Digital Commons Network^™

Evaluation Of Sirna-Mediated Knockdown Of Heat Shock Protein 16.2 In Adult Acropora Cervicornis, Edward P. Gniffke, Benjamin D. Young, Keir J. Macartney, Michael S. Studivan, Ian C. Enochs, Erin E. Easton

School of Earth, Environmental, & Marine Sciences Faculty Publications

Acropora cervicornis is a critically endangered Caribbean coral species facing a precipitous decline due to disease and thermal bleaching. To combat the loss of ecologically important reef-building corals, novel molecular approaches can be used to understand mechanisms of resilience and ultimately provide a means to modulate coral performance. In this study, we tested a recently developed method for gene knockdown in adult corals that utilizes short interfering RNAs (siRNAs) and the RNA interference pathway. The Heat Shock Protein 16.2 (HSP16.2) gene in fragments of adult A. cervicornis was targeted for knockdown using a single siRNA construct. RT-qPCR analysis of HSP16.2 …

The Role Of Interactions In Moiré Quantum Materials, Liangtao Peng

Arts & Sciences Graduate Student Theses and Dissertations

Moiré quantum materials have emerged as a highly tunable platform for studying strongly interacting electrons in low dimensions. By twisting or lattice-mismatching atomically thin crystals, one can generate long-wavelength moiré superlattices that reconstruct the electronic bands, produce narrow bandwidths, and greatly enhance the importance of Coulomb interactions. As a result, these systems host a wide range of correlated phenomena, including symmetry breaking, magnetism, and superconductivity. This dissertation develops and applies Green's-function-based many-body methods to understand the role of interactions in moiré quantum materials. This dissertation consists of four chapters. The first chapter introduces moiré quantum materials and reviews the basic …

Advancing High-Energy Astrophysics Through X-Ray Spectropolarimetry And Balloon-Borne Instrumentation, Sohee Chun

Arts & Sciences Graduate Student Theses and Dissertations

The strong gravitational field around black holes provides a unique laboratory for investigating the behavior of matter and radiation under extreme conditions. As matter accretes, the interplay between the accretion disk, hot corona, and surrounding stellar environment produces complex spectral and polarimetric X-ray signatures. Thus, X-ray spectropolarimetry has become a powerful tool for constraining the accretion geometry. Building on these advances, this dissertation integrates theoretical modeling, observational data analysis, and instrumentation development to address open questions in high-energy astrophysics. On the theoretical side, relativistic ray-tracing simulations test the truncated disk-hot inner flow accretion geometry against observations of Cygnus X-1, identifying …

Gopher: Efficient Dynamic Graph Pattern Mining Via Dag-Driven Execution, Yi Zhang, Yu Huang, Chaoqiang Liu, Haifeng Liu, Juntao Chen, Jingrui Yuan, Jianhui Yue, Xiaofei Liao, Hai Jin, Jingling Xue

Michigan Tech Publications

Graph pattern mining is essential for analyzing dynamic networks, where graphs evolve over time. To accommodate these changes, existing solutions update match sets incrementally, avoiding the need to re-mine the entire graph and achieving significant performance improvements. However, these methods suffer from inefficiencies due to redundant set intersection operations across subgraph instances, causing performance degradation. In this paper, we propose Gopher, a DAG-driven dynamic graph pattern mining system that leverages computation locality for enhanced performance. Gopher uses DAGs to represent set operations, identifying and merging common subexpressions at compile time. This reduces redundant computations during runtime. To maximize performance, we …

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.

Strategic Layout And Optimization Paths Of The Standard System For The Exploration And Exploitation Of Deep Coalbed Methane, Chen Xinjun, Liu Zengqin, Shen Baojian, Zhao Shihu, Ye Jincheng, Zhang Jiaqi, Wang Tianyun

Coal Geology & Exploration

Objective In China, the deep coalbed methane (CBM) industry has emerged as a key sector for the reserve growth and production addition of natural gas. However, the current CBM technical standard system, which was developed based on shallow CBM practices, is incompatible with the demands for the exploration and development of deep CBM. To address this challenge, this study proposes the optimization paths of the current standard system, aiming to provide standardization support for large-scale exploitation of deep CBM.Methods Deep CBM reservoirs exhibit complex geological conditions of high temperature, pressure, and in-situ stress, as well as the CBM occurrence …

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

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

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Microburst Day Potential Index: Improving Launch And Public Safety, Zachary Kopel

Ocean Engineering and Marine Sciences Student Publications

Microbursts

•  A common convective wind event 
• Capable of causing damage from wind exceeding 30kts (NWS) 
• Cape Canaveral is a prime microburst environment 
• Can affect launch ops and daily activities by exceeding safety standards

Brevards Microplastics: A Literature Review And Proposed Methods To Study Florida Beaches, Kenna Peters

Ocean Engineering and Marine Sciences Student Publications

With populations along coastlines rapidly increasing so does the risk of microplastics entering the environment. Previous studies have found that 68% of microplastics found in the environment come from man-made cellulosic materials like rayon. Little information is known about the distribution, transportation, and final location of microplastics once they are introduced. Brevard County contains multiple types of beaches that differ substantially in human activity levels, urban density, and land-use patterns. Having a full picture of microplastic distribution and intensity is vital to understanding and reversing the impact these pollutants have on the environment.

Worldview-Bench: A Benchmark For Evaluating Global Cultural Perspectives In Large Language Models, Abdullah Mushtaq, Imran Taj, Rafay Naeem, Ibrahim Ghaznavi, Junaid Qadir

All Works

Background: Large Language Models (LLMs) are predominantly trained and aligned in ways that reinforce Westerncentric epistemologies and socio-cultural norms, leading to cultural homogenization and limiting their ability to reflect global civilizational plurality. Existing benchmarking frameworks fail to adequately capture this bias, as they rely on rigid, closed-form assessments that overlook the complexity of cultural inclusivity. Objectives: To address this cultural bias problem, we introduce WorldView-Bench, a benchmark designed to evaluate Global Cultural Inclusivity (GCI) in LLMs by analyzing their ability to accommodate diverse worldviews. Methods: Our approach is grounded in the Multiplex Worldview proposed by Senturk et al., which distinguishes …

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, …

Reminders App Execution, Alexander Gardner

Harrisburg University Other Works

This poster highlights the creation of an Android mobile application for reminders. This app is titled It's Time, Remind! Developed with the Flutter SDK and SQLite for the local database. Firebase Authentication was utilized for user account registration, login/ logout, and profile management. Users can additionally choose to use the app without an account. Users are able to view, create, and delete reminders and notes. Reminders also notify at specified times.

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, …

An Analysis Of Surface-Level Macro And Microplastics Along Western Central Leyte, Philippines, Graham B. Morrison-Plumley

Electronic Theses and Dissertations 2020 - Present

With the pervasive use of plastics worldwide, their environmental effects can reach even the most remote ecosystems. This study examines both macro and microplastic pollution in the Greater Baybay region in western Leyte, Philippines. Samples from 10 separate sites within Baybay and its adjacent communities represent the unique environmental and geologic diversity of the central Visayan region of the Philippines. At each of the 10 sampling sites, 3 excavations were conducted, yielding 30 samples. Macroplastics were collected in the field, immediately around microplastic digs. Microplastic samples were analyzed in Florida Atlantic University’s Environmental Change Lab. It was determined that spatial …

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 …

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.

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 …

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. …

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 …

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.

Grm-179-195 A Retrieval-Augmented Generation (Rag) System For Bible Question Answering Using Scriptural Text And Commentary, Maryam Koya, Pragya Mishra

C-Day Computing Showcase

This project develops and evaluates a question-answering (QA) system designed to address theological and interpretive questions about the New Testament. It uses a Retrieval-Augmented Generation (RAG) framework that integrates a pretrained large language model with a structured knowledge base consisting of public-domain Berean Standard Bible (BSB) New Testament and New Testament commentaries. User queries are embedded to retrieve semantically relevant passages, which are then supplied as contextual input for answer generation. The system is evaluated based on retrieval quality, answer faithfulness, and comparison to ground truth. Performance is benchmarked against a baseline BM25 keyword retrieval system without commentary, demonstrating that …

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.

Grm-175-233 From Leakage To Reliability In Dementia Detection, Crystal Tubbs

C-Day Computing Showcase

Automated dementia detection from speech offers a scalable approach to cognitive screening, but its reliability depends on rigorous experimental design. In this work, we reconstructed a Wav2Vec2-based dementia classification pipeline and identified critical methodological flaws, including speaker leakage, nondeterministic preprocessing, and invalid test partitions. We rebuilt the dataset using strict speaker-level separation, deterministic segmentation, and validation checks to ensure reproducibility. The corrected baseline achieved an accuracy of 44.74 percent and macro F1 score of 0.4439, reflecting a more realistic performance estimate than prior inflated results. This work establishes a scientifically valid foundation for evaluating augmentation strategies such as SpecAugment in …

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-123-140 P15-T2 Boating Safety Game Us Army Corps | Boating Mvp, Maryam Hamza, Saleh Hamza, Will Vanwinkle, Trevor Caffrey, Tobi Akinsunmi

C-Day Computing Showcase

This project is an interactive 2D educational boating safety game developed in Unity to teach students essential water navigation and life jacket safety practices in an engaging and immersive format. Designed in collaboration with a real-world sponsor, the game simulates a dynamic boating environment where players navigate obstacles, identify hazards, and make safety decisions under time constraints. The experience integrates instructional modules, guided character narration, and a final quiz phase that reinforces knowledge through immediate feedback, scoring, and achievement-based rewards. Players learn critical concepts such as proper life jacket fit, safe boating procedures, hazard identification, and shallow water awareness. The …

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.

Grm-153-198 Safecircle: Ai And Micro-Radar-Based Remote Monitoring For Patients With Ad/Adrd, Awan-Ur- Rahman, Soarov Borty, Gowtham Ankolu

C-Day Computing Showcase

Alzheimer's disease and related dementias (AD/ADRD) are irreversible and degenerative neurological conditions that severely impacts neurons, resulting in cognitive decline and memory loss. This study explores a mHealth system, including a SafeCircle iOS prototype, a novel solution that combines artificial intelligence with cutting-edge micro-radar technology. The platform offers a variety of features, including management of patient and caregiver profiles, real-time alerts in case of emergencies, emergency contact lists, one-touch SOS support, sharing of live locations, and recording of unusual events in video. It is a responsive and reliable care assistant that optimizes patient safety while reducing caregiver burden.