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Articles 61 - 90 of 5596
Full-Text Articles in Entire DC Network
Method For Target Detection In A High Noise Environment Through Frequency Analysis Using An Event-Based Vision Sensor, Will Johnston, Shannon Young, David Howe, Rachel Oliver, Zachary Theis, Brian Mcreynolds, Michael L. Dexter
Method For Target Detection In A High Noise Environment Through Frequency Analysis Using An Event-Based Vision Sensor, Will Johnston, Shannon Young, David Howe, Rachel Oliver, Zachary Theis, Brian Mcreynolds, Michael L. Dexter
Faculty Publications
Event-based vision sensors (EVSs), often referred to as neuromorphic cameras, operate by responding to changes in brightness on a pixel-by-pixel basis. In contrast, traditional framing cameras employ some fixed sampling interval where integrated intensity is read off the entire focal plane at once. Similar to traditional cameras, EVSs can suffer loss of sensitivity through scenes with high intensity and dynamic clutter, reducing the ability to see points of interest through traditional event processing means. This paper describes a method to reduce the negative impacts of these types of EVS clutter and enable more robust target detection through the use of …
A Molecular Dynamics Study Of Single Crystal And Intergranular Crack Growth Behavior In WX M1−X Binary Aloys (M = V, Mo, Ta, Re), Samuel C. Wagers, Adib J. Samin
A Molecular Dynamics Study Of Single Crystal And Intergranular Crack Growth Behavior In WX M1−X Binary Aloys (M = V, Mo, Ta, Re), Samuel C. Wagers, Adib J. Samin
Faculty Publications
This study employs molecular dynamics simulations to investigate the fracture behavior of four binary refractory alloys WxM1−x (M = V, Mo, Ta, Re) and their dependence on crystallographic orientation, composition, and grain boundary (GB) structure, focusing on six distinct low-sigma grain boundaries. The simulations reveal that the effect of composition is complex with the most pronounced effect, accompanied by the maximum or minimum stress intensity factor, generally occurring at intermediate compositions. All compositions showed a higher fracture resistance in the [110] orientation compared to the [100] orientation. There was a strong thermodynamic tendency for Mo and V, …
Exploring The Translation Lookaside Buffer (Tlb) For Low-Level Task Differentiation And Classification, Cristian Agredo, Daniel F. Koranek, Christine M. Schubert, Jose A. Gutierrez Del Arroyo, Tor J. Langehaug, Scott R. Graham
Exploring The Translation Lookaside Buffer (Tlb) For Low-Level Task Differentiation And Classification, Cristian Agredo, Daniel F. Koranek, Christine M. Schubert, Jose A. Gutierrez Del Arroyo, Tor J. Langehaug, Scott R. Graham
Faculty Publications
The primary focus of modern Central Processing Unit (CPU) technologies is performance improvement, with security often considered a secondary concern. As a result, vulnerabilities within the system are overlooked. While significant research, both offensive and defensive, has been conducted on CPU caches, relatively little attention has been given to the Translation Lookaside Buffer (TLB) due to its perceived lack of data granularity. Prior studies have typically combined multiple Hardware Performance Counters (HPCs) or relied on timing analysis to extract meaningful insights. In contrast, this study introduces a novel methodology that leverages only TLB related HPCs for multi-task classification, without incorporating …
Library Resources @ The D'Azzo Research Library, Sara Craycraft
Library Resources @ The D'Azzo Research Library, Sara Craycraft
AFIT Library Knowledge Base
No abstract provided.
The Spectral Response Of Time-Resolved Piv In A Turbulent Boundary Layer, Peter Manovski, Wagih Abu Rowin, Henry Ng, Paul Gulotta, Matteo Giacobello, Charitha De Silva, Nicholas Hutchins, Ivan Marusic
The Spectral Response Of Time-Resolved Piv In A Turbulent Boundary Layer, Peter Manovski, Wagih Abu Rowin, Henry Ng, Paul Gulotta, Matteo Giacobello, Charitha De Silva, Nicholas Hutchins, Ivan Marusic
Student Publications
This study presents the application of time-resolved particle image velocimetry (TR-PIV) to measure the mean and fluctuating velocity components in a turbulent boundary layer (TBL) over an axisymmetric body of revolution. A narrow wall-normal strip of the flow was captured using a synchronised high-speed laser and camera at a recording frequency of up to 80 kHz. The resulting streamwise and wall-normal velocity TR-PIV data were validated against hot-wire anemometry measurements and direct numerical simulations (DNS) of a flat plate under matched flow conditions. The mean flow results showed good agreement between all methods, while the expected attenuation due to the …
Isotope Production Modeling In Sodium-Cooled Fast Reactors, Aaron W. Burkhardt
Isotope Production Modeling In Sodium-Cooled Fast Reactors, Aaron W. Burkhardt
Theses and Dissertations
The accurate prediction of isotopic compositions in Sodium-Cooled Fast Reactors (SFRs) is essential for nuclear forensic analyses and international nuclear treaty monitoring, particularly with the increased global deployment of Generation-IV reactors. This research developed and validated a detailed computational model tailored specifically to the Prototype Fast Breeder Reactor (PFBR), employing advanced Monte Carlo neutron transport methods, sophisticated burnup modeling, and variance reduction techniques. Validation against empirical data from the Experimental Breeder Reactor-II confirmed the model’s accuracy, producing a comprehensive database of isotopic compositions across 301 assembly locations and 580 isotopes through the reactor’s initial operation and equilibrium cycle. The model …
Residential Factors Associated With Mental Health In United States Veterans, Air Force Military, And Air Force Employees, Andrew J. Hoisington, Christopher A. Stamper, Molly Penzenik, Meredith Reitter, Elizabeth J. Kovacs, Nazanin H. Bahraini, Lisa A. Brenner
Residential Factors Associated With Mental Health In United States Veterans, Air Force Military, And Air Force Employees, Andrew J. Hoisington, Christopher A. Stamper, Molly Penzenik, Meredith Reitter, Elizabeth J. Kovacs, Nazanin H. Bahraini, Lisa A. Brenner
Faculty Publications
Individuals in Westernized countries spend most of their time indoors. However, exploration of residential building factors that may influence occupants’ mental health is limited in scientific literature. The purpose of this study was to explore investigator's perceived areas of importance in residences to mental health via survey methods. To that end, we administered the Housing, Occupancy, Materials, and Environment (HOME) survey to assess factors that may influence mental health to those working in the United States (US) Air Force (n = 230) or past military members, US Veterans (n = 180). Self-reported mental health surveys were also administered to the …
War, Wounds, And Strategy: Patient Movement Lessons From The World Wars For Great Power Competition, Phillip R. Jenkins
War, Wounds, And Strategy: Patient Movement Lessons From The World Wars For Great Power Competition, Phillip R. Jenkins
Faculty Publications
This thesis examines the evolution of the U.S. military's patient movement system during World War I and World War II to evaluate how well it may perform under the conditions of future large-scale combat operations. It asks whether the United States can move, treat, and sustain wounded personnel at the scale and pace required to preserve combat power in a prolonged, high-intensity conflict. Using detailed case studies of the Meuse-Argonne Offensive and the Battle of the Bulge, the analysis focuses on how transportation platforms, organizational structure, and standard operating procedures (SOPs) shaped patient movement under conditions of attrition, disruption, and …
Contract Quality Feature Extraction Using Llm, Aaron C. Washington
Contract Quality Feature Extraction Using Llm, Aaron C. Washington
Theses and Dissertations
This study explored the potential insights generated from linguistic complexity measurements and large language model (LLM) based assessments on the quality of contract documents. By combining structured True/False prompts with log-probability analysis and ambiguity scoring, the study introduced novel contract-quality assessment methods. Results support a feature-driven approach to contract evaluation, one that offers automated, scalable insights for triaging risk and improving drafting practices. These assessment methods contribute to the growing field of legal natural language processing by offering modular tools for effective contract analysis.
Equiangularity From Compatible Orthobiangularity, Tyler J. Myers
Equiangularity From Compatible Orthobiangularity, Tyler J. Myers
Theses and Dissertations
An equiangular tight frame (ETF) is an equal norm sequence of vectors in a Hilbert space whose coherence achieves equality in the Welch bound. Such sequences necessarily have minimal coherence and thus are, in some sense, as "spread out" in space as possible. ETFs have a variety of applications, such as compressed sensing and waveform design. The main problem in the study of ETFs is determining the pairs (D, N) for which an ETF with N vectors in a D-dimensional space exists. Real ETFs are moreover equivalent to a special subset of a well-studied class of graphs known as strongly …
Rotating Scatter Mask System Optimization Study For Determining Optimal Image Recreation, Seth L. Grover
Rotating Scatter Mask System Optimization Study For Determining Optimal Image Recreation, Seth L. Grover
Theses and Dissertations
The Rotating Scatter Mask (RSM) system is a radiation imaging technology currently limited by the mask design and governing identification algorithm parameters. To optimize the RSM design, Dakota—an optimization software—was integrated with a ray tracing code that simulates particle interactions with the RSM detector, and with the Locally Competitive Algorithm (LCA), which reconstructs the source image based on the ray tracing code’s Detector Response Matrix (DRM). Since the original ray tracing code was developed in MATLAB, it was translated into Python to improve compatibility with both Dakota and LCA. The Python version of the ray tracing code was then integrated …
Fused-Silica Microelectromechanical Systems For Relative Gravimetry, Ethan Doerstling
Fused-Silica Microelectromechanical Systems For Relative Gravimetry, Ethan Doerstling
Theses and Dissertations
Gravimeters are devices that measure gravitational acceleration which can be used by the United States Air Force (USAF) in the areas of navigation and remote sensing. Fused-silica microelectromechanical systems (MEMS) devices offer capabilities to make inexpensive relative gravimeters with higher thermal stability than common silicon devices while maintaining good gravitational sensitivity. The fused-silica devices in this research were designed, simulated, fabricated, and tested to observe their performance as gravimeters. The devices exhibit properties of highly sensitive accelerometers but the current designs do not qualify as gravimeters. This study provides information to improve the sensitivity and stability of these fused-silica MEMS …
Leveraging Large Language Models (Llms) For Automated Generation Of Sysml V2 State Machines In Guidance, Navigation, And Control (Gnc) Systems, Andrea Louise Ames
Leveraging Large Language Models (Llms) For Automated Generation Of Sysml V2 State Machines In Guidance, Navigation, And Control (Gnc) Systems, Andrea Louise Ames
Theses and Dissertations
Modern defense systems continue to grow in complexity, placing increasing pressure on engineering workflows to be faster and more adaptable. While Model-Based Systems Engineering (MBSE) with the emerging SysML v2 standard provides a framework for capturing system behavior, its practical use is often limited by the expertise and time required for manual modeling. This research investigates whether large language models (LLMs) can help overcome that barrier by automatically generating SysML v2 state machines from Guidance, Navigation, and Control (GNC) textual inputs. Three LLM Flowise-based models were developed and evaluated: the Structured Transformation Model (STM), which uses a structured extraction and …
Constraining Nuclear Data Uncertainty Requirements For The 19F(A, N)22Na Reaction For Non-Proliferation Applications, Tyler R. M. Smith
Constraining Nuclear Data Uncertainty Requirements For The 19F(A, N)22Na Reaction For Non-Proliferation Applications, Tyler R. M. Smith
Theses and Dissertations
This thesis explores the requirements on nuclear data uncertainties needed for the use of the 19F(α, n)22Na reaction for nuclear non-proliferation applications. An overview of how neutrons are produced from alpha decays in a UF6 medium is discussed. Calculation demonstrate the role nuclear data uncertainties effect the neutron yield and energy spectra as a function of enrichment.
Enhanced Nuclear Binding Near The Proton Drip Line Opens Possible Bypass Of The 64Ge Rapid Proton Capture Process Waiting Point, Zachary Meisel, W.-J Ong, J. S. Ranhawa
Enhanced Nuclear Binding Near The Proton Drip Line Opens Possible Bypass Of The 64Ge Rapid Proton Capture Process Waiting Point, Zachary Meisel, W.-J Ong, J. S. Ranhawa
Faculty Publications
We performed astrophysics model calculations with updated nuclear data to identify a possible bypass of the 64Ge waiting point, a defining feature of the rapid proton capture (rp) process that powers type I X-ray bursts on accreting neutron stars. We find that the rp-process flow through the 64Ge bypass could be up to 36% for astrophysically relevant conditions. Our results call for new studies of 65Se, including the nuclear mass, β-delayed proton emission branching, and nuclear structure as it pertains to the 64As(p, γ) reaction rate at X-ray burst temperatures.
Overview Of Reference Managers, Phillip Elam
Overview Of Reference Managers, Phillip Elam
AFIT Library Knowledge Base
No abstract provided.
Global Sporadic-E Prediction And Climatology Using Deep Learning, J. A. Ellis, Daniel J. Emmons, M. B. Cohen
Global Sporadic-E Prediction And Climatology Using Deep Learning, J. A. Ellis, Daniel J. Emmons, M. B. Cohen
Faculty Publications
Sporadic-E (Es) is an ionospheric phenomenon defined by strong layers of plasma which may interfere with radio wave propagation. In this work, we develop deep learning models to improve the understanding of Es, including the presence, intensity and height of the layers. We developed three separate models. The first, building off earlier work in (J. A. Ellis et al., 2024, link in AFIT Scholar, 10.1029/2023sw003669), includes only the main features from radio occultation (RO) measurements. The second adds to that time, date, location, geomagnetic and solar indices, solar winds, x-ray flux, weather and lightning. A …
Simultaneous Observations Of Irregular Sporadic E Structures Using The Lwa And A Dps4d, Kenneth S. Obenberger, C. A. Taylor, Jonah J. Colman, Eugene Dao, J. Dowell, J. D. Eccles, Daniel J. Emmons, C. T. Fallen, J. M. Holmes, G. B. Taylor
Simultaneous Observations Of Irregular Sporadic E Structures Using The Lwa And A Dps4d, Kenneth S. Obenberger, C. A. Taylor, Jonah J. Colman, Eugene Dao, J. Dowell, J. D. Eccles, Daniel J. Emmons, C. T. Fallen, J. M. Holmes, G. B. Taylor
Faculty Publications
Multi-instrument studies have recently shed new light on the morphology of sporadic E, especially intense sporadic E. Here we present simultaneous observations of dense sporadic E (Es) structures using the Long Wavelength Array (LWA) radio telescopes and a Digisonde Portable Sounder 4D (DPS4D). Our coordinated observations show that the LWA radio telescopes in central New Mexico can reliably locate regions of dense Es structures as they pass over a Digisonde located over 500 km away in Texas. The LWA appears to be most sensitive to the densest Es structures, which also appear to contain irregularities with vertical …
Global Ionospheric F Region Parameters From Gnss-Pod Limb Measurements: Evaluations And Comparisons With Two Empirical Models - Iri-2020 And Nequick-2, Nimalan Swarnalingam, Dong L. Wu, Dieter Bilitza, Daniel J. Emmons, Cornelius Csar Jude H. Salinas, Artem Smirnov, Yenca Migoya-Oru
Global Ionospheric F Region Parameters From Gnss-Pod Limb Measurements: Evaluations And Comparisons With Two Empirical Models - Iri-2020 And Nequick-2, Nimalan Swarnalingam, Dong L. Wu, Dieter Bilitza, Daniel J. Emmons, Cornelius Csar Jude H. Salinas, Artem Smirnov, Yenca Migoya-Oru
Faculty Publications
An optimal estimation (OE) technique has recently been developed for F region electron density (Ne) using Global Navigation Satellite System (GNSS) limb sounding on low Earth orbit (LEO) satellites (COSMIC-2, Spire, and FengYun-3). This method provides unprecedented spatiotemporal sampling for global monthly Ne climatology within 100–500 km in 2 hr intervals. The global dataset, collected during mid to moderately high solar activity, is compared with leading models: IRI-2020 and NeQuick-2. Diurnal variations in summer, winter, and equinoctial months are examined for the F2-layer peak, as well as the topside and bottomside of the F region. The observed and modeled NmF2 …
Machine Learning For Reactor Power Monitoring With Limited Labeled Data, C. L. Stewart, B. L. Goldblum, R. G. Abbott, L. Appleby, Brett J. Borghetti, V. Hollingshead, J. H. Whetzel
Machine Learning For Reactor Power Monitoring With Limited Labeled Data, C. L. Stewart, B. L. Goldblum, R. G. Abbott, L. Appleby, Brett J. Borghetti, V. Hollingshead, J. H. Whetzel
Faculty Publications
Real-time reactor power monitoring is critical for a variety of nuclear applications, spanning safety, security, operations, and maintenance. While machine learning methods have shown promise in monitoring reactor power levels, there is limited research on their efficacy in label-starved environments. The goal of this work is to assess the feasibility of classifying nuclear reactor power level using multisource data in scenarios with limited labels. Data were collected using low-resolution multisensors at four nuclear reactor facilities: two large research reactors and two TRIGA reactors. Within each pair, one reactor dataset served as the source and the other as the target in …
Analyzing Stability Of Estimates At Completion For Long Duration Development Efforts, Bradley Vuu, Jonathan D. Ritschel, Brandon M. Lucas, Edward D. White
Analyzing Stability Of Estimates At Completion For Long Duration Development Efforts, Bradley Vuu, Jonathan D. Ritschel, Brandon M. Lucas, Edward D. White
Faculty Publications
Defense program managers utilize Earned Value Management (EVM) methodologies to measure, report, and predict the cost and schedule performance of their programs. Previous research conducted by Christensen (1996) and Kim et al. (2019) has shown varied results in the stability of EVM Estimates at Completion (EACs). Stability is defined as a 10% or less deviation from the final EAC at a specified percent completion point of the program. The Christensen (1996) and Kim et al. (2019) studies also noted that program-specific factors, such as phase, can impact the accuracy of EVM metrics. This study builds upon those works by assessing …
Distribution Comparisons Of Eac Cost Growth For Aircraft Work Breakdown Structure Elements, Kyle P. Marquis, Edward D. White, Brandon M. Lucas, Robert D. Fass, Jonathan D. Ritschel, Shawn M. Valentine
Distribution Comparisons Of Eac Cost Growth For Aircraft Work Breakdown Structure Elements, Kyle P. Marquis, Edward D. White, Brandon M. Lucas, Robert D. Fass, Jonathan D. Ritschel, Shawn M. Valentine
Faculty Publications
This article analyzes and investigates the distribution of cost growth of the Estimate at Completion (EAC) for the Work Breakdown Structure (WBS) elements of approximately 60 historical United States Acquisition Category I Research, Development, Test and Evaluation aircraft programs. Using the method of maximum likelihood in conjunction with the Akaike Information Criterion, the authors suggest that both the lognormal and Weibull distributions provide relatively good fit to EAC cost growth, with the lognormal slightly edging out the Weibull. As a summarized finding, the authors present their empirical results for the mean, coefficient of variation (CV), the 15th and 85th percentiles …
The Effects Of Snow Cover On The Dynamic Pressure Of Nuclear Detonation Blast Waves, Adam Card, Andrew W. Decker
The Effects Of Snow Cover On The Dynamic Pressure Of Nuclear Detonation Blast Waves, Adam Card, Andrew W. Decker
Faculty Publications
Blast pressure is the primary military targeting metric for nuclear weapons. Any local conditions that affect blast pressure have the potential for altering nuclear plans, both from defensive and offensive standpoints. Understanding the impact of snow to the blast wave, therefore, provides a benefit both to military planners and to warfighters on the ground, for any operation occurring in arctic environments. No existing data provides a quantitative description of how snow on the ground affects a nuclear detonation blast wave passing over it. Similar blast waves passing over dust have experimentally proven to enhance blast pressure in a localized region.1 …
The Impact Of Solar Angle And Cloud Shadows On 3d Reconstruction Of Rolling Stock Cargo, Carlina M. Ostrand, Adam D. Reiman, Frank W. Ciarallo, Scott L. Nykl, Clark N. Taylor, Joshua F. Krutz
The Impact Of Solar Angle And Cloud Shadows On 3d Reconstruction Of Rolling Stock Cargo, Carlina M. Ostrand, Adam D. Reiman, Frank W. Ciarallo, Scott L. Nykl, Clark N. Taylor, Joshua F. Krutz
Faculty Publications
Meeting the relentless demand for more efficient air cargo transportation is of paramount importance for commercial needs and military missions. This study describes an experiment to test an innovative approach that harnesses cutting-edge stereoscopic vision technology to create 3D point clouds of rolling stock cargo across varying solar angles and cloud shadow conditions. Virtual cargo point clouds are generated by calibrating and systematically organizing the depth and location points from an RGB-D camera and then reprojecting them in a virtual environment. Measurement accuracy was rigorously tested across six camera positions in various combinations of weather conditions against physical ground truth …
On Large Language Models In National Security Applications, William N. Caballero, Phillip R. Jenkins
On Large Language Models In National Security Applications, William N. Caballero, Phillip R. Jenkins
Faculty Publications
The overwhelming success of GPT-4 in early 2023 highlighted the transformative potential of large language models (LLMs) across various sectors, including national security. This article explores the implications of LLM integration within national security contexts, analyzing their potential to revolutionize information processing, decision-making, and operational efficiency. Whereas LLMs offer substantial benefits, such as automating tasks and enhancing data analysis, they also pose significant risks, including hallucinations, data privacy concerns, and vulnerability to adversarial attacks. Through their coupling with decision-theoretic principles and Bayesian reasoning, LLMs can significantly improve decision-making processes within national security organizations. Namely, LLMs can facilitate the transition from …
Exploring Emotion Classification Of Indonesian Tweets Using Large Scale Transfer Learning Via Indobert, Connor Shaw, Phillip M. Lacasse, Lance E. Champagne
Exploring Emotion Classification Of Indonesian Tweets Using Large Scale Transfer Learning Via Indobert, Connor Shaw, Phillip M. Lacasse, Lance E. Champagne
Faculty Publications
Business, political, and other social structures create strong motivation to understand the attitudes, motivations, feelings, and emotions of a population of interest. Social media is a rich source of self-disclosed information by individuals from all walks of life about virtually every domain of the human experience, but the vast quantity of data is impossible to effectively analyze without advanced natural language processing algorithms. This research creates a transfer learning based emotion classification model for Indonesian language Twitter data. Transfer learning consists of two steps: pre-training and fine tuning. Three variations of Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT) are tested …
A Multi-Objective Reinforcement Learning Framework For Title Autonomous On-Orbit Inspections, Austin C. Reynolds
A Multi-Objective Reinforcement Learning Framework For Title Autonomous On-Orbit Inspections, Austin C. Reynolds
Theses and Dissertations
The rapidly evolving landscape of space operations necessitates dynamic and autonomous systems to address complex challenges such as Resident Space Object (RSO) inspections. This research explores the application of a Multi-Objective Reinforcement Learning (MORL) framework to rendezvous and proximity operations (RPO), enabling agents to balance conflicting objectives like time efficiency, fuel conservation, and information gain. Unlike traditional reinforcement learning, MORL allows dynamic reweighting of objectives without retraining, offering adaptability and efficiency in multi-objective environments. The study demonstrates MORL's capabilities through custom 2D and 3D simulations of Hill-Clohessy-Wiltshire (HCW) environments and comparing its performance to traditional RL in RPO scenarios. Tasks …
Lethality And Survivability Of Autonomous Self-Sensing Uavs, Jeffrey T. Wilson
Lethality And Survivability Of Autonomous Self-Sensing Uavs, Jeffrey T. Wilson
Theses and Dissertations
Unmanned Aerial Vehicles (UAVs) have seen increased usage over the past two decades during the Global War on Terrorism (GWOT), operating in low-risk environments against dispersed enemies with minimal counter-drone capabilities. However, as the U.S. military shifts focus to Multi-Domain Operations (MDO) and Large Scale Combat Operations (LSCO), UAVs face significantly higher risks, including frequent and successful attacks, as well as the exploitation of their technology. Battle damage assessment (BDA) is not new; however, autonomous self-assessment by UAVs represents a novel advancement. Currently, UAV BDA relies on manual inspection, requiring approximately eight hours per drone. By adopting self-sensing technology, UAVs …
Evaluating Dry Air Personnel Decontamination Of Methyl Salicylate As A Chemical Agent Surrogate In Extreme Cold Environments To Reduce Airborne Risks Using A Manikin, Lance E. Campbell
Evaluating Dry Air Personnel Decontamination Of Methyl Salicylate As A Chemical Agent Surrogate In Extreme Cold Environments To Reduce Airborne Risks Using A Manikin, Lance E. Campbell
Theses and Dissertations
This research investigated a mobile air shower as an alternative to water washing for personnel chemical decontamination in Arctic environments, where traditional methods like disrobing and rinsing are impractical. Interest in Arctic operations has grown with recent U.S. military focus on strategic advancements by Russia and China, yet research on air shower effectiveness for chemical decontamination remains limited. This study examined whether a commercial off-the-shelf air shower could effectively decontaminate methyl salicylate (MES), a surrogate for chemical warfare blister agents, from a manikin outfitted in military cold-weather gear. Researchers used a ppbRAE 3000 photo-ionization detector to measure MES concentrations following …
Emergency Response Digital Twin: Integrating Augmented Reality And Live Position Data With Simulation-Aided Decision-Making In Real-Time, Joseph Fuentes
Emergency Response Digital Twin: Integrating Augmented Reality And Live Position Data With Simulation-Aided Decision-Making In Real-Time, Joseph Fuentes
Theses and Dissertations
With the growing use of simulation across industries, the digital twin remains an underexplored research area, particularly in emergency management and response. Its real-time updating capability is often overlooked due to the misconception that "digital twin" is merely a complex term for simulation. This paper highlights its distinctiveness through an evasion exercise involving two independent entities in a collocated environment. Using a highly integrated virtual environment (HIVE) and internet of things (IoT) devices, we link the physical system with an analytical simulation, demonstrating the impact of lag times in high-pressure scenarios. The computational model leverages agent-based modeling (ABM) and discrete-event …