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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.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Repeated Induction of Inattentional Blindness in a Simulated Aviation Environment

The study reported herein is a subset of a larger investigation on the role of automation in the context of the flight deck and used a fixed-based, human-in-the-loop simulator. This paper explored the relationship between automation and inattentional blindness (IB) occurrences in a repeated induction paradigm using two types of runway incursions. The critical stimuli for both runway incursions were directly relevant to primary task performance. Sixty non-pilot participants performed the final five minutes of a landing scenario twice in one of three automation conditions: full automation (FA), partial automation (PA), and no automation (NA). The first induction resulted in a 70 percent (42 of 60) detection failure rate with those in the PA condition significantly more likely to detect the incursion compared to the FA condition or the NA condition. The second induction yielded a 50 percent detection failure rate. Although detection improved (detection failure rates declined) in all conditions, those in the FA condition demonstrated the greatest improvement with doubled detection rates. The detection behavior in the first trial did not preclude a failed detection in the second induction. Group membership (IB vs. Detection) in the FA condition showed a greater improvement than those in the NA condition and rated the Mental Demand and Effort subscales of the NASA-TLX (NASA Task Load Index) significantly higher for Time 2 compared Time 1. Participants in the FA condition used the experience of IB exposure to improve task performance whereas those in the NA condition did not, indicating the availability and reallocation of attentional resources in the FA condition. These findings support the role of engagement in operational attention detriment and the consideration of attentional failure causation to determine appropriate mitigation strategies.

Kennedy, Kellie D.↗
DOE OSTI2020

Machine learning for natural resource assessment: An application to the blind geothermal systems of Nevada

A study is underway to apply machine learning methods to evaluate natural resource potential. In particular, we are considering the search for blind geothermal systems in Nevada. Beginning with the data and experience from the previous Nevada play fairway analysis project, we are building models in TensorFlow/Keras and gaining experience toward predicting the geothermal resource potential as a probability map. During the first year of this project we have encountered several issues particular to using geological and geophysical data sets with these tools. Through an illustrative example we develop a promising workflow for future use as more data become available and are analyzed.

15 GEOTHERMAL ENERGY↗
NASA NTRSDate not supplied

Blind Validation Study of PRICE TruePlanning and SEER-H

Two of the primary parametric costing tools used to estimate the development and production cost of future spacecraft hardware are PRICE TruePlanning by PRICE Systems, and System Estimation and Evaluation of Resources-Hardware (SEER-H) by Galorath. These are standard tools used by NASA and industry to estimate the cost of new aerospace hardware. This study is an independent verification of the accuracy of these tools and was originally published in 2018. The purpose of this presentation is to circulate the findings of that study among the NASA cost community. Both PRICE Systems and Galorath have completed internal validation studies of their parametric cost estimating tools; however, they only provided the results of the studies and did not detail the exact methods used to perform the validation. In the present study, cost estimators used PRICE TruePlanning and SEER-H to estimate the cost of twelve different past NASA science missions. The estimators were prevented from knowing the actual cost of the missions in an effort to minimize cognitive biases. In the present study, SEER-H had an average error of 23%, median error of -0.3%, with a standard deviation of 43%. PRICE had an average error of 52%, median error of 50%, and standard deviation of 45%. Nine of the twelve mission's actual costs fell within the 80% confidence interval of SEER's probabilistic estimates; however, none of the missions fell within PRICE’s confidence intervals. There were several factors independent of PRICE and SEER-H which may have affected the accuracy of the results in the present study including: uncertainty in the technical data used for the estimates, the methods used to estimate uncertainty in spacecraft component mass and numbers of prototypes, and the experience of the estimators.

SEER↗
DOE OSTI2020

Flying Blind, or Just Flying Under the Radar? The Underappreciated Power of De Novo Methods of Mass Spectrometric Peptide Identification

Mass spectrometry-based proteomics is a popular and powerful method for precise and highly multiplexed protein identification. The most common method of analyzing untargeted proteomics data is called database searching, where the database is simply a collection of protein sequences from the target organism, derived from genome sequencing. Experimental peptide tandem mass spectra are compared to simplified models of theoretical spectra calculated from the translated genomic sequences. However, in several interesting application areas, such as forensics, archaeology, venomics and others, a genome sequence may not be available, or the correct genome sequence to use is not known. In these cases, de novo peptide identification can play an important role. De novo peptide identification infers peptide sequence directly from the tandem mass spectrum without reference to a sequence database, usually using graph-based or machine learning algorithms. In this review, we provide a basic overview of de novo peptide identification methods and applications, briefly covering de novo algorithms and tools, and focusing in more depth on recent applications from venomics, metaproteomics, forensics, and characterization of antibody drugs.

proteomics, mass spectrometry, forensics, de novo ↗
DOE OSTI2022

Randomized Phase 3, Double-blind, Placebo-controlled Study of Prophylactic Gabapentin for the Reduction of Oral Mucositis Pain During the Treatment of Oropharyngeal Squamous Cell Carcinoma

The purpose of this paper is to determine whether prophylactic gabapentin usage in patients undergoing definitive concurrent chemotherapy and radiation therapy (chemoRT) for oropharyngeal cancer (OPC) improves treatment-related oral mucositis pain, opioid use, and feeding tube (FT) placement.

62 RADIOLOGY AND NUCLEAR MEDICINE↗