Search NASA⌕ Search

SEARCH · Search NASA

Results for “RECOMMENDATIONS”

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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

T-Rex: The NASA Technology Taxonomy Recommender System

NASA tracks over 16,000 technology projects across the Agency, from propulsion systems to software. These projects are classified according to NASA’s Technology Taxonomy to facilitate data search, extraction, and application. In 2020, the Taxonomy was revised to better align strategic goals with project technical disciplines. Manual re-classification of cur-rent and historical projects was estimated to take thousands of technologist labor hours. Instead of manual classification, our team developed T-Rex, a recommender engine, trained on just a small set of manually classified projects. T-Rex was used to classify the projects and then integrate the data into TechPort to recommend classes to users when updating projects. The system and methodology are used in other NASA projects, and T-Rex has achieved 95% accepted accuracy overall.

Space Technology↗
DOE OSTI2021

Environmental life cycle impacts of small wastewater treatment plants: Design recommendations for impact mitigation

The objective of this study was to quantify potential mitigation of environmental impacts from the operation and construction of wastewater treatment plants (WWTP) from implementing specific design recommendations. The study investigated small WWTPs, many of which are serving slow growing or declining populations. Life Cycle Assessment methodology was used to evaluate and compare the inventory and environmental impacts of nine small WWTP case studies. Detailed inventory data was collected from the facilities’ engineering design plans and utility bills. One recommended practice was to avoid significant overdesign by planning for no lower than a 75% capacity utilization by the facilities’ end-of-life. A theoretical correction to a 75% capacity utilization was estimated to mitigate 0.4% of lifetime electricity usage and 1% of secondary process concrete for every 1% reduction in design average flow rate. Relatedly, a 0.4% mitigation in the Carcinogenic and Global Warming impacts could be achieved for every 1% reduction in design average flow toward a 75% capacity utilization. Other suggested practices were focused on conveyance, namely, to minimize non-process facility area and to use polyvinyl chloride pipe instead of ductile iron pipe where possible. Furthermore, the latter practice was estimated to mitigate between 1.1 and 4.8% of the Carcinogenic impact in the nine case studies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A machine learning Automated Recommendation Tool for synthetic biology

Abstract Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, fatty acids, and tryptophan. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The case for data science in experimental chemistry: examples and recommendations

The physical sciences community is increasingly taking advantage of the possibilities offered by modern data science to solve problems in experimental chemistry and potentially to change the way we design, conduct and understand results from experiments. Successfully exploiting these opportunities involves considerable challenges. In this Expert Recommendation, we focus on experimental co-design and its importance to experimental chemistry. We provide examples of how data science is changing the way we conduct experiments, and we outline opportunities for further integration of data science and experimental chemistry to advance these fields. Our recommendations include establishing stronger links between chemists and data scientists; developing chemistry-specific data science methods; integrating algorithms, software and hardware to ‘co-design’ chemistry experiments from inception; and combining diverse and disparate data sources into a data network for chemistry research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge

This paper describes outcomes of the 2019 Cryo-EM Model Challenge. The goals were to (1) assess the quality of models that can be produced from cryogenic electron microscopy (cryo-EM) maps using current modeling software, (2) evaluate reproducibility of modeling results from different software developers and users and (3) compare performance of current metrics used for model evaluation, particularly Fit-to-Map metrics, with focus on near-atomic resolution. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived by 13 participating teams from four benchmark maps, including three forming a resolution series (1.8 to 3.1 Å). The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual experiments and structure data archives such as the Protein Data Bank. We recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed cryo-EM map density.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Simple and statistically sound recommendations for analysing physical theories

Physical theories that depend on many parameters or are tested against data from many different experiments pose unique challenges to statistical inference. Many models in particle physics, astrophysics and cosmology fall into one or both of these categories. These issues are often sidestepped with statistically unsound ad hoc methods, involving intersection of parameter intervals estimated by multiple experiments, and random or grid sampling of model parameters. Whilst these methods are easy to apply, they exhibit pathologies even in low-dimensional parameter spaces, and quickly become problematic to use and interpret in higher dimensions. In this article we give clear guidance for going beyond these procedures, suggesting where possible simple methods for performing statistically sound inference, and recommendations of readily-available software tools and standards that can assist in doing so. Our aim is to provide any physicists lacking comprehensive statistical training with recommendations for reaching correct scientific conclusions, with only a modest increase in analysis burden. Furthermore, our examples can be reproduced with the code publicly available at Zenodo.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Community recommendations on cryoEM data archiving and validation

In January 2020, a workshop was held at EMBL-EBI (Hinxton, UK) to discuss data requirements for the deposition and validation of cryoEM structures, with a focus on single-particle analysis. The meeting was attended by 47 experts in data processing, model building and refinement, validation, and archiving of such structures. This report describes the workshop's motivation and history, the topics discussed, and the resulting consensus recommendations. Some challenges for future methods-development efforts in this area are also highlighted, as is the implementation to date of some of the recommendations.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Recommended Functionalities for Improving Cybersecurity of Distributed Energy Resources

The current electric grid is transitioning through increasing penetration of distributed energy resources (DERs), which include intermittent renewable generation resources on the distribution side. Both the monitoring and control of DERs require extensive data-exchange and communication networks. These networks lead to cyber vulnerabilities and risks of new kinds of cyberattacks that may be extremely destructive for power system operations. Although current standards, such as IEEE Std. 1547-2018, do not discuss cybersecurity measures for DERs, cybersecurity controls should be developed for securing DER systems at the device level, communications level, and applications level. This paper discusses the current industry's best practices related to DER cybersecurity and proposes recommended functionalities for improving the cybersecurity posture of DERs, specifically at the device/distribution level. These practical recommendations have been discussed and verified with the industry through a DER cybersecurity working group.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A Multi-Objective Bayesian Optimized Human Assessed Multi-Target Generated Spectral Recommender System for Rapid Pareto Discoveries of Material Properties

Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameter and function spaces need a rapid strategic search through active learning. However, in all cases prior to optimization, the target material properties are assumed known and fixed, which mostly deviates from real-world scenarios in material synthesis. This can be critical for running expensive experiments on new materials, when the experimental results are fuzzy for any scientific outcomes due to improper target setting, ultimately wasting time and cost. The failure rate and cost are even higher over exploring on multi-target space, where we want to learn the pareto among multiple properties, to jointly optimize during material synthesis for desired applications. To address the challenge, here we introduce the human-operator attempt flexibility in the active learning based automated experiment framework, with generating multiple human assessed targets through a voting-based recommender system during real-time microscope measurements over the large material image space, sequentially learn/update multiple desired targets through a weighting system, and adaptively search in multiple material properties functional space for non-dominated pareto discoveries to maximize the custom structural similarity based acquisition function. We term this a multi-objective Bayesian optimized human assessed multi-target generated spectral recommender systems (MOBO-HAM-SRS). The approach has been demonstrated to peizoresponse force spectroscopy of a ferroelectric thin film, exploring with different kernels and acquisition functions. This work shows an advancement towards human-AI collaborated automated experiments, steering optimization trajectories through human overpowering AI at the early stage when uncertainty is high and AI overpowering human at the later stage with rapid exploration towards optimal goal, following human-assessed multiple targets properties.

Biswas, Arpan↗
DOE OSTI2024

RAP: Resource-aware Automated GPU Sharing for Multi-GPU Recommendation Model Training and Input Preprocessing

Ensuring high-quality recommendations for newly onboarded users requires the continuous retraining of Deep Learning Recommendation Models (DLRMs) with freshly generated data. To serve the online DLRM retraining, existing solutions use hundreds of CPU computing nodes designated for input preprocessing, causing significant power consumption that surpasses even the power usage of GPU trainers. To this end, we propose RAP, an end-to-end DLRM training framework that supports Resource-aware Automated GPU sharing for DLRM input Preprocessing and Training. The core idea of RAP is to accurately capture the remaining GPU computing resources during DLRM training for input preprocessing, achieving superior training efficiency without requiring additional resources. Specifically, RAP utilizes a co-running cost model to efficiently assess the costs of various input preprocessing operations, and it implements a resource-aware horizontal fusion technique that adaptively merges smaller kernels according to GPU availability, circumventing any interference with DLRM training. In addition, RAP leverages a heuristic searching algorithm that jointly optimizes both the input preprocessing graph mapping and the co-running schedule to maximize the end-to-end DLRM training throughput. The comprehensive evaluation shows that RAP achieves 78.3× speedup on average over CPU-based DLRM input preprocessing frameworks. In addition, the end-to-end training throughput of RAP is only 2.04% lower than the ideal case, which has no input preprocessing overhead.

Wang, Zheng↗
DOE OSTI2026

Responsible AI Recommendations (RAIR)

This dataset contains recommendations culled from academic and gray literature on the implementation of responsible or ethical AI. Recommendations are tagged with 1 to 5 topical labels, and a label regarding the source type of their parent document.

Artificial intelligence↗
DOE OSTI2020

COVID-19 global pandemic planning: Performance and electret charge of N95 respirators after recommended decontamination methods

Shortages of N95 respirators for use by medical personnel have driven consideration of novel conservation strategies, including decontamination for reuse and extended use. Decontamination methods listed as promising by the Centers for Disease Control and Prevention (CDC) (vaporous hydrogen peroxide (VHP), wet heat, ultraviolet irradiation (UVI)) and several methods considered for low resource environments (bleach, isopropyl alcohol and detergent/soap) were studied for two commonly used surgical N95 respirators (3M™ 1860 and 1870+ Aura™). Although N95 filtration performance depends on the electrostatically charged electret filtration layer, the impact of decontamination on this layer is largely unexplored. As such, respirator performance following decontamination was assessed based on the fit, filtration efficiency, and pressure drop, along with the relationship between (1) surface charge of the electret layer, and (2) elastic properties of the straps. Decontamination with VHP, wet heat, UVI, and bleach did not degrade fit and filtration performance or electret charge. Isopropyl alcohol and soap significantly degraded fit, filtration performance, and electret charge. Pressure drop across the respirators was unchanged. Modest degradation of N95 strap elasticity was observed in mechanical fatigue testing, a model for repeated donnings and doffings. CDC recommended decontamination methods including VHP, wet heat, and UV light did not degrade N95 respirator fit or filtration performance in these tests. Extended use of N95 respirators may degrade strap elasticity, but a loss of face seal integrity should be apparent during user seal checks. NIOSH recommends performing user seal checks after every donning to detect loss of appropriate fit. Decontamination methods which degrade electret charge such as alcohols or detergents should not be used on N95 respirators. The loss of N95 performance due to electret degradation would not be apparent to a respirator user or evident during a negative pressure user seal check.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Sensor Recommendations for Long Term Monitoring of the F-Area Seepage Basins

In mid-2018, a new paradigm for long-term monitoring was developed after of decade of applied research projects funded by the Department of Energy’s office of Environmental Management Technology Development program. The program at SRNL was focused on transitioning complex environmental waste sites from active to passive remediations strategies. A key result of these studies was that the use of enhanced attenuation approaches at radiologically contaminated sites will result in the creation of secondary source areas in the subsurface that will require monitoring for decades. Alternative monitoring approaches are being developed and tested at the Savannah River Site’s F-Area Hazardous Waste Management Facility, the new paradigm provides innovative solutions that will significantly lower costs of monitoring through the coupling of data collection, machine learning and deterministic groundwater modeling. The foundation of this approach is a well-optimized network of sensors for measuring hydrogeochemical master variables that control, and therefore act as indicators of groundwater contaminant transport. By monitoring changes in the controlling master variables over time arising from geological and environmental shifts, predictive modelling can assist with identifying new strategies for ensuring regulatory requirements are met if trends toward conditions for potential remobilization of attenuated contaminants are detected. In this report, we evaluated commercially available single parameter sensor platforms (e.g., temperature/depth) and configurable multi-parameter sensor platforms (e.g., pH, oxidation-reduction potential, temperature, depth, dissolved oxygen, and conductivity). Each was scored using an optimization function based on how well the system supports the proposed long-term monitoring paradigm, in general, and the site-specific conditions at F-Area, in particular. Several viable sensor systems were identified. Of these, a combined platform including the In-Situ Aqua TROLL 500 multi-parameter sensor platform and the In-Situ temperature/depth sensor had the highest rating and was identified as the most suitable candidate for installation and monitoring of the master variables and potentiometric surface that control groundwater contaminant plumes emanating from the F-Area Seepage Basins. The discussion of recommended potential deployment locations builds upon recommendations made by Denham et al (2019).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Multitask Recommender Systems for Cancer Drug Response

The problem we are currently trying to address is that there are many types of cancer drugs and many types of cancers and there is not always experimental data for a specific cancer type and cancer drug interaction. While there is a large possible set of feasible drug and cancer combinations, testing each pair is not realistic due to the high monetary cost of cell-based assays. Thus, this leaves researchers with a difficult choice of what drugs they should test on specific cancer types. This issue is known as the cold-start problem. Our focus is on developing recommender systems capable of addressing the cold-start problem as it relates to interaction between cancer types and cancer drugs. One of the most effective ways to address the cold-start problem is through large data analysis, however due to the cost prohibitive nature of cancer research the largest available data set size is the Genomics of Drug Sensitivity in Cancer with 494,973 genomic associations. To achieve optimal model performance on the cold-start problem, it is advantageous to employ multitask algorithms that are capable of transferring information between cancer datasets. The aim of this report is to draw from adaptations and state of the art developments in both algorithms for recommender systems and multitask learning to model the interaction between cancer cell lines and cancer drugs. Cancer cell lines are defined by the US National Cancer Institute as "cancer cells that keep dividing and growing over time, under certain conditions in a laboratory". This paper will focus on evaluating the performance of Neural Collaborative Filtering and Gaussian Processes, as well as their multitask adaptations, on cancer datasets from CCLE, NCI60, GDSC and CTRP. These methods will be evaluated on model performance in regression prediction but also in interpretability.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Guidance and Recommendations for Streamlining Reporting for Federal Energy and Water Efficiency Projects

Federal agencies are required to report on their progress in meeting various energy and water management requirements. These reporting requirements encompass energy and water projects at federal facilities, including projects that are alternatively financed, e.g., conducted through energy savings performance contracts (ESPCs) or utility energy service contracts (UESCs). The purpose of this guidance is to provide recommendations to streamline federal agency reporting. The guidance recommends the use of eProject Builder (ePB), a project development and archiving tool for energy projects. ePB carries additional value in its simplification of federal agency reporting by dovetailing with the Federal Energy Management Program’s (FEMP’s) EISA 432 Compliance Tracking System (CTS).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Recommendations for Distributed Energy Resource Patching

While computer systems, software applications, and operational technology (OT)/Industrial Control System (ICS) devices are regularly updated through automated and manual processes, there are several unique challenges associated with distributed energy resource (DER) patching. Millions of DER devices from dozens of vendors have been deployed in home, corporate, and utility network environments that may or may not be internet-connected. These devices make up a growing portion of the electric power critical infrastructure system and are expected to operate for decades. During that operational period, it is anticipated that critical and noncritical firmware patches will be regularly created to improve DER functional capabilities or repair security deficiencies in the equipment. The SunSpec/Sandia DER Cybersecurity Workgroup created a Patching Subgroup to investigate appropriate recommendations for the DER patching, holding fortnightly meetings for more than nine months. The group focused on DER equipment, but the observations and recommendations contained in this report also apply to DERMS tools and other OT equipment used in the end-to-end DER communication environment. The group found there were many standards and guides that discuss firmware lifecycles, patch and asset management, and code-signing implementations, but did not singularly cover the needs of the DER industry. This report collates best practices from these standards organizations and establishes a set of best practices that may be used as a basis for future national or international patching guides or standards.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Recommended Target Fields for Commissioning the Vera C. Rubin Observatory

The commissioning team for the Vera C. Rubin observatory is planning a set of engineering and science verification observations with the Legacy Survey of Space and Time (LSST) commissioning camera and then the Rubin Observatory LSST Camera. The time frame for these observations is not yet fixed, and the commissioning team will have flexibility in selecting fields to observe. In this document, the Dark Energy Science Collaboration (DESC) Commissioning Working Group presents a prioritized list of target fields appropriate for testing various aspects of DESC-relevant science performance, grouped by season for visibility from Rubin Observatory at Cerro Pachon. Our recommended fields include Deep-Drilling fields (DDFs) to full LSST depth for photo-$z$ and shape calibration purposes, HST imaging fields to full depth for deblending studies, and an $\sim$200 square degree area to 1-year depth in several filters for higher-level validation of wide-area science cases for DESC. We also anticipate that commissioning observations will be needed for template building for transient science over a broad RA range. We include detailed descriptions of our recommended fields along with associated references. We are optimistic that this document will continue to be useful during LSST operations, as it provides a comprehensive list of overlapping data-sets and the references describing them.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Scoping Analysis of MACCS Modeling Improvements for the Study of Protective Action Recommendations

In late 2004, the U.S. Nuclear Regulatory Commission (NRC) initiated a project to analyze the relative efficacy of alternative protective action strategies in reducing consequences to the public from a spectrum of nuclear power plant core melt accidents. The study is documented in NUREG/CR-6953, “Review of NUREG-0654, Supplement 3, ‘Criteria for Protective Action Recommendations for Severe Accidents,’” Volumes 1, 2, and 3. The Protective Action Recommendations (PAR) study provided a technical basis for enhancing the protective action guidance contained in Supplement 3, “Guidance for Protective Action Strategies,” to NUREG-0654/FEMA-REP-1, Rev. 1, “Criteria for Preparation and Evaluation of Radiological Emergency Response Plans and Preparedness in Support of Nuclear Power Plants, ” dated November 2011. In the time since, a number of important changes and additions have been made to the MACCS code suite, the nuclear accident consequence analysis code used to perform the study. The purpose of this analysis is to determine whether the MACCS results used in the PAR study would be different given recent changes to the MACCS code suite and input parameter guidance. Updated parameters that were analyzed include cohorts, keyhole evacuation, shielding and exposure parameters, compass sector resolution, and a range of source terms from rapidly progressing accidents. Results indicate that using updated modeling assumptions and capabilities may lead to a decrease in predicted health consequences for those within the emergency planning zone compared to the original PAR study.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗