Digital Commons Network^™

Analysis Of Popular Songs In The Us Market, Michael Myers

Theses, Dissertations and Culminating Projects

In today’s digitally driven music landscape, understanding what drive’s a song’s popularity requires insight not only into its acoustic and lyrical content, but also into patterns of listener engagement across platforms. This thesis explores the predictive and descriptive dimensions of song popularity by applying supervised and unsupervised machine learning models to a multi-source dataset integrating audio features, sentiment analysis, and temporal consumption behavior. Drawing from a novel, multi-platform dataset that includes Billboard Hot 100 rankings, Spotify acoustic features and popularity scores, streaming, airplay, and sales metrics as reported on Luminate’s Music Connect, and lyrics from AZLyrics, the study investigates the …

Assessing The Impact Of Pll And Ple Concentrations And Aging On Protein Condensates Formation Via Llps, Nabil Harche

Theses, Dissertations and Culminating Projects

Protein droplets, also referred to as condensates, are membrane-less organelles formed through a biophysical process known as liquid-liquid phase separation (LLPS). These structures play an indispensable role in a wide range of cellular functions, including compartmentalizing biochemical reactions, regulating gene expression, and responding to cellular stress. Unlike traditional membrane-bound organelles, these condensates form dynamically and reversibly within the cytoplasm or nucleus, without the need for lipid membranes. The formation of these protein droplets is affected by several physicochemical factors, including pH, temperature, ionic strength, concentration of biomolecules, and specific molecular interactions such as electrostatics and hydrophobic forces. LLPS results in …

Human-Ai-Collaboration-For-Coding, Siddhardha Ravi

Theses, Dissertations and Culminating Projects

AI-generated code, while rapidly producing functional solutions, often falls short in aspects like comprehensive error handling, robust documentation, and optimal architectural design, areas where human expertise excels. Conversely, humans can greatly benefit from AI's rapid code generation capabilities. This project proposes and evaluates "A Framework to Improve Code Quality by Utilizing Generative AI Coding Along With Human-Written Code", designed to create a synergy between AI and human intelligence for enhanced software development. Conducted over four weeks, the research leverages BigCodeBench as its core dataset to rigorously investigate how human intervention can improve AI-generated code quality, identify the most effective human-AI …

Comparative Expression Of Cgmp-Dependent Protein Kinase G (Pkg) From Plasmodium And Toxoplasma, Tsopmo Asaah

Theses, Dissertations and Culminating Projects

Malaria and Toxoplasmosis are life threatening diseases to humans. The search for a cure and control of these diseases is still one of the major challenges of scientists worldwide. In this research we focused on two Malaria species; Plasmodium bergei² (Pb) and Plasmodium falciparum¹ (Pf) and also on Toxoplasma gondii. Part of the life cycle of these organisms is regulated by the enzyme cyclic GMP-dependent protein kinase G (PKG)⁴. This is our drug target in this research. We believe that inhibiting this enzyme will be a great step in fighting these diseases. In this research, we devised a comparative study …

Simulation Of Photochemical Effects On Air Quality, Grace Li

Chemical Engineering Undergraduate Honors Theses

Nitrogen dioxide (NO₂) and ozone (O₃) are both EPA criteria air pollutants in the troposphere, largely generated by combustion and therefore present in significant concentrations in urban areas. Sunlight facilitates chemical reactions between pollutants and ambient air. Aerosols modulate the amount of available energy required to drive such photochemical reactions, ultimately impacting air quality. Simulating photolysis rates with FastJX software is a resource-efficient strategy to study how these aerosols can affect air quality.

Investigating The Influence Of Experimental Parameters On The Formation Of Gold Nanoparticles: A Green Synthesis And Design Of Experiments Approach, Isaac Opeyemi Subuloye

Master's Theses

This thesis examines the green synthesis of gold nanoparticles using catechin, a naturally occurring plant-derived compound, as both a reducing and capping agent. The synthesis process was guided by a structured Design of Experiments (DOE) methodology to ensure systematic investigation and optimization. A two-phase experimental approach was adopted. First, a full factorial screening design was used to evaluate the effects of catechin concentration, NaOH concentration, and pre-reaction time. This was followed by an optimization phase using a face-centered composite design to identify conditions that produce nanoparticles with ideal characteristics. The nanoparticles were analyzed using UV-visible spectroscopy and dynamic light scattering …

Nebline, August/September 2025

NEBLINE Newsletter Archive from Nebraska Extension in Lancaster County

Contents

Summertime’s Natural Light Show: Fireflies by Kait Chapman

Insect Science with Kids: Bug Collecting as a Tool to Learn

The Perfect Partnership: EFNEP and SNAP-Ed Join Forces for Lifelong Nutrition and Health by Emily Gratopp

Recipe of the month by Emily Gratopp: Fruit salsa & cinnamon chips

Southeast nebraska Pasture & Acreage Expo

Southeast Nebraska Alfalfa & Wheat Expo

Landlord/Tenant Cash Rent workshop

Vegetable Gardening in the Fall by Mary Jane Frogge

GRO Big Red virtual learning series

2025 perennial plant of the year: Blue mountain mint (Pycnanthemum muticum)

Garden guide: Things to do this month by …

Navigating Transportation Barriers: Older Adults’ Familiarity With New Mobility Options And Perceptions Toward Autonomous Vehicles In Arkansas, Arna Nishita Nithila, Suman Kumar Mitra, Michelle Gray, Alishia Juanelle Ferguson, Jennifer D. Webb

Civil Engineering Faculty Publications and Presentations

The objective of this study is to analyze older adults’ familiarity with new transportation options (ride-hailing services, bike-share services, and shared e-scooter services) and their perception towards autonomous vehicles (fully autonomous cars), as well as how transportation barriers influence their familiarity and perceptions, in Arkansas, a predominantly rural state. Data from 775 older adults aged 60 years or older were collected between October 2021 and October 2022. To fulfill the study objective, the study used Latent Class Cluster Analysis to segment older adults into classes based on their familiarity with new transportation options and their perceptions of autonomous vehicles. The …

Integrated Sustainability: Unlocking Environmental Potential In Electric Vehicles And Solar Panels, Zachary C. Anderson

Honors Theses

This thesis investigates the environmental, economic, and individual level factors influencing consumer adoption of electric vehicles and solar panels. Through a comprehensive scoping review and case-based analysis, it identifies key incentives and barriers related to purchase intentions for each alternative energy technology. Central factors influencing adoption include cost-related concerns, individual purchasing power, demographic effects, and policy impact. These findings suggest that targeted interventions – specifically those used to amplify incentives and mitigate barriers – can positively impact individual consumer purchase intention, including through bundling options. Highlighting the importance of integrated sustainability, this study explored how electric vehicles and solar panels …

An Injectable, Dual-Curing Hydrogel For Controlled Bioactive Release In Regenerative Endodontics, Meisam Omidi, Daniela S. Masson-Meyers, Jeffrey M. Toth

Biomedical Engineering Faculty Research and Publications

Regenerative endodontics seeks to restore the vascularized pulp–dentin complex following conventional root canal therapy, yet reliable neovascularization within the constrained root canal remains a key challenge. This study investigates the development of an injectable, dual-curing hydrogel based on methacrylated decellularized amniotic membrane (dAM-MA) and compares its performance to a conventional gelatin methacryloyl (GelMA). The dAM-MA platform was designed for biphasic release, incorporating both free vascular endothelial growth factor (VEGF) for an initial burst and matrix-metalloproteinase-cleavable VEGF conjugates for sustained delivery. The dAM-MA hydrogel achieved shape-fidelity via thermal gelation at 37 °C and possessed tunable stiffness (0.5–7.8 kPa) after visible-light irradiation. …

The Recreation Opportunity Spectrum In Practice: A Classification Of Visitors And Sites In The Roaring Fork Valley, Colorado, Usa, Jessica Bruck

All Graduate Theses and Dissertations, Fall 2023 to Present

Researchers and public land managers continue to investigate what motivates visitors to recreate and how to best sustain environmental conditions to provide for these recreation demands and support conservation goals. Much recreation research has successfully identified visitor motivations at a single recreation area (spatial scale), but few studies broadly assess the manifestation of visitor motivations at an array of recreation areas that present unique recreation experience opportunities. Additionally, it is known that there are a diversity of visitor motivations and preferences, which presents a complex challenge to public land managers who must ensure that a diversity of recreation opportunities are …

Operationalizing Camera-Based Hydrologic Monitoring With Ai And Edge Computing: Towards Real-Time Water Level And Discharge Measurements, Razin Bin Issa

All Graduate Theses and Dissertations, Fall 2023 to Present

Monitoring river water levels and flows is critical for managing water supplies, protecting against floods, supporting ecosystems, and informing infrastructure planning. However, traditional methods rely on sensors installed directly in rivers, which can be expensive, difficult to maintain, and vulnerable to damage—particularly in remote or hazardous locations. In many regions, limited budgets and access challenges result in large gaps in river monitoring networks.

This research explores an innovative alternative: using fixed cameras and artificial intelligence (AI) to monitor rivers from a distance. The approach allows for non-contact observation of water levels and flows by analyzing images captured by cameras installed …

Exploring Biotic And Abiotic Drivers Of Ecosystem Properties: The Influence Of Waterbirds And Photodegradation On Wetland Ecosystems, Lindsay Miller

All Graduate Theses and Dissertations, Fall 2023 to Present

Wetlands provide a wide range of valuable ecosystem services: these are benefits from the environment that support human well-being, such as water filtration, fish and bird habitat, and shoreline protection. Despite their importance, wetlands are among the most threatened ecosystems, facing increasing pressure from natural and human activities. To better predict the future of wetlands and guide effective management and restoration practices, we need to understand the various forces shaping their ecological properties. In this thesis, we examined the influence of two forces: waterbirds and photodegradation (the process by which sunlight breaks down plant material). To explore each of these …

Ctso Implementation In Secondary Education: Insights Into Teacher Motivation, Barriers And Training Needs, Ariane Crystal

All Graduate Theses and Dissertations, Fall 2023 to Present

This study explores the role of Career and Technical Student Organizations (CTSOs) in enhancing the educational experiences of secondary Career and Technical Education (CTE) teachers in Utah. By surveying CTE educators, the research identifies key factors that motivate teachers to implement CTSOs, as well as barriers they face in doing so. The findings indicate that a significant majority of teachers recognize the value of CTSOs for fostering student engagement and career readiness. Key motivators include support from school administration and access to professional development, while challenges such as time constraints and resource limitations hinder participation. The study highlights the importance …

Beyond The Click: Investigating Mental Models, Targeted Attacks, And Behavioral Interventions Against Clickbait, Ankit Shrestha

All Graduate Theses and Dissertations, Fall 2023 to Present

Clickbait is misleading or exaggerated content on social media that tricks people into clicking on links by making them curious. For instance, posts that use headlines such as “This is the worst day to visit a restaurant”. These clickbait posts can lead to fake news, wrong information, and even harmful websites. Even though many people know clickbait can be risky, they often still fall for it. Existing tools to stop clickbait don’t always consider how people think, the situations they are in, or their different needs. This research looks at how people understand and react to clickbait, and develops different …

Examining The Development Of Collective Classroom Efficacy At The Secondary Level, Bradley K. Dutson

All Graduate Theses and Dissertations, Fall 2023 to Present

How can educators develop cohesive and productive learning communities at the secondary level? Prior research has investigated how classrooms develop collective classroom efficacy (CCE) or the shared belief that, as a classroom, they can achieve their learning goals together as a community. Previous studies suggest that CCE at the elementary level is developed with a teacher acting in the role of a community organizer. The teacher employs social and academic practices which incorporate the use of relevance, relationship, and responsibility. These help to establish four key elements of developing CCE which include: 1) a positive classroom environment, (2) persistence and …

Pressure Recovery Analysis Downstream Of Valves And Meters: An Experimental And Numerical Investigation, Stephen J. Sanders

All Graduate Theses and Dissertations, Fall 2023 to Present

In pressurized water systems, flow control devices such as valves and meters cause pressure losses that can affect energy efficiency and measurement accuracy. Industry standards assume that all devices recover within the same distance downstream, but this may not be true for all device types. In this study we used laboratory testing and computer modeling to determine how far downstream pressure stabilizes after flowing through various valves and meters. Devices like Venturi meters recovered pressure quickly, while orifice plates required longer distances and resulted in greater permanent energy loss. In most cases, pressure recovered within four to six pipe diameters. …

Machine Learning And Clinical Eeg Data For Multiple Sclerosis: A Systematic Review, Badr Mouazen, Ahmed Bendaouia, El Hassan Abdelwahed, Giovanni De Marco

Manufacturing & Industrial Engineering Faculty Publications

Multiple Sclerosis (MS) is a chronic neuroinflammatory disease of the Central Nervous System (CNS) in which the body’s immune system attacks and destroys the myelin sheath that protects nerve fibers, leading to a wide range of debilitating symptoms and causing disruption of axonal signal transmission. Accurate prediction, diagnosis, monitoring and treatment (PDMT) of MS are essential to improve patient outcomes. Recent advances in neuroimaging technologies, particularly electroencephalography (EEG), combined with machine learning (ML) techniques — including Deep Learning (DL) models — offer promising avenues for enhancing MS management. This systematic review synthesizes existing research on the application of ML and …

Equivalence And Similarity Refutation For Probabilistic Programs, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider the problems of statically refuting equivalence and similarity of output distributions defined by a pair of probabilistic programs. Equivalence and similarity are two fundamental relational properties of probabilistic programs that are essential for their correctness both in implementation and in compilation. In this work, we present a new method for static equivalence and similarity refutation. Our method refutes equivalence and similarity by computing a function over program outputs whose expected value with respect to the output distributions of two programs is different. The function is computed simultaneously with an upper expectation supermartingale and a lower expectation submartingale for …

Explainable Multimodal Sentiment Analysis Of Social Media Visual Content For Child Safety, Yee Sen Tan, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Ensuring the safety and well-being of children is increasingly important, especially in a world where visual content is pervasive. This paper proposes a novel multimodal, multilingual, and multiclass sentiment analysis method for social media content, aimed at improving content moderation for child safety. Our approach integrates textual, visual, and audio data from videos, categorizing sentiment into four levels: positive, slightly negative, negative, and strongly negative, enabling granular detection of harmful content. To enhance explainability and trust, we also leverage interpretable mechanisms to analyze the contributions of each modality. Evaluation of our method demonstrates strong generalization across diverse video types, and …

Rl4co: An Extensive Reinforcement Learning For Combinatorial Optimization Benchmark, Federico Berto, Et. Al

Research Collection School Of Computing and Information Systems

Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation. Deep reinforcement learning (RL) has recently shown significant benefits in solving CO problems, reducing reliance on domain expertise and improving computational efficiency. However, the absence of a unified benchmarking framework leads to inconsistent evaluations, limits reproducibility, and increases engineering overhead, raising barriers to adoption for new researchers. To address these challenges, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 27 CO problem environments and 23 state-of-the-art baselines. Built on efficient software libraries and best practices in …

Learning Frame-Level Classifiers For Video-Based Real-Time Assessment Of Stroke Rehabilitation Exercises From Weakly Annotated Datasets, Ana Rita Cóias, Min Hun Lee, Alexandre Bernardino, Asim Smailagic, Mariana Mateus, David Fernandes, Sofia Trapola

Research Collection School Of Computing and Information Systems

Autonomous rehabilitation support solutions, such as virtual coaches, should provide real-time feedback to improve motor function and maintain patient engagement. However, fully annotated dataset collection for real-time exercise assessment is time-consuming and costly, posing a barrier to evaluating proposed methods. In this work, we present a novel framework that learns a frame-level classifier using weakly annotated videos for real-time assessment of compensatory motions in stroke rehabilitation exercises by generating pseudo-labels at a frame level. We consider three approaches: 1) a baseline approach that uses a source dataset to train a frame-level classifier, 2) a transfer learning approach that uses target …

The 6th International Workshop On Talent And Management Computing (Tmc 2025), Hengshu Zhu, Yong Ge, Hui Xiong, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with talent and management-related tasks in a quantitative manner. Indeed, thanks to the era of big data, the availability of large-scale talent data provides unparalleled opportunities for leaders to deliver intelligence for effective management for organizations. In the past few years, talent and management computing have increasingly attracted attention from KDD communities, and a number of research/applied data science efforts have been devoted. To this end, the purpose of this workshop, i.e., the 6th International Workshop on Talent and Management Computing …

Inference-Time Gaze Refinement For Micro-Expression Recognition: Enhancing Event-Based Eye Tracking With Motion-Aware Post-Processing, Panahetipola Mudiyanselage Nuwan Bandara, Thivya Kandappu, Archan Misra

Research Collection School Of Computing and Information Systems

Event-based eye tracking holds significant promise for fine-grained cognitive state inference, offering high temporal resolution and robustness to motion artifacts, critical features for decoding subtle mental states such as attention, confusion, or fatigue. In this work, we introduce a model-agnostic, inference-time refinement framework designed to enhance the output of existing event-based gaze estimation models without modifying their architecture or requiring retraining. Our method comprises two key post-processing modules: (i) Motion-Aware Median Filtering, which suppresses blink-induced spikes while preserving natural gaze dynamics, and (ii) Optical Flow-Based Local Refinement, which aligns gaze predictions with cumulative event motion to reduce spatial jitter and …

Affinitytune: A Prompt-Tuning Framework For Few-Shot Anomaly Detection On Graphs, Jingyan Chen, Guanghui Zhu, Guansong Pang, Chunfeng Yuan, Yihua Huang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD) is a critical task with applications in domains such as networking, finance, and bioinformatics. % However, the scarcity of labeled anomalies and the limitations of unsupervised methods hinder effective detection. % While semi-supervised and few-shot learning approaches offer improvements, they struggle with knowledge transfer and rely heavily on labeled data. % Recent advancements in prompt tuning on graphs provide a promising direction, but their application to heterophilous graphs in anomaly detection remains underexplored. % In this work, we propose AffinityTune, a novel framework for few-shot graph anomaly detection based on prompt tuning. % Our approach introduces …

Tetd: Trusted Execution In Trust Domains, Zhanbo Wang, Jiaxin Zhan, Xuhua Ding, Fengwei Zhang, Ning Hu

Research Collection School Of Computing and Information Systems

Intel TDX empowers cloud service providers to construct confidential virtual machines called trust domains (TDs) on x86 platforms. Similar to its counterparts from AMD and Arm, TDX's hardware based protection over integrity and secrecy of virtual machine memory and vCPU states inevitably hinders legitimate virtual machine management such as introspection. At the presence of compromised high-privileged software (e.g., the guest kernel), neither the cloud service provider nor the TD owner can securely carry out a task within the TD. To tackle this problem, we propose TETD, an in-TD trusted execution technique without trusting any TD system software. Our design does …

Oblivious Digital Tokens, Mihael Liskij, Xuhua Ding, Gene Tsudik, David A. Basin

Research Collection School Of Computing and Information Systems

A computing device typically identifies itself by exhibiting unique measurable behavior or by proving its knowledge of a secret. In both cases, the identifying device must reveal information to a verifier. Considerable research has focused on protecting identifying entities (provers) and reducing the amount of leaked data. However, little has been done to conceal the fact that the verification occurred.We show how this problem naturally arises in the context of digital emblems, which were recently proposed by the International Committee of the Red Cross to protect digital resources during cyber-conflicts. To address this new and important open problem, we define …

Achilles: A Formal Framework Of Leaking Secrets From Signature Schemes Via Rowhammer, Junkai Liang, Zhi Zhang, Xin Zhang, Qingni Sheng, Yansong Gao, Xinliang Yuan, Haiyang Xue, Pengfei Wu, Zhonghai. Wu

Research Collection School Of Computing and Information Systems

Signature schemes are a fundamental component of cyber-security infrastructure. While they are designed to be mathematically secure against cryptographic attacks, they are vulnerable to Rowhammer fault-injection attacks. Since all existing attacks are ad-hoc in that they target individual parameters of specific signature schemes, it remains unclear about the impact of Rowhammer on signature schemes as a whole.In this paper, we present Achilles, a formal framework that aids in leaking secrets in various real-world signature schemes via Rowhammer. Particularly, Achilles can be used to find potentially more vulnerable parameters in schemes that have been studied before and also new schemes that …

Mpo: Multilingual Safety Alignment Via Reward Gap Optimization, Weixiang Zhao, Yulin Hu, Yang Deng, Tongtong Wu, Wenxuan Zhang, Jiahe Guo, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have become increasingly central to AI applications worldwide, necessitating robust multilingual safety alignment to ensure secure deployment across diverse linguistic contexts. Existing preference learning methods for safety alignment, such as RLHF and DPO, are primarily monolingual and struggle with noisy multilingual data. To address these limitations, we introduce Multilingual reward gaP Optimization (MPO), a novel approach that leverages the well-aligned safety capabilities of the dominant language (e.g., English) to improve safety alignment across multiple languages. MPO directly minimizes the reward gap difference between the dominant language and target languages, effectively transferring safety capabilities while preserving the …

A Review: The Beauty Of Serendipity Between Integrated Circuit Security And Artificial Intelligence, Chen Dong, Decheng Qiu, Bolun Li, Yang Yang, Chenxi Lyu, Dong Cheng, Hao Zhang, Zhenyi. Chen

Research Collection School Of Computing and Information Systems

Integrated circuits are the core of a cyber-physical system, where tens of billions of components are integrated into a tiny silicon chip to conduct complex functions. To maximize utilities, the design and manufacturing life cycle of integrated circuits rely on numerous untrustworthy third parties, forming a global supply chain model. At the same time, this model produces unpredictable and catastrophic issues, threatening the security of individuals and countries. As for guaranteeing the security of ultra-highly integrated chips, detecting slight abnormalities caused by malicious behavior in the current and voltage is challenging, as is achieving computability within a reasonable time and …