Digital Commons Network^™

(R2137) Smarandache Curved Ruled Surfaces And Their Characterizations According To Modified Orthogonal Frame In E^3, Gülnur Şaffak Atalay

Applications and Applied Mathematics: An International Journal (AAM)

In this study, the ruled surfaces generated by Smarandache curves are expressed according to the modified orthogonal frame defined according to both curvature and torsion of the given unit speed curve in 3-dimensional Euclidean space; some special characterizations of these surfaces such as developability, striction curves, distribution parameters are given. In addition, some examples of these surfaces are given in graphical form. We use Maple to draw the graphs of the examples.

(R2130) Cusum-Test For Unconditional Variance Change Detection In Bilinear Garch Models, Edoh Katchekpele, Abdou Kâ Diongue, Ben Célestin Kouassi

Applications and Applied Mathematics: An International Journal (AAM)

We examine CUSUM-type test for detecting changes in unconditional variance within Bilinear GARCH models. We derive the asymptotic distribution of the test statistic under both null and alternative hypotheses and assess test effectiveness in identifying single structural breaks. Simulation studies support our theoretical results and demonstrate the practical utility of the test.

A Combined Multi-Isotope And Machine Learning Approach To Determine Groundwater Salinity And Solute Sources, Lower Valley Area, El Paso County, Texas, Gloria A. Ortiz Gamboa

Open Access Theses & Dissertations

Groundwater salinization is increasingly troublesome in aquifers in and around El Paso, Texas, where concern for available water resources has grown due to frequent droughts and increasing population. Both groundwater and surface water have experienced an increase in total dissolved solids (TDS), a trend that can pose future health and economic problems for residents who primarily rely on groundwater. To address this issue, this research focuses on the Hueco Bolson Aquifer and the Rio Grande Alluvial Aquifer within the Lower Valley area of El Paso, Texas, utilizing a multi-analysis approach to evaluate and determine solute sources and their possible end-members …

Marsanywhere: Dataset And Cross-View Diffusion Model For Satellite-To-Ground View Synthesis With Mars Data, Benjamin T. Hinchliff

Master's Theses

Satellite-to-ground view synthesis aims to create a realistic ground view image from a corresponding satellite view image. This is a well-studied problem for street level imagery, with good results being achieved by using modern image synthesis techniques such as diffusion models. However, despite the public availability of satellite and ground level imagery on Mars, these techniques have yet to be applied to the domain due to difficulties in collating and processing the data into a usable form. We address this deficiency by creating a dataset consisting of ground view panorama imagery from the Perseverance rover, along with associated satellite view …

The Moderating Effect Of Income Inequality On The Income–Emissions Relationship In G20 Countries, Zahra Rizky Fadilah, Budiasih Budiasih

Economics and Finance in Indonesia

This study analyzes the moderating effect of income inequality on the income–emissions relationship in the environmental Kuznets curve (EKC) framework. Findings indicate that the relationship is inverted U-shaped in middle-income G20 countries, but monotonically increasing in high-income G20 countries. Interestingly, income inequality moderates this relationship only in the latter group. These findings suggest that middle-income G20 countries should focus on raising income per capita to mitigate environmental degradation, while their high-income counterparts need to prioritize reducing income inequality to effectively decouple income from emissions.

Numerical Calculations Of The Electromagnetic And Gravitational Wave Signatures Of Unequal Mass Black Hole Binary Inspirals, Madeline Clyburn

All Dissertations

In 2015, the Laser Interferometer Gravitational-Wave Observatory (LIGO) measured the first gravitational waves (GWs) from the coalescence of two stellar-mass black holes. This detection marked a historic milestone in astrophysics, confirming general relativity and establishing the existence of gravitationally bound black holes. Since then, researchers have extended the search to even more massive black hole mergers. Recently, the International Pulsar Timing Array (IPTA), including the North American Nanohertz Observatory for Gravitational Waves (NANOGrav), detected a nanohertz GW background from supermassive black hole binaries (SMBHBs), black holes with masses M ≃10^7−10^9 M⊙ orbiting throughout the universe. However, a single SMBHB has …

Dual-Model Approach For Accurate Chest Disease Detection Using Gvit And Swin Transformer V2, Kamal Ahmad, Hafeez Ur Rehman, Babar Shah, Farman Ali, Irfan Hussain

All Works

The precise detection and localization of abnormalities in radiological images are very crucial for clinical diagnosis and treatment planning. To build reliable models, large and annotated datasets are required that contain disease labels and abnormality locations. Most of the time, radiologists face challenges in identifying and segmenting thoracic diseases such as COVID-19, Pneumonia, Tuberculosis, and lung cancer due to overlapping visual patterns in X-ray images. This study proposes a dual-model approach: Gated Vision Transformers (GViT) for classification and Swin Transformer V2 for segmentation and localization. GViT successfully identifies thoracic diseases that exhibit similar radiographic features, while Swin Transformer V2 maps …

Curvilinear Image Segmentation Using Multiscale Variational U-Net, Rebekah Fortes

LSU New Orleans Theses and Dissertations

Segmentation of curvilinear structures such as water contours, cracks in cement, and vascular networks in biomedical imaging, poses unique challenges due to extreme class imbalance, irregular morphology, low contrast against complex backgrounds, and the need to preserve global connectivity while detecting fine-scale details. We propose a Multiscale Variational U-Net (MSVU-Net) architecture designed specifically to address these challenges. The model integrates multiscale convolutional filters to capture both global context and local detail, while embedding a variational model in the bottleneck layer to enhance structural representation. To mitigate class imbalance and improve fidelity, the network optimizes a hybrid loss function that combines …

Coupled Machine Learning Models: Combining Observations And Numerical Analysis In A Physics-Regularized Approach, Austin B. Schmidt

LSU New Orleans Theses and Dissertations

This dissertation investigates surrogate modeling for fixed-location environmental forecasting using novel data-combination techniques. The work surveys the landscape of observational measurements and numerically generated data, identifying similar research and gaps in current methodologies. The ratio-coupled training framework is introduced to combine two data sources per predicted feature through a tunable parameter that weights training signal strength. An optimization scheme is developed to simultaneously tune surrogate weights and the coupled signal ratio, allowing relative influence between signals to act as an explicit regularizer. Three case studies demonstrate the methodology and approach in a variety of contexts. The first study is based …

Peptoid-Based Molecular Analysis Utilizing High-Speed Molecular Dynamics Methods, In Chul Hwang

LSU New Orleans Theses and Dissertations

Polypeptoids are N-substituted glycine polymers, which differ from peptides in the placement of the side chain being present on the amide nitrogen rather than the backbone C_α. While both peptoids and peptides are composed of linked amino acids, the structural changes resulting from the differing origin of the side chain leads to distinct backbone structure, as well as remove the chirality found in the peptide structure. These differences lead peptoids to have different physiochemical and biological properties, such as being resistant to proteolysis and diverse three-dimensional structures that are not observed in peptides. With the shifting of the …

(R2115) Analysis Of Single Server Queueing System With Differentiated Vacations And Differentiated Breakdowns, V. Karthick, V. Suvitha

Applications and Applied Mathematics: An International Journal (AAM)

This research work considers a single server queueing model with differentiated vacations. In addition there is a possibility of two types of failures when the server is in a busy period; namely hard failure and soft failure. In the time of soft failure server may work with a slow service rate. We analyzed as a Quasi-Birth-and-Death (QBD) process, using the matrix geometric method, the steady state probability vector of the number of customers in the queue and the stability conditions are produced. Busy period analysis of the proposed model in given. The effects of various parameters on the system performance …

(R2132) A Multi Server Markovian Working Vacation Queue With Randomly Varying Environment, A. Sundaramoorthy, R. Kalyanaraman

Applications and Applied Mathematics: An International Journal (AAM)

In this article, we consider a multi server Markovian queueing system with working vacation. During busy period, the arrival and service completion are generated by K distinct randomly varying environments. At a service completion epoch, if no customer in the system, the servers take vacation, the vacation policy is multiple vacation policy and the vacation period follows negative exponential distribution. In addition, during vacation period the servers serve customers if they arrive. Based on the vacation termination point we define two Models. For the two models, the steady state probability vector of number of customers in the queue, the stability …

(R2135) System Dynamical Analysis For Ann-Based Numerical Solutions Of A Compartmental Model: A Bio-Mathematical Model Of Drug Diffusion Through The Compartments Of Blood And Tissue, Rakesh Kumar, Sudarshan Dhua

Applications and Applied Mathematics: An International Journal (AAM)

This paper provides a considerably efficient numerical approach to acquire the solutions of a biomathematical model administrating oral and intravenous distribution of pharmaceuticals in the human body. The proposed numerical approach based on an artificial neural network is employed to extract numerical solutions for a detailed set of ordinary differential equations and analyze the change in concentration of drug diffusion via the compartments of blood and tissue medium. We primarily focus on analyzing three different models established on the diffusion process, exercising laws of mass action and Fick’s principle. In this work, the existing model is reformulated as an optimization …

(R2107) Analysis Of A Bivalent Vaccine Model With Peer Influence Effect On Testing, Manoj Kumar Singh, Anjali .

Applications and Applied Mathematics: An International Journal (AAM)

The coronavirus caused havoc around the world. There was a terrible situation in villages and cities, and no one knew how to deal with it. Although the governments of each country tried their best to save the common people, vaccination programs and testing centers were built everywhere. However, people were not utilizing it due to fear. Because of this, the infection spread rapidly. The qualitative study of the mathematical model here is in context with the situation when bivalent vaccination and testing are available for an epidemic. The mathematical model combines the exposed period and influenza model with vaccination included …

Topic Modeling And Culturomic Analysis Of 30,000 Books Over 100 Years Using Gensim, Michael A. Freeman

Electronic Theses and Dissertations

This thesis explores the cultural influence of historical events on English-language fiction published between 1820 and 1929. Using a corpus of 30,256 digitized books from Project Gutenberg, Latent Dirichlet Allocation (LDA) topic modeling was applied to identify recurring themes across eleven decades. The study sought to determine whether historically significant events could be detected within fictional narratives. One clear instance emerged: Napoleon Bonaparte and the Napoleonic Wars appeared explicitly in the 1820s corpus. Beyond this, several thematic patterns were observed—such as maritime language in the 1840s, national identity in the 1880s, and youth-oriented dialogue in the early 20th century—that plausibly …

By The Numbers: Numeracy, Religion, And The Quantitative Transformation Of Early Modern England (Book Review), Calvin Jongsma

Faculty Work Comprehensive List

Reviewed Title: By the Numbers: Numeracy, Religion, and the Quantitative Transformation of Early Modern England by Jessica Marie Otis. New York, NY: Oxford University Press, 2024. 264 pp. ISBN: 9780197608777.

Understanding Bias And Fairness In Large Language Models: An Empirical Study, Joshua Johnson

Electrical Engineering and Computer Science Undergraduate Honors Theses

This thesis investigates demographic bias in large language models (LLMs) through the use of evaluating outcome disparities when utilized in decision making tasks as well as underlying associations that could contribute to furthering these disparities. Using profiles from the Adult dataset, we analyze how Gemini 2.0 Flash performs in an income prediction task using zero-shot and few-shot prompting methods. Our findings show that models exhibit measurable differences in demographic parity and false positive rates, with the use of few-shot prompting reducing these disparities. Alongside this line of testing, we tested associational bias in Qwen 2.5 using probability based association tests …

Geochemical Analysis Of The Eagle Ford Shale In Webb And Dimmit Counties, Tx, Natalie G. Girlinghouse

Electronic Theses and Dissertations

The Late Cretaceous Eagle Ford Shale of South Texas is a mixed carbonate–siliciclastic succession that serves as both a prolific hydrocarbon source and reservoir. This study integrates chemostratigraphy, mineralogical analysis, and elemental ratio proxies to reconstruct depositional environments and assess controls on detrital influx, paleoproductivity, and redox conditions across multiple cores in Webb and Dimmit counties. Major, trace, and redox-sensitive elements were evaluated alongside Total Organic Content (TOC) and carbonate content to identify stratigraphic trends and environmental shifts.

Results reveal distinct geochemical signatures between the Upper and Lower Eagle Ford Shale, with the Lower Eagle Ford characterized by elevated Al-rich …

Structural Geology Of The Brady Mountain Area, Ouachita Mountains, Arkansas: Integrating Field Mapping, Geospatial Techniques, Photogrammetry And 3d Modeling For Structural Analysis, Emmanuel Darko, Zachariah Fleming

Electronic Theses and Dissertations

Much of the Ouachita orogenic belt, representing the deformed Paleozoic rocks flanking the southern margin of the North American craton, is buried beneath post-orogenic Mesozoic and Tertiary sediments composing the Gulf Coastal Plain. The Ouachita Mountains of Arkansas and Oklahoma are the largest areas of outcrop for the Pennsylvanian-age Ouachita orogenic belt, making it an ideal place to study the orogeny. This study focuses on the Brady Mountain area within the Benton Uplift, the orogenic core of the Ouachita Mountains in western Arkansas. The region is comprised of numerous ridges trending nearly east-west and are for the most part densely …

Fractional Order Hierarchical Decompositions Using Multigrid Components, Panayot S. Vassilevski

Mathematics and Statistics Faculty Publications and Presentations

Motivated by the fractional order multilevel decompositions of finite element spaces developed previously, we exploit additive representations of popular multigrid (MG) cycles to design fractional order MG decompositions. The additive representations enable us to scale the individual hierarchical components thus ending up with fractional order hierarchical decompositions that are based on the readily available MG components. This results in a highly efficient and scalable (in terms of high-performance) fractional order hierarchical MG decompositions that we tested in the setting of finite element white noise sampling as an alternative to PDE-based white noise sampling using fractional order shifted Laplacians.

Navigating Seismic Anisotropy, With Applications To Alaska, Aakash Gupta

Geosciences

Seismic anisotropy characterizes how elastic waves travel through solid materials with different speeds, depending on the direction of the waves and the direction of particle motion of the waves. Evidence of seismic anisotropy in the Earth is widespread, from seismic waves sampling the crust, mantle and even the core, to active-source, industry-scale seismic experiments, to laboratory mea­ surements of rock samples from boreholes or at the surface. Global models of the Earth provide a highly averaged image of anisotropy. Studies based on shear wave splitting measurements from local and global earthquakes indicate that the anisotropy in the Earth is much …

Examination Of Differences Of Similar-Sized Martian Craters At The Simple-Complex Transition As Revealed By High-Resolution Imaging And Stereo-Derived Topography, Lindsey Dorn

Geosciences

Impact craters on Mars have a wide range of morphologies, and the relationship between the mechanics of their formation and their eventual morphologies is still not completely understood. By examining craters within a narrow diameter range, impact energies are constrained to similar values - enabling focused investigation of target properties and their influence on crater excavation and modification. There is a transition from simple bowl-shaped depressions to complex craters with terraced walls and central floor structures with increasing crater diameter. We analyzed target influences on morphology by examining eleven well-preserved craters of varying morphologies in the 7 to 9 km …

Ice Surface Change And Flow Dynamics Analysis Of Sít’ Tlein (Malaspina Glacier) Through Remote Sensing, Victor Devaux-Chupin

Geosciences

Sít’ Tlein, located in southeast Alaska, is the largest piedmont glacier in the world. Periglacial lake expansion, low-lying bed, and proximity to the Pacific Ocean put its glacier lobe at risk of rapid retreat. Its disappearance would equate to removing an area of land about half the size of Rhode Island. Sít’ Tlein exhibits complex ice flow responsible for its iconic folded moraines, and for its heterogeneous ice resupply in its lobe, which occasionally surges. This lobe is gradually mantled by debris and vegetation as the ice flow fades towards its margins, protecting it from melting. This the­ sis is …

Multi-Scale Insar Investigations Of Anthropogenic And Natural Processes In The American Southwest, Emily Jo Graves

Geosciences

Arid regions in the American Southwest undergo measurable surface deformation driven by variations in subsurface fluid pressure, and other natural and anthropogenic processes. These processes range from regional scale aquifer dynamics to local oil and gas wellsite activities. This dissertation produces and then utilizes InSAR-derived average velocity fields and displacement time series to reveal and understand these multi-scale processes, their interactions, and impacts for a portion of the American Southwest. Chapter 1 discusses the climate and natural and anthropogenic processes present in our study area. In Chapter 2, we generate detailed large scale average velocity maps that reveal deformation associated …

Security Vulnerabilities And Defense Tactics For Generative Ai Application Development, Kyle Klein

University Honors Theses

Generative AI (GenAI) applications such as OpenAI's ChatGPT leverage large language models (LLMs) trained on enormous amounts of data to accomplish tasks such as document editing, summarization, and query response. Chatbots and LLM programs that are equipped with retrieval-augmented generation (RAG) have the ability to draw upon data provided by developers and users to improve the quality of the program's responses. LLM technology has even expanded to generate images, audio, and video from user instructions. Designed around unpredictable user input and typically composed of many opaque components, LLM software products face a paradigm shift of new, constantly evolving security challenges. …

Content And Consequences: Impact Of Representation In Stem Higher Education Instructional Content On Marginalized Students, Nichole Ventura

Doctorate in Education

This qualitative study examined representation of historically marginalized students in STEM instructional content at the higher education level and its impact on their learning experiences. Despite growing diversity initiatives in STEM enrollment, curricular materials often fail to reflect the identities of underrepresented students. Using critical theory and interpretivist approaches, this research investigated how representation—or its absence—shapes students' sense of belonging, academic identity formation, and persistence. Through semi-structured interviews with undergraduate students from historically marginalized backgrounds, and purposeful sampling, this study captured the lived experiences of students engaging with STEM instructional materials. Interview protocols explored how students perceive their representation in …

Changepoint Detection As Model Selection: A General Framework, Michael A. Grantham

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation presents a general framework for changepoint detection based on ℓ₀ model selection. The core method, Iteratively Reweighted Fused Lasso (IRFL), improves upon the generalized lasso by adaptively reweighting penalties to enhance support recovery and minimize criteria such as the Bayesian Information Criterion (BIC). The approach allows for flexible modeling of seasonal patterns, linear and quadratic trends, and autoregressive dependence in the presence of changepoints.

Simulation studies demonstrate that IRFL achieves accurate changepoint detection across a wide range of challenging scenarios, including those involving nuisance factors such as trends, seasonal patterns, and serially correlated errors. The framework is …

Interactions Between Black, Brown, Blue And Green Carbon From Terrestrial To Marine Ecosystems: A Critical Review, Allison White, Richard Kline, Md. Saydur Rahman

School of Earth, Environmental, & Marine Sciences Faculty Publications

Black and brown carbon are byproducts of the incomplete combustion of biomass or fossil fuels. These light-absorbing aerosols influence climate by altering the radiative balance through the absorption and scattering of sunlight, exacerbating global climate change. Their subsequent deposition on terrestrial and marine environments affects surface albedo, snow and ice melt, and biogeochemical processes, leading to further environmental consequences. In contrast, green and blue carbon refer to carbon stored in terrestrial vegetation and marine ecosystems, respectively. These ecosystems play a crucial role in mitigating climate change by sequestering atmospheric carbon; however, they are increasingly threatened by climatic disturbances, anthropogenic activities, …

Leveraging Disparate Wildland Fire Datasets To Address Longstanding Fire Management Questions, Kendra Fallon

Boise State University Theses and Dissertations

While wildfire season is perceived as occurring in the warm months, and expanding, wildfire management is a year-long process. Juxtaposed to wildfire seasons are the reactive seasons of mitigating for post-fire effects, namely first-order effects of potential hydrological impacts in burn footprints, and the proactive implementation of fuels treatments to modify fire behavior to lessen the reactive workload. In this dissertation, I focus on answering lingering questions that are critical to the management of the shoulder seasons of wildfire season. To assist in resource allocation and identification of post-hydrological risks, I present a model identifying the major drivers of soil …

Deep Reinforcement Learning For Adaptive Mesh Refinement: A Training Parameter Study, Antone Chacartegui

Boise State University Theses and Dissertations

Adaptive mesh refinement (AMR) enables efficient solution of partial differential equations by dynamically allocating computational resources to regions requiring higher resolution. It has been demonstrated that deep reinforcement learning agents could learn effective mesh adaptation policies. However, practical deployment requires a systematic understanding of how training parameters affect performance across diverse computational scenarios.

This thesis presents a parameter optimization study analyzing the relationship between deep reinforcement learning for adaptive mesh refinement (DRL-AMR) training parameters and model performance. We implement an independent DRL-AMR system with a discontinuous Galerkin solver and actor-critic (A2C) algorithm for one-dimensional linear advection. The study explores four …