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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern

A prototype application for machine-readable literature is investigated. The program is called pyDataRecognition and serves as an example of a data-driven literature search, where the literature search query is an experimental data set provided by the user. The user uploads a powder pattern together with the radiation wavelength. The program compares the user data to a database of existing powder patterns associated with published papers and produces a rank ordered according to their similarity score. The program returns the digital object identifier and full reference of top-ranked papers together with a stack plot of the user data alongside the top-five database entries. The paper describes the approach and explores successes and challenges.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry

Structure determination workloads in neutron diffractometry are computationally expensive and routinely require several hours to many days to determine the structure of a material from its neutron diffraction patterns. The potential for machine learning models trained on simulated neutron scattering patterns to significantly speed up these tasks have been reported recently. However, the amount of simulated data needed to train these models grows exponentially with the number of structural parameters to be predicted and poses a significant computational challenge. To overcome this challenge, we introduce a novel batch-mode active learning (AL) policy that uses uncertainty sampling to simulate training data drawn from a probability distribution that prefers labelled examples about which the model is least certain. We confirm its efficacy in training the same models with ∼ 75% less training data while improving the accuracy. We then discuss the design of an efficient stream-based training workflow that uses this AL policy and present a performance study on two heterogeneous platforms to demonstrate that, compared with a conventional training workflow, the streaming workflow delivers ∼ 20% shorter training time without any loss of accuracy.

Wang, Tianle [Brookhaven National Laboratory (BNL)↗
DOE OSTI2022

Occasions for laughter and dementia risk: Findings from a six‐year cohort study

Aim Currently, there is little evidence on the relationship between laughter and the risk of dementia, and since laughter is mainly a social behavior, we aimed to examine the association between various occasions for laughter and the risk of dementia in Japanese older adults. Methods We draw upon 6‐year follow‐up data from the Japan Gerontological Evaluation Study, including 12 165 independent older adults aged 65 years or over. Occasions for laughter were assessed using a questionnaire, while dementia was diagnosed using the standardized dementia scale of the long‐term care insurance system in Japan. Cox proportional hazards models were estimated, yielding hazard ratios and 95% confidence intervals (CIs). Results The multivariable hazard ratio of dementia incidence for all participants in the groups for high versus low variety of occasions for laughter was 0.84 (95% CI: 0.72–0.98, P for trend <0.001). A greater variety of occasions for laughter was associated with a lower risk of dementia 0.78 (95% CI: 0.63–0.96, P for trend <0.001) among women, but was less pronounced for men, with significant associations only for the medium group. Laughing during conversations with friends, communicating with children or grandchildren, and listening to the radio were primarily associated with decreased risk. Conclusion A greater variety of laughter occasions in individual and social settings was associated with a reduced risk of dementia. Geriatr Gerontol Int 2022; 22: 392–398 .

Wang, Yu↗
DOE OSTI2018

Intersectoral strategies between health and education for preventing adolescent pregnancy in Chile: Findings from a qualitative study

Abstract Background In Chile, despite its steady decrease overall, adolescent pregnancy is concentrated in the most vulnerable population. Efforts in intersectoral collaboration between health and education to address the problem are being developed, but they have not been assessed. Objective To describe intersectoral strategies between health and education to address adolescent sexual and reproductive health, prevent adolescent pregnancy, and to explore adolescents’ and health professionals’ perceptions regarding those strategies. Design A qualitative ethnographic study was carried out in five municipalities in the Metropolitan Region of Chile. A sample of five key informants, 23 health professionals and 50 adolescents participated in a total of 38 semi‐structured interviews and five discussion groups. Results Two intersectoral strategies to respond to adolescents’ sexual and reproductive health needs were identified: (a) the “in‐and‐out” strategy, where health professionals provide health care mostly in health centres and carry out specific actions in schools and (b) the school‐based strategy in which health professionals carry out continuous actions in schools as part of the curriculum. The second is perceived as responding better to adolescents’ needs in sexual and reproductive health issues and in preventing adolescent pregnancy. Discussion The school‐based strategy, with the constant presence of health professionals and lack of bureaucratic procedures, facilitates adolescents to access sexual and reproductive health care. This strategy enables sexual and reproductive health to be understood as an integral dimension of adolescents’ lives, and it reinforces a holistic idea of health in which it is approached as a whole.

Obach, Alexandra↗
DOE OSTI2022

Finding new friends and revisiting old ones – how plant lipid droplets connect with other subcellular structures

Summary The number of described contact sites between different subcellular compartments and structures in eukaryotic cells has increased dramatically in recent years and, as such, has substantially reinforced the well‐known premise that these kinds of connections are essential for overall cellular organization and the proper functioning of cellular metabolic and signaling pathways. Here, we discuss contact sites involving plant lipid droplets (LDs), including LD‐endoplasmic reticulum (ER) connections that mediate the biogenesis of new LDs at the ER, LD‐peroxisome connections, that facilitate the degradation of LD‐stored triacylglycerols (TAGs), and the more recently discovered LD‐plasma membrane connections, which involve at least three novel proteins, but have a yet unknown physiological function(s).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

On-Sun Characterization of Microchannel Supercritical Carbon Dioxide Solar Thermal Receivers - Preliminary Findings

The design of, and preliminary on-sun tests on, an 8 cm x 8 cm microchannel supercritical carbon dioxide (sCO2) receiver is presented. The receiver has a laminated design, wherein sheets of Haynes 230 nickel superalloy are patterned and diffusion bonded to form microscale flow passages. The microscale pattern is in the form of square pins with width and height of 500 μm and 200 μm respectively. The pins are arranged in an in-line pattern with respect to the flow direction. The longitudinal and transverse pitch ratios of the micro pin fins are identical and equal to two times the side width of the pillar. A sCO2 test facility is developed with the ability to supply sCO2 at 200 bar pressure and at temperatures between 300-500°C to the receiver inlet. The sCO2 facility is coupled to a seven meter diameter parabolic dish with a 25 kW rating and a concentration ratio of about 800. On-sun tests are performed at a receiver inlet pressure of 150 bar and a receiver inlet temperature between 110-130°C. Receiver and thermal efficiencies in excess of 0.91 and 0.96 respectively for the incident heat flux ranging from 8 to 80 W/cm2, and average surface temperatures ranging from 150-550°C are obtained in these experiments.

14 SOLAR ENERGY↗
DOE OSTI2018

A Cobalamin Activity-Based Probe Enables Microbial Cell Growth and Finds New Cobalamin-Protein Interactions across Domains

Understanding the factors that regulate microbe function and microbial community assembly, function, and fitness is a grand challenge. A critical factor and an important enzyme cofactor and regulator of gene expression is cobalamin (vitamin B 12 ). Our knowledge of the roles of vitamin B 12 is limited, because technologies that enable in situ characterization of microbial metabolism and gene regulation with minimal impact on cell physiology are needed. To meet this need, here we show that a synthetic probe mimic of B 12 supports the growth of B 12 -auxotrophic bacteria and archaea. We demonstrate that a B 12 activity-based probe (B 12 -ABP) is actively transported into Escherichia coli cells and converted to adenosyl-B 12 -ABP akin to native B 12 . Identification of the proteins that bind the B 12 -ABP in vivo in E. coli, a Rhodobacteraceae sp. and Haloferax volcanii, demonstrate the specificity for known and novel B 12 protein targets. The B 12 -ABP also regulates the B 12 dependent RNA riboswitch btuB and the transcription factor EutR. Our results demonstrate a new approach to gain knowledge about the role of B 12 in microbe functions. Our approach provides a powerful nondisruptive tool to analyze B 12 interactions in living cells and can be used to discover the role of B 12 in diverse microbial systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Finding a New B1-Type Phase in Single Crystals of Fe–Al and Fe–Ga Soft Magnetic Alloys

The atomic structure of Fe–Al (7 and 9 at % Al) and Fe–Ga (18 at % Ga) alloys is studied by X‑ray diffraction using a laboratory four-circle diffractometer. After refining annealing, single-crystal alloy samples were annealed in the ferromagnetic state (T < T{sub C}). One sample of the Fe–18 at % Ga alloy, after short holding in the paramagnetic state (T > T{sub C}), was quenched in room temperature water. Earlier, the authors reported on the peculiarities of the ordering of alloying atoms in B2 and D0{sub 3} phase structures in quenched and annealed samples of these alloys. Here, we present and discuss the results of our observations in these alloys of a new phase with a face-centered cubic (fcc) lattice (B1-type structure with NaCl prototype and unit cell parameter ~5.2 nm). The fcc phase appears in the Fe–Al alloy as the aluminum concentration increases from 7 to 9 at %; it is observed in the Fe–18 at % Ga alloy, and its volume fraction increases after annealing in the ferromagnetic state in comparison with a quenched alloy sample. In these alloys (9 at % Al) and (18 at % Ga), different ways of embedding fcc crystals in the bcc phase of single crystals are realized; i.e., the axes of the fcc lattice are directed in four different ways relative to the axes of the bcc lattice.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Finding MIDDLE Ground: Scalable and Secure Distributed Learning

Edge computing methods allow devices to efficiently train a high-performing, robust, and personalized model for predictive tasks. However, these methods succumb to privacy and scalability concerns such as adversarial data recovery and expensive model communication. Furthermore, edge computing methods unrealistically assume that all devices train an identical model. In practice, edge devices have varying computational and memory constraints which may not allow certain devices to have the space or speed to train a specific model. To overcome these issues, we propose MIDDLE: a model independent distributed learning algorithm which allows heterogeneous edge devices to assist each other’s training while communicating only non-sensitive information. MIDDLE unlocks the ability for edge devices, regardless of computational or memory constraints, to assist each other even with completely different model architectures. Furthermore, MIDDLE does not require model or gradient communication which greatly reduces communication size and time. We prove that MIDDLE attains the optimal convergence rate O(1/sqrt(TM)) of stochastic gradient descent for convex and non-convex smooth optimization (for total iterations T and batch size M). Finally, our experimental results demonstrate that MIDDLE (even in non-IID data settings) attains robust and high-performing models without model or gradient communication.

Bornstein, Marc I.↗
DOE OSTI2020

DelPlasmid: Finding Plasmids with Deep Learning and Machine Learning v1

Extrachromosomal genetic elements or plasmids are an important driver of DNA exchange and genetic innovation in prokaryotes. The success of plasmids has been attributed to their independent replication from host cell chromosomes and their frequent self-transfer. In recent years, the Department of Energy has recognized the importance of plasmid DNA to the viability and success of microbial communities and JGI has responded with funding to support the research and development of new methods and tools for the efficient sequencing of plasmid DNA. LBNL #2019-037 is responsive to this call.

Andreopoulos, William↗
DOE OSTI2026

fpm-find v0.1.0

This program searches the fortran-lang community's package index (https://github.com/fortran-lang/webpage/blob/main/data/package_index.yml). The program is designed for use as a Fortran Package Manager (fpm) plugin.

Rouson, Damian [Lawrence Berkeley National Laborat↗
DOE OSTI2024

Neural Networks to Find the Optimal Forcing for Offsetting the Anthropogenic Climate Change Effects

Abstract Of great relevance to climate engineering is the systematic relationship between the radiative forcing to the climate system and the response of the system, a relationship often represented by the linear response function (LRF) of the system. However, estimating the LRF often becomes an ill-posed inverse problem due to high-dimensionality and nonunique relationships between the forcing and response. Recent advances in machine learning make it possible to address the ill-posed inverse problem through regularization and sparse system fitting. Here, we develop a convolutional neural network (CNN) for regularized inversion. The CNN is trained using the surface temperature responses from a set of Green’s function perturbation experiments as imagery input data together with data sample densification. The resulting CNN model can infer the forcing pattern responsible for the temperature response from out-of-sample forcing scenarios. This promising proof of concept suggests a possible strategy for estimating the optimal forcing to negate certain undesirable effects of climate change. The limited success of this effort underscores the challenges of solving an inverse problem for a climate system with inherent nonlinearity. Significance Statement Predicting the climate response for a given climate forcing is a direct problem, while inferring the forcing for a given desired climate response is often an inverse, ill-posed, problem, posing a new challenge to the climate community. This study makes the first attempt to infer the radiative forcing for a given target pattern of global surface temperature response using a deep learning approach. The resulting deeply trained convolutional neural network inversion model shows promise in capturing the forcing pattern corresponding to a given surface temperature response, with a significant implication on the design of an optimal solar radiation management strategy for curbing global warming. This study also highlights the technical challenges that future research should prioritize in seeking feasible solutions to the inverse climate problem.

Ren, Huiying↗