Digital Commons Network^™

A Data-Driven Machine-Learning Framework For Intermittent Demand Classification And Forecasting Of Electrical Spare Parts In Dubai’S Water Pumping Stations, Ayesha Khamis

Theses

Irregularity in spare parts demand has been a recurring problem in many critical industries. The same problem is found in Dubai's water pumping stations, where demand is highly intermittent, with long periods of no usage followed by sudden increases. These irregularities are usually caused by maintenance activities or equipment failures. Forecasting such demand is challenging, as irregular patterns can lead to stockouts or overstocking. In this research, a machine learning (ML) forecasting framework is developed to handle intermittent demand for electrical spare parts in Dubai's pumping stations. The framework includes demand classification and prioritization, as well as the application of …

Imaging Performance Analysis Of Euv Mask Stacks For Sub-24 Nm Features At High And Hyper Numerical Aperture, Abdusame Omran N. Berish

Theses

As extreme ultraviolet (EUV) lithography advances toward high numerical apertures (NAs) of 0.55 and 0.75, mask-3D effects degrade image fidelity, limiting resolution and the process window. This work investigates the performance of alternative EUV absorber materials for sub-12 nm half-pitch patterning under high-NA and hyper-NA conditions. The lithographic simulations were conducted using the PROLITH Maxwell rigorous model under dipole illumination optimized for sub-24 nm resolution dense line/space features. The EUV mask stacks were derived from five fabricated and tested absorbers: TaBN, Ru/Ta, Pt2W, Ni/CrN, and TaSi2.  The absorber thickness was recalibrated to an optimal value, ensuring a maximum normalized image …

Full-Field Experimental Analysis Of 3d Printed Open-Hole Plates Reinforced With Isotropic And Concentric Carbon Fibers, Mozah Alyammahi

Theses

Additive Manufacturing technology (AM) has become an attractive process for innovative products in recent decades. The applications of AM materials widen to include composite materials. The fabrication of composite materials via AM makes the process more cost-effective than traditional fabrication methods. Nowadays, there are 3D printers that can produce composite materials with different strategies which results in different mechanical properties. This study examines the impact of carbon fiber reinforcement, both isotropic and concentric, on 3D printed open-hole plates utilizing full-field experimental analysis.  Problem Statement  The research on investigating different fiber reinforcement is increasing rapidly. However, most of the studies are …

Cancer Detection System Using Binary Neural Network On Dna Based Architecture, Antar Narayan Chowdhury

Theses

Deoxyribonucleic acid (DNA) is among the most durable chemical storage media, capable of encoding the fundamental instructions for protein synthesis (the central dogma). Each human cell contains a unique DNA sequence characterized by identifiable markers that facilitate pattern recognition. These molecular features offer significant potential for personalized drug development and disease identification. Recent advancements in DNA based research have demonstrated that fundamental arithmetic operations can be executed directly through molecular interactions, bypassing the need for silicon-based computational assistance. These biochemical applications can be further scaled to support Binary Neural Network (BNN) models, which are particularly well-suited for mitigating stochastic noise …

Effect Of Geometry, Cell Size, And Carbon Fiber Reinforcement On The Charpy Impact Strength Of Additively Manufactured Tpms Lattice Structures, Ahmed Yousuf Mohammad Bin Yaroof (Alsuwaidi)

Theses

Scientific research indicates a growing utilization of lightweight, high-performance materials across various disciplines, including engineering, driven by the demands of modern technological advancements. Recent developments within these fields include the creation of advanced lattice structures through additive manufacturing (AM) processes. One such lattice structure is the triply periodic minimal surface (TPMS) structure. TPMS structures possess unique mechanical properties and energy absorption characteristics that distinguish them from conventional AM lattice structures. However, published research to date has largely focused on the quasi-static behavior of TPMS structures, with limited attention given to the impact performance of composite- reinforced TPMS structures. The purpose …

Preparing Future-Ready Graduates For Technological And Workforce Transformations, Ghia El Dirani

Theses

The United Arab Emirates (UAE) is undergoing rapid economic transformation driven by technolog- ical innovation and national strategies such as Vision 2031 and UAE Centennial 2071, positioning STEM (Science, Technology, Engineering, and Mathematics) education as critical to building a knowledge-based economy. However, a persistent gap exists between the competencies developed in STEM higher education programs and the skills demanded by emerging sectors such as artificial intelligence, renewable energy, and advanced manufacturing. While the UAE has introduced pro- gressive education policies and invested in digital infrastructure, most curriculum reforms remain reactive and disconnected from long-term workforce projections. This research applies strategic …

Investigation Of Machining (Drilling) Of Bio-Composite Reinforced With Jute Fibers Under Different Machining Conditions, Mohammed Abdul Mujeeb Ansari

Theses

This study investigates the drilling performance of jute fibre reinforced bio-composites under different machining conditions to reduce performance parameters like surface roughness, tool type and delamination damage (entry and exit). Jute composites were fabricated using a process called hand layup. Drilling experiments were carried out using full factorial method focusing on three parameters: Feed Rate (0.05mm/rev, 0.1mm/rev and 0.15mm/rev), Drill bit Type (Titanium Nitride-TiN, High Speed Steel-HSS and High-Speed Steel Cobalt HSS-Co) and Lubrication condition (Dry, Minimum Quantity Lubrication-MQL and Cryogenic-LN2). A total of 27 experimental trials were performed, and the responses were measured for each condition. Statistical analysis was …

Predicting Property Sale Prices In Dubai Using Machine Learning Regression Models, Mohammad Buabdulla

Theses

It has been observed that the fastness of digital real estate’s application has resulted in availability of large-scale property data which forms an opportunity to more precise and open property valuation practices. A lot of conventional real estate appraisal methodologies with much emphasis on manual evaluation and historical comparative value find it difficult to respond to the market dynamics or the dynamics that exist within the market at an alarming rate. This research will help mitigate these shortcomings by generating and testing machine learning regression models to determine the price of residential property at sale in Dubai by using real-life …

The Best Practices For Prefabrication Of Industrial Buildings, Derek Gines

Theses

Prefabrication has demonstrated measurable and repeatable advantages in productivity, cost certainty, and environmental performance, yet its adoption within industrial building typologies remains largely inconsistent. Existing research largely evaluates prefabrication through downstream performance outcomes, while offering limited insight into the upstream design and organizational decisions that enable or undermine its reliability. This thesis reframes prefabrication as a design‑led methodology rather than a construction optimization, arguing that successful hybrid prefabrication is determined primarily by early decision timing, governance structures, and the control of spatial and logistical interfaces. This study adopts a qualitative design‑research approach that combines comparative case study analysis with expert …

Predicting Luxury Car Sales Using Machine Learning: A Comparative Study Of Linear Regression, K-Nearest Neighbors, And Support Vector Machines, Khalifa Jamal Mohammad Saleh Alblooshi

Theses

This paper explores the use of machine learning to predict the sales of luxury cars in the globe, and BMW as a case study of its sales data in the global market between the period 2010 and 2024. The study focuses on three regression algorithms, which include the Linear Regression, K-Nearest Neighbours (KNN) and Support Vector Machines (SVM) and sees their predictive accuracy, generalisation performance and business applicability. In Python with the help of the Google Colab, an end-to-end analytical pipeline was developed entailing data preprocessing, outlier management, feature engineering, and time-sensitive traintest division. RMSE, MAE, MAPE, and R2 were …

Toward Reliable Computational Social Science: Inconsistency-Aware Methods For Human Annotation And Ai Inference, Sujan Dutta

Theses

As artificial intelligence (AI) becomes increasingly common in computational social science, \textit{inconsistency} has emerged as a key challenge. AI models often contradict themselves when given equivalent inputs, disagree with other models on the same data, and diverge from human judgments in seemingly opaque ways. Human annotators exhibit their own inconsistencies, both within individuals and across groups shaped by differing values and identities. Rather than treating these inconsistencies simply as noise, this dissertation argues that they contain meaningful signals that can be leveraged to improve learning efficiency, strengthen evaluation, and increase the reliability of large-scale social measurement. To study this phenomenon, …

Toward A Unified Framework For Open World Visual Learning, Yuansheng Zhu

Theses

Artificial intelligence systems have achieved remarkable performance across a wide range of visual tasks. However, most existing models operate under the unrealistic closed-world assumption, where training and test data are drawn from the same distribution. In real-world applications such as anomaly detection, autonomous driving, and medical diagnosis, learning systems frequently encounter novel or out-of-distribution scenarios. These settings require models that can recognize unknown inputs, adapt to new information over time, and maintain reliable performance under evolving conditions. This dissertation studies the problem of Open World Visual Learning, a paradigm that enables visual learning systems to operate robustly in dynamic and …

Optimizing Virtual Scrolling Performance In Angular: A Comparative Study Of Cdk And Custom Implementation, Guri Sokoli

Theses

Modern web applications often display large datasets with tens of thousands of items, such as e-commerce catalogs, data tables, and social media feeds. Rendering all items in the Document Object Model (DOM) at once causes browser freezing, high memory use, and slow interfaces. Virtual scrolling solves this problem. It is widely adopted but rarely studied through direct performance comparison. Few empirical studies measure how different implementations behave under varying dataset sizes, devices, or browsers. This research conducts a comparative analysis of Angular CDK Virtual Scroll as an industry-standard baseline and develops an optimized implementation incorporating framework-specific enhancements: OnPush change detection …

An Artist’S Playground: Building Bridges Between Children’S Education And Their Knowledge Of Art Through Multi-Modal Learning, Alice J. Cauchi

Theses

Not all museums welcome children as part of their main audience with open arms. Research suggests that experiencing museums as a child supports classroom learning and development, but only when the museums engage young children properly. This paper explores how incorporating multi-modal interactives into art museums can effectively engage children, ages 4 to 8, and build a foundation that supports lifelong learning. Drawing on ideas from early-childhood education, museum visitor-engagement studies, and interactivity sciences, this project results in a framework that guides art museums on how they can overlap play, interactives, and content to incorporate this age group effectively to …

Nlp Crowdsourcing For Predominantly Oral Languages: The Case Of Bambara, Allahsera Auguste Tapo

Theses

Predominantly oral languages (POLs) face a significant "digital divide," as they are often excluded from the benefits of modern natural language processing (NLP) technologies, due to a lack of extensive, readily available machine learning (ML) datasets. We investigate methods to overcome this data scarcity for Bambara, a Manding language, spoken primarily in Mali, with a rich oral tradition but limited digital presence.     The research leverages crowdsourcing and community engagement to build high-quality ML ready dataset resources. Key contributions include methods for automatic speech recognition (ASR) and machine translation (MT) dataset collection and curation and for educational resource creation.      Our findings …

Beyond Anomaly Detection: Classifying Attacker Automation Level From Ssh Honeypot Behavioral Signatures, Ashley Alt

Theses

The proliferation of AI-assisted offensive tools has introduced a new category of cyber attacker that combines the speed of automation with the adaptive reasoning previously associated only with skilled human operators. Despite the richness of behavioral data captured by SSH honeypots, existing analyses treat interaction logs primarily as evidence of malicious activity rather than as a dataset capable of distinguishing between attacker types. This thesis investigates whether human-driven, traditionally automated, and AI-assisted attackers produce distinguishable behavioral signatures within SSH honeypot interactions, and whether machine learning techniques can reliably classify attacker behavior from session-level features. A controlled experimental architecture was developed …

No Intelligence Without Statistics: The Invisible Backbone Of Artificial Intelligence, Ernest Fokoue

Articles

The rapid ascent of artificial intelligence (AI) is often portrayed as a revolution born from computer science and engineering. This narrative, however, obscures a fundamental truth: the theoretical and methodological core of AI is, and has always been, statistical. This paper systematically argues that the field of statistics provides the indispensable foundation for machine learning and modern AI. We deconstruct AI into nine foundational pillars—Inference, Density Estimation, Sequential Learning, Generalization, Representation Learning, Interpretability, Causality, Optimization, and Unification—demonstrating that each is built upon century-old statistical principles. From the inferential frameworks of hypothesis testing and estimation that underpin model evaluation, to the …

On The Scientific Stature Of Data Science: The Epistemological Unicorn, Ernest Fokoue

Articles

Data Science has ignited unprecedented academic, industrial, and pedagogical fervor, yet its status as a \textit{science} in the classical sense---comparable to physics or biology---remains profoundly unsettled. This article interrogates the epistemological foundations of Data Science by examining its hybrid theoretical lineage, from the Universal Approximation Theorem to the No-Free-Lunch Theorems, with special emphasis on the fundamental Bayesian optimality results for both regression and classification. We argue that Data Science is in a vigorous \textit{gestational period}, characterized not by an absence of principles but by a creative tension between empirical pragmatism and deep mathematical theory. The Cross-Validation score emerges as the …

Using Deep Learning To Increase Eye-Tracking Robustness, Accuracy, And Precision In Virtual Reality, Kevin Barkevich

Theses

Algorithms for the estimation of gaze direction from mobile and videobased eye trackers typically involve tracking a feature of the eye that moves through the eye camera image in a way that covaries with the shifting gaze direction, such as the center or boundaries of the pupil. Tracking these features using traditional computer vision techniques can be difficult due to partial occlusion and environmental reflections. Although recent efforts to use machine learning (ML) for pupil tracking have demonstrated superior results when evaluated using standard measures of segmentation performance, little is known of how these networks may affect the quality of …

Horizontal Gene Transfer In Neisseria, Wen Ting Dong

Theses

Antibiotics are a special category of drugs that help treat bacterial infections by killing bacteria or hindering their growth and replication. Antibiotics are greatly significant in modern medical practices, providing an effective treatment for bacterial infections as well as enabling organ transplants, open surgeries, and chemotherapy to be possible. Unfortunately, cases of antibiotic resistance were observed shortly after the introduction of antibiotics. The appearance and spread of these resistant strains poses a significant threat to public health safety. The Centers for Disease Control (CDC) estimates there are 2,868,700 antibiotic-resistant bacterial and fungal infection cases per year in the United States, …

Enhancing Engineering Education For Students With Intellectual Disabilities Through Universal Design For Learning, Bree Jimenez, Ginevra Courtade

Journal of Science Education for Students with Disabilities

This study explores the impact of a Universal Design for Learning (UDL) aligned engineering curriculum on teacher confidence and student engagement in special education classrooms, specifically focusing on students with intellectual and developmental disabilities (IDD). The research investigates how the curriculum, combined with targeted professional development (PD), supports teachers in delivering high-quality engineering instruction. Through a mixed methods approach, including surveys, interviews, focus groups, and classroom observations, data revealed significant improvements in teacher self-efficacy, particularly in areas such as scaffolding, differentiation, and accessibility. Teachers reported increased confidence in their ability to implement engineering lessons and foster engineering mindsets in their …

What Do Future Nutrition Professionals Think About Wic Jobs? A Study Of Nutrition Career Perceptions, Madison Degenfelder

Theses

Background: The Special Supplemental Nutrition Program for Women, Infants and Children (WIC) program faces a critical workforce shortage, yet little is known about the knowledge, attitudes, and beliefs of nutrition students and recent graduates regarding WIC career pathways, requirements, and compensation. Understanding these factors is essential to design strategies to strengthen recruitment and build a sustainable WIC workforce. Objective: To identify and describe knowledge, attitudes and beliefs on entering the WIC workforce among college nutrition and dietetics students and recent graduates. Design: A mixed-methods, sequential study design which included a cross-sectional survey and two focus groups was employed to ascertain …

Selecting Without Replacement From A Population Of Bands Of Serially Connected Objects, James E. Marengo, Dominick Banasik, Joseph Voelkel, David L. Farnsworth

Articles

The sampling procedure from a finite population of objects that are serially attached into bands is described and analyzed. One object is randomly selected and removed at a time, which results in that object’s band being broken into two bands or shortened by one object. The main result gives the probability of choosing an object that is part of a band of serially connected objects of any specified size at each stage of the selection process.

Ultrarenderer: Real-Time Heterogeneous Cpu-Gpu Memory Management And Adaptive View-Dependent Rendering For Massive 3d Environments, Huadong Zhang

Theses

Rendering large-scale 3D datasets in real-time presents significant challenges due to the widening gap between the ever-growing scale of dataset complexity and the limited memory capacity of modern GPUs. As datasets increasingly exceed available GPU memory, traditional methods encounter severe bottlenecks, such as restricted CPU-GPU bandwidth and inefficient memory usage, which significantly hinder rendering performance. This is especially problematic in interactive applications where high frame rates and low latency are critical. To address these limitations, this dissertation presents UltraRenderer, a novel out-of-core rendering method designed to maximize visual fidelity under strict memory and bandwidth constraints. The core of our approach …

Enhancing Science Accessibility And Engagement For Students With Visual Impairments: A Randomized Trial Of The Talking Labquest In Nigerian Schools, Sariat A. Adelakun Dr

Journal of Science Education for Students with Disabilities

Students with Blindness in Nigeria are often erroneously perceived as incapable of engaging with science at the basic education level, contributing to their low enrolment in science-related careers. Although various techniques and materials have been proposed to support students with blindness in science, many lack empirical validation. This study investigated one such approach by examining the impact of the Talking LabQuest on the interest of Students with blindness in science subjects.

A randomized controlled trial was conducted in inclusive junior secondary schools in Nigeria, involving 20 classrooms over an 18-week period. Twelve classrooms were randomly assigned to the intervention group, …

Engineering Human Microphysiological Models To Investigate Bacterial Extracellular Vesicle–Driven Endothelial And Blood–Brain Barrier Dysfunction, Louis P. Widom

Theses

Pathogenic bacterial extracellular vesicles (BEVs) are nanoscale particles derived from bacteria that contain pro-inflammatory cargo. During bacterial infections, BEVs provoke the host inflammatory response and may cause widespread damage. Furthermore, antibiotic treatment can boost BEV production and thereby increase the number of toxic signals traveling through the circulatory system. This is especially dangerous in brain blood vessels since evidence suggests that BEVs may destabilize the protective blood–brain barrier (BBB), resulting in neuroinflammation associated with cognitive decline and development of neurological disorders. Our understanding of BEV interactions with the host remains limited, necessitating the development of in vitro models to better …

The Role Of Self-Awareness, Advocacy, And Support In Stem Success: Stories From Landmark College's Aie-Stemplos Program, Anastasio Bonhomme, Jaden Clark, Madison Wan, Gabrielle Woolgar, Rebecca Matte, Christin B. Monroe

Journal of Science Education for Students with Disabilities

Neurodivergent individuals bring unique perspectives and talents to STEM fields while facing distinct challenges, particularly in higher education environments that often rely on traditional learning models. Landmark College’s AIE-STEMPLOS program provides an innovative framework to empower neurodivergent STEM scholars by fostering self-awareness, advocacy, and community support. Grounded in Universal Design for Learning (UDL) principles and strengths-based approaches, the program leverages tools like the Birkman Method® to help students articulate their strengths and communication styles in professional terms. This paper examines how accessible learning strategies, peer mentorship, and individualized advising cultivate belonging, confidence, and resilience among neurodivergent STEM scholars, offering a …

Optimizing Traffic Signal Timings Using Real Time Data Analytics, Hamad Abdulla Binsultan

Theses

City traffic jams have become a major challenge for contemporary cities, causing delays, increased fuel consumption, and other environmental impacts. Conventional traffic signal controlling systems usually depend on fixed or preset signal plans that are incompetent to adjust themselves with changing traffic conditions. Traffic signal timings are optimized across the intersection health of the system by processing real-time data analytics. Through real-time data analytics, traffic flow efficiency is improved while waiting time at signalized intersections is reduced. Using traffic real-time data like vehicle counts, vehicle types, time of day and day of the week to see howtraffic behaves under different …

Model Free Sliding Mode Control For Uavs, Aashrita Mandalapu

Theses

Sliding Mode Control (SMC) is a robust and adaptable control strategy recognized for its ability to withstand uncertainties, disturbances, and parameter variations. Although the straightforward implementation of SMC along with its insensitivity to modeling inaccuracies make it effective for a wide range of applications, it still relies heavily on an accurate system model. In dynamic environments where system characteristics vary over time, a model-independent SMC framework becomes necessary. So, a Model-Free Sliding Mode Control (MFSMC) approach is proposed as a compelling solution for complex nonlinear systems where deriving an accurate model is difficult. In contrast to conventional SMC approach, MFSMC …

A Pilot Study Evaluating Child Resistant Closures In Pharmaceuticals In The Developing Nation Of Ghana: Informing Global Paediatric Safety And Usability Standards, Samuel Ofosu, Kojo Agyapong Afrifah, Laura Bix

Journal of Applied Packaging Research

Despite their proven effectiveness in preventing unintentional paediatric medication poisoning in high-income countries, the performance and usability of Child Resistant Closures (CRCs) remain underexplored in low and middle-income countries (LMICs).

To provide pilot data regarding the potential efficacy of using CRCs in LMICs, we conducted a study of the approach in a low-resourced region (Ghana, West Africa) by adapting testing protocols utilized in the United States (16 CFR Part 1700.20). The study utilized a convenience sample of children (n=50; 42-51 months) and adults (n=50; 50-70 years) to develop preliminary data related to the efficacy of a single design of CRC …