Digital Commons Network^™

Marking Estimation In Petri Nets Using Dynamic Mode Decomposition, Aditya Kale

Theses

Petri Nets (PNs) are a well-established framework for modeling and analyzing complex systems with interacting, concurrent processes. This thesis extends traditional Petri Net methodologies by integrating Dynamic Mode Decomposition (DMD), a technique originally developed for fluid dynamics, to analyze Continuous Petri Nets (CPNs). By applying DMD to CPN marking evolution, this research constructs a reduced-order model that captures its dynamics through dynamic modes and eigenvalues, enabling prediction of future markings without detailed knowledge of transition firings or underlying deterministic models. The principal contribution of this research is extending the kit of tools available for analysis of CPN dynamics, providing insights …

Targeted Drug Delivery: Investigating Protein Corona Behavior, Atharva Markale

Theses

Due to high specificity and less toxicity, nanoparticles have become promising for targeted drug delivery against cancer, although they face challenges when they enter the bloodstream, making their behavior unpredictable. The deposition of proteins on nanoparticles, the protein corona, changes their biosignature, affecting their circulation, drug release potency, targeting ability, and immunogenicity. The continuous exchange of protein layers on a nanoscale is complex to analyze.

Nanoparticle tracking analysis (NTA) is a method to characterize nanoparticles in blood plasma by combining principles of light scattering microscopy and Brownian motion. In this work, time-dependent experiments with plasma were conducted to analyze the …

Electrochemical Nitrate Reduction To Ammonia On Cu Based Nanoparticles Using Quantum Mechanical Simulations, Kathan Bhavin Shukla

Theses

Nitrate reduction to ammonia (NRA) is critical for environmental remediation and energy conservation, as it can remove harmful nitrate (NO₃^-) from water sources while producing usable ammonia (NH₃). Cu represents one of the most promising non-noble-metal NRA electrocatalysts. However, the intrinsic catalytic activity of Cu facets and their influence under different pH conditions remain unclear, particularly in the presence of ions commonly found in wastewater such as Cl^-, Na⁺, Mg²⁺, and Ca²⁺. Using density functional theory (DFT) calculations, we evaluated the nitrate reduction to ammonia (NRA) pathways …

Optimizing Social Determinant Of Health Concept Extraction, Niharika Singh

Theses

Generative Artificial Intelligence has recently garnered enormous attention for a varied number of reasons, one of them being its capacity to automate complex tasks. As the potential for integrating Generative Artificial Intelligence across technology, media, and healthcare grows, many tasks previously reliant on manual or algorithm-intensive methods can be simplified significantly. One such challenge is concept extraction, which is a labor-intensive process, especially when performed manually for building robust ontologies. It involves analyzing text to identify and extract relevant concepts, key phrases, entities, and relationships between these entities. This thesis explores various challenges of concept extraction from the ABCD (Adolescent …

Methodology For Developing Biodegradable Polymer Blends And Composites For Agriculture, Fangzhong Xing

Theses

The use of polyethylene (PE) films to improve soil conditions and to increase crop yield has produced microplastics that are harmful to our ecosystem. The overall objective of this thesis is to develop an effective biodegradable polymer/composite to replace PE, and hence, to reduce microplastic pollution. The focus of this research is to develop a systematic protocol for assessing the suitability of the biodegradable films based on industry standards in agriculture. For this purpose, a range of biodegradable polymer blends are prepared using polylactic acid (PLA) and polybutylene adipate-co-terephthalate (PBAT) with varying PLA to PBAT ratios. Solvent casting combined with …

A Programmable Look-Up Table-Based Processing In Memory Architecture For Massively Parallel, Energy-Efficient Processing Of Data-Intensive Applications Within Dram, Purab Ranjan Sutradhar

Theses

As modern computing workloads become increasingly data-intensive, the limitations of traditional digital computing hardware, especially in terms of communication bandwidth between memory and processors, are becoming more apparent. Surprisingly, the performance and energy efficiency of the state-of-the-art are not primarily constrained by the computing power of advanced processing architectures but rather by the low communication bandwidth between the memory and the processor. This challenge necessitates a clever re-architecting of digital computing hardware to make the communication between the processor and the memory more energy-efficient, faster, and capable of supporting significantly higher bandwidth. Memory-centric computing emerges as a novel, alternate computing …

Human-Machine Interactions For Behavioral Health Care, Zhiwei Yu

Theses

About one in four American adults (26%) and 20.3% of children aged 8 to 16 years reported behavioral health concerns in 2023. However, a substantial gap remains between demand for behavioral healthcare services and access to such services. In response, research efforts have been made to advance effective and accessible approaches to provide assessments and treatments for populations with behavioral health needs. Yet, two primary challenges exist in current practice and research: 1) self-reports and manual observation-based assessments are subjective and labor-intensive, as evaluations are limited to the availability of clinical professionals; 2) they only target the general experiences but …

Machine Learning Models For Enhanced Stock Trading Strategies, Alyaa Al Ali

Theses

The purpose of this research is to develop sophisticated machine-learning models that can predict stock price movements, taking into account the multifaceted influences that create volatility in financial markets. To navigate the complexities of market dynamics effectively, investors, financial institutions, and academia rely on precision stock price predictions. This research generates actionable buy and sell signals by using historical stock data, technical market indicators, Linear Support Vector Machines (LSVM), Neural Networks, and Logistic Regression. A distinctive feature of this model is it uses market indicators such as PPO, MACD, RSI Signal, Bollinger Bands, ROC Signal, and DX Signal instead of …

Unravelling Micro-Mysteries: Separating Microscopic Entities Using Electrokinetic Microsystems, Nuzhet Nihaar Nasir Ahamed

Theses

There is an increasing demand for the development of rapid and reliable methods for microparticle and cell separations in applications that range from clinical analysis to food and environmental safety assessments. Microfluidic systems, being portable and offering a response within minutes, are particularly attractive. Electrokinetic (EK) methods are a growing area in microfluidics, due to their robustness, rapid response time, low cost, and label-free analysis of micron-sized particles, including intact microorganisms. EK methods require the application of an electric stimulation, such as direct-current (DC) or alternating-current (AC) potentials, to induce different EK phenomena, such as electrophoresis, electroosmotic flow and/or dielectrophoresis, …

Spatiotemporal Alignment Of Event Stream With Images And Active Illumination, Abhijan Wasti

Theses

Unlike conventional frame-based cameras that form images by sampling all pixels within the duration of the global/rolling shutter, a pixel in an event camera can be triggered independently when the log intensity change in scene luminance at the pixel exceeds a threshold. This unique feature provides several advantages over conventional sensors, including high dynamic range (HDR) (≈120dB), high temporal rate (≈10,000Hz), low latency (< 1ms), and low power requirements (≈10mW). These properties make them excellent candidates for applications such as high-speed photography, HDR image reconstruction, object tracking, depth estimation, simultaneous localization and mapping, and surveillance and monitoring. Despite their potential, the asynchronous and spatially sparse nature of events poses challenges to event processing and interpretation. This is because most advanced image processing and computer vision algorithms are designed to work with conventional image formats, and not with temporally dense streams of asynchronous pixel events (i.e., the event stream). Although emerging techniques in supervised machine learning demonstrate promise, continued and rapid progress relies on the availability of labeled event datasets, which are scarce, and difficult to produce. Moreover, generating reliable events for training models is challenging due to the scene-dependent nature of event generation, which is further complicated by varying illumination and relative motion. In this thesis, we attempt to address these limitations with a novel imaging paradigm involving the capture of frames from a conventional frame-based camera that has been spa tially aligned and temporally synchronized with an event sensor. Our active illumination source allows us to generate events more consistently even under challenging illumination and motion in the scene. We demonstrate the feasibility of such a setup for a mobile eye tracking system and acquire subpixel and microsecond accurate spatiotemporal alignment. Our method facilitates the use of pre-trained models that operate on conventional images to detect features that can then be used to train models that can be applied directly to the event stream. This serves as the next step in event-based imaging and unlocks new possibilities in computer vision and imaging technology.

Nano Biosensing Platform For Infectious Disease Diagnostics, Li Liu

Theses

CRISPR-Cas enabled biosensors offer great potential to be at the forefront of diagnostic medicine. This thesis focuses on the principles of CRISPR-Cas technology and its application to biosensors and virus diagnostics. It also provides a detailed discussion of the different detection methods available at this stage. The breakthrough achievements of CRISPR biosensors in nucleic acid detection are summarized. The CRISPR-Cas nano biosensors exhibit high accuracy, sensitivity, selectivity, and versatility, offering great potential for next-generation diagnostic and point-of-care devices. The prospects and future trends of CRISPR biosensors are also described. From an engineering perspective, this thesis develops and applies several methods …

Deep Learning Approach For Detecting And Classifying Chaotic Driving Activity Using Video Surveillance, Fatma Abdulla Desmal Alhaddad

Theses

The growing rate of urbanization in the city of Dubai has contributed to the increase in vehicular activity and the prevalence of risky driving behavior on the road. This project proposes a deep-learning-based system, VGG-16, for the identification and classification of different patterns of driving behavior. The research methodology used for the study relies on the compilation of datasets from various resources, including ImageNet, Kaggle, UAH-DriverSet, D²-City Dataset, and confidential videos obtained from the Dubai Police command center. The findings of the study will be categorized into two parts. The first part will involve the compilation of the features based …

The Future Of Smart Transport Technologies: Examining Dubai's Technological Readiness Levels (Trls) To Adopt New Smart Transport Technologies, Hamad Omran Alshizawi

Theses

This research aims to understand what Dubai is willing to do and to provide in terms of smIntelligent Transportation Systems with emphasis on capacity optimization of traffic and understanding TRLs and absorption capacity for AI in Intelligent Transportation Systems Traffic Prediction and Control Systems. The research is important in the context of the concept of a smart city for Dubai and the argument that perfect for the object of examination – transport – can and should integrate new technologies easily into the existing context. Drawing from a population of smart city literature development, traffic and Smart technology, the present study …

Using Prediction Ml Algorithm For Predicting Early Student Attrition In Higher Education, Noora Ali Mohsen Alattar Alhashmi

Theses

Abstract This study aims to use predictive models in order to figure out students that are in likelihood of attrition as well as determine the factors that may contribute to this attrition. Student attrition is the occurrence in which students leave the institution without finishing their studies or taking the degree. It includes students either willingly or involuntarily terminating their education and failing to graduate. The findings will enable relevant parties to establish successful strategies or approaches and activities to assist in reducing the number of students who leave. With the testing and analysis approaches, it was recognized that SVM …

High Heat Flux Dissipation Techniques Using Enhancement Structures And Subcooled Boiling Chamber For High Performance Computing Applications, Maharshi Y. Shukla

Theses

With a rising need for high-performance computing in data centers, enhanced heat dissipation from microprocessors becomes critical for maintaining their surface temperature in a compact setting. While air cooling has been sufficient until now, liquid cooling proves to be superior in its heat dissipation capabilities. The present work investigates enhancing liquid cooling with boiling and enhanced boiling structures in a novel subcooled boiling chamber. The single-phase forced convection heat transfer is first studied by enhancing the heat transfer performance (CHF and HTC) by more than 50 % with the injection of air bubbles at the heater surface. A self-sustaining method …

Adaptive Parameter Tuning For Robust Event-Based Slam In Diverse Scenarios, Saber Muawiyah Sakhrieh

Theses

In the ever-changing field of contemporary robotics, Visual Simultaneous Localization and Mapping (V-SLAM) stands as a crucial technology, facilitating navigation and mapping in various environments. While traditional V-SLAM approaches excel in static environments, the need for their adaptation to handle dynamic and realistic scenes is becoming increasingly apparent. This thesis delves into the advancements within the V-SLAM field, with a focus on enhancing its adaptability and performance in dynamic and challenging conditions. Initially, a comprehensive review of V-SLAM methods in the literature is presented in this thesis, offering an in-depth examination of current cutting-edge techniques, their strengths, weaknesses, and prospects …

A Foresight Study For Sustainable Urban Transport Systems: The Case Of Dubai, Salahuddin Bassem Masadeh

Theses

Mainly, this research proposes a future study for sustainable transportation systems in the city and the importance of applying these systems in Dubai to prevent accidents, to make it more sustainable and save more lives. It will identify the practical actions and results of applying these technologies and their social, economic, urban, and environmental effects. These actions will increase the growth of the economy and the quality of life in Dubai City by achieving their transportation sustainability objectives. Another goal of the research is to foster the association between city shareholders, construction companies, government, and traffic officers to apply sustainable …

Investigating Key Challenges In Managing Inventories Of Spares In The Ev Sector, Keasav Ram R

Theses

Exploring the major issues in spare inventory management in the EV industry is the main purpose of the study. The current practices and challenges associated with inventory management of spares in the US EV sector have been investigated in this study. A "secondary qualitative data collection" method along with an exploratory approach has been utilised in this study to drive the study in a proper way to achieve a successful outcome. The collected data has been analysed in terms of using a thematic analysis. Objective and research questions specific themes have been generated to analyse the collected secondary data. It …

A Study Of Gate Valve Supply Chain And Life Cycle Impacts, Saud Bindarwish

Theses

and sustainable water supplies depend on water infrastructure. However, old infrastructure, especially valves that leak and break, can cause significant water loss, economic strain, and environmental damage. This study explores best choice of gate valves to use while replacing defected existing gate valve in the networks by examining gate valve supply chains and their life cycle consequences. Traditional gate valve supply chains are lengthy and complicated. Extracting iron ore and copper destroys habitats and pollutes air and water. Factory processing and production of these materials increases energy use and air/water pollution. Valve shipping, usually overseas, emits lots of greenhouse gases. …

Smart Technology For Enhancing Indoor Air Quality, Abdulaziz Mohammed

Theses

The COP28 emission reduction plan underscores the imperative of transitioning away from fossil fuels to combat climate change. Concurrently, the rapid adoption of Internet of Things technology has emerged as a transformative force in urban governance, offering cities enhanced resilience and operational frameworks through data analytics and real-time monitoring. In response to these trends, the present research focused on leveraging IoT technology to monitor indoor air quality within a “restaurant A” in an urban area in Dubai. Employing advanced data analytics and smart sensor technologies, particularly LoRa, the project aimed to analyze and monitor air quality parameters. Disposing the sensor …

Decentralisation Of Drone Authentication Within Iod Networks., Jarryd Tayler Brits

Theses

The applications of drones have become increasingly more versatile, with their popularity also increasing rapidly in the last decade. These applications span from entertainment within civilian markets to more military-based applications, including private security firms. However, with this rise in use, the security risks involved have also increased drastically, predominantly in a drone’s lack of ability to authenticate surrounding drones efficiently and securely without the aid of a third party. Although there have been copious amounts of research to support drone authentication, they predominantly rely on a single source of truth, such as a ground station server (GCS), which presents …

A Framework For Filtering Irrelevant Notams, Roudha Abdulrahman

Theses

The Notice to Airmen (NOTAM) system is essential for aviation safety, giving critical information on risks and operating limitations. However, the volume and complexity of NOTAM data complicate interpretation, which could compromise safety and efficiency. This study tries to address these issues by creating a predictive algorithm for analyzing NOTAM data and predicting whether to keep or remove them. The methodology combines effective machine learning algorithms to reveal insights that improve safety and operational efficiency. Using a dataset of NOTAM entries, multiple prediction algorithms are tested to forecast possible problems and enable proactive risk management. The study's findings help to …

Utilization And Justification Of Generative Ai Technologies For Schematic Architectural Design, Aaric Celeste

Theses

Today, architecture is heavily assisted by computer programs. As technology advances, these programs will be more impactful in the design space and will become more inseparable as a result. It is nearly certain that architectural design will no longer be as time-consuming and reiterative as it is today. Computer software and AI-assisted tools will be able to interpret information and produce practical designs that architects otherwise couldn’t. It may seem futuristic and a not-in-our-lifetime phenomenon, but it in fact already exists today. Algorithms are already developed to assist and in some cases take over design work. This thesis discovered existing …

Unraveling The Role Of Asymmetry In Structural Porous Media On The Growth Of Fluid Fingers, Amir Ibrahim

Theses

Instabilities at fluid-fluid interfaces are ubiquitous in porous media, even at small flow rates. The growth of fluid fingers during fluid-fluid immiscible displacement in a porous medium results in partial saturation of the medium, and inefficiency in displacement has a tremendous economic impact on oil recovery, chemical reactors, and water filtration industries. In this study, we demonstrate that an inherent asymmetry in the structure of a porous medium controls the development of fluid fingers due to instabilities at fluid interfaces. We construct a 2D porous medium consisting of a matrix of triangles using soft lithography to investigate air displacement by …

Exploring The Positive Impact Of Emotionally Designed Furniture On Alleviating Negative Symptoms In Individuals With Complex Post-Traumatic Stress Disorder (C-Ptsd): A Case Study Of Chinese Families Influenced By Traditional Confucian Culture., Guangxin Zhang

Theses

This study aims to explore whether emotionally designed furniture has a positive impact on alleviating negative symptoms in individuals with Complex Post-Traumatic Stress Disorder (C-PTSD). Through a combination of literature review and empirical research, the characteristics of C-PTSD individuals and their common negative symptoms, including anxiety, depression, fatigue, etc., were first reviewed and analyzed. Then, the hypothesis of the positive impact of emotionally designed furniture on individuals with C-PTSD was proposed. The study further explores how emotionally designed furniture enhances environmental comfort and fosters emotional connections in personal spaces to provide psychological support and alleviate symptoms in individuals with C-PTSD. …

Improving Automatic Refactoring Candidate Identification, Ryan Devoe

Theses

Extract method refactoring is pivotal for enhancing code readability, maintainability, and modularity by segmenting complex code into clearer, isolated methods. Identifying opportunities for such refactorings necessitates a deep understanding of the codebase’s evolution and its intricate relationships. Current methodologies utilize developer commit messages, advanced graph analysis, and diverse machine learning approaches to automate this identification process. This research delves into the application of deep learning-based Large Language Models (LLMs) to tackle the complexities inherent in extract method refactoring. We introduce innovative approaches, including the use of LLMs to cluster code blocks based on complex patterns and dependencies, and the analysis …

Emotional First Aid Kit Design, Wenxi Qi

Theses

In contemporary society, the shift towards holistic well-being has highlighted the critical importance of emotional health in maintaining overall quality of life. Research indicates that emotional health issues now pose a greater threat to individual well-being and productivity than physical ailments. Despite this, there exists a significant gap in awareness and emphasis between physical and emotional health across various sectors, contributing significantly to the prevalence of mental health issues. This research paper introduces the "Emotional First Aid Kit," a novel design concept aimed at addressing emotional distress in everyday scenarios similar to conventional first aid kits managing physical injuries. The …

On The Naming Of Identifiers: A Survey Of Undergraduate Students, Stacy Skalicky

Theses

Alsuhaibani et al. state that 60% of the participants do not have strict or any naming standards in the workplace and developers surveyed desired clear method naming standards to promote code comprehension [2]. Martin indi-cated identifier names are responsible for 90% of what makes software readable and understandable [25] . Borstler et al. indicated readability was important to developers at every level of experience but few report their academic influences being effectively leveraged to make quality identifiers [5]. Keuning et al. surveyed teachers in programming-related fields and found that code quality although important, was not a big part of their …

Improving Design And Physical Boundary In Temporary Post-Disaster Relief Shelters: Analysis Of Existing Literature, Min-Hye Jang

Theses

Natural disasters such as floods, hurricanes, cyclones, storms, earthquakes, wildfires, and landslides displace people. As responses at the community resilience level, the temporary post-relief shelter is critical for the community's rapid recovery. Converting existing public facilities is the fastest way to respond to a crisis. However, the Hurricane Katrina Superdome Shelter alerted society to the prevalence of sexual assault in transitory catastrophe contexts. Not only has sexual abuse occurred, but prior shelters did not equip every user with physical barriers to suit their needs. This study examined academic journals, articles, and books to identify issues with natural disaster shelters created …

Optimizing Customs Efficiency: Detection For Illicit Shipments In Cargo, Ohood Alharbi

Theses

Aim- The study aimed for creating a machine learning algorithm which succeeded customs control procedures through estimating shipments being either illicit or non-illicit. The study aimed at boosting the level of accuracy in determining the critical shipments, which would in turn, increase customs productivity, minimize false positive and false negative, as well as augment security and ensure the smoothness of trade flow, amid a palpable surge in importation rates. Methods- The study implemented the CRISP-DM methodology (Cross-Industry Standard Process for Data Mining), which has a well-structured approach of achieving standard data mining solutions. At the beginning, the efforts have been …