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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Reduced solar-induced chlorophyll fluorescence from GOME-2 during Amazon drought caused by dataset artifacts

In a recent study, Yang et al. reported a decrease in solar-induced chlorophyll fluorescence (SIF) during 2015/2016 El Niño event albeit the increase in enhanced vegetation index (EVI). They interpreted the reduced SIF as a signal of reduced ecosystem photosynthesis. However, we argue that the reduced SIF during 2015/2016 is caused by a decreasing trend of SIF due to sensor degradation and the satellite overpass time is critical for drought impact assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Testing for changes in biomass dynamics in large‐scale forest datasets

Abstract Tropical forest responses to climate and atmospheric change are critical to the future of the global carbon budget. Recent studies have reported increases in estimated above‐ground biomass (EAGB) stocks, productivity, and mortality in old‐growth tropical forests. These increases could reflect a shift in forest functioning due to global change and/or long‐lasting recovery from past disturbance. We introduce a novel approach to disentangle the relative contributions of these mechanisms by decomposing changes in whole‐plot biomass fluxes into contributions from changes in the distribution of gap‐successional stages and changes in fluxes for a given stage. Using 30 years of forest dynamic data at Barro Colorado Island, Panama, we investigated temporal variation in EAGB fluxes as a function of initial EAGB (EAGB i ) in 10 × 10 m quadrats. Productivity and mortality fluxes both increased strongly with initial quadrat EAGB. The distribution of EAGB (and thus EAGB i ) across quadrats hardly varied over 30 years (and seven censuses). EAGB fluxes as a function of EAGB i varied largely and significantly among census intervals, with notably higher productivity in 1985–1990 associated with recovery from the 1982–1983 El Niño event. Variation in whole‐plot fluxes among census intervals was explained overwhelmingly by variation in fluxes as a function of EAGB i , with essentially no contribution from changes in EAGB i distributions. The high observed temporal variation in productivity and mortality suggests that this forest is very sensitive to climate variability. There was no consistent long‐term trend in productivity, mortality, or biomass in this forest over 30 years, although the temporal variability in productivity and mortality was so strong that it could well mask a substantial trend. Accurate prediction of future tropical forest carbon budgets will require accounting for disturbance‐recovery dynamics and understanding temporal variability in productivity and mortality.

Rutishauser, Ervan↗
DOE OSTI2018

Russulaceae: a new genomic dataset to study ecosystem function and evolutionary diversification of ectomycorrhizal fungi with their tree associates

The family Russulaceae is considered an iconic lineage of mostly mushroom-forming basidiomycetes due to their importance as edible mushrooms in many parts of the world, and their ubiquity as ectomycorrhizal symbionts in both temperate and tropical forested biomes. Although much research has been focused on this group, a comprehensive or cohesive synthesis by which to understand the functional diversity of the group has yet to develop. Interest in ectomycorrhizal fungi, of which Russulaceae is a key lineage, is prodigious due to the important roles they play as plant root mutualists in ecosystem functioning, global carbon sequestration, and a potential role in technology development toward environmental sustainability. As one of the most species-diverse ectomycorrhizal lineages, the Russulaceae has recently been the focus of a dense sampling and genome sequencing initiative with the Joint Genome Institute aimed at untangling their functional roles and testing whether functional niche specialization exists for independent lineages of ectomycorrhizal fungi. In this paper, we present a review of important studies on this group to contextualize what we know about its members' evolutionary history and ecosystem functions, as well as to generate hypotheses establishing the Russulaceae as a valuable experimental system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

TU-H-CAMPUS-JeP2-03: Machine-Learning-Based Delineation Framework of GTV Regions of Solid and Ground Glass Opacity Lung Tumors at Datasets of Planning CT and PET/CT Images

Purpose: In radiation treatment planning, delineation of gross tumor volume (GTV) is very important, because the GTVs affect the accuracies of radiation therapy procedure. To assist radiation oncologists in the delineation of GTV regions while treatment planning for lung cancer, we have proposed a machine-learning-based delineation framework of GTV regions of solid and ground glass opacity (GGO) lung tumors following by optimum contour selection (OCS) method. Methods: Our basic idea was to feed voxel-based image features around GTV contours determined by radiation oncologists into a machine learning classifier in the training step, after which the classifier produced the degree of GTV for each voxel in the testing step. Ten data sets of planning CT and PET/CT images were selected for this study. The support vector machine (SVM), which learned voxel-based features which include voxel value and magnitudes of image gradient vector that obtained from each voxel in the planning CT and PET/CT images, extracted initial GTV regions. The final GTV regions were determined using the OCS method that was able to select a global optimum object contour based on multiple active delineations with a level set method around the GTV. To evaluate the results of proposed framework for ten cases (solid:6, GGO:4), we used the three-dimensional Dice similarity coefficient (DSC), which denoted the degree of region similarity between the GTVs delineated by radiation oncologists and the proposed framework. Results: The proposed method achieved an average three-dimensional DSC of 0.81 for ten lung cancer patients, while a standardized uptake value-based method segmented GTV regions with the DSC of 0.43. The average DSCs for solid and GGO were 0.84 and 0.76, respectively, obtained by the proposed framework. Conclusion: The proposed framework with the support vector machine may be useful for assisting radiation oncologists in delineating solid and GGO lung tumors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Constraints on lepton number violation with the 2 tonne · year CUORE dataset

Matter-antimatter asymmetry underlines the incompleteness of the current understanding of particle physics. Neutrinoless double-beta decay (0νββ) may help explain this asymmetry while unveiling the Majorana nature of the neutrino. The CUORE (Cryogenic Underground Observatory for Rare Events) experiment searches for 0νββ of 130 Te using a tonne-scale cryogenic calorimeter operated at milli-kelvin temperatures. We report no evidence of 0νββ and place a lower limit on the half-life of T 1/2 > 3.5 × 10 25 years (90% credibility interval) with over 2 tonne·years of TeO 2 exposure. Finally, the tools and techniques developed for this result and the 5-year stable operation of nearly 1000 detectors demonstrate crucial infrastructure for future-generation experiments capable of searching for 0νββ across multiple isotopes.

Adams, D. Q. [Department of Physics and Astronomy,↗
DOE OSTI2022

Characterization of the correlated background for a sterile neutrino search using the first dataset of the JSNS$^2$ experiment

JSNS 2 (J-PARC Sterile Neutrino Search at J-PARC Spallation Neutron Source) is an experiment that is searching for sterile neutrinos via the observation of ν¯ μ → ν¯ e appearance oscillations using muon decay-at-rest neutrinos. Before dedicated data taking in the first-half of 2021, we performed a commissioning run for 10 days in June 2020. Using the data obtained in this commissioning run, in this paper, we present an estimate of the correlated background which imitates the ν¯ e signal in a sterile neutrino search. In addition, in order to demonstrate future prospects of the JSNS 2 experiment, possible pulse shape discrimination improvements towards reducing cosmic ray induced fast neutron background are described.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A search for $$\upmu ^+ \rightarrow \textrm{e}^+ \upgamma $$ with the first dataset of the MEG II experiment

Abstract The MEG II experiment, based at the Paul Scherrer Institut in Switzerland, reports the result of a search for the decay$$\upmu ^+ \rightarrow {\textrm{e}}^+ \upgamma $$ μ + → e + γ from data taken in the first physics run in 2021. No excess of events over the expected background is observed, yielding an upper limit on the branching ratio of$${\mathcal {B}} (\upmu ^+ \rightarrow {\textrm{e}}^+ \upgamma ) < 7.5 \times 10^{-13}$$ B ( μ + → e + γ ) < 7.5 × 10 - 13 (90% CL). The combination of this result and the limit obtained by MEG gives$${\mathcal {B}} (\upmu ^+ \rightarrow {\textrm{e}}^+ \upgamma ) < 3.1 \times 10^{-13}$$ B ( μ + → e + γ ) < 3.1 × 10 - 13 (90% CL), which is the most stringent limit to date. A ten-fold larger sample of data is being collected during the years 2022–2023, and data-taking will continue in the coming years.

Physics↗
DOE OSTI2022

Stealthy Cyber Anomaly Detection On Large Noisy Multi-material 3D Printer Datasets Using Probabilistic Models

As Additive Layer Manufacturing (ALM) becomes pervasive in industry, its applications in safety critical component manufacturing are being explored and adopted. However, ALM's reliance on embedded computing renders it vulnerable to tampering through cyber-attacks. Sensor instrumentation of ALM devices allows for rigorous process and security monitoring, but also results in a massive volume of noisy data for each run. As such, in-situ, near-real-time anomaly detection is very challenging. The ideal algorithm for this context is simple, computationally efficient, minimizes false positives, and is accurate enough to resolve small deviations. In this paper, we present a probabilistic-model-based approach to address this challenge. To test our approach, we analyze current measurements from a polymer composite 3D printer during emulated tampering attacks. Our results show that our approach can consistently and efficiently locate small changes in the presence of substantial operational noise.

Yoginath, Srikanth↗
DOE OSTI2024

Avian Activity Classification Using Recurrent Networks to Fuse Videos with Metadata on Imbalanced Datasets

Activity classification plays a crucial role in various real-life scenarios involving both humans and animals. There is an increasing need for precise activity classification focused on avian-solar interactions, as the usage of solar energy facilities, such as photovoltaic array power stations, has been observed to impact bird species richness, behavior, and activity. However, there has been no work to develop an automated system to monitor and classify these avian-solar interactions. All current methods rely on human observers, which is time and human resources costly and subject to errors related to searcher efficiency. With the recent success of Deep Learning models in activity classification problems, this paper develops a recurrent neural network-based model to automatically classify six avian activities around solar energy facilities. Our proposed model integrates critical feature engineering metadata with video frame data, enabling improved learning and more accurate activity classification. Furthermore, we address the challenge of data imbalance during training and demonstrate the efficacy of our model in detecting and classifying different activities within video tracks. Additionally, we analyze the saliency/backpropagation map of the trained proposed model and validate its decision-making rationale.

Avian activity classification; bidirectional LSTM;↗
DOE OSTI2025

A Survey on Error-Bounded Lossy Compression for Scientific Datasets

Error-bounded lossy compression has been effective in significantly reducing the data storage/transfer burden while preserving the reconstructed data fidelity very well. Many error-bounded lossy compressors have been developed for a wide range of parallel and distributed use cases for years. They are designed with distinct compression models and principles, such that each of them features particular pros and cons. In this article, we provide a comprehensive survey of emerging error-bounded lossy compression techniques. The key contribution is fourfold. (1) We summarize a novel taxonomy of lossy compression into six classic models. (2) We provide a comprehensive survey of 10 commonly used compression components/modules. (3) We summarized pros and cons of 47 state-of-the-art lossy compressors and present how state-of-the-art compressors are designed based on different compression techniques. (4) We discuss how customized compressors are designed for specific scientific applications and use-cases. We believe this survey is useful to multiple communities including scientific applications, high-performance computing, lossy compression, and big data.

Error-Bounded Lossy Compression↗
DOE OSTI2026

The LCLStream Ecosystem for Multi-Institutional Dataset Exploration

We describe a new end-to-end experimental data streaming framework designed from the ground up to support new types of applications – AI training, extremely high-rate X-ray time-of-flight analysis, crystal structure determination with distributed processing, and custom data science applications and visualizers yet to be created. Throughout, we use design choices merging cloud microservices with traditional HPC batch execution models for security and flexibility. This project makes a unique contribution to the DOE Integrated Research Infrastructure (IRI) landscape. By creating a flexible, API-driven data request service, we address a significant need for high-speed data streaming sources for the X-ray science data analysis community. With the combination of data request API, mutual authentication web security framework, job queue system, high-rate data buffer, and complementary nature to facility infrastructure, the LCLStreamer framework has prototyped and implemented several new paradigms critical for future generation experiments.

Rogers, David [ORNL] (ORCID:0000000251871768)↗