Digital Commons Network^™

Thermodynamic Assessment Of The Li, Na, K, Cs | F, I Reciprocal Salt For Msr Applications, Clara M. Dixon, Mina Aziziha, Amir Mofrad, Jorge Paz Soldan Palma, Ronald E. Booth, Aiswarya Padinhare Manissery, Jack A. Wilson, Hans Conrad Zur Loye, Hunter B. Tisdale, Juliano Schorne-Pinto, Theodore M. Besmann

Faculty Publications

Gibbs energy models necessary to describe the thermochemical behavior of cesium and iodine in molten FLiNaK (46.5LiF-11.5NaF-42KF mol%), a proposed molten salt reactor coolant and fuel solvent, have been developed as they are of concern due to their high radiotoxicity and volatility. The six constituent pseudo-binary systems LiI-CsI, LiI-KI, LiI-NaI, NaI-KI, KI-CsI and NaI-CsI were evaluated using available phase equilibria and mixing enthalpy measurements. The models for the ternary reciprocal salt systems Li,Cs|F,I, Li,K|F,I, Li,Na|F,I, Na,K|F,I, Na,Cs|F,I and K,Cs|F,I were obtained by interpolation of the optimized pseudo-binary systems and parameters fit as necessary based on observed behavior. The additive (common …

Cattlefever: An Automated Cattle Fever Estimation System, Trong Thang Pham, Ethan Coffman, Beth Kegley, Jeremy G. Powell, Jiangchao Zhao, Ngan Le

Electrical Engineering and Computer Science Faculty Publications and Presentations

Farmers face the critical challenge of monitoring cattle well-being for both ethical and economic success, relying on signals like body temperature and facial expressions to assess their animals' health. However, these indicators have traditionally relied on human observation with manual measurement, which is time-consuming and subjective. Despite this clear need, no automated system currently exists for monitoring cattle body temperature, and available datasets remain limited in scope. To address these challenges, we make two key contributions: (i) We introduce CattleFace-RGBT, a novel RGB-Thermal (RGB-T) Cattle Facial Landmark dataset consisting of 2,300 paired RGB and thermal images (4,600 images in total), …

The Impact Of Screentime In Childhood, Kyla P. Shirley

Informatics

The increased dependency on technological devices as a result of the digital age being upon us impacts a multitude of individuals from all age groups. For older groups, they might find an easier time navigating the world wide web due to their awareness of the potential dangers these digital spaces may bring. On the other hand, younger age groups are more susceptible to the dangers posed due to their lack of maturity which in turn makes them extremely vulnerable to being negatively impacted when using these electronic devices. The goal of this project was to generate awareness concerning screen time …

Digital Communications Between Firms And Investors: Impact Of Explanatory Responses On Investor Engagement In Online Financial Q&A, Runyu Wang, Zili Zhang, Keng Siau, Ziqiong Zhang

Research Collection School Of Computing and Information Systems

The emerging trend of digital communications between firms and investors through online question-and-answer (Q&A) platforms is recognized as a vital strategy for managing investor relations, contributing to enhanced market efficiency and information transparency through increased information exchange. Potential investors can seek responses from firm managers to address their information needs, thereby mitigating market uncertainties. To provide foundational insights, we conduct a survey of investors to assess their awareness, usage, and perceptions of firm-investor Q&A platforms. In the subsequent empirical study, we specifically focus on the substance of managers’ responses, which are primarily aimed at clarifying firm events or information. In …

Coresets For Clustering Under Stochastic Noise, Lingxiao Huang, Zhize Li, Nisheeth K. Vishnoi, Runkai Yang, Haoyu Zhao

Research Collection School Of Computing and Information Systems

We study the problem of constructing coresets for $(k, z)$-clustering when the input dataset is corrupted by stochastic noise drawn from a known distribution. In this setting, evaluating the quality of a coreset is inherently challenging, as the true underlying dataset is unobserved. To address this, we investigate coreset construction using surrogate error metrics that are tractable and provably related to the true clustering cost. We analyze a traditional metric from prior work and introduce a new error metric that more closely aligns with the true cost. Although our metric is defined independently of the noise distribution, it enables approximation …

Reliable-Data-Split (Rds): Maximizing Model Potential With Reinforced Selection Strategy, Hoang D. Nguyen, Xuan-Son Vu, Quoc Tuan Truong, Duc-Trong Le

Research Collection School Of Computing and Information Systems

The nexus between data characteristics and parametric models is fundamental for developing effective and reliable artificial intelligence (AI) systems. Mismatches in data properties for model development may lead to deleterious effects on AI model performance in machine learning practice. This paper proposes a Reliable Data Split (RDS) procedure to learn how to select data points that will generalise the target domain adequately by employing prior knowledge of the data generative process. We introduce a reinforced selection strategy using deep reinforcement learning with diverse black box predictors in maximising ensemble rewards as the proxy of model performance potential while maintaining an …

Scalable Graph Indexing Using Gpus For Approximate Nearest Neighbor Search, Zhonggen Li, Xiangyu Ke, Yifan Zhu, Bocheng Yu, Baihua Zheng, Yunjun Gao

Research Collection School Of Computing and Information Systems

Approximate nearest neighbor search (ANNS) in high-dimensional vector spaces has a wide range of real-world applications. Numerous methods have been proposed to handle ANNS efficiently, while graph-based indexes have gained prominence due to their high accuracy and efficiency. However, the indexing overhead of graph-based indexes remains substantial. With exponential growth in data volume and increasing demands for dynamic index adjustments, this overhead continues to escalate, posing a critical challenge.In this paper, we introduce Tagore, a fasT library accelerated by GPUs for graph indexing, which has powerful capabilities of constructing refinement-based graph indexes such as NSG and Vamana. We first introduce …

Applications Of Machine Learning In Gravitational-Wave Research With Current Interferometric Detectors, Elena Cuoco, Marco Cavaglià, Ik Siong Heng, David Keitel, Christopher Messenger

Physics Faculty Research & Creative Works

This article provides an overview of the current state of machine learning in gravitational-wave research with interferometric detectors. Such applications are often still in their early days but have reached sufficient popularity to warrant an assessment of their impact across various domains, including detector studies, noise and signal simulations, and the detection and interpretation of astrophysical signals. In detector studies, machine learning could be useful to optimize instruments like LIGO, Virgo, KAGRA, and future detectors. Algorithms could predict and help in mitigating environmental disturbances in real time, ensuring detectors operate at peak performance. Furthermore, machine-learning tools for characterizing and cleaning …

Soil Characterization With Proximal Geophysics, Sophia M. Becker

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

High quality soil data is crucial for addressing numerous threats to human well-being such as water scarcity, food insecurity, and intensified natural disasters. True soil property values are determined by analyzing soil samples in a laboratory, but soil sampling is often expensive and time-consuming. Some in-field point sensors and remote sensing products are available to measure soil properties, but they have inherent limitations in measurement extent and resolution. Geophysical methods can fill the measurement scale gap between point measurements and remote sensing products, but various research questions remain open. This dissertation aims to advance understanding of geophysics-derived soil property measurements …

Residential Hvac Installation Faults: Field Prevalence, Drivers, And Energy Impacts, Seyed Ali Rooholghodos

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Faults in residential comfort systems can have detrimental effects on the performance of the system, reducing the energy efficiency, affecting indoor comfort, and shortening equipment lifespan. Many of the faults occur at installation. However, there has not previously been a systematic and nationally representative study to indicate the prevalence of the faults, meaning the distribution of faults by fault type and fault intensity. Understanding true fault prevalence is essential to be able to identify derivers, and to assess the incremental electricity consumption of the new installation systems.

This study presents on-site data from 341 new central cooling systems—air conditioners and …

Large-Scale Experimental Study On The Impact Of Air Cleaning & Ventilation On Indoor Air Quality And Student Illness-Related Absenteeism, Daud Nosham

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This large-scale randomized experimental field study investigated the effect of portable air purifiers (PAPs) on classroom’s air quality and student’s illness related absenteeism. PAPs were installed in 317 classrooms from various school districts located in Eastern Nebraska. The classrooms were randomly assigned into four conditions: three treatment conditions (T1, T2 & T3) and one control condition (device without any filter). T1 was equipped with HEPA filters, T2 added Activated Carbon (AC) layer to HEPA, and T3 further integrated Germicidal Ultraviolet (GUV) alongside HEPA and AC filters. The measured variables were carbon dioxide (CO2), fine particles (PN2.5) and coarse particles (PNcoarse), …

Dancing With Logic: The Impact Of Integrating Dance In Teaching Introductory Computer Science Concepts On Student Understanding And Engagement, April Monk

Master's Theses

The purpose of this study was to investigate the effectiveness of dance-integrated pedagogical methods in enhancing the learning experiences of students in an introductory computer science course. In particular, the research aimed to measure the impact of creative movement on student comprehension, engagement, and perceptions of computer science. To guide this investigation, the study explored three research questions: Does integrating dance into lessons impact students’ comprehension of fundamental computer science concepts? How does dance integration affect student perceptions of both dance and computer science? And what elements of dance contribute to differences in comprehension between dance-integrated and traditional lessons? A …

N-Aryl Phenoxazines As Polymer Supported Photocatalysts, Mary Kathryn Hall

Master's Theses

In photosynthesis, the energy of sunlight is perfectly harnessed to facilitate the production of small molecules that are essential for plant life on earth. Inspired by this model of energy efficiency and simplicity, this work seeks to mimic that ability to use the energy in visible light to perform new and more efficient chemistries. Just like in photosynthesis, photocatalysts can act as chromophores absorbing light to excite electrons opening new pathways for previously untapped reactivity. Recent research has provided a wide range of small molecule reactions that are made possible or easier by the use of a photocatalyst. However, these …

A Learning‑Augmented Dynamic Programming Approach For Orienteering Problem With Time Windows, Guansheng Peng, Lining Xing, Fuyan Song Ma, Aldy Gunawan, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Recent years have witnessed a surge of interest in solving combinatorial optimization problems (COPs) using machine learning techniques. Motivated by this trend, we propose a learning-augmented exact approach for tackling an NP-hard COP, the Orienteering Problem with Time Windows, which aims to maximize the total score collected by visiting a subset of vertices in a graph within their time windows. Traditional exact algorithms rely heavily on domain expertise and meticulous design, making it hard to achieve further improvements. By leveraging deep learning models to learn effective relaxations of problem restrictions from data, our approach enables significant performance gains in an …

Semi‑Supervised Graph Anomaly Detection Via Robust Homophily Learning, Guoguo Ai, Hezhe Qiao, Hui Yan, Guansong Pang

Research Collection School Of Computing and Information Systems

Current semi-supervised graph anomaly detection (GAD) methods utilizes a small set of labeled normal nodes to identify abnormal nodes from a large set of unlabeled nodes in a graph. These methods posit that 1) normal nodes share a similar level of homophily and 2) the labeled normal nodes can well represent the homophily patterns in the entire normal class. However, this assumption often does not hold well since normal nodes in a graph can exhibit diverse homophily in real-world GAD datasets. In this paper, we propose RHO, namely Robust Homophily Learning, to adaptively learn such homophily patterns. RHO consists of …

Llm-Assisted Cwe Identification, Severity Assessment, And Vulnerability Description Generation, Mohammad Jalili Torkamani

School of Computing: Dissertations, Theses, and Student Research

Identifying the underlying weakness types and assessing their severity using CWE and CVSS standards are critical steps in software vulnerability management. While timely assessment of vulnerabilities mitigates the impact of severe security incidents, automating joint CWE identification and severity assessment remains challenging due to the heterogeneity of vulnerabilities across different code granularities and programming languages. In addition, generating vulnerability descriptions is often time-consuming, as it requires extensive manual review, validation, and writing by security experts.

In this thesis, we leverage the capabilities of Large Language Models (LLMs) to automate the identification of CWE identifiers and the assessment of their severity …

Enhancing Ad/Adrd Management Through Ihelpcare: A Compliant And Culturally Sensitive Ai-Driven Digital Healthcare Platform, Trisha Bhowmick

Master's Theses

The digital healthcare field is expanding fast, and now it requires platforms that use advanced technology and maintain robust data security and compliance practices. In the present paper, we present the main structure, key methods, and compliance strategies of the digital healthcare system iHelpCare, which, while fully meeting the HIPAA/GDPR requirements, provides health services more accessible, efficient, and inclusive. The proposed platform is powered by AI for personalized care solutions, with the main emphasis on preventive health management and providing tools for people with disabilities.

iHelpCare achieves real-time patient monitoring while securing medical data management and easy communication between patients, …

Topic Modeling And Culturomic Analysis Of 30,000 Books Over 100 Years Using Gensim, Michael A. Freeman

Electronic Theses and Dissertations

This thesis explores the cultural influence of historical events on English-language fiction published between 1820 and 1929. Using a corpus of 30,256 digitized books from Project Gutenberg, Latent Dirichlet Allocation (LDA) topic modeling was applied to identify recurring themes across eleven decades. The study sought to determine whether historically significant events could be detected within fictional narratives. One clear instance emerged: Napoleon Bonaparte and the Napoleonic Wars appeared explicitly in the 1820s corpus. Beyond this, several thematic patterns were observed—such as maritime language in the 1840s, national identity in the 1880s, and youth-oriented dialogue in the early 20th century—that plausibly …

By The Numbers: Numeracy, Religion, And The Quantitative Transformation Of Early Modern England (Book Review), Calvin Jongsma

Faculty Work Comprehensive List

Reviewed Title: By the Numbers: Numeracy, Religion, and the Quantitative Transformation of Early Modern England by Jessica Marie Otis. New York, NY: Oxford University Press, 2024. 264 pp. ISBN: 9780197608777.

Spatial–Temporal Evolution Of The Box Springs Plutonic Complex, Riverside, Ca, Mateo Ospino Díaz

Loma Linda University Electronic Theses, Dissertations & Projects

The mechanisms of pluton emplacement into continental crust have long been debated in igneous geology, with early models emphasizing forceful processes such as ballooning, later contrasted by alternative emplacement mechanisms including diapirism, incremental assembly, stoping and, emplacement by sheets and dikes. These contrasting perspectives highlight the complexity of the construction of mid-crustal intrusions and the need for case studies that integrate multiple datasets. The Box Springs Plutonic Complex (BSPC) in the northern Peninsular Ranges Batholith (PRB) preserves structural, textural, and compositional features that record its emplacement and internal construction. Although its lithology, structure, and geochemistry have been described, an integrated …

Security Vulnerabilities And Defense Tactics For Generative Ai Application Development, Kyle Klein

University Honors Theses

Generative AI (GenAI) applications such as OpenAI's ChatGPT leverage large language models (LLMs) trained on enormous amounts of data to accomplish tasks such as document editing, summarization, and query response. Chatbots and LLM programs that are equipped with retrieval-augmented generation (RAG) have the ability to draw upon data provided by developers and users to improve the quality of the program's responses. LLM technology has even expanded to generate images, audio, and video from user instructions. Designed around unpredictable user input and typically composed of many opaque components, LLM software products face a paradigm shift of new, constantly evolving security challenges. …

What Environmental Circumstances Generate Hazardous Wave Overtopping Conditions For Infrastructure Disruption: Dawlish (United Kingdom) Case Study, Zbigniew Tomasz Zielonka

The Plymouth Student Scientist

Coastal infrastructure faces increasing threats from wave overtopping due to climate change, rising sea levels, and intensifying storms. This research investigates the environmental conditions that drive hazardous wave overtopping using Dawlish, a vulnerable section of the UK’s railway, as a case study. Understanding these conditions is essential for improving knowledge, developing effective mitigation strategies, and enhancing the resilience of shoreline defences against future challenges. Using one-year observations from WireWall instruments, offshore wave buoy, and meteorological data, the research applies a Random Forest model to assess the influence of metocean conditions on overtopping. The results indicate that wave height, tidal level, …

Collective Agency And Community Resilience Among Farmworkers In The Lower Rio Grande Valley, Angel Velazquez

Theses and Dissertations

This research assesses how farmworkers in the Lower Rio Grande Valley of Texas use everyday practices to build resilience and adapt to changes in their lives. I apply two interconnected theories, White’s (2017, 2019) framework of collective agency and community resilience and Castro and Sen’s (2022) framework of everyday adaptation actions, to study these practices. I conducted 19 semi-structured interviews with Latino farmworkers and professionals who work with local farmworker organizations in the Rio Grande Valley (RGV) of Texas. Interview transcripts were analyzed to determine if and how Latino farmworkers in the RGV can work towards community resilience and implement …

Streams Guidelines: Standards For Technical Reporting In Environmental And Host-Associated Microbiome Studies, Julia M. Kelliher, Chloe Mirzayi, Sarah R. Bordenstein, Aaron Oliver, Christina A. Kellogg, Eneida L. Hatcher, Maureen Berg, Petr Baldrian, Mashael Aljumaah, Cassandra Maria Luz Miller, Christopher Mungall, Vlastimil Novak, Alexis Palucki, Ethan Smith, Nazifa Tabassum, Gregory Bonito, J. Rodney Brister, Patrick S. Chain, Mingfei Chen, Samuel Degregori, Xuefeng Peng, Et. Al.

Faculty Publications

The interdisciplinary nature of microbiome research, coupled with the generation of complex multi-omics data, makes knowledge sharing challenging. The Strengthening the Organization and Reporting of Microbiome Studies (STORMS) guidelines provide a checklist for the reporting of study information, experimental design and analytical methods within a scientific manuscript on human microbiome research. Here, in this Consensus Statement, we present the standards for technical reporting in environmental and host-associated microbiome studies (STREAMS) guidelines. The guidelines expand on STORMS and include 67 items to support the reporting and review of environmental (for example, terrestrial, aquatic, atmospheric and engineered), synthetic and non-human host-associated microbiome …

Sopo: Text-To-Motion Generation Using Semi-Online Preference Optimization, Xiaofeng Tan, Hongsong Wang, Xin Geng, Pan Zhou

Research Collection School Of Computing and Information Systems

Text-to-motion generation is essential for advancing the creative industry but often presents challenges in producing consistent, realistic motions. To address this, we focus on fine-tuning text-to-motion models to consistently favor highquality, human-preferred motions—a critical yet largely unexplored problem. In this work, we theoretically investigate the DPO under both online and offline settings, and reveal their respective limitation: overfitting in offline DPO, and biased sampling in online DPO. Building on our theoretical insights, we introduce Semi-online Preference Optimization (SoPo), a DPO-based method for training text-to-motion models using “semi-online” data pair, consisting of unpreferred motion from online distribution and preferred motion in …

Uniteformer: Unifying Node And Edge Modalities In Transformers For Vehicle Routing Problem, Dian Meng, Zhiguang Cao, Jie Gao, Yaoxin Wu, Yaqing Hou

Research Collection School Of Computing and Information Systems

Neural solvers for the Vehicle Routing Problem (VRP) have typically relied on either node or edge inputs, limiting their flexibility and generalization in real-world scenarios. We propose UniteFormer, a unified neural solver that supports node-only, edge-only, and hybrid input types through a single model trained via joint edge-node modalities. UniteFormer introduces: (1) a mixed encoder that integrates graph convolutional networks and attention mechanisms to collaboratively process node and edge features, capturing cross-modal interactions between them; and (2) a parallel decoder enhanced with query mapping and a feed-forward layer for improved representation. The model is trained with REINFORCE by randomly sampling …

Learning Memory-Enhanced Improvement Heuristics For Flexible Job Shop Scheduling, Jiaqi Wang, Zhiguang Cao, Peng Zhao, Rui Cao, Yubin Xiao, Yuan Jiang, You Zhou

Research Collection School Of Computing and Information Systems

The rise of smart manufacturing under Industry 4.0 introduces mass customization and dynamic production, demanding more advanced and flexible scheduling techniques. The flexible job-shop scheduling problem (FJSP) has attracted significant attention due to its complex constraints and strong alignment with real-world production scenarios. Current deep reinforcement learning (DRL)-based approaches to FJSP predominantly employ constructive methods. While effective, they often fall short of reaching (near-)optimal solutions. In contrast, improvement-based methods iteratively explore the neighborhood of initial solutions and are more effective in approaching optimality. However, the flexible machine allocation in FJSP poses significant challenges to the application of this framework, including …

Instance-Level Video Depth In Groups Beyond Occlusions, Yuan Liang, Yang Zhou, Ziming Sun, Tianyi Xiang, Guiqing Li, Shengfeng He

Research Collection School Of Computing and Information Systems

Depth estimation in dynamic, multi-object scenes remains a major challenge, especially under severe occlusions. Existing monocular models, including foundation models, struggle with instance-wise depth consistency due to their reliance on global regression. We tackle this problem from two key aspects: data and methodology. First, we introduce the Group Instance Depth (GID) dataset, the first large-scale video depth dataset with instance-level annotations, featuring 101,500 frames from real-world activity scenes. GID bridges the gap between synthetic and real-world depth data by providing high-fidelity depth supervision for multi-object interactions. Second, we propose InstanceDepth, the first occlusion-aware depth estimation framework for multi-object environments. Our …

Robust Hallucination Detection In Llms Via Adaptive Token Selection, Mengjia Niu, Hamed Haddadi, Guansong Pang

Research Collection School Of Computing and Information Systems

Hallucinations in large language models (LLMs) pose significant safety concerns that impede their broader deployment. Recent research in hallucination detection has demonstrated that LLMs’ internal representations contain truthfulness hints, which can be harnessed for detector training. However, the performance of these detectors is heavily dependent on the internal representations of predetermined tokens, fluctuating considerably when working on free-form generations with varying lengths and sparse distributions of hallucinated entities. To address this, we propose HaMI, a novel approach that enables robust detection of hallucinations through adaptive selection and learning of critical tokens that are most indicative of hallucinations. We achieve this …

Sempo: Lightweight Foundation Models For Time Series Forecasting, Hui He, Kun Yi, Yuanchi Ma, Qi Zhang, Zhengdong Niu, Guansong Pang

Research Collection School Of Computing and Information Systems

The recent boom of large pre-trained models witnesses remarkable success in developing foundation models (FMs) for time series forecasting. Despite impressive performance across diverse downstream forecasting tasks, existing time series FMs possess massive network architectures and require substantial pre-training on large-scale datasets, which significantly hinders their deployment in resource-constrained environments. In response to this growing tension between versatility and affordability, we propose SEMPO, a novel lightweight foundation model that requires pretraining on relatively small-scale data, yet exhibits strong general time series forecasting. Concretely, SEMPO comprises two key modules: 1) energy-aware SpEctral decomposition module, that substantially improves the utilization of pre-training …