Digital Commons Network^™

Strengthening Early Childhood Education: Increasing Family Engagement At The Sprout School Of Richmond, Virginia, Tara Davison, Jennifer Meadows

Doctor of Education Capstones

The Young Women’s Christian Association (YWCA) of Richmond is dedicated to fostering community and combating discrimination through various initiatives. Their early childhood education program, the Sprout School, is one initiative that strives to serve families from all walks of life within the Richmond community. However, a significant challenge faced by the Sprout School is increasing family engagement, which is crucial for strengthening the relationships among parents, teachers, and students. Research indicates that effective family engagement can positively influence children's educational experiences and outcomes. To address this, the research team conducted a mixed-methods study to assess the current state of family …

A Call To Action For Transport Justice: Exploring Transportation Disadvantages Faced By Justice-Impacted Individuals Reintegrating Into Rural Communities, Tarnika Edmunds

Master of Urban and Regional Planning Capstone Projects

This study explores the intersection of rural reentry and transportation planning, emphasizing the importance of Transport Justice in overcoming barriers faced by transit-dependent populations, particularly formerly incarcerated individuals. While urban transportation planning is widely discussed, this research highlights the overlooked challenges that rural environments pose for individuals reentering society. Through a multi-method approach that involved document analysis, participant observations, and informal discussions, this study uncovers a range of themes related to accessibility, reliability, vulnerability, and the quality of commuting experiences. The findings underline the necessity for transparency, accountability, and collaboration across sectors to address these gaps and inform policies that …

Uri Undergraduate And Graduate Course Catalog 2025-2026, University Of Rhode Island

URI Course Catalogs Archive

This is a downloadable PDF version of the University of Rhode Island course catalog.

Predicting Crises On The African Frontier Stock Markets With Investor Sentiment Indicators: A Machine Learning Approach, David Korsah, Lord Mensah

Journal of International Technology and Information Management

This study examined the predictive ability of machine learning algorithms in identifying crises within African stock markets. The study employed seven distinct machine-learning models, analyzing historical stock prices from eight stock markets, three major sentiment indicators, and the exchange rates of local currencies against the US dollar, with each data spanning from May 1, 2007, to April 1, 2023. Extreme Gradient Boosting (XGBoost) emerged as the most effective algorithm for predicting crises. Historical stock prices and exchange rates were identified as the most critical features for prediction. On the sentiment side, investors’ perceptions of potential volatility on the S&P 500, …

Crop2cloud Platform: Real-Time Data Integration For Agricultural Water Monitoring, Bryan Nsoh, Abia Katimbo, Kendall Dejonge, Wei-Zhen Liang, Hongzhi N. Guo, Yufeng Ge, Derek M. Heeren, Yeyin Shi, Xin Qiao, Daran R. Rudnick, Hope Njuki Nakabuye, Birru Girma, Isa Kabenge, Joshua Wanyama

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Efficient water management is vital for sustainable agriculture, yet integrating real-time data for precise irrigation remains a challenge. This study designed the Crop2Cloud (C2C) platform, a system that leverages advanced sensors using Internet of Things (IoT), edge and cloud computing techniques, and computed Water Stress Indices (WSIs) and machine learning models (i.e., fuzzy logic), to provide scalable and real-time irrigation decisions. The C2C platform aggregates several data including Volumetric Water Content (VWC) from TDR sensors (Acclima Inc., US) installed at four multiple depths, canopy temperatures (Tc) measured by Infrared Radiometers (IRTs) (Apogee Instruments, US), as well as weather information and …

How Can Chinese Culture Go Abroad? Evidence From Successful Establishment Of Chinese Academic Foreign Translation Projects, Tian Lan

Dissertations and Theses Collection (Open Access)

Compared to Chinese economy scale, the global understanding and recognition of Chinese culture are quite superficial, it is crucial to promote Chinese culture go abroad through academic exchanges between China and other countries. This study explores and validates the relationship and applicability of Chinese culture “Going Global” based on the translation project of Chinese academic works. Through case analysis and the development of data models, it aims to provide theoretical guidance and practical tools for relevant institutions and individuals.

The study begins with qualitative research on cases, describing and analyzing typical examples to demonstrate how Chinese Academic Translation (CAT) projects …

Cultural Intelligence For Advanced Practice Providers: Moving A Contextually Based Model Forward For Workforce Training, René Padilla

Yale School of Nursing Digital Theses

Cultural Intelligence for Advanced Practice Providers: Moving a Contextually Based Model Forward for Workforce TrainingCultural Intelligence (CQ) among clinicians in primary care for the Hispanic population is inadequate (Da Costa, 2023). Cultural diversity training for patient care has been identified as a need to improve outcomes since the 1960s (Lin et al., 2017). In 2000, the Office of Minority Health (OMH) at the US Department of Health released the National Standards for Culturally and Linguistically Appropriate Services in Healthcare and updated those standards in 2013. Mainous et al. (2020) found that only 35% of primary care providers (PCPs) knew of …

Promoting Mental Health Awareness And Reducing Stigma In Spanish-Speaking Communities Through Educational Interventions, Kelly Medina Prado

Yale School of Nursing Digital Theses

Background: Spanish-speaking Latino populations in the U.S. experience significant disparities in mental health access driven by language barriers, low mental health literacy, and cultural stigma. These factors contribute to the underutilization of services, early treatment dropout, and poor outcomes.

Objective: This quality improvement project aimed to develop, implement, and evaluate a culturally and linguistically tailored mental health education intervention designed to increase mental health literacy and reduce stigma among Spanish-speaking Latino adults in a federally qualified health center.

Methods: A total of four group sessions were conducted over an eight-week period. The sessions were delivered in Spanish and included culturally …

Political Communication In A Multicultural Metropolis: Chinese Ethnic Media And The 2023 Toronto Mayoral By-Election, Dongwei Lin

Theses and Dissertations (Comprehensive)

Chinese are the second largest visible minority group in Canada and the most frequently reported ethnic or cultural origin in Toronto. Mandarin and Cantonese are, respectively, the largest and third largest non-official languages in Toronto. However, little research exists on the role of the Chinese ethnic community in Toronto municipal politics, especially the role of Chinese ethnic media in Toronto municipal elections.

The key argument of this thesis is that “Chinese ethnic media represent complementary public sphere(s) for informing and engaging Chinese Canadians in local political participation and discussion.” Through the 2023 Toronto Mayoral By-Election case study, this thesis examines …

Children's Interpretation Of Others' Faces Covered With Medical Masks, Sevval Cinar

Theses and Dissertations (Comprehensive)

How we recognize and regulate emotions is fundamental to overall health and wellbeing. Emotional recognition and understanding are key to proper communication and emotion regulation. Barriers, such as masks, might have an impact on the way children recognize and interpret others' emotions, and therefore the way they learn, communicate, and regulate their own. The COVID-19 pandemic necessitated widespread mask-wearing, raising questions about its impact on children’s emotional development. Using a sample of children with varying levels of exposure to mask wearing adults (N= 13, ages 4 to 6), we investigated how prolonged mask wearing predicts emotion recognition, particularly …

Art/Place Making In A Settler Colonial Context: A Critical Place Inquiry On The Haldimand Tract/Kitchener-Waterloo From A White Settler Perspective, Haley Sheppard

Theses and Dissertations (Comprehensive)

Although critical place scholars implicate artists in settler colonial processes of placemaking, there is little information on how white settler artists (WSAs) might use their creative practice to support practices of accountability and solidarity with Indigenous resurgence. This research responds to this gap by investigating the creative practices of WSAs located on the Haldimand Tract/in Kitchener-Waterloo (KW), and how they might approach critical, contextual practices in their creative work. A critical place inquiry was combined with ethnographic research principles and a transformative critical research paradigm. Six WSAs participated in mobile interviews, which took place in a location significant to them. …

Multi-Lingual And Cross-Domain Frontiers In Machine-Generated Content Detection, Gurunameh Singh Chhatwal

Theses and Dissertations (Comprehensive)

The rapid advancement of generative artificial intelligence, particularly Large Language Models (LLMs) such as GPT-4 and their multilingual capabilities, has significantly blurred the distinction between human-authored and machine-generated content. This technological evolution introduces critical challenges concerning the detection and attribution of textual authenticity and authorship, exacerbating societal issues like misinformation proliferation and compromising academic and professional integrity. Traditional detection methodologies, predominantly monolingual and heuristic-based, have demonstrated inadequate generalizability and efficacy against the sophisticated, multilingual capabilities of contemporary generative models.

This thesis addresses two major problems arising from these advancements. Firstly, it introduces novel multilingual detection methodologies explicitly designed to differentiate …

Unveiling Trauma And Seeking Justice: Exploring Mass Atrocities Against Tamils Through The Lens Of Religion, Culture, And Global Justice, Arrabe Vigneswaran

Theses and Dissertations (Comprehensive)

This paper examines the mass atrocities committed against the Tamil population in Sri Lanka, analyzing them through the interconnected lenses of religion, culture, and global justice. It explores how Sinhalese-Buddhist nationalism, codified through legal and constitutional mechanisms such as the Sinhala Only Act and state-sponsored cultural erasure, facilitated the systemic marginalization, displacement, and dehumanization of Tamils. The study investigates the legal, political, and cultural underpinnings of these acts ranging from war crimes and ethnic cleansing to potential genocide while assessing the reluctance of both domestic and international actors to enforce meaningful accountability. Drawing on hybrid court models, transitional justice theories, …

Examining The Impact Of Early Literacy Interventions On Teachers’ And Parents’ Learning And Perceptions Of Early Literacy Development, Abigael Mary Hogarth Todd Miss

Theses and Dissertations (Comprehensive)

Parents and teachers play a vital role in fostering early literacy development; however, many require additional training to enhance their understanding of alphabet knowledge and phonological awareness. This study examined the effectiveness of two instructional videos—one on phonological awareness and one on evaluating alphabet books—in increasing teachers’ (n = 35) and parents’ (n = 34) perceptions about these domains and the practical application of information covered in the videos. The 69 participants viewed each video, and results indicated a general decrease in confidence following exposure to these videos, likely reflecting an increased awareness of the complexity of the …

Technology-Enhanced Professional Development For Early Literacy: A Comparative Study Of Online Teacher Learning Models In Kenya, Constanza Uribe-Banda

Theses and Dissertations (Comprehensive)

Growing recognition of early literacy as essential for lifelong learning, along with evidence that teachers’ mastery of foundational literacy instruction influences student outcomes (Darling-Hammond et al., 2017; UNESCO, 2020), has led to increased investment in teacher professional development. Despite these efforts, many educators face significant challenges in accessing high-quality training, particularly in resource-constrained settings where geographical barriers and limited institutional support persist. This is especially true in rural Kenyan communities, where teachers often work in under-resourced schools with unreliable electricity and internet connectivity yet bear responsibility for laying critical literacy foundations. The present study examines the potential to deliver effective …

Negotiated Cultural Identity And Belonging Of Self-Identified Indo-Guyanese Immigrant Women, Neta (Goomatee) Deonarain-Gear

Theses and Dissertations (Comprehensive)

This dissertation examines how young, self-identified Indo-Guyanese women who immigrated to Southwestern Ontario in the 1970s negotiate cultural identity and belonging. Utilizing a bio-ethnographic bricolage narrative methodology, the study explores the impact of postcolonial diaspora and social displacement on these women’s experiences within the "third space" or borderlands. Grounded in relativist ontology and constructivist epistemology, storytelling serves as the primary data collection method through a critical theory lens. Interviews with 12 participants were analyzed thematically and reflexively, revealing how participants construct meaning and navigate cultural identities. The analysis, conducted through feminist, decolonial, and trauma-informed perspectives, offers insights into "psychological homelessness" …

A Pilot Study Examining Children’S Evaluations And Reasoning About Concealment And Instrumental Forms Of Antisocial Lying, Noah Battaglia

Theses and Dissertations (Comprehensive)

Children as young as 4 years of age judge self-benefitting lies (i.e., antisocial lying) as wrong. Although this has traditionally been interpreted as evidence that young children view honesty as a moral obligation, contemporary moral development theory and research suggests that right/wrong judgments fail to capture important aspects of children’s social cognition. Research from a perspective known as social domain theory (Smetana et al., 2014; Turiel, 1983) has shown that children’s understanding of morality (i.e., norms concerning others’ rights and wellbeing) is distinct from their understanding of other types of socially unacceptable behaviour. While children judge many social events as …

Exploring The Shadows: How Taylor Swift's Music Embodies Dark Sound As A Popular Music Artist, Kristyn E. Cole

Theses and Dissertations (Comprehensive)

To challenge the conceptual matter of “dark sound”, this major research paper centers around the popular music artist, Taylor Swift to demonstrate an overarching feminist wisdom in an alternative style of music to identify how Taylor Swift presents and embodies qualities of “dark sound”. Research done by D. Ferrett, Theodor Adorno, Roland Barthes and Paul C. Jasen are used to examine her popular music, and its intersections and influence on our culture and society. Using multimodal critical discourse analysis, this research paper discusses three of Swift’s songs; “willow” (evermore, 2020) for her sound, All Too Well: The Short …

Blessed Be The Holiness Tie That Binds, Don Thorsen

The Asbury Journal

No abstract provided.

Detecting Ai-Generated News Articles Using Unsupervised Machine Learning Algorithms, Lilou Sicard-Noel

Master's Projects

The widespread adoption of Large Language Models (LLMs) has revolutionized text generation and heightened concerns over misinformation and the erosion of journalistic integrity. Detecting AI-generated text is critical to addressing these challenges, yet current detection methods face adaptability, scalability, and accuracy limitations. This research paper uses machine-learning techniques to explore the classification of human and AI-generated articles, including a mix of human and AI-written content. The primary focus is on evaluating the effectiveness of clustering algorithms (K-Means and Agglomerative Clustering), auto-encoders, and Part-Of- Speech Tag Transition Matrix Log-Likelihood for distinguishing between AI-generated and human-written texts. Our findings reveal that while …

Hierarchical Bloom Filter Tree (Hbft): Scalable Geospatial Metadata Indexing For Bigdata Systems, Mrudula Patteparapu

Master's Projects

Big Data infrastructure growth has produced overwhelming metadata volumes which create extensive problems for spatial indexing and both system scalability and database queries. Traditional solutions consisting of R-trees and conventional Bloom filters manage to provide either range query support or approximate membership testing, yet they face performance issues when used at large-scale metadata management. This research proposes Hierarchical Bloom Filter Tree (HBFT) as an improved framework that integrates hierarchical spatial partitioning with partitioned, scalable, cuckoo, and striped Bloom filter variants based on existing studies in hierarchical and probabilistic indexing. The complete evaluation process shows that HBFT outperforms PostGIS (an industry-standard …

Medilightrag: A System For Medical Query Response Using Fine-Tuned Llms And Graph Based Retrieval, Rajiv Karthik Reddy Kodimala

Master's Projects

The exponential increase in medical data has created a greater demand for precise and efficient information retrieval systems. Existing Large Language Models (LLMs) face domain-specific difficulties such as sophisticated medical jargon, situational comprehension, and the continual advancement of healthcare knowledge. To tackle these challenges, we present MediLightRAG, an innovative two-stage system which integrates parameter-efficient fine-tuning of Large Language models with LightRAG’s graph-based retrieval. The first stage focuses on enabling accurate resource-efficient model adaptation for the medical domain through QLoRA fine-tuning. In the second stage, LightRAG’s two-tiered retrieval architecture that combines graph-based indexing with dynamic knowledge retrieval is employed to enhance …

Edurag: Improving Ai Teaching Assistants With Retrieval-Augmented Generation, Geethika Vadlamudi

Master's Projects

This paper is based on the emerging need for AI-driven teaching assistants to deliver personalized, effective, and responsive educational assistance. Earlier proposals for educational technology such as rule-based systems and adaptive learning platforms have been limited by the lack of flexibility, domain accuracy, and slow, non-interactive support. With the launch of large language models (LLMs) like GPT-3, GPT-4, they have demonstrated that they can generate the kind of human-responsive text. When it comes to more substantive education matters, though, such models suffer from domain accuracy, in addition to whether accurate information can be imparted. And that is where the aspect …

Semanticgraphrec: Lightweight Hybrid Recommendations Powered By Semantic Item Representations And Graph Collaborative Filtering, Devi Surya Kumari Akula

Master's Projects

Graph neural networks (GNNs) have emerged as a powerful paradigm for collaborative filtering. However, they often fall short in fully leveraging side textual content, resulting in suboptimal recommendations. To address this limitation, we explore the synergy between GNNs and deep contextual embeddings of item descriptions, aiming to enhance recommendation quality on the Amazon-Books dataset. We propose SemanticGraphRec, which combines GNNs with Large Language Models (LLMs) to leverage both collaborative filtering and textual item content. Experimental results demonstrate that incorporating semantic item embeddings produced by fine-tuning LLMs consistently improves performance. Our approach enhances recommendation relevance in sparse data scenarios by leveraging …

Image-To-Text Transcription: Analyzing And Describing Visual Content, Zixiao Fan

Master's Projects

Image captioning, which provides a textual understanding of visual content, is the fundamental support for the advancement of Human-A.I. Interaction technology. In the hope of exploring the application of such technology, this project focuses on two specific goals. One is to directly explore the application of the image informationretrieving abilities, and the other is to dive into the specifics of the pipeline and components of image captioning models. As a result, this project presents a working app that exploits the text retrieval functionalities to enable image storage with functions like tagging and transcription. It also supports search functionality with a …

Extraction Of A Knowledge Graph Of Biomedical Relationships, Brian Tran

Master's Projects

Rapid release in biomedical literature poses a challenge in linking information. This thesis aims to extract data from expanding datasets to identify and form meaningful relationships between biomedical entities. Large language models (LLMs) enable us to learn at a rapid pace. Creation of LLms from scratch are impractical. This thesis aims to collect a small dataset, containing biomedical papers, and use it to train large language models (LLMs) to extract entities from the text and learn the relationships between these entities. The experiment will be divided into two stages and utilize EU-ADR and ChemProt dataset. Starting with named entity recognition …

Galora: A Lightweight Graph-Aware Llm Framework For Node Classification On Text-Attributed Graphs, Mayur Choudhary

Master's Projects

With the exponential rise of language models (LMs) and their potential to understand semantic relationships, large LMs are being used across a wide range of applications. Text-attributed graphs (TAGs) are one notable example where LLMs can be combined with Graph Neural Networks (GNNs) to enhance node classification results. TAGs associate textual content with each node and are commonly seen in various domains such as social networks, citation graphs, recommendation systems, etc. Effectively modeling TAGs would enable deeper insights into different aspects of the graph and improve decision-making in relevant domains. We present GaLoRA, a parameter-efficient framework to integrate structural information …

Maple: Malware Analysis Through Projection Of Low-Dimensional Embeddings, Quang Duy Tran

Master's Projects

Machine learning has become a popular and powerful tool for malware analysis and detection. With the rise in popularity of natural language processing (NLP) techniques, researchers can now extract contextual embeddings from malware opcode sequences, enabling the capability to analyze hidden malware patterns and advanced code obfuscation strategies. However, unlike malware binaries, which can be directly visualized as images, these embeddings exist in high-dimensional spaces, making it difficult to observe their global patterns or spatial structures. In this paper, we propose a framework for visualizing malware embeddings in lower-dimensional space using various dimensionality reduction techniques. Our approach converts malware binaries …

Large Language Models For Bacterial Genomic Analysis, Manvendra Chavan

Master's Projects

Identification of bacterial gene sequences with agricultural applications has the potential to transform agricultural biotechnology. These genes can be used in environmentally friendly pest control strategies. One such use case is identifying genes with potential insecticidal properties. With an increasing number of genomic information and decreasing numbers of available annotated sequences, finding new insecticidal genes has become more challenging.The traditional methods relying on sequence alignment and annotated databases are not effective in detecting functionally relevant genes lacking close homology to known cases. This project investigates the data-driven classification of genes by sequence modeling. This research is focused on learning DNA …

Equity In Public Budgeting: Community Engagement In Morgan Hill, Ariana Perez

Master's Projects

Through this research, I will examine what citizen participation strategies have been implemented and their role and effectiveness in addressing wealth inequities. My research will explore these topics and examine what programs and policies the City of Morgan Hill can implement to increase residents' sense of belonging and ultimately push for greater social equity. My primary research question is, what are the programs and policies that the City of Morgan Hill can implement to engage immigrant communities, specifically Spanish-speaking residents, and low-income residents in shaping decisions around public investments and funding? This project will serve as a needs assessment to …