Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- University of Nebraska - Lincoln (9660)
- Singapore Management University (7282)
- TÜBİTAK (5357)
- China Simulation Federation (3924)
- Missouri University of Science and Technology (3766)
-
- Old Dominion University (3651)
- Air Force Institute of Technology (2946)
- Wright State University (2899)
- Utah State University (2696)
- University of Kentucky (2666)
- Chinese Chemical Society | Xiamen University (2213)
- University of Nevada, Las Vegas (1909)
- China Coal Technology and Engineering Group (CCTEG) (1822)
- University of Arkansas, Fayetteville (1660)
- City University of New York (CUNY) (1603)
- University of South Florida (1591)
- Portland State University (1584)
- Embry-Riddle Aeronautical University (1403)
- Purdue University (1384)
- Washington University in St. Louis (1318)
- Montana Tech Library (1317)
- California Polytechnic State University, San Luis Obispo (1279)
- University of Texas at El Paso (1211)
- University of Central Florida (1207)
- Louisiana State University (1174)
- Edith Cowan University (1163)
- Technological University Dublin (1156)
- University of Texas Rio Grande Valley (1112)
- Chulalongkorn University (1100)
- New Jersey Institute of Technology (1087)
- Keyword
-
- Machine learning (1573)
- Deep learning (937)
- Machine Learning (804)
- Artificial intelligence (758)
- Computer Science (681)
-
- Climate change (608)
- Sustainability (534)
- Engineering (531)
- Simulation (519)
- Security (494)
- Optimization (470)
- Groundwater (451)
- Cybersecurity (446)
- Deep Learning (434)
- Water quality (424)
- Applied sciences (420)
- Artificial Intelligence (395)
- Education (389)
- Mathematics (374)
- Remote sensing (370)
- United States (366)
- Algorithms (363)
- Chemistry (360)
- 2014 International Conference on Hydroinformatics HIC (355)
- Classification (351)
- Montana (342)
- Computer science (341)
- Physics (341)
- Modeling (309)
- Neural networks (309)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (6876)
- Theses and Dissertations (5310)
- Journal of System Simulation (3924)
- Nebraska Tractor Tests (3402)
- Turkish Journal of Electrical Engineering and Computer Sciences (3106)
-
- Journal of Electrochemistry (2213)
- Coal Geology & Exploration (1822)
- Faculty Publications (1715)
- Electronic Theses and Dissertations (1661)
- Computer Science & Engineering Syllabi (1312)
- Dissertations (1157)
- USF Tampa Graduate Theses and Dissertations (985)
- Turkish Journal of Chemistry (885)
- Chulalongkorn University Theses and Dissertations (Chula ETD) (865)
- Masters Theses (829)
- Silver Bow Creek/Butte Area Superfund Site (820)
- Theses (794)
- Open Access Theses & Dissertations (793)
- Physics Faculty Publications (775)
- Computer Science Faculty Research & Creative Works (760)
- Department of Computer Science Technical Reports (704)
- Graduate Theses and Dissertations (701)
- All Computer Science and Engineering Research (683)
- UNLV Theses, Dissertations, Professional Papers, and Capstones (666)
- Applications and Applied Mathematics: An International Journal (AAM) (637)
- Articles (631)
- Reports (612)
- School of Natural Resources: Faculty Publications (585)
- Publications and Research (580)
- World of Coal Ash Proceedings (580)
- Publication Type
Articles 541 - 570 of 119351
Full-Text Articles in Entire DC Network
Effects Of Ab Mix Hydroponic Solution And Ameliorant Mixtures On Cayenne Pepper Growth And Yield In An Inceptisols, Tien Turmuktini, Betty Natalie Fitriatin, Tualar Simarmata, Firda Widayanti, Nuryanti, Lia Amalia, Elly Roosma Ria, Linlin Parlinah
Effects Of Ab Mix Hydroponic Solution And Ameliorant Mixtures On Cayenne Pepper Growth And Yield In An Inceptisols, Tien Turmuktini, Betty Natalie Fitriatin, Tualar Simarmata, Firda Widayanti, Nuryanti, Lia Amalia, Elly Roosma Ria, Linlin Parlinah
Jurnal Kultivasi
Cayenne pepper (Capsicum frustescens L.) cultivation faces several problems, including acidic pH and inadequate nutrient management. These problems can be addressed by using a mixture of various ameliorants and AB mix nutrient solutions. This study aimed to determine the effect of ameliorant mixture dosage and AB mix solution concentration on soil chemical properties, growth, and yield of cayenne pepper. The experiment used a randomized block design, consisting of two factors: nutrient solution concentration and ameliorant dosage. The factor of nutrient solution concentration consisted of four levels, i.e., 700, 1000, 1300, and 1600 ppm, while the factor of ameliorant dosage also …
Re: Approval Letter For The Diggings East 100% Remedial Design Package Submittal (Dated July 23, 2026), The 100% Sbcca Programmatic Technical Specifications Submittal (Technical Specifications) (Dated May 29, 2026), The Final Revised Silver Bow Creek Conservation Area (Sbcca) Construction Quality Assurance Plan (Cqap) (Dated July 29, 2026), The Final Sbcca Construction Monitoring Quality Assurance Project Plan (Qapp) (Dated July 29, 2026), And The Final Sbcca Materials Management Plan (Mmp) (Dated July 29, 2026), Emma Rott
Silver Bow Creek/Butte Area Superfund Site
No abstract provided.
Clark Tailings Consolidated Waste Management Area Site Investigation Quality Assurance Project Plan (Qapp), Woodard & Curran
Clark Tailings Consolidated Waste Management Area Site Investigation Quality Assurance Project Plan (Qapp), Woodard & Curran
Silver Bow Creek/Butte Area Superfund Site
No abstract provided.
Development Of Robust Ratio Linear Fitting Method Of Temperature And Emissivity Separation For High-Temperature Data, Mitchell Manzardo, Michael L. Dexter, Shannon R. Young, John Bowlan, Anthony L. Franz
Development Of Robust Ratio Linear Fitting Method Of Temperature And Emissivity Separation For High-Temperature Data, Mitchell Manzardo, Michael L. Dexter, Shannon R. Young, John Bowlan, Anthony L. Franz
Faculty Publications
Accurate temperature and emissivity separation from thermal infrared radiance is essential for characterizing materials under high-temperature laboratory conditions. Existing temperature and emissivity separation methods have largely been developed for multispectral remote sensing applications, where long atmospheric path lengths require extensive atmospheric compensation. In contrast, the current work considers hyperspectral laboratory measurements acquired over a short optical path, where atmospheric effects are comparatively small but increased measurement uncertainty remains within portions of the measured spectrum. The ABB MR304 FTIR spectrometer used in this study exhibits reduced optical transmission below approximately 2.5 μm, producing increased measurement uncertainty within the spectral region containing …
Palmnet: Confidence-Calibrated Edge-Cloud Ai For Field Diagnosis Of Date Palm Diseases, Muntadher Kareem, Raed Majeed
Palmnet: Confidence-Calibrated Edge-Cloud Ai For Field Diagnosis Of Date Palm Diseases, Muntadher Kareem, Raed Majeed
Karbala International Journal of Modern Science
Date palm (Phoenix dactylifera L.) is a cornerstone crop for Iraq and the wider MENA region, yet reliable in-field diagnosis of leaf disorders remains slow, labour-intensive, and constrained by a limited pool of agronomists. This paper presents PalmNet, a full-stack diagnostic system that classifies nine leaf conditions through a calibrated edge-cloud framework. The system is developed and evaluated on a public dataset of 3,089 field images spanning the nine classes, using a 70/15/15 stratified split. A ShuffleNetV2 student network, distilled from a ConvNeXt-Tiny teacher, is deployed on two complementary edge endpoints: a Raspberry Pi Zero 2 W field station …
Proactive Deep Q-Learning Approach For Anomaly Detection In Iot Idss, Hawraa A. Habeeb, Mehdi E. Manaa
Proactive Deep Q-Learning Approach For Anomaly Detection In Iot Idss, Hawraa A. Habeeb, Mehdi E. Manaa
Journal of Intelligent Informatics, Networking, and Cybersecurity
Breach rates and unparalleled vulnerabilities are a constant feature of the cyber landscape these days, and the increasing complexity of the proliferation of Internet of Things (IoT) nodes is to be expected. With these challenges, the conventional intrusion detection systems (IDS) are proven to be unable to deal with the extensive and varied data streams. Such systems can be fundamentally attributed to the classical nature of these systems, which are lacking in flexibility to analyze traffic in real-time and thus have no proactive capabilities of identifying patterns of unknown attacks. Considering these technical barriers, in this paper, an offensive-defensive system …
Greenovation As A Strategic Leverage For Sustainable Competitive Advantage, Amiya Kumar Mohapatra, Anil Kumar, Yiğit Kazançoğlu
Greenovation As A Strategic Leverage For Sustainable Competitive Advantage, Amiya Kumar Mohapatra, Anil Kumar, Yiğit Kazançoğlu
Management Dynamics
Greenovation is the new currency of the global economy which focuses on ‘green and innovation’ that generates environmental benefits through reduced resource consumption, lower carbon emissions, and improved ecological performance aligned with Sustainable Development Goals (SDGs). Greenovation focuses on long-term value creation by integrating environmental externalities, resource circularity, and multi-stakeholder governance and accountability structures. Greenovation integrated with corporate strategies can provide greater strategic competitive advantages which is also termed as green competitive advantage. By systematically integrating product and process improvements through Greenovation, firms can attain competitive advantage for long-term value creation for both internal and external stakeholders. It is imperative …
Multi-Ion Permeation And Dynamic Conductance Modulation In Connexin Gap Junction Channels, Bassam G. Haddad, Daniel M. Zuckerman, Steve L. Reichow
Multi-Ion Permeation And Dynamic Conductance Modulation In Connexin Gap Junction Channels, Bassam G. Haddad, Daniel M. Zuckerman, Steve L. Reichow
Chemistry Faculty Publications and Presentations
Gap junction channels formed by connexins mediate direct intercellular communication and are essential for electrical signaling and tissue homeostasis. Despite their large, solvent-accessible pores, connexin channels exhibit distinct conductance, selectivity, and rectification properties, but the molecular mechanisms underlying these behaviors remain incompletely understood. Here, we performed ~67 μs of all-atom computational electrophysiology simulations of connexin-46 (Cx46), connexin-50 (Cx50), and heterotypic Cx46/50 gap junction channels based on high-resolution open-state structures, enabling characterization of both ion permeation and long-timescale channel dynamics. Simulations reveal a multi-ion, multi-pathway permeation mechanism governed by isoform-specific energetic barriers and transient ion coordination sites that shape conductance and …
Gw240925 And Gw250207: Astrophysical Calibration Of Gravitational Wave Detectors, A. G. Abac, I. Abouelfettouh, F. Acernese, Teviet Creighton, Mario C. Diaz, Raul Alberto Espinosa Perez, J. Lawrence, Francisco Llamas Villarreal, Soma Mukherjee, Gaukhar Nurbek, Volker Quetschke, Miriam Ramos Arevalo, Wenhui Wang
Gw240925 And Gw250207: Astrophysical Calibration Of Gravitational Wave Detectors, A. G. Abac, I. Abouelfettouh, F. Acernese, Teviet Creighton, Mario C. Diaz, Raul Alberto Espinosa Perez, J. Lawrence, Francisco Llamas Villarreal, Soma Mukherjee, Gaukhar Nurbek, Volker Quetschke, Miriam Ramos Arevalo, Wenhui Wang
Physics & Astronomy Faculty Publications
GW240925 and GW250207 are two loud gravitational-wave signals from binary black hole coalescences observed with network signal-to-noise ratios ∼32 and ∼69, respectively, by the LIGO Hanford-LIGO Livingston-Virgo network. Gravitational-wave signals from coalescing binaries have characteristic phase and amplitude evolution predicted by general relativity. These signal waveforms, together with measured instrumental calibration uncertainties, are used to infer source parameters. However, for sufficiently loud detections, it is possible to constrain the calibration of the detectors directly using the signals themselves. We present the first informative astrophysical measurements of gravitational-wave detector calibration. For GW240925, we verify the inference of Hanford calibration from the …
Machine Learning Applications To Physical Processes, William Charles
Machine Learning Applications To Physical Processes, William Charles
Arts & Sciences Graduate Student Theses and Dissertations
This thesis demonstrates how machine learning techniques can solve computationally challenging problems across diverse areas of physics, from high-energy astrophysics to condensed matter systems, accelerating traditional computation. My first contribution addresses the computational expense of Monte Carlo calculations for radiative processes in relativistic plasmas. I develop a neural network sampling method that enables fast sampling from an arbitrary probability density, and demonstrate the method on inverse Compton scattering, achieving a speedup of up to an order of magnitude beyond standard methods. My second contribution addresses the structure and radiation of neutron star magnetospheres. I use physics-informed neural networks to model …
Human-Centered Modeling Of Traffic As A Complex System, Poorendra P. Ramlall
Human-Centered Modeling Of Traffic As A Complex System, Poorendra P. Ramlall
Discovery Day - Daytona Beach
Traffic systems are driven not only by motion, but by interaction: vehicles influence one another, drivers continuously adapt to surrounding behaviour, and cognitive processes shape decisions that can propagate through the flow of traffic. Understanding these layered interactions is essential for improving traffic safety and for designing the next generation of intelligent, connected, and automated transportation systems. This PhD research develops a multiscale, data-driven framework for identifying, modelling, and ultimately interpreting interaction structure in traffic systems. The work first established an information-theoretic basis for this problem, demonstrating how information flow can uncover directional relationships in traffic dynamics and help infer …
Quantifying Grain Size In Scanning Electron Microscopy Images, Katherine Hoffsetz
Quantifying Grain Size In Scanning Electron Microscopy Images, Katherine Hoffsetz
Discovery Day - Daytona Beach
This project explores advanced image analysis techniques to assess the microstructure of highly strained austenitic stainless steel. Utilizing Python imaging libraries such as scikit-image and OpenCV, we aim to extract precise measurements for grain size from scanning electron microscopy (SEM) images. These metrics will be examined against the computed grain sizes of the sample from electron backscatter diffraction measurements. By automating the extraction of grain size measurements from SEM images, this study contributes to steamlining the quality assurance/ quality control of industrially processed materials.
Learning Casual Structures From Aviation Accident Narratives Using Natural Language Processing And Graph-Based Knowledge Representation, Stephanie Ramsey, Katherine Hoffsetz, Madeline Gorman, Logan Lambeth
Learning Casual Structures From Aviation Accident Narratives Using Natural Language Processing And Graph-Based Knowledge Representation, Stephanie Ramsey, Katherine Hoffsetz, Madeline Gorman, Logan Lambeth
Discovery Day - Daytona Beach
Understanding the complex causal relationships underlying aviation accidents is critical for improving safety and preventing future incidents. However, much of this information exists in unstructured narrative reports, making large-scale analysis difficult. This project aims to automatically extract and model causal chains from National Transportation Safety Board (NTSB) accident narratives using a combination of traditional natural language processing (NLP) techniques, transformer-based architectures, and graph-based knowledge representation. Traditional NLP methods, including named entity recognition, dependency parsing, and rule-based pattern matching, will be used to identify structured cause–effect relationships. These approaches will be compared with transformer-based models, including a lightweight encoder for classification …
High-Level Trajectory Learning For Non-Prehensile Object Manipulation With Hierarchical Reinforcement Learning, Gulsum Tuba Cibuk Girgin
High-Level Trajectory Learning For Non-Prehensile Object Manipulation With Hierarchical Reinforcement Learning, Gulsum Tuba Cibuk Girgin
Discovery Day - Daytona Beach
Site exploration requires in-situ resource utilization when the physical properties of resources are unknown. Therefore, a generalizable object manipulation method is crucial for extraterrestrial environments. Existing studies develop reinforcement learning policies that enable interaction with objects, in which quadruped robots learn to reach commanded goals with one foot while balancing with the remaining legs. However, in these studies, goal-oriented task execution relies on high-level trajectories provided by human experts, which limits autonomous robotic operations. In this study, we propose a hierarchical DRL in which a high-level pedipulation policy outputs commands for a low-level reach policy, enabling autonomous, smooth and affordable …
Sequential Causal Architecture For Multimodal Aviation Accident Prediction, Kaitlyn Cavanaugh, Isaac Morrison
Sequential Causal Architecture For Multimodal Aviation Accident Prediction, Kaitlyn Cavanaugh, Isaac Morrison
Discovery Day - Daytona Beach
Aviation accidents are rarely the result of a single failure but rather from a complex causal chain of latent failures. While traditional data mining models often predict incident occurrence, they frequently overlook the sequential mechanics defined by known accident causation theoretical frameworks like the Swiss Cheese Model and the FAA's HFACS. This project addresses the need for interpretable, reliable, multi-stage forecasting by proposing a Sequential Causal Architecture that transforms theoretical causation models into a structured Directed Acyclic Graph (DAG) for multimodal accident causation chain prediction. Data from the NTSB and DOT is used and connected together in a meaningful way …
Energy-Aware Bimodal Contact Detection For Leg Odometry, Emre Girgin
Energy-Aware Bimodal Contact Detection For Leg Odometry, Emre Girgin
Discovery Day - Daytona Beach
Autonomous exploration of extraterrestrial environments using legged robots requires robust GNSS-free 3D state estimation. Standard leg odometry relies on Zero-Velocity Updates (ZUPT), which assume a grounded foot remains completely stationary. This assumption consistently fails on deformable granular terrain due to unobservable slippage, rapidly degrading state estimation. To mitigate this critical failure mode, we propose a dual contact-detection framework designed to robustly gate an Error-State Extended Kalman Filter (ESEKF) tracking pose, velocity, and IMU biases. The architecture isolates physical load and kinematics by modeling contact detection as two independent parallel Hidden Markov Models (HMMs). The Load HMM processes Ground Reaction Forces, …
Learning Motion Primitive Selection And Environment Abstraction, Edison Alberto Martinez Samaniego, Natalie Alexander, Kaelyn Weddle
Learning Motion Primitive Selection And Environment Abstraction, Edison Alberto Martinez Samaniego, Natalie Alexander, Kaelyn Weddle
Discovery Day - Daytona Beach
Learning Motion Primitive Selection and Environment Abstraction Advanced Air Mobility (AAM) is emerging as a transformative solution for short and medium range transportation; however, it introduces an operational model that differs significantly from conventional aviation. AAM vehicles are expected to operate closer to populated areas, with increased autonomy, in dense urban and suburban environments. These settings present constrained maneuvering conditions which highlights the importance of maintaining safe operation under degraded flight conditions. Abnormal conditions may endanger onboard passengers, people on the ground, and surrounding infrastructure, making rapid detection and mitigation essential to prevent loss of control. Recent research has explored …
Modeling Aircraft Collision Risk Using Machine Learning And Traffic Density Data Ac, Jadia Renee Ewing, Alexander Van Baelan, Conrad Prisby, Rafal P. Smietana
Modeling Aircraft Collision Risk Using Machine Learning And Traffic Density Data Ac, Jadia Renee Ewing, Alexander Van Baelan, Conrad Prisby, Rafal P. Smietana
Discovery Day - Daytona Beach
Air traffic congestion is an increasingly important factor in aviation safety as global flight activity continues to grow. This project investigates whether higher traffic density is associated with an increased risk of aviation incidents and identifies key contributing factors. Using publicly available flight (ADS-B) and incident (NTSB) data, we apply several machine learning models to analyze traffic patterns and predict risk. We begin with logistic regression to evaluate the relationship between density and incident probability, followed by decision trees to extract interpretable rules describing high-risk conditions. K-nearest neighbors (KNN) is used to examine similarity in traffic patterns among incident flights, …
Low-Rank Spectral Analysis For The Reddening Of The Seven Sisters Star Cluster, Eric Rodarte, Angelina Scalice, Madison Warner, Kevin Numbe
Low-Rank Spectral Analysis For The Reddening Of The Seven Sisters Star Cluster, Eric Rodarte, Angelina Scalice, Madison Warner, Kevin Numbe
Discovery Day - Daytona Beach
The Pleiades, also known as the Seven Sisters, is a stunning star cluster located approximately 440 light-years from Earth. This vibrant assemblage of hot blue stars in the Taurus constellation can be admired with the naked eye or through binoculars during early autumn. In this presentation, we utilize spectral theory to measure the reddening in the Pleiades star cluster. To evaluate the impact of interstellar dust on reddening, we employ principal component analysis (PCA) on a matrix representing color indices from various photometric bands linked to the cluster’s photometric data. This dataset was obtained from VIZIER. Our PCA analysis of …
Vibration And Thermal-Vacuum Feasibility For Micro Carbon Fiber Filled Nylon Filament Lunar Applications, Andrew Murphy, Shannon O'Sullivan, Daniel Lopez
Vibration And Thermal-Vacuum Feasibility For Micro Carbon Fiber Filled Nylon Filament Lunar Applications, Andrew Murphy, Shannon O'Sullivan, Daniel Lopez
Discovery Day - Daytona Beach
The proposed work seeks to improve the fundamental understanding of the properties concerning 3-D printing filaments that have potential to be used for lunar applications. The associated properties in focus for the proposed study, vibration and thermal-vacuum-resistance, are fundamental aspects of spaceflight and are critical to mission success for objectives associated with the environmental factors in space. The successful outcome of the proposed work will answer questions relating to the feasibility of micro carbon fiber filled nylon 3-D printing filaments such as Markforged’s Onyx® for application for projects in the Space Technologies Laboratory, including for the development of structures relating …
Advancements In Spacecraft Trajectory Generation Through Matrix Decomposition Techniques, David Stoev, Oshani Jayawardane, Kaitlyn Cavanaugh, Kristiyan Stefanov, Andrew Murphy
Advancements In Spacecraft Trajectory Generation Through Matrix Decomposition Techniques, David Stoev, Oshani Jayawardane, Kaitlyn Cavanaugh, Kristiyan Stefanov, Andrew Murphy
Discovery Day - Daytona Beach
The Circular Restricted Three-Body Problem (CR3BP) is renowned for its intricate and chaotic dynamics, leaving it without a closed-form solution. In this poster, we introduce an innovative approach to determine spacecraft trajectories within the CR3BP framework using matrix factorization techniques. We formulate a matrix equation where the right-hand side vector is constructed from the spacecraft's position and velocity data, while the coefficient matrix is derived from the spacecraft's temporal data. Subsequently, we apply several matrix decomposition techniques, including modified Gram-Schmidt, the Householder technique, and Givens Rotation, to analyze the coefficient matrix and derive the spacecraft trajectories. Finally, we evaluate the …
Earthquake-Resistant Design And Base Isolation Techniques, Christian George, Jacob Sweeten, Gabriela Cotto, Savion Stewart
Earthquake-Resistant Design And Base Isolation Techniques, Christian George, Jacob Sweeten, Gabriela Cotto, Savion Stewart
Discovery Day - Daytona Beach
Earthquake-resistant design has become a critical feature in construction near active fault lines, where seismic activity is most frequent and potentially destructive. In recent years, technological advancements have significantly improved methods for protecting buildings from earthquake damage. Among these innovations, base isolation systems have appeared as one of the most effective solutions. By decoupling a building from ground motion, base isolators reduce the transmission of seismic forces, helping to prevent structural damage and support building stability during earthquakes. In addition to improving safety, base isolation systems can also reduce long-term costs associated with earthquake-related repairs and maintenance. Base-isolated structures are …
Analyzing Fungal Growth Dynamics Under Different Environmental Conditions Using A Lotka–Volterra Competition System, Maria Ordonez, Fabrio Araujo
Analyzing Fungal Growth Dynamics Under Different Environmental Conditions Using A Lotka–Volterra Competition System, Maria Ordonez, Fabrio Araujo
Discovery Day - Daytona Beach
Fungi play a critical role in ecosystems as decomposers that recycle nutrients and maintain environmental balance. Their populations are influenced by multiple environmental factors such as temperature, humidity, nutrient availability, and interactions with other organisms. In this project, the Lotka–Volterra model is used to analyze how competing fungal species interact and how these interactions influence population dynamics over time. By modeling two fungal populations competing for the same limited resources, the equations illustrate how environmental conditions and competition coefficients determine whether one species dominates; both species coexist, or one species becomes extinct. The model provides insight into how changes in …
Characterization Of An Ambe Tagged Neutron Source For A 30-Ton Wbls Detector, Rylee Grover
Characterization Of An Ambe Tagged Neutron Source For A 30-Ton Wbls Detector, Rylee Grover
Discovery Day - Daytona Beach
Understanding the detection capabilities of water-based liquid scintillator (WbLS) is critical for its deployment in next-generation neutrino detectors such as THEIA and Phase II of the Deep Underground Neutrino Experiment (DUNE). This study focuses on the characterization and alignment testing of an Americium-Beryllium (AmBe) radioactive neutron source. We plan to dope the 30-ton WbLS detector at Brookhaven National Laboratory (BNL) with Gadolinium (Gd) to improve neutron detection capabilities. This will be tested by the implementation of an AmBe source as a calibration metric. The AmBe source emits neutrons coincident with a 4.4 MeV gamma ray, making it possible to perform …
Lightweight Uav-To-Uav Detection And Tracking For Advanced Air Mobility Applications, Taylor Hostetter
Lightweight Uav-To-Uav Detection And Tracking For Advanced Air Mobility Applications, Taylor Hostetter
Discovery Day - Daytona Beach
Lightweight UAV-to-UAV Detection and Tracking for Advanced Air Mobility Applications addresses the significant challenge of reliable UAV-to-UAV detection on resource-constrained platforms, particularly within Advanced Air Mobility (AAM) environments where dense, low-altitude airspace requires robust detect-and-avoid capabilities. This work presents the development and experimental evaluation of a lightweight detection and tracking framework for autonomous detect-and-avoid applications. The approach is designed to support real-time onboard operation in multi-vehicle environments characteristic of emerging AAM systems. The proposed framework integrates optical and LiDAR sensing with a low-complexity machine learning decision-support layer that reduces false detections without replacing the underlying control-oriented detection pipeline. This design …
Project Minerva, Lena Wang, Gannon English, Ethan Yemm, Gabrielle Guirguis, Jose Murphy
Project Minerva, Lena Wang, Gannon English, Ethan Yemm, Gabrielle Guirguis, Jose Murphy
Discovery Day - Daytona Beach
Project Minerva is a multidisciplinary engineering initiative at Embry-Riddle Aeronautical University (ERAU) dedicated to the design, integration, and deployment of high-altitude balloon (HAB) systems for stratospheric research and aerospace hardware validation. The project challenges student researchers to engineer flight-ready payloads capable of maintaining structural and electronic integrity in extreme temperatures and low-pressure environments in the stratosphere. Typically utilizing 600g latex balloons filled with helium, Project Minerva missions aim to achieve altitudes exceeding 22,000 meters to facilitate vertical atmospheric profiling of variables such as CO₂ concentration, humidity, temperature, and pressure. Beyond its technical objectives, the project serves as a professional training …
System And Method For Detecting Subsurface Voids Using Controlled Heat Addition, Chase Nilsson
System And Method For Detecting Subsurface Voids Using Controlled Heat Addition, Chase Nilsson
Discovery Day - Daytona Beach
This study evaluates controlled heat addition in a confined space to estimate enclosed volumes for irregularly shaped spaces. The intent is to detect hidden voids in cave environments. Conventional methods for identifying such voids are often costly, logistically difficult, and limited in accuracy. Preliminary experiments were conducted with a controlled heat source, a temporary thermal barrier to isolate the test chamber, and simple temperature sensors. Temperatures were recorded for the pre-test baseline, the heating interval, followed by a cooling period. The thermal response showed clear engineering trends that warrant further exploration and modeling. Preliminary results indicate that transient temperature changes …
The Impact Of Environmental Contributing Factors In Spatial Disorientation And Non-Spatial Disorientation Related General Aviation Accidents, Victoria Cornaro, Molly Mersinger
The Impact Of Environmental Contributing Factors In Spatial Disorientation And Non-Spatial Disorientation Related General Aviation Accidents, Victoria Cornaro, Molly Mersinger
Discovery Day - Daytona Beach
The Impact of Environmental Contributing Factors in Spatial Disorientation and Non-Spatial Disorientation Related General Aviation Accidents Spatial disorientation (SD) is an inherent risk of flying and with a high risk of resulting in a fatal accident (Gibbs et al, 2011). SD related accidents occur when a pilot’s perception of the aircraft altitude, position, or relative motion conflict with reality (Benson, 1999). SD accidents are significantly more likely to result in a fatality than non-SD cases. The purpose of this study was to investigate the role of different contributing factors on SD and non-SD general aviation accidents. We used the NTSB …
Ai-Driven Predictive Analysis Of Airline Profitability Under Macroeconomic And Sustainable Aviation Fuels, Shivika Singh
Ai-Driven Predictive Analysis Of Airline Profitability Under Macroeconomic And Sustainable Aviation Fuels, Shivika Singh
Discovery Day - Daytona Beach
This study presents an AI-enhanced predictive model to assess airline profitability under the combined influence of macroeconomic trends and the adoption of sustainable aviation fuel (SAF). By including economic indicators such as GDP growth, inflation rates, and fuel price volatility with airline operational data, including ticket prices and fuel costs, the model simulates profitability across multiple carriers. Scenario-based analyses, encompassing optimistic, moderate, and pessimistic projections, illustrate different financial sensitivities between low-cost and legacy airlines.
Supply Chain Analysis: The Oregonator Autocatalytic Case Study, Abigail Butcher
Supply Chain Analysis: The Oregonator Autocatalytic Case Study, Abigail Butcher
Discovery Day - Daytona Beach
Understanding stability in complex supply chains remains a critical challenge due to nonlinear feedback, delayed responses, and sensitivity to parameter changes. This project presents a novel framework that applies bifurcation analysis to evaluate system stability, using the Oregonator autocatalytic chemical reaction model as an analog for supply chain dynamics. A parameter sweep of key model variables, particularly the stoichiometric factor f and the reaction rate constants k, is used to identify transitions between stable and oscillatory regimes. These transitions provide insight into how variations in feedback strength can drive instability in real-world systems. The framework will then be extended to …