Digital Commons Network^™

Australian Teen Voices On Age Verification And Age Assurance Measures, Giselle Woodley, Harrison W. See, Brian O'Neill, Lelia Green, Elisabeth Staksrud, Paul Haskell-Dowland

Research outputs 2022 to 2026

Age verification and age assurance technologies, including biometrics, facial recognition software and digital identification, have been explored as potential measures to restrict access of young people to social media and sexually explicit media (SEM), including pornography. However, there is limited data regarding how teens targeted by such restrictions feel about such strategies. Drawing on qualitative interviews with Australian teens and pre-teens (aged 11–17), this paper presents their views of age verification technologies, particularly in relation to restricting access to SEM. The participants in this study welcomed improved online protections but were ambivalent about proposed solutions. Age assurance and age verification …

A Decade Of Knowledge Without Borders: Open Access And Global Reach Of School Social Work Research, Leticia Villarreal Sosa

School of Social Work Faculty Publications

As we celebrate the 10th anniversary of the International Journal for School Social Work (IJSSW), this article provides a reflection on a decade the last decade of breaking barriers and building bridges across practitioners, researchers, communities, and continents and looks forward to our responsibility for the future. This article discusses the benefits and challenges related to Open Access publishing, and addresses how Open Access is a public good—ensuring that practitioners, researchers, students, and policymakers can engage the best evidence regardless of geography, resources, or institutional affiliation. The impact of IJSSW is reviewed, including a summary of accomplishments, most downloaded …

Developing American Indian/Alaska Native Children As Leaders In The Climate Movement, Joseph Burns, Alessandra Angelino, Danielle Heims-Waldron, Allison Empey, Jason Deen

Journal of Youth Development

American Indian/Alaska Native (AI/AN) voices are critical in the climate movement, as numerous social drivers have rendered these communities particularly vulnerable to the consequences of environmental change. In recent years, specific events, including the Dakota Access Pipeline, have galvanized AI/AN youth, who have been increasingly involved as leaders in the climate movement both in the United States and internationally. This yields a significant opportunity to promote leadership development for Indigenous youth, both through local, national, and international organizing and through curricular development to spark interests in environmental science. This review aims to discuss the role of AI/AN youth leadership in …

Identification And Characterization Of Host-Modulating Effectors Encoded By The Cluster F1 Mycobacteriophage Normanbulbiejr, Bethany M. Wise, Kaylia Edwards, Cole R. Jirsa, Sabrina Abbruzzese, Amina Adebiyi, Sarika Bapat, Tyler Barnhardt, Naomi Bastiampillai, Ella Begovic, Mary G. Berchick, Gilda Bocco, John Bonoris, Elizabeth Boos, Margaret Cassady, Julia Chehab, Gene Cooper, Hunter Coyle, Joseph Davis, Natalia De La Cruz Vargas, Matthew Delach, Carly Dowiak, David Ferraro, Madison Fuller, Bootsie Glasser, Kyle Gordon, Hannah Hoch, Sarah Holleman, Luke Hood, Brett Hurrell, Nicholas Jacobs, Danny Jiang, Steven Kefalos, Salma Maher, Jocelyn Martin, Rohebot Mengesha, Daniel Merenich, Ashwin Nayak, Dasha Nesterova, Joshua Nguyen, Chukwudumebi Okonkwo, Taylor Pompan, Lauren Redwood, Christina Scanlon, Lily Schneider, Aparna Shenai, Colton Siatkowski, Lisa Strausner, Joseph Vizzarri, Jordan Watson, Angela Wilson, Catherine Wynne, Anthony Zygmunt, Michelle Kanther, Kimberly M. Payne, Richard S. Pollenz, C N. Sunnen, Danielle M. Heller

PCOM Scholarly Works

NormanBulbieJr (NBJ) is a temperate siphovirus isolated on the host Mycobacterium smegmatis mc²155 that encodes 102 gene products, 60 of which have no known function (NKF). Based on gene content, NBJ is classified as a Cluster F, Subcluster F1 phage and shares 70% of its encoded gene phamilies with Girr, another F1 mycobacteriophage that was recently analyzed in a genome-wide overexpression screen and found to encode 29 diverse gene products capable of inhibiting growth of M. smegmatis. Similar functional screens in other mycobacteriophages have uncovered a growing repertoire of diverse, phage-encoded bacterial growth inhibitors, providing prime candidates …

Bridging The Gut And The Heart-Exploring Pathophysiology, Risk Factors, And Therapeutic Implications Of Cardiovascular Disease In Inflammatory Bowel Disease, Diana Othon-Martinez, Sylvia Valeria Peña-Muñoz, Margarita Riojas-Barrett, Genesis Vidales-López, Josué Moisés Sánchez Guzmán, Lukasz Kwapisz

School of Medicine Publications

Inflammatory bowel disease (IBD), including Crohn’s disease and ulcerative colitis, is a chronic systemic immune dysregulated disorder affecting the gastrointestinal tract that often has extraintestinal manifestations. Limited data exist on the cardiovascular (CV) implications of IBD, but a higher prevalence of CV disease (CVD) has been observed compared to the general population, resulting in increased mortality risk. This review examines the pathophysiology and risk factors linking IBD to CVD, highlighting several key mechanisms: The “lipid paradox” in active IBD, arterial stiffness and endothelial dysfunction, proinflammatory cytokine activity, gut microbiota dysbiosis, and drug-induced myocardiopathy. The main findings include information about IBD …

Signaling Through National Security Lawmaking, Weijia Rao

Faculty Scholarship

An often-overlooked element of the ongoing TikTok saga is China’s export control law, which prohibits the sale of TikTok’s core content recommendation algorithm without prior approval from the Chinese government. This law is just one example of the extensive national security lawmaking China has undertaken in recent years. Closely paralleling similar institutions in the U.S., China has established a comprehensive national security legal framework that authorizes the government to restrict exports, sanction foreign entities, screen foreign investments, and block transactions involving cyber infrastructure.

Why turn towards legality when China has the ability to employ these actions without a formal legal …

Genocidal Accusation, Steven Arrigg Koh

Faculty Scholarship

To accuse of genocide — what does it mean? Genocidal accusation is ubiquitous today, evident more in the public square than in any courtroom. At first glance, such accusation seemingly relies on a central assumption: genocidal accusation is critical to preventing atrocity. This Article argues that this widespread assumption is incomplete, obscuring genocidal accusation’s dual nature. In fact, genocidal accusation encompasses not only laudable atrocity prevention (for example, the Rwandan genocide), but also problematic punitive, carceral discourse that brands the “other” as morally polluted (such as President Putin’s Ukraine invasion). It is thus challenging to evaluate such accusation in the …

The Problem With The Pursuit Of Pleasure And Why It Isn’T The Same As Joy, Seow Hon Tan

Research Collection Yong Pung How School Of Law

One is fleeting and depends on circumstances, the other comes from persevering in what one values and loving others.

Building A Restructuring Hub: Lessons From Singapore, Aurelio Gurrea-Martinez

Research Collection Yong Pung How School Of Law

This article seeks to analyze the legal, market, and institutional features needed to become an international hub for debt restructuring. To that end, it examines the strategy adopted by Singapore as well as the market and institutional factors generally found in other leading legal and financial centers such as the United States, the United Kingdom, and Hong Kong. It is argued that in jurisdictions that have traditionally had creditor-oriented insolvency systems, such as Singapore, the United Kingdom, and Hong Kong, one of the primary challenges when enhancing the restructuring framework for debtors is ensuring that the insolvency system remains protective …

Assessing U.S. Fatal & Nonfatal Injury Trends By Worker Age, Alexander Mcdaniel

All-Inclusive List of Electronic Theses and Dissertations

Occupational injuries and fatalities in the United States have declined over time; however, meaningful differences by worker age remain a concern for employers and safety professionals. Younger workers often experience higher non-fatal injury rates due to limited experience and developing hazard-recognition skills, while older workers face increased risk of severe and fatal outcomes related to age-associated physical changes. The purpose of this study was to analyze long-term injury and fatality trends by age group and determine whether differences exist over time. National injury data from the Census of Fatal Occupational Injuries (1992–2023) and the Survey of Occupational Injuries and Illnesses …

John Henryism And The African American Experience: An Exploration Of Risk And Protective Factors And Health Outcomes, Joseph L. Twitdy

All-Inclusive List of Electronic Theses and Dissertations

Racial discrimination has been tied to adverse mental and physical health outcomes among African Americans. In the face of this adversity, African Americans have developed strategies for coping with everyday experiences of discrimination. One such coping method is based on the concept of John Henryism. The folktale of John Henry tells the story of an African American steel-driver racing against a steam-powered drill to clear a path for trains through a mountain. Although John Henry wins the race, he later dies due to exhaustion, ultimately passing with his hammer in his hand. The psychological concept of John Henryism (JH) refers …

Association Of Area Deprivation Index With Perinatal And Covid-19 Outcomes During The Early Pandemic: A Secondary Analysis Of A Prospective Cohort Study, Abigail Bell, Megan Foeller, Candice Woolfolk, Emily Diveley, Fan Zhang, Amy Scheffer, Ruizhi Huang, Daniel Jackson, Nandini Raghuraman, Ebony Carter, Indira U Mysorekar, Jeannie C Kelly

Center on Aging Staff Publications

Objective: We sought to evaluate the relationship between neighborhood-level deprivation and perinatal outcomes, particularly preterm birth, as well as COVID-19 outcomes among pregnant patients during the early pandemic.

Methods: We performed a secondary analysis of a prospective longitudinal cohort study that investigated the impact of prenatal COVID-19 exposure on perinatal outcomes. Pregnant patients were recruited 12/23/20-7/18/22 and serially assessed for COVID-19 exposure during pregnancy, with serum antibody testing, electronic medical record review, and patient report. For this secondary analysis, address at the time of delivery was used to categorize patients into low ADI (≤50th percentile Area Deprivation Index, indicating low …

Rise Of Social Media Hacking: Ai-Based Ip Tracking For Uae Law Enforcement, Mounikha Naarrayen Chakravarthula

Theses

Social media has evolved into a critical channel for communication, expression, and public influence, but it has also become a prevalent avenue for cybercrime, particularly in digitally advanced nations such as the United Arab Emirates (UAE). The rising complexity of online offences, coupled with anonymisation tools and cross-border digital behaviour, has made the attribution of social-media-based cyber incidents increasingly challenging for law enforcement. In this context, artificial intelligence (AI) offers the potential to strengthen digital investigations by providing intelligent, scalable, and evidence-driven attribution capabilities. This research develops an AI-assisted Internet Protocol (IP) attribution framework tailored specifically for UAE law enforcement …

The Role Of Ai And Predictive Policing In Crime Prevention, Saif Salem Mohammad Hassan Abdulla

Theses

The current thesis examines the application of Artificial Intelligence (AI) in the arena of predictive policing and crime forecasting using an integrated case study based on the empirical approach supported by a narrative review of literature. Due to the growing use of digital data by the law-enforcement agencies, AI techniques, including machine learning and spatio-temporal modelling, are implemented to detect patterns of crimes, predict high-risk areas, and assist law-enforcement decision-making. Though these technologies have the potential to make the processes of accuracy and resource allocation better, they also bring up the issue of the fairness, transparency, and disproportionate effects on …

Defaking Deepfakes: Designing And Evaluating Ai-Powered Digital Media, Saniat Javid Sohrawardi

Theses

The rapid spread of generative AI has revolutionized media production, creating new challenges for information integrity as convincing deepfakes proliferate. Journalists play a critical role in upholding credible public information, yet existing deepfake detection technologies often overlook their specific workflows and requirements. This dissertation addresses these needs by identifying what journalists require from detection tools and evaluating usability in realistic scenarios through the following works: Journalists' Needs and Tool Design: Through qualitative user studies, we uncover journalists' preferences for tools that provide transparent, explainable evidence and context-aware analysis integrated into news verification routines. These insights drive the design of DeFake, …

Predictive Modeling Of Tokenized Real Estate Prices Using Machine Learning, Mohammed Alteneiji

Theses

In this paper, we propose a machine learning-based model to predict the prices of tokenized real-estate assets, combining blockchain data, real-estate data, and sentiment data. Tokenization refers to partitioning a physical asset into fractional tokens on a blockchain, where each token represents a fraction of that asset. These include token liquidity, trading volume, platform activity, and the on-chain activity of investors. These new variables require data-based analytics that are capable of accounting for more complex relationships than customary real estate valuation approaches. To address this, we propose a multimodal dataset that incorporates on-chain data (token supply, number of wallet-holders, transaction …

An Interpretable Machine Learning Framework For Detecting Phishing Urls Based On Lexical Features, Khalid Alrokhaimi

Theses

Phishing attacks represent one of the most significant and persistent threats in the cybersecu- rity landscape, with attackers increasingly using sophisticated URL manipulation techniques to deceive users and steal sensitive information. Traditional detection methods, which rely primarily on blacklists and heuristic rules, struggle to identify zero-day phishing URLs that have not yet been catalogued in security databases. This research addresses this critical gap by developing an interpretable machine learning framework for detecting phishing URLs usingclexical, structural, content-based, and domain metadata features. The study employs a comprehensive dataset of 11,430 labeled URLs (5,715 legitimate and 5,715 phishing) with 87 extracted features, …

Persuasive Pathways In Digital Apologies: The Role Of Persuasion Routes In Engagement With Youtuber Apology Videos, Darrin Majocha

Theses

This study explores how viewers process YouTube apology videos and how those processing methods relate to their judgments of the creator and their willingness to continue engaging with the channel. Guided by the Elaboration Likelihood Model, a single-session online survey experiment randomly assigned 413 adult participants to watch one of five publicly available apology videos from well-known YouTubers. After viewing, participants reported the extent to which they engaged in central versus peripheral processing, rated the creator's credibility and the apology's sincerity, and indicated their intentions to engage. Central processing was positively associated with engagement intentions, whereas peripheral processing was weakly …

Predicting Violent Crime Hotspots, Mohammad Alhammadi

Theses

This thesis presents a systematic literature review and an empirical demonstration focused on predicting violent-crime hotspots. Drawing on 50 studies published between 2010 and 2025, the review synthesises methodological developments across hotspot mapping, spatio-temporal modelling, risk terrain analysis, and machine-learning approaches. The review highlights a clear evolution from retrospective density maps to more dynamic, data-driven techniques, while also identifying persistent challenges related to data bias, temporal granularity, environmental context, fairness, and operational implementation. To complement the review, the thesis applies kernel density estimation (KDE) and three ensemble machine-learning models—Random Forest, Gradient Boosting, and XGBoost—to 769,680 geocoded violent-crime incidents recorded in …

Potential And Low-Cost Football Talents For Uae Clubs Based On Data-Driven Analysis, Rashid Alqemzi

Theses

The use of data analytics in professional football has changed the way clubs view, value and invest in players. Meanwhile, elite European teams have consistently exploited the data-driven strategies that offer them a competitive edge, but in up-and-coming football markets, such as the UAE Pro League, nepotism has remained strong, with reputation-based and agentdriven recruitment being the transformation choice of even the biggest profile moves. This work explores whether predictive analytics can discover hidden high-quality football talent from ”hotbeds” of talent including Brazil and Argentina, in a way that matches the economic capabilities of clubs in the UAE, given the …

Ai-Powered Multimodal Tour Guide: Enhancing Cultural Tourism With Image Recognition, And Personalized Storytelling, Abdulla Ibrahim Aljawi

Theses

This thesis presents the design and evaluation of an AI-powered multimodal tour guide that uses image recognition and personalised storytelling to enhance cultural heritage experiences. Traditional approaches to learning about monuments rely on static plaques, generic tour content, or manual web searches, which limit personalisation, interactivity, and accessibility. To address these limitations, the study implemented a “Multimodal Monument Explorer” that allows users to upload a photo of a landmark or describe it in natural language and then receive rich, context-aware explanations in both text and audio form. The system integrates a persistent vector database of monument images, OpenCLIP-based visual embeddings, …

Event-Driven Traffic Management, Abdulla Humaid Alhosani

Theses

Traffic congestion during peak hours and large public events is a persistent challenge in urban areas, affecting mobility, economic productivity, and quality of life. While many cities are moving towards smart, data-driven traffic management, the practical effectiveness of predictive models for event-driven traffic control remains uncertain. This thesis presents an offline, data-driven feasibility study that investigates whether ma- chine learning and time-series models can predict traffic volume patterns under different conditions, including weather and the presence of events. Using a historical traffic dataset with derived trend variables, the study applies exploratory data analysis (EDA) and two predictive approaches: ARIMA for …

Ai-Powered Mobile Phone Activity Insights: Developing Predictive Models For Smarter Decision-Making, Maryam Al Ali

Theses

This study investigates how artificial intelligence can enhance telecom network management by forecasting internet usage, predicting congestion, and identifying user behavior patterns from mobile phone activity data. The study made use of anonymized logs for calls, SMS and internet, and put up a multi-model analytical pipeline, which was composed of time-series forecasting (ARIMA, LSTM), clustering (K-Means), and classification (XGBoost), to perform the analysis. Among the time-series methods, ARIMA ranked first in the forecast performance (RMSE=0.31) and gave LSTM a convincing defeat in the case of this particular short and stable dataset. Based on K-Means segmentation, users were sorted into five …

Deepfake Audio Detection, Rashed Alfalasi

Theses

The rise of deepfake audio technology has introduced a serious threat to information credibility, personal security, and media integrity. This thesis investigates the application of machine learning techniques for detecting synthetic audio through the analysis of acoustic features, including Mel-Frequency Cepstral Coefficients (MFCCs), spectral centroid, chroma_stft, and zero-crossing rate. The dataset used in this study was sourced from Kaggle and contains labeled samples of real and fake audio clips. The research aimed to train and evaluate multiple machine learning models—Support Vector Machines (SVM), Random Forest, XGBoost, Logistic Regression, and Neural Networks—to determine the most effective approach for deepfake audio classification. …

Analyzing Airline Customer Experience Challenges And Their Impact On Dubai's Tourism Sector, Suhail Alfalasi

Theses

The paper explores whether there is a connection between operational performance, customer sentiment, and digital administrative complexity among major regional Middle Eastern airlines, such as Flag Carriers (e.g., Emirates) and Low-Cost carriers (LCCs) (e.g., Air Arabia). With the use of a highly detailed dataset of customer review and operations data, the study proves that a large service paradox is present in which, despite the high Net Promoter Scores (NPS: 48.004) of the carriers, which are positively reinforced by delivering world-class soft products, the loyalty is constantly disrupted by low-frequency but high-severe operational delays (Delay_Minutes). Since it has been analyzed that …

Beyond Detection: A Batch-Based Ai Framework For Temporal And Event-Correlated Trend Analysis Of Misinformation On Social Media, Vishnu Tejas Vijayaraghavan

Theses

The rapid spread of misleading information on social media influences public behaviour and complicates crisis communication. Although transformer models such as BERT accurately detect misinformation at the post level, most studies analyse posts in isolation and overlook howmisinformation fluctuates over time or responds to major events. This study addresses that gap by developing an end-to-end analytical workflow that integrates BERT-based classification with temporal aggregation, topic clustering, anomaly detection, and event alignment. The analysis uses 10,700 COVID-19–related tweets (6,420 training, 2,140 validation, and 2,140 testing). Because timestamps were unavailable, synthetic timestamps were assigned using an evenly spaced date range between 1 …

Chatgpt As A Mental Health Ally: A Study On College Students’ Adoption Of Ai For Therapy, Alya Albastaki

Theses

This research investigates college students’ use of ChatGPT for mental health support, addressing a population with high unmet mental health needs due to barriers like accessibility and cost. Through a mixed-methods study, which included a survey of 126 students and sentiment analysis of 1,200+ social media posts, the research examined adoption prevalence, gender influences, and perceived benefits and limitations. Survey findings show 40.5% of students use ChatGPT for mental health, especially those with self-reported challenges. Female students reported higher adoption, linked to greater mental health needs and openness to supplementary support. Key benefits included 24/7 access, anonymity, and low cost, …

Assessing Large Language Models As An Interpretive Layer In Marketing Mix Modeling: Implications For Marketing Analytics, Mohammad Bin Haider

Theses

Marketing mix modelling (MMM) remains a core technique for guiding budget allocation, yet its outputs are often difficult for non-technical planners to interpret and govern. At the same time, large language models (LLMs) offer new possibilities for translating complex model artefacts into narrative guidance, but raise concerns about hallucination, reproducibility, and alignment with model-risk governance. This thesis examines whether an open-source MMM framework can be engineered as a repro- ducible, governance-ready pipeline and then augmented with a tightly constrained LLM interpretive layer. The empirical setting is a multi-brand, multi-country retail portfolio with several years of digital marketing and outcome data …

Desiccation Tolerance In Tetradesmus Green Algae, Kristen Patten

Theses

In the face of the global climate change threat, understanding the adaptations that organisms have evolved to handle environmental variation is of particular interest to scientists. With climate change impacting global water availability and increasing the risk of drought for many traditional agricultural areas in the United States (IPCC, 2014, IPCC, 2021, and Kuwayama et al., 2019), desiccation tolerance in vegetative states is one adaptation that is currently receiving a lot of attention. Green algae are particularly useful organisms for understanding this adaptation due to their ubiquity across environments, which has given rise to independently evolved organisms displaying different levels …

Glacial Deposits, Vol. 50, 2025, Illinois State University

Glacial Deposits

Newsletter of the Illinois State University Department of Geography, Geology, and the Environment