Digital Commons Network^™

Visualizing The Dynamics Of Neuroevolution With Genetic Distance Projections, Evan Patterson

Theses

Evolutionary algorithms have shown substantial progress in recent years, especially in neural architecture search and neuroevolution applications. Despite their effectiveness, analyzing and understanding the evolutionary paths these algorithms traverse to reach solutions remains challenging. These algorithms often involve distributed computing strategies, which can include subpopulations or islands, and they explore massive or even unbounded search spaces in both continuous and non-continuous domains. Manually examining individual solutions to understand the evolutionary dynamics is often infeasible due to large population sizes, large genome sizes, and high generation counts. This work introduces a new methodology for visualizing neuroevolution population dynamics called genetic distance …

A Systematic Literature Review Of Behavior Analysis In Psychiatric Inpatient Settings, Stephanie Henry, Robbie Hanson

Theses

Applied behavior analysis (ABA) may be best known for the treatment of individuals with intellectual or developmental disabilities, such as those with autism spectrum disorder (ASD). However, ABA also has a long history of effective outcomes with other populations, such as with individuals who are admitted to inpatient psychiatric facilities (Dougher, 2004; Harvey et al., 2009). In 2018, 187,877 individuals in the United States were reported as receiving treatment for mental health disorders (Lutterman, 2022). Given behavior analysis’ success implementing operant procedures with these populations, an updated review of the literature on the use of behavior analysis within these settings …

Evaluating The Effects Of Co₂ And Uv-B On Sporobolus Virginicus (L.) Kunth Growth Under Open Top Chamber: A Comparison Of Silicon Nanoparticle-Treated And Untreated Plants, Suhaila Humaid Ablooshi

Theses

The study focused at how silicon nanoparticles (SiNPs; 50 mg/L) affected the growth, photosynthetic efficiency, and biochemical responses of the salt-tolerant halophyte Sporobolus virginicus in four different environments. They are control, high CO₂, UV-B radiation, and a mix of high CO₂ and UV-B. Plants that were treated with SiNPs showed big changes in their shape, such as more biomass, bigger leaf area, and better root systems in all conditions. The use of combined stress treatments caused a big increase in the levels of chlorophyll a, chlorophyll b, total chlorophyll, and carotenoids. As a result, that the plants were better at …

Identifying Barriers, Challenges And Opportunities Of Implementing Retrofitting Strategies In Existing Buildings In The Uae, Ahmad Ghalib Masri

Theses

The built environment of the UAE faces sustainability challenges because of its high energy usage and greenhouse gas emissions which require successful retrofitting methods to enhance aging buildings' performance and efficiency. This research investigates the barriers and challenges and opportunities of retrofitting strategy implementation in the UAE to provide direction for policymakers and stakeholders regarding sustainable practices. The research employed a mixed-methods approach through literature reviews and stakeholder surveys and interviews. The study shows that high upfront costs (68% of respondents) and building owner awareness limitations (54%) and fragmented regulations with inconsistent thermal insulation standards across emirates represent major barriers. …

A Study Of The Impact Of Balancing, Geometric Transformation, Generative Networks, And Roi Techniques In Eye Diseases Classification, Sghaira Hareb Alnuaimi

Theses

Automatic detection of ocular diseases helps medical professionals efficiently identify eye disorders, reduce diagnostic errors, and accelerate diagnoses to prevent blindness. Deep learning has been successfully utilized in various fields, including medical image classification. However, in spite of these advancements, challenges remain in ocular disease classification.

The objective of this work is to address these challenges using data processing, data augmentation in combination with Region of Interest (ROI) techniques. Medical datasets often suffer from scarcity, imbalance, and low-quality images, leading to inaccurate classification. To mitigate these issues, we utilize the ODIR dataset, which contains 7,000 labelled training images for both …

تحول سياسات العقاب والإصلاح للأحداث في دولة الإمارات: دراسة مقارنة لقانون اتحادي رقم 9 لسنة 1976 بشأن الاحداث الجانحين والمشردين وقانون اتحادي رقم 6 لسنة 2022 بشأن الاحداث الجانحين والمعرضين للجنوح, Hamda Abdulla Almahri

Theses

The Transformation of Juvenile Punishment and Reform Policies in the United Arab Emirates: A Comparative Study of a Federal Law No. 9 of 1976 Regarding Juvenile Delinquents and Vagrants, and Federal Law No. 6 of 2022 Regarding Juvenile Delinquents and those at Risk of Delinquency

This study explores the concept of juvenile criminal responsibility under Federal Law No. (6) of 2022 in the United Arab Emirates, which restructured the legal framework for dealing with juvenile offenders and those at risk of delinquency in line with social developments and international standards. The research focused on analyzing the legal foundations of juvenile …

Encapsulation Of Lactobacillus Reuteri Dsm 17938 In W/O/W Emulsion: Influence Of Oil Type On Viability And Oxidative Stability, Nora Mohamed Alantali

Theses

Probiotics, known for their wide range of health benefits, have been gaining recognition on their effects on the gut-brain axis. This axis, operated via various pathways, can be modulated by the type and functions of probiotics found in the gut microflora. However, probiotics face several stress factors before reaching the gut which hinders them from being functional. The main objective of this thesis was to construct a stable double-emulsion-based delivery system for probiotics, which can act as a barrier against the harsh conditions of the digestive system. Lactobacillus reuteri DSM 17938 was loaded in a water-in-oil-in-water Pickering double emulsion system, …

Groundwater Storage Dynamics In Abu Dhabi Emirate Using In-Situ And Grace Data, Tala Jalal Maksoud

Theses

Water scarcity presents a significant challenge to the sustainable development; especially in arid regions like Abu Dhabi Emirate. Addressing concerns over nonrenewable aquifer depletion, caused by agricultural expansion and lifestyle advancements, has, therefore, become crucial in such regions. This thesis investigates Groundwater Storage (GWS) dynamics in Abu Dhabi Emirate by integrating satellite data from the Gravity Recovery and Climate Experiment (GRACE) with in-situ data from 257 wells over two decades (2002-2022), employing GIS and RS technologies. The main aim of this study is to provide a comprehensive approach to evaluate GWS dynamics considering multiple perspectives including: dealing with missing datasets, …

Wide Lock-In Energy Harvesting From Vortexinduced Vibrations Of A Deformable Cylinder, Ahmed Raafat Mostafa

Theses

Energy harvesting from ambient sources has gained attention due to increasing energy demands. Despite VIV-based harvesters showing significant potential, their lock-in region, where significant power is generated, is narrow. Given the continuously varying ambient conditions of fluid currents, harvesters can easily fall into de-synchronization, yielding low energy output. Existing solutions like tunable masses or multiple degrees of freedom systems increase complexity and weight, limiting practical applications. This work introduces a novel variable diameter cylinder mechanism—a practical technique that actively tunes the cylinder’s geometry in real time to enhance energy harvesting efficiency from VIV. The mechanism employs an expanding pulley system …

Evaluating Faults Detection And Their Impact On Photovoltaic (Pv) Modules, Mohammad Ahmed Almasoum

Theses

Photovoltaic (PV) modules are critical to the transition toward renewable energy, offering a sustainable solution for global power generation. However, faults in PV modules significantly reduce energy output, increase maintenance costs, and compromise system reliability. Traditional fault detection methods, while useful, often lack the precision and efficiency required for real-time applications, leading to prolonged downtime and revenue losses. Machine learning (ML) offers a promising approach for detecting and classifying faults in PV modules with greater accuracy and speed. This study evaluates fault detection techniques in PV modules and their impact on operational efficiency, focusing on machine learning-based classification models. A …

Through Legitimate Dissent, The Supreme Court Repairs Itself, Afnaan A. Qureshi

Theses

The decisions of the Supreme Court of the United States, along with its reasoning, influence the direction and scope of historical events. Through an analysis of landmark dissents from four of the worst Supreme Court decisions, Curtis’ dissent in Dred Scott v. Sandford (1857), Harlan’s dissent in Plessy v. Ferguson (1896), Holmes’ dissent in Abrams v. United States (1919), and Murphy’s dissent in Korematsu v. United States (1944), we can take steps towards catalyzing its self repair by stepping away from digital echo chambers and engaging in legitimate dissent. The four cases marked turning points in the efforts to protect …

Glue Laminated Timber: A New Way Of Framing The Residential Architecture Of Upstate New York To Improve Thermal Performance & Energy Use Intensity, Griffin Jones

Theses

This thesis addresses the issue present within the current construction techniques for the residential architecture of Upstate New York. Modern platform framing has long been the dominant fashion for constructing homes in the United States, however the nature of this construction is instilled with the issue of thermal bridging, where the wood studs and structure allow heat to escape due to gaps in the continuous insulation. This study proposed a new way of framing residential architecture for the upstate region of New York, relying on glue-laminated timber (GLT) to replace dimensional sawn lumber. GLT has increased structural strength and stability …

Resonating Patterns: Adaptive Resonance Theory And Self-Organizing Maps, Meeti Dixit

Theses

Adaptive Resonance Theory (ART) represents a powerful neural network architecture designed to address the stability-plasticity dilemma. Its primary objective is to enable rapid learning without compromising retention. ART embodies characteristics of self-organization and self-stabilization, distinguishing it from traditional neural networks. Unlike error-based learning prevalent in conventional models, ART employs competitive learning mechanisms. One of the distinguishing features of ART is its involvement in hypothesis testing, a departure from the approach of deep learning. This capability allows ART to learn dynamically in real-time environments. Over time, various ART models have emerged that cater to different types of data, such as binary …

Enhancing Influence Estimation In Gradient Boosted Decision Trees Through Hierarchical Analysis, Anurag Choubey

Theses

This report presents a novel influence function for gradient-boosted decision trees (GBDTs), a widely-used class of predictive models. Influence estimation aims to quantify how individual training samples affect a model’s predictions, offering valuable insights for model debugging, data quality analysis, and in- terpretability. Existing influence functions for GBDTs—such as LeafInfluence, LeafInfluenceSP, and BoostIn—have shown varying degrees of success, with BoostIn currently recognized as the state-of-the-art in terms of estimation quality and computational efficiency. In this work, we propose BoostInLCA, a new influence function that extends BoostIn by incorporating information from non-leaf nodes via the Lowest Common Ancestor (LCA) path, thereby …

Beyond The Badge: Predicting Military Employee Attrition In The Police Force, Alia Abdulla Alhajji

Theses

This study explores the issue of employee attrition within the military police force, with a focus on predicting which employees are at risk of leaving. Attrition in law enforcement is costly and disruptive, especially in roles that require long training periods and operational readiness. The research specifically targets the internal organizational factors influencing turnover in military police environments and aims to leverage machine learning models to support the early identification of high-risk individuals. Guided by the CRISP-DM framework, the study followed a structured process across six stages: business understanding, data exploration, preparation, modeling, evaluation, and insight generation. A simulated dataset …

Under The Mask: Labeling And Self-Perception In Augmented Reality, Ruiyuan Guo

Theses

Under the Mask is a speculative AR experience that critically explores how social labels shape both perception and self-perception in everyday life. The project reimagines augmented reality not as a technological novelty, but as a conceptual mirror—a way to visualize how identity is continuously negotiated in social space. Users interact with two distinct labeling functions: when they assign a tag to themselves, it becomes visible to everyone, symbolizing the ways in which self-identification enters public discourse. In contrast, when users label others, those tags remain private—visible only to the individual user—highlighting how our assumptions primarily influence our own vision, not …

Classification Of Linear Systems Of Equations For Quantum Computing Implementation, Mark Danza

Theses

Drawing nearer to an error-corrected era of quantum computing, it is necessary to understand the suitability of certain post-NISQ algorithms for practical problems. One of the most promising, applicable, and yet difficult to implement in practical terms is the Harrow, Hassidim and Lloyd (HHL) algorithm for linear systems of equations. An enormous number of problems can be expressed as linear systems of equations, from machine learning to fluid dynamics to electrical circuit analysis. However, in most cases, HHL will not be able to provide a practical, reasonable solution to these problems. This work seeks to determine whether problems can be …

Instrapix – An Image-Based Digital Audio Workstation Design, Dan Qiao

Theses

InstraPix, a UI project designed by Dan Qiao, is groundbreaking image-and-audio conversion software and a digital audio workstation (DAW). enables bidirectional conversion between images and sound, opening new creative possibilities. With powerful image processing tools, it allows users to directly use pictures as audio sources, shaping unique sonic compositions. InstraPix also supports sound presets, VST plugins, and note editing, allowing seamless integration with other instruments. This all-in-one audio workstation could mark the beginning of a new era where music can be created entirely from images. Assigning images significant importance in audio production and minimizing the middle steps, it breaks the …

Intellimad: A Framework For Secure Machine Learning Models Evaluation And Fine-Tuning In Federated Setting, Dmitrii Korobeinikov

Theses

In contemporary artificial intelligence (AI) appli- cations, Machine Learning (ML) models are core components enabling AI-driven functionalities, yet selecting and fine-tuning a model and its hyperparameters remains challenging. ML model architecture, as well as key model training parameters, such as the number of training epochs, batch size, and learning rate, are highly dependent on both the dataset modalities and the specific task resolved in a particular application. More sophisticated execution setups may require determination of additional environment-related parameters, such as identifica- tion of computational capabilities required for execution of a particular AI-driven task, or discovery and establishment of desired security-related …

Back To The Basics: Extracellular Protein Interaction Networks Of The Human Infant Immune System, Mavis Amity Irwin

Theses

Datamining without an age filter proves challenging, especially when searching for direct data from human infants. Although online databases provide immune interaction networks, they often lack information about the age of data sources, resulting in categorizations as age-unspecified. This limitation underrepresents the physiology of naïve immune systems in infants, particularly since full-term infant immunity transitions to an adult-like phenotype between 24 and 30 months of age. This study aims to reconstruct an age-specific immune interaction network for full-term infants by integrating literature-based evidence with existing online database content. A list of 60 extracellular protein candidates involved in immune responses was …

Developing Meld-Accelerated Molecular Dynamics Protocols To Simulate The Binding Of The P53-Derived Ligand To The Mdm-2, X Protein, Maria Ciko

Theses

In this study, we focus on developing computational methods to predict protein-ligand binding affinities, with applications in peptide drug discovery. Molecular Dynamics (MD) simulations can capture the complex conformational behavior of proteins, but their high computational cost limits their efficiency. MELD, or Modeling Employing Limited Data, is a Bayesian approach that integrates external information to accelerate sampling of low- energy, high-probability conformations. Building on previous work by Morrone et al., which successfully applied MELD to P53-MDM2 complexes, we hypothesize that we can effectively compute the relative binding affinities while reducing steric clashes and mitigating the effect of slowed diffusion on …

Innovating Criminal Justice: Predictive Analytics For Effective Recidivism Management, Dina Ali Haidar

Theses

Recidivism—the tendency of previously convicted individuals to reoffend—poses significant challenges to criminal justice systems worldwide. In the United States, a study by the Bureau of Justice Statistics revealed that approximately 68% of released prisoners were rearrested within three years, and 83% within nine years (Bureau of Justice Statistics, 2018). These high rates underscore systemic deficiencies in rehabilitation processes and resource allocation. Traditional risk assessment tools often rely on static factors, failing to account for the dynamic and individualized nature of reoffending risks. This limitation highlights the need for innovative, data-driven methodologies to enhance offender management strategies. This study proposes a …

Predicting Student Dropout Risk Using Machine Learning, Fatma Alameri

Theses

Student dropout remains a persistent challenge in higher education, undermining institutional performance, reducing workforce preparedness, and limiting students’ academic and economic opportunities. Accurately identifying students at risk of attrition is complex, due to the interplay of academic, financial, and behavioral factors. This thesis addresses this challenge by applying a combined machine learning framework—integrating both unsupervised and supervised techniques—to predict student dropout using structured, first-year academic and financial data. The study utilizes a comprehensive dataset of 4,424 undergraduate student records from a European higher education institution, covering ten academic years and comprising 35 variables related to academic performance, enrollment behavior, and …

Fake News Detection: Leveraging Natural Language Processing And Machine Learning For Reliable Information Verification, Roshni Rajendra Salve

Theses

This dissertation details the creation and assessment of a machine learning algorithm designed to identify fake news utilizing Natural Language Processing (NLP) methods. The research employs several machines learning models, including Long Short-Term Memory (LSTM) and other deep learning techniques, to detect and classify misleading information. Data is sourced from a variety of platforms, such as social media and online news outlets, to compile a thorough dataset. The data is pre-processed to eliminate noise, address missing values, and extract essential features through techniques like tokenization, stop-word removal, and lemmatization. The performance of the models is evaluated using key metrics such …

A Review Of Smart Public Transport Systems: Challenges, Technological Innovations, And Future Directions, Ahmed Masam Alfalasi

Theses

Urbanization has created greater demand for effective and sustainable public transport systems in smart cities. Yet, overcrowding, congestion, and old infrastructure remain the main challenges to the effectiveness of these systems. This literature review discusses the major issues affecting smart public transport, such as reliability issues, environmental sustainability, and improved integration and coordination between modes of transport. Technologies like the Internet of Things (IoT), artificial intelligence (AI), and big data analytics are being applied to enhance public transportation efficiency, alleviate congestion, and improve passenger experience. Qualitative research methods were used, relying on secondary data from academic journals, government reports, and …

Stochastic Variational Autoencoder, Shounak Desai

Theses

This work explores a novel generative modeling approach inspired by variational autoencoders (VAEs). Traditional VAEs rely on a recognition model (encoder) that approximates the latent posterior with a single gaussian distribution for each input, limiting their flexibility in capturing complex data distributions. In contrast, we propose a modified recognition model that utilizes stochastic mixtures of gaussians, allowing for a more expressive latent representation. By leveraging stochastic neural networks within the VAE framework, we aim to achieve a tighter evidence lower bound (ELBO) on the log-likelihood of the data. Our approach is a preliminary investigation to enhance the latent space structure …

Future Unleashed: Reimagining Marketing Strategies With Future Foresight, Afra Sultan Al Suwaidi

Theses

In today’s fast-paced and dynamic business landscape, marketing plays a crucial role in influencing business strategy and decision-making in an organisation. Marketing not only helps organisations identify and anticipate trends, threats, and opportunities, but also enables them to formulate and implement effective strategies to capitalise on these insights and achieve the organisation’s vision and goals. Future Foresight in marketing empowers organisations to stay ahead of the curve and gain a deeper understanding of the target audience and industry landscape. The evolving nature of marketing driven by advancements in technology, particularly artificial intelligence, machine learning, and digital marketing channels, makes it …

Investigating The Performance Variation For Graphene-Based Field-Effect Transistor, Tzu-Jung William Huang

Theses

As silicon CMOS technology approaches its scaling limits, two-dimensional (2D) materials such as graphene offer promising alternatives due to its atomically thin material property and high carrier mobility. Graphene field-effect transistors (GFETs) are especially suited for high-frequency and RF applications; however, large-scale integration is hindered by substantial device-to-device variability. Primary contributors include inconsistent graphene transfer, contact resistance, poor dielectric interfaces, and underlying substrate topography. This work presents a comparative study of three GFET architectures to address variability: (a) raised Al gate with 15 nm Al2O3, (b) raised Al gate with monolayer hexagonal boron nitride (hBN), and (c) recessed Al gate …

Evaluating Leatherback Turtle Conservation Using The Iucn Red List, Hannah Mcguiness-Piasecki

Theses

The International Union for Conservation of Nature (IUCN) Red List is a key resource for assessing species extinction risks and serves as a framework for global conservation planning. This study focused on the Leatherback Turtle (Dermochelys coriacea), a species currently classified as Vulnerable on the IUCN Red List, evaluating the conservation policies and measures implemented by selected English-speaking countries. The study employed a policy analysis approach, reviewing national and international treaties relevant to leatherback conservation. The findings revealed that while many countries align their policies with global treaties, some gaps remain, particularly in addressing threats to adult leatherbacks …

Mary Kay Reimagined, Allison Terveer

Theses

This project is taking the brand Mary Kay and turning it into a new brand that would be enticing to younger generations and become a staple in everyone and anyone’s routines. Mary Kay was a huge makeup brand in the 1990s and early 2000s, but the cosmetic industry has grown tremendously, and Mary Kay has fallen behind. They mostly target the older generations, the generations that were supporting them in the 1990s and 2000s. They need to expand their target range and get younger consumers to pay attention to them. With updated packaging, new marketing and a new way to …