Jody L. Callihan, Jennifer J. Rogers

Plant and Soil Sciences Faculty Publications

Unionid mussel populations in a section of the Clinch River in Virginia, USA, has declined substantially, but the causes of the decline remain unknown. To investigate this zone of decline (ZOD), we deployed juvenile freshwater mussels (Villosa iris in 2012 and Lampsilis fasciola in 2013) in both cages and silos at sites within the Clinch River System. We analyzed mussel tissues for trace element and organic contaminant concentrations, shells for trace elements, and environmental media (total water, dissolved water, particulate sediment, and bedload sediment) for both inorganic and organic contaminants. We found a few differences between mussels deployed in cages …

Personalized Physics Learning Through Ai: Insights From Problem Generation, Chatbot Dialogues, And Intelligent Tutoring Systems, Atharva Dange

Physics Dissertations - Archive

Artificial intelligence (AI) is poised to transform science education, yet questions remain on how best to integrate these technologies into teaching and learning. This dissertation investigates the use of AI-driven tools in university physics courses through three complementary studies. In the first study, a generative language model (ChatGPT) was used to create novel physics homework problems aligned with course objectives. Analysis showed that, after expert vetting, AI-generated questions can foster higher-order problem-solving and reduce student reliance on solution memorization, though careful instructor oversight is required to ensure accuracy. The second study embedded an AI chatbot as a learning aid in …

Development And Application Of Gpu Based Monte Carlo Simulation Techniques In Radiation Medicine, Satzhan Sitmukhambetov

Physics Dissertations - Archive

Cancer remains a significant public health challenge, with treatments like radiation therapy forming a cornerstone of modern care. To address this challenge, advanced computer simulations can improve radiotherapy in two primary ways. From one hand, mechanistic simulations help elucidate the fundamental radiobiological working principles, which could contribute to the clinical advancements from a bottom-up framework. On the other hand, modality simulations could help design better clinical systems with better detection and therapeutic capabilities. This research enhances these critical simulation tools by introducing two significant developments.

First, this work introduces a metaphase DNA model into a microscopic Monte Carlo simulation framework …

Uncertainty In Solar Wind Propagation: An Analysis Of L1 To Earth Variability And The Implications For Geospace Modeling In Space Weather Operations, Espen T. Fredrick

Physics Dissertations - Archive

As humanity's technological dependence grows, so does its vulnerability to space weather. Space weather describes solar-driven phenomena in the near-Earth space environment and their terrestrial effects. Many of these effects occur due to the interaction of the solar wind and Earth's magnetosphere. The solar wind originates at the solar corona and carries solar plasma and magnetic field outward through the solar system. This magnetic field, known as the interplanetary magnetic field (IMF) during transit, can undergo magnetic reconnection with Earth's geomagnetic field when oppositely aligned field lines converge. This process transfers energy into Earth's magnetosphere, driving space weather phenomena.

Space …

Characterizing The Earth's Magnetosheath With In Situ Measurements, Hector A. Salinas

Physics Dissertations - Archive

Earth’s magnetosheath is a region of shocked plasma that mediates energy and momentum transfer from the solar wind to the magnetosphere. The dynamics and thus the plasma structures of this region are highly dependent on upstream solar wind conditions. As such, the region is often characterized as a turbulent environment, with the most intense periods of turbulence occurring often under quasi-parallel bow shock conditions. This magnetosheath turbulence displays as variable and large-scale fluctuations with in-situ measurements. Given that turbulence is a characteristic feature of the magnetosheath, this dissertation asks two questions: (1) How are plasma structures inside the sheath affected …

On The Orbital Dynamics Of Exoplanets, Exomoons, And Submoons, Shaan Dharmesh Patel

Physics Dissertations - Archive

Although exoplanet detection has long been a central focus in astronomy, growing interest in exomoons has highlighted the increasing need for theoretical frameworks to interpret emerging candidates. Furthermore, while submoons, natural satellites orbiting moons, are not presently observed in the Solar System and remain speculative (e.g., the hypothesized past submoon of Saturn’s Iapetus), their existence is plausible within the context of the complex and diverse architectures now identified across planetary and stellar systems. We develop a generalized framework to assess the orbital stability of exomoons and submoons through comprehensive N-body simulations incorporating both three-body dynamics and tidal interactions, with the …

Study Of Structural Changes In Bone From Osteogenesis Imperfecta Using Positron Annihilation Lifetime Spectroscopy, Pablo Kohlmann Garcia, Richard Vallery

Student Summer Scholars Manuscripts

Osteogenesis Imperfecta (OI), commonly referred to as Brittle Bone Disease, is a genetic disorder that results in increased bone fragility. There are many known variations, with four of them being the most common and each with a different level of severity. However, the exact causes of OI and their molecular and structural effects on bones are largely unknown. To investigate the relationship between nanoscale bone structure and disease severity, this research investigates OI by using positron annihilation lifetime spectroscopy (PALS) to understand whether normal bone structure differs from OI-infected bones. Using this method, positrons are directed towards mice’s bones, forming …

Assessing Arbuscular Mycorrhizal Fungal Community Composition And Diversity Across A Land-Use Gradient In Eastern Washington Prairies, Katherine I. Cole

EWU Masters Thesis Collection

Arbuscular mycorrhizal fungi (AMF) are key to healthy, functioning terrestrial ecosystems worldwide. Associating with over 80% of all extant land plants, they play a pivotal role in nutrient cycling, and contribute to overall plant health. Given their significance to terrestrial ecosystems, AMF have a crucial role to play in the successful restoration of Eastern Washington’s native prairies. However, AMF community composition is significantly altered by the commercial agricultural practices used extensively in this region for over 100 years. The Eastern Washington University (EWU) Prairie Restoration Project aims to restore ~120 acres of farmland to native prairie. This research provides insight …

Calibration Of A Low-Cost Air Quality Sensor Package Integrated Into The New York State Mesonet, Ellie Hojeily

Electronic Theses & Dissertations (2024 - present)

The University at Albany designed and manufactured 59 low-cost air quality sensor packages to continuously measure PM₂.₅, CO, O₃, NO₂, and NO at 38 New York State Mesonet (NYSM) sites located in the New York City Metropolitan Area. Prior to use for monitoring, low-cost sensors require calibration to correct for environmental sensitivities. Calibration models can be developed using data collected from co-location periods in which low-cost sensors are installed at sites with Federal Reference Methods and/or Federal Equivalent Methods instruments. In this study, packages were periodically co-located (calibrated) for 18 to 162 days at the New York State Department of …

Investigation Of The Binding Mechanism Between The Supplemental And Tail Regions Of Mirna And Mrna Within Argonaute 2, Christina Ms Karadiakos

Electronic Theses & Dissertations (2024 - present)

MicroRNAs (miRNA) are a class of small non-coding RNAs that play a key role in gene silencing. miRNAs form an RNA-induced silencing complex (RISC) with the argonaute 2 (AGO2) protein in order to control gene expression through silencing messenger RNAs (mRNA). The ability for a miRNA-mRNA complex to fit into AGO2 and form the RISC is based on the location and characteristics of a central bulge, but the mechanism and interactions that lead to a functional RISC remain unknown. Using available experimental data, we modeled the miR-34 and NOTCH1 duplex inside of AGO2 with only the seed region base paired. …

Polarimetric Capture And Differentiable Rendering, Katherine Anne Salesin

Dartmouth College Ph.D Dissertations

Many scientific fields rely on the capture and modeling of light to extract underlying information about the world. Often, more information can be extracted by capturing more about the nature of the light, such as its spectral shape or polarization state. While polarization is a relatively unexplored topic in computer graphics, when used in tandem with other recent advancements in the field it has enormous potential to improve both forward and inverse models in other scientific disciplines. We demonstrate this potential in two distinct settings in this thesis.

First, we apply the capture of polarized light to an inverse problem …

Potsdam: Pareto Optimization Targeting Security, Data, And Mediation, J Peter Brady

Dartmouth College Ph.D Dissertations

Given the growing amount and variety of data handled by modern systems, it is crucial to guarantee the accuracy and protection of input data without errors or malicious intentions. The need to improve security in software programs often conflicts with the assurance of maximum performance, making developers and maintainers hesitant to incorporate more testing.

LangSec (Language-Theoretic Security) is a security approach that treats input validation as a formal language recognition problem, ensuring that only well-defined, unambiguous inputs are processed to eliminate exploitable parsing flaws. This dissertation explores integrating LangSec principles with Pareto optimization to enhance safety and robustness in digital …

Learning From Non-Stationary Data Streams, Gabriel Jonas Aguiar

Theses and Dissertations

The rapid growth of data from sources such as mobile applications, sensors, and network monitoring has increased the need for machine learning algorithms capable of handling non-stationary data streams. However, learning from such streams presents significant challenges due to their evolving nature and the presence of concept drift. One of the most complex issues is learning from imbalanced data streams, where shifting data distributions, combined with feature space drifts, complicate continuous adaptation. These challenges become even more pronounced in multi-class scenarios, which are common in real-world applications. Detecting concept drift in such contexts is particularly demanding, as it requires tracking …

Extremal Trees For Random Walks, Ben Bridenbaugh

Mathematics, Statistics, and Computer Science Honors Projects

A random walk is a sequence of adjacent vertices that are chosen uniformly at random from the neighbors of the previous vertex. An access time is the average length of time that a random walk takes to reach a target probability distribution from a starting probability distribution, given an optimal stopping rule. This paper deals with characterizing the trees of diameter d and on n vertices that extremize three different types of access times.

Clea Labs For Planetary Astronomy Bottled For Silicon Macintosh, Yuktha Shetty, Hansika Reddy Bandela, Jack C. Straton

Physics Faculty Publications and Presentations

Project CLEA -- CONTEMPORARY LABORATORY EXPERIENCES IN ASTRONOMY -- develops laboratory exercises that illustrate modern astronomical techniques using digital data and color images.

These Labs were developed for Windows, but the authors have bottled them for Silicon Macintosh computers.

Multiphysics Modeling Of Material Response To High-Intensity X-Ray And Laser Pulses: Heating, Ablation, And Plasma Expansion, Youssef Abouhussien

Theses and Dissertations

This dissertation presents a computational framework to investigate material response under high-intensity X-ray fluxes produced by an exo-atmospheric nuclear detonation and laser-material irradiations, with a focus on heating, ablation, and plasma expansion phenomena relevant to satellite vulnerability and high-energy-density environments. A hybrid Monte Carlo and Two-Temperature Model (MC-TTM) was developed to simulate X-ray and laser energy deposition and thermal relaxation in metals and semiconductors across a range of X-ray and laser pulse durations from femtoseconds to nanoseconds. Results demonstrate distinct thermal behavior between materials, with ablation thresholds and phase transitions captured in good agreement with experimental data.

In parallel, a …

Multitask Learning For Named Entity Recognition And Relationship Extraction, Adrienne D. Hembrick

Theses and Dissertations

Information Extraction (IE) is a fundamental task in Natural Language Processing (NLP), involving the identification of structured information from unstructured text. Two core components of IE—Named Entity Recognition (NER) and Relation Extraction (RE)—are widely used to extract key concepts and the relationships between them across various domains. However, the sequential dependency of RE on the output of NER makes it vulnerable to error propagation: inaccuracies in entity recognition can negatively affect downstream relation extraction.

To mitigate this issue, Multitask Learning (MTL) has been proposed as an approach that jointly models NER and RE, aiming to improve overall performance and reduce …

Leveraging Counterfactuals For Enhanced Natural Language Inference: A Multitask Knowledge Distillation Approach, Brian J. Davis

Browse all Theses and Dissertations

Natural-language inference (NLI) asks whether a hypothesis is entailed by, contradicts, or is neutral with respect to a premise. Modern transformers reach high raw accuracy on benchmarks such as SNLI, MNLI, and ANLI, yet they often rely on brittle lexical shortcuts and provide little insight into their decision process. This thesis shows that counterfactual-augmented knowledge distillation can simultaneously boost robustness and supply faithful, token-level explanations—without scaling model size. Four T5-v1_1 students (60M, 220M, 770M, 3B parameters) are trained under four curricula: (1) standard fine-tuning, (2) fine-tuning with free-text rationales, (3) multi-task distillation with naive counterfactuals, and (4) multi-task distillation with …

Searching For Cages In Graphs And Signed Graphs, Isaac Partee

Browse all Theses and Dissertations

The girth of a graph G is the minimum length of a cycle in G. A (k,g)-graph is a k-regular graph of girth g. A (k,g)-cage is a (k,g)-graph with the smallest possible number of vertices. For example, K4 is the unique (3,3)-cage, K3,3 is the unique (3,4)-cage, and the Petersen Graph is the unique (3,5)-cage. The search for cages particular values of (k,g) is an ongoing area of considerable research. For values of (k,g) where the number vertices in a (k,g)-cage is not known, covering graphs have recently been used for constructing progressively smaller and smaller (k,g)-graphs. The covering …

Optimizing Cloud Computing Resources For Operational Cost And Application Performance Using Machine Learning, Isaac K. Matthew

Browse all Theses and Dissertations

As AI-driven workloads accelerate the growth of cloud initiatives and spending, resource waste also increases due to persistent inefficiencies in cloud compute and infrastructure management. Overprovisioned resources and suboptimal configurations often lead to operational inefficiencies and unnecessary financial overhead. These challenges arise from the difficulty of anticipating resource demands in dynamic workloads and selecting suitable virtual machines to ensure optimal performance. Our research proposes a holistic, data-driven framework for managing cloud compute resources that reduces costs without compromising application performance. We integrate a predictive, model-driven, threshold-based autoscaling solution for cloud-native applications with an optimized instance right-sizing approach to select cost-effective …

Reducing Operator Training Time Through Virtual Reality: A Case Study On The Lpkf Protomat E44 Machine, Joshua C. Patel

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This thesis presents the development of an immersive virtual reality (VR) simulation that replicates the operation of the LPKF ProtoMat E44 PCB milling machine. Aimed at reducing operator training time and improving procedural understanding, the simulation offers an interactive and realistic environment where users can safely engage with machine workflows and start-up sequences. The emphasis is on accurate representation, usability, and maintaining immersion to support intuitive learning. Although formal evaluation is outside the scope of this work, the system is designed to serve as a foundation for cost-effective, scalable training in technical and manufacturing contexts, offering a modern alternative to …

Scalable Real-Time Stream Clustering For Unbounded Text Streams, Nathaniel C. Crossman

Browse all Theses and Dissertations

Social media, AI systems, IoT sensors, and other platforms generate vast amounts of streaming data. Given this vast volume of information, techniques that can reduce and aggregate data into meaningful topics are essential. One such technique is the two-phase stream clustering approach. In the first, online micro-clustering phase, the system forms micro-clusters from the incoming data stream, incrementally merges new items into related existing micro-clusters, and prunes or fades micro-clusters as they become inactive, producing a constantly updating yet compact set of micro-clusters representing potential topics and subtopics of the stream. In the second, offline macro-clustering phase, these micro-clusters are …

Patient Subset Classification Using Encoded Embeddings And Knowledge Graph Retrieval-Augmented Generation, Benjamin A. Holmes

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The widespread adoption of electronic medical records has created a vast reservoir of clinical data that can be leveraged to better understand how interventions relate to patient outcomes. Much of this information, however, exists as unstructured free-text, posing significant challenges for traditional statistical and machine-learning methods. Solving these challenges would allow the extraction of specific patient subpopulations (clinically relevant cohorts of individuals who share overlapping symptoms, risk factors, or diagnostic criteria), which could be used in precision medicine. Despite this promise, extracting these subpopulations from unstructured medical notes is an ongoing challenge due to the variability of clinical language and …

Generative Adversarial Networks (Gans) For High-Dimensional Biological Data, Harigovind Harikumar

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This thesis investigates the application of Generative AI models, mainly Generative Adversarial Network (GAN) models to high dimensional and low sample size biological datasets like Motion Sickness, Breast Cancer, Crohn, and Melanoma. We utilized and compared three generative AI frameworks: Vanilla GAN, Wasserstein GAN (WGAN), Locality-Sensitive Hashing GAN (LSH-GAN) and Omics GAN. To address the challenges associated with high-dimensionality and low sample size, which was leading to very poor outputs of biological synthetic samples, we came up with an approach to stop the model when it reaches its saturation level. That is, we printed the loss plots to see where …

Pixmix Attack: Implementation And Evaluation Of A Novel Pixel Injection On Digital Video Port (Dvp) Interface In Embedded Camera Systems With Pcb Hardware Trojan, Sayed Md Tashfi Nowroz

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Image sensors are at the heart of machine vision systems in robotics, industrial automation, and surveillance systems which ideally operate with minimal human supervision and only occasional maintenance. The image sensors convert visible light into electrical signals which are locally decoded to image on the printed circuit board (PCB) by an ordinary embedded processor System on Chip (SoC). This thesis investigates a critical vulnerability in such systems, targeting the communication protocol at the signal level during runtime. Specifically, it focuses on a novel attack in the Digital Video Port (DVP) protocol, possible to exploit with PCB-based hardware Trojans, to craft …

A Secure Ml-Assisted Framework For Resilient And Efficient Prediction Of Physiotherapy Sequence In Bilateral Carpal Tunnel Syndrome, Pratik Pandurang Kharat

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Bilateral idiopathic carpal tunnel syndrome (CTS) is a neuromuscular disorder characterized by compression of the median nerve at both wrists, leading to symptoms such as pain, numbness, tingling, and muscle weakness. Unlike unilateral cases, bilateral idiopathic CTS presents distinct therapeutic challenges due to the simultaneous involvement of both hands and the lack of an identifiable underlying cause. This study explores the application of machine learning techniques to predict the optimal sequence of physiotherapeutic interventions Stretching followed by Myofascial Mobilization (S/M) or the reverse (M/S) in female patients with bilateral idiopathic CTS and right hand dominance. Data were drawn from a …