Digital Commons Network^™

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 …

Evaluating The Effects Of Two Key Temporal Intervals During Pavlovian Training On Conditioned Reinforcement, Gabrielle M. Sutton

All Graduate Theses and Dissertations, Fall 2023 to Present

Reinforcers are anything that increases or maintains the frequency of a behavior that precedes their occurrence. Reinforcers can be separated into two types, primary and secondary. Primary reinforcers are biologically relevant stimuli like food, water, or drugs that can influence behavior without any prior training. Secondary reinforcers are those that impact behavior through their trained relationship with a primary reinforcer, like money and tokens. For this reason, they are also known as conditioned reinforcers. Conditioned reinforcers are used in therapies and training designed to increase desirable behaviors, but they are also involved in the development and maintenance of maladaptive behaviors …

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 …

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 …

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. …

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 …

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 …

Revitalizing Literary Engagement: Innovative Approaches To Teaching Willa Cather In High School English Classes, Sarah Hoyt, Phu Vu

Language Arts Journal of Michigan

This study explores methods to enhance student engagement and interest in high school English literature classes, focusing on the analysis of Willa Cather's novels. It addresses the challenge of motivating students to engage deeply with full-length novels, crucial for cultural and literary education. Employing a qualitative approach and triangulated data sources, the study reveals significant improvements in student engagement, comprehension, and literary analysis. Additionally, students demonstrated a heightened ability to connect with the themes and characters in the prose.

Unburying The "Bury Your Gays" Trope In Young Adult Literature, Brooke Armbrustmacher

Language Arts Journal of Michigan

In this paper, I unpack what the "Bury Your Gays" trope is, how it originated, its purpose, and its contemporary use and impacts. Through analysis of example novels and referenced scholarly articles, I explain how character deaths do or do not align with the trope, exemplifying the broader patterns of the trope and its impacts on young adults. Educators can learn to contextually identify the trope, gain the tools to spark classroom discussion through analysis, and identify examples of novels that disrupt this trope to be implemented in classroom libraries and reading lists.

Reimagining Positionality In Curriculum: Unlocking Consciousness Beyond Hegemonic Boundaries, Haniyeh Kheirkhah Abkenari

Language Arts Journal of Michigan

This paper examines the interplay between hegemonic systems, curriculum, and individual consciousness in diverse life contexts. It underscores the need for critical thinking skills and self-examination to challenge hegemonic ideologies embedded within curriculum materials. The paper emphasizes the potential for curriculum to shape and influence consciousness, yet it also highlights the autonomy of consciousness to critically engage with curriculum content. Positionality and identity are explored, illustrating how they impact an individual's social role and their ability to promote social justice.

Through the Currere as a autoethnography research method, the paper offers a comprehensive reflection on the author's experiences as an …

Independent- August 2025, Leslie Lozada

The Independent (1988-present)

No abstract provided.

Labor, Leisure, And Law, Aneil Kovvali, Jonathan S. Masur

Articles

Americans work harder than their counterparts in many other advanced economies. While many predicted that technological progress would eventually make work obsolete, Americans continue to pull long hours. Although stories of worker burnout and “quiet quitting” suggest that this situation is not a good fit for everyone, any problems are largely invisible to the conceptual frameworks that dominate the analysis of law. These frameworks normally assume the goal of maximizing production and often treat the human preference for leisure as a problem to be solved. This Article analyzes the problem of overwork. In doing so, it surfaces legal and policy …

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 …

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 …

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 …

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, …

Towards Context-Aware Traffic Classification Via Time-Wavelet Fusion Network, Ziming Zhao, Zhuoxue Song, Xiaofei Xie, Zhaoxuan Li, Jiongchi Yu, Fan Terry Zhang, Tingting Li

Research Collection School Of Computing and Information Systems

Encrypted traffic classification occupies a significant role in cybersecurity and network management. The existing encrypted traffic classification technology mostly relies on intra-flow semantics for extracting features. However, considering that some attack behaviors inherently have similar patterns to legitimate behaviors, and powerful adversaries could simulate benign users to conceal their attack intentions, intra-flow features may be similar between different categories. In this paper, we propose TrafficScope, a time-wavelet fusion network based on Transformer to enhance the performance of encrypted traffic classification. Specifically, in addition to using intra-flow semantics, TrafficScope also extracts contextual information to construct more comprehensive representations. Moreover, to cope …

Cami: A Counselor Agent Supporting Motivational Interviewing Through State Inference And Topic Exploration, Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Phey Ling Kit, Nicholas Gabriel Lim, Cameron Shi Ern Tan, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Conversational counselor agents have become essential tools for addressing the rising demand for scalable and accessible mental health support. This paper introduces CAMI, a novel automated counselor agent grounded in Motivational Interviewing (MI) – a client-centered counseling approach designed to address ambivalence and facilitate behavior change. CAMI employs a novel STAR framework, consisting of client’s state inference, motivation topic exploration, and response generation modules, leveraging large language models (LLMs). These components work together to evoke change talk, aligning with MI principles and improving counseling outcomes for diverse clients. We evaluate CAMI’s performance through both automated and expert evaluations, utilizing simulated …

Assessing The Robustness Of Test Selection Methods For Deep Neural Networks, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Wei Ma, Mike Papadakis, Lei Ma, Yves Le Traon

Research Collection School Of Computing and Information Systems

Regularly testing deep learning-powered systems on newly collected data is critical to ensure their reliability, robustness, and efficacy in real-world applications. This process is demanding due to the significant time and human effort required for labeling new data. While test selection methods alleviate manual labor by labeling and evaluating only a subset of data while meeting testing criteria, we observe that such methods with reported promising results are simply evaluated, e.g., testing on original test data. The question arises: are they always reliable? In this article, we explore when and to what extent test selection methods fail. First, we identify …

Knowledge Boundary Of Large Language Models: A Survey, Moxin Li, Yong Zhao, Wenxuan Zhang, Shuaiyi Li, Wenya Xie, See-Kiong Ng, Tat-Seng Chua, Yang Deng

Research Collection School Of Computing and Information Systems

Although large language models (LLMs) store vast amount of knowledge in their parameters, they still have limitations in the memorization and utilization of certain knowledge, leading to undesired behaviors such as generating untruthful and inaccurate responses. This highlights the critical need to understand the knowledge boundary of LLMs, a concept that remains inadequately defined in existing research. In this survey, we propose a comprehensive definition of the LLM knowledge boundary and introduce a formalized taxonomy categorizing knowledge into four distinct types. Using this foundation, we systematically review the field through three key lenses: the motivation for studying LLM knowledge boundaries, …

Beware Of Your Po! Measuring And Mitigating Ai Safety Risks In Role-Play Fine-Tuning Of Llms, Weixiang Zhao, Yulin Hu, Yang Deng, Jiahe Guo, Xingyu Sui, Xinyang Han, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu

Research Collection School Of Computing and Information Systems

Although large language models (LLMs) store vast amount of knowledge in their parameters, they still have limitations in the memorization and utilization of certain knowledge, leading to undesired behaviors such as generating untruthful and inaccurate responses. This highlights the critical need to understand the knowledge boundary of LLMs, a concept that remains inadequately defined in existing research. In this survey, we propose a comprehensive definition of the LLM knowledge boundary and introduce a formalized taxonomy categorizing knowledge into four distinct types. Using this foundation, we systematically review the field through three key lenses: the motivation for studying LLM knowledge boundaries, …

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 …

Comparative Nitrene-Transfer Chemistry To Olefins Mediated By First-Row Transition Metal Catalysts Supported By A Pyridinophane Macrocycle With N4 Ligation, Himanshu Bhatia, Lillian P. Adams, Ingrid Cordsiemon, Suraj Kumar Sahoo, Amitava Choudhury, Thomas R. Cundari, Pericles Stavropoulos

Chemistry Faculty Research & Creative Works

A 12-membered pyridinophane scaffold containing two pyridine and two tertiary amine residues is examined as a prototype ligand (^tBuN4) for supporting nitrene transfer to olefins. The known [(^tBuN4)M^II(MeCN)2]²⁺ (M = Mn, Fe, Co, and Ni) and [(^tBuN4)Cu^I(MeCN)]⁺ cations are synthesized with the hexafluorophosphate counteranion. The aziridination of para-substituted styrenes with PhI=NTs (Ts = tosyl) in various solvents proved to be high yielding for the Cu(I) and Cu(II) reagents, in contrast to the modest efficacy of all other metals. For α-substituted styrenes, aziridination is accompanied by products of aziridine …

Visceral, Neural, And Immunotoxicity Of Per- And Polyfluoroalkyl Substances: A Mini Review, Pietro Martano, Samira Mahdi, Tong Zhou, Yasmin Barazandegan, Rebecca Iha, Hannah Do, Joel Burken, Paul Ki-Souk Nam, Qingbo Yang, Ruipu Mu

Chemistry Faculty Research & Creative Works

Per- and polyfluoroalkyl substances (PFASs) have gained significant attention due to their widespread distribution in the environment and potential adverse health effects. While ingestion, especially through contaminated drinking water, is considered the primary route of human exposure, recent research suggests that other pathways, such as inhalation and dermal absorption, also play a significant role. This review provides a concise overview of the toxicological impacts of both legacy and emerging PFASs, such as GenX and perfluoro butane sulfonic acid (PFBS), with a particular focus on their effects on the liver, kidneys, and immune and nervous systems, based on findings from recent …

Exploring Rehabilitation Therapists' Knowledge And Perspectives On The Use Of Artificial Intelligence And Machine Learning For Persons Poststroke, Hannah Clark, Mia Delvecchio, Min Hun Lee, Elena D. Brown, Kaia Mikula, Robert Halyama, Kasey Stepansky

Research Collection School Of Computing and Information Systems

Research Objectives: The use of technology such as robotics, gaming systems, self-monitoring apps, or other sensor-based devices in standard practice is infrequent. Due to the rapid development of artificial intelligence (AI) and machine learning (ML) applications, it is important to look at how therapists perceive AI/ML, and design applications with potential barriers in mind. to support future integration into practice. The purpose of this research project is to gain rehabilitation therapists’ perspectives on AI/ML in post-stroke assessment and intervention.Design: This ongoing study uses a mixed methods design with surveys and focus groups. Participants engaged in a 30-minute webinar to learn …

Dreamanime: Learning Style-Identity Textual Disentanglement For Anime And Beyond, Chenshu Xu, Yangyang Xu, Huaidong Zhang, Xuemiao Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

Text-to-image generation models have significantly broadened the horizons of creative expression through the power of natural language. However, navigating these models to generate unique concepts, alter their appearance, or reimagine them in unfamiliar roles presents an intricate challenge. For instance, how can we exploit language-guided models to transpose an anime character into a different art style, or envision a beloved character in a radically different setting or role? This paper unveils a novel approach named DreamAnime, designed to provide this level of creative freedom. Using a minimal set of 2-3 images of a user-specified concept such as an anime character …

If Alcohol Consumption Were Eliminated, Which Us Counties Would Still Experience High Rates Of Breast Cancer Among Women?, Hina Javed

Boise State University Theses and Dissertations

Background

Breast cancer is the most prevalent cancer among women in the United States, accounting for roughly one-third of all newly diagnosed female cancers (American Cancer Society, 2024). The risk factors for breast cancer include both non-modifiable and modifiable factors. Non-modifiable factors include age at menarche, early menstrual cycles and late menopause. Modifiable factors include smoking, diabetes, obesity and environmental factors such as air pollution. The interplay between lifestyle factors and environmental exposures in cancer development has long been debated. While alcohol consumption is widely recognized as a major breast cancer risk factor, the role of environmental exposure such as …

Integrating Uas And Satellite Data To Improve Classification Of Plant Functional Types In A Forest Landscape, Sky Mae Gennette

Boise State University Theses and Dissertations

Remotely sensed land-use/land-cover (LULC) data products are an important tool for understanding landscape processes at all scales. The types of inference researchers can derive from remote sensing data depend on the quality and characterization of these LULC products. Currently, gaps in the detail of widely available vegetation cover products limit inference about processes at the level of plant functional type (PFT). Advanced remote sensing tools, machine learning, and data fusion can be employed to contribute novel methods to produce detailed, accurate vegetation cover products. Here, we combine high resolution Unoccupied Aerial Systems (UAS) multispectral, structural and textural data with large-scale …

Preparing Pre-Service Teachers For Writing Instruction: Best-Practices And Insights Into Self-Efficacy, Lindsey Wiggins

Boise State University Theses and Dissertations

Many pre-service teachers (PSTs) report feeling under-prepared to teach writing, a challenge often linked to limited attention to writing instruction in teacher preparation programs (TPPs). While writing methods courses offer opportunities to build PSTs’ self-efficacy and pedagogical skill, questions remain about how to best structure these experiences and measure their impact. This three-article dissertation examines how PSTs develop self-efficacy for writing and writing instruction during teacher preparation and how those beliefs relate to instructional practices.

In Study I, I conducted a systematic literature review to examine how researchers measure the effects of effective writing and writing instruction interventions with elementary …