Search NASA⌕ Search

SEARCH · Search NASA

Results for “human performance data”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Artificial Intelligence for Accelerating Nuclear Applications, Science, and Technology

Artificial intelligence (AI) and machine learning (ML) methods have had significant impacts in science and technology in recent years. These methods for generating models from datasets or logic-based algorithms that emulate aspects of human performance can similarly accelerate the fields of nuclear applications, science, and technology toward the IAEA goals of contributing to peace, health, and prosperity. In order to accomplish advances with AI in general and ML in particular across these fields, IAEA can play a significant role by establishing, hosting and curating centralised resources, including databases, adhering to FAIR (findable, accessible, interoperable and reusable) principles and Open Science best practices, providing stewardship of data sharing, supporting training efforts and development of relevant workforces, as well as enabling connections among the scientific, technology, mathematics, AI and ethics communities. Many areas can benefit from the use of AI in the realm of nuclear applications. In human health, these areas include clinical research, epidemiology, nutrition, medical imaging, radiotherapy and education of health professionals. AI-based tools are also being used to facilitate different clinical tasks in imaging, computer-assisted diagnosis in mammography and lung cancer screening programmes, and dose prediction in nuclear medicine procedures. ML methods in particular may also increase the efficiency and accuracy of the analysis of computerised tomography and dual-energy absorptiometry scans for body composition and bone analysis. The application of AI methods to nuclear and related technologies in food and agriculture can lead to significant advances and improved efficiency in the optimisation of agricultural production, food product development, management of supply chains, food safety and food authenticity control. In the water and environmental sector, AI can help inform policies to mitigate the world’s water problems. The application of AI techniques to hydrology and environmental sciences is expected to improve patterns identification and enable model predictions under a changing climate.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRSDate not supplied

Adjusting a Full Body Model to Mitigate Inverse Kinematics Artifacts in Opensim

BACKGROUND: In support of Vibration Isolation and Stabilization (VIS) system development for Human Health Countermeasures (HHC) exercise systems in space, such as the European Enhanced Exploration Exercise Device (E4D) [1], dynamic quantities required to model the response of a proposed VIS while considering the effect of VIS motion on the forces between the human and VIS platform were obtained using motion capture data [2]. On occasion, large-amplitude oscillatory spikes were found in the subject’s linear and angular momentum derivatives, affecting analyses that depend on forces and moments derived from motion capture. The purpose of this investigation was to identify causes of these artifacts and techniques for their resolution. METHODS AND RESULTS: To obtain the required human dynamic quantities to drive the VIS simulation, motion capture data was collected containing recorded trajectories of passive retroreflective markers on body landmarks of an exercising subject. Since the full body Rajagopal model [3] was originally used to enhance gait analysis, upper body joints did not require large Ranges of Motion (ROM). We thus modified the Rajagopal model [4, 5] to allow it to be used for upper body intensive exercises like those common to the E4D. OpenSim Inverse Kinematics (IK) [6] was performed using these scaled subject models to generate the joint angles throughout the exercise while minimizing marker error. At times, the arms were observed to ‘snap’ from one configuration to another, causing spike artifacts. Following IK, a custom OpenSim plugin [7] was used to determine the required dynamic quantities including the rates of change of the linear and angular momenta of the human. Since motion capture is recorded at a larger time step than required by the VIS simulation, the human center of mass location was fit with splines and a second derivative taken to obtain the momentum derivative, allowing the VIS simulation to maintain conservation of momentum when appropriate. In a few cases during this stage, artifacts much larger than the expected noise of the second derivatives were introduced. Investigation of cases containing artifacts revealed several modifications that could be made to the OpenSim model to improve IK results. Since OpenSim models use Euler angles and rotation sequences, ‘gimbal lock’ would be encountered in the arms when raised 90 degrees to the side (e.g., T-pose, some hang clean exercise, etc.). This was resolved by reorienting the horizontal axes at the shoulder joint by 45 degrees, placing ‘gimbal lock’ outside common arm ROM, with the arm ROMs adjusted following this change. Elbow and wrist ROMs could also be adjusted to allow realistic motion while at the same time limiting the likelihood of unrealistic orientations. On occasion, the arms flipped backwards when raised above the head. This was prevented by using medial elbow markers in scaling and IK. When medial markers were not available, the acromial joint location in the unscaled model was shifted before model scaling to better align the arm with available markers. Lastly, artifacts which became apparent after taking the second derivatives of spline-fit data were found to occur in cases when the pelvis rotation limit prevented the full range of motion of an exercise. These issues were resolved by unclamping the pelvis rotation limit. Through this investigation, an understanding of conditions leading to IK artifacts was acquired allowing the automation of artifact detection. These artifact detection and mitigation techniques can be applied toward modeling of upper body motions in aerospace and other fields for improved IK results.

C A Bell↗
NASA NTRSDate not supplied

Advances in Entry Modeling for Impact Risk Assessment

A summary of recent advancements in the detailed modeling of asteroid atmospheric entry processes made through NASA’s Asteroid Threat Assessment Project (ATAP) is presented. Understanding, and accurately modeling these processes and their associated uncertainties is critical to predicting all downstream impact effects, such as blast wave and thermal damage footprints. Furthermore, there is (perhaps thankfully) a dearth of empirical data for large impactors of the kind that would pose a threat to human populations, on which to anchor and/or validate models used in risk assessments. Therefore, we must rely heavily on detailed theoretical and numerical modeling to develop robust assessments for decision makers. To that end, ATAP has made some significant progress in advancing the capabilities in this area. Two areas in particular are highlighted in the present work: meteoroid ablation mechanisms, and bolide luminosity. The first research area – meteoroid ablation mechanisms – has focused on performing novel high-enthalpy wind tunnel experiments on meteorites and meteorite analogs, and utilizing the resulting data to develop high-fidelity models for impactor mass loss at scale. These efforts have resulted in several insights. Of note, these data suggest a differential vaporization process where volatiles are liberated preferentially when the asteroidal material is subject to high heat, while refractory components remain in the molten layer on the surface. A numerical model has been developed which considers this phenomena, and its effect on the bulk impactors effective heat of ablation is examined. The second research thrust that is discussed is focused on accurate modeling of impactor radiation phenomena. While another submission to the conference will discuss the application of our approach to thermal ground damage modeling, here, we present an overview of our extensive efforts to utilize available ground- and space-based observations of large bolides (~1m diameter, and above) to inform and validate our detailed modeling approaches. These methods have been shown to accurately reconstruct the detailed spectra for the Benesov bolide, as well as approximate the burn footprint for the Tunguska event. Recent effort has focused on reconciling light curve data from multiple sources (all-sky camera networks, GLM, US government sensors), using our validated model, and providing a calibrated model for luminous efficiency which can then be utilized to infer impactor properties such as shape, mass, and composition. This work will be demonstrated through an exemplar case study focusing on a large bolide event with observational data (e.g. Chelyabinsk, Flensburg, etc.). Finally, our team’s assessment on the current maturity of atmospheric entry models, and priorities for future research will be provided.

ATAP↗
DOE OSTI2018

Understanding Dynamical Systems in High-Dimensional Parameter Spaces (Final Report)

In the last few decades, computational models, especially of the complex dynamical systems of greatest interest, i.e., climate, high energy density science, combustion, etc. have become increasingly accurate and sophisticated. Consequently, they are now routinely used to predict outcomes of experiments or to inform design decisions. However, many such simulations depend on a (large) number of parameters in order to, for example, approximate physical phenom whose exact solution is unknown or describe material properties in situations with little or no experimental data. Choosing these parameters and understanding how the outcomes of such a simulation changes with respect to the inputs are rapidly becoming some of the primary challenges in many application areas. Simulations have long been too complex for humans to fully comprehend and thus are often treated as black-box systems. The inputs to the systems are typically a collection of parameters with expert-selected ranges, and the outputs are various quantities of interest (QoIs) describing the performance of the system. Therefore, the fundamental challenge can be described as one of understanding a high-dimensional (many input parameters), multi-variate (many QoIs) function. Unfortunately, existing analysis tools are only of limited use for this task as most techniques can neither deal with many input dimensions nor offer opportunities to jointly study several QoIs. Furthermore, data analysis has long relied on an exploratory process in which scientist first visualize the available data, for example, using scatter plots or 3D renderings, form new hypotheses on some phenomena of interest, and subsequently test these hypotheses using dedicated analysis or statistics approaches. However, for high-dimensional functions this exploration is difficult if not impossible using existing tools as there are too many QoIs to simultaneously observe, and visualizations of high-dimensional spaces are abstract and difficult to understand if they exist at all. The goal of this project is to build new analysis techniques and tool that offer high level insights for exploring high-dimensional scientific data. And to extend existing approach beyond single scalar functions and instead consider multiple QoIs simultaneously.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2016

Differing Air Traffic Controller Responses to Similar Trajectory Prediction Errors

A Human-In-The-Loop simulation was conducted in January of 2013 in the Airspace Operations Laboratory at NASA's Ames Research Center. The simulation airspace included two en route sectors feeding the northwest corner of Atlanta's Terminal Radar Approach Control. The focus of this paper is on how uncertainties in the study's trajectory predictions impacted the controllers ability to perform their duties. Of particular interest is how the controllers interacted with the delay information displayed in the meter list and data block while managing the arrival flows. Due to wind forecasts with 30-knot over-predictions and 30-knot under-predictions, delay value computations included errors of similar magnitude, albeit in opposite directions. However, when performing their duties in the presence of these errors, did the controllers issue clearances of similar magnitude, albeit in opposite directions?

trajectory prediction uncertainty↗
NASA NTRS2017

Evaluation of a Tactical Surface Metering Tool for Charlotte Douglas International Airport Via Human-in-the-Loop Simulation

NASA has been working with the FAA and aviation industry partners to develop and demonstrate new concepts and technologies that integrate arrival, departure, and surface traffic management capabilities. In March 2017, NASA conducted a human-in-the-loop (HITL) simulation for integrated surface and airspace operations, modeling Charlotte Douglas International Airport, to evaluate the operational procedures and information requirements for the tactical surface metering tool, and data exchange elements between the airline controlled ramp and ATC Tower. In this paper, we focus on the calibration of the tactical surface metering tool using various metrics measured from the HITL simulation results. Key performance metrics include gate hold times from pushback advisories, taxi-in/out times, runway throughput, and departure queue size. Subjective metrics presented in this paper include workload, situational awareness, and acceptability of the metering tool and its calibration

air traffic management↗
NASA NTRS2016

How Important is Conflict Detection to the Conflict Resolution Task?

To determine the capabilities and limitations of human operators and automation in separation assurance roles, the second of three Human-in-the-Loop (HITL) part-task studies investigates air traffic controllers ability to detect and resolve conflicts under varying task sets, traffic densities, and run lengths. Operations remained within a single sector, staffed by a single controller, and explored, among other things, the controllers conflict resolution performance in conditions with or without their involvement in the conflict detection task. Whereas comparisons of conflict resolution performance between these two conditions are available in a prior publication, this paper explores whether or not other subjective measures display a relationship to that data. Analyses of controller workload and situation awareness measures attempt to quantify their contribution to controllers ability to resolve traffic conflicts.

function allocation↗
DOE OSTI2018

Pyroptosis by caspase11/4-gasdermin-D pathway in alcoholic hepatitis in mice and patients

Alcoholic hepatitis (AH) continues to be a disease with high mortality and no efficacious medical treatment. Although severe AH is presented as acute on chronic liver failure, what underlies this transition from chronic alcoholic steatohepatitis (ASH) to AH is largely unknown. To address this question, unbiased RNA sequencing and proteomic analyses were performed on livers of the recently developed AH mouse model, which exhibits the shift to AH from chronic ASH upon weekly alcohol binge, and these results are compared to gene expression profiling data from AH patients. This cross-analysis has identified Casp11 (CASP4 in humans) as a commonly up-regulated gene known to be involved in the noncanonical inflammasome pathway. Immunoblotting confirms CASP11/4 activation in AH mice and patients, but not in chronic ASH mice and healthy human livers. Gasdermin-D (GSDMD), which induces pyroptosis (lytic cell death caused by bacterial infection) downstream of CASP11/4 activation, is also activated in AH livers in mice and patients. CASP11 deficiency reduces GSDMD activation, bacterial load in the liver, and severity of AH in the mouse model. Conversely, the deficiency of interleukin-18, the key antimicrobial cytokine, aggravates hepatic bacterial load, GSDMD activation, and AH. Furthermore, hepatocyte-specific expression of constitutively active GSDMD worsens hepatocellular lytic death and polymorphonuclear leukocyte inflammation. Conclusion: These results implicate pyroptosis induced by the CASP11/4-GSDMD pathway in the pathogenesis of AH. (Hepatology 2018;67:1737-1753).

Khanova, Elena↗
NASA NTRS1989

Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events, and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system. The artificial intelligence approach is useful because it provides (1) the use of human experts' knowledge of sensor behavior and faulty engine conditions in interpreting data; (2) the use of engine design knowledge and physical sensor locations in establishing relationships among the events of multiple sensors; (3) the use of stored analysis of past data of faulty engine conditions; and (4) the use of knowledge-based reasoning in distinguishing sensor failure from actual faults. The neural network approach appears promising because neural nets (1) can be trained on extremely noisy data and produce classifications which are more robust under noisy conditions than other classification techniques; (2) avoid the necessity of noise removal by digital filtering and therefore avoid the need to make assumptions about frequency bands or other signal characteristics of anomalous behavior; (3) can, in effect, generate their own feature detectors based on the characteristics of the sensor data used in training; and (4) are inherently parallel and therefore are potentially implementable in special-purpose parallel hardware.

Ali, Moonis↗
NASA NTRS2017

Airborne Trajectory Management (ABTM): A Blueprint for Greater Autonomy in Air Traffic Management

The aviation users of the National Airspace System (NAS) - the airlines, General Aviation (GA), the military and, most recently, operators of Unmanned Aircraft Systems (UAS) - are constrained in their operations by the design of the current paradigm for air traffic control (ATC). Some of these constraints include ATC preferred routes, departure fix restrictions and airspace ground delay programs. As a result, most flights cannot operate on their most efficient business trajectories and a great many flights are delayed even getting into the air, which imposes a significant challenge to maintaining efficient flight and network operations. Rather than accepting ever more sophisticated scheduling solutions to accommodate the existing constraints in the airspace, a series of increasingly capable airborne technologies, integrated with planned improvements in the ground system through the Federal Aviation Administration (FAA) Next Generation Air Traffic Management System (NextGen) programs, could produce much greater operational flexibility for flight path optimization by the aviation system users. These capabilities, described in research coming out of NASA's Aeronautics Research Mission Directorate, can maintain or improve operational safety while taking advantage of air and ground NextGen technologies in novel ways. The underlying premise is that the nation's physical airspace is still abundant and underused, and that the delays and inefficient flight operations resulting from artificial structure in airspace use and procedural constraints on those operations may not be necessary for safe and efficient flight. This article is not an indictment of today's NAS or the people who run it. Indeed, it is an exceptional achievement that Air Traffic Management (ATM) - the complex human/machine conglomeration of communications, navigation and surveillance equipment and the rules and procedures for controlling traffic in the airspace - has both the capacity and enables the degree of efficiency in air travel that it does. But it is also true that sixty years of the "radar religion" (i.e., reliance on radar-based command and control) has produced several generations of ATM system operators and researchers who believe that introducing automation within the existing functional structure of ATM is the only way to "modernize" the system. Even NextGen, which began as a proposal for "transformational" change in the way ATC is performed, has morphed over the last decade and a half to become just the inclusion of Global Positioning System (GPS) for navigation, Automatic Dependent Surveillance Broadcast (ADS-B) for surveillance, and Data Communications (Data Comm) for communications, while still operating in rigidly structured airspace with human controllers being responsible for separation and traffic flow management (TFM) within defined sectors of airspace, using the same horizontal separation standards that have been in use since raw primary radar was introduced in the 1950s. No system as massive as the current NAS ATM can be replaced with a better system while simultaneously meeting the transportation and other aviation needs of the nation. A new generation of more flexible operations must emerge and yet coexist in harmony with the current operation (i.e., share the same airspace without segregation), thereby enabling a long-term transformation to take place in the way increasing numbers of flights are handled. Market forces will be the ultimate driver of this transformation, and investment realities mandate that real benefits must accrue to the first operators to adopt these new capabilities. In fact, the kinds of missions envisioned in the emerging world of UAS operations, unachievable under conventional ATM, demand that this transformation take place. Airborne Trajectory Management (ABTM) is proposed as a series of transformational steps leading to vastly increased flexibility in flight operations and capacity in the airspace to accommodate many varied airspace uses while improving safety. As will be described, ABTM enables the gradual emergence of a new paradigm for user-based trajectory management in ATM that brings tangible benefits to equipped operators at every step while leveraging the air and ground investments of NextGen. There are five steps in this ABTM transformation.1 NASA has extensively studied the first and last of these steps, and a roadmap of increasing capabilities and benefits is proposed for bridging between these operational concepts.

Cotton, William B.↗
DOE OSTI2017

Potentials for Platooning in U.S. Highway Freight Transport

Smart technologies enabling connection among vehicles and between vehicles and infrastructure as well as vehicle automation to assist human operators are receiving significant attention as a means for improving road transportation systems by reducing fuel consumption - and related emissions - while also providing additional benefits through improving overall traffic safety and efficiency. For truck applications, which are currently responsible for nearly three-quarters of the total U.S. freight energy use and greenhouse gas (GHG) emissions, platooning has been identified as an early feature for connected and automated vehicles (CAVs) that could provide significant fuel savings and improved traffic safety and efficiency without radical design or technology changes compared to existing vehicles. A statistical analysis was performed based on a large collection of real-world U.S. truck usage data to estimate the fraction of total miles that are technically suitable for platooning. In particular, our analysis focuses on estimating 'platoonable' mileage based on overall highway vehicle use and prolonged high-velocity traveling, and established that about 65% of the total miles driven by combination trucks from this data sample could be driven in platoon formation, leading to a 4% reduction in total truck fuel consumption. This technical potential for 'platoonable' miles in the United States provides an upper bound for scenario analysis considering fleet willingness and convenience to platoon as an estimate of overall benefits of early adoption of connected and automated vehicle technologies. A benefit analysis is proposed to assess the overall potential for energy savings and emissions mitigation by widespread implementation of highway platooning for trucks.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2016

Energetic Light Fragment Production Capability in MCNP6

Monte Carlo Methods, Codes, and Applications group within the Computational Physics Division at Los Alamos National Laboratory has led the development of the transport code MCNP6 (Monte Carlo N-Particle transport code, version 6). MCNP6 is a general-purpose, continuous energy, generalized-geometry, time-dependent, Monte-Carlo radiation-transport code designed to track many particle types over broad ranges of energies. It is used around the world in applications ranging from radiation protection and dosimetry, nuclear-reactor design, nuclear criticality safety, detector design and analysis, decontamination and decommissioning, accelerator applications, medical physics, space research, and beyond. At lower energies, the code uses tables of evaluated nuclear data, while for higher energies (> 150 MeV), MCNP6 uses the cascade-exciton model, version 03.03 (CEM03.03), and the Los Alamos quark-gluon string model, version 03.03 (LAQGSM03.03) to model nuclear reactions. Emission of energetic heavy clusters heavier than {sup 4}He from nuclear reactions play a critical role in several applications, including electronics performance in space, human radiation dosages in space or other extreme radiation environments, proton- and hadron-therapy in medical physics, accelerator and shielding applications, and more. None of the available models are able to accurately predict emission of light fragments (LF) from arbitrary reactions. The CEM and LAQGSM event generators in MCNP6 describe quite well the spectra of fragments with sizes up to {sup 4}He across a broad range of target masses and incident energies (up to ∼ 5 GeV for CEM and up to ∼ 1 TeV/A for LAQGSM). However, they do not predict the high-energy tails of LF spectra heavier than {sup 4}He well. Most LF with energies above several tens of MeV are emitted during the precompound stage of a reaction. The 03.03 versions of CEM and LAQGSM do not account for precompound emission of LF larger than {sup 4}He. The goal of this research is to enable MCNP6 to produce high-energy light fragments. These energetic LF may be emitted by our models through three processes: Fermi breakup, preequilibrium, and coalescence. We explore the emission of light fragments through each of these mechanisms and demonstrate an improved agreement with experimental data achieved by extending precompound models to include emission of fragments heavier than {sup 4}He. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

SU-C-BRD-05: Non-Invasive in Vivo Biodosimetry in Radiotherapy Patients Using Electron Paramagnetic Resonance (EPR) Spectroscopy

Purpose: Medical intervention following a major, unplanned radiation event can elevate the human whole body exposure LD50 from 3 to 7 Gy. On a large scale, intervention cannot be achieved effectively without accurate and efficient triage. Current methods of retrospective biodosimetry are restricted in capability and applicability; published human data is limited. We aim to further develop, validate, and optimize an automated field-deployable in vivo electron paramagnetic resonance (EPR) instrument that can fill this need. Methods: Ionizing radiation creates highly-stable, carbonate-based free radicals within tooth enamel. Using a process similar to nuclear magnetic resonance, EPR directly measures the presence of radiation-induced free radicals. We performed baseline EPR measurements on one of the upper central incisors of total body irradiation (TBI) and head and neck (H&N) radiotherapy patients before their first treatment. Additional measurements were performed between subsequent fractions to examine the EPR response with increasing radiation dose. Independent dosimetry measurements were performed with optically-stimulated luminescent dosimeters (OSLDs) and diodes to more accurately establish the relationship between EPR signal and delivered radiation dose. Results: 36 EPR measurements were performed over the course of four months on two TBI and four H & N radiotherapy patients. We observe a linear increase in EPR signal with increasing dose across the entirety of the tested range. A linear least squares-weighted fit of delivered dose versus measured signal amplitude yields an adjusted R-square of 0.966. The standard error of inverse prediction (SEIP) is 1.77 Gy. For doses up to 7 Gy, the range most relevant to triage, we calculate an SEIP of 1.29 Gy. Conclusion: EPR spectroscopy provides a promising method of retrospective, non-invasive, in vivo biodosimetry. Our preliminary data show an excellent correlation between predicted signal amplitude and delivered dose. With further development, a robust means of predicting delivered radiation dose from EPR measurements is expected. This project was funded by the Biomedical Advanced Research and Development Authority (BARDA) within the U.S. Department of Health and Human Services subcontracted through the Geisel School of Medicine at Dartmouth and by the Dartmouth Physically-Based Biodosimetry Center for Medical Countermeasures Against Radiation (Dart-Dose CMCR) Pilot Program.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

PHASE: Personalized Head-based Automatic Simulation for Electromagnetic properties in 7T MRI

Accurate and individualized human head models are becoming increasingly important for electromagnetic (EM) simulations. These simulations depend on precise anatomical representations to realistically model electric and magnetic field distributions, particularly when evaluating Specific Absorption Rate (SAR) within safety guidelines. State of the art simulations use the Virtual Population due to limited public resources and the impracticality of manually annotating patient data at scale. Here, this paper introduces Personalized Head-based Automatic Simulation for EM properties (PHASE), an automated open-source toolbox that generates high-resolution, patient-specific head models for EM simulations using paired T1-weighted (T1w) magnetic resonance imaging (MRI) and computed tomography (CT) scans with 14 tissue labels. To evaluate the performance of PHASE models, we conduct semi-automated segmentation and EM simulations on 15 real human patients, serving as the gold standard reference. The PHASE model achieved comparable global SAR and localized SAR averaged over 10 grams of tissue (SAR-10g), demonstrating its potential as a promising tool for generating large-scale human model datasets in the future. The code and models of PHASE toolbox have been made publicly available: https://github.com/hrlblab/PHASE.

Deep learning↗
NASA NTRS2023

A Meta-Analytic Approach to Investigating the Relationship Between Human-Automation Trust and Attention Allocation

Trust and attention allocation are pivotal determinants in human-automation interaction. However, there are scarce empirical findings regarding the relationship between trust and attention allocation. Observations from our previous work suggested there may be a negative correlation between trust in automation and eye movement towards automation, though no formal analysis of these data had been conducted to quantify this relationship. The present meta-analysis examined the relationship between three dimensions of trust in automation (performance, process, and purpose) and visual attention allocation to the automation. Specifically, we applied Cumming’s (2014) meta-analysis technique to combine evidence across three experiments. Results indicated a negative correlation between trust in automation and visual sampling of the automated system monitoring task for performance-based trust, but not for process- or purpose-based trust. These findings suggest that operators scanned the automation’s behavior less frequently when indicating higher performance-based trust towards the automation.

Human-Autonomy Teaming (HAT)↗
NASA NTRS2014

Complete Decoding and Reporting of Aviation Routine Weather Reports (METARs)

Aviation Routine Weather Report (METAR) provides surface weather information at and around observation stations, including airport terminals. These weather observations are used by pilots for flight planning and by air traffic service providers for managing departure and arrival flights. The METARs are also an important source of weather data for Air Traffic Management (ATM) analysts and researchers at NASA and elsewhere. These researchers use METAR to correlate severe weather events with local or national air traffic actions that restrict air traffic, as one example. A METAR is made up of multiple groups of coded text, each with a specific standard coding format. These groups of coded text are located in two sections of a report: Body and Remarks. The coded text groups in a U.S. METAR are intended to follow the coding standards set by National Oceanic and Atmospheric Administration (NOAA). However, manual data entry and edits made by a human report observer may result in coded text elements that do not follow the standards, especially in the Remarks section. And contrary to the standards, some significant weather observations are noted only in the Remarks section and not in the Body section of the reports. While human readers can infer the intended meaning of non-standard coding of weather conditions, doing so with a computer program is far more challenging. However such programmatic pre-processing is necessary to enable efficient and faster database query when researchers need to perform any significant historical weather analysis. Therefore, to support such analysis, a computer algorithm was developed to identify groups of coded text anywhere in a report and to perform subsequent decoding in software. The algorithm considers common deviations from the standards and data entry mistakes made by observers. The implemented software code was tested to decode 12 million reports and the decoding process was able to completely interpret 99.93 of the reports. This document presents the deviations from the standards and the decoding algorithm. Storing all decoded data in a database allows users to quickly query a large amount of data and to perform data mining on the data. Users can specify complex query criteria not only on date or airport but also on weather condition. This document also describes the design of a database schema for storing the decoded data, and a Data Warehouse web application that allows users to perform reporting and analysis on the decoded data. Finally, this document presents a case study correlating dust storms reported in METARs from the Phoenix International airport with Ground Stops issued by Air Route Traffic Control Centers (ATCSCC). Blowing widespread dust is one of the weather conditions when dust storm occurs. By querying the database, 294 METARs were found to report blowing widespread dust at the Phoenix airport and 41 of them reported such condition only in the Remarks section of the reports. When METAR is a data source for an ATM research, it is important to include weather conditions not only from the Body section but also from the Remarks section of METARs.

METAR Decoder/Parser↗
DOE OSTI2023

Data-Driven Discovery of Linear Molecular Probes with Optimal Selective Affinity for PFAS in Water

Approaches to tackle the wide and growing variety of highly persistent per- and polyfluoroalkyl substances (PFAS) are of pressing global need because of their detrimental human health effects, such as cancer, birth defects, and hormone imbalance. Sensitive, selective, and easy-to-use real-time sensors to monitor and detect PFAS and sorbents to extract them are critical to meeting government-mandated environmental concentrations. In this work, we combine all-atom molecular dynamics simulations, enhanced sampling, deep representational learning, and Bayesian optimization to perform high-throughput virtual screening for highly sensitive and selective molecular probes. Our molecular design space consists of 3850 linear hydrocarbon chains with varying degrees of halogenation with and without amine- and phosphine-based headgroups. By employing a data-driven search process, we efficiently explore the molecular design space to optimize the sensitivity to perfluorooctanesulfonic acid (PFOS) as a prototypical PFAS analyte and selectivity relative to a sodium dodecyl sulfate (SDS) interferent. We calculate 504 Gibbs free energies of probe-analyte and probe-interferent interactions and identify probes with PFOS association free energies of up to (-ΔG PFOS ) = 9.8 ± 0.2 kJ/mol and selectivities relative to SDS of (-ΔΔG PFOS–SDS ) = 3.1 ± 1.5 kJ/mol. A C 11 Br 23 P(CH 3 ) 2 probe containing 11 backbone brominated carbons and a tertiary phosphine headgroup possesses the most sensitive binding constant to PFOS within the defined search space of K b PFOS = 177.4 ± 12.7, and a semibrominated probe C 5 H 11 C 7 Br 14 N(CH 3 ) 2 containing 12 backbone carbons and a tertiary amine headgroup possesses the highest selectivity relative to SDS of K b PFOS /K b SDS = 4.6 ± 1.7. A retrospective analysis of our data to extract interpretable design rules reveals that the sensitivity of linear hydrogenated probes increases by approximately 1 kJ/mol per C–C bond. The addition or removal of halogen atoms and amine or phosphine headgroups produces nonmonotonic changes in both sensitivity and selectivity with changes to the sensitivity of up to 2.5 kJ/mol. Finally, this work places empirical limitations on the performance of a wide range of linear probes for PFOS detection and offers a generic strategy for high-throughput computational screening to promote selective and sensitive binding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A method to fuse multiphysics waveforms and improve predictive explosion detection: theory, experiment and performance

Natural and human-made sources of transient energy often emit multiple geophysical signatures that include mechanical and electromagnetic waveforms. We present a constructive method to fuse and evaluate statistics that we derive from such multiphysics waveforms that improves our capability to detect small, near-ground explosions over similar methods that consume single signature waveforms. Our method advances Fisher's Combined Probability Test (Fisher's Method) to operate under both hypotheses of a binary test on noisy data and provide researchers with the density functions required to forecast the ability of Fisher's Method to screen fused explosion signatures from noise. We apply this method against 12 d, multisignature explosion and noise records to show (1) that a fused multiphysics waveform statistic that combines radio, acoustic and seismic waveform data can identify explosions roughly 0.8 magnitude units lower than an acoustic emission, STA/LTA detector for the same detection probability and (2) that we can quantitatively predict how this fused, multiphysics statistic performs with Fisher's Method. Our work thereby offers a baseline method for predictive waveform fusion that supports multiphenomenological explosion monitoring (multiPEM) and is applicable to any binary testing problem in observational geophysics.

58 GEOSCIENCES↗