Engineering Commons^™

Effects Of The Psb-To-Ow Ratio And Light Intensity On Biogas Composition During Phototrophic-Assisted Anaerobic Fermentation Of Organic Waste, Kridsana Jirasansawat, Sirilak Prasertkulsak, Nitiwist Teangthai

Applied Environmental Research

The integration of phototrophic processes into anaerobic fermentation offers a promising strategy for improving biogas quality; however, the combined effects of the photosynthetic bacteria-to-organic waste (PSB-to-OW) ratio and light intensity remain unclear. In this study, the photosynthetic bacteria (PSB)-assisted anaerobic fermentation of municipal organic waste under varying PSB-to-OW ratios and light intensities was evaluated. An optimal PSB-to-OW ratio of 1:2 resulted in significantly higher CH4 concentrations and lower CO2 and H2S concentrations than the other tested ratios did (p < 0.05). At this ratio, moderate illumination (2000 lx) resulted in the highest CH4 concentration (68.2 ± 0.3%), compared with 1000 lx (61.5 ± 0.4%) and 4000 lx (63.1 ± 0.5%). Under illumination, the CO2 and H2S concentrations were lower than those under dark conditions, indicating a more favorable biogas composition. Kinetic analysis indicated that CO2 and H2S exhibited apparent first-order behavior under illumination, with the highest rates occurring at 2000 lx (kH2S = 0.093 day-1; kCO2 = 0.059 day-1), suggesting light-dependent transformation processes. Metagenomic analysis revealed a microbial community dominated by sulfide-oxidizing (Chlorobium sp.), photoheterotrophic (Rhodopseudomonas sp.), and syntrophic bacteria, supporting coupled carbon conversion and sulfur transformation. Overall, optimization of the …

Artificial Sense-Making Dataset, Jason A. Bengtson, John Sandstrom, Nathan Camp

NMSU Library: Datasets

No abstract provided.

Numerical Study Of Air Vents Influence On Wave Forces Acting On Box-Type Bridge Girders With Cavities Under Storm Surge, Khalida H. Jihad

UNF Graduate Theses and Dissertations

Along with the continuous rise of sea level, direct interaction between elevated coastal structures, such as coastal bridges, and seawater during storm surge, has been shown to potentially cause bridge failures in the past. For example, Hurricane Irma (2017) generated storm surge levels exceeding 2-3 m in portions of South Florida, causing widespread transportation disruption including roadway washouts, erosion, and bridge approach damage along US-1 in Florida Keys. Similar failures have occurred during Hurricane Ivan (2004), where uplift forces displaced spans of the Escambia Bay Bridge, and Hurricane Ian (2022), which caused partial collapse of the Sanibel Causeway when elevated …

Investigating The Effects Of Pressure On Fe And Co Intercalated 2h-Tas2 Through First-Principle Calculations, Ronald E. Putnam Jr.

UNF Graduate Theses and Dissertations

Using first-principles methods, we investigate the effects of applied isotropic pressure on the electronic and magnetic properties of intercalated Fe1/3TaS2 and Co1/3TaS2. Fe1/3TaS2 has been shown to be a ferromagnet that undergoes structural and magnetic phase transitions under applied pressure. The magnetic phase transitions are observed to be linked to the structural shift, and a mixture of magnetic phase states is present between 3 GPa and 6 GPa. Co1/3TaS2 was found to be antiferromagnetic, as confirmed by experimental work. One- and two-unit-cell systems of Co1/3TaS2 are analyzed, both showing band splitting that disappears under pressure. The application of pressure forces …

Efficiently Stowed Flashers With Uniform-Thickness Panels Based On Non-Developable Origami, Larry L. Howell, Robert J. Lang, Specer P. Magleby, Davis Wing

Faculty Publications

We present the mathematical construction of a deployable flasher pattern with near-uniform nonzero thickness in the deployed form and efficient face-to-face packing in the stowed form. We demonstrate its fabrication, deployment, and stowage. The results facilitate the design and manufacture of deployable systems with panels whose substantial thickness is dictated by the end application, with particular utility for space systems.

Improving Efficiency In Noma Schemes Having Inter-User Interference Using Mechanism Design, Zory Marantz

Publications and Research

Modern wireless systems utilize non-orthogonal multiple access to increase their rate capacities; however, the efficiency of the individual utility defined in bits per Joule has yet to be considered. Multiple variations of non-orthogonal multiple access have the interference of the signal-to-interference-plus-noise ratio as a function of the received power from multiple other users due to code implementations that are non-orthogonal or non-ideal cancellation in successive-interference-cancellation methods. Game theoretic concepts are used to improve user bits-per-Joule performance. Previous solutions increment transmit power and are not based on closed form systematic methods. The mechanism design presented here led to a non-cooperative Nash …

Ansys Discovery And Fluent Files For Modeling A Hydrocyclone With Scco2 And Coal Fly Ash, Isaiah Morones, Catherine Brewer

Chemical & Materials Engineering: Datasets

No abstract provided.

Sustainable Paper Production From Date Palm And Reed Leaves Through The Valorization Of Agricultural Waste Products, Imane Belyamani, Alreem Alameri, Jacqueline Soghman

All Works

The environmental consequences of wood-based paper production, including greenhouse gas emissions, have accelerated the search for sustainable alternatives. This study investigates the use of reed and date palm fibers as eco-friendly raw materials for paper production, focusing on starch's influence on their thermal, structural, and mechanical properties. Reed fibers exhibited a higher pulp yield (58.2 %) and lower lignin content (7.8 %) compared to date palm fibers (55.9 % yield, 14.1 % lignin), contributing to papers with smoother textures and greater flexibility. The incorporation of starch into both fiber types resulted in notable performance improvements, though the effects were more …

Probing Proficiency-Related Neural Representations With Pca And Ica, Onila R. Narayana Mudalige Don

Graduate Student Theses, Dissertations, & Professional Papers

How proficiency-related information is represented in task fMRI depends not only on the data themselves, but on the representational lens used to summarize them. This thesis examines second-language (L2) proficiency as a problem of representational organization rather than as a simple classification exercise. Using task fMRI from adult language learners performing semantic animacy judgments in their native language (L1) and second language (L2), I derive shared low-dimensional network representations with principal component analysis (PCA) and independent component analysis (ICA), then evaluate those representations under matched leakage-controlled decoding pipelines.

Across analyses, the central comparison is between representational frameworks rather than between …

Beyond The Meter: Frameworks For Evaluating The Broad Spectrum Of Microgrid Benefits, Elias Henderson

Cal Poly Humboldt theses and projects

Community-scale microgrids can create a range of social benefits. These include resilient electricity, cleaner air, decarbonization, and economic development, alongside more qualitative values like energy sovereignty and community self-determination. Many such benefits, however, are illegible to California’s market and regulatory frameworks. This thesis traces this disconnect, with particular attention to grid-edge communities in Northern California.

Microgrid benefits are classified as monetized, modeled, or incommensurable according to their inclusion/exclusion in existing regulatory and revenue mechanisms. To explore each category, a mixed-methods analytic approach is employed, pairing Tribal and stakeholder interviews with the development of a microgrid valuation calculator. This tool is …

Learning-Based Graph Shrinking For Quantum Optimization Of Constrained Combinatorial Problems, Monit Sharma, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Graph shrinking has recently emerged as a powerful preprocessing technique for hybrid classical–quantum optimization, enabling variable and constraint reduction before quantum solving. Conventional approaches rely on Semi-Definite Programming (SDP) relaxations to compute vertex correlations, but these methods suffer from high computational overhead, instance-specific tuning, and limited generalizability. In this work, we replace the handcrafted SDP correlation stage with a reinforcement learning (RL) based correlation estimator, trained to predict merge quality directly from graph structure. We reformulate the graph shrinking process as a Markov Decision Process (MDP), design a Graph Neural Network (GNN) policy to guide vertex merging, and integrate the …

Global Perspectives On Esg And Sdgs In Aviation, Leila Halawi

Publications

ESG implementation in aviation and airline operations has progressed significantly in the past few years. ESG is thought of as a source of competitive advantage. This research investigates the complex connection between ESG practices, SDG alignment, and trust-building in airline operations. We examine how airlines navigate these complex dynamics by drawing on literature and case studies from three distinct regions: the United States, Europe, and South America. We synthesize existing knowledge to illuminate pathways toward a more sustainable and trusted aviation industry and offer insights into successful approaches, persistent challenges, and emerging trends. © 2026 selection and editorial matter, Joanna …

Geometric Effects Of Warfighters On Body Armor Fit And Protective Capabilities Against Shock Wave Energy, Melissa Lynn Sutter

Doctoral Dissertations

Body armor is a vital piece of protective equipment for warfighters to defend against threats, necessitating continued development to improve comfort, weight, and protection. However, female warfighters often wear unisex body armor, designed primarily for the male torso. Current research has evaluated the short- and long-term detriments of female warfighters wearing these armors, focusing on comfort and performance. However, these studies do not comprehensively consider how a non-form-fitting armor compromises warfighter safety from battlefield threats. This research examines the geometric effects of female warfighters on armor protection level when defending against shock threats by evaluating the energy distribution on a …

Assessment Of Simulation Software Used For Cubesat Gnc Verification And Validation By University Research Teams, Alexander Taiyo Newett

Masters Theses

As the growth in university satellite teams continues, along with the greater trend in the small satellite market, the need for a guidance, navigation, and control verification and validation pipeline suitable for these young and inexperienced teams becomes evident. Much of the mathematical theory and software implementation of GNC concepts are large hurdles for teams largely composed of undergraduate students.

Many software packages exist that can help these teams achieve GNC verification and validation. If the learning curves of these software packages can be overcome, new satellite teams have the opportunity to better build and test GNC algorithms that are …

Machine Learning For Predictive Energy And Emissions Modeling Of Vehicles And Power Grids In The United States, S M Tanvir Faysal Alam Chowdhoury

Dissertations

The environmental benefits of electric vehicle (EV) adoption depend on more than replacing internal combustion engine vehicles with electric powertrains. EV adoption reshapes electricity demand, interacts with regional generation mixes, and influences travel behavior and congestion, creating a coupled transportation-energy system in which vehicle and power-plant emissions must be evaluated together. This dissertation develops machine-learning frameworks for predicting energy consumption and emissions from vehicles and power grids under rising EV adoption. The first component forecasts grid emissions from EV charging. Using simulation data from NREL's Cambium database, a Prophet-based time-series framework predicts carbon dioxide, nitrous oxide, and methane emission rates …

Cognitive Load Classification Using Functional Near-Infrared Spectroscopy, Pratham Shah

Theses and Dissertations (Comprehensive)

This thesis investigates the classification of cognitive load using functional near-infrared spectroscopy (fNIRS) signals recorded during an N-back working memory task. The study introduces a novel short-channel correction layer designed to suppress superficial physiological noise adaptively, addressing limitations of traditional General Linear Model (GLM) based regression. A single participant dataset comprising 69 validated sessions was analyzed using both conventional machine learning and deep learning approaches. Traditional classifiers: Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Random Forests, and Gradient Boosting were first evaluated using statistical features (mean, variance, peak, and slope). Among these, Gradient Boosting achieved the highest accuracy (55.6%), …

Taylorkan-Vit: Parameter-Efficient Vision Transformers For Medical Image Classification, Kaniz Fatema, Dr. Emad Mohammed, Dr. Sukhjit Singh Sehra

Theses and Dissertations (Comprehensive)

Effective and interpretable classification of medical images remains a critical challenge in computer-aided diagnosis, particularly in data-scarce and resource-constrained clinical settings where traditional deep learning models prove impractical. This study addresses the fundamental barrier to Vision Transformer adoption in medical imaging—massive parameter counts and data requirements—through a systematic two-phase methodology. Phase 1 evaluates three spline-based Kolmogorov–Arnold Network (KAN) variants to identify the optimal nonlinear approximation function for parameter-efficient medical image classification: SBTAYLOR-KAN (B-splines with Taylor series), SBRBF-KAN (B-splines with Radial Basis Functions), and SBWAVELET-KAN (B-splines with Morlet wavelets). Comprehensive experiments across brain MRI, chest X-rays, and tuberculosis datasets—without any image …

Sapphire Optical Fiber Bragg Grating Sensors Based On Dispersive Microwave-Photonic Frequency-Time Domain Analysis, Ruimin Jie, Chen Zhu, Farhan Mumtaz, Koustav Dey, Bohong Zhang, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Sapphire fiber Bragg gratings (SFBGs) have attracted growing interest for high temperature sensing in harsh environments, yet their interrogation typically relies on optical spectrum measurements, demanding a high-resolution optical spectrum analyzer (OSA) that is bulky, expensive, and constrained in acquisition speed. Moreover, the inherently multimode nature of sapphire fiber further complicates spectrum-based demodulation, thereby limiting the achievable sensing resolution. In this paper, we propose and experimentally demonstrate a microwave-photonic interrogation approach for SFBG sensors. Instead of measuring the optical reflection spectrum, the complex frequency response in the microwave domain of an SFBG is acquired using a vector network analyzer (VNA) …

Lidar-Based Framework For Detecting Suspicious Human Activities, Ahd Aljumah, Charalampos Antoniadis, Hakim Ghazzai, Nawfal Guefrachi, Ahmad Alsharoa, Gianluca Setti

Electrical and Computer Engineering Faculty Research & Creative Works

This study explores the development of Human Activity Recognition (HAR) systems capable of identifying suspicious activities to enhance security in public spaces. We propose an innovative solution that integrates LiDAR sensors with deep learning technologies. Our method employs advanced models operating on LiDAR point cloud, PV-RCNN for human detection, and LidarGait++ for classifying activities into categories such as standing or walking (non-suspicious) and sneaking or fighting (suspicious). Due to the scarcity of suitable real-world datasets for training such systems, we utilize a 3D simulation tool, Blender, to create realistic environments and generate labeled point cloud data. This synthetic dataset allows …

Deep Learning Approach For Microwave Imaging Based On Deep Convolutional Asymmetric Encoder-Decoder Structure And Physics-Induced Loss, He Ming Yao, Shiji Song, Michael Kwok Po Ng, Lijun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, we introduce an innovative deep learning (DL) methodology designed for real-time quantitative microwave imaging (MWI). Our approach is centered around the utilization of a deep convolutional asymmetric encoder-decoder structure (DCAEDS), which requires only a single-frequency far-field measurement of the electromagnetic (EM) scattered field as input and subsequently predicts the contrasts (permittivities) of the target materials. During the offline training process, we incorporate an EM forward solver specifically crafted to compute the EM scattered field generated by the predicted target contrasts (permittivities) produced by the DCAEDS. The DCAEDS is seamlessly integrated with this EM forward solver to optimize …

Identifying Industry-Preferred Automation Software In Engineering: An Indeed-Based Analysis To Inform Engineering Technology Curriculum Design, Triet Minh Phan, Collins Okafor, Winifred Okafor, Devang Mehta, Mohsen Souissi, Jenora Waterman, Connie Mayberry, Misty Thomas, Orlando Ayala, Angie Price, Maurizio Manzo

Engineering Technology Faculty Publications

In the evolving field of automation engineering, staying aligned with industry software demands is critical to preparing graduates for the modern workforce. This study investigates the prevalence of leading industrial automation platforms—Rockwell / Allen-Bradley (RSLogix / Studio 5000), Siemens (TIA Portal / Step 7), and Schneider Electric (EcoStruxure / Unity Pro)—across job postings collected from Indeed using the keyword "automation engineering." The research compiles a structured dataset of job postings with seven fields: ID, Job Title, Organization, Rockwell / Allen-Bradley, Siemens, Schneider Electric, and Posting URL. Each entry is manually coded to indicate whether the listed software platforms are mentioned, …

Interpretable Machine Learning For Bridge-Pier Scour Prediction And Flood Resilience, Adil Khan, Dalya Ismael

Engineering Technology Faculty Publications

Bridge-pier scour is a leading cause of flood-induced bridge failure, yet practice still lacks transparent, physics-informed tools that link data-driven prediction with design guidance. This study develops an interpretable, physics-aware machine-learning framework to predict equilibrium scour depth and translate those predictions into actionable strategies for flood-resilient infrastructure. Using the 2014 U.S. Geological Survey Pier-Scour Database (569 laboratory cases), five models: Gradient Boosting, AdaBoost (Tree), XGBoost, Gaussian Process (RBF kernel), and Kernel Ridge (polynomial), were trained and evaluated with K-fold cross-validation. Model performance was evaluated using R², RMSE, and MAE. Gradient Boosting performed best, achieving training and testing R² of 0.99 …

Generative Ai And Llm Applications In Renewable Energy And Smart Grids: A Systematic Review For The Sustainable Energy Transition, Umit Cali, Ugur Halden, Merlinda Andoni, Ferhat Ozgur Catak, Si Chen, Benoit Couraud, Emre Kantar, Samuel Knapper, Ibrahim Kucukdemiral, Huseyin Kusetogullari, Murat Kuzlu, Yashar Mousavi, Sonam Norbu, Taha Selim Ustun, David Flynn

Engineering Technology Faculty Publications

The global energy transition toward decarbonization and digitalization requires advanced methods to manage decentralized, data-intensive cyber-physical energy systems. This systematic review analyzes 106 research studies on Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) in renewable energy and smart grids, organized into seven application clusters covering forecasting, system design, operation, reliability, data and cybersecurity, and energy markets. The review situates these applications within a Cyber-Physical-Social Systems (CPSS) framework. Results show that GANs dominate current applications (47.2%), followed by LLMs (10.4%) and VAEs (9.4%), with growing adoption of diffusion and score-based models (7.5% each). Selected studies report improved probabilistic forecasting …

Analytical And Semi-Analytical Modeling Of Solar Cells Using The Lambert W Function: A Comprehensive Review Of Equivalent Circuits, Adel El-Shahat, Martin Ćalasan, Snežana Vujoševic, Shady H. E. Abdel Aleem

Engineering Technology Faculty Publications

The modeling of photovoltaic (PV) cells through equivalent circuits forms a central element in the analysis, simulation, and optimization of solar energy systems. Traditional approaches often depend on iterative numerical methods to solve the implicit current–voltage (I–V) equations. In contrast, the Lambert W function has emerged as an effective mathematical tool that enables closed-form or semi-analytical expressions for a wide range of PV models. This paper presents a Lambert W-centered review of analytical and semi-analytical formulations for PV equivalent-circuit models, covering classical single-diode and multi-diode structures and modern variants incorporating additional elements, voltage-dependent parameters, and topology rearrangements. The models are …

Artificial Intelligence (Ai) In Educating Next Generation Of Engineering Technology Students, Adel El-Shahat, Murat Kuzlu, Vukica M. Jovanovic, Katherine Smith, Abdullah Al Mamun, Otilia Popescu

Engineering Technology Faculty Publications

Artificial Intelligence (AI) is transforming education, particularly for electrical engineering technology (EET) students, by presenting adaptive learning, immediate responses, and unconventional tools. Therefore, this paper proposes investigating modern learning to employ AI in educating future electrical engineering technology students. Firstly, the paper explores how to shape AI knowledge for EET students, supplying them with hands-on skills in AI tasks, clarifying coding, data analysis, and AI ethical usage. Then, as educators, what are the efficient AI tools to utilize in teaching, such as tailored tutoring, automated code assessment, AI-driven design/simulation, lecture dictation, and smart content creation? Key tools, for instance, Google …

Deep Learning-Based High-Resolution Spectrogram Reconstruction For Robust Bearing Fault Diagnosis, Sabyasachi Biswas, Saifur Rahman Tushar, Md Ebtidaul Karim, Abdullah Alam Mamun, Mahathir Mohammad Bappy

Engineering Technology Faculty Publications

Accurate and reliable bearing fault diagnosis is critical for the safe and efficient operation of rotating machinery. Conventional time–frequency (TF) analysis techniques, such as the short-time Fourier transform (STFT), are limited by their fixed window sizes and the inherent trade-off between time and frequency resolution, often leading to suboptimal fault representation under varying operating conditions. To address these challenges, this paper proposes a deep learning-based framework that reconstructs high-resolution, noise-robust TF representations directly from raw vibration signals. The proposed model integrates a denoising encoder with a learnable TF transformation network to adaptively capture fault related frequency patterns without manual parameter …

Using A Study Journal To Support Class Engagement And The Development Of Entrepreneurial Mindset Habits, Otilia Popescu, Dimitrie C. Popescu

Engineering Technology Faculty Publications

Engineering Technology programs were historically introduced to support non-traditional students and offer college pathways for students working and having already a developing career. This is even more true in the current academic environment, with a large percentage of students enrolled in engineering technology programs being either fully or part-time employed, active or retired military, and at different stages in their lives, usually with families to care for. Often, non-traditional students attend classes online, either synchronously or even more often asynchronously, due to their schedule constraints. Course instructors regularly face schedule or time management constraints from the students’ side, and they …

Wip: Arduino-Based Pbl To Foster Entrepreneurial Mindset, Nathan Q. Holland, Vukica M. Jovanovic

Engineering Technology Faculty Publications

This work-in-progress paper examines a semester-long project-based learning initiative begun in an introductory engineering course during the Fall 2025 semester. The project aims to enhance students' hands-on experience by integrating information literacy, the engineering design process (EDP), and an entrepreneurial mindset (EM) [1]. The objective is to boost student confidence in teamwork, technical problem-solving, and application of skills, preparing them for future courses in their discipline. Students in this introduction to engineering course received an Arduino Uno R3 Controller board kit and additional sensors. They were tasked to develop a device addressing engineering in the medicine challenge of their choosing. …

Modeling Healthcare Data With Logistic Quantile And Uniform-Based Mixture Polynomial Distributions, Mohan D. Pant, Aditya Chakraborty, Jovanna A. Tracz

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Continuous healthcare data often deviate from normality, which can substantially increase the risk of making invalid inferences, given that many inferential statistical procedures rely on normality assumption. To obviate this issue, we propose a new family of non-normal distributions based on a linear combination of the quantile functions of standard logistic and uniform (0, 1) distributions. This new family of non-normal distributions is studied within three different methods: L-moments, conventional moments, and percentiles. Its performance is compared among the three methods in the context of parameter estimation and data modeling. The results of Monte Carlo simulation and bootstrapping techniques indicate …

Flood Damage And Social Vulnerability In Coastal New Hampshire, Mitchell S. Berry

Honors Theses and Capstones

This study investigates the relationship between flood-induced building damage and social vulnerability in coastal New Hampshire, with a focus on communities increasingly affected by sea-level rise, storm surge, and high tide events. Using a dataset of 2,528 single-family homes, the analysis integrates flood depth maps (coastal, pluvial, and fluvial), building-level damage estimates, and a housing-burden-based metric to assess how physical and social factors interact to shape flood impacts. Results show that flood depth is the primary driver of building damage, with the most severe impacts concentrated in coastal areas experiencing deeper inundation. However, when buildings are grouped by vulnerability, higher …