Engineering Commons^™

Assessing Water Quality In Drinking Water Distribution Systems: Rural Infrastructure Challenges, Hydrodynamic Effects On Biofilms, And Biofilm Contribution To Disinfection By-Products Formation, Vinila Vasam

Graduate Theses, Dissertations, and Problem Reports (ETD)

Access to clean and safe drinking water remains one of the most persistent public health and environmental challenges in the United States. Maintaining water quality after leaving the treatment plant in drinking water distribution systems (DWDS) is a complicated and often overlooked challenge. Most research focuses on water treatment and source water quality, but not on the miles of pipe that follow. Aging infrastructure, complex flow patterns in the DWDS, and limited resources make water quality management especially difficult. This dissertation includes three core studies: 1) A system-level analysis of small and rural utilities in the geographically complex region of …

Orchestrating Bone Healing: Biomaterial-Driven Immunoengineering Of The Bone Microenvironment For Advanced Regenerative Therapies, Ah Joung Rachel Yu, Mohammad Reza Zare, Edikan Ogunnaike, Mostafa Yazdi

Chemical and Biochemical Engineering Faculty Research & Creative Works

Bone regeneration is a multistage and tightly regulated process driven by coordinated interactions between the immune and skeletal systems. While inflammation is essential for initiating repair, its dysregulation contributes to delayed union, nonunion, and impaired healing commonly seen in trauma, chronic disease, and aging. Traditional interventions, including autografts and inert biomaterials, provide structural support but fail to engage or modulate the immune microenvironment that governs successful regeneration. Emerging research in osteoimmunology has revealed the centrality of immune cell populations such as macrophages, T regulatory cells, dendritic cells, and myeloid-derived suppressor cells in directing osteogenesis, angiogenesis, and the resolution of inflammation. …

Sustainable Safety Planning On Two-Lane Highways: A Random Forest Approach For Crash Prediction And Resource Allocation, Fahmida Rahman, Cidambi Srinivasan, Xu Zhang, Mei Chen

Civil Engineering Faculty Publications

During the safety planning stage, accurate crash prediction tools are critical for prioritizing countermeasures and allocating resources effectively. Traditional statistical approaches, while long applied in this field, often depend on distributional assumptions that may introduce bias and limit model accuracy. To address these issues, studies have started exploring Machine Learning (ML)-based techniques for crash prediction, particularly for higher functional class roads. However, the application of ML models on two-lane highways remains relatively limited. This study aims to develop an approach to integrate traffic, geometric, and critically, speed-based factors in crash prediction using Random Forest (RF) and SHapley Additive exPlanations (SHAP) …

Deep Learning Approaches For Voltammetric Analysis Of Coffee, Ryan Koes

Honors Theses

This thesis investigates deep learning approaches for voltammetric analysis of brewed coffee using a low-cost electrochemical system and screen-printed electrodes (SPEs). Traditional analytical methods, such as high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS), provide precise quantification of key compounds but require expensive instrumentation and specialized expertise, limiting accessibility. While SPEs offer a more accessible alternative, they yielded poor results with traditional processing; however, when combined with a neural network, the system proved more effective. In experiments with 132 coffee samples, mean errors for caffeine, CGA, and TDS predictions were 52.98 ppm, 70.48 ppm, and 0.08%, respectively. These findings …

A Web-Based Wizard-Of-Oz Platform For Collaborative And Reproducible Human-Robot Interaction Research, Sean O'Connor

Honors Theses

The Wizard-of-Oz (WoZ) technique is widely used in Human-Robot Interaction (HRI) research, but two persistent problems limit its effectiveness: existing tools impose technical barriers that exclude non-engineering domain experts (the Accessibility Problem), and the fragmented landscape of robot-specific implementations makes interaction scripts difficult to port across platforms (the Reproducibility Problem- concerning execution consistency and portability, not third-party replication). Through a literature review, I identified three design principles to address both: a hierarchical specification model, an event-driven execution model, and a plugin architecture that decouples experiment logic from robot-specific implementations. I realized these principles in HRIStudio, an open-source, web-based platform providing …

Combining Haptic Feedback With Electromyography To Study Knee Injury In Stationary Cycling, Christopher Kirby

Honors Theses

Patellofemoral Pain Syndrome (PFPS) is the most prevalent overuse injury in cycling, often linked to altered neuromuscular activation patterns of the quadriceps and hamstrings. While sensory feedback has successfully modified cyclists posture, there is a critical lack of evidence-based interventions targeting the underlying muscle activation imbalances associated with chronic knee pain. This study aimed to develop and validate a novel, real-time biofeedback system that utilizes electromyography (EMG) to trigger vibrotactile cues, aiming to shift muscle onset timing earlier in the pedal stroke to mitigate PFPS-related imbalances. A closed-loop system was engineered by integrating a Delsys EMG system with a SageMotion …

Design And Analysis Of Energy Recovery Methods For Reduced Aircraft Emissions, Joshua C. Hauck

Honors Theses

Aircraft flights are an increasingly popular mode of transportation. However, their harmful impacts on the environment are a growing concern. Many engineers have worked to develop fully electric aircraft to address this issue. Although they are much more sustainable than conventional aircraft, electric aircraft encounter severe limitations imposed by current battery technology. One alternative route engineers have taken is developing energy recovery methods (ERMs). These are marketed as devices that reduce aircraft fuel consumption without significantly changing their structure and functionality, making them an excellent short-term solution. However, there is little to no consideration of the tradeoffs induced by the …

Buckling Analysis Of Auxetic Composite Laminates And Optimal Design Using Lamination Parameters And Machine Learning, Hans Bendon Maria Tamil Selvan

Mechanical and Aerospace Engineering Theses

Composite materials are widely used as structural panels in aerospace, automotive, and civil engineering applications, where buckling is often a critical failure mode. This thesis focuses on the analysis and design of composite laminates that maximize buckling performance under prescribed stiffness and thickness constraints.

The first part of the study investigates the buckling behavior of auxetic laminates, which exhibit a negative Poisson's ratio. While previous studies suggest that auxetic laminates can achieve higher critical buckling loads than non-auxetic laminates under simply supported boundary conditions with lateral restraint, the influence of other boundary conditions and plate aspect ratios has not been …

Effects Of Metal-Modified Catalysts On The Desorption Performance Of Mixed Amine Solutions And Machine Learning Prediction, Xunxuan Heng, Zhenzhen Zhang, Longhua Zhu, Li Yang, Shugang Xie, Zeyu Wang, Dongtai Han, Fang Liu, Kunlei Liu

Mechanical Engineering Faculty Publications

The high energy penalty associated with solvent regeneration is still a major bottleneck in amine-based CO₂ capture. In this work, the effects of five solid acid catalysts on the desorption performance of a mixed-amine solvent were compared, and the HY catalyst with superior desorption behavior was selected and further modified with four transition metals (Co, Mn, Gr and Ce) to enhance its catalytic activity. The findings indicate that the CO₂ desorption capacity and maximum desorption rate of the Co-modified HY catalyst reach 48.96 mmol and 0.02211 mmol/s, corresponding to increases of 36.80% and 35.39% relative to the blank …

Sewer Pipe Condition Assessment Using An Ensemble Machine Learning Framework For Infrastructure Decision Support, Mahnaz Rouhi

Civil Engineering Dissertations

Aging wastewater infrastructure presents significant challenges for municipalities across the United States, with many sewer networks approaching or exceeding their design life. Conventional inspection methods, such as closed-circuit television (CCTV), are limited by subjectivity, cost, and inefficiency. To address these challenges, this study develops an ensemble machine learning framework for assessing the structural condition of sewer pipelines using inspection records enriched with geospatial attributes.

The primary objective of this research is to enhance predictive accuracy, interpretability, and decision-support for risk-based asset management. The scope of the study encompasses 4,802 CCTV inspection records from Dallas, TX, and Tampa, FL, integrating physical, …

Videoscoop: A Non-Traditional, Domain-Independent Framework For Video Analysis, Umme Hafsa Billah

Computer Science and Engineering Dissertations

Due to the proliferation of cameras in handheld devices and the widespread use of CCTV, images and videos have become a preferred alternative for capturing and disseminating information. Automated analysis for understanding image or video contents (e.g., objects, activities, backgrounds, situations of interest, etc.) is critical for many applications such as Civic Monitoring, Surveillance (in general), monitoring activities in Assisted Living environments, and many more. Image and Video Analysis (IVA) research has been ongoing for several decades, resulting in numerous techniques for algorithmically analyzing and understanding image and video contents.

Image Analysis (IA) has advanced in several areas, including object …

Deep Learning For Wireless Communications, Swarada Ajit Kulkarni

Electrical Engineering Dissertations

The rapid evolution of wireless communication imposes stringent requirements for ultra-reliable, low-latency transmission in dynamic, interference-prone environments. Traditional model-driven signal processing struggles to adapt to nonlinear hardware effects, time-varying channels, and complex interference patterns. Deep learning (DL) offers a transformative, data-driven alternative, enabling end-to-end optimization and robust adaptation under uncertain propagation conditions.

This dissertation investigates deep learning architectures for intelligent and resilient wireless communication through three complementary contributions. The first introduces a Vision Transformer (ViT)-based modulation classification framework that leverages self-attention to capture local and global dependencies in spectrogram representations of Quadrature Amplitude Modulation (QAM) signals. The ViT achieves superior …

Sainik Vol 1 Issue 2, Sastra Deemed To Be University

SAINIK

No abstract provided.

Training A Cellpose Model For T-Cell Cytoplasm And Nucleus Segmentation, William Laws, Matthew Lindley, Irene Georgakoudi

Wetterhahn Science Symposium Posters

Accurate single-cell segmentation of cytoplasm from nucleus in autofluorescence images is critical for quantifying cellular metabolism using the optical redox ratio (FAD / FAD+NAD(P)H). However, automated cytoplasmic segmentation of T cell two-photon excitation fluorescence images remains challenging due to low signal-to-noise ratio, small cell size, and variability in cell brightness across imaging conditions. Here, we present a pipeline combining a custom-trained Cellpose-SAM model with an adapted cytoplasmic post-processing algorithm (CPPA) for automated single-cell metabolic analysis of T cell NAD(P)H autofluorescence images. Raw 2048×2048 pixel images were spatially binned 4×4 in MATLAB to improve signal-to-noise ratio and reduce processing time. A …

Powering The Machine, Draining The Planet: Whether U.S. Environmental Law Is Equipped To Regulate The Energy And Water Demands Of Ai Data Centers, Michael Marcu

Journal of Earth and Life Science

Artificial intelligence (AI) data centers have become one of the United States' fastest-growing and least-regulated sources of environmental stress. In 2024 alone, U.S. data centers consumed 183 terawatt-hours (TWh) of electricity more than the entire nation of Pakistan and consumed an estimated 17 billion gallons of water (IEA, 2025; Berkeley Lab, 2024). By 2030, electricity demand from these facilities is projected to reach 426 TWh, a 133% increase in six years (Pew Research Center, 2025). This paper examines whether the existing U.S. environmental regulatory framework put by the National Environmental Policy Act (NEPA), the Clean Water Act (CWA), and the …

Fiber-Reinforced Composites For Vaginal Tissue Engineering Applications, Samyuktha S. Kolluru, Abir Hamdaoui, Annabella M. Mascot, Siobhan S. Sutcliffe, Jerry L. Lowder, Michelle L. Oyen, Samantha G. Zambuto

Mechanical Engineering Faculty Publications

The vagina is a fibromuscular tube-shaped organ that plays critical roles in menstruation, pregnancy, and female sexual health. Vaginal tissue constituents, including cells and extracellular matrix components, contribute to tissue structure, function, and prevention of injury and pathology. However, much microstructural function remains unknown, including how the fiber-cell and cell–cell interactions influence macromechanical properties. A deeper understanding of these interactions will provide critical information needed to reduce and prevent vaginal pathologies. Our objective for this work is to design a novel tissue-mimicking biomaterial for vaginal tissue engineering, and characterize its biological and mechanical performance in the vaginal microenvironment. We successfully …

First Water-To-Ligand Substitution Dictates Co2+/Ni2+ Extraction Selectivity, Kailong Zhang, Yuxuan Zhang, Bin Ji, Rick Q. Honaker, Isabel C. Escobar, Jian Shi, Qing Shao

Mining Engineering Faculty Publications

Solvent extraction is a crucial technology for the large-scale recovery of critical metals. One key challenge is to develop ligands with high ionic selectivity, especially for ions with similar properties. Manipulating ionic solvation emerges as a viable approach to enhance the selectivity. However, such manipulation requires a thorough understanding of the thermodynamics underlying ligand-induced variation in ionic solvation. We investigated the thermodynamics governing the binding of two organophosphorus ligands (Cyanex 272 and D2EHPA) with Co²⁺ and Ni²⁺ at the aqueous-organic liquid–liquid interface using a combination of experiments and computations. UV-vis and IR spectroscopy confirmed that extraction induces solvation …

Field-Based Device To Test Possible Tbis, Colin Laidlaw, Lauren Powers

Student Research Design & Innovation Symposium

Traumatic brain injury (TBI) remains a major cause of death in the United States. In emergency field care, first responders must quickly assess neurological status and determine appropriate patient transport. The Glasgow Coma Scale (GCS), the primary neurological assessment used in the field, correctly identifies TBI only about 40 percent of the time. In Rhode Island, state protocols require all patients with suspected TBI to be transported to a Level I trauma center. This creates challenges when trauma resources are limited or when symptoms are subtle and difficult to evaluate in the field. To address this issue, a reaction time …

Story Builders, Jillian Mercer

Student Research Design & Innovation Symposium

The objective of this project was to create an engaging and educational game for children in the 1st - 3rd grade that strengthened reading comprehension skills and fostered positive associations with reading through collaboration, tactile learning, and fun. The COVID-19 pandemic forced the shutdown of schools and switched learning to online only, which caused a lasting negative impact to childhood literacy in America. Research and first-hand testimony revealed that children developed a more positive relationship to reading when it was a group activity, as opposed to solitary, screen-based learning. In the early stages of prototyping, words in colored blocks were …

Futura Type Book, Ava Nuckolls

Student Research Design & Innovation Symposium

This creative piece was the final project for GDES: 2000 Typography. The goal of the project was to create a book about a chosen font that consisted of at least 6 pages. The book would tell a brief history of the font, while showcasing weights, styles, and anatomy. Each book would have a cohesive theme throughout. The font chosen specifically for this project was Futura by Paul Renner. The theme was retro-futuristic and consisted of green text and graphics on top of a black background. The process of this piece started with choosing a font and creating thumbnail sketches. After …

One Product, One Hook, Thousands Of Views, Mackenzie Lauto

Student Research Design & Innovation Symposium

The video highlights how a single, well-positioned product, Cocokind lip balm, with a great discounted deal of running, can generate strong engagement through strategic short-form content. By combining a clear value proposition (a high-quality product at a discounted price) with a scroll-stopping hook, the video demonstrates how brands can quickly capture audience attention. This approach has urgency, relatability, and visual appeal to drive views, interaction, and potential conversions, showcasing the effectiveness of simplicity in social media marketing.

Burghi Variable Candy Land Type Book, Lily Guyon

Student Research Design & Innovation Symposium

This project showcases a comprehensive understanding of a chosen typeface by exploring its visual identity and emotional connection. The student developed a type book focused on the font's aesthetic and its creator, incorporating two artistic quotes that align with the project's overall theme. The book features a detailed analysis of both Burghi Big and Burghi Small, including a labeled breakdown of letter anatomy and a full display of the alphabet, numbers, and symbols. Concluding with a historical overview of the font and its designer, the project finishes with a formal references page containing the student's credentials and course information.

Standard Issue, Tyler Miles

Student Research Design & Innovation Symposium

Created for GDES 2000, STANDARD ISSUE explored the font Helvetica and rigid masculinity using archival military testing imagery. The project deconstructed the typeface's neutrality, personifying Helvetica as a clinical figure of, "structured, pervasive, and subverted" nature. By juxtaposing 1950s Swiss design with the physical vulnerability of military experimentation, the work examined the "bureaucracy of language," ultimately framing the "standard" as a form of armor that humans wear as much as it wears them.

Paraná Fiz, Cloey Dias

Student Research Design & Innovation Symposium

This project focused on the conceptualization and design of a beverage brand inspired by the Paraná River located in Argentina. The river, the second longest in South America, was used as both a cultural and visual reference, symbolizing movement, energy, and natural vitality. These characteristics guided the overall concept and aesthetic direction of " Paraná Fiz", a yerba mate-infused soda water brand. The product aimed to merge traditional Argentine influences with a modern beverage format through flavors inspired by popular Argentine fruits. The project involved extensive visual branding development, including the creation of a custom logo, four distinct flavor identities, …

6 Degrees Of Freedom Robotic Arm, Vansh Garg , ‘26, Eric Chen , ‘26, Kaw Moo , ‘26

Senior Theses, Projects, and Awards

This project involved the design, fabrication, and testing of a 6 degree of freedom robotic arm. The goal was to demonstrate that a low-cost, 3D-printed system could achieve useful strength, modularity, and basic manipulation for simple pick-and-place tasks. Potential applications include education, prototyping, hobbyist robotics, and low-precision automation.

The mechanical system uses 3D-printed arm links, cycloidal gearboxes, belt-driven transmission, bearings, and a modular end effector. These components were selected to balance cost, manufacturability, and performance, with gear reduction used to increase available joint torque. The electrical system provides motor actuation, power distribution, and controller integration, while the software enables command-based …

System-Level Assessment Of Non-Ideal Phase Change Material Behavior In Low Earth Orbit Spacecraft Thermal Control Models, Nathan S. Bruursema

Dissertations, Master's Theses and Master's Reports

Phase change materials (PCMs) are of interest in spacecraft thermal control because their latent heat capacity can provide passive thermal buffering during transient or cyclic heat loads. PCM behavior is often treated using idealized assumptions such as repeatable phase transition temperatures, consistent thermal accessibility across cycles, and full latent recovery. However, practical PCM behavior may be affected by non-ideal phenomena such as supercooling, interfacial resistance, and degradation of internal transport accessibility. This thesis examines the system-level relevance of these effects for low Earth orbit (LEO) spacecraft thermal control and identifies the operating conditions under which non-ideal PCM behavior becomes design-relevant. …

Typology And Spatiotemporal Patterns Of Illegal Fishing Activities In The Philippines: Implications For Fisheries Management, Marlowe O. Acevedo, Tzu-Yun Ching, Chih-Shin Chen

Journal of Marine Science and Technology–Taiwan

Illegal, unreported, and unregulated (IUU) fishing activity is a global threat and continues to undermine the effectiveness of fisheries regulations and management measures. However, the extent of illegal fishing in Philippine waters remains unclear. This study aims to identify the types of illegal fishing in Philippine waters and analyzed their temporal and spatial patterns. Data were obtained from the records of the Philippine Coast Guards (PCG) and Maritime Law Enforcement Agencies from 2015 to 2020. A total of 602 apprehensions were recorded, resulting in 813 violations, of which 630 cases were fishery violations. Most of the watercrafts involved in the …

Delay-Doppler Integrated Sensing And Communications (Dd-Isac) With Predictive Beamforming, Mohammad Abdul Mobin, Yanjun Pan, Jingxian Wu

Electrical Engineering and Computer Science Faculty Publications and Presentations

A new delay-Doppler (DD) integrated sensing and communications (ISAC) framework is proposed for unmanned aerial vehicle (UAV) systems. In the DD-ISAC framework, both sensing and communications are performed by using the orthogonal delay Doppler division multiplexing (ODDM) waveforms, which unify sensing and communication designs through the unique ODDM waveform properties, such as local DD-domain bi-orthogonality and dual-resolution. Specifically, the dual-resolution property enables the generation of a range-Doppler map for accurate and low complexity sensing, and the bi-orthogonality minimizes interference for both sensing and communications. The ODDM waveforms are used in combination with the phase comparison monopulse technique and a scaled …

Cats 2.0: Leveraging Large Language Models And Graph Databases For Robust Arabic Sms E-Commerce Systems, M. Samir Abou El-Seoud, Daoud M. Daoud 2

Computer Science

Arabic SMS-based e-commerce platforms pose unique challenges due to the spontaneous and noisy nature of user-generated text (e.g., abbreviations, dialectal Arabic, or “Arabizi” transliterations). In this paper, we present Classified Ads Text Service (CATS) 2.0, an improved classified ads system that combines probabilistic large language models (LLMs) with deterministic graph-based knowledge representations to achieve robust understanding and matching of Arabic SMS content. Building on earlier work that emphasized the importance of integrating sublanguage analysis with content-oriented methods, our approach uses a hybrid pipeline: an LLM interprets free-text messages and extracts structured information, which is then inserted into a Neo4j graph …

Machine-Learning- And Iot-Based Approach For Predicting Water Quality Using Data Classification And Explainable Ai Technique, M. Samir Abou El-Seoud, Omar H. Karam 2, Hosam El-Sofany 3

Computer Science

Maintaining water quality is crucial for both public health and environmental sustainability. This study proposes a machine-learning- (ML-) and Internet-of-Things- (IoT-) based approach to water quality prediction that utilizes explainable artificial intelligence (XAI) and data classification techniques. The proposed approach integrates IoT devices to enhance real-time data collection, facilitating continuous monitoring and early anomaly detection. Ten different ML classifiers—Decision Trees, K-Nearest Neighbors, XGBoost, Naïve Bayes, Logistic Regression, AdaBoost, Random Forests, Support Vector Machines, Voting, and Multi-Layer Perceptron—were evaluated to find out which one works best for predicting water quality. We used three distinct approaches for feature selection: analysis of variance …