Digital Commons Network^™

Programmable Metal−Organic Complex Arrays With Precisely Controlled Metal Sequences As Tunable Multinuclear Scaffolds, Ahmed Alzamly, Sajid Ali

Faculty Scholarship

Metal−Organic Complex Arrays (MOCAs) offer a modular framework for creating multinuclear systems with precise control over metal ion identity and sequencing at the molecular scale. This structural accuracy allows researchers to investigate how specific metal arrangements dictate cooperative phenomena, such as catalytic efficiency, electronic interactions, and photophysical responses. Recent breakthroughs in synthetic strategies  including orthogonal protection and periodic metalation have enabled the fabrication of highly predictable heterometallic structures. This Review explores the fundamental principles of MOCAs’ sequence control, contemporary fabrication techniques, and the relationship between metal placement and emergent properties. Additionally, it examines applications of MOCA in catalysis, sensing, and …

2026 - The Thirtieth Annual Symposium Of Student Scholars

Symposium of Student Scholars Program Books

The full program book from the 30th Annual Symposium of Student Scholars, held on April 22-24, 2026. Includes abstracts from the presentations and posters.

Joint Inversion For Heat Transfer Parameters Of Porous Media With Geothermal-Seepage Coupling Based On A Physics-Informed Neural Network, Zhang Xiao, Yang Duoxing, Cong Lin, Cheng Yimei, Zhang Lianzhong

Coal Geology & Exploration

Background The thermal diffusivity and vertical seepage velocity of porous media are critical to characterizing the mechanisms underlying subsurface heat transfer. These two parameters are typically determined through inversion using temperature data since they are difficult to measure directly. Fiber-optic distributed temperature sensing (FO-DTS) can yield high-resolution, continuous temperature profiles, providing data for parameter identification. However, conventional numerical inversion suffers from limitations such as mesh dependency and sensitivity to initial values when used to process continuous monitoring data. Methods This study proposed a joint geothermal parameter inversion method that integrates FO-DTS data with a physics-informed neural network (PINN). Specifically, the …

Program And Proceedings, The Nebraska Academy Of Sciences 1880–2026: 146th Anniversary Year, One Hundred-Thirty-Sixth Annual Meeting

Nebraska Academy of Sciences: Programs and Proceedings

Program and abstracts of the proceedings for the Nebraska Academy of Sciences 136th annual meeting, 2025

Sessions

Aeronautics and Space Science

Anthropology

Biological and Medical Sciences

Earth Sciences

Ecology, Sustainability, and Environmental Science

Physics and Engineering

General Poster Session

2026 Maiben Lecture: Shifting Extremes: Understanding Nebraska’s Changing Climate and Preparing for What Lies Ahead, Deborah Bathke

2026 Friends of Science Awards: Julie Shaffer and Irina Filina

2026 C. Bertrand and Marian Othmer Schultz Colleaguate Schoalrship Award: Piper Ryschon and Bryce Reeson

In Memoriam: Paul Royster

Repurposing An Old Pc Into A Self-Hosted Cloud And Media Server, Riley Biggins

Honors Theses

Commercial cloud platforms rely heavily on subscription models, so everyday users fall into a "cloud trap" summarized by perpetual fees, forfeited privacy, and a lack of true asset ownership. At the same time, functioning consumer electronics get discarded as e-waste when they fail to meet the hardware requirements of modern operating systems. This project presents a sustainable and cost-effective alternative to both issues by repurposing a 2017 HP Pavilion laptop into a personal cloud and media server.

Despite severe hardware constraints, my software architecture rivals the utility of paid services. By replacing Windows with a headless installation of Ubuntu Server, …

Draft Uniform Federal Policyquality Assurance Project Plan Addendum, Hydrogeologic, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

The Role Of Human-Centered Artificial Intelligence In Crewed Mars Missions, Jordan Neuman

Student Publications and Presentations

The suggestion of implementing Artificial Intelligence (AI) in crewed missions to the Martian surface can revolutionize mission operations, psychological support, and ethical decision‑making under challenging conditions. This study is a structured, qualitative literature review of current AI research for the future of human space exploration. It uses publicly available peer‑reviewed journal articles and NASA/ESA technical reports on AI for Mars missions. Using Perplexity, the researcher first prompted the AI tool to identify recurring topics across these sources and generate themes. The researcher independently reviewed all sources to confirm and refine those themes. Three themes—mission autonomy, local resource utilization and life‑support, …

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 …

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. …

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.

Grm-174-228 Mitigating Prompt-Induced Variability In Llm Outputs, Crystal Tubbs

C-Day Computing Showcase

Large language models in enterprise settings often produce structurally invalid outputs when users communicate informally, creating silent failure modes that pass unnoticed in downstream systems. This study investigates how prompt variation alone impacts schema compliance and output reliability. We evaluate three architectures across two tasks and four prompt styles, isolating the effect of interaction style on model behavior. Results show that baseline systems achieve 100% compliance under structured prompts but fail completely under ambiguous and casual inputs. A minimal reliability pipeline consisting of generation, self critique, and schema validation restores 100% compliance across all conditions at a predictable computational cost.

Grp-03-141 Stress-Testing Parkinson’S Disease Screening: A Cross-Modal Analysis Of Drawing And Speech Models, Rehma Razzak

C-Day Computing Showcase

Medical AI systems are increasingly deployed in clinical settings, yet most published models report only clean accuracy, dataset details, and training procedures—while omitting security‑critical evaluations such as robustness to perturbations, adversarial vulnerability, and failure modes under realistic noise. This project addresses that gap by building a cross‑modal robustness assessment for Parkinson’s disease (PD) screening models across handwriting trajectories, speech‑derived acoustic features, and an LLM‑based preprocessing layer. Despite strong clean performance (visual subject‑level ROC AUC ≈ 0.99; audio ≈ 1.0), the visual pipeline proved highly brittle to realistic acquisition distortions. Downsampling and point‑dropout caused near‑chance collapse, while pressure noise and XY …

Grp-08-168 Diagnosing Faults In Electrical Power Systems Of Satellites, Jared Lasley, Nguyen Thi Binh Nguyen

C-Day Computing Showcase

Satellite systems cost hundreds of millions of dollars or more to launch. To be resistant to catastrophic failures (and total loss of investment), satellite systems are designed with redundant sub-systems and are further equipped with numerous sensors and other health-monitoring sub-systems. In this poster, we consider an approach to fault diagnosis based on probabilistic logic programming. In particular, we propose to use ProbLog to model and reason with the electrical power system (EPS) of a satellite. Once we model a system using (probabilistic) first-order logic, we can take the system state and any (unexpected) sensor readings, and through automated reasoning, …

Grp-180-147 Quantum Augmented Microgrids (Quam) Simulator, Nitin Jha, Prateek Paudel

C-Day Computing Showcase

Small modular nuclear reactors (SMRs) are redefining the energy generation landscape by enabling the deployment of modular, scalable, and pre-built power units that can be used to build distributed autonomous microgrids for critical infrastructure and burgeoning AI factories. Often, these microgrids are linked together to provide a resilient, decentralized power generation infrastructure. Consequently, the cybersecurity of microgrids is of critical importance. In this work, we propose a quantum augmented network framework for resilient microgrids. We integrate the ideas of secure quantum networking, quantum anonymous notification, and quantum random number generation to strengthen the integrity, confidentiality, and privacy of microgrid networks. …

Uc-135-129 Fishgame: Mobile Hyper-Casual Fishing Game, Lauren Rousell, Lisbeth Martinez, Jacob Portillo, Jacob Miller

C-Day Computing Showcase

fishGame is a strategy-driven hyper-casual mobile game that combines the accessibility of traditional mobile gameplay with the progression depth of a roguelike. The project was developed over a three-month period using Unity 6.3 and related production tools. Following the completion of an alpha build, testing was conducted through gameplay sessions and a detailed follow-up survey to gather feedback on player experience, clarity, and engagement. Results indicated that fishGame was well received, with players reporting low levels of confusion and strong replayability. These findings suggest a clear interest in mobile games that offer greater depth while preserving the immediacy and simplicity …

Uc-142-177 Multiplayer Spsu Tub Racing Video Game, William Pitts, William Urvan, Thomas Powell, Joshua Young

C-Day Computing Showcase

Multiplayer Bathtub Racing revives a well-known Southern Polytechnic State University tradition through a digital multiplayer experience built in Unity. The project extends a prior single-player tub racing game by adding online multiplayer

Uc-158-205 Scrappyfin — Ind, Simulated Digital Wallet &​ Fraud Detection Platform, Logan Davis, Dante Galvan, Zahaira Jordan, Avery Bracey, Chris Pham

C-Day Computing Showcase

ScrappyFin is an educational FinTech platform developed in partnership with The Home Depot to simulate a digital wallet system and demonstrate fraud detection techniques. Users can create virtual wallets and perform synthetic transactions in a controlled environment, enabling analysis of financial behavior without real risk. The platform combines rule-based logic with machine learning models to identify suspicious activity, such as unusual transaction amounts or patterns. An admin dashboard provides clear explanations for flagged transactions. ScrappyFin showcases how modern financial systems detect risk while serving as a practical learning tool for software engineering and data-driven applications.

Grp-125-144 Mapping The Affordances Of Human-Ai Interaction: A Large-Scale Text Mining And Statistical Analysis Of Llm Usage Patterns, Anil Vallepu

C-Day Computing Showcase

People increasingly communicate with AI for schoolwork, office tasks, and daily needs. This study investigates the affordances of human-AI interaction using modern text mining and statistical analysis on the WildChat-1M dataset of over 1.1 million real-world ChatGPT user conversation logs. We apply BERTopic to extract latent interaction topics, compute a probabilistic topic-document matrix P(T|D), and perform rigorous statistical testing including Welch’s T-test and ANOVA to compare affordance patterns between GPT-3.5 and GPT-4.0 Results reveal that creative writing, Coding, message drafting and many interesting topics are the dominant affordances, while a spatio-temporal trend analysis maps how interaction patterns evolve globally over …

Grm-134-128 Neurovision: Mapping Brain Signals To Language And Visual Meaning, Siri Yellu

C-Day Computing Showcase

NeuroVision presents a unified framework for mapping electroencephalography (EEG) signals to language and visual meaning. Extracting semantic information from EEG remains a fundamental challenge due to its low signal-to-noise ratio, high dimensionality, and inter-subject variability. To address these challenges, we propose a multimodal representation learning framework that aligns EEG signals with both textual and visual embeddings through temporal modeling, spatial brain-region decomposition, and contrastive learning. The framework integrates self-supervised pretraining with supervised multimodal alignment to learn robust and transferable representations. Experimental results demonstrate BLEU-1 of 0.1106 and ROUGE-1 of 0.1493 for EEG-to-text generation, alongside a 52% improvement in retrieval performance …

Gc-172-139 Detection Of Sms Spam Using Transformer Bert Model, Nathan Bonner, Zachary Kandell, Michael Hayes, David Quintanilla, Leon Greenberg

C-Day Computing Showcase

This project evaluates automated SMS spam classification by comparing traditional machine learning against modern transformer architectures. We built a Bidirectional LSTM (BiLSTM) baseline using TF-IDF feature extraction and NearMiss-1 undersampling to handle severe class imbalances. We then compared this against a fine-tuned Hugging Face Sentence-BERT model. Preliminary results show Sentence-BERT significantly outperformed the BiLSTM baseline (99.01% vs. 95.65% accuracy). These findings demonstrate that transformer-based embeddings offer a highly accurate, scalable solution for spam mitigation without relying on aggressive data undersampling.

Gc-154-201 Evaluation Of Generative Ai Responses To Pharmacy Prompts, Abrar Syed

C-Day Computing Showcase

Generative AI (GenAI) is increasingly used in pharmacy for drug information and decision support, yet accuracy remains variable. We systematically reviewed studies that reported full prompts and model responses to evaluate correctness across pharmacy‑relevant tasks. GenAI performed well on basic drug facts but was inconsistent for patient-specific recommendations and interaction checking, with occasional hallucinations. Findings support cautious, supplementary use in pharmacy practice and education.

Grp-0100-193 Are Tgnn-Based Intrusion Detection Results Trustworthy? A Dataset Audit And Evaluation Framework, Faysal Chowdhoury, Sait Suer, Yinning Zhang

C-Day Computing Showcase

Temporal Graph Neural Networks (TGNNs) have reported near-perfect accuracy in Network Intrusion Detection (NID). However, this research reveals these results are often artifacts of dataset flaws rather than genuine model capability. Through a systematic audit of five benchmark datasets, we identify critical issues: node identity leakage, feature extraction artifacts, train/test contamination, and temporal sparsity. We demonstrate that models often learn to recognize specific attacker IP addresses instead of generalizing attack behavior. We propose a standardized evaluation framework featuring leakage-aware relabeling and attack-aware chronological splitting to provide a more reliable basis for future TGNN-NID research.

Uc-164-215 Hootnest: Ai-Powered Ksu Student Assistant, Tabitha Washington, Aspen Steele

C-Day Computing Showcase

HootNest helps prospective Kennesaw State students get clear, reliable answers about college life. It is designed for students who may not have easy access to counselors, mentors, or campus visits. The chatbot allows students to ask the chatbot anything they need to know.

Ur-171-118 Aidflow: A Predictive Financial Aid Transparency System For Students, Chaathurya Nakkana

C-Day Computing Showcase

Students frequently experience delays and confusion regarding financial aid refunds due to unclear system statuses and lack of communication. This project introduces AidFlow, a predictive financial aid transparency system that translates complex financial data into clear explanations, predicts refund timelines, and provides actionable guidance. A rule-based model and system pipeline were developed to simulate real-world scenarios and improve student understanding and decision-making.

Gc-126-148 Allies Connect- Georgia's Nonprofit And Volunteer Coordination Platform, Molly Calhoun, Takeshia Banks, David Castro, Ryan Hanrahan, Tarik Davis

C-Day Computing Showcase

Georgia's nonprofit services face an issue of discoverability. While many nonprofits have the resources to help their community members succeed, they have trouble actually connecting to members of the community that need their support. Connecting with these resources is challenging for community members because their avenues of communication are spread across the internet. Some have their own websites, some have a Facebook page where they post events, some rely on word of mouth and fliers, and others rely on phone chains to keep their community members informed. This means that community members seeking support need to be able to access …

Maximizing Uinta Basin Oil Well Yield Through Geochemical Fingerprinting Of Produced Water, Troy W. Day

Theses and Dissertations

The Uinta Basin is an unconventional oil play that has, since 2017, annually increased hydrocarbon extraction through modern drilling and fracing techniques with increased frac loading. The primary production target is the Uteland Butte Member of the Green River Formation, which consists of interbedded limestones, dolostones, and thick, organic-rich mudstones. Along with increased hydrocarbon production, volumes of produced water from the Uteland Butte Member have also risen. Water is co-produced with hydrocarbons and is an undesirable byproduct of hydrocarbon extraction. Large frac operations now consistently have highly variable percentages of recovered water volume relative to oil. This increase in water …

Developing Physics And Computing Identities In A Computationally Integrated Physics Course, Elizabeth Hallock

Engineering & Physics Student Scholarship

No abstract provided.

A Computational Approach To Periodic Orbits Of State-Dependent Delay Differential Equations, Noah Corbett

Electronic Theses and Dissertations 2020 - Present

The field of delay differential equations (DDEs) concerns the study of systems whose evolution depends on certain past states of the system. Of particular interest are the state-dependent DDEs, whose delay terms are non-constant and depend on the current state itself. In this thesis, we provide rigorous solution-finding techniques for a certain class of one-dimensional state-dependent DDEs, as well as a state-dependent delayed Van der Pol equation. This technique is inspired by the classical Picard-Lindelof theorem and is successful in proving the existence and uniqueness of orbits in such systems under certain reasonable restrictions. We then employ the Lagrange-Chebyshev interpolating …

A Masked-Perturbation Model For Destabilization Security Games In Feedback Systems, Gabriel Michael Perry

Theses and Dissertations

In this thesis, we present a mathematical formulation for increasing cyberphysical system security. This model allows a system administrator to use assumptions about exposed signals to derive defense strategies meant to mitigate the system's vulnerability to feedback perturbation attacks. We begin with a survey of game theory with the goal of building the concepts of resource and security games. We then introduce our model, which is a security game; targets in this game are exposed signals in the model map of the system, and resources are perturbed or introduced dynamical relationships between these signals. This formulation, called the masked-perturbation model, …

Templating Effect Of Neutral Dithiane Ligands On The Self-Assembly Of Cationic Au(I)-Phosphine Units, Farzane Osanlou, Abdollah Neshat, Mohammad Reza Yousefshahi, Vaclav Eigner, Michal Dusek, Shahram Seidi

Turkish Journal of Chemistry

The interactions between the dinuclear Au(I) precursor (dppm)Au2Cl2 and neutral sulfur-containing dithianes (specifically 1,3- and 1,4-dithiane) were examined in the presence of NH₄PF₆. Although these sulfur-based ligands were initially employed to facilitate the formation of mixed phosphine/sulfur Au(I) complexes, single-crystal X-ray diffraction analysis revealed the unexpected generation of multinuclear Au(I) assemblies exclusively coordinated by phosphine ligands. When 1,3-dithiane was present, a trinuclear Au(I) complex was isolated, characterized by a central four-coordinate Au(I) center exhibiting a distorted see-saw geometry, flanked by two terminal Au(I) centers with T-shaped coordination environments. Importantly, the dithiane ligand did not coordinate directly to the gold centers …