Digital Commons Network^™

The Vampire In Video Games: Narrative Structures, Game Mechanics, And Cultural Significance, Ricardo Longo Minervino

Theses

This thesis examines how vampires are represented in video games by analyzing narrative structures, game mechanics, and cultural meaning. Using the existing games of Vampyr, V Rising, Vampire: The Masquerade – Bloodlines, The Sims 4: Vampires, Skyrim: Dawnguard, Baldur’s Gate 3, and Bloodlines 2, the study identifies which design strategies successfully create an engaging and believable vampire experience for the player, as well as determining which development decisions could hurt the experience for players. Using formalism, symbolism, feminist methodology, and player reception theory, this thesis argues that vampire games function best when players confront moral dilemmas, experience consequences for feeding …

Rubbing Away Respect: How Repeated Touch Alters The Meaning Of Women’S Statues, Olivea Kittrell

Theses

This thesis examines how repeated public interactions with statues of women, particularly the ritualized rubbing of their breasts, alter both the material surfaces and symbolic meanings of statues. We will focus on three bronze sculptures: the Molly Malone statue in Dublin, the Juliet statue in Verona, and the Bust of Dalida in Paris. Through these, we will investigate how statues created with commemorative intent become sites of sexualized behavior based on gender. The problem this thesis addresses is the lack of scholarly analysis on how gendered public interaction physically and symbolically transforms statues of women. While some scholarship covers either …

Frameworks Of Genius: Fresco, Labor, Scaffolding, And Patronage In The Making Of Michelangelo's Sistine Chapel Ceiling, Jackelyn Eileen Adkins

Theses

This thesis examines how the materiality of production and the patronage of Pope Julius II influenced the visual and conceptual outcome of Michelangelo’s ceiling frescoes in the Sistine Chapel. It argues the physical demands of the fresco medium, the architectural constraints of the chapel, and the material infrastructure, such as the pigments, scaffolding, and spatial dynamics related to the political, theological, and ideological imperatives of papal authority. The materials served as a symbolic extension of Pope Julius II’s ambitions, embedding his message directly into the frescoes. By foregrounding the complex web of labor and materials that supported the project, this …

Firing Through Adversity: How Women Ceramicists Sustained Stoke-On-Trent’S Pottery Industry Between The Wars, Zachary Carlson

Theses

Between the First and Second World Wars, the pottery industry of Stoke-on-Trent endured economic depression, material shortages, and social upheaval. Within this volatile context, women ceramicists such as Susie Cooper and Clarice Cliff emerged as central figures in sustaining, and redefining British ceramic production. This thesis examines how these artists transformed adversity into innovation through design, entrepreneurship, and leadership, arguing that their work both stabilized the industry and advanced a distinctly feminist modernism grounded in labor, creativity, and resilience. Drawing on feminist design theory, class and economic analysis, and material culture studies, this research situates Cooper and Cliff within broader …

Terracottas: A Case Study On Late Pre-Classic Colima Figurine Functions And Origins, Gracie Isabella Roades

Theses

This paper investigates the origins and functions of Late Preclassic Colima ceramic figurines from West Mexico through a case study approach. It argues that key stylistic features of the Colima canon have roots in the Early Preclassic period, particularly through the comparison with figurine examples from the site of El Opeño. The study further examines the role of these figurines within mortuary contexts, interpreting them as representational vessels of real individuals used in ancestral remembrance practices. Although many Colima figurines are well represented in museum collections today, their original contexts were often lost due to early twentieth-century looting. Recent excavations …

Identity And Representation In Contemporary Art: An Inclusive 400/500-Level Art History Seminar Course, Laura E. Franz

Theses

This project is an upper-level art history seminar addressing identity and representation – specifically related to race, ethnicity, gender, and sexuality – in contemporary art. Primarily a discussion-based course, the course is focused on inclusivity and is highly structured, with weekly low-stakes assignments that provide students with ample opportunities to practice engaging with academic texts. Designed for students with varying levels of academic skills, it is built around current non-academic articles that students select via an online survey. Students then “unpack” the “big ideas” in each of the four chosen non-academic articles via a cluster of academic readings and videos …

Accessible Preservation: Developing A Low-Cost, Iiif-Compliant Workflow For Small Art Collections, Robyn Palescandolo

Theses

This project explores the development of an affordable, standards-based digital collections management system designed for small galleries, independent artists, and emerging institutions. Using the artist’s own studio collection as a case study, the research integrates accessible hardware and open-source technologies, including the International Image Interoperability Framework (IIIF), Artwork Archive, GitHub Pages, and the Internet Archive, to establish a transparent, replicable workflow for cataloguing and publication. The project demonstrates how interoperability, metadata consistency, and open access can be achieved without reliance on costly proprietary systems, such as TMS Collections or PastPerfect.

The workflow includes high-resolution image capture, 3D scanning, data normalization, …

Detecting Illicit Bitcoin Transactions On The Dark Web, Abdulla Alsuwaidi

Theses

Dark web markets use Bitcoin and related cryptocurrencies to transfer and launder proceeds of crime while obscuring the real-world identities of operators. This creates an increasingly challenging environment for regulators and law-enforcement agencies, who must analyse suspi- cious transactions on decentralised, public, pseudonymous blockchains and increasingly dense and noisy Bitcoin transaction graphs. This thesis investigates whether, and how, blockchain analytics and machine learning can help detect illicit transactions on Bitcoin networks. The first part of the thesis is a systematic literature review of recent contributions from academia and industry that examine major solution approaches for identifying illicit flows on public …

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 …

Whisper Wall: Transforming Global Superstitions Into A Unified, Sound-Reactive Immersive Experience, Hardika Nitin Patil

Theses

Superstitions, often dismissed as irrational in today’s data-driven world, remain deeply rooted in human culture and behavior, offering meaning and a sense of control amid uncertainty. Whisper Wall is an immersive, sound-reactive installation that invites audiences to explore these enduring beliefs through whispered audio narration and symbolic, shadow-inspired visual storytelling. This project re-engages audiences with rich, cross-cultural superstitions, encouraging reflection beyond skepticism to thoughtfully examine their origins and significance. Blending visual communication, motion design, sound, and interaction, Whisper Wall creates a multisensory experience where users activate intimate stories via interactive elements—whisper-like audio delivered through red paper cups coupled with minimalist, …

Transgender Adolescents' Healthcare Rights: The Gap Between Gender-Affirming-Care (Gac) Policymakers’ And Healthcare Providers’ Goals, Jayden Galli

Theses

Transgender people have marginalized healthcare experiences that result in decreased health outcomes. For transgender adolescents, gender-affirming-care (GAC) is even more difficult to access and being restricted with increasing frequency. This mixed methods study was conducted to examine transgender healthcare through the gaps between GAC policymakers’ and healthcare providers’ goals. General research has focused on the relationship between GAC and transgender health, GAC policy development, and the discrepancies between GAC policy outcomes and medical professionals’ advice. This leads to the research question about how policymakers and healthcare providers can collaborate to bridge the gap between GAC policies and transgender minors' healthcare …

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, …

Physics Meets Data: Merging Physics-Based Methods With Deep Learning To Model Complex Systems, Maryam Toloubidokhti

Theses

Accurate modeling of complex systems is crucial in domains such as healthcare, where personalized diagnosis and treatment planning are essential. Traditional physics-based models provide structured, theoretically grounded insights but are often computationally intensive and constrained by simplified assumptions that limit adaptability to patient-specific conditions. In contrast, data-driven models are computationally efficient and capable of capturing complex patterns, yet they often lack interpretability and fail to incorporate essential physical principles, reducing robustness and generalization. This disconnect between mechanistic understanding and computational practicality presents significant challenges in critical applications such as healthcare, where both physical accuracy and real-time performance are vital. To …

The Impact Of The 2008 Financial Crisis On Crime And Cybercrime In The United States, Rashed Adel Alsuwaidi

Theses

This thesis will examine how the traditional crime and cy- were affected by the 2008 financial crisis. United States are also experiencing a rise in crime between 2005 and 2012. Based on the FBI data at the national level. The Internet Crime Complaint Center (IC3), Uniform Crime Reports and important economic indicators. The study, which involves tors, including unemployment, GDP, rates of foreclosures, and mortgage rates, is a combination. correlationbased interpretation supported by exploratory descriptive trend analysis. model-fit checks. The results indicate that contrary to the conventional expectations, violent and property crime also maintained their long-term reduction during the period …

Predicting Employee Attrition With Machine Learning: Data-Driven Strategies For Enhancingworkforce Retention, Ali Almheiri

Theses

In this research, the dual prediction and prescription model is developed and validated so that this model can not only predict employee turnover risk, but it also proposes the appropriate retention interventions, which apply across industries. On the basis of the IBM HR Analytics Attrition dataset (n=1,470), we preprocessed demography, job and satisfaction variables and trained three machine-learning classifiers, Random Forest, Logistic Regression and XGBoost to predict voluntary turnover. XGBoost model recorded the best discrimination (AUC=0.87), sensitivity (0.76), and specificity (0.81), which signifies strong predictive power. Analysis of feature-importance was conclusive with time, rate of frequent business travel and compensation …

Predictive Maintenance For It Equipment Using Machine Learning Models, Rashed Khalid Alshirawi

Theses

Predictive maintenance was recently introduced as a paradigm shift in the field of smart manufacturing, and it allows organizations to predict equipment failures and streamline maintenance planning based on the data-driven insights. The present work addresses the concept of the implementation of a decision tree-based predictive maintenance model based on AI4I 2020 Predictive Maintenance Dataset, a simulated environment of an actual industry through sensor data including temperature, torque, rotational velocity, and toolwear. The study uses a quantitative approach that is based on positivism philosophy, which is the focus on objectivity, empirical validation, and statistical testing of the model performance. Synthetic …

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 …

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 …

Detecting Fraud In Police Reports Using Machine Learning And Natural Language Processing, Maryam Almarar

Theses

The paper explores how statistical analysis and machine learning can be used to identify the fraud patterns in the police reports. The study aims at establishing the most important predictive factors and indicators distinguishing fraudulent and valid cases with the use of structured data of police databases. The work was done in the background of the increase in financial fraud instances and the rising necessity of the introduction of automated detection systems in police departments. Police reports of the pastwere mined down to data and analyzed on SPSS 1, to carry out statistical operations. The sample was structured data which …

Exploring Engineering Students' Utilization Of Resources In Calculus Using Self-Regulated Learning, Kriz George

Theses

In response to high failure rates of engineering students in introductory math courses such as calculus, a wide variety of interventions have been implemented. A common intervention is targeted at modifying the curricula. Additionally, a major initiative to improve pass rates is to provide resources to students to help them better learn the concepts and get continual support as they complete assignments and other coursework. Despite these interventions, pass rates continue to remain low. I posit that merely the availability of resources is not enough for student success in mathematics courses. Students who lack knowledge of how to use these …

Predicting Inmate Overcrowding To Improve Facility Management, Mayed Ali Alameri

Theses

Overcrowding is a major challenge to correction systems because the conventional forecasting techniques are inaccurate and inadequate in most cases. This paper will solve this by constructing and testing a machine learning-based model to predict facility-level overcrowding. With the use of the XGBoost Regressor model on a dataset comprising of U.S. correctional facilities, the research identified key structural drivers but showed that the static facility attributes alone have limited predictive power (r-square approx 0.18) The discussion shows that overcrowding is non-linear, and a complicated problem not confined to the facility characteristics but to the larger, non-measurable regional influences, of which …

Predicting Residential Real Estate Prices In Dubai Using Integrated Data Analytics: A Machine Learning Approach, Mohammad Nasser

Theses

Dubai’s rapidly evolving real estate market attracts substantial global investment yet remains characterized by information asymmetry, pricing volatility, and fragmented data sources. This study examines how integrated data analytics and ensemble machine learning can be used not primarily for precise price prediction, but rather to identify and quantify the key behavioral and transactional drivers of residential property prices in Dubai. A unified dataset was constructed by integrating over 100,000 residential property transactions from the Dubai Land Department with macroeconomic indicators, web-scraped listing attributes, and sentiment measures derived from online reviews and social media discussions. Supervised learning models including Linear Regression, …

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 …

The Klik Chair - Designed To Move, Built To Last, Neeraj Sugumaran Menon

Theses

For the modern individual who moves housing frequently, buying furniture has become an increasingly tedious and frustrating process. Traditional furniture often presents challenges due to its bulk, weight, and complex assembly requirements, making it difficult to transport and reassemble when moving to a new living space. As a result, the true value of a piece of furniture is no longer determined solely by its price tag or aesthetic appeal but by its ability to adapt to a nomadic lifestyle. Additionally, furniture that holds long-term sentimental or functional value has become even more significant in an era where disposability is routine. …

Predicting Loan Defaults: A Behavioral Scoring Approach For Portfolio Risk Monitoring In Banking And P2p Lending, Humaid Sultan Almarri

Theses

The critical challenge of lending institutions and peer-to-peer investors is to single out high-risk active borrowers in order to avoid defaults and corresponding losses. This thesis is devoted to the development of a data-driven approach to predict loan defaults, which balances predictive accuracy with interpretability and cost-sensitive evaluation. Using a large dataset of loans from LendingClub, we applied the CRISP-DM methodology, performing extensive data preprocessing and exploratory analysis before training several machine learning models-logistic regression, decision tree, random forest, and XGBoost-to classify loans as default or non-default. Class imbalance was addressed through resampling, and models were tuned via cross-validation with …

Play Of Curiosity: A Table-Top Game That Cultivates A Creative Attitude By Encouraging Curiosity In Young Adults, Kajal Shah

Theses

In a time that increasingly demands instant creativity, adaptability, and innovative thinking on a constant basis, nurturing a curious attitude among young adults must become a critical focus. Creativity and curiosity can be encouraged throughout various stages of life, and for myriads of personality types, the focus demographic of this paper is young adults, ages 18 to 26 years old. Young adulthood is when one starts to recognize post-formal thoughts. This stage of cognitive development helps to approach concepts beyond binary thinking, developing the unique abilities of ambiguous thinking and relativism. The enigmatic, ever-changing, and easygoing mind will often try …

Strainx : A Technical Exploration Of Cinema4d'S Native Particle System In An Experimental Title Sequence, Farah Ahmad

Theses

This paper presents a practice-based investigation into the capabilities of Cinema 4D’s native particle system through the creation of an experimental title sequence titled StrainX. The project was conceived as a technical and aesthetic exploration, prioritizing abstraction, form, and motion over narrative content. Through iterative testing and simulation, the study examines how Cinema 4D’s built-in particle, geometry, and material tools can generate visually complex results without relying on third-party plugins, such as X-Particles. All assets, simulations, and textures were developed natively within Cinema 4D, allowing for a unified and efficient workflow. The methodology emphasizes procedural experimentation, simulation caching using Alembic …

Macroscale Thermal Modelling For The Molten Metal Droplet Jetting Additive Manufacturing Process, Khushbu Zope

Theses

Molten Metal Jetting (MMJ) has become a promising pathway for next-generation metal additive manufacturing because it avoids the need for powders or high-power energy sources and enables precise, drop-on-demand deposition of structural alloys. Despite this promise, the field lacks a predictive thermal modeling framework that can describe the full temperature history of a part as thousands of droplets accumulate. Without such a model, it is difficult to control heat buildup, to understand how bonding conditions evolve across layers, and to avoid defects such as incomplete fusion, porosity, and geometrical distortion. This dissertation presents the first validated macroscale thermal modeling framework …

Predictive Analysis Of Residential Property In Urban Areas, Hamad Adnan Alzarooni

Theses

This paper aims to determine the predictive modelling of residential property prices in urban Scotland with the help of an integrated framework in which machine learning methods are applied in combination with detailed socio-economic, health, housing, and geographic indicators. Conventional valuation methods tend to be based on the concept of few structural variables and ignore the effect of multidimensional variables in determining spatial variation in housing markets. To cope with this, the study uses Linear Regression, random forest, Multi-layer perceptron, and XGBoost models, which are assisted by a broad range of feature engineering, outlier management, and data preprocessing. Compared with …