Engineering Commons^™

Economic Material For Large-Scale H2 Storage And H2-Co2 Separation, Hussein R. Abid, Alireza Keshavarz, Header Jaffer, Basim K. Nile, Stefan Iglauer

Research outputs 2022 to 2026

Hydrogen is a clean fuel that can potentially completely decarbonize the energy supply chain and mitigate global warming. Hydrogen – a highly volatile gas – however, needs to be separated from CO2 during H2 production, and also from cushion gas in H2 geo-storage projects; in addition, large-scale H2 storage is a key obstacle. We thus tested and chemically upgraded common sub-bituminous coal as a material for H2-CO2 separation and H2 storage. The coal adsorbed significant amounts of H2 and CO2 and demonstrated an excellent H2-CO2 separation efficiency if chemically modified. The work presented here thus provides fundamental data required for …

Serious Game-Based Training For Improved Utilization Of A Novel Temporalis Emg Interface For Controlling Powered Wheelchairs, Calvin Macdonald

Honors Undergraduate Theses

Amyotrophic lateral sclerosis (ALS) is a terminal neurodegenerative disease that leads to a lack of independent mobility. One solution uses a unilateral surface EMG (sEMG) interface on the temporalis muscle to provide autonomous control of a powered wheelchair. Limbitless Journey, an EMG-controlled serious game, intends to provide users with a virtual environment to train in before use in a real-world scenario. A recent study analyzed the effect of video game training on the use of sEMG systems on the forearm, showing significant improvement in the usage of the interface but no difference between Free Play and structured play. The study …

Robotic Gas Source Localization And Distribution Mapping Via Deep Reinforcement Learning, Iliya Kulbaka

UNF Graduate Theses and Dissertations

This research aims to advance the fields of Gas Source Localization (GSL) and Gas Distribution Mapping (GDM) by developing deep reinforcement learning (DRL) methodologies suitable for complex, real-world environments. GSL and GDM are crucial for applications such as environmental monitoring, hazardous material detection, and search-and-rescue missions, where safe and efficient exploration is essential. Traditional methods often fall short in dynamic settings influenced by factors like wind and obstacles. To address these limitations, this study proposes novel neural network architectures and learning frameworks for adaptive exploration and mapping, integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) layers, and Deep Q-Networks …

A Method For Investigation Of Small-Scale Morphologic Change Around Low Crested Oyster-Based Structures, Makaya S. Malila

UNF Graduate Theses and Dissertations

In recent years, a novel method for shoreline stabilization, Pervious Oyster Shell Habitat (POSH) units, comprised of recycled eastern oyster shells, has been developed. These mound-shaped structures have been deployed along several shoreline sites in Northeast Florida. This case study sought to develop and implement a method for investigation of small-scale morphologic change in the vicinity of these structures using smartphone LiDAR technology, surveying instruments, and 2D/3D mapping software. Results suggest that the POSH units are helping to mitigate erosion by trapping sediments around them. Moving forward, it may be possible to adapt the methodology used here to assess morphologic …

An Enhanced Evaluation Of Bioslurry For Use In Coastal Erosion Mitigation, Megan Elizabeth Dunton

UNF Graduate Theses and Dissertations

This paper discusses the treatment of Florida beach sand by surface percolating Bioslurry, a microbially-induced calcite precipitation (MICP) variant that was developed and refined in recent years. Researchers examined how variations in pore volume and surface area affected the cemented depth of soil treated via surface percolation. Results suggested that while the pore volume is an important factor that governs cementation in soil treated via surface percolation, it does not appear to be the only controlling factor and treatment volume per unit surface area is another important factor to consider when treating soil with Bioslurry using surface percolation. The implications …

Investigating The Role Of Tesserae In Energy Absorption In Shark Cartilage, Molly Dobrow

UNF Graduate Theses and Dissertations

Unlike mammals, shark endoskeletons are composed of cartilage, which is less stiff compared to bone. Despite this structural disadvantage, sharks have risen to become apex predators, with reported maximum posterior bite force estimates approximating 5914 N in bull sharks. Shark jaws are estimated to undergo high cycle loading within the organism’s lifecycle without evidence of failure. Fatigue resistance may relate to mineral distribution within a component of the endoskeletal system – tesserae. Tesserae are thin, roughly hexagonal tiles composed of calcium phosphate hydroxyapatite (HA) crystals and grow by accretion to eventually surround the hyaline cartilage core. Though tesserae dimensions are …

Finops-Driven Cloud Optimization Models For Enterprise Applications, Manikantha Varaprasad Inakollu

Computer Science and Engineering Faculty Publications

Cloud computing has revolutionized enterprise IT infrastructure, yet escalating costs and resource inefficiencies threaten to undermine these benefits. This research examines FinOps-driven optimization models that enable organizations to balance cloud performance, cost efficiency, and business value. The study addresses the critical challenge enterprises face in managing cloud expenditures while maintaining operational excellence. Through comprehensive analysis of FinOps principles and practical optimization frameworks, we develop models that integrate financial accountability, technical efficiency, and business alignment. Our research demonstrates that organizations implementing structured FinOps practices achieve 25-40% cost reductions without compromising application performance. The study contributes both theoretical frameworks for understanding cloud …

Enhancing Erp Auditability And Compliance Using Permissioned Blockchain, A Framework For Transparent And Immutable Enterprise Resource Planning Systems., Manikantha Varaprasad Inakollu

Computer Science and Engineering Faculty Publications

Enterprise Resource Planning systems serve as the backbone of modern organizational operations, yet their centralized architecture creates significant challenges for auditability and regulatory compliance. This research proposes a permissioned blockchain framework to enhance ERP auditability by creating immutable, transparent, and traceable records of all system transactions and modifications. The study addresses critical gaps in current ERP systems where transaction histories can be altered, audit trails prove insufficient, and compliance verification remains cumbersome. Through examination of existing ERP limitations and blockchain capabilities, we develop an integrated architecture that maintains operational efficiency while providing cryptographic assurance of data integrity. Our framework employs …

Tailored Micromagnet Sorting Gate For Simultaneous Multiple Cell Screening In Portable Magnetophoretic Cell-On-Chip Platforms, Jonghwan Yoon, Yumin Kang, Hyeonseol Kim, Abbas Ali, Keonmok Kim, Sri Ramulu Torati, Mi-Young Im, Changyeop Jeon, Byeonghwa Lim, Cheolgi Kim

Center for Bioelectronics Publications

Conventional magnetophoresis techniques for manipulating biocarriers and cells predominantly rely on large-scale electromagnetic systems, which is a major obstacle to the development of portable and miniaturized cell-on-chip platforms. Herein, a novel magnetic engineering approach by tailoring a nanoscale notch on a disk micromagnet using two-step optical and thermal lithography is developed. Versatile manipulations are demonstrated, such as separation and trapping, of carriers and cells by mediating changes in the magnetic domain structure and discontinuous movement of magnetic energy wells around the circumferential edge of the micromagnet caused by a locally fabricated nano-notch in a low magnetic field system. The motion …

A Framework For Scientific Data Indexing, Searching And Sharing, Apoorva Mohite

Master's Projects

Scientific data continues to grow. Wildfire simulation experiments performed by the WIRC team at SJSU have generated over 138 TB of data so far and it is expected to keep growing. It becomes difficult for researchers to search through that data to find the data of their interest. This data is stored on an HPC cluster that external users do not have access to. The WIRC team also conducts experiments and publishes their research, but the size of data makes it difficult to share these datasets. This project introduces a novel solution to indexing scientific data, searching through the data …

Fine-Tuning Large Language Models For Folder Structure Generation, Likhith Nemani

Master's Projects

Starting a new project is a significant challenge in the software development world. Building a new project skeleton and configurations will require vast amounts of time and effort. This project aims to overcome the difficulty presented by this challenge using advanced large language models, specifically fine-tuning LLMs. Our initial focus with the implementation is to use the powerful capabilities of advanced modern models to simplify and accelerate the complicated process of getting new projects started. The solution process begins with a user posting a README file to a predetermined repository. This README file then is used as a source for …

Credit Score-Based Lending System On The Ethereum Platform, Mayuri Shimpi

Master's Projects

Traditional banking systems act as intermediaries, assessing risks and profiting from interest rate differentials. Credit scores, provided by trusted bureaus, are commonly used to evaluate the creditworthiness of borrowers. Cryptocurrencies have emerged as a significant and innovative medium due to their decentralized nature, operating without reliance on a central authority, such as a government.

This report describes a project to implement the Autonomous Lending system on the Ethereum Platform (ALOE), as proposed in [1], aiming to seamlessly integrate traditional credit scoring methodologies for evaluating a borrower's risk of default. The objective of this project report is to establish a robust …

Ml-Based User Identification Through Mouse Dynamics, Rakshit Gupta

Master's Projects

User authentication and identification plays a crucial role in ensuring the security and integrity of digital systems. Traditional authentication methods, such as passwords and biometrics, have inherent limitations that can compromise system security. This research proposes a novel approach to user authentication by leveraging machine learning techniques and behavioral biometrics, specifically mouse dynamics. The primary objective is to develop a sophisticated framework that can accurately identify individuals based on their unique mouse behavior patterns. The study explores and compares multiple deep learning architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Transformer models, to generate embeddings from …

Analysis And Application Of Adaptive Ml Algorithms For Malware Classification, Rashmi Boddukuri

Master's Projects

Malware classification is the process of distinguishing malware samples into categories of malware families that it is associated with and remains a critical step in the process of mitigating malware-related threats. In recent years, machine learning techniques have emerged as a powerful tool for such malware classification tasks. In this study, we explore the application of adaptive machine learning models to malware classification in order to analyze and determine how they compare in performance to similar but non-adaptive algorithms. The results achieved in this study share insight into the strengths and limitations of adaptive learning models when applied towards malware …

Distinguishing Chatbot From Human, Gauri Anil Godghase

Master's Projects

There have been many recent advances in the field of Generative Artificial Intelligence and Large Language Models, with GPT 3 or ChatGPT model being one of the frontrunners in this field. These large language models have become so powerful that it has become difficult to differentiate between text written by humans and machine-generated text. This paper proposes a solution to the problem of classification of the origin of data (human or chatbot) by using Machine Learning. In addition, the proposed solution also helps us analyze the text generated by such Language Models and understand the underlying patterns present in the …

Exploring Gender Bias In Large Language Models: Cross-Linguistic Comparisons And Evaluation Letters Analysis, Athira Kumar

Master's Projects

Large language models (LLMs) play a significant role in modern human-computer interaction. They have exploded in popularity recently, becoming widely used for various tasks. However, concerns persist regarding potential biases within these models. This project investigates gender bias in the popular LLMs - GPT-3.5, GPT-4, Gemini, and LLAMA. The first part of our study focuses on analyzing biases using ambiguous sentences across three languages - English, Malayalam, and Tamil. We evaluate the LLMs to see if they associate occupations with commonly held gender stereotypes, by using specific professions within our test sentences. Through the use of two low-resource languages, this …

Lexigen: Lexical-Driven Image Generation, Sangram Prashant Chincholkar

Master's Projects

This research project proposes a novel approach to user-driven image editing via natural language descriptions. The aim is an accurate change of certain features of an image with respect to the descriptive text while maintaining, with equal concern, the integrity of the remaining parts of the image not affected by the description. The task is particularly relevant for fields like content creation, personalized design, and automated image editing that require both coherence of a visual scene and textual description. We propose a generative model, LexiGen, which perfectly integrates natural language descriptions with their corresponding visual changes within an image. The …

Adaptive Metric-Driven Load Balancing For Specialized Clusters Using Nginx, Juhi Raju Malkani

Master's Projects

Adaptive Metric-Driven Load Balancer is an innovative two-tier load-balancing system that uses NGINX and Prometheus to optimize resource allocation in specialized cloud clusters. This framework is built to give great performance and flexibility and runs on Google Kubernetes Engine (GKE), but it may also be deployed on local cloud environments for added security. The first tier of our system uses an NGINX-based load balancer to route incoming requests based on content type, sending traffic to hardware-optimized clusters for processing requests through specialized hardware. In our algorithm, the second tier dynamically modifies load distribution throughout each cluster by calculating pod weights …

Electron-Induced Chemical Transformations In Polymer Films: Understanding The Role Of Electrons In Euv Lithography, Maximillian Mueller

Master's Theses

The next generation of computer processors, memory, and nano-scale technologies all rely on our ability to create patterns at the nanometer scale reliably. Extreme ultraviolet (EUV) lithography can create sub-10 nm features by exposing photoresist materials, typically polymers or organic networks, to 13.5 nm, 92 eV light. At these energies, absorption of photons by the photoresist leads to the emission of a primary electron around 80 eV which inelastically scatters throughout the material, initiating chemistry and creating secondary electrons. The resulting cascade of electrons and chemical reactions remains poorly understood for most photoresist materials systems. This work aims to characterize …

A Lab-Scale Mold Simulator Employing An Optical-Fiber-Instrumented Mold To Characterize Initial Steel Shell Growth Phenomena, Muhammad A. Nazim, Rony K. Saha, Mario F. Buchely, Ronald J. O'Malley, Jie Huang, Arezoo Emdadi

PSMRC Faculty Research

A mold simulator was developed to replicate the mold oscillation and casting speed conditions of a continuous caster on the lab scale. A mold was designed incorporating fiber optics to capture internal temperature gradients and transient heat transfer phenomena during the initial solidification of steel. Casting parameters (casting speed, oscillation stroke and oscillation frequency) were investigated using the mold simulator. Solidified steel shells and mold thermal data were collected and characterized after initial solidification to investigate the impacts of mold oscillation on shell growth and mold heat transfer.

Femtosecond Laser–Inscribed Fiber Bragg Grating Sensors: Enabling Distributed High- Temperature Measurements And Strain Monitoring In Steelmaking And Foundry Applications, Ogbole Collins Inalegwu, Yeshwanth Reddy Mekala, Rony Kumer Saha, Farhan Mumtaz, Dinesh Reddy Alla, Deva Prasaad Neelakandan, Jeffrey D. Smith, Ronald J. O'Malley, Rex Gerald, Jie Huang

PSMRC Faculty Research

This study demonstrates the use of fiber Bragg grating (FBG) sensors for distributed temperature and strain monitoring in steelmaking and foundry applications. Integrated into inexpensive optical fibers, FBGs offer accurate and real-time remote sensing, detecting shifts in wavelength due to temperature (up to 1,800 °C), strain, or structural wear and tear. Furthermore, the intrinsic features of FBG sensors: compact size, immunity to electromagnetic interference and corrosion, robustness to vibration, ease of integration into existing composite structures, and non-intrusive measurement capacity in harsh environments make them ideal for steelmaking. FBGs optimize production, ensure quality, and enhance safety within the steel industry.

Enhanced Bottom Anode Monitoring In Dc Electric Arc Furnaces Using Fiber Optic Sensors, Yeshwanth Reddy Mekala, Rony Kumer Saha, Ogbole Collins Inalegwu, Muhammad Roman, Farhan Mumtaz, Rex Gerald, Jeffrey D. Smith, Jie Huang, Ronald J. O'Malley

PSMRC Faculty Research

A pin style bottom anode employs conductive steel rods that serve as the pathway for the high electrical power through rammed refractory at the bottom of a DC Electric Arc Furnace (EAF). Anode wear during operation is important to monitor, as anode replacement is expensive and impacts EAF productivity. Liquid steel penetration into the un-sintered refractory layer can result from rapid electrical power ramp-up, dips in furnace temperature, or operating the anode for too long between EAF campaigns. In extreme cases, the liquid steel may penetrate the bottom of the furnace when anode wear progresses too close to the bottom …

Fiber-Optic Raman Probe For On-Line Eaf Slag Analysis, Bohong Zhang, Hanok Tekle, Jeffrey D. Smith, Todd Sander, Ronald J. O'Malley, Jie Huang

PSMRC Faculty Research

In Electric Arc Furnace (EAF) steelmaking, the push for improved efficiency requires accurate analysis of the chemical composition of its slag system to control slag foaming, provide refractory protection, and maintain high furnace iron yield. Therefore, the ability to obtain real-time slag chemistry data would provide a useful tool to improve the control and efficiency of the process. The work reported here aims to assess the structure and chemistry of EAF slags at high temperatures using a portable fiber-optic Raman probe. The ability to relate Raman spectra peaks to chemistry and structure is demonstrated in experimental result with EAF slags …

The Melting Behavior Of Hydrogen Direct Reduced Iron In Molten Steel And Slag: An Integrated Computational And Experimental Study, Fabian Andres Calderon Hurtado, Joseph Govro, Arezoo Emdadi, Ronald J. O'Malley

PSMRC Faculty Research

Direct reduced iron (DRI) and hot briquetted iron (HBI) are essential feedstocks for tramp element control in the electric arc furnace (EAF). Due to greenhouse gas (GHG) concerns related to CO₂ emissions, hydrogen as a substitute for natural gas and a reductant in DRI production is being widely explored to reduce GHG emissions in ironmaking. This study examines the melting behavior of hydrogen DRI (H-DRI) pellets in the EAF containing low-carbon (0.1 wt.%) molten steel and molten slag. A computational heat transfer model was developed to predict the melting behavior of H-DRI pellets. To validate the model, a set …

Methylene Blue-Mediated Photodynamic Therapy In Combination With Doxorubicin: A Novel Approach In The Treatment Of Ht-29 Colon Cancer Cells, Nima Rastegar-Pouyani, Jaber Zafari, Alireza Nasirpour, Hossein Vazini, Nabbaa Najjar, Seyedeh Zohreh Azarshin, Fatemeh Javani Jouni

Electrical & Computer Engineering Faculty Publications

Introduction: With an alarmingly growing number of patients diagnosed with colorectal cancer, adopting innovative anti-cancer approaches has recently garnered great attention. One interesting concept is the co-administration of cytotoxic agents and safer modalities such as photodynamic therapy (PDT), which can subsequently improve therapeutic efficacy and potentially reduce the risks of severe adverse effects and drug resistance. In the course of PDT, a locally injected photosensitizer (PS) is irradiated with a light source, which subsequently generates reactive oxygen species (ROS) and induces programmed cell death in tumor cells.

Methods: In this study, to evaluate the potential anti-cancer effects of chemotherapy combined …

Energy And Empowerment In The High Arctic, Alyssa Pantaleo

Dartmouth College Ph.D Dissertations

National governments continue to depend on fossil fuels for electricity and heat generation in Arctic communities. This dependence threatens the economic, environmental, and cultural sustainability of Arctic communities and subjects them to future volatility and uncertainty. In Greenland, the centralized government structure creates additional challenges for northern communities by limiting the inclusion of local knowledge and priorities in favor of national, standardized solutions. This research identifies pathways towards fossil fuel reduction in northern Greenlandic communities via 1.) analyzing the potential for renewable energy inclusion in grid-scale or residential energy generation and 2.) analysis of the potential for energy reduction in …

Accelerating Cavity Fault Prediction Using Deep Learning At Jefferson Laboratory, Md M. Rahman, A. Carpenter, K. Iftekharuddin, C. Tennant

Electrical & Computer Engineering Faculty Publications

Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive …

Integrated Energy-Efficient Distributed Link Stability Algorithm For Uav Networks, Altaf Hussain, Shuaiyong Li, Tariq Hussain, Razaz Waheeb Attar, Farman Ali, Ahmed Alhomoud, Babar Shah

All Works

Ad hoc networks offer promising applications due to their ease of use, installation, and deployment, as they do not require a centralized control entity. In these networks, nodes function as senders, receivers, and routers. One such network is the Flying Ad hoc Network (FANET), where nodes operate in three dimensions (3D) using Unmanned Aerial Vehicles (UAVs) that are remotely controlled. With the integration of the Internet of Things (IoT), these nodes form an IoT-enabled network called the Internet of UAVs (IoU). However, the airborne nodes in FANET consume high energy due to their payloads and low-power batteries. An optimal routing …

Desirable Candidates For High-Performance Lead-Free Organic–Inorganic Halide Perovskite Solar Cells, Sajid Sajid, Salem Alzahmi, Imen Ben Salem, Nouar Tabet, Yousef Haik, Ihab M. Obaidat

All Works

Perovskite solar cells (PSCs) are currently demonstrating tremendous potential in terms of straightforward processing, a plentiful supply of materials, and easy architectural integration, as well as high power conversion efficiency (PCE). However, the elemental composition of the widely utilized organic–inorganic halide perovskites (OIHPs) contains the hazardous lead (Pb). The presence of Pb in the PSCs is problematic because of its toxicity which may slow down or even impede the pace of commercialization. As a backup option, the scientific community has been looking for non-toxic/less-toxic elements that can replace Pb in OIHPs. Despite not yet matching the impressive results of Pb-containing …

An Enhanced Deep Autoencoder For Flight Delay Prediction, Desmond B. Bisandu, Dan Andrei Soviani-Sitoiu, Irene Moulitsas

Journal of Aviation/Aerospace Education & Research

Accurate and timely flight delay prediction cannot be overemphasized because of the ever-increasing demand for air travel and its importance in deploying intelligent transportation systems. Nonetheless, there has not been a universal solution to the problem, as more intelligent flight decision systems are required for the aviation industry's future growth. Existing flight delay classification and prediction approaches are mainly shallow traffic models and do not satisfy many applications in the real world. Our motivation to rethink the deep architecture model for predicting flight delays emanates from the problem. In this research, we proposed a technique that modified stacked autoencoder architecture …