Digital Commons Network^™

Enabling Sustainability Transitions For Great Salt Lake: Framing Of Public Opinion, Boundary Work, And Policy, Anna Mcentire

All Graduate Theses and Dissertations, Fall 2023 to Present

This dissertation explores how people think about, talk about, and take action to save Great Salt Lake, which reached historic low levels in 2021. Using surveys, public records, and legislative documents primarily from 2022 to 2024, the research examines public opinions, collaborative efforts, and policy changes related to the lake. The first chapter looks at what people value about the lake and what actions they want to see taken. It finds that people are very concerned and want significant efforts to stop the lake's decline. The second chapter investigates how different groups work together publicly to negotiate new understandings, create …

Linking Variation In Mule Deer Nutritional Condition And Age To Population Performance In A Temperate Environment, Kent R. Hersey

All Graduate Theses and Dissertations, Fall 2023 to Present

The amount of body fat animals have as they enter and exit winter can have major impacts on how well they survive and reproduce. Mule deer are widely distributed herbivores whose survival and reproduction are strongly tied to weather patterns and the quality of vegetation they consume, which in turn impacts nutritional condition. The objective of this study was to identify and quantify the factors (animal specific, climatic, or habitat) influencing mule deer body fat during both summer and winter in Utah and examine how nutritional condition influences survival. We found December body fat was most affected by December lactation …

Aquatic Habitat Representation For Robust Water Resources Management And Fish Conservation Decision-Making, Gregory C. Goodrum

All Graduate Theses and Dissertations, Fall 2023 to Present

We rely on rivers to provide water supply, flood control, and hydroelectricity for people and to sustain aquatic ecosystems. The development of dams and reservoirs to provide water for people did not consider environmental impacts to rivers, leading to widespread decline of freshwater species and habitat. Improving environmental outcomes of water management is needed to protect and restore aquatic ecosystems. However, rivers are complex and variable, making them difficult to represent as objectives for water management. This dissertation explores approaches to improve aquatic habitat representation and environmental objectives for water resources management and river conservation and restoration.

Chapter 2 uses …

An Ant-Colony Approach To Scheduling Charging Sessions Of Electric Vehicle Fleets With Heterogeneous Scheduling Constraints, Derek D. Redmond

All Graduate Theses and Dissertations, Fall 2023 to Present

Electric vehicles and electric-vehicle fleets are gaining widespread usage. A major challenge of managing an electric-vehicle fleet is scheduling when the vehicles can charge and when the vehicles can complete their tasks. Finding smart ways to schedule electric-vehicle charging reduces both the cost of charging vehicles and increases the health of the grid and the environment. This best-of-both-worlds outcome is because of the pricing structure that electric utility companies use. When minimizing the cost of charging the vehicles, it is important to consider that vehicle fleets have constraints on when they can charge. Vehicles are usually limited by the tasks …

From Supercomputers To Desktops: An Interactive And Portable System For Particle In Cell Simulation And Visualization Using Commodity Hardware, Kim Peterson

All Graduate Theses and Dissertations, Fall 2023 to Present

Generating and analyzing visual representations of simulation data in real time (i.e., during execution), has become increasingly important for handling the complexity and scale of modern computational models. Traditionally, this approach has been limited to High-Performance Computing (HPC) environments, leaving some researchers, students and educators without resources to explore cutting-edge simulations and analyses. This work attempts to show that powerful scientific simulations and real-time analysis can be made available by using more easily obtained commodity hardware. This approach may enable a broader participation in the scientific computing discovery process.

In this paper, we explore how advanced simulation and visualization frameworks …

A Focus On Student Education: Determining Student Attitudes Towards Transparent Autograding And Developing Artificially Intelligent Tools To Help Students Succeed, Andra Rice

All Graduate Theses and Dissertations, Fall 2023 to Present

This thesis is composed of two parts both relating to helping students succeed. First, the focus is on determining how we can help students feel more comfortable using an AI tool that can provide them immediate feedback. Second, machine learning algorithms are explored in relation to tracking student tasks to encourage healthy study habits.

The development of effective autograders is key for scaling assessment and feedback. While AI based autograding systems for open-ended response questions have been found to be beneficial for providing immediate feedback, autograders are not always liked, understood, or trusted by students. Our research tested the effect …

Impacts Of 4r Nitrogen Management And Water Optimization On Soil Health, Kamaldeep Kamaldeep

All Graduate Theses and Dissertations, Fall 2023 to Present

Farmers in Utah and Idaho are facing growing challenges due to high fertilizer costs, limited irrigation water, and pressure to improve long-term soil health. To better understand current practices and grower attitudes, a survey was conducted in 2020 targeting 5,000 producers of small grains, corn, and potatoes. Although the response rate was low, 146 growers representing over 25,000 hectares of cropland participated. Survey results showed that most growers are open to improving nitrogen and irrigation practices and care about their soil health, even if not all are fully using 4R or precision agriculture methods.

This thesis includes two field studies …

Temperature Effects On Fruit Doubling And Bloom Prediction In Tart Cherry, Josette M. Moon

All Graduate Theses and Dissertations, Fall 2023 to Present

Tart cherry (Prunus cerasus) is a key horticultural crop in Utah. Extreme summer heat and cold weather in late spring are challenges to tart cherry production causing economic losses. These problematic weather conditions are influenced by the local landscape and are exacerbated by changing climate. This research explores (1) the impact of summer heat stress on fruit deformities, and (2) the accuracy of temperature-based predictions of spring bud development. High temperatures and water stress after harvest are known to cause a deformity known as twin or double fruits, resulting in unmarketable fruit. This disorder has been studied in …

Understanding Hydroclimate In The Contiguous United States: Roles Of Temperature Vs Precipitation And Subtropical Pacific Warming, Grace Esenam Affram

All Graduate Theses and Dissertations, Fall 2023 to Present

This study investigates the physical drivers and mechanisms of hydroclimate variability across the Contiguous United States (U.S.), using a combination of observable data, reanalysis data, land modeling, and coupled climate model experiments. One of the major findings is that the historic 2021 drought event across the Western U.S. was primarily driven by a severe lack of precipitation rather than increased temperatures. The study further finds that anthropogenic forcing has substantially increased the risk of this extreme drought by approximately 26 times relative to a world without the influence.

Additionally, the study reveals that the Pacific Meridional Mode (PMM) characterized by …

Optimization And Utilization Of Microbial Electrolysis Cells In Waste To Product Applications, Hayden Harrison

All Graduate Theses and Dissertations, Fall 2023 to Present

As the world searches for cleaner and more sustainable energy solutions, turning waste into fuel is an increasingly attractive strategy. This research focuses on microbial electrolysis cells, a new technology that uses bacteria and a small amount of electricity to convert organic waste—like food scraps or wastewater—into useful gases such as hydrogen and methane. These gases can be used as energy sources to reduce dependency on more traditional fuels.

This thesis tackles two key challenges that limit the real-world use of microbial electrolysis systems. First, it introduces a simpler and more effective method to stop hydrogen losses in single-chamber cells. …

The Pharmacology Of Gpr171 Ligands In The Treatment Of Pain: An In Vitro And In Vivo Preclinical Characterization, Max V. Mcdermott

All Graduate Theses and Dissertations, Fall 2023 to Present

Opioids are addictive, ineffective for long-term treatment, readily produce withdrawal and tolerance, and can cause overdose and death. These properties have contributed to an epidemic of addiction and overdose, which continues unabated in the United States. Therefore, new drugs to treat acute and chronic pain are needed. Opioids bind to receptors belonging to a superfamily of proteins called G protein-coupled receptors (GPCRs). GPCRs are the most commonly targeted proteins among FDA-approved drugs. As such, under-researched GPCRs are a viable source of new targets in the field of drug development. One such underexplored GPCR is GPR171. Studies have demonstrated that a …

Authentic Digital Writing Parameters In College Composition: Design, Implementation, And Assessment Of A Culture Jam, Mary Ellen Greenwood

All Graduate Theses and Dissertations, Fall 2023 to Present

As technology reshapes communication, college classrooms must adapt to prepare students for writing in a quickly changing world. This dissertation investigates how authentic writing—writing that feels meaningful, relevant, and tied to real-world purposes—can enhance student motivation, confidence, and writing skills in college composition courses.

The study introduces a multimodal assignment called a Culture Jam, where five participants critiqued and remixed media messages, such as advertisements or social media posts, to create their own critical digital compositions. Over three weeks, participants created their Culture Jams, shared feedback with peers, and reflected on their experiences. This project focused on five key elements …

A Brief Cognitive-Behavioral Intervention For Emotion Regulationand Aggression In Justice-Involved Adolescents: Preliminary Outcomes From A Non-Randomized Trial In Short-Term Juvenile Detention, Cassidy L. Tennity

Dissertations

Justice-involved youth are at risk for a variety of psychosocial difficulties, including anger, aggression, and emotion regulation challenges. Short-term juvenile detention settings offer a unique opportunity to provide psychological interventions to youth before they are returned to the community or sentenced to long-term placements. However, implementation barriers within these settings often hinder the delivery of evidence-based care. This study developed and tested a brief, two-session anger management intervention for adolescents detained in a short-term juvenile detention center in the Southeastern United States, drawing on evidence-based practices from prior work with justice-involved youth. The present study examined implementation outcomes (i.e., feasibility, …

Enhanced Point Cloud Generation From A Novel 360° Underwater Lidar, Olagoke E. Daramola

Dissertations

This dissertation presents novel algorithms to improve the mapping capabilities of a 360-degree underwater Pulsed Laser Line Scanner LiDAR (PLLS-360°). Due to its 360° field-of-view (FOV), the PLLS-360° is a compact full-waveform omnidirectional imager suitable for seafloor mapping, underwater asset inspection, object detection, ice-sheet mapping, and construction progress monitoring. The proposed methodology includes an improved waveform fitting technique for saturated waveform recovery, detection array response correction, radiometric corrections, and fusion of LiDAR and sonar bathymetric datasets. The first part of this dissertation assesses the LiDAR’s performance and describes how the data for this unique 360° FOV architecture is processed. The …

Building Elementary Student Science Skills And Interest: A Reciprocal Engagement Partnership, Lindley Mcdavid, Sandra F. San Miguel

ELRC Presentations

Scholar-practitioner partnerships grounded in community engagement principles focus on mutually beneficial relationships that meet critical community needs while integrating learning, discovery and service. Our research aims to identify how and the degree that a physical activity-based science learning program contributes to students’ science learning and interest while aiming to support the overall wellbeing of a community elementary school that serves families from under resourced (98% free/reduced lunch) and diverse racial and ethnic backgrounds (48% non-white). Partner school teachers and staff members were invited to participate in an interview where they answered questions about their perspectives and experiences with the program …

Identifying And Examining Stressors In The Secondary And Post-Secondary Classroom, Benjamin S. Alter

All NMU Master's Theses

Students in secondary and post-secondary classrooms are openly exposed to stressors that can be considered impactful in their day-to-day lives.These stressors can be categorized as either “positive,” which serve as motivators, or “negative,” which often cause anxiety. In order to understand the stressors that students experience, students in a rural teacher education program at Northern Michigan University were asked to respond anonymously to a survey asking about stressors at the secondary and post-secondary levels. The survey was given to students enrolled in the Winter & Fall semesters of 2023, and the Winter semester of 2024. Students identified stressors from their …

The Effect Of An Acute Bout Of Rock Climbing On Cognitive Functioning And Cortisol Levels, Vanessa Steigauf

All NMU Master's Theses

Rock climbing is an understudied physical activity (PA) that demands active decision-making, integration of sensory and visual input, and continuous regulation of nervous system activity to produce complex motor outputs. The unique psychological demands of climbing warrant studying its effect on cognition and brain health. I recruited 26 generally healthy adults for a randomized-controlled study. Climbing experience, PA and perceived stress were self-reported. Each participant was randomly assigned to a climbing (10x2 minutes climb, moderate intensity) or control group (seated, watching climbing-related video). The primary outcomes, cognitive performance and salivary cortisol levels were assessed by the Stroop test. A linear …

Introduction To C++ (Volume I), Hussam Ghunaim Ph.D.

All Open Educational Resources

This book is written as an Open Education Resource (OER) to replace expensive commercial materials currently used at the Department of Computer Science at Fort Hays State University. It has two volumes corresponding to the CSCI 121 and CSCI 221 courses. These courses are developed to introduce college freshmen students to Object-Oriented Programming utilizing C++. The author tried to bridge the gap in the current programming textbooks by avoiding lengthy and, on many occasions, unnecessary details. This book’s main feature is to present the discussed principles in the least wording possible while providing adequate examples and exercises to reinforce students’ …

Are We Safe Yet? Experimental Analysis Of Emergency Post-Alert Messages, Hugh D. Walpole, Jeannette Sutton, Michele K. Olson

Emergency Preparedness, Homeland Security, and Cybersecurity Faculty Scholarship

Wireless Emergency Alerts can offer timely warnings and crucial guidance for people at risk from natural and technological hazards. However, at the time an alert is issued, it is rarely clear how long hazardous conditions will persist and when affected people can return to normal activities. This highlights the potential benefits of issuing post-alert messages to indicate that the hazard has passed; yet in practice, these post-alert messages are rarely used. When they are used, they frequently use operational language focused on the status of the associated alert (i.e. “the alert has expired” or has been “canceled”) rather than information …

Development Of A New Groundbased Instrumentation Technique For Low-Resolution Exoplanet Transmission Spectroscopy, Mary Anne Limbach, Luke M. Schmidt, Ryan J. Oelkers, Darren L. Depoy

Physics & Astronomy Faculty Publications

We present a new instrumentation technique for high-precision, ground-based spectrophotometric measurements ideal for low-resolution (R ∼ 20–60) exoplanet transmission spectroscopy. This technique employs novel thin-film coating technology for wide-band, simultaneous multi-band imaging, enabling high-precision differential photometry through self-referencing and comparison stars. Theoretical calculations and on-sky results show that this method effectively reduces 96% of amplitude scintillation noise and other systematics that typically limit ground-based spectrophotometric precision for bright stars. On-sky results are demonstrated using the custom-built Exoplanet Transmission Spectroscopy Imager (ETSI), deployed at the McDonald Observatory 2.1 m telescope in 2022. For a V = 9.7 mag star, ETSI …

Short: Breaking The Charge: Exploiting State Manipulation In Ev Charging, Ce Zhou, Qiben Yan, Zhiyuan Yu, Eshan Dixit, Ning Zhang, Huacheng Zeng, Alireza Safdari Ghanhdari

Computer Science Faculty Research & Creative Works

Electric vehicles (EVs) have become one of the promising solutions to the ever-evolving environmental and energy crisis. The key to the wide adoption of EVs is a pervasive charging infrastructure, composed of both private/home chargers and public/commercial charging stations. However, the security of electric vehicle charging has not been thoroughly investigated. This paper investigates the communication mechanisms between the chargers and EVs and exposes the lack of protection for the authenticity in the SAE J1772 charging control protocol. To showcase our discoveries, we propose a new class of attacks, ChargeX, which aims to manipulate the charging states of EV chargers …

Gcot: Chain-Of-Thought Prompt Learning For Graphs, Xingtong Yu, Chang Zhou, Zhongwei Kuai, Xinming Zhang, Yuan Fang

Research Collection School Of Computing and Information Systems

Chain-of-thought (CoT) prompting has achieved remarkable success in natural language processing (NLP). However, its vast potential remains largely unexplored for graphs. This raises an interesting question: How can we design CoT prompting for graphs to guide graph models to learn step by step? On one hand, unlike natural languages, graphs are non-linear and characterized by complex topological structures. On the other hand, many graphs lack textual data, making it difficult to formulate language-based CoT prompting. %Therefore we cannot directly adopt the CoT prompting methods used in the language domain. In this work, we propose the first CoT prompt learning framework …

Ai-Assisted Triage And Decision Support Of Head And Neck Cancer Screening And Diagnosis In Low-Resourced Settings, Min Hun Lee, Sean Shao Wei Lam, Shaun Xin Hong Liew, Michael Dorosan, Nicholas Graves, Jonas Karlström, Hiang Khoon Tan, Walter Tsong Lee

Research Collection School Of Computing and Information Systems

The mortality burden of head and neck cancer (HNC) is increasing globally and disproportionately affects people in low-and middle-income countries with limited medical workforce. To address this issue, artificial intelligence (AI) algorithms are increasingly being explored to process medical imaging data, demonstrating competitive performance. However, the clinical adoption of AI remains challenging as clinicians struggle to understand how complex AI works and trust it to use in practice. In addition, AI may not perform well on varying data qualities of endoscopy videos for HNC screening and diagnosis from multiple sites.In this project, our international and interdisciplinary team will collaborate with …

Faithfulrag: Fact-Level Conflict Modeling For Context-Faithful Retrieval-Augmented Generation, Qinggang Zhang, Zhishang Xiang, Yilin Xiao, Le Wang, Junhui Li, Xinrun Wang, Jinsong Su

Research Collection School Of Computing and Information Systems

Large language models (LLMs) augmented with retrieval systems have demonstrated significant potential in handling knowledge-intensive tasks. However, these models often struggle with unfaithfulness issues, generating outputs that either ignore the retrieved context or inconsistently blend it with the LLM’s parametric knowledge. This issue is particularly severe in cases of knowledge conflict, where the retrieved context conflicts with the model’s parametric knowledge. While existing faithful RAG approaches enforce strict context adherence through well-designed prompts or modified decoding strategies, our analysis reveals a critical limitation: they achieve faithfulness by forcibly suppressing the model’s parametric knowledge, which undermines the model’s internal knowledge structure …

Causalabstain: Enhancing Multilingual Llms With Causal Reasoning For Trustworthy Abstention, Yuxi Sun, Aoqi Zuo, Wei Gao, Jing Ma

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) often exhibit knowledge disparities across languages. Encouraging LLMs to abstain when faced with knowledge gaps is a promising strategy to reduce hallucinations in multilingual settings. Current abstention strategies for multilingual scenarios primarily rely on generating feedback in various languages using LLMs and performing self-reflection. However, these methods can be adversely impacted by inaccuracies and biases in the generated feedback. To address this, from a causal perspective, we introduce CausalAbstain, a method that helps LLMs determine whether to utilize multiple generated feedback responses and how to identify the most useful ones. Extensive experiments demonstrate that CausalAbstain effectively …

R2dqg: A Quality Meets Diversity Framework For Question Generation Over Knowledge Bases, Yimeng Ren, Yanhua Yu, Lizi Liao, Yuhu Shang, Kangkang Lu, Mingliang Yan

Research Collection School Of Computing and Information Systems

The task of Knowledge-Based Question Generation (KBQG) involves generating natural language questions from structured knowledge sources, posing unique challenges in balancing linguistic diversity and semantic relevance. Existing models often focus on maximizing surface-level similarity to ground-truth questions, neglecting the need for diverse syntactic forms and leading to semantic drift during generation. To overcome these challenges, we propose Refine-Reinforced Diverse Question Generation (R2DQG), a two-phase framework leveraging a generation-then-refinement paradigm. The Generator first constructs a diverse set of expressive templates using dependency parse tree similarity, capturing a wide range of syntactic patterns and styles. These templates guide the creation of question …

Xfinbench: Benchmarking Llms In Complex Financial Problem Solving And Reasoning, Zhihan Zhang, Yixin Cao, Lizi Liao

Research Collection School Of Computing and Information Systems

Solving financial problems demands complex reasoning, multimodal data processing, and a broad technical understanding, presenting unique challenges for current large language models (LLMs). We introduce **XFinBench**, a novel benchmark with 4,235 examples designed to evaluate LLM’s ability in solving comple**X**, knowledge-intensive **Fin**ancial problems across diverse graduate-level finance topics with multi-modal context. We identify five core capabilities of LLMs using XFinBench, i.e., _terminology understanding_, _temporal reasoning_, _future forecasting_, _scenario planning_, and _numerical modelling_. Upon XFinBench, we conduct extensive experiments on 18 leading models. The result shows that o1 is the best-performing text-only model with an overall accuracy of 67.3%, but still …

L3net: Localized And Layered Reparameterization For Incremental Learning, Xuandi Luo, Huaidong Zhang, Yi Xie, Hongrui Zhang, Xuemiao Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

Model-based class incremental learning (CIL) methods aim to address the challenge of catastrophic forgetting by retaining certain parameters and expanding the model architecture. However, retaining too many parameters can lead to an overly complex model, increasing inference overhead. Additionally, compressing these parameters to reduce the model size can result in performance degradation. To tackle these challenges, we propose a novel three-stage CIL framework called Localized and Layered Reparameterization for Incremental Learning (L3Net). The rationale behind our approach is to balance model complexity and performance by selectively expanding and optimizing critical components. Specifically, the framework introduces a Localized Dual-path Expansion structure, …

Preface, Special Issue For The 16th International Conference On Graph Transformation (Icgt 2023), Maribel Fernández, Christopher M. Poskitt

Research Collection School Of Computing and Information Systems

This special issue contains six extended versions of papers presented at the 16th International Conference on Graph Transformation (ICGT 2023), held in Leicester, UK, on 19–20 July 2023. The conference was part of STAF 2023 (Software Technologies: Applications and Foundations) and was held under the auspices of the European Association for Theoretical Computer Science (EATCS), the European Association of Software Science and Technology (EASST), and the IFIP Working Group 1.3, Foundations of Systems Specification.

Evaluating And Mitigating Linguistic Discrimination In Large Language Models: Perspectives On Safety Equity And Knowledge Equity, Guoliang Dong, Haoyu Wang, Jun Sun, Xinyu Wang

Research Collection School Of Computing and Information Systems

By training on text in various languages, large language models (LLMs) typically possess multilingual support and demonstrate remarkable capabilities in solving tasks described in different languages. However, LLMs can exhibit linguistic discrimination due to the uneven distribution of training data across languages. That is, LLMs are hard to keep the consistency of responses when faced with the same task but depicted in different languages. In this study, we first explore the consistency in the LLMs’ outputs responding to queries in various languages from two aspects: safety and quality. We conduct this analysis with two datasets (AdvBench and NQ) based on …