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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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We developed a methodology to correlate catalyst nanoparticle size and shape with reactivity and explain the disparity among literature experimental data. The method uses GCN and machine learning models as a proxy of structure sensitivity.
Graph convolution incorporates topological information of a graph into learning. Message passing corresponds to traversal of a local neighborhood in classical graph algorithms. We show that incorporating additional global structures, such as shortest paths, through distance preserving embedding can improve performance. Our approach, Gavotte, significantly improves the performance of a range of popular graph neu-ral networks such as GCN, GA T,Graph SAGE, and GCNII for transductive learning. Gavotte also improves the performance of graph neural networks for full-supervised tasks, albeit to a smaller degree. As high-quality embeddings are generated by Gavotte as a by-product, we leverage clustering algorithms on these embed dings to augment the training set and introduce Gavotte+. Our results of Gavotte+ on datasets with very few labels demonstrate the advantage of augmenting graph convolution with distance preserving embedding.
The accuracy of distribution system state estimation may be significantly impacted by the existence of bad measure-ments and unexpected topology errors. This paper proposes a data-driven Graph Convolutional Network (GCN) for anomaly detection, including bad measurements and topology change events. Compared to many existing machine learning approaches, the proposed approach embeds both spatial-temporal measure-ment correlations, which allows us to detect and distinguish different anomalies. Numerical results carried out on the IEEE 37-node system demonstrate that the proposed-based method can obtain high accuracy in detecting bad data and topology changes as compared to other approaches, even in the presence of high PV penetrations.
This paper presents a 1-D convolutional and graph convolutional networks for fault detection in microgrids. The combination of 1-D convolutional neural networks (1D-CNN) and graph convolutional networks (GCN) helps extract both spatial-temporal correlations from the voltage measurements in microgrids. The fault detection scheme includes fault event detection, fault type and phase classification, and fault location. There are five neural network model training to handle these tasks. Transfer learning and fine-tuning are applied to reduce training efforts. The combined 1-D convolutional and graph convolutional networks (1D-CGCN) is compared with the traditional ANN structure on the Potsdam 13-bus microgrid dataset. The accuracy of 99.5%, 98.4%, 99.2%, and 95.5% are achieved in fault event detection, fault type classification, fault phase identification, and fault location respectively. The detailed confusion matrices of fault type and fault phase classification are provided for validation.
Power distribution systems are increasingly integrating heterogeneous sensors with varying data reporting rates and types, which pose challenges to achieving observability at the desired temporal resolution of distribution system state estimation (DSSE). Multisensor failures caused by extreme events exacerbate these issues, introducing substantial uncertainties into DSSE. This article proposes a novel solution to these challenges by ensuring high-resolution system observability despite heterogeneous data sources and multisensor failures. First, a deep learning architecture combining long short-term memory (LSTM) and graph convolutional network (GCN) is employed to synchronize meters with different reporting rates, aiming to achieve system observability. A random-walk-model-based approach is introduced to generate pseudo-measurements while properly characterizing their uncertainties under multisensor failures. Finally, a disaster-risk-informed observability metric (RiOM) is defined to quantify the uncertainty associated with state estimation results. The proposed framework offers deeper insights into the system observability on the fly compared with conventional analysis. The effectiveness of the framework is demonstrated on an IEEE standard test case and a large-scale real-world distribution feeder in mid-Minnesota in the U.S.
Grid emergency voltage control (GEVC) is paramount in electric power systems to improve voltage stability and prevent cascading outages and blackouts in case of contingencies. While most deep reinforcement learning (DRL)-based paradigms perform single agents in a static environment, real-world agents for GEVC are expected to cooperate in a dynamically shifting grid. Moreover, due to high uncertainties from combinatory natures of various contingencies and load consumption, along with the complexity of dynamic grid operation, the data efficiency and control performance of the existing DRL-based methods are challenged. To address these limitations, we propose a multi-agent graph-attention (GATT)-based DRL algorithm for GEVC in multi-area power systems. Here, we develop graph convolutional network (GCN)-based agents for feature representation of the graph-structured voltages to improve the decision accuracy in a data-efficient manner. Furthermore, a cutting-edge attention mechanism concentrates on effective information sharing among multiple agents, synergizing different-sized subnetworks in the grid for cooperative learning. We address several key challenges in the existing DRL-based GEVC approaches, including low scalability and poor stability against high uncertainties. Test results in the IEEE benchmark system verify the advantages of the proposed method over several recent multi-agent DRL-based algorithms.
The code ingests Zeek logs derived from network packet captures and goes through data preprocessing before it gets passed into a K-Means model that labels each device as either a client or server. Graph Convolutional Network (GCN) model is used to obtain the embeddings to represent the features in lower dimension. Last, K-means cluster analysis is used to cluster the embeddings for each class.
This dataset contains environmental and soil measurements from 18 different locations across the globe including the SPRUCE experiment site and multiple sampling depths, with 17 of these locations having samples processed between 2012-2013 and one location (SPRUCE) collected in 2021 and processed in 2022. Environmental measurements include: mean annual temperature, mean annual precipitation, and 30-day presampling temperature. Soil physicochemical measurements include: particle size analysis (PSA), pH, gravimetric moisture content (GMC), bulk soil carbon (C) and nitrogen (N), total organic C and N, C:N ratio, and dissolved organic carbon (DOC). Soil biological measurements include: microbial biomass carbon (MBC) measured through chloroform fumigation extraction (CFE), gene copy numbers (GCN) of bacteria, fungi, and archaea measured through quantitative polymerase chain reaction (qPCR), DNA yield measured through Nanodrop spectrophotometry, and phospholipid fatty acids (PLFA) of bacteria and fungi measured through PLFA analysis. This data set contains one file in comma separate (*.csv) format.
Human Interferon (IFN) alpha, beta, and gamma participate in the body's natural immune response to lethal virus infection and disease.The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013 - 2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013-2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. Herein, PNNL sub-projects provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open-access to viral Omics datasets and project lifecycle metadata. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Transcriptomics (T) dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific Human Interferon (IFN), interferon alpha (IFNα), interferon beta (IFNβ), and/or interferon gamma (IFNγ) stimulated response to an experimental virus infection treatment study. Host sample types include cerebellum ["CB", BTO:0000232], cortical neurons ["CN", BTO:0004102], cortex ["CT", BTO:0000233], dendritic cells ["DC", BTO:0002042], granule cell neurons ["GCN", BTO:0003393], lymph node ["LN", BTO:0000784], and serum ["SE", BTO:0001239] from mouse (Mus musculus) tissue collections.
West Nile virus (WNV) is classified as a Category B priority pathogen (mosquito-borne Flavivirus) by the National Institute of Allergy and Infectious Diseases (NIAID), and are known to cause severe infections in humans where lethal host-associated mechanisms are not clearly defined. The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013 - 2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013-2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. Herein, PNNL sub-projects provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open-access to viral Omics datasets and project lifecycle metadata. Secondary host-pathogen viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics (P), metabolomics (M), lipidomics (L), and transcriptomics (T) dataset downloads each have a direct relationship to a primary sample submission corresponding a specific West Nile virus [NCBITAXON:11082] (WNV-NY99 382) experimental infection study. Host sample types include cerebellum ["CB", BTO:0000232], cortical neurons ["CN", BTO:0004102], cortex ["CT", BTO:0000233], dendritic cells ["DC", BTO:0002042], granule cell neurons ["GCN", BTO:0003393], lymph node ["LN", BTO:0000784], and serum ["SE", BTO:0001239] from mouse (Mus musculus) tissue collections.
Among multimessenger observations of the next Galactic core-collapse supernova, Super-Kamiokande (SK) plays a critical role in detecting the emitted supernova neutrinos, determining the direction to the supernova (SN), and notifying the astronomical community of these observations in advance of the optical signal. In 2022, SK has increased the gadolinium dissolved in its water target (SK-Gd) and has achieved a Gd concentration of 0.033%, resulting in enhanced neutron detection capability, which in turn enables more accurate determination of the supernova direction. Accordingly, SK-Gd’s real-time supernova monitoring system has been upgraded. SK_SN Notice, a warning system that works together with this monitoring system, was released on 2021 December 13, and is available through GCN Notices. When the monitoring system detects an SN-like burst of events, SK_SN Notice will automatically distribute an alarm with the reconstructed direction to the supernova candidate within a few minutes. In this paper, we present a systematic study of SK-Gd’s response to a simulated Galactic SN. Assuming a supernova situated at 10 kpc, neutrino fluxes from six supernova models are used to characterize SK-Gd’s pointing accuracy using the same tools as the online monitoring system. The pointing accuracy is found to vary from 3° to 7° depending on the models. However, if the supernova is closer than 10 kpc, SK_SN Notice can issue an alarm with three-degree accuracy, which will benefit follow-up observations by optical telescopes with large fields of view.
The origin of the short (<2 s) class of gamma-ray bursts (GRBs) is finally becoming clear after decades of search. The first one localized to a few arcseconds accuracy, GRB 050509B, was found to have a highly probable association with a nearby (z = 0.225) elliptical galaxy. A second one with arcsecond localization, GRB 050709, was also associated with a low redshift (z = 0.16) galaxy. We report here the detection of short GRB 050724 with remarkable properties; in particular, it has low energy gamma-ray emission that lasts for 100 s after the main short pulse, strong early X-ray afterglow, and an unusual lightcurve that rebrightens at 3x10(exp 4) s. A position on the sky accurate to 9 arcsec was determined and provided as a GCN alert to ground-based telescopes within 80 s. A subsequent high-resolution X-ray image provided a sub-arcsec position coincident with ground-based optical and radio observations of the afterglow. Like GRB 050509B, this burst is located off-center in an elliptical galaxy. The energy output of the GRB at the host distance of z = 0.258 is 2-3 orders of magnitude less than for long bursts. The low level of star formation in such galaxies is strong evidence against a collapsar or hypernova origin like that associated with long GRBs. Based on these new data, it is highly probable that short GRBs are produced by the coalescence of orbiting neutron stars (NSs) or black holes (BHs), with some evidence for a NS-BH merger in this burst.
The Swift gamma-ray burst explorer was launched on Nov. 20,2004 from Cape Canaveral, Florida. The first instrument onboard became fully operational less than a month later. Since that time the Burst Alert Telescope (BAT) on Swift has detected more than one hundred gamma-ray bursts (GRBs), most of which have also been observed within two minutes by the Swift narrow-field instruments: the X-Ray Telescope (XRT) and the Ultra-Violet and Optical Telescope (UVOT). Swift trigger notices are distributed worldwide within seconds of the trigger through the Gamma-ray burst Coordinates Network (GCN) and a substantial fraction of GRBs have been followed up by ground and space-based telescopes, ranging in wavelength from radio to TeV. Results have included the first rapid localization of a short GRB and further validation of the theory that short and long bursts have different origins; detailed observations of the short-term power-law decay of burst afterglows leading to an improved understanding of the fireball model; and detection of the most distant GRB ever found. Swift is also a sensitive X-ray observatory with capabilities to monitor galactic and extragalactic transients on a daily basis, carry out the first all-sky hard X-ray survey since HEAO-1, and study in detail the spectra of X-ray transients. significant results, both in GRB science and in the search for and study of hard X-ray sources. In this talk I will provide a broad overview of the Swift mission and its most significant results, both in GRB science and in the search for and study of hard X-ray sources.
As part of the automated response to a new gamma-ray burst (GRB), the Ultraviolet and Optical Telescope (UVOT) instrument on Swift starts a 200-second exposure with the V filter within approximately 100 seconds of the BAT burst trigger. The instrument searches for sources in a 8' x 8' region, and sends the list of sources and a 160" x 160" sub-image centered on the burst position to the ground via Tracking and Data Relay Satellite System (TDRSS). These raw products and additional products calculated on the ground are then distributed through the GCN within a few minutes of the trigger. We describe the sensitivity of these data for detecting afterglows, summarize current results, and outline plans for rapidly distributing future detections.
The Swift gamma-ray burst explorer was launched on Nov. 20, 2004 from Cape Canaveral, Florida. The first instrument onboard became fully operational less than a month later. Since that time the Burst Alert Telescope (BAT) on Swift has detected more than 150 gamma-ray bursts (GRBs), most of which have also been observed within two minutes by the Swift narrow-field instruments: the X-Ray Telescope (XRT) and the Ultra-Violet and Optical Telescope (UVOT). Swift trigger notices are distributed worldwide within seconds of the trigger through the Gamma-ray burst Coordinates Network (GCN) and a substantial fraction of GRBs have been followed up by ground and space-based telescopes, ranging in wavelength from radio to TeV. Results have included the first rapid localization of a short GRB and further validation of the theory that short and long bursts have different origins; detailed observations of the power-law decay of burst afterglows leading to an improved understanding of the fireball and afterglow models; and detection of the most distant GRB ever found. Swift is also a sensitive X-ray observatory with capabilities to monitor galactic and extragalactic transients on a daily basis, carry out the first all-sky hard X-ray survey since HEAO-1, and study in detail the spectra of X-ray transients. The talk will emphasize the connection between Swift/BAT GRB observations and source monitoring and TeV observations.
We obtained 98 R-band and 18 B, r', i' images of the optical afterglow of GRB 060526 (z = 3.21) with the MDM 1.3 m, 2.4 m, and the PROMPT telescopes at CTIO over the five nights following the burst trigger. Combining these data with other optical observations reported in GCN and the Swift XRT observations, we compare the optical and X-ray afterglow light curves of GRB 060526. Both the optical and X-ray afterglow light curves show rich features, such as flares and breaks. The densely sampled optical observations provide very good coverage at T > 10(exp 4) s. We observed a break at 2.4 x 10(exp 5) sin the optical afterglow light curve. Compared with the X-ray afterglow light curve, the break is consistent with an achromatic break supporting the beaming models of GRBs. However, the prebreak and postbreak temporal decay slopes are difficult to explain in simple afterglow models. We estimated a jet angle of theta(sub j) approx. 7deg and a prompt emission size of R(sub prompt) approx. 2 x 10(exp 14) cm. In addition, we detected several optical flares with amplitudes of (Delta)m approx. 0.2,0.6, and 0.2 mag. The X-ray afterglows detected by Swift have shown complicated decay patterns. Recently, many well-sampled optical afterglows also show decays with flares and multiple breaks. GRB 060526 provides an additional case of such a complex, well-observed optical afterglow. The accumulated well-sampled afterglows indicate that most of the optical afterglows are complex.
With 3 years of on-orbit operations (launched in Nov 2004), Swift has detected over 300 gammaray bursts (GRBs). The unique combination of quick and accurate position determinations by the BAT instrument, fast autonomous spacecraft slewing, and multi-band instrumentation (XRT and UVOT) has allowed Swift to accumulate a long list of discoveries about GRBs. These positions are also available to the ground follow-up community (via TDRSS and GCN) within 15-30 sec. A summary of these discoveries will be given (e.g. long and short GRB host associations, SN associations, flaring and on-going activity in the central engine). The Swift spacecraft and instruments are in fine working order with no signs of performance degradation. With an expected orbital lifetime well past 2020, the overlap with GLAST (launch in mid-2008) will yield greater than 30 GRBs/year with observations by both missions. This will provide an unprecedented wavelength coverage from optical to 100 GeV. The coordination of pointing Swift with GLAST will be described.
The Fermi Gamma-ray Burst Monitor (GBM) is an all-sky monitoring instrument sensitive to energies from 8 keV to 40 MeV. Over the past 8 years of operation, the GBM has detected over 240 gamma-ray bursts per year and provided timely GCN notices with localization to few-degree accuracy for follow-up observations. In addition to GRBs, Galactic transients, solar flares, and terrestrial gamma-ray flashes have also been observed. In recent years we have also been searching the continuous GBM data for electromagnetic counterpart to astrophysical neutrinos and gravitational wave events, as these are believed to be associated with gamma-ray bursts. With continuous data downlink every few hours and a temporal resolution of 2 microseconds, GBM is well suited for observing transients and supporting EM follow-up in the era of multi-messenger astronomy.