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Overidentification Of Learning Disabilities Among African Immigrants English Language Learners, Ololade Khafayat Banire

All Electronic Theses and Dissertations

This thesis explores the over-identification of learning disabilities among African immigrant English Language Learners (ELLs) in U.S. schools, examining both the individual and systemic factors contributing to disproportionality. By reviewing existing literature, the study identified five major factors: assessment and diagnostic bias, teacher knowledge and beliefs, contextual and school-level factors, structural inequities, and evidence gaps in research, revealing that overidentification often arises from the intersection of these factors. The review also highlights evidence-based solutions, including culturally and linguistically responsive assessment, continuous teacher professional development, family and community engagement, structural and policy reforms, and improved research practices. While limited by the …

005. M.F. Arey (1905-06) [Biography], Iowa Academy Of Science.

Presidents of the Iowa Academy of Science

No abstract provided.

Law And Industrial Policy: The East Asian Experience, James Si Zeng

Vanderbilt Journal of Transnational Law

No abstract provided.

The New Atrocity In International Law, Natalie R. Davidson

Vanderbilt Journal of Transnational Law

No abstract provided.

China's Approach To The Anti-Foreign Sanctions Mechanism And Its International Legality, Songling Yang

Vanderbilt Journal of Transnational Law

No abstract provided.

Assessing Residents' Water Knowledge, Communication Preferences, And Risk Perceptions: Insights For Practice And Future Communication, Ashleigh M. Day, Eric S. Cerino, Zoe Zidzik, Laura Umphrey

Iowa Journal of Communication

The purpose of this research was to better understand Flagstaff, Arizona (AZ) residents’ water knowledge, media uses, information sources, and water-related risk perceptions. After collecting survey responses from residents (n=476), results highlighted that most residents know their water supplier, but their risk perceptions varied as related to waterborne diseases and water scarcity. Particularly, participants felt more susceptible to water scarcity issues than waterborne diseases and felt that water scarcity was more severe than waterborne diseases. Further, when considering getting water-related information, many participants preferred receiving information via digital devices. Regarding social media use, participants held the strongest preference for receiving …

Toxic Sweetness And Abstinence: The Citationality Of Robert Lustig's Anti-Sugar Rhetoric, Tyler J. Snelling

Iowa Journal of Communication

Robert Lustig’s (2009) lecture on YouTube “Sugar: The Bitter Truth” animated a widespread conversation about whether sugar poisons bodies. Discourse following the lecture warrants analysis to gage how the meaning of the lecture transformed from a critique of food systems into individual actions. I argue that citationality of “Sugar: The Bitter Truth” fosters healthism by grounding sugar’s toxicity in scientific and affective reasoning that compels individual actions like abstinence from sugar. The pediatrician's lecture enabled the construction of a supposedly "moral eater" who removes sugar from consumption to avoid the poison's consequences. This paper offers tools for examining the spread …

Front Matter - Table Of Contents - Preface

Iowa Journal of Communication

No abstract provided.

Back Matter - Call For Submissions

Iowa Journal of Communication

No abstract provided.

Fixation On Viruses: Eye-Tracking, Image-Based Agency, And Fear-Appeal Desensitization, Braden Bagley, Lijie Zhou, Hayden Coombs, Matthew Barton

Iowa Journal of Communication

This study examined the effects of image-based assignment of agency (IAA) on COVID-19 and flu fear-appeals by employing eye-tracking technology. Volunteers participated in a 2x2x2 between-groups experiment. Fixation-count and fixation-duration were collected by a Tobii Pro X3-120 Screen-Based Eye-Tracker. After reviewing a fact sheet, participants answered a 29-item survey that included items aligning with the extended parallel process model. The flu/agentic-image/virus-agentic-language message produced significantly higher fixation-frequency to the image and a higher perception of severity than the COVID-19/real-image/human-agentic-language message. Fixation-frequency on the image produced significantly higher fear perceptions, but significantly lower message-quality and message-credibility perceptions. Flu messages led to significantly …

Moldy Whoppers And Vitamin Donuts: Health Halos In Food Advertisements, Debbie Danowski

Iowa Journal of Communication

Health Halos have been used in food advertisements since the late nineteenth and early twentieth centuries when the advent of mass-produced consumer goods took hold and manufactures began advertising their products. It wasn’t until nearly eight decades later, however, that theoretical framework specifically involving health halos was recognized in advertising research. Though subsequent research suggests the effectiveness of health halos in making unhealthy food appear healthier, there has been little, if any, attention given to identification of the health halos used in food advertising. This study aims to fill the gap by identifying two of the health halo techniques used …

Heterogeneous Clustering Of Multiomics Data For Breast Cancer Subgroup Classification And Detection, Joseph Pateras, Musaddiq Lodi, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

The rapid growth of diverse -omics datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation–maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense. The proposed optimization subroutines enhance survival analysis and improve runtime performance. This article presents a framework for distinguishing cancer subtypes and identifying potential biomarkers for breast cancer. Key insights into individual subtype expression and function were obtained through differentially expressed gene …

Lrtm Left-Right Transition Matrices For Molecular Interaction Prediction, Kaitlin Zheng, Guihua Duan, Mengyun Yang, Wei Wu, Yao-Hang Li, Jianxin Wang

Computer Science Faculty Publications

Molecular interactions are central to most biological processes. The discovery and identification of potential associations between molecules can provide insights into biological exploration, diagnostic and therapeutic interventions, and drug development. So far many relevant computational methods have been proposed, but most of them are usually limited to specific domains and rely on complex preprocessing procedures, which restricts the models’ ability to be applied to other tasks. Therefore, it remains a challenge to explore a generalized approach to accurately predicting potential associations. In this study, We propose Left-Right Transition Matrices (LRTM) for molecular interaction prediction. From the perspective on the diffusion …

A Data-Driven Sliding-Window Pairwise Comparative Approach For The Estimation Of Transmission Fitness Of Sars-Cov-2 Variants And The Construction Of The Evolution Fitness Landscape, Md Jubair Pantho, Richard Annan, Landen Alexander Bauder, Sophia Huang, Letu Qingge, Hong Qin

Computer Science Faculty Publications

Estimating the transmission fitness of SARS-CoV-2 variants and understanding their evolutionary fitness trends are important for epidemiological forecasting. Existing methods are often constrained by their parametric natures and do not satisfactorily align with the observations during COVID-19. Here, we introduce a sliding-window data-driven pairwise comparison method, the differential population growth rate (DPGR) that uses viral strains as internal controls to mitigate sampling biases. DPGR is applicable in time windows in which the logarithmic ratio of two variant subpopulations is approximately linear. We apply DPGR to genomic surveillance data and focus on variants of concern (VOCs) in multiple countries and regions. …

The Invisible Influencer In Information Infrastructure, Herbert Van De Sompel, Michael L. Nelson

Computer Science Faculty Publications

The UPS Prototype was a proof-of-concept web portal built in preparation for the Universal Preprint Service Meeting held in October 1999 in Santa Fe, New Mexico. The portal provided search functionality for a set of metadata records that had been aggregated from a range of repositories that hosted preprints, working papers, and technical reports. Every search result was overlaid with a dynamically generated menu, called an SFX-menu, that provided a selection of value-adding links for the described scholarly work. The meeting eventually led to the Open Archives Initiative and its Protocol for Metadata Harvesting (OAI-PMH), which remains widely used in …

Cath-Ddg: Towards Robust Mutation Effect Prediction On Protein-Protein Interactions Out Of Cath Homologous Superfamily, Guanglei Yu, Xuehua Bi, Teng Ma, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Protein-protein interactions (PPIs) are fundamental aspects in understanding biological processes. Accurately predicting the effects of mutations on PPIs remains a critical requirement for drug design and disease mechanistic studies. Recently, deep learning models using protein 3D structures have become predominant for predicting mutation effects. However, significant challenges remain in practical applications, in part due to the considerable disparity in generalization capabilities between easy and hard mutations. Specifically, a hard mutation is defined as one with its maximum TM-score < 0.6 when compared to the training set. Additionally, compared to physics-based approaches, deep learning models may overestimate performance due to potential data leakage.

Results: We propose new training/test splits that mitigate data leakage according to the CATH homologous superfamily. Under the constraints of physical …

Coldstartcpi: Induced-Fit Theory-Guided Dti Predictive Model With Improved Generalization Performance, Qichang Zhao, Haochen Zhao, Linyuan Gao, Kai Zheng, Yajie Li, Qiao Ling, Jing Tang, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Predicting compound-protein interactions (CPIs) plays a crucial role in drug discovery. Traditional methods, based on the key-lock theory and rigid docking, often fail with novel compounds and proteins due to their inability to account for molecular flexibility and the high sparsity of CPI data. Here, we introduce ColdstartCPI, a framework inspired by induced-fit theory, which leverages unsupervised pre-training features and a Transformer module to learn both compound and protein characteristics. ColdstartCPI treats proteins and compounds as flexible molecules during inference, aligning with biological insights. It outperforms state-of-the-art sequence-based models, particularly for unseen compounds and proteins, and shows strong generalization capability …

Coming Back Differently: An Exploratory Case Study Of Near Death Experiences Of Webpages, Lesley Frew, Michael L. Nelson, Michele Weigle

Computer Science Faculty Publications

In this case study, we use web archives to analyze 8,824 webpages that were taken offline and subsequently put back online, thus experiencing a “near death experience.” We enumerate the stages of a webpage’s near death experience, including the change from a successful HTTP status code to non-successful and back, the intermediate stage with markers such as an under construction banner, and an analysis of how the pages came back differently.

From Philosophy To Nlu: Evolving Definitions With Research Hypotheses, Jian Wu, Sarah Rajtmajer

Computer Science Faculty Publications

Over the past decades, alongside advancements in natural language processing, significant attention has been paid to training models to automatically extract, understand, test, and generate hypotheses in open and scientific domains. However, interpretations of the term hypothesis for various natural language understanding (NLU) tasks have migrated from traditional definitions in the natural, social, and formal sciences. Even within NLU, we observe differences defining hypotheses across literature. In this paper, we overview and delineate various definitions of hypothesis. Especially, we discern the nuances of definitions across recently published NLU tasks. We highlight the importance of well-structured and well-defined hypotheses, particularly as …

From Philosophy To Nlu: Evolving Definitions Of Research Hypotheses, Jian Wu, Sarah Rajtmajer

Computer Science Faculty Publications

Over the past decades, alongside advancements in natural language processing, significant attention has been paid to training models to automatically extract, understand, test, and generate hypotheses in open and scientific domains. However, interpretations of the term hypothesis for various natural language understanding (NLU) tasks have migrated from traditional definitions in the natural, social, and formal sciences. Even within NLU, we observe differences defining hypotheses across literature. In this paper, we overview and delineate various definitions of hypothesis. Especially, we discern the nuances of definitions across recently published NLU tasks. We highlight the importance of well-structured and well-defined hypotheses, particularly as …

Optiselect And Enshap: Integrating Machine Learning And Game Theory For Ischemic Stroke Prediction, Pritam Chakraborty, Anjan Bandyopadhyay, Sricheta Parul, Sujata Swain, Partha Sarathy Banerjee, Tapas Si, Hong Qin, Saurav Mallik

Computer Science Faculty Publications

Stroke analysis using game theory and machine learning techniques. The study investigates the use of the Shapley value in predictive ischemic brain stroke analysis. Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. For each sample, the top 3, 4, and 5 features are evaluated and selected to evaluate their performance. The Shapley value method was used to rank the models using their best four features based on their predictive capabilities. As a result, better-performing …

Neural Topic Modeling Via Contextual And Graph Information Fusion, Jiyuan Liu, Jiaxing Yan, Chunjiang Zhu, Xingyu Liu, Qing Li, Yanghui Rao

Computer Science Faculty Publications

Topic modeling is a powerful unsupervised tool for knowledge discovery. However, existing work struggles with generating limited-quality topics that are uninformative and incoherent, which hindering interpretable insights from managing textual data. In this paper, we improve the original variational autoencoder framework by incorporating contextual and graph information to address the above issues. First, the encoder utilizes topic fusion techniques to combine contextual and bag-of-words information well, and meanwhile exploits the constraints of topic alignment and topic sharpening to generate informative topics. Second, we develop a simple word co-occurrence graph information fusion strategy that efficiently increases topic coherence. On three benchmark …

Understanding Pii Leakage In Large Language Models: A Systematic Survey, Shuai Cheng, Zhao Li, Shu Meng, Mengxia Ren, Haitao Xu, Shuai Hao, Chuan Yue, Fang Zhang

Computer Science Faculty Publications

Large Language Models (LLMs) have demonstrated exceptional success across a variety of tasks, particularly in natural language processing, leading to their growing integration into numerous facets of daily life. However, this widespread deployment has raised substantial privacy concerns, especially regarding personally identifiable information (PII), which can be directly associated with specific individuals. The leakage of such information presents significant real-world privacy threats. In this paper, we conduct a systematic investigation into existing research on PII leakage in LLMs, encompassing commonly utilized PII datasets, evaluation metrics, and current studies on both PII leakage attacks and defensive strategies. Finally, we identify unresolved …

Humans Vs. Llms On Open Domain Scientific Claim Verification: A Baseline Study, Benjamin Curtis, Stefania Dzhaman, Matthew Maisonave, Jian Wu

Computer Science Faculty Publications

Verifying scientific claims is challenging for the general public because most people lack domain knowledge. Manual verification by subject domain experts is accurate, but it is obviously not scalable to meet the rising number of scientific claims on the Web. Whether the emerging large language models and large reasoning models can be used for scientific claim verification, and how their performances compare to humans, are still research questions. To this end, we developed a new benchmark MSVEC2 that consists of 138 claims from credible fact verification websites and science news outlets. Two tasks were given to both human and LLM …

Rejecting The Racialization Of Indianness, Andrea J. Martin

Faculty Scholarship

No abstract provided.

Promoting Financial Empowerment Via 401(K) Plan Domestic Abuse Victim Distributions, Samantha Prince

Faculty Scholarship

Domestic violence is sadly and shockingly all too prevalent in the United States. According to the U.S. Centers for Disease Control, more than one in four women and one in seven men in this country are subject to domestic abuse “affecting an estimated 10 million people every year.”

Finances and financial abuse play a significant role in 99% of domestic abuse cases. “[L]acking financial knowledge or resources is the number one indicator of whether a domestic violence victim will stay, leave, or return to an abusive relationship.”When abusers have control over financial assets, victims are monetarily paralyzed and have little …

Border Decriminalization As A State Project: Lessons From Marijuana And Assisted Suicide Legalization Across The United States, Victor C. Romero

Faculty Scholarship

My prior work argued for the decriminalization of border crossings without proof of specific intent to violate another law (like drug trafficking), which is even less likely to happen now than it was when the piece was published, given the current presidential administration’s zealous deportation strategy and Congress’s seeming acquiescence. As such, I advocate for a second-best solution involving federal-state cooperative agreements wherein border states abutting both Mexico and Canada are given the option by the federal government to pass local legislation decriminalizing border crossings into their states as their communities desire. To the extent that the current presidential administration …

Embracing A New Approach To Academic Success: How The Adoption Of A Growth Mindset Can Enhance Legal Education, Titichia M. Jackson

Faculty Scholarship

No abstract provided.

Should The X-Minute City Include Commutes? An Evaluation Using Commute And Well-Being Data From The Singapore Life Panel®, Sock Yong Phang, Yen Cong Wong, Stephen Hoskins

Research Collection School Of Economics

Over the past decade, the x-minute city — or “chrono-urbanism” — has gained traction intransport planning, emphasizing residents’ ability to access essential destinations within15-min travel time from their homes. Some cities, including Sydney and Singapore, haveextended this concept to commuting, with Singapore’s Land Transport Authority targetinga “45-min city” for daily commutes by 2040. While well-intentioned, applying time-basedgoals to commuting remains controversial. This study evaluates the validity of thex-minute commute city as a transport planning objective by examining the link betweencommuting duration and well-being. Using high-frequency panel data from 2,535Singapore residents aged 55 to 75, we analyze how commuting affects life …

A Virus Of The Mind: The Neurological Impact Of Neonatal Herpes Simplex Virus Infection, Abigail Jeanne Dutton

Dartmouth College Ph.D Dissertations

The ability of herpes simplex virus (HSV) to establish lifelong latency in sensory neurons makes it one of the most pervasive viruses worldwide. Although most HSV infections are asymptomatic or cause limited cutaneous symptoms, some give rise to serious central nervous system (CNS) manifestations. Both primary HSV infection and subsequent reactivation events can cause viral replication and spread within the brain, ultimately leading to pathologic inflammation and direct CNS damage. In addition to fulminant presentations of HSV encephalitis, subclinical HSV CNS infections have been implicated in neurodevelopmental, cognitive, and neurodegenerative impairment. To model the lifelong effects of subclinical HSV infection, …