Engineering Commons^™

Design And Fabrication Of 3d Bioprinted Scaffold: Experimental And Machine Learning Methods, Mohan K. Dey

LSU Doctoral Dissertations

The development of reliable hydrogel-based scaffolds for extrusion bioprinting remains limited by the poor structural fidelity of low-viscosity bioinks and the lack of robust, high-throughput quality evaluation methods. This dissertation can resolve such challenges by developing the combination of optimized hydrogel formulations, cryogenic-assisted bioprinting, and artificial intelligence (AI)-based scaffold evaluation to the use of tissue engineering and preclinical cancer modelling applications. To assess the rheological behavior, printability, mechanical properties, and biocompatibility of the alginate – gelatin (Alg–Gel) system, a novel system of hydrogel was developed and characterized using Alg–Gel hydrogel system. The 7% alginate, 8% gelatin mixture was found to …

Experimental Evaluation Of Membranes In The Recovery Of Hydrogen From Serpentinization, Rafael Dos Santos

LSU Master's Theses

Natural hydrogen generated through serpentinization reactions has emerged as a promising low-carbon energy resource. However, serpentinization-derived gas streams commonly contain methane (CH₄) and carbon dioxide (CO₂), requiring purification before hydrogen can be utilized in industrial applications. Membrane-based gas separation technologies offer a potentially cost-effective solution for hydrogen recovery; however, to the best of the author's knowledge, no previous studies have specifically investigated their application to serpentinization-derived gas streams.

This work evaluates the performance of two commercial polymeric membrane modules (Evonik and Generon) and one palladium-copper (Pd-Cu) membrane module (Okaya) for hydrogen recovery from gas compositions representative of serpentinization environments. An …

Impact Of Asphalt Binder Chemical Composition On The Performance Of Asphalt Mixture., Sagar Parajuli

LSU Master's Theses

Asphalt binder is composed of four chemical fractions, namely, saturates, aromatics, resins, and asphaltenes (SARA). Binders with the same performance grade (PG) can exhibit different chemical compositions due to variability in crude oil sources and refining processes. The objective of this study was to evaluate how such chemical variations influence the mechanical performance of asphalt mixtures at high, intermediate and low temperatures.

Eighteen 12.5-mm nominal Maximum Aggregate Size mixtures were designed using two aggregate types (limestone and gravel), and three binder grades (PG 67-22, PG 70-22m and PG 76-22m), each from three different sources with varying chemical fractions. A suite …

Explainable Machine Learning For Biomedical Diagnostics: Optical Imaging And Eeg Signal Analysis, Fozia Rajbdad

LSU Doctoral Dissertations

The growing convenience of complex biomedical data begins new roads for better disease detection and functional identification via artificial intelligence (AI). Nevertheless, conventional analysis methods often rely on basic metrics that drop sensitive biotic differences, and various AI systems are difficult to infer, limiting their clinical reliability and practical use. There is a growing need for explainable, physiologically relevant computational models that can extract key biomarkers from diverse biomedical data sources. This dissertation addresses this problem by obtaining explainable machine learning and deep learning procedures for studying biomedical signals and optical imaging data.

This dissertation is divided into two parts; …

A Reduced-Order Framework For Stochastic Criticality Estimation In Granular Energetic Materials, Philip T. Melton

LSU Master's Theses

Granular energetic materials (EMs) exhibit stochastic shock initiation because pore collapse, frictional dissipation, and localized thermal activation depend on microstructural descriptors that vary between nominally identical samples. Fully resolved mesoscale and atomistic calculations can represent these mechanisms, but their computational cost limits direct ensemble evaluation of microstructure-conditioned criticality thresholds. This thesis introduces a reduced-order framework for stochastic criticality estimation in granular EMs by coupling five explicitly defined model components: first, a one-dimensional steady compaction-shock model that maps initial solid volume fraction and shock pressure to a bulk mass-specific dissipated-work budget; second, an SEM/synthetic-image segmentation workflow that extracts pore area, perimeter, …

Direct Measurement Of Stem Position For Gas-Lift Valves While Flowing At High Pressure, Paulo E. Reitz

LSU Master's Theses

The present work challenges the idea of estimating the gas lift valves true dynamic stem position by providing an alternative capable of tracking the real stem position during flow tests. This is of interest for the improvement of performance determination tests and procedures that involve critical safety concerns, complex training and considerable investments, both in time and money. This scenario, together with the strong interest of industry for valve optimization and the scarcity of works available in literature, encouraged this study. An apparatus named Vision System was developed for tracking and measuring the stem position during flow tests. The developed …

Evaluating Soil Health And Crop Yield In Louisiana Agricultural Systems: Impacts Of Best Management Practices And Prediction Models, Hector J. Mendoza Lagos

LSU Doctoral Dissertations

The adoption of conservation management practices is critical for improving soil health, enhancing nutrient use efficiency, and sustaining crop productivity in row crop systems in Louisiana. This study evaluated the role of conservation agronomic practices, soil biochemical indicators, and machine learning predictive models to improve soil nutrient dynamics, soil health indicators, microbial communities (MC), and crop productivity on a corn (Zea mays L.) research plot scale and in a commercial forty-hectare cotton (Gassypium hirsutum L.)-corn-soybean (Glycine max L.) rotation system in northeast Louisiana. The objectives of the study were to evaluate soil nutrient dynamics and MCs under …

Techno-Economic Assessment And Life Cycle Analysis Of Electrocatalytic Reduction Of Co2 To Ethanol., Omotolani Elizabeth Oduyebo

LSU Master's Theses

This study presents a techno-economic analysis (TEA) and life cycle assessment (LCA) of the electrocatalytic reduction of CO₂ to ethanol, a multi-carbon (C₂) product with significant market value. Prior TEA studies have relied on simplified lump-sum separation cost estimates, and prior LCA studies have rarely examined the combined effect of CO₂ source and electricity supply on carbon intensity gaps that this work addresses through process-simulation-grounded analysis. An Aspen Plus process simulation was developed for an anion-exchange membrane (AEM) electrolyzer system coupled with an extractive distillation separation train using ethylene glycol as the entrainer, achieving 99.9 wt.% ethanol purity …

Public Understanding Of Ai-Enabled Cyber Threats And Its Impact On Cybersecurity Governance In The United States, Charlotte M. Barbrick

LSU Master's Theses

Artificial intelligence is spreading quickly in workplaces and everyday life, and that fast growth is creating cybersecurity and privacy risks that current laws and organizational practices do not fully address. This thesis asks whether AI-specific knowledge shapes public support for cybersecurity governance of AI-enabled systems in the United States. Using nationally representative survey data from the Pew Research Center’s American Trends Panel (ATP) Wave 119, fielded December 12--18, 2022, I estimate weighted regression models to test how objective AI knowledge shapes support for cybersecurity governance. I also test whether concern about data misuse helps explain that relationship and whether AI …

Implantable, Sensor-Embedded Vascular Graft Towards Wireless Monitoring Of Stenosis, Nnamdi Dike

LSU Master's Theses

Arteriovenous (AV) grafts are commonly used to provide vascular access for hemodialysis in patients with end-stage renal disease. Despite their widespread use, AV grafts are prone to complications such as stenosis and thrombosis. Early detection of these conditions remains challenging with current monitoring methods too costly or insufficient. This work presents the design, fabrication, and validation of an LC pressure sensor embedded within a model AV graft to enable real-time monitoring. The proposed system integrates a parallel-plate capacitive pressure sensor with a spiral inductor to form an LC circuit embedded within an elastomeric graft wall. The Ecoflex 00-30 dielectric layer …

Movable Bed Physical Model Investigation Of Bed Level Changes Caused By River Sediment Diversions, Hayden Cole Franklin

LSU Master's Theses

Since 1932, due to human and natural processes, over 2,000 square miles of Louisiana’s coast have been lost. River sediment diversions have been proposed as sustainable options to combat land loss. These projects, like the proposed Mid-Barataria Sediment Diversion, are designed to deliver sediment-rich Mississippi River water into nearby bays and estuaries, helping build and maintain land. However, river sediment diversions may alter river hydraulics and sediment transport, potentially inducing upstream degradation and downstream aggradation. Using the Lower Mississippi River Physical Model (LMRPM), this study quantitatively analyzed bed level changes as well as hydraulic conditions associated with the proposed Mid-Barataria …

Gas Lift Valve Operation Under Elevated Pressure And Temperature Conditions, Pedro O. De Melo Flores

LSU Master's Theses

Gas lift valves are critical components in artificial lift systems, and their performance directly influences well unloading efficiency and long-term production. Current industry models used to correct valve opening and closing pressure for temperature rely on assumptions that have not been extensively validated against experimental data. This study experimentally investigates the opening and closing behavior of injection pressure-operated (IPO) gas lift valves under controlled elevated-temperature conditions. A load rate test bench was modified to include a heating system capable of maintaining valve temperatures up to 220°F. Five gas lift valve designs from multiple manufacturers were tested over a range of …

A Pretraining-Based Framework For On-Device Training Of Imu-Based Locomotion Mode Detection For Wearable Active Exoskeletons, Muhammad Tahir Khan

LSU Master's Theses

Active exoskeletons are being developed to support human movement in physically demanding industries such as construction. For these systems to work effectively, they must be able to correctly identify the user’s current activity. This process is known as locomotion mode detection and plays an important role in selecting the appropriate control parameters for exoskeletons. Many existing approaches use inertial measurement units (IMUs) to recognize these activities and have shown strong performance. However, most of these methods depend on large amounts of labeled data collected under specific conditions. As a result, they often do not perform well when applied to new …

Wearable Sensor System Used To Measure Linear And Angular Acceleration Of The Head Of American Football Players, David A. Emerson

LSU Master's Theses

American football players are repeatedly subject to high-velocity impacts; collisions can exceed 100 g in the NFL [1]. Rotational accelerations of the head caused by high-velocity collisions have been proven to be extremely damaging to the highly organized brain fibers at the brain-skull interface [2]. The sensor system (SS) measures the nonplanar acceleration at four locations on a chin strap using ADXL314 accelerometers and utilizes rigid-body kinematics to estimate the head rotational state from the measured acceleration of the SS [3]. The SS was validated through progressive testing, including steady-state, linear impact, and rotational impact testing. The ADLX314 sensor exhibited …

Marine Vehicle Dynamics Using Koopman Operator Theory With Hybrid Observables, Mikhalib A L Green

LSU Master's Theses

Accurate modeling of marine vehicle dynamics remains challenging due to strong nonlinear hydrodynamic effects, environmental disturbances, and sensitivity to configuration changes, particularly for small-scale platforms. Classical physics-based models require extensive parameter identification and often exhibit degraded performance outside narrow operating regimes, while purely data-driven approaches may lack structure or impose high computational cost. This thesis presents a data-driven Koopman operator framework with hybrid observables for modeling the dynamics of unmanned marine vehicles. The proposed approach combines structured monomial observables with a learned neural network embedding to construct a lifted state representation in which the nonlinear vehicle dynamics are approximated by …

Large-Scale File Fragment Classification Via Multi-View Learning, Samuel Hildebrand

LSU Master's Theses

File reassembly is one of the most fundamental tasks in digital forensics, enabling recovery of data from potentially damaged storage media even when file system metadata is unavailable. This thesis reviews more than two decades of work in the realm of file carving, with a particular focus on fragmented file carving, which remains a focus of research, and file fragment classification, a principal component of fragmented file carving. This thesis serves a literature review of both file carving and fragmented file carving, surveys the massive amounts of data needed for the task of fragment classification and the datasets that serve …

Identification Of Thruster Faults In Underwater Vehicles By Using Custom Encodings In Spiking Neural Networks, Donovan Gegg

LSU Master's Theses

Autonomous Underwater Vehicles (AUVs) are untethered robotic platforms used for tasks such as seafloor mapping, infrastructure inspection, and environmental monitoring. Recent technological advances have produced smaller, more affordable platforms, broadening access to research teams and small companies alike. This miniaturization comes at the cost of them handling drawbacks associated with a more compact machine such as reduced battery capacity as well as limited processing and sensing capabilities. These constraints make small-sized marine vehicle’s reliability critical as they can cause malfunctions, making the loss of a vehicle more likely. Actuator faults are particularly consequential as unintended and unstable control in an …

Polymer Microstructures For Advanced Biomanufacturing, Tongyao Wu

LSU Doctoral Dissertations

With the continued growth of the biopharmaceutical industry, the demand for scalable, robust, and resource-efficient platforms for large-scale mammalian cell culture is amplified. Recent developments in microfluidic technology, such as precise control of the microenvironment, showed the potential to improve the performance of cell culture systems. However, constrained by scalability and operational efficiency, applying such approaches to large-scale cell culture and biopharmaceutical production presents challenges. This dissertation addresses these challenges through three independent but conceptually related technological developments. First, a roll-to-roll (R2R) fabrication process was developed for the scalable production of hollow microcarriers (HMCs). HMCs provide three-dimensional microenvironments suitable for …

Quantifying And Integrating Community Hardship Into Two-Stage Stochastic Grid Reliability Optimization With Battery Storage, Fredrica Arthur

LSU Master's Theses

Traditional reliability planning for conventional distribution systems is largely utility-oriented, with a focus on collective system performance metrics like Expected Energy Not Supplied (EENS), where implicitly all unserved energy is considered of equal weight in terms of post-outage economic hardship. Yet, it is well understood that extended outage durations cause an uneven level of hardship to socioeconomically disadvantaged communities. This thesis proposes a community-informed reliability planning framework where the hardship caused by outages is explicitly considered in the battery energy storage system (BESS) location and sizing problem. First, a hardship-weighted Energy Not Supplied (WENS) measure is proposed, where income, education, …

Time-Robust Evaluation For Multi-Dataset Intrusion Detection Reveals Temporal Shortcuts And Strong Baselines, Kyle A. Mccleary

LSU Master's Theses

Pooled multi-dataset benchmarks are an attractive way to evaluate intrusion detection systems (IDS) across heterogeneous public corpora, but they can quietly reward shortcut features tied to capture schedules and dataset identity. This work introduces TRACER, an auditable benchmark specification that standardizes seven public IDS corpora into a shared transaction-window prediction unit and a shared label ontology, enabling controlled comparisons between compact sequence backbones and strong tabular baselines under matched splits, training budgets, and scoring rules.

Under this protocol, absolute clock time is a strong shortcut under pooled random splits. Enforcing time-robust controls (timestamp rebasing, circular shifts, and schedule-token masking) reduces …

Multimodal Thermal And Mechanical Characterization Of Cryopreservation Effects In Biological Cells, Subhrajyoti Sourav Kumar Kundu

LSU Master's Theses

Cryopreservation is critical for long-term storage of cells, tissues, organs, and reproductive cells in medicine, biotechnology, and conservation. However, its success is limited by ice formation and cryoinjury, prompting extensive research into analytical tools for understanding and improving cryopreservation outcomes. We outline the principles of each technique and how they are used to detect key thermal and physical events such as ice nucleation, vitrification, devitrification, and cryoinjury. DSC enables quantitative thermal characterization including critical cooling/warming rates (ranging from 1-200°C/min for various systems) and glass transition temperatures. Cryomicroscopy provides direct real time visualization of ice crystal dynamics, distinguishing intracellular versus extracellular …

Developments Of New Metal-Free Batteries And Self-Charging Batteries For Sustainability, Abhishek Paudel

LSU Doctoral Dissertations

The growing concerns over the limitations and environmental impact of conventional lithium-ion batteries (LIBs) have driven the search for more sustainable energy storage solutions. Metal-free batteries, such as those utilizing ammonium ions (NH₄⁺), offer a compelling alternative due to their non-toxic nature, abundant availability, and unique electrochemical properties. The NH₄⁺ ion's lower molar mass and distinct tetrahedral structure enable efficient charge transfer and intercalation, distinguishing it from traditional metal ions and potentially improving battery performance. Self-charging batteries, which integrate piezoelectric materials to harvest ambient mechanical energy, present a significant advancement in energy storage technology. By capturing and converting mechanical energy …

Applications For The Quasi-Atomic Orbital And Constrained Density Functional Theory Methods On Catalytic Reactions, Alvaro David Loaiza Orduz

LSU Doctoral Dissertations

Selective activation of C–O, C–H, and C–C bonds underpins biomass upgrading, alkane functionalization, and CO₂ conversion. Despite their importance, the electronic factors governing catalytic performance remain incompletely understood, and many industrial processes still rely on empirical trends or material-specific observations. This dissertation addresses this gap by applying density functional theory (DFT), thermodynamic decomposition, and electronic-structure analysis to identify unifying principles of reactivity across transition-metal phosphides, vanadate oxides, copper-based electrocatalysts, and mixed IrO₂–RuO₂ layers. This work examines how charge transfer, orbital localization, and ligand-induced perturbations control reaction pathways in diverse catalytic systems. C–O bond scission in 2-methyltetrahydrofuran (MTHF) and methanol was …

The Role Of Laminin Isoforms In Glioblastoma Migration, Aseel Ahmed, Faezeh Ghobadi, Hanna Devillier, Qi Cai

Distinguished Undergraduate Researcher Program

Glioblastoma (GBM) remains the most prevalent and lethal invasive brain tumor, denoted by a median survival of 12-15 months despite standard procedures such as safe surgical resection, radiotherapy, and chemotherapy. This limited effectiveness largely arises from the infiltrative nature of GBM cells, which interact with the brain’s extracellular matrix (ECM) by migrating along blood vessels and axonal pathways. The ECM contains the interstitial matrix (IM) and the
basement membrane (BM) made up of proteins and glycans which interact with the malignant cells. While research has mainly been focused on understanding how GBM cells interact with IM components, our knowledge about …

The Healing Power Of Nanoparticles: Bioengineering Gelatin Films With Lignin-Graft-Plga Nanoparticles For Enhanced Tissue Repair, Marie Howe, Cristina Sabliov, Thanida Chuacharoen, Willyam Nikiema

Distinguished Undergraduate Researcher Program

Fish gelatin, a biocompatible material, can be methacrylated to enable its photo-crosslinking and formation of hydrogels for tissue-repair applications (1). This study investigated the incorporation of lignin-grafted PLGA nanoparticles (LNPs) into 3D-printed fGelMA hydrogels as controlled drug delivery systems. Lignin has UV absorbing properties, which could potentially interfere with photocrosslinking of fGelMA (2). These LNPs possess hydrophobic PLGA core and hydrophilic lignin forming the shell providing ample opportunities for delivery of drugs of different chemistries (3). Fluorescent LNPs (FLNPs) were engineered by covalently bonding a fluorophore to the shell prior to nanoparticle synthesis to allow for fluorophore tracking. The understanding …

Innovative Smart Sensing System For Accurate Monitoring Of Uhpc Setting Time, Ted Atera, Khalilullah Taj, Masoud Pasbani, Yen-Fang Su

Distinguished Undergraduate Researcher Program

Ultra-High-Performance Concrete (UHPC) is an advanced concrete material known for its strength and durability. Its superior mechanical properties make it an ideal material for demanding structural applications such as bridges. Accurate setting time measurements are crucial in helping to optimise the performance of UHPC. All conventional methods and standards for measuring the setting time of concrete rely on using expensive specialised equipment, such as the Vicat apparatus, which assesses penetration depth or resistance. However, these methods may not be well-suited for UHPC due to its rapid surface drying while the interior remains fresh. This prevents free needle penetration and leads …

Cache-Conscious Sparse Matrix Dense Matrix Multiplication On Gpus, Haoqiang Guo

LSU Doctoral Dissertations

Over the past decade, high-performance deep learning has evolved into a critical research domain, driven by the demand for efficient models and high inference throughput. Deep learning architectures have shifted from stacked convolutional layers to transformer-based models, while pruning techniques and graph-structured data have established sparse matrix–dense matrix multiplication (SpMM) as a fundamental kernel—particularly in graph neural networks (GNNs). Modern GPUs, with their massive parallelism and high-bandwidth memory, offer immense potential for accelerating these workloads. While SpMM implementations using the compressed sparse row (CSR) format remain common to avoid conversion overhead, preprocessing-based methods have recently demonstrated superior potential. In GNN …

Investigating The Fatigue Behavior Of Inconel 939 And Inconel 718 Through Micromechanical Testing, Mohammad Hossein Shahini

LSU Doctoral Dissertations

A detailed evaluation of fatigue crack growth (FCG) in a silicon-modified Inconel 939 alloy, fabricated via laser powder bed fusion additive manufacturing (L-PBF AM), was performed using cyclic bending tests on pre-notched microscale cantilevers with a cross-sectional dimension of 25 µm × 25 µm. To our knowledge, this study is the first to demonstrate that such microscale cantilever tests can effectively capture both the threshold and Paris-law regimes of FCG, despite the limited specimen size. The precise fabrication of these small-scale specimens allows for controlled fatigue crack propagation relative to the microstructure while minimizing the presence of volumetric defects. Notably, …

Equity-Aware Natural Hazards Resilience Assessment And Improvement Of Road Networks, Naqib Mashrur

LSU Doctoral Dissertations

As climate change intensifies the frequency and severity of hurricanes, the resulting flood events pose escalating threats to transportation infrastructure and community well-being. This dissertation addresses the intersection of flood resilience, network connectivity, and social equity by integrating principles from network analysis, equity planning, and disaster resilience. The research begins by examining the social dimensions of accessibility loss during hurricane-induced flooding. Spatial and demographic analysis reveals that Native American and Hispanic populations in the case study region experience the most significant reductions in access to essential service facilities, highlighting inequities in systems.

Investigating a region southern Louisiana road network demonstrates …

Bridging Modalities: Enhancing Multimodal Sentiment Analysis For Social Media Networks, Misbah Ul Hoque

LSU Doctoral Dissertations

Social media platforms like X (formerly Twitter) serve as rich sources of textual and visual information, making multimodal sentiment analysis essential for understanding complex human emotions. This dissertation aims to advance multimodal sentiment analysis by improving the semantic alignment and fusion of textual and visual features, thereby enabling more accurate and context-aware sentiment interpretation of social media content.

To address challenges in multimodal integration, this work proposes two complementary MSA approaches. The first approach introduces a similarity-based multi-layer attention neural network (SiMANN) that enhances modality integration through cosine-based similarity fusion and modality-specific attention to emphasize salient features in text and …