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Language Arts Journal of Michigan

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Independent- August 2025, Leslie Lozada

The Independent (1988-present)

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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 …

Tradeoffs Between Elemental Homeostasis And Growth Govern Freshwater Phytoplankton Responses To Salinization, Samuel A.T. Dias, Clay Prater, Adriana Diaz-Delgado, Natalie A. Clay, Sally A. Entrekin, Michelle A. Evans-White

Entomology and Plant Pathology Faculty Publications and Presentations

Anthropogenic salinization resulting from road salt application can degrade aquatic environments by altering the structure and function of phytoplankton communities, ultimately reducing flows of resources through aquatic food webs. However, physiological mechanisms underlying taxon-specific responses to salinization are often poorly linked to higher-order ecosystem dynamics, limiting our ability to predict community responses to salinization. To this end, we tested hypotheses derived from Subsidy-Stress and Ecological Stoichiometry theory by growing two cosmopolitan genera, Dolichospermum (prokaryotic, cyanobacteria) and Scenedesmus (eukaryotic, green algae), across NaCl gradients and contrasting differences in their growth rates, degree of Na homeostasis, and cellular C : N : …

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 …

Localized Multivariate Statistical Assessment Of United States Construction Labor Shortages, Ahmed Shiha, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Craft labor shortage is a persistent challenge facing the construction industry across various markets. The historically low geographic mobility of construction labor exacerbates and localizes labor shortages. Several research efforts relied on subjective evaluations to quantify the existence of labor shortages on a national level. As such, there is a lack of empirical quantification of labor shortages at different geospatial levels. This paper fills this knowledge gap. To this end, the authors adopted a methodology that encompassed: (1) conducting a literature review to identify and collect national-level and subnational indicators that explain the changes in labor supply and demand; (2) …

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 …

Outcomes And Resource Utilization In Hyperkalemic Emergency Department Patients Treated With Patiromer Or Sodium Zirconium Cyclosilicate, William Frank Peacock, Zubaid Rafique, Julie Gayle, James Neuenschwander, Harold Brown, Jennifer Kammerer, Jeffrey Budden, Ning Rosenthal

Faculty, Staff and Students Publications

OBJECTIVES: Hyperkalemia (HK) is a life-threatening condition commonly treated in emergency department (ED). Real-world outcomes associated with ED administration of potassium binders are limited. This study assessed ED discharge disposition and resource utilization in hyperkalemic ED patients treated with patiromer or sodium zirconium cyclosilicate (SZC).

METHODS: We performed a retrospective cohort study using discharge data from 230 hospitals in the Premier PINC AI Healthcare Database. ED patients were included if they were ≥18 years old, had potassium ≥5 mEq/L, and received patiromer/SZC during January 1, 2019 to December 31, 2021. Descriptive statistics and multivariable logistic regression analysis were used to …

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 …

Engineering Properties And Stress-Strain Behavior Of Bcsa Concrete: Flexural Design Parameters, Strain Measurement Methods, And Models, Gabriel R. Johnson

Graduate Theses and Dissertations

Belitic calcium sulfoaluminate (BCSA) cement concrete has gained popularity due to advantages over portland cement (PC), including, its fast-setting, high early strength gain, high durability, and reduced global warming potential. Though primarily used for pavement repair, it has good potential for structural applications. Building codes like ACI 318:19 permit alternative cement materials like BCSA cement if they can be shown to have adequate and predictable structural, durability, and fire performance comparable to PC concrete. This study evaluates the structural and mechanical behavior of BCSA cement concrete, measuring standard engineering properties and comparing results to PC concrete and code prediction equations. …

The Role Of Managerial Support In Influencing The Attitudes Of Highly Educated Millennials In Serbia, Dimitrije Gasic, Vladimir Dzenopoljac, Mladen Cudanov, Jozsef Poor, Nemanja Berber

All Works

This study explores the influence of managerial support on the attitudes of highly educated (BSc, MSc, and PhD) millennials in Serbia, focusing on job satisfaction, turnover intention, and job stress. Understanding this relationship is crucial for enhancing employee retention and organisational efficiency given the evolving workplace dynamics. Managerial support, encompassing emotional, instrumental, and informational assistance, is hypothesised to positively impact job satisfaction while reducing turnover intention and job stress. Data were collected through an online survey from 367 respondents in two waves (the first wave in January 2024 and the second in July 2024) and analysed using partial least squares …

Artificial Intelligence Integration And Teachers' Self-Efficacy In Physics Classrooms, Fouad Yehya, Areej Elsayary, Ghadah Al Murshidi, Ahmed Al Zaabi

All Works

The United Arab Emirates (UAE), in its vision 2021 and the UAE centennial 2071 plan, highlights the essential role of artificial intelligence (AI) and technology in shaping a knowledge-based, future-ready society. This study explores the integration of AI in physics classrooms, focusing on secondary education in the UAE. It also investigates the perceptions and self-efficacy of physics teachers regarding the use of AI tools in classroom settings. A qualitative research design was employed to gather in-depth insights from 15 physics teachers across schools in Sharjah, assessing their confidence and readiness for AI integration through the lens of the attitude and …

Youth Attitudes Towards The Effects Of Social Media Addiction A Study On University Students In The United Arab Emirates, Fokiya Akhtar, Fawzia Abdulla Alali

All Works

Objectives: This study aims to understand youth’s attitudes toward the effects of social media usage and its risk factors. It also investigates the impact of social media addiction on youth, and determines the mediating factors in this relationship, such as ‘FOMO’ (fear of missing out) and nocturnal social media usage. Methods :For the purpose of this research, 327 university students were surveyed using the Social Media Addiction Scale, the FOMO Scale, the Nocturnal Social Media Use Scale, and the Pittsburgh Sleep Index Scale. Results: The results reveal statistically significant correlations between high social media usage and adverse effects on mental …

Psilocybin And Death Anxiety: A Dual-Mechanism Approach To Existential Distress, Annabelle Swain

University Honors Theses

This literature review looks at how psilocybin, the active compound in "magic mushrooms," can help people deal with death anxiety; the deep fear of dying or losing control that shows up in lots of mental health struggles. While traditional treatments like SSRIs and talk therapy work for some people, they don't always get to the root of the problem. Psilocybin works differently. It changes brain activity, especially in networks tied to repetitive, self-focused thinking, as well as creates powerful, emotional experiences that help people rethink how they see life and death. This paper covers recent research showing how one or …

Birth Outcomes In The Hispanic Population In The United States: Trends, Variation, And Determinants (2011–2021), Yanchao Yang

Business Analytics Faculty Publications

Background: Cesarean section (C-section) is the most common surgical procedure in the United States, yet its use varies widely across regions and institutions. Although clinical risk factors are central to delivery decisions, geographic context, health system capacity, and local practice patterns may also influence C-section use. Understanding both the determinants and predictability of C-section delivery is important for improving obstetric quality and equity.

Objective: This study aims to document geographic variation in C-section use across the United States, identify maternal and county-level factors associated with C-section delivery, and evaluate the predictive performance of machine learning models across clinically defined risk …