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Strainx : A Technical Exploration Of Cinema4d'S Native Particle System In An Experimental Title Sequence, Farah Ahmad
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 …
Optimizing Waste Management And Recycling Patterns Using Data Analytics, Mohammad Omar Almarri
Optimizing Waste Management And Recycling Patterns Using Data Analytics, Mohammad Omar Almarri
Theses
The increasing pace of urbanisation and consumption has increased the burden of waste generation in the world and it is a huge burden on the current waste management systems. The United Arab Emirates (UAE) is a region that is intensifying this challenge through the accelerated urbanization, high sustainability targets, and the necessity of effective recycling policies. The thesis that is being examined explores the ways in which the data analytics can be utilized to streamline waste management and recycling trends, emphasizing the enhancement of the collection process, forecasting the trends in the waste generation as well as facilitating the use …
Evaluating Streambank And In-Stream Erosion As Acontributors To Water Quality In Two Illinois Agricultural Watersheds, Chibueze Chigozie Iguegbe
Evaluating Streambank And In-Stream Erosion As Acontributors To Water Quality In Two Illinois Agricultural Watersheds, Chibueze Chigozie Iguegbe
Theses
This study investigates its contribution to sediment and phosphorus (P) export in two agriculturally dominated watersheds in south central Illinois—Lost Creek and East Fork Shoal Creek (EFS). Using cross-section surveys, and erosion pin measurements over a two-year monitoring period, we quantified bank erosion rates, sediment yields, and associated phosphorus loads. Hydrologic data from the Illinois State Water Survey (ISWS), alongside local precipitation and soil data, were utilized in the interpretation of erosion dynamics in the study watersheds. Key findings from this study show spatial and temporal variability in erosion rates, which is driven by seasonal flow events, riparian vegetation type, …
Survivorship In Virginia Opossums And The Management Implications For Burmese Pythons In Key Largo, Florida, Arya J. Sanjar
Survivorship In Virginia Opossums And The Management Implications For Burmese Pythons In Key Largo, Florida, Arya J. Sanjar
Theses
This study employed a telemetry-based framework, using radio-collared Virginia opossums (Didelphis virginiana), to quantify survival patterns and estimate detection probabilities of python predation events. Between April 2022 and January 2025, 135 adult opossums were monitored using VHF collars across protected habitats within Key Largo. Annual survival was estimated using a staggered-entry Kaplan-Meier model, producing an annual survival rate of 0.17 (SE = 0.089) for females, and male survival was estimated at 0.04 (SE = 0.032) at 10 months since no male lived 10 months after collaring. Cause-specific monthly survival analyses indicated python predation peaked during the late summer months of …
Effect Of Water-Deficit Stress On Cannabis Sativa Production And Secondary Metabolite Levels, Shiksha Sharma
Effect Of Water-Deficit Stress On Cannabis Sativa Production And Secondary Metabolite Levels, Shiksha Sharma
Theses
Cannabis (Cannabis sativa L.) is an emerging high-value crop that belongs to the Cannabaceae family and the genus Cannabis, which is known to produce more than 200 cannabinoids. Although genetic variation is the main factor in cannabinoid production, water-deficit stress is believed to induce its production. The objective of this master’s thesis was to determine the effects of water-deficit stress frequencies and timing on growth, physiology, yield, and cannabinoid concentration in cannabis. Heidi cultivars were planted in a controlled environmental growth unit. One period of water-deficit stress was found to produce the lowest concentration of total CBD and THC. In …
Flood Susceptibility Assessment Through Gis Integrated Analytical Hierarchy Process And Machine Learning Models, Sujan Shrestha
Flood Susceptibility Assessment Through Gis Integrated Analytical Hierarchy Process And Machine Learning Models, Sujan Shrestha
Theses
Flooding is among the most destructive natural disasters globally, and it inflicts severe damage on both natural environments and human-made structures. The frequency of floods has been increasing due to unplanned urbanization, climate change, and changes in land use. Flood susceptibility maps help identify at-risk areas, supporting informed decisions in disaster preparedness, risk management, and mitigation. This study aims to generate a flood susceptibility map for two regions: Davidson County of Tennessee using an integrated geographic information system (GIS) and analytical hierarchical process (AHP), and the Briar Creek watershed of Mecklenburg County, North Carolina using an integrated GIS and machine …
Beyond The Mozart Effect: How Musical Experience And Emotional Response Shape Individual Differences In Spatial Reasoning, Connor Watkinson
Beyond The Mozart Effect: How Musical Experience And Emotional Response Shape Individual Differences In Spatial Reasoning, Connor Watkinson
Theses
Research on the Mozart effect (the short-term enhancement of spatial-temporal reasoning after music listening) has yielded inconsistent results, often due to overlooking individual differences and using limited stimuli. This study examined multiple musical conditions (popular lyrical, classical, and white noise control) while considering the moderating roles of musical experience and the mediating influence of emotional responses. Undergraduate participants (N = 162) completed spatial reasoning assessments before and after an eight-minute listening session, alongside measures of musical background, listening habits, mood, and music-evoked emotions.Analyses revealed no overall difference in spatial reasoning improvement between music and white noise conditions. However, substantial individual …
Adaptive Reuse Of Underutilized Strip Malls: Creating Mixed-Use Continuum Care Campuses For Seniors, Julie Le Nguyen
Adaptive Reuse Of Underutilized Strip Malls: Creating Mixed-Use Continuum Care Campuses For Seniors, Julie Le Nguyen
Theses
As suburban commercial landscapes continue to decline, underutilized strip malls present an opportunity to reimagine aging-supportive environments through adaptive reuse. This thesis investigates how these sites can be transformed into mixed-use senior housing and continuum-care campuses through adaptive reuse strategies. Using a comparative case study methodology, the research examines a range of precedents—including senior housing retrofits, dementia villages, and community-based mixed-use developments—to identify key spatial, programmatic, and environmental principles that support aging populations. A five-part evaluative framework assesses project performance across site integration and green space, social and intergenerational engagement, programmatic and service integration, mixed-use activation, and cognitive and environmental …
Early Diabetes Prediction Using Machine Learning: A Comparative Study Of Classification Models, Ahmed Abdelqadir Bawazir
Early Diabetes Prediction Using Machine Learning: A Comparative Study Of Classification Models, Ahmed Abdelqadir Bawazir
Theses
The current dissertation investigates the application of machine learning in diabetes prediction at early stages through a comparison of the performances of three classification models, including Logistic Regression, Decision Tree, and Random Forest. Inspired by the increased and spread cases of diabetes worldwide, the research objective is to facilitate early diagnosis by providing interpretable and accurate predictive models. Based on Pima Indians Diabetes Dataset containing 768 clinical records, the research implemented data preprocessing including KNN imputation and outlier processing, feature scaling, and formation of interaction features. Quantitative and comparative approach was made to train and test the models based on …
Three Forks, Virginia Van Enckevort
Three Forks, Virginia Van Enckevort
Theses
Three Forks is a thesis drama series about a 19th-century woman sent West to live with her estranged family in Muscogee, a lawless town where justice bends to power and survival means learning the game — or being buried by it. As we follow our lead, Eleanor Davis, out West and eventually her settlement into town, we gain a look into her new world, one where tribal council contend with encroaching U.S. jurisdiction and where settlers, freedmen, and railroad men jostle for influence in a town on the brink of statehood. The many fabled stories and myths about the West …
Analyzing The Hidden Impact Of Work Stress On Mental Health, Shaikha Aljabry
Analyzing The Hidden Impact Of Work Stress On Mental Health, Shaikha Aljabry
Theses
Occupational stress and deteriorating mental health constitute an increasing issue in the global community, and the problem of how job demands, personal resources, and cognitive processes interrelate to determine employee well-being is hardly known. This dissertation will study these dynamics by incorporating theory-driven Structural Equation Modeling (SEM) with data-driven Machine Learning (ML) methods that offer explanatory and predictive information. The study examines the influences of job demands, personal resources, effort–reward imbalance (ERI), emotional labor, cognitive appraisal, and social support on the stress and mental health outcomes of a sample of 5,000 employees working in various industries and regions of the …
Trust Calibration In Human-Ai Teaming: Within-Session Dynamics, Transparency, And Performance Effects, Mohamed Ahmed Aljaziri
Trust Calibration In Human-Ai Teaming: Within-Session Dynamics, Transparency, And Performance Effects, Mohamed Ahmed Aljaziri
Theses
Trust plays a decisive role in the effectiveness of human-AI teams, particularly in tasks that depend on coordinated decision-making under uncertainty. While prior research acknowledges that trust in automation is dynamic, current work provides limited insight into how trust evolves within an interaction, what causes it to become miscalibrated, and how transparency affects these processes. This thesis examines trust calibration in a controlled 2-D grid-world search-and-rescue environment, where 54 participants collaborated with an AI teammate presented through four communication modes based on the Ability, Benevolence, and Integrity (ABI) framework. The study uses secondary analysis of experimental data to observe: (1) …
Symbiotic Furniture Design: A Single Chair Integrating Epiphytes With Taiwanese Window Grille Structures, Kuan-Wen Lin
Symbiotic Furniture Design: A Single Chair Integrating Epiphytes With Taiwanese Window Grille Structures, Kuan-Wen Lin
Theses
Plants have been integrated into residential and interior environments for centuries, yet research on the psychological effects and interactive potential of plants in furniture design is still relatively limited. Traditional planting methods, such as individual potted plants or large green walls, often require additional floor or wall space, which is not ideal for environments with limited living space. Therefore, this study investigates how plants can be integrated into furniture design to enhance the effect of interior greenery and user engagement, and to redefine the relationship between furniture and the natural environment. This research explores a furniture design that incorporates epiphytic …
The Impact Of The 2008 Financial Crisis On Crime And Cybercrime In The United States, Rashed Adel Alsuwaidi
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 …
Detecting Fraud In Police Reports Using Machine Learning And Natural Language Processing, Maryam Almarar
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 …
Predicting Residential Real Estate Prices In Dubai Using Integrated Data Analytics: A Machine Learning Approach, Mohammad Nasser
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, …
The Klik Chair - Designed To Move, Built To Last, Neeraj Sugumaran Menon
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
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 …
Predictive Analysis Of Residential Property In Urban Areas, Hamad Adnan Alzarooni
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 …
Segment-Level Machine Learning For Detecting Partial Deepfake Audio: An Rnn–Svm Hybrid Approach For Real-World Adversarial Environments, Ahmad Alhelli
Segment-Level Machine Learning For Detecting Partial Deepfake Audio: An Rnn–Svm Hybrid Approach For Real-World Adversarial Environments, Ahmad Alhelli
Theses
This study investigates the detection of real, fully fake, and partially fake (PF) audio using classical machine-learning models as well as a segment-level analysis model. Unlike most existing research, which focuses solely on binary real-vs-fake classification, this work introduces a three-class detection framework and constructs realistic PF samples by inserting short synthetic speech segments into otherwise genuine audio recordings. Its method combines MFCC and spectral feature engineering,Wav2Vec2 embeddings, controlled PF synthesis and various models such as SVM, Random Forest, XGBoost and an attention based RNN. Segment level windowing allows fine-grained study of transition of time and breaks of manipulation. The …
Leveraging Ai In Traffic Monitoring For Improved Accident Prediction In Uae, Mahra Alattar
Leveraging Ai In Traffic Monitoring For Improved Accident Prediction In Uae, Mahra Alattar
Theses
This paper investigates how Machine Learning (ML) models can be used in traffic surveillance and predict accidents in the United Arab Emirates (UAE). In spite of large infrastructure developments and stringent traffic laws, traffic accidents continue to be a major problem with an increase in fatalities and injuries. There is a gap in effective detection and responses since traditional monitoring and forecasting techniques are unable to capture the intricate, nonlinear, and time-dependent character of traffic patterns. This thesis fills that void by utilizing sophisticated the potential use of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Deep Neural Network (DNN), …
Enhancing Airport Operations With Ai For A Seamless Passenger Experience, Rashed Alsubousi
Enhancing Airport Operations With Ai For A Seamless Passenger Experience, Rashed Alsubousi
Theses
The paper is research exploring the importance of Artificial Intelligence (AI) and Data Analytics to optimize airport operations and the passenger experience in the environment of the expanding air traffic in the world and the smart city movement. With increasing tasks that airports currently experience, congestion, flight delays, mishandling of baggage, and limited capacity, nowadays AI-based technologies integration is crucial to the operational efficiency, sustainability, and customer satisfaction. This study discusses the use of predictive analytics, machine learning, and clustering models to enhance passenger flows, resource allocation, and performance in general at airports. The research is based on the working …
Supervised Learning For Predicting Mental Health And Burnout In Healthcare Workers, Afra Alfalasi
Supervised Learning For Predicting Mental Health And Burnout In Healthcare Workers, Afra Alfalasi
Theses
This study aims to predict and analyze burnout among healthcare workers using supervised machine learning techniques and Exploratory Data Analysis (EDA). Leveraging the Healthcare Workforce Mental Health Dataset, the research identifies key demographic, occupational, and psychological factors most strongly associated with burnout. The methodology involves data preprocessing, feature selection, and model training using algorithms such as logistic regression, decision trees, random forests, and gradient boosting. Model performance will be evaluated through standard metrics, including accuracy, precision, recall, and ROC-AUC. The expected outcome is a predictive framework that highlights high-impact burnout predictors and generates actionable insights to support early intervention and …
Predicting Cryptocurrency Prices Using Stochastic Modeling, Reem Hani Al Omari
Predicting Cryptocurrency Prices Using Stochastic Modeling, Reem Hani Al Omari
Theses
Cryptocurrencies are digital currencies that operate independently of central banks and governments. They were designed to overcome the limitations of traditional financial systems through a decentralized, peer-to-peer electronic cash mechanism. Trading in cryptocurrencies offers several advantages, including decentralized and efficient transactions, reduced costs through the elimination of intermediaries, investment opportunities across exchanges, and seamless cross-border remittances. Modeling cryptocurrency prices is therefore essential, not only due to these advantages but also because of the substantial market capitalization of cryptocurrencies, estimated to exceed 900 billion dollars according to CoinMarketCap [6]. The main objective of this thesis is to propose a predictive framework …
Massive Stars Behaving Badly: Exceptional Interactions And Their Remnants, Kaila Nathaniel
Massive Stars Behaving Badly: Exceptional Interactions And Their Remnants, Kaila Nathaniel
Theses
Massive stars are some of the most dynamic and influential objects in the Universe, driving galactic evolution through violent explosions and serving as progenitors to neutron stars and black holes. While the evolution of isolated massive stars already produces remarkable phenomenae, massive stars that interact with companions or evolve within extreme environments may produce even more extraordinary outcomes. In this dissertation, I investigate some exceptional circumstances in which a massive star might find itself. In a first investigation, I analyze the strong interactions between isolated pairs of stars in low-density environments. If a binary consisting of a massive star and …
Advancing 3d Infrastructure Utilities Mapping Through The Integration Of Multi-Method Digital Modelling Techniques, Adil Abdulla Aldashti
Advancing 3d Infrastructure Utilities Mapping Through The Integration Of Multi-Method Digital Modelling Techniques, Adil Abdulla Aldashti
Theses
With urban growth accelerating, cities increasingly need precise 3D information about their underground service networks to support sustainability goals and digital systems. Older 2D maps are often outdated, incomplete, and misaligned, making them difficult to use within 3D GIS environments. Moreover, conventional surveying tools may fall short in accuracy, struggle to reach buried assets, and require high costs when dealing with existing utilities. This thesis aims to develop and evaluate a field to 3D methodology for mapping underground utilities in Dubai, integrating multi-sensor and surveying approaches, and GIS 3D models in alignment with ASCE 38-22(American Society of Civil, 2022a), PAS …
Machine Learning Approaches For Predicting And Preventing Vehicle Theft, Hamad Almutawa
Machine Learning Approaches For Predicting And Preventing Vehicle Theft, Hamad Almutawa
Theses
This study develops a data-driven framework for predicting vehicle theft using machine-learning techniques applied to a comprehensive crime dataset containing demographic, spatial ,and operational policing variables. The dataset, sourced from publicly available crime records, included 9,712 cases and 30 variables, with minimal missing data and balanced class distribution. After performing extensive preprocessing—including outlier analysis, missing-value imputation, categorical encoding, feature selection, and correlation testing—four supervised classification models were implemented: Logistic Regression, Random Forest, Support Vector Machine, and a Neural Network. Bivariate statistical tests, including Chi-square and Mann–Whitney U, revealed significant relationships between theft occurrence and factors such as crime area, luxury …
Advancements In Ml Via Efficient Generative Modeling, Robust Domain Adaptation, And Explainable Multimodal Retrieval, Prasanna Reddy Pulakurthi
Advancements In Ml Via Efficient Generative Modeling, Robust Domain Adaptation, And Explainable Multimodal Retrieval, Prasanna Reddy Pulakurthi
Theses
The rapid evolution of AI heightens the need for learning systems that are efficient, robust, and explainable. This dissertation advances these three pillars through innovations in classification, generative modeling, domain adaptation under data-scarce conditions, and multimodal retrieval. Collectively, the methods reduce dependence on large, labeled datasets, improve adaptability under distribution shifts, enable deployment on resource-constrained platforms, and enhance interpretability. For classification, the Iterative Maximum Likelihood Classifier (IMLC) recasts regularized maximum likelihood training as a fixed-point contraction with convergence guarantees, enabling faster and more stable optimization. Results on synthetic data and MNIST validate its efficiency. For generative models, we introduce Parametric …
Assessing The Impact Of Gis Data Inaccuracies On Urban Infrastructure, Majid Aldashti
Assessing The Impact Of Gis Data Inaccuracies On Urban Infrastructure, Majid Aldashti
Theses
Urban infrastructure delivery is a complex process influenced by project type, spatial distribution, stakeholder coordination, and data quality. This thesis examines the application of Microsoft Power BI, a no-code visual analytics tool, as a means to analyze performance patterns in urban projects and inform decision-making in smart city governance. The study uses a structured secondary dataset simulating over 600 infrastructure projects across various departments and neighborhoods. By transforming raw data into an interactive dashboard, the research explores relationships between delay rates, customer satisfaction, budget compliance, and technology adoption. The analysis revealed that project categories such as metal furniture and police …
Rise Of Social Media Hacking: Ai-Based Ip Tracking For Uae Law Enforcement, Mounikha Naarrayen Chakravarthula
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 …