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NASA NTRSDate not supplied

Quality of Candidate Flights and Submission Prediction in Collaborative Digital Departure Reroute

Collaborative Digital Departure Reroute (CDDR) enables the reroute of flights using a flight operator proposed set of alternative route options, referred to as Trajectory Option Set (TOS), in order to reduce delay on the airport's surface and in the Metroplex environment. The reroute functionality is enabled through NASA's Digital Information Platform (DIP). TOS candidate flights are defined as flights with an alternative route with delay savings greater than the flight operator defined relative trajectory cost. This paper analyzes the TOS candidate flights at Dallas/Fort Worth International Airport (KDFW) in the North Texas Metroplex to gain insight into which candidate flights are higher quality through a scoring method. This insight will inform refinements to help CDDR focus on high quality reroute opportunities. Binary classification models for predicting the flight operator's submission of candidate flights are also explored in this paper.

Machine Learning↗

NASA NTRSDate not supplied

Quality of Candidate Flights and Submission Prediction in Collaborative Digital Departure Reroute

Machine Learning↗

DOE OSTI2021

Real-time streamflow forecasting: AI vs. Hydrologic insights

In this paper, we propose a set of simple benchmarks for the evaluation of data-based models for real-time streamflow forecasting, such as those developed with sophisticated Artificial Intelligence (AI) algorithms. The benchmarks are also data-based and provide context to judge incremental improvements in the performance metrics from the more complicated approaches. The benchmarks include temporal and spatial persistence, persistence corrected for baseflow and streamflow, as well as river distance weighted runoff obtained from space-time distributed rainfall. In the development of the benchmarks, we use basic hydrologic insights such as flow aggregation by the river network, scale-dependence in basin response, streamflow partitioning into quick flow and baseflow, water travel time, and rainfall averaging by the basin width function. The study uses 140 streamflow gauges in Iowa that cover a range of basin scales between 7 and 37,000 km 2 . The data cover 17 years. This work demonstrates that the proposed benchmarks can provide good performance according to several commonly used metrics. For example, streamflow forecasting at half of the test locations across years achieves a Kling-Gupta Efficiency (KGE) score of 0.6 or higher at one-day ahead lead time, and 20% of cases reach the KGE of 0.8 or higher. The proposed benchmarks are easy to implement and should prove useful for developers of data-based as well as physics-based hydrologic models and real-time data assimilation techniques.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

The reference genome for the northeastern Pacific bull kelp, Nereocystis luetkeana

Bull kelp, Nereocystis luetkeana, is a northeastern Pacific kelp with broad distribution from Alaska to central California. Its population declines have caused severe concerns in northern California, the Salish Sea in Washington, and recently in some populations in Oregon. Despite bull kelp's accumulated ecological and physiological studies, an assembled and annotated genomic reference was still unavailable. Here, we report the complete and annotated genome of Nereocystis luetkeana, produced by the California Conservation Genomics Project (CCGP), which aims to reveal genomic diversity patterns across California by sequencing the complete genomes of approximately 150 carefully selected species. The genome was assembled into 1562 scaffolds with 449.82 Mb, 80x of coverage and 22 952 gene models. BUSCO assembly showed a completeness score of 72% for the stramenopiles gene set. The mitochondria and chloroplast genome sequences have 37 Kb and 131 Mb, respectively. The orthology analysis between 10 Phaeophycean genomes showed 1065 expanded and 286 unique orthogroups for this species. Pairwise comparisons showed 542 orthogroups present only in N. luetkeana and M. pyrifera, another large-body kelp. The enrichment analysis of these orthogroups showed important functions related to central metabolism and signaling due to ATPases enrichment in these two species. This genome assembly will provide an essential resource for the ecology, evolution, conservation, and breeding of bull kelp.

California Conservation Genomics Project—CCGP↗

NASA NTRS1974

Development of techniques to enhance man/machine communication

A four-state random stimulus generator, considered to function as an ESP teaching machine was used to investigate an approach to facilitating interactions between man and machines. A subject tries to guess in which of four states the machine is. The machine offers the user feedback and reinforcement as to the correctness of his choice. Using this machine, 148 volunteer subjects were screened under various protocols. Several whose learning slope and/or mean score departed significantly from chance expectation were identified. Direct physiological evidence of perception of remote stimuli not presented to any known sense of the percipient using electroencephalographic (EEG) output when a light was flashed in a distant room was also studied.

Targ, R.↗

DOE OSTI2023

Leverage Score Sampling for Parametric PDEs (Final Technical Report)

This final technical report summarizes the accomplishments of work performed under DOE Office of Science Award DE-SC0022266, which is titled “Leverage Score Sampling for Parametric PDEs”. The goal of the project was to extend methods from Randomized Numerical Linear Algebra (RandNLA) to tackle central computational challenges in model order reduction and uncertainty quantification (UQ) for parametric partial differential equations (PDEs). In particular, we sought to use importance sampling methods originally developed for RandNLA to develop sample efficient active learning algorithms for approximating high-dimensional scalar functions, e.g. by polynomials, Gaussian process models, and simple neural networks. Such methods can be immediately applied to developing surrogate models or to approximating quantity of interest (QoI) surfaces. In the context of PDEs, each sample used for learning equates to the solution of the differential equation for a particular set of parameters, so sample efficiency translates to improved computational efficiency for a variety of downstream tasks.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2018

FAIRness and Usability for Open-Access Omics Data Systems

Omics data sharing is especially crucial to the biological research community, and the last decade or two has seen a huge rise in collaborative analysis systems, databases, and knowledge bases for omics and other systems biology data. We assessed the "FAIRness" of NASA's GeneLab Data Systems (GLDS) along with four similar kinds of systems in the research omics data domain, using 14 FAIRness metrics. 14 metrics. The range of Pass ratings was 29-79% of the 14 metrics, Partial Pass 0-21%, and Fail 7-50%. The range of overall FAIRness scores was 5-12 (out of 14). The systems we evaluated performed the best in the areas of data findability and accessibility, and worst in the area of data interoperability. We propose two new principles that Big Data systems, in particular, should consider for increasing data accessibility. We relate our experiences implementing semantic integration of omics data from several systems for the federated querying and retrieval functions of the GLDS, given the shortcomings in data interoperability of these systems.

Berrios, Daniel C.↗

NASA NTRS2023

An Ensemble Neural Network Model for Predicting Rare-Earth Oxide and Silicate Heat Capacities at High Temperature

In this work, a neural network model was developed to predict the constant pressure heat capacity for materials in the rare-earth oxide—silica material space. Several model architectures were trained and tested on heat capacity data generated from first-principles density functional theory calculations. Hyperparameter optimization was performed, and the optimal model was selected for heat capacity predictions. The optimal model architecture was found to have a root-mean-squared error of 5.12 ± 3.37 J/mol-K. The optimal model architecture was then used in a bagging ensemble model trained using the leave-one-group-out method to provide error estimates for model predictions. The out-of-bag score for the ensemble model was 0.997. The predicted heat capacities agree well with the DFT and experimental results and were computed orders of magnitude faster than DFT simulations. Machine learning shows the potential to provide a suitable surrogate model for thermochemical property predictions for candidate environmental barrier coating materials but refining of input material features and model architectures could further improve accuracy for these models.

environmental barrier coatings↗

NASA NTRS1992

Fuzzy set approach to quality function deployment: An investigation

The final report of the 1992 NASA/ASEE Summer Faculty Fellowship at the Space Exploration Initiative Office (SEIO) in Langley Research Center is presented. Quality Function Deployment (QFD) is a process, focused on facilitating the integration of the customer's voice in the design and development of a product or service. Various input, in the form of judgements and evaluations, are required during the QFD analyses. All the input variables in these analyses are treated as numeric variables. The purpose of the research was to investigate how QFD analyses can be performed when some or all of the input variables are treated as linguistic variables with values expressed as fuzzy numbers. The reason for this consideration is that human judgement, perception, and cognition are often ambiguous and are better represented as fuzzy numbers. Two approaches for using fuzzy sets in QFD have been proposed. In both cases, all the input variables are considered as linguistic variables with values indicated as linguistic expressions. These expressions are then converted to fuzzy numbers. The difference between the two approaches is due to how the QFD computations are performed with these fuzzy numbers. In Approach 1, the fuzzy numbers are first converted to their equivalent crisp scores and then the QFD computations are performed using these crisp scores. As a result, the output of this approach are crisp numbers, similar to those in traditional QFD. In Approach 2, all the QFD computations are performed with the fuzzy numbers and the output are fuzzy numbers also. Both the approaches have been explained with the help of illustrative examples of QFD application. Approach 2 has also been applied in a QFD application exercise in SEIO, involving a 'mini moon rover' design. The mini moon rover is a proposed tele-operated vehicle that will traverse and perform various tasks, including autonomous operations, on the moon surface. The output of the moon rover application exercise is a ranking of the rover functions so that a subset of these functions can be targeted for design improvement. The illustrative examples and the mini rover application exercise confirm that the proposed approaches for using fuzzy sets in QFD are viable. However, further research is needed to study the various issues involved and to verify/validate the methods proposed.

Masud, Abu S. M.↗

DOE OSTI2025

Plasma phosphorylated tau217 strongly associates with memory deficits in the Alzheimer’s disease spectrum

Abstract Plasma phosphorylated tau (p-tau) biomarkers open unprecedented opportunities for identifying carriers of Alzheimer’s disease pathophysiology in early disease stages using minimally invasive techniques. Plasma p-tau biomarkers are believed to reflect tau phosphorylation and secretion. However, it remains unclear to what extent the magnitude of plasma p-tau abnormalities reflects neuronal network disturbance in the form of cognitive impairment. To address this question, we included 103 cognitively unimpaired elderly and 40 cognitively impaired, amyloid-β-positive individuals from the TRIAD cohort, in addition to 336 cognitively unimpaired and 216 cognitively impaired, amyloid-β-positive older adults from the BioFINDER-2 cohort. Participants had tau PET scans, amyloid PET scans or amyloid CSF, p-tau217, p-tau181 and p-tau231 blood measures, structural T1-MRI and cognitive assessments. In this cross-sectional study, we used regression models and correlation analyses to assess the relationship between plasma biomarkers and cognitive scores. Furthermore, we applied receiver operating characteristic curves to assess cognitive impairment across plasma biomarkers. Finally, we categorized participants into amyloid (A), p-tau (T1) and tau PET (T2) positive (+) or negative (−) profiles and ran non-parametric comparisons to assess differences across cognitive domains. We found that plasma p-tau217 was more associated with cognitive performance than p-tau181 and p-tau231 and that this relationship was particularly strong for memory scores (TRIAD: βp-tau217 = −0.53, βp-tau181 = −0.35 and βp-tau231 = −0.24; BioFINDER-2: βp-tau217 = −0.52, βp-tau181 = −0.24 and βp-tau231 = −0.29). Associations in amyloid-β-positive participants resembled these results, but other cognitive scores also showed strong associations in cognitively impaired individuals. Moreover, plasma p-tau217 outperformed plasma p-tau181 and plasma p-tau231 in identifying memory impairment (area under the curve values for TRIAD: p-tau217 = 0.86, p-tau181 = 0.77 and p-tau231 = 0.75; and for BioFINDER-2: p-tau217 = 0.86, p-tau181 = 0.76 and p-tau231 = 0.81) and in identifying executive function impairment only in the BioFINDER-2 cohort (p-tau217 = 0.82, p-tau181 = 0.76 and p-tau231 = 0.76). Lastly, we showed that subtle memory deficits were present in A+T1+T2− participants for plasma p-tau217 (P = 0.007) and plasma p-tau181 (P = 0.01) in the TRIAD cohort and for all biomarkers across cognitive domains in A+T1+T2− and A+T1+T2− individuals (P < 0.001 in all) in the BioFINDER-2 cohort. The A+T1+T2− individuals showed cognitive deficits in both cohorts (P < 0.001 in all). Together, our results suggest that plasma p-tau217 stands out as a biomarker capable of identifying memory deficits attributable to Alzheimer’s disease and that memory impairment certainly occurs in amyloid-β- and plasma p-tau-positive individuals who have no significant amounts of tau in the neocortex.

Neurosciences & Neurology↗

NASA NTRS1973

Sensitivity of a critical tracking task to alcohol impairment

A first order critical tracking task is evaluated for its potential to discriminate between sober and intoxicated performances. Mean differences between predrink and postdrink performances as a function of BAC are analyzed. Quantification of the results shows that intoxicated failure rates of 50% for blood alcohol concentrations (BACs) at or above 0.1%, and 75% for BACs at or above 0.14%, can be attained with no sober failure rates. A high initial rate of learning is observed, perhaps due to the very nature of the task whereby the operator is always pushed to his limit, and the scores approach a stable asymptote after approximately 50 trials. Finally, the implementation of the task as an ignition interlock system in the automobile environment is discussed. It is pointed out that lower critical performance limits are anticipated for the mechanized automotive units because of the introduction of larger hardware and neuromuscular lags. Whether such degradation in performance would reduce the effectiveness of the device or not will be determined in a continuing program involving a broader based sample of the driving population and performance correlations with both BACs and driving proficiency.

Tennant, J. A.↗

NASA NTRS2009

Group Capability Model

The Group Capability Model (GCM) is a software tool that allows an organization, from first line management to senior executive, to monitor and track the health (capability) of various groups in performing their contractual obligations. GCM calculates a Group Capability Index (GCI) by comparing actual head counts, certifications, and/or skills within a group. The model can also be used to simulate the effects of employee usage, training, and attrition on the GCI. A universal tool and common method was required due to the high risk of losing skills necessary to complete the Space Shuttle Program and meet the needs of the Constellation Program. During this transition from one space vehicle to another, the uncertainty among the critical skilled workforce is high and attrition has the potential to be unmanageable. GCM allows managers to establish requirements for their group in the form of head counts, certification requirements, or skills requirements. GCM then calculates a Group Capability Index (GCI), where a score of 1 indicates that the group is at the appropriate level; anything less than 1 indicates a potential for improvement. This shows the health of a group, both currently and over time. GCM accepts as input head count, certification needs, critical needs, competency needs, and competency critical needs. In addition, team members are categorized by years of experience, percentage of contribution, ex-members and their skills, availability, function, and in-work requirements. Outputs are several reports, including actual vs. required head count, actual vs. required certificates, CGI change over time (by month), and more. The program stores historical data for summary and historical reporting, which is done via an Excel spreadsheet that is color-coded to show health statistics at a glance. GCM has provided the Shuttle Ground Processing team with a quantifiable, repeatable approach to assessing and managing the skills in their organization. They now have a common frame of reference across NASA/contractor lines to communicate and mitigate any critical skills concerns.

Olejarski, Michael↗

DOE OSTI2023

Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15

Abstract Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between structural models is proven useful for estimating the quality of protein tertiary structural models, but it has been rarely applied to predicting the quality of quaternary structural models. Moreover, the pairwise similarity approach often fails when many structural models are of low quality and similar to each other. To address the gap, we developed a hybrid method (MULTICOM_qa) combining a pairwise similarity score (PSS) and an interface contact probability score (ICPS) based on the deep learning inter‐chain contact prediction for estimating protein complex model accuracy. It blindly participated in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 and performed very well in estimating the global structure accuracy of assembly models. The average per‐target correlation coefficient between the model quality scores predicted by MULTICOM_qa and the true quality scores of the models of CASP15 assembly targets is 0.66. The average per‐target ranking loss in using the predicted quality scores to rank the models is 0.14. It was able to select good models for most targets. Moreover, several key factors (i.e., target difficulty, model sampling difficulty, skewness of model quality, and similarity between good/bad models) for EMA are identified and analyzed. The results demonstrate that combining the multi‐model method (PSS) with the complementary single‐model method (ICPS) is a promising approach to EMA.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Mechanically induced thermal runaway severity analysis for Li-ion batteries

Thermal runaway is one of the most important safety concerns in the deployment of Li-ion batteries. For this study, we developed a standardized single-side indentation test protocol to induce an internal short-circuit. Cell voltage, temperature, and applied compressive force are monitored as a function of time. Each cell is given an observed hazard severity (OHS in five categories) modified from the EUCAR table. Meanwhile a calculated hazard severity (CHS) is obtained solely based on temperature and voltage curves. The calculation formula considers the cell temperature, rate of temperature increase, cell capacity, state of charge (SOC), voltage drop, and voltage drop rate. Each term is assigned with an appropriate weight to scale the calculated score from 0 to100. This method was applied to >100 Li-ion batteries with various SOCs and chemistries, and their OHS and CHS were displayed against SOC. The purpose is to provide battery designers, manufacturers, and end-users a clear comparison of thermal runaway severity of different batteries.

25 ENERGY STORAGE↗

DOE OSTI2020

A scalable method of applying heat and humidity for decontamination of N95 respirators during the COVID-19 crisis

A lack of N95 Filtering Facepiece Respirators (FFRs) during the COVID-19 crisis has placed healthcare workers at risk. It is important for any N95 reuse strategy to determine the effects that proposed protocols would have on the physical functioning of the mask, as well as the practical aspects of implementation. Here we propose and implement a method of heating N95 respirators with moisture (85°C, 60-85% humidity). We test both mask filtration efficiency and fit to validate this process. Our tests focus on the 3M 1860, 3M 1870, and 3M 8210 Plus N95 models. After five cycles of the heating procedure, all three respirators pass both quantitative fit testing (score of >100) and show no degradation of mask filtration efficiency. We also test the Chen Heng V9501 KN95 and HKYQ N95 finding no degradation of mask filtration efficiency, however even for unheated masks these scored <50 for every fit test. The heating method presented here is scalable from individual masks to over a thousand a day with a single industrial convection oven, making this method practical for local application inside health-care facilities.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2010

Walk on Floor Eyes Closed Test as a Measure of Postflight Ataxia

INTRODUCTION: Astronauts returning from space flight universally exhibit impaired posture and locomotion. Measurement of this impairment is an evolving process. The walk on the floor line test with the eyes closed (WOFEC) provides a unique procedure for quantifying postflight ataxia. Data from a modified WOFEC were obtained as part of an ongoing NASA interdisciplinary pre- and postflight study (Functional Task Test, FTT) designed to evaluate astronaut postflight functional performance. METHODS: Seven astronauts (5 short duration with flights of 12-16 days; 2 long duration crewmembers with flights of 6 months) were tested twice before flight, on landing day (short duration only), and 1, 6, and 30 days after flight. The WOFEC consisted of walking for 10 steps (repeated twice) with the feet heel to toe in tandem, arms folded across the chest and the eyes closed. The performance metric (scored by three examiners from video) was the percentage of correct steps completed over the three trials. A step was not counted as correct if the crewmember sidestepped, opened their eyes, or paused for more than three seconds between steps. RESULTS/ CONCLUSIONS: There was a significant decrease in percentage of correct steps on landing day (short duration crew) and on first day following landing (long duration) with partial recovery the following day, and full recovery beginning on day sixth after flight. Both short and long duration fliers appeared to be unaware of foot position relative to their bodies or the floor. Postflight, deviation from a straight path was common, and the test for two crewmembers elicited motion sickness symptoms. These data clearly demonstrate the sensorimotor challenges facing crewmembers after returning from spaceflight. The WOFEC test has value providing the investigator or crew surgeon with a simple method to quantify vestibular ataxia, as well as providing instant feedback of postural ataxia without the use of complex test equipment.

Reschke, M. F.↗

NASA NTRS2017

Unmanned Aircraft Systems Minimum Operations Performance Standards End-to-End Verification and Validation (E2-V2) Simulation

As Unmanned Aircraft Systems (UAS) make their way to mainstream aviation operations within the National Airspace System (NAS), research efforts are underway to develop a safe and effective environment for their integration into the NAS. Detect and Avoid (DAA) systems are required to account for the lack of "eyes in the sky" due to having no human on-board the aircraft. The current NAS relies on pilot's vigilance and judgement to remain Well Clear (CFR 14 91.113) of other aircraft. RTCA SC-228 has defined DAA Well Clear (DAAWC) to provide a quantified Well Clear volume to allow systems to be designed and measured against. Extended research efforts have been conducted to understand and quantify system requirements needed to support a UAS pilot's ability to remain well clear of other aircraft. The efforts have included developing and testing sensor, algorithm, alerting, and display requirements. More recently, sensor uncertainty and uncertainty mitigation strategies have been evaluated. This paper discusses results and lessons learned from an End-to-End Verification and Validation (E2-V2) simulation study of a DAA system representative of RTCA SC-228's proposed Phase I DAA Minimum Operational Performance Standards (MOPS). NASA Langley Research Center (LaRC) was called upon to develop a system that evaluates a specific set of encounters, in a variety of geometries, with end-to-end DAA functionality including the use of sensor and tracker models, a sensor uncertainty mitigation model, DAA algorithmic guidance in both vertical and horizontal maneuvering, and a pilot model which maneuvers the ownship aircraft to remain well clear from intruder aircraft, having received collective input from the previous modules of the system. LaRC developed a functioning batch simulation and added a sensor/tracker model from the Federal Aviation Administration (FAA) William J. Hughes Technical Center, an in-house developed sensor uncertainty mitigation strategy, and implemented a pilot model similar to one from the Massachusetts Institute of Technology's Lincoln Laboratory (MIT/LL). The resulting simulation provides the following key parameters, among others, to evaluate the effectiveness of the MOPS DAA system: severity of loss of well clear (SLoWC), alert scoring, and number of increasing alerts (alert jitter). The technique, results, and lessons learned from a detailed examination of DAA system performance over specific test vectors and encounter cases during the simulation experiment will be presented in this paper.

Ghatas, Rania W.↗

DOE OSTI2026

The Sensitivity of Variational Bayesian Neural Network Performance to Hyperparameters

In scientific applications, predictive modeling is often of limited use without accurate uncertainty quantification (UQ) to indicate when a model may be extrapolating or when more data needs to be collected. Bayesian Neural Networks (BNNs) produce predictive uncertainty by propagating uncertainty in neural network (NN) weights and offer the promise of obtaining not only an accurate predictive model but also accurate UQ. However, in practice, obtaining accurate UQ with BNNs is difficult due in part to the approximations used for model training (such as those made in variational inference) and in part to the need to choose a suitable set of hyperparameters; these hyperparameters outnumber those needed for traditional NNs and often have opaque effects on the results. We aim to shed light on the effects of hyperparameter choices for variational BNNs by performing a global sensitivity analysis of variational BNN performance under varying hyperparameter settings. Our results indicate that many of the hyperparameters interact with each other to affect both predictive accuracy and UQ. For improved usage of variational BNNs in real-world applications, we suggest that thorough hyperparameter tuning, including tuning of prior hyperparameters and loss function parameters, is essential for accurate UQ in variational BNNs.

97 MATHEMATICS AND COMPUTING↗