Physical Sciences and Mathematics Commons^™

Development Of An Experimentally Validated Computational Fluid Dynamics Model For A Pbi Membrane-Based Anhydrous Hcl Electrolyzer, Kris Likit-Anurak, Hunter R. Teel, Ishwor Karki, Ben I. Howard, Brian C. Benicewicz, Sirivatch Shimpalee, Benjamin H. Meekins

Faculty Publications

We report the development of an experimentally-validated computational fluid dynamics (CFD) model for simulation of an anhydrous HCl electrolyzer. The experimental data from 3 membrane variants was used to provide kinetic and membrane parameters for the model. The model not only accurately simulates overall electrolyzer performance, but it also provides key insights into the transport phenomena within the electrolyzer. The model allows simulation of experimental parameters like high HCl flowrates and increased cell pressure that pose a high safety risk to researchers. The model shows hotspots in the temperature distribution that will need to be addressed by flow field modification …

Latent Auto-Recursive Composition Engine: A Generative System For Creative Expression In Human-Ai Collaboration, Yenkai Huang

Computer Science Senior Theses

This thesis investigates the shifting boundaries of art in the era of Generative AI, crit-
ically examining the essence of art and the legitimacy of AI-generated works. Despite
significant advancements in the quality and accessibility of art through generative
AI, such creations frequently encounter skepticism regarding their status as authentic
art. To address this skepticism, the study explores the role of creative agency in var-
ious generative AI workflows and introduces an ”artist-in-the-loop” system tailored
for image generation models like Stable Diffusion. This system aims to deepen the
artist’s engagement and understanding of the creative process. Additionally, a novel
tool, …

Scalable Pythagorean Mean-Based Incident Detection In Smart Transportation Systems, Md Jaminur Islam, Jose Paolo Talusan, Shameek Bhattacharjee, Francis Tiausas, Abhishek Dubey, Keiichi Yasumoto, Sajal K. Das

Computer Science Faculty Research & Creative Works

Modern smart cities need smart transportation solutions to quickly detect various traffic emergencies and incidents in the city to avoid cascading traffic disruptions. to materialize this, roadside units and ambient transportation sensors are being deployed to collect speed data that enables the monitoring of traffic conditions on each road segment. in this article, we first propose a scalable data-driven anomaly-Based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. …

Inchi Isotopologue And Isotopomer Specifications, Hunter N. B. Moseley, Philippe Rocca-Serra, Reza M. Salek, Masanori Arita, Emma L. Schymanski

Markey Cancer Center Faculty Publications

This work presents a proposed extension to the International Union of Pure and Applied Chemistry (IUPAC) International Chemical Identifier (InChI) standard that allows the representation of isotopically‑resolved chemi‑ cal entities at varying levels of ambiguity in isotope location. This extension includes an improved interpretation of the current isotopic layer within the InChI standard and a new isotopologue layer specification for representing chemical intensities with ambiguous isotope localization. Both improvements support the unique isotopically‑ resolved chemical identification of features detected and measured in analytical instrumentation, specifically nuclear magnetic resonance and mass spectrometry.

Scientific contribution

This new extension to the InChI standard …

Improved Models Of Elastic Scheduling, Marion Baumli Sudvarg

McKelvey School of Engineering Graduate Student Theses & Dissertations

In real-time computing systems, \textit{timely} execution is a requirement of \textit{correct} execution. Such systems are widely found in robotics and autonomous vehicle applications, mobile spectrometry of atmospheric aerosols, real-time hybrid simulation for natural hazards engineering, and in prompt localization of transients such as gamma-ray bursts for time-domain and multi-messenger astrophysics. \textit{Elastic scheduling} provides a framework to adjust computational rates and workloads in systems for which timeliness cannot otherwise be guaranteed. While originally proposed for periodic tasks executing on a single processor, elastic scheduling has since been extended to sequential and parallel execution on multiple processors and to earliest deadline first …

Tree Recovery By Dynamic Programming, Gustavo Alberto Gratacos

McKelvey School of Engineering Graduate Student Theses & Dissertations

Tree-like structures are common, naturally occurring objects that are of interest to many fields of study, such as plant science and biomedicine. Analysis of these structures is typically based on skeletons extracted from captured data, which often contain spurious segments or cycles that need to be removed. We propose a dynamic programming algorithm which seeks to recover directed trees from these noisy skeletons. Our method recovers trees by removing edges and duplicating nodes while adhering to edge-label constraints. Our algorithm proceeds by iteratively merging graph nodes, such that the solution on the original graph can be obtained from those on …

Exploring Neural Networks For Breast Cancer Tissue Classification, Stephen Jacobs, Md Abdullah Al Hafiz Khan

Symposium of Student Scholars

Last year, more than 240 thousand women in the United States were diagnosed with breast cancer. These patients are benefitting from decades of data that have been collected by cancer research institutions around the world. Tissue samples are analyzed and cataloged by these institutions, and several facilities like the University of Wisconsin are sharing this historical data to promote the advancement of new cancer treatments. Deep learning and neural network models are being built for this data to help doctors diagnose faster and design treatment options for patients by comparing their tissue samples with these historical datasets. We will use …

Effects Of Ti Addition On The Characteristics Of Al-10zn-6mg-2si/Zro2 Composites Produced By Squeeze Casting, Qesha Diva Prameshvara, Pipin Indah Lestari, Bondan Tiara Sofyan

Journal of Materials Exploration and Findings

Metal matrix composite (MMC) with 7xxx aluminum matrix is potential for ballistic applications due to the combination of strength, toughness, and light weight. Previous study successfully produced aluminum-based composites with SiC particles which were able to stop type III bullet, however cracks remained on back of the plate. Therefore, in this research, SiC was replaced by zirconia (ZrO₂) due to its high fracture toughness. Ti-B grain refiner was added to further improve toughness through grain boundary strengthening mechanism. This research developed 5 vol.% ZrO₂ strengthened Al-10Zn-6Mg-2Si composite with addition of Al-5Ti-1B grain refiner produced through squeeze casting …

Integration Of The Ashby Technique And Pahl-Beitz Quantitative Ranking For Railway Axle Material Selection, Helya Chafshoh Nafisah, Jaka Fajar Fatriansyah, Siti Norasmah Surip

Journal of Materials Exploration and Findings

Railway axle serves as a vital connection between the train's wheels and its body. However, cyclic loading and high speed can induce fatigue in railway axle, which potentially leads to damage human safety. Therefore, it is important to find materials that have good mechanical properties with the lowest weight and cost. In this paper, a comprehensive method using Ashby chart has been performed to select candidate materials of railway axle. The methods begin with analyzing function by determining the problem, objective function, and constraints. After that, the results obtained are ranked using Pahl and Beitz quantitative weighting method. The results …

Analysis Of The Effect Of Different Surface Preparation Methods On Corrosion Resistance And Adhesion Strength Of Astm A36 Steel Substrate With Surface Tolerant Epoxy Paint As Coating Material, Irwan Wijaya Santoso, Daffa Aqila, Rini Riastuti, Rizal Tresna Ramadhani

Journal of Materials Exploration and Findings

In the industrial world, to extend the service life of materials, protection methods are carried out to slow down the material's corrosion rate. The protection method that is often used is the coating method. The coating method is a protection method by coating the substrate material using a coating material to prevent contact between the substrate material and the environment. In this research, the substrate material used is ASTM A36 steel and the coating material used is Surface Tolerant Epoxy paint. The independent variable used in this study lies in the surface preparation method which consists of: solvent cleaning, hand …

Impacts Of El Niño On The Florida Reef: A Case Study Of The 2016 El Niño Event, Evvie Yendra

Department of Environmental Studies: Undergraduate Student Theses

Coral reefs are vital ecosystems to coastal communities and the world’s oceans. They support other organisms, protect shorelines, and benefit coastal economies through fishing and recreation. Today, coral reefs face many threats, including ocean acidification, bleaching due to increased ocean temperatures, and disease. These threats are typically heightened during El Niño events across the globe. With a focus on the Florida Reef, these data encompass three key regions (Southeastern Florida, The Florida Keys, and Dry Tortugas) over the years 2014, 2016 (an El Niño event), and 2018. As 2024 is interpreted to be an El Niño year, this study predicts …

Strongly Stated And Loosely Held: Examining Stewart Brand's Role In The Ecomodernist Project, Liam J. Baker

Department of Environmental Studies: Undergraduate Student Theses

With the ecomodernist environmental perspective emerging in the wake of the 2015 publication of An Ecomodernist Manifesto, many researchers have sought to critique the ecomodernists and their theories of separating humanity from nature, intensifying agriculture, expanding energy production, and relying on technology to resolve the environmental challenges of the 21st century. In this paper, I add to these critiques by examining the history of Stewart Brand, one of the coauthors of An Ecomodernist Manifesto, to highlight connections between his personal environmental perspective, his material impacts, and the ecomodernist project as a whole. By analyzing the discourse produced by and about …

Accord: Constraint-Driven Mediation Of Multi-User Conflicts In Cloud Services, Abhiroop Tippavajjula, Primal Pappachan, Anna Squicciarini, Jose Such

Computer Science Faculty Publications and Presentations

When multiple users adopt collaborative cloud services like Google Drive to work on a shared resource, incorrect or missing permis- sions may cause conflicting or inconsistent access or use privileges. These issues (or conflicts) compromise resources confidentiality, integrity, or availability leading to a lack of trust in cloud services. An example conflict is when a user with editor permissions changes the permissions on a shared resource without consent from the orig- inal resource owner. In this demonstration, we introduce ACCORD, a web application built on top of Google Drive able to detect and resolve multi-user conflicts. ACCORD employs a simulator …

Toward Intuitive 3d Interactions In Virtual Reality: A Deep Learning- Based Dual-Hand Gesture Recognition Approach, Trudi Di Qi, Franceli L. Cibrian, Meghna Raswan, Tyler Kay, Hector M. Camarillo-Abad, Yuxin Wen

Engineering Faculty Articles and Research

Dual-hand gesture recognition is crucial for intuitive 3D interactions in virtual reality (VR), allowing the user to interact with virtual objects naturally through gestures using both handheld controllers. While deep learning and sensor-based technology have proven effective in recognizing single-hand gestures for 3D interactions, research on dual-hand gesture recognition for VR interactions is still underexplored. In this work, we introduce CWT-CNN-TCN, a novel deep learning model that combines a 2D Convolution Neural Network (CNN) with Continuous Wavelet Transformation (CWT) and a Temporal Convolution Network (TCN). This model can simultaneously extract features from the time-frequency domain and capture long-term dependencies using …

05.13.2024 Orsp Connect, Liz Williamson

ORED Newsletter

Distinguished Research and Creative Achievement Award, Center for Practical Ethics, DARPA Connect, Air Force Internship

Landscape Influences On Thermal Sensitivity And Predicted Spatial Variability Among Brook Trout Streams In The Southeastern Usa, George P. Valentine, Xinyi Lu, C. Andrew Dolloff, Craig N. Roghair, Jacob M. Rash, Mevin B. Hooten, Yoichiro Kanno

Mathematics and Statistics Faculty Publications

Warming water temperatures as a result of climate change pose a major threat to coldwater organisms. However, the rate of warming is not spatially uniform due to surface-ground-water interactions and stream and watershed characteristics. Coldwater habitats that are most resistant to warming serve as thermal refugia and identifying their locations is critical to regional aquatic conservation planning. We quantified the thermal sensitivity of 203 streams providing current and potential habitat for brook trout (Salvelinus fontinalis) across nearly 1000 linear km of their native range in the southern and central Appalachian Mountains region, USA, and characterized their spatial variability …

Building A Data Pipeline And Machine Learning Model For Insurance Data, Connor Weyers

Honors Program: Senior Projects (Public)

Insurance telematics is an emerging and exciting field. It combines the advancements in GPS tracking, computational analytics, data processing, and machine learning into a useful tool to help insurance companies make the best product for their consumers. This is why National Indemnity looked to implement a telematics portion to their business processes of underwriting insurance policies and sponsored a School of Computing Senior Design project. In this report, we will first review existing solutions that been used to solve problems and subproblems similar to that we are given in this project. We then propose designs for the data pipeline and …

Assessing Reproducibility Of Brain-Behavior Associations Using Bootstrap Aggregation Methods, Zhetao Chen

Arts & Sciences Graduate Student Theses and Dissertations

在本论文中，随着越来越多地利用静息态功能连接 MRI (rs-fcMRI) 将神经活动与病理状况联系起来，我们面临着对此类数据可靠性的普遍担忧。我们的探索集中于提高人类连接组计划（HCP）数据集框架内大脑行为关联的可重复性。我们采用两种不同的引导聚合方法来研究功能连接可靠性的增强：使用循环块引导（CBB）的单独时间序列装袋和使用线性支持向量回归（LSVR）模型的主题级装袋。我们对 CBB 个体时间序列 bagging 的调查表明，这种方法并不能显着增强大脑行为关联的可重复性。这一发现指出了实现可靠的功能连接措施的复杂性以及某些聚合方法在克服这一挑战方面的局限性。相比之下，我们的学科水平考试 通过 LSVR 模型装袋呈现出更有希望的结果。这种方法显着增强了分析之间模型权重的可靠性，证明了其在提高数据稳健性和可重复性方面的功效。两种方法的这种不同影响强调了适当的分析策略在提高神经影像数据可靠性方面的关键作用。通过描述这两种方法的结果，本论文有助于对神经影像领域的数据可靠性进行更广泛的讨论。它强调了在不同数据集上持续进行方法创新和验证的必要性，以提高 rs-fcMRI 研究的可靠性和可解释性。

Mending Trust In Ai: Trust Repair Policy Interventions For Large Language Models In Visual Data Journalism, Hangxiao Zhu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Trust in Large Language Models (LLMs) emerged as a pivotal concern. This is because, despite the transformative potential of LLMs in enhancing the interpretability and interactivity of complex datasets, the opacity of these models and instances of inaccuracies or biases have led to a significant trust deficit among end-users. Moreover, there is a tendency for people to personify AI tools that utilize these LLMs, attributing abilities and sensibilities that they do not truly possess. This thesis exploits this personification and proposes a comprehensive framework of trust repair policies tailored to address the challenges inherent in LLM annotations within data journalism …

Characteristics Based Factor Models - Comparison Of Estimation Procedures, Henri Ohl

McKelvey School of Engineering Graduate Student Theses & Dissertations

Understanding cross-sectional and time series variation of asset returns is fundamental in finance, particularly in asset pricing. This thesis explores the integration of factor theory with machine learning to deepen our comprehension of these dynamics. Characteristics based factor models offer a systematic framework for quantifying an asset's underlying risk-return structure, leveraging time-varying conditional information on model parameters carried by firm-specific characteristics. These models serve as valuable tools for discerning the driving components of an asset's expected excess return. Recent research established a novel methodology for consistent parameter estimation within this framework, only requiring a large cross-section but not a long …

Thermal Physiology Of Sceloporus Olivaceus And Sceloporus Variabilis In South Texas, Ian Rockel

Masters Theses (Archived)

This study explores the thermal physiology of two sympatric lizard species, Sceloporus olivaceus (Texas spiny lizard) and Sceloporus variabilis (rose-bellied lizard), in South Texas. I document the metabolic rate and its temperature sensitivity, thermal limits, and performance at different temperatures to better understand the impact of rising temperatures and urbanization on these ectotherms, whose body temperature and, consequently, metabolic rate, activity level, and reproductive success depend on their thermal environment. We hypothesize that S. olivaceus will exhibit greater thermal resilience owing to its broader latitudinal range, variable habitat usage within Texas, and presumed more versatile thermoregulatory strategies. Scelorporus olivaceus's higher …

America's Next Top Ej Model: Assessing The Role Of Food Access In Environmental Justice Models, Allyson Barry

Department of Environmental Studies: Undergraduate Student Theses

Disruptions to weather patterns and ecosystems due to climate change are a global challenge. However, previous research has found that communities of color, traditionally underprivileged communities, and socioeconomically disadvantaged communities are disproportionately burdened with adverse health outcomes, higher propensities of exposure to weather disasters and pollution, and well-being inequalities. The field of environmental justice (EJ) seeks to eliminate such disparities through environmental laws, regulations, and policies. Published in 2022, the Centers for Disease Control and Prevention (CDC) and the Agency for Toxic Substances and Disease Registry (ATSDR) formulated the Environmental Justice Index (EJI) as a data tool to identify communities …

Sub-Stage Climatic Shifts During Mis 11 Refined From Diatom Assemblage Reconstruction In The Valles Caldera, New Mexico, Savannah Cutler

Earth and Planetary Sciences ETDs

Diatom assemblage reconstruction from the Valles Caldera sediment core (VC-3) was utilized to infer climatic and aquatic changes during a 50-thousand-year [ka] period in the Mid-Pleistocene (435-385 ka). This 50 ka interval spans most of Marine Isotope Stage 11 (MIS 11) – one of the most prominent interglacials of the past 500 ka – and is widely considered an analog for the Holocene and future climate regimes. Nearly all records from MIS 11 are either marine or ice-core based, making VC-3 unique among lacustrine sediment records as it captures insolation variations and climatic changes during the mid-Pleistocene. Analysis of the …

Analysis And Computation Of Constrained Sparse Coding On Emerging Non-Von Neumann Devices, Kyle Henke

Mathematics & Statistics ETDs

This dissertation seeks to understand how different formulations of the neurally inspired Locally Competitive Algorithm (LCA) represent and solve optimization problems. By studying these networks mathematically through the lens of dynamical and gradient systems, the goal is to discern how neural computations converge and link this knowledge to theoretical neuroscience and artificial intelligence (AI). Both classical computers and advanced emerging hardware are employed in this study. The contributions of this work include:

1. Theoretical Work: A comprehensive convergence analysis for networks using both generic Rectified Linear Unit (ReLU) and Rectified Sigmoid activation functions. Exploration of techniques to address the binary …

Avoiding Pitfalls In Molecular Simulation Of Vapor Sorption: Example Of Propane And Isobutane In Metal–Organic Frameworks For Adsorption Cooling Applications, Filip Formalik, Haoyuan Chen, Randall Q. Snurr

Physics & Astronomy Faculty Publications

This study introduces recommendations for conducting molecular simulations of vapor adsorption, with an emphasis on enhancing the accuracy, reproducibility, and comparability of results. The first aspect we address is consistency in the implementation of some details of typical molecular models, including tail corrections and cutoff distances, due to their significant influence on generated data. We highlight the importance of explicitly calculating the saturation pressures at relevant temperatures using methods such as Gibbs ensemble Monte Carlo simulations and illustrate some pitfalls in extrapolating saturation pressures using this method. For grand canonical Monte Carlo (GCMC) simulations, the input fugacity is usually calculated …

Burning Calories, Burning Ocean: Metabolic Rate In Bivalves As A Predictor Of Extinction Selectivity Through Time And During Rapid Global Warming, Siddharth Gavirneni

Theses - ALL

Organismal metabolic rate is linked to environmental temperature and oxygen consumption, and as such, may be a useful predictor of extinction risk during major climate- driven crises. However, metabolic attributes can be quantified in many ways, highlighting differing aspects of organisms’ ecology. Here, we estimate resting whole-body and mass-specific metabolic rates in post-Carboniferous bivalve taxa using body size and seawater paleotemperature to assess how metabolic rate determines survival both during and outside of intervals of rapid climate warming, or ‘hyperthermals’. Accounting for the effects of geographic range size, we find a pattern of preferential extinction of bivalves with lower total …

Stochastic Algorithms For Simulating Protein Assembly In Biological Cells, Advait Shukla

Theses - ALL

Understanding membrane protein assembly using computational methods is critical in developing drug delivery strategies for tight junction diseases such as Alzheimer’s and Crohn’s diseases. While experimental methods, such as freeze-fracture micrographs, provide microscopic details of the protein strand networks, they cannot offer molecular-level information, which is necessary for drug design. This work attempts to close the gap by computationally predicting strand networks of the claudin family of proteins that form tight junctions across neighboring cells in epithelial and endothelial tissues. Using claudin dimer data collected by the Protein AssociatioN Energy Landscape (PANEL) method coupled with some Markovian approximations, we built …

Development And Application Of Photoresponsive Small Molecule Probes To Modulate And Characterize Neuroreceptors, Spencer Kim

Dissertations - ALL

The field of neuroscience is rapidly evolving and so the development of novel tools that support innovative research is urgently needed. Two tools that have proven to be particularly impactful to the field of neuroscience are activity-based protein profiling (ABPP) and optogenetics, both of which have seen widespread application but with relatively limited advancement of the underlying technology. Limitations of ABPP and optogenetics have become increasingly apparent and research output is stalling due to lack of technological advancement. For example, few systems exist for targeting low-abundance and unstable proteins via ABPP and application of ABPP and optogenetic systems thus far …

Development And Progress Towards Functional Photoresponsive Systems, Jasmine Gomez

Dissertations - ALL

Chapter 2: Surface Functionalization of Thermally-Expandable Microspheres with Gold Nanoparticles The advancement of methods for controlling motion on a variety of scales is significant in order to develop finely tuned next-generation devices. Here, we’ve created a micron-scale actuator system triggered via photothermal activation and capable of powering macro-scale movement in an elastomer matrix. This system fundamentally relies on thermally-expandable microspheres (TEMs) – a core-shell complex containing a polymeric shell and liquid core that expands dramatically upon heating. To improve the ability of TEMs as soft-actuators controlled via light, we grafted a secondary silicate-silane copolymer shell to attach gold nanoparticles. The …

A Molecular Toolbox To Study Dopamine And Serotonin Receptors For The Development Of Improved Therapeutics, Emma Joanna Doukmak

Dissertations - ALL

Dopaminergic and serotonergic signaling pathways are involved in numerous biological processes including behavior, mood regulation, and cognition. Dysfunction in these pathways can lead to neurological and mental health disorders, which are often treated with drugs targeting the dopamine and serotonin receptors. However, many of these drugs, along with those targeting other G protein couple receptors (GPCRs), suffer a lack of target specificity, leading to nonspecific binding and therefore side effects. In recent years, new advances in chemical biological techniques, including affinity-based protein profiling (ABPP) and optogenetics, have further elucidated the structure and function of GPCRs, which can in turn aid …