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Deep Tissue Massage-Induced Muscle Damage Associated With Delayed Onset Muscle Soreness (Doms), Stress, And Rhabdomyolysis, Lily M. Ekert, Ella R. Halopka, Micah Maddio, Marisa Wood, Bailey Kerkow, Logan Murphy, Ashley Wise Apr 2026

Deep Tissue Massage-Induced Muscle Damage Associated With Delayed Onset Muscle Soreness (Doms), Stress, And Rhabdomyolysis, Lily M. Ekert, Ella R. Halopka, Micah Maddio, Marisa Wood, Bailey Kerkow, Logan Murphy, Ashley Wise

Research & Creative Achievement Day

Deep tissue massage is a common treatment method used to aid in muscle recovery, reduce pain, and promote overall physical well-being. However, there is limited research explaining its biochemical effects at a cellular level, particularly in relation to muscle damage and systemic inflammation. Severe cases of muscle tissue breakdown are Rhabdomyolysis, which is discussed with an emphasis on clinical evidence rather than biochemical aspects. The goal of this study is to determine whether deep tissue massages cause cellular damage and tissue inflammation. This testing will be done through quantitative measurements of biomarkers within the bloodstream. In previous studies, four specific …

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Growing Alum Crystals, Jackson A. Struhar, Alexis M. Freiboth, Ana N. Schneider Apr 2026

Growing Alum Crystals, Jackson A. Struhar, Alexis M. Freiboth, Ana N. Schneider

Research & Creative Achievement Day

Alum, also known as Potash, is used to can pickles. Alum powder was used to create seed crystals. The seed crystal was hung in a jar submerged in distilled water with alum powder dissolved to attempt to grow an Alum crystal over the Spring semester. Three different concentrations were used to determine changes associated with crystal growth and quality. Numerous strategies were employed throughout the semester as growth methods worked and did not work.

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Growing Large Alum Crystals, Wyatt N. Laborde, Will Gunderson, Kyle R. Behrens Apr 2026

Growing Large Alum Crystals, Wyatt N. Laborde, Will Gunderson, Kyle R. Behrens

Research & Creative Achievement Day

Alum forms a colorless cubic crystal that can grow to the size of a small apple over the span of 2-3 months of growth in a concentrated solution. Many different seed crystal solutions were used to obtain large clear seed crystals. The best seed crystal was attached to a string and suspended in various concentrations of Alum solution to allow for the seed crystal to grow into a large crystal. By monitoring the weight of the crystal, the solution concentration was adjusted for optimum growth.

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Purification And Investigation Of Bromelain Enzyme From Pineapple, Elsie J. Dahnert, Leah O. Hanson, Regan E. Stafanoni, Reyna Olson, Eion D. Hinkle, Muaj-Infinite S. Yang Apr 2026

Purification And Investigation Of Bromelain Enzyme From Pineapple, Elsie J. Dahnert, Leah O. Hanson, Regan E. Stafanoni, Reyna Olson, Eion D. Hinkle, Muaj-Infinite S. Yang

Research & Creative Achievement Day

Bromelain proteins are a group of digestive protease enzymes that act as a chemical defense to prevent grazing. Bromelain is often taken as a dietary supplement in an attempt to support digestion, and it is used as a meat tenderizer in the restaurant industry. Extracting Bromelain from pineapple, especially the skin, leaves, and stem of the plant, can create a value-added product from what is widely considered to be agricultural waste. In this research, we examined the difference in Bromelain concentrations between the flesh of the pineapple and the skin, leaves, and stem of the pineapple, and the efficacy of …

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Mechanism Of Improved Miscibility Of Co2–Oil Systems By Multiester-Headgroup Surfactants, Ning Xu, Yanling Wang, Baojun Bai, Yu Zhang, Wenjing Shi, Zhaonian Zhang, Wenhui Ding, Peixu Ma, Zan Gao Apr 2026

Mechanism Of Improved Miscibility Of Co2–Oil Systems By Multiester-Headgroup Surfactants, Ning Xu, Yanling Wang, Baojun Bai, Yu Zhang, Wenjing Shi, Zhaonian Zhang, Wenhui Ding, Peixu Ma, Zan Gao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

In the pursuit of carbon neutrality, the CO2-enhanced oil recovery provides dual benefits by enabling both carbon sequestration and incremental oil production. However, its application is limited by high minimum miscibility pressure. CO2-philic and oil-affinitive surfactants have emerged as an effective, low-dosage, and cost-efficient strategy to reduce the minimum miscibility pressure. In this study, macroscopic phase behavior experiments combined with molecular dynamics simulations were employed to systematically elucidate the influence of multiester-head surfactants on CO2–oil miscibility. Using a modified pressure–volume–temperature apparatus equipped with optical power monitoring, we determined that multiester-head surfactants reduced the first-contact …

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A Sticky Situation: Mannose Glycosides, Morgan Romanski, Scott Hasty Apr 2026

A Sticky Situation: Mannose Glycosides, Morgan Romanski, Scott Hasty

2026 Student Academic Showcase

Mannose chemistry is notoriously difficult due to the electronic structure of the molecule. The dipole moments on the molecule provide stability, meaning adding and removing substituents come with many obstacles. This results in numerous byproducts, prevents reactions from going quickly, or prevents the reaction from occurring at all. In this project, the issue of producing a mannose molecule with the desired leaving group for glycosylations is investigated using two different methods of synthesis of attaching 2-mercaptopyridimine to a benzylated mannose on carbon 1. These two methods investigate and aim to solve the issue of epoxide formation and the mannose molecule …

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Complete Synthesis Of A Beta-Linked Disaccharide, Abby Dunn Apr 2026

Complete Synthesis Of A Beta-Linked Disaccharide, Abby Dunn

2026 Student Academic Showcase

The continual need to develop strategies and methods for oligosaccharide synthesis drives carbohydrate chemists to discover new glycosides. This push has led to the discovery of an attractive 6-methylpyrid-2-yl leaving group for chemical glycosylation. Presented are three syntheses of the molecules 2-mercapto-6-methylpyridine, glycosyl donor 6-methylpyrid-2-yl-2,3,4,6-O-acetyl-1-thio-β-D-glucopyranoside, and glycosyl acceptor methyl-2,3,4-O-benzyl-α-D-glucopyranoside. The 2-mercapto-6-methylpyridine molecule was affixed to the requisite sugar to produce the glycosyl donor, 6-methylpyrid-2-yl-2,3,4,6-O-acetyl-1-thio-β-D-glucopyranoside. This sugar was then coupled with methyl-2,3,4-O-benzyl-α-D-glucopyranoside using silver triflate (AgOTf) to obtain the targeted disaccharide.

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Theory And Simulations Of Delayed Stochastic And Deterministic Models Of Prion Diseases, Gangadhara Boregowda, Omar Sharif, Daniel Gutierrez Iii, Allegra Simmons, Laurent Pujo-Menjouet, Tamer Oraby, Michael R. Lindstrom Apr 2026

Theory And Simulations Of Delayed Stochastic And Deterministic Models Of Prion Diseases, Gangadhara Boregowda, Omar Sharif, Daniel Gutierrez Iii, Allegra Simmons, Laurent Pujo-Menjouet, Tamer Oraby, Michael R. Lindstrom

School of Mathematical & Statistical Sciences Faculty Publications

Neurodegenerative diseases (NDs), such as Alzheimer’s, Parkinson’s, and prion diseases, are characterized by the dynamical spread of toxic proteins through the brain. In prion diseases, cellular prion protein (PrPC), produced by neurons, misfolds into a toxic form, known as scrapie prion protein (PrPSc). PrPSc induces neuronal stress which ultimately leads to cell death. In this paper, we develop mathematical models for the progression of prion diseases, incorporating a cellular defense mechanism that introduces a delay term affecting protein translation and a volatility term accounting for unaccounted biological factors influencing the system. We also extend the model to capture the spatial …

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Mcgehee Blow Up And Collision Manifold Of Planar (2+2)-Body Problem, Nathaniel Scott Sill Apr 2026

Mcgehee Blow Up And Collision Manifold Of Planar (2+2)-Body Problem, Nathaniel Scott Sill

Theses and Dissertations

This thesis analyzes the dynamics of the Planar (2+2)-Body Problem which consists of two asteroids moving under the gravitational force of each other and of two larger primaries. We use the McGehee blow up technique to remove the singularities in the dynamics associated with a triple collision with one of the primaries by introducing a new set of variables. Additional variables are introduced to reduce the dimension of the problem. We then derive the dynamics for these new variables. We then use the energy relation that comes from the original Hamiltonian to describe the collision manifold which is pasted in …

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Optimization Of Pediatric Multidetector Ct Imaging Parameters Using A Machine Learning–Based Monte Carlo Simulation Model, Ali O. Masoud, Adithya Rajnaryanan, Khamis O. Amour, Ahmed M. Jusabani, Justin E Ngaile, Manoj Kumar, Mwingereza John Kumwenda Dr Apr 2026

Optimization Of Pediatric Multidetector Ct Imaging Parameters Using A Machine Learning–Based Monte Carlo Simulation Model, Ali O. Masoud, Adithya Rajnaryanan, Khamis O. Amour, Ahmed M. Jusabani, Justin E Ngaile, Manoj Kumar, Mwingereza John Kumwenda Dr

Tanzania Journal of Science

This study utilized Monte Carlo (MC) simulations to optimize radiation doses in pediatric multidetector computed tomography (MDCT) head scans by analyzing key parameters like tube current (mA), tube voltage (kV), pitch, and slice thickness. The findings indicate that reducing tube current significantly lowers the Computed Tomography Dose Index (CTDIvol) and Dose Length Product (DLP), effectively minimizing patient radiation exposure. Higher pitch values (0.7–0.9) further reduced radiation by decreasing beam overlap, while using a thinner slice thickness (0.6 mm) improved dose efficiency. A comparison highlighted the effectiveness of optimization: simulated parameters kVp 100, mAs 81, pitch 0.98 yielded a CTDIvol of …

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Re: Approval Letter For The Butte Priority Soils Operably Unit (Bpsou) Revised Draft Final Quarterly Operations And Maintenance Report: Butte Treatment Lagoon System (Btl) Second Quarter 2025 (Dated April 10, 2026), Emma Rott Apr 2026

Re: Approval Letter For The Butte Priority Soils Operably Unit (Bpsou) Revised Draft Final Quarterly Operations And Maintenance Report: Butte Treatment Lagoon System (Btl) Second Quarter 2025 (Dated April 10, 2026), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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Synthesis, Structural Elucidation, In Vitro Antibacterial Activity Of Mononuclear Ag(I) Complex Derived From (E)-N-(4-Fluorophenyl)-1-(Pyridin-2-Yl) Methanimine And Triphenylphosphine Ancillary Ligand., Olufemi Stephen Odulaja, Saliu Alao Amolegbe, Muritala Adeniyi Olusola Apr 2026

Synthesis, Structural Elucidation, In Vitro Antibacterial Activity Of Mononuclear Ag(I) Complex Derived From (E)-N-(4-Fluorophenyl)-1-(Pyridin-2-Yl) Methanimine And Triphenylphosphine Ancillary Ligand., Olufemi Stephen Odulaja, Saliu Alao Amolegbe, Muritala Adeniyi Olusola

Tanzania Journal of Science

Chemo-therapeutic application of metal complexes to remediation and subjugation of emerging infectious diseases with a view to improving potency and efficacy of a variety of drugs is gaining more momentum, especially in the 21st century. This research work designed a new biologically active complex [AgL(PPh3)2]NO3,C, obtained from the reaction of Ag(I) nitrate with bidentate pyridinyl Schiff base ligand (E)-N-(4-fluorophenyl)-1-(pyridin-2-yl) methanimine L, with triphenylphosphine (PPh3) as co-ligand. Characterisation was done by FT-IR, UV-Vis, NMR, (TGA/DTA), X-ray crystallography, and elemental analysis. The combined effects of pyridinyl Schiff base ligand and PPh3 on the antibacterial activities against Staphylococcus aureus, Escherichia coli, Klebsiella pneumonia, …

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Detecting Eigenvectors Of An Operator That Are Near A Specified Subspace, David Darrow, Jeffrey S. Ovall Apr 2026

Detecting Eigenvectors Of An Operator That Are Near A Specified Subspace, David Darrow, Jeffrey S. Ovall

Mathematics and Statistics Faculty Publications and Presentations

In modeling quantum systems or wave phenomena, one is often interested in identifying eigenstates that approximately carry a specified property; scattering states approximately align with incoming and outgoing traveling waves, for instance, and electron states in molecules often approximately align with superpositions of simple atomic orbitals. These examples—and many others—can be formulated as the following eigenproblem: given a selfadjoint operator L on a Hilbert space H and a closed subspace W ⊂ H, can we identify all eigenvectors of L that lie approximately in W? We develop an approach to answer this question efficiently, with a userdefined tolerance and range …

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Differentiable Objectives For 3d Scene Relighting Via Gradient Descent On Olat Basis Coefficients, Anson Savage Apr 2026

Differentiable Objectives For 3d Scene Relighting Via Gradient Descent On Olat Basis Coefficients, Anson Savage

Theses and Dissertations

Designing effective lighting is an iterative and often time-consuming process. This work contributes to automatic lighting design research by presenting a render-engine agnostic optimization routine: gradient descent on RGB multipliers of one-light-at-a-time (OLAT) basis images. We compare several objective functions to accomplish lighting tasks and show that our method is capable of quickly and effectively exploring different lighting styles using either text prompts or reference images. We also present several datasets specific to lighting tasks and show that fine-tuning on these datasets can improve performance.

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Grp-02-199 Topological Constraints For Protein Folding, Charles Fanning Apr 2026

Grp-02-199 Topological Constraints For Protein Folding, Charles Fanning

C-Day Computing Showcase

We study whether topological loss-based constraints improve multidomain whole-chain protein structure prediction beyond the ColabFold baseline by better preserving the topologies of folded proteins. We benchmark against Wasserstein metrics with our own virtual persistence and RKHS semi-metric constraints as well as higher-order virtual persistence diagrams.

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Uc-177-226 Scrapper Kinetics Llc, Mikhail Rudenko, Andrew Torimoto, Jimmy Lock, Marco Cheng Apr 2026

Uc-177-226 Scrapper Kinetics Llc, Mikhail Rudenko, Andrew Torimoto, Jimmy Lock, Marco Cheng

C-Day Computing Showcase

Scrapper Kinetics LLC is a multiplayer and multimodal physics puzzle game. Where players get to choose between playing in VR or Desktop mode, and then, with up to 7 friends (8 players total), try to make a profit in the harsh dead space hulks they have been hired to scrap. We made the game as a test to see how easy it is to have completely different devices interact in the same play space.

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Ur-166-204 Multi-User Stem Learning Experience Powered By Llm Conversational Agents, Devon Haynes, Chance Boecker, Oliver Haggard Apr 2026

Ur-166-204 Multi-User Stem Learning Experience Powered By Llm Conversational Agents, Devon Haynes, Chance Boecker, Oliver Haggard

C-Day Computing Showcase

While Extended Reality (XR) provides experiential and interactive foundations for STEM education, current storytelling and narrative-driven applications often lack responsive nonplayer characters (NPCs), limiting interactive potential through pre-scripted stories. Additionally, despite the growth of Large Language Model (LLM) integration in XR, limited research explores the combined use of multi-user XR systems and conversational Artificial Intelligence (AI) to facilitate real-time, adaptive instruction. This project seeks to address these gaps by 1) Developing a narrative-driven STEM learning XR prototype that incorporates synchronous multi-user interaction and an embedded LLM-driven conversational agent and 2) Exploring the effectiveness of combining these technologies to improve learning …

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Ex-04-142 Modeling Distress And Evaluating Chatbot Safety For Suicide-Related Social Media Texts, Rehma Razzak Apr 2026

Ex-04-142 Modeling Distress And Evaluating Chatbot Safety For Suicide-Related Social Media Texts, Rehma Razzak

C-Day Computing Showcase

This project addresses the urgent need for transparent chatbot safety evaluations amid rising concerns about AI-facilitated self-harm. Using public social media datasets, we simulate two tasks: (1) detecting suicidal ideation via emotion-based risk scoring, and (2) stress-testing a support-style chatbot against 888 high-risk prompts, including euphemisms and “for a story” framing. A multi-label classifier trained on GoEmotions feeds emotion profiles into a logistic regression model to generate suicidality risk scores. These scores guide a local chatbot built with Ollama’s llama3, which analyzes user messages and steers responses toward safe, empathetic behavior. Evaluation shows ~90% of replies were safe or supportive. …

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Gc-113-125 Carter’S Lake Visitor Center Boating Safety Game, Hunter Blake, Chancelor Brown, Lauren Robbins, Will Bryant, Kendrick Bryant Apr 2026

Gc-113-125 Carter’S Lake Visitor Center Boating Safety Game, Hunter Blake, Chancelor Brown, Lauren Robbins, Will Bryant, Kendrick Bryant

C-Day Computing Showcase

The Boating Safety Game is an educational, kiosk-based touchscreen game created for the U.S. Army Corps of Engineers and the Carters Lake Visitor Center. It is designed to improve the knowledge and engagement of boating safety concepts for visitors, particularly for students and youth. The project was developed using multiple game scenarios meant to reinforce safe boating practices through tutorial scenes, top down navigation, life jacket and required item selection tasks, and player motivation through quizzes, feedback, scores, and a star ranking system. The game’s design emphasizes accessibility and retention through simple touchscreen interaction, guided instruction, and repeated feedback on …

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Gc-141-167 Smart Resume Screening & Interview Preparation Assistant, Loreli Olien, Tomas King, Hanzhi Chen, Kawanda Gray, Brittany Frazier Apr 2026

Gc-141-167 Smart Resume Screening & Interview Preparation Assistant, Loreli Olien, Tomas King, Hanzhi Chen, Kawanda Gray, Brittany Frazier

C-Day Computing Showcase

The hiring process often relies on manual resume review and keyword matching, which can lead to inconsistent and biased candidate evaluations. This project introduces a Smart Resume Screening and Interview Preparation Assistant designed to improve transparency and consistency in early-stage candidate evaluation. The system allows recruiters to upload resumes and job descriptions, then uses embedding-based semantic matching and large language models to assess candidate alignment across skills, experience, education, and projects. The application generates structured rankings, explainable insights, and tailored interview questions. This project focuses on developing a functional prototype that demonstrates how AI can enhance decision support while maintaining …

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Gc-161-210 Hybrid Path Planning Using Genetic Algorithm, Caitlin Tigani, Ramisa Fariha Joyee, Wasif Mohammad Apr 2026

Gc-161-210 Hybrid Path Planning Using Genetic Algorithm, Caitlin Tigani, Ramisa Fariha Joyee, Wasif Mohammad

C-Day Computing Showcase

This research investigates whether uninformed search (BFS) or informed search (A*) is more effective when combined with a Genetic Algorithm for maze path planning. We design and implement four algorithms: baseline BFS and A*, hybrid GA+A*, and hybrid GA(BFS+A*). Our findings show that while A* alone performs optimally, integrating it with GA can produce alternative quality solutions, though with computational trade-offs. The study demonstrates that GA+A* provides the best balance between solution quality and runtime efficiency.

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Gc-178-191 Communication App: Ai-Assisted Aac Platform​, Alex Wills, Maryam Koya Apr 2026

Gc-178-191 Communication App: Ai-Assisted Aac Platform​, Alex Wills, Maryam Koya

C-Day Computing Showcase

The Communication App is an accessibility-focused mobile application designed to support individuals with speech impairments, strong or unrecognized accents, and neurodivergent communication needs. The system leverages AI assisted speech-to-text (STT) and text-to-speech (TTS) technologies to enable real-time and seamless communication between users.This project aims to bridge communication gaps by providing a customizable, adaptive platform that learns user speech patterns over time. The application integrates cloud-based services, secure communication protocols, and an intuitive user interface to ensure usability, performance, and accessibility.

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Grm-010-170 Ai-Enabled Water Quality Framework For E. Coli Prediction And Forecasting, Sangeetha Devaraj, Jui Mhatre Apr 2026

Grm-010-170 Ai-Enabled Water Quality Framework For E. Coli Prediction And Forecasting, Sangeetha Devaraj, Jui Mhatre

C-Day Computing Showcase

Water quality monitoring is essential for public health and environmental sustainability, yet existing monitoring infrastructures remain sparse, fragmented, and incomplete. Data from the United States Geological Survey (USGS) indicate that while over 1.5 million sites are cataloged in the USGS Water Data for the Nation, only a small fraction are actively reporting water quality measurements, with significant reductions observed in recent years. Moreover, critical parameters such as pH, water temperature, dissolved oxygen, turbidity, and microbial indicators like Escherichia coli are inconsistently measured, with widespread missing and irregular data. This work presents an AI-enabled water quality data framework designed to address …

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Grm-012-173 Can You Trust Ai Code? Understanding And Detecting Breaking Changes Using Llms, K M Ferdous, Kowshik Chowdhury Apr 2026

Grm-012-173 Can You Trust Ai Code? Understanding And Detecting Breaking Changes Using Llms, K M Ferdous, Kowshik Chowdhury

C-Day Computing Showcase

AI-generated code is increasingly prevalent in software engineering practices, yet its reliability in preserving backward compatibility remains underexplored. This paper presents a unified study of (i) how often AI-generated code introduces breaking changes and (ii) whether large language models (LLMs) can detect such changes from commit-level diffs with explanations. We analyze 7,191 agent-generated and 1,402 human-authored pull requests from Python repositories using an AST-based approach to identify potential breaking changes. Our results show that AI agents introduce fewer breaking changes overall than humans (3.45% vs. 7.40%) in code generation tasks. However, agents show higher risk in maintenance tasks, where refactoring …

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Grm-081-207 Leveraging Non-Parametric Longitudinal Rank Sum Tests (Lrst) For Robust Global Treatment Effect Estimation In Alzheimer’S Disease, Imaan Shahid Apr 2026

Grm-081-207 Leveraging Non-Parametric Longitudinal Rank Sum Tests (Lrst) For Robust Global Treatment Effect Estimation In Alzheimer’S Disease, Imaan Shahid

C-Day Computing Showcase

Parametric approaches, such as Mixed Models for Repeated Measures (MMRM), are standard in Alzheimer’s Disease (AD) clinical trials. However, these models often falter when data violates assumptions of normality or follows non-linear trajectories—common occurrences in AD due to floor/ceiling effects on cognitive scales and heterogeneous disease progression. This study evaluates Longitudinal Rank Sum Tests (LRST) as a non-parametric alternative to maintain statistical power and robustness.

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Grm-094-176 Influence Of Speech Disfluencies And Prompt Optimization On Llm-Based Alzheimer's Detection, Muhammad Awais Arshad Apr 2026

Grm-094-176 Influence Of Speech Disfluencies And Prompt Optimization On Llm-Based Alzheimer's Detection, Muhammad Awais Arshad

C-Day Computing Showcase

This study evaluates how speech disfluencies and prompting strategies impact LLM-based Alzheimer’s Disease (AD) detection. We compared transcripts with preserved disfluencies (ADReSS) against clean transcripts (ADReSSo) using four state-of-the-art LLMs. Key Discovery: Complex prompts induce a "mirror-image" classification bias, where DeepSeek models severely over-classify AD, and GPT-5.2 over-classifies Cognitively Normal (CN) individuals. Optimization Fix: Applying DSPy MIPROv2 effectively mitigated bias in simpler prompts, while TextGrad successfully optimized complex, multi-step prompts.

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Grm-095-230 A Multimodal Llm Framework For Automated Construction Blueprint Analysis With Real-Time Decision Support, Muhammad Awais Arshad Apr 2026

Grm-095-230 A Multimodal Llm Framework For Automated Construction Blueprint Analysis With Real-Time Decision Support, Muhammad Awais Arshad

C-Day Computing Showcase

This study addresses the high hallucination rates of Vision-Language Models (LLMs) when analyzing complex, hybrid construction blueprints. We developed a dual-input pipeline that pairs high-resolution images with a four-layer JSON "Digital Twin" (vector text, raster OCR, geometry) to mathematically ground the LLM's visual interpretation. Key Engineering Achievement: We processed a massive 137-sheet civil engineering project with zero errors. By introducing a multi-tier JSON pruning strategy, we cut token usage by up to 70% and processed the entire batch from $13-$15 to just $0.93. Decision Support Extension: We integrated real-time traffic data (TomTom) and GDOT procedural policies to transform the pipeline …

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Grm-132-159 Integrating Causal Inference With Graph Neural Networks For Alzheimer’S Disease Analysis, Pranay Kumar Peddi Apr 2026

Grm-132-159 Integrating Causal Inference With Graph Neural Networks For Alzheimer’S Disease Analysis, Pranay Kumar Peddi

C-Day Computing Showcase

Deep graph learning has advanced Alzheimer’s disease (AD) classification from MRI, but most models remain correlational, confounding demographic and genetic factors with disease-specific features. We present Causal-GCN, an interventional graph convolutional framework that integrates do-calculus-based back-door adjustment to identify brain regions exerting stable causal influence on AD progression. Each subject’s MRI is represented as a structural connectome where nodes denote cortical and subcortical regions and edges encode anatomical connectivity. Confounders such as age, sex, and APOE4 genotype are summarized via principal components and included in the causal adjustment set. After training, interventions on individual regions are simulated by severing their …

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Grm-159-214 Transportation Energy And Emission Modeling And Analysis Tool (Teemat), Laeticia Neno Aloyem Apr 2026

Grm-159-214 Transportation Energy And Emission Modeling And Analysis Tool (Teemat), Laeticia Neno Aloyem

C-Day Computing Showcase

The Transportation Energy and Emission Modeling and Analysis Tool (TEEMAT) is a web-based decision-support framework for evaluating the environmental impacts of EV adoption across U.S cities. TEEMAT integrates a feedforward neural network trained on MOVES 4.0 for tract-level vehicle emissions (CO₂, NOₓ, PM₂.₅), a macroscopic traffic and activity-based model capturing congestion-driven emission spikes, and a Meta-Prophet model trained on NREL Cambium data for grid emissions (CO₂, CH₄, N₂O). Results show that while EV adoption reduces tailpipe emissions, rising travel demand and congestion-induced low speeds can significantly offset these gains underscoring that meaningful decarbonization requires coordinated transportation and energy grid strategies.

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Grm-169-137 Predicting The Stock Market's Next Move: How Neural Network Architecture Shapes Forecasting Accuracy, Roderick Powell Apr 2026

Grm-169-137 Predicting The Stock Market's Next Move: How Neural Network Architecture Shapes Forecasting Accuracy, Roderick Powell

C-Day Computing Showcase

Three feedforward neural network (FFNN) architectures — bottleneck, parallel multi-path, and residual parallel — were trained on ten years of daily S&P 500 (SPY ETF) price and volume data to predict next-day market direction (Up/Down). All three demonstrated predictive ability above random chance. Architectural choice directly determined class prediction bias: the bottleneck concentrated errors on Up days, the parallel architecture distributed them evenly, and residual connections inverted the bias toward Down days. Model 2 (parallel) achieved the highest test accuracy (58.2%) and the most balanced class predictions among the three configurations.

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