Digital Commons Network^™

Evaluating Matching Frontier Methods For Crash Modification Factor Estimation, Usama Elrawy Shahdah, Eman K . Ali, Sania Reyad Elagamy

Mansoura Engineering Journal

This study evaluates "matching frontier" methods against conventional approaches for estimating Crash Modification Factors (CMFs). Thirty-four method-model combinations were tested, including 16 frontier variants, 8 propensity-score-matching algorithms with negative binomial and mixed-effects models, and two benchmarks (empirical Bayes and cross-sectional), across multiple sample sizes and three true CMF values (0.80, 1.00, and 1.30). Of these, 30 converged successfully; 4 mixed-effects models with replacement-based matching failed due to duplicate control observations. Each scenario was evaluated across 10 Monte Carlo replications to quantify performance uncertainty.

Synthetic crash data were generated with known treatment effects, examining scenarios ranging from ideal (386 treated, 21,000 …

Remote Sensing-Based Rice Detection: Novel Indices From Optical And Sar Synergy, Kaifi Chomani

Mansoura Engineering Journal

Accurate rice mapping is challenging due to the lack of a simple, efficient approach and the complexity and limited ability of existing methods to distinguish rice fields from non-rice fields. This study proposes two novel indices: the Multispectral Rice Detection Index (MRDI), which utilises optical imagery, and the OptiRadar Rice Index (ORRI), which incorporates both optical and Synthetic Aperture Radar (SAR) data. Ground-truth data from the Cropland Cropping Systems (CROS) were used to assess the performance of both indices. The findings indicated that MRDI achieved an overall accuracy of 97.97% with perfect recall (100%) but lower precision (78.65%). However, the …

An Iterative & Analytical Hybrid Deep Learning Framework For Enhanced Brain Tumor Detection And Segmentation In Mri Video Scans, Swati V. Sakhare

Mansoura Engineering Journal

The critical need for accurate and robust brain tumor detection in MRI video scans is for early diagnosis and treatment planning operations. However, the existing methods suffer from several limitations, such as not being able to capture spatio-temporal features, handling class imbalances, and generalizing across datasets with diverse characteristics. To address these challenges, I propose a hybrid deep learning framework that integrates multiple advanced techniques to enhance the detection and segmentation of brain tumors. My approach starts by using the 3D CNN-LSTM (ResNet3D-LSTM) to extract spatio-temporal feature, which utilizes ResNet3D for spatial detail extraction and LSTM for temporal coherence across …

Exploring The Interplay Of Operational Conditions, Microbial Communities, And Water Quality In Drinking Water Ozone-Biofiltration, Kara Yvonne Cunningham

Graduate Theses, Dissertations, and Problem Reports (ETD)

Ensuring reliable access to safe drinking water is a growing global challenge as water utilities face variable source water quality driven by population growth, land‑use change, and climate change impacts. To address these challenges, utilities are implementing advanced treatment technologies designed to improve reliability under variable source water conditions. Ozone-biofiltration has emerged as a treatment approach that can reduce a wide range of natural and anthropogenic contaminants. However, despite its increased use, there is limited insight into the biological processes shaping filter function or how they respond to operational and environmental changes. Advances in molecular microbiology and high-throughput sequencing now …

Optimization Of Carbon Capture And Sequestration In The Tri-State Hub West Virginia, Ohio, And Pennsylvania, Saif Abdullah Saif Al-Nabhani

Graduate Theses, Dissertations, and Problem Reports (ETD)

Carbon Capture and sequestration (CCS) is a critical way for the decarbonization of power and industrial sectors. The Tri-State area between West Virgina, Ohio and Pennsylvania are a diverse region with various CO₂ sources, deep saline, depleted reservoirs, pipeline network, and the midstream infrastructure that makes it possible for Captured CO₂ to be safely and economically moved and stored over long periods of time. This project addresses the need for an integrated optimization framework that includes CO₂ sources to potential storing sites using a transportation network design and a subsurface model that secures storage and minimizes leakage risk.

The model …

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 …

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) …

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 …

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 …

Sainik Vol 1 Issue 2, Sastra Deemed To Be University

SAINIK

No abstract provided.

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 …

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 …

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 …

Fairness Without Demographic Attributes In Medical Vision–Language Models, Ahsan Habib Akash

Graduate Theses, Dissertations, and Problem Reports (ETD)

Vision–language models (VLMs) and the embedding models that underlie them have become the default representation layer for multimodal artificial intelligence. Trained by contrastive alignment over enormous, loosely curated image–text corpora, they inherit the demographic skew of that data, and they encode it in the geometry of the shared embedding space itself. The consequence is a model that performs unevenly across demographic groups even when no protected attribute is ever supplied as an input. In consumer applications this is an equity problem; in medicine it is a safety problem, because an embedding that is systematically less discriminative for one subpopulation translates …

Real-Time Lidar-Based Vehicle Detection Using Pointpillars For Connected And Automated Vehicle Applications, Jared Townsend Behr

Graduate Theses, Dissertations, and Problem Reports (ETD)

This work explores the implementation of a real-time LiDAR processing model for autonomous vehicle design. Automotive perception systems are becoming increasingly important as Advanced Driver Assistance Systems (ADAS) and Connected and Automated Vehicle (CAV) technologies continue to progress toward higher levels of autonomy. Reliable perception is paramount for autonomous features, such as adaptive cruise control (ACC), where identifying and tracking a lead vehicle is a safety critical task.

Many production ADAS perception systems primarily rely on radar and camera sensors for object detection and sensor fusion. These sensors work well to achieve classification and accurate distance detections; however, they are …

Experimental Evaluation Of 100 Ft X 10 Ft Frp Truss Pedestrian Bridge, Ashish Dhital

Graduate Theses, Dissertations, and Problem Reports (ETD)

ABSTRACT

Experimental Evaluation of 100 ft x 10 ft FRP Truss Pedestrian Bridge

Ashish Dhital

FRP truss bridges are now increasingly used in pedestrian bridge applications due to their high strength-to-weight ratio, corrosion resistance, better impact performance compared with conventional materials, and ease of assembly. These advantages make FRP systems attractive for accelerated construction, remote-site installation, and environments where steel or reinforced concrete bridges may experience corrosion-related deterioration. Due to insufficient data on both strength and serviceability (i.e deflection, frequency) performances. Full-scale testing of FRP pedestrian bridges of longer spans (50’-100’) is needed to better understand the dynamic behavior, long-term …

Process Optimization And Quality Control In Manufacturing Of Frp Composites By Vartm, Mohamed Emadeldin Omar Abdelhadi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Railroad tank cars, being one of the most used means of transportation of hazardous material across the country, have not been structurally upgraded for a long time. Thus, the Pipeline and Hazardous Materials Safety Administration (PHMSA), a United States Department of Transportation (USDOT) agency, released a rule for safer transportation of highly hazardous and flammable liquids by railroad to enhance the design specifications of both the new and existing tank cars in May 2015. A multifunctional composite jacket is proposed as a new material to structurally enhance the railroad tank cars to withstand any accidents that could happen en route. …

Swarm Of One: Self-Organization In Multicellular Robots, Trevor Riley Smith

Graduate Theses, Dissertations, and Problem Reports (ETD)

Designing robots traditionally relies on centralized, top-down methods that constrain adaptability and resiliency, producing systems optimized for specific tasks. However, such systems often fail in complex environments, such as extraterrestrial, disaster, or natural settings, where designers cannot foresee every scenario a robot may encounter. Achieving autonomy in these contexts requires robots that are resilient and capable of adapting to the unknown. Multicellular organisms exhibit this adaptability through self-organization, where complex structures and behaviors emerge from local interactions among their cells. Such organisms can be viewed simultaneously as a single entity and a swarm of cooperating cells, i.e., a Swarm-of-One.

This …

Cell-To-Pack Modeling Of Lithium-Ion Batteries: From Bayesian Parameterization To Pack-Level Optimization, Derek Slack

Graduate Theses, Dissertations, and Problem Reports (ETD)

One of the major hurdles for electric or hybrid aviation is the comparative energy density between lithium-ion batteries and jet fuel. To help obtain maximal power-to-weight ratio in electric or hybrid powertrains the battery pack design should be optimized to be as light as possible while meeting thermal safety and power requirements. The proposed solution is an optimization routine beginning with a cell-level parameter estimation of lithium-ion battery cells, where data driven Bayesian Gaussian Processes replace empirically or analytically derived parameters in a first principles electrochemical model in PyBaMM, as functions of the transient state of the cell. The resulting …

Assessment Of Emissions Factors For Produced Water And Other Atmospheric Liquid Storage Tanks On Natural Gas Sites, David Glenn Childs

Graduate Theses, Dissertations, and Problem Reports (ETD)

As the US natural gas (NG) sector continues to grow, concerns have increased regarding the effects and increasing volume of greenhouse gases emitted into the atmosphere. While NG is a cleaner burning fuel than diesel and gasoline, unburned methane (CH₄) has a global warming potential (GWP) 27-30 times that of carbon dioxide (CO₂). To better quantify CH₄ emissions on NG sites, the US Department of Energy (DOE) granted West Virginia University (WVU) funding to collect direct measurements at well sites and compressor stations.

In this work, a total of 18 sites, including controlled and uncontrolled …

Innovative Strategies For Stabilizing Zinc Anodes: From Molecular Design To Practical Zinc-Ion Batteries, Nhat Anh Thi Thieu

Graduate Theses, Dissertations, and Problem Reports (ETD)

Aqueous zinc-ion batteries (AZIBs) have become increasingly attractive as grid-scale energy storage solutions due to their safety, low cost, and environmental friendliness. However, the anode-electrolyte interface in AZIBs is prone to instability due to severe dendrite growth, self-corrosion, parasitic water-induced side reactions, and hydrogen evolution reactions (HER), which collectively lead to poor cyclability. To date, various interfacial engineering strategies have been investigated to address these challenges and improve the stability of Zn metal anodes. This dissertation presents a unified strategy for regulating Zn deposition through synergistic electrode and electrolyte design in laboratory-scale coin cells and then applies these concepts to …

Dielectrophoretic Biomarkers For Cancer Detection And Cell Analysis: Integrating Microfluidics And Ai, Raphael Olumide Oladokun

Graduate Theses, Dissertations, and Problem Reports (ETD)

Pancreatic ductal adenocarcinoma (PDAC) carries a five-year survival rate of 13%, driven largely by late-stage diagnosis and the absence of sensitive noninvasive screening tools. Current diagnostic methods lack the sensitivity to detect disease-associated systemic changes at early and precancerous stages, representing a critical clinical gap. This dissertation addresses the central hypothesis that PDAC and its pathological subtypes induce measurable, disease-associated electrophysiological changes in circulating PBMCs that can serve as correlative liquid-biopsy biomarkers. To test this hypothesis, this work asks: can dielectrophoresis (DEP), impedance and zeta potential analysis with AI-assisted classification distinguish benign pancreatic conditions (non-malignant control), precancerous lesions (primarily IPMNs), …

Development And Validation Of A Multi-Output Neural Network-Based Virtual Temperature Sensor For Electric Vehicle Thermal Management, Dalton Michael Wiggins

Graduate Theses, Dissertations, and Problem Reports (ETD)

Battery electric vehicles (BEVs) rely on accurate thermal management to ensure component performance, efficiency, safety, and long-term durability. Critical propulsion system components, including electric motors and high-voltage battery packs, are commonly monitored using physical temperature sensors. However, these sensors increase system cost, introduce additional hardware complexity, and may be impractical for directly measuring temperatures at critical internal locations. Virtual temperature sensors (VTSs) provide a software-based alternative by estimating component temperatures using readily available vehicle operating data.

This research presents the development and validation of a long short-term memory (LSTM) neural network-based virtual temperature sensor capable of simultaneously estimating the motor …

Evaluation Of Engineering Controls In Mitigation Of Fecal And Chloride Contamination To An Urban Stream, Gabriel J. Zenny

Graduate Theses, Dissertations, and Problem Reports (ETD)

Fecal coliform contamination is a major contributor to surface water pollution in the United States’ (U.S.) waterways, whether from combined sewer overflows (CSOs) (point source pollution), runoff from precipitation and snowmelt (nonpoint source pollution), or exfiltration from aging and failing infrastructure. In addition to fecal bacteria, chloride contamination is also a major, prevalent surface water contaminant impacting U.S. waterways. Chloride contamination is predominantly released into U.S. waterways as nonpoint source pollution, endemic to northern areas that use industrial brine and salt as a form of mitigation for ice accumulation on roadways. The purpose of this research was to investigate impairment …

Semantic-Weighted Lidar–Camera Slam For Robust Mapping In Vegetation-Rich Environments, David Aigbovboise Akhihiero

Graduate Theses, Dissertations, and Problem Reports (ETD)

Many LiDAR odometry and SLAM methods are lightweight and effective, but they assume that all LiDAR point returns are equally reliable. In practice, this assumption is often violated in vegetation rich or semi transparent environments, where in a single beam footprint, LiDAR beams may intersect multiple scattering surfaces like leaves, branches or glass. These ambiguities in returns produces probabilistic or biased range measurements that violate the assumptions of most geometric registration frameworks and can lead to drift and inconsistency.

To address this challenge, this dissertation proposes a semantic-weighted LiDAR-camera SLAM framework that integrates class dependent weights into a front-end LiDAR …