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The author has identified the following significant results. Of significance are the continued detection and analysis of such short-lived events as forest fires, oil spills, vegetation damage, volcanoes, storm ridges, and earthquakes.
The author has identified the following significant results. Significant results are the continued detection of short-lived events. The following have been detected and analyzed: forest fires, oil spills, vegetation damage, volcanoes, storm ridges, and earthquakes. It is hoped that the Mississippi River flood scenes will arrive shortly and then floods be added to the list of identified short-lived events.
There are no author-identified significant results in this report.
The author has identified the following significant results. Forest fires, oil spills, vegetation damage, volcanoes, storm ridges, earthquakes, and floods have been detected and analyzed.
The author has identified the following significant results. Detection of short-lived events has continued. Forest fires, oil spills, vegetation damage, volcanoes, storm ridges, earthquakes, and floods have been detected and analyzed.
There are no author-identified significant results in this report.
The macroscopic properties of inverted V events measured by the Atmospheric Explorer-D satellite are described, along with the spatial distributions and the extents of individual events. The satellite carried 19 detectors, each comprising a cylindrical electrostatic analyzer followed by an electron multiplier. A total of 430 events was recorded, none of which occurred in cusp precipitation. The observed characteristics of the inverted V events are compared with particle distribution properties predicted by a double layer mechanism, an electrostatic shock model, extended field-aligned potential, and anomalous resistivity. Widespread, continuous electric fields were found parallel to the magnetic field lines, and anomalous resistivity was determined as the most favorable mechanism for producing the electron precipitation structure observed.
The observed textures and chemistry of Ca-Al-rich inclusions (CAIs) are presumed to be the culmination of a series of repeated heating and cooling events in the early history of the solar nebula. We have examined the effects of these heating/cooling cycles experimentally on a bulk composition representing an average Type B Ca-Al-rich inclusion composition. We have tested the effect of the nature of the starting material. Although the most recent and/or highest temperature event prior to incorporation into the parent body dominates the texture and chemistry of the CAI, prior events also affect the phase compositions and textures. We have determined that heating precursor grains to about 1275 C prior to the final melting event increases the likelihood of anorthite crystallization in subsequent higher temperature events and a prior high temperature even that produced dendritic melilite results in melilite that shows evidence of rapid crystallization in subsequent lower temperature events. Prior low temperature pre-crystallization events produce final ran products with pyroxene compositions similar to Type B Ca-Al-rich inclusions, and the glass (residual liquid) composition is more anorthitic than any other experiments to date. The addition of Pt powder to the starting material appears to enhance the ability of anorthite to nucleate from this composition.
The PAMELA (Payload for Antimatter Matter Exploration and Light-nuclei Astro-physics) spaceborne experiment was launched on 15 June 2006 and has been continuously collecting data since then. The apparatus measures electrons, positrons, protons, anti-protons and heavier nuclei from about 100 megaelectronvolts to several hundreds of gigaelectronvolts. The on-board instrumentation is built around a permanent magnet with a silicon microstrip tracker, providing charge and track detection information. During solar maximum conditions of solar cycle 24, PAMELA has been providing key information about solar energetic particles (SEPs) and their influence at Earth. We discuss here the recent 2012 March 7 SEP event with a brief comment on the subsequent Forbush decrease, registered by PAMELA. This event was also observed by Fermi/LAT (Large Area Telescope) exhibiting unprecedented time-extended gamma-ray emission (greater than 100 megaelectronvolts) lasting nearly 20 hours. We compare the derived accelerated ion population at the Sun with the ion population measured in space by PAMELA and discuss the implications for particle acceleration.
The 1982-83 El Nino/Southern Oscillation (ENSO) event was accompanied by the largest interannual variation in the Earth's rotation rate on record.
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Laser powder bed fusion is a mainstream additive manufacturing technology widely used to manufacture complex parts in prominent sectors, including aerospace, biomedical, and automotive industries. However, during the printing process, the presence of an unstable vapor depression can lead to a type of defect called keyhole porosity, which is detrimental to the part quality. In this study, we developed an effective approach to locally detect the generation of keyhole pores during the printing process by leveraging machine learning and a suite of optical and acoustic sensors. Simultaneous synchrotron x-ray imaging allows the direct visualization of pore generation events inside the sample, offering high-fidelity ground truth. A neural network model adopting SqueezeNet architecture using single-sensor data was developed to evaluate the fidelity of each sensor for capturing keyhole pore generation events. Our comparative study shows that the near infrared images gave the highest prediction accuracy, followed by 100 kHz and 20 kHz microphones, and the photodiode sensitive to processing laser wavelength had the lowest accuracy. Using a single sensor, over 90% prediction accuracy can be achieved with a temporal resolution as short as 0.1 ms. A data fusion scheme was also developed with features extracted using SqueezeNet neural network architecture and classification using different machine learning algorithms. Our work demonstrates the correlation between the characteristic optical and acoustic emissions and the keyhole oscillation behavior, and thereby provides strong physics support for the machine learning approach.
Although first recognized in 1971, the quasi-continuous record since 1979 of the appearance of coronal mass ejections (CMEs-perhaps more appropriately called coronal magnetic ejections) has resulted in a stable understanding of their properties, at least from a statistical viewpoint. These eruptions occur every few days during solar activity minimum and many times per day during maximum. They are believed to play an important role throughout the heliosphere in such diverse events as removing helicity from the corona; modulating the energetic particle environment in the inner heliosphere; causing severe geomagnetic storms at Earth and other magnetic bodies throughout the solar system; and controlling the galactic cosmic ray flux. It is therefore understandable that researchers have studied both individual events and the ensemble of CMEs observed over several solar cycles. We will present an overview of these statistics, some new recent observations, and a personal perspective on potential paths of future research.
In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single-trial multichannel EEG data from event-related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. Both the data and their decomposition are displayed using a new visualization tool, the "ERP image," that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Results show that ICA can separate artifactual, stimulus-locked, response-locked, and non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single-trial EEG records, (2) identification and segregation of stimulus- and response-locked EEG components, (3) examination of differences in single-trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between-subject component stability of ICA decomposition of single-trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data. Copyright 2001 Wiley-Liss, Inc.
Management of the nation’s power grid over the coming decades will need to factor multiple climate-driven threats to the power system that can be stressful or even detrimental to its operations. During disasters, grid operators suffer from cognitive overload where the electric grid is severely impacted by the weather, yet grid operators have limited awareness of these factors. Meanwhile, the electric grid is facing an explosion of data coming from a variety of sources which can enable the operator to evaluate the risks and develop mitigation strategies against hazardous events. In this work, we processed heterogeneous environmental and power grid data to learn and model grid behavior caused by extreme weather events. In this study, we focused on two weather-driven hazards, hurricanes and wildfires, which were analysed for the electric grid of Texas. We the used this data to train a Reinforcement Learning (RL) agent by analysing and predicting future behaviour of the grid during those hazard events. All these parts are incorporated in one framework to have a geophysically informed power simulators that can be used to train RL agents.
Events detection is a key challenge in power grid frequency disturbances analysis. Accurate detection of events is crucial for situational awareness of the power system. In this paper, we study the problem of events detection in power grid frequency disturbance analysis using synchrophasors data streams. Current events detection approaches for power grid rely on individual detection algorithm. This study integrates some of the existing detection algorithms using the concept of machine committee to develop improved detection approaches for grid disturbance analysis. Specifically, we propose two algorithms—an Event Detection Machine Committee (EDMC) algorithm and a Change-Point Detection Machine Committee (CPDMC) algorithm. Both algorithms use parallel architecture to fuse detection knowledge of its individual methods to arrive at an overall output. The EDMC algorithm combines five individual event detection methods, while the CPDMC algorithm combines two change-point detection methods. Each method performs the detection task separately. The overall output of each algorithm is then computed using a voting strategy. The proposed algorithms are evaluated using three case studies of actual power grid disturbances. Compared with the individual results of the various detection methods, we found that the EDMC algorithm is a better fit for analyzing synchrophasors data; it improves the detection accuracy; and it is suitable for practical scenarios.
Context. The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed, including the main science targets, Sgr A* and M 87, and various calibrators. Sixteen sources were AGN.Aims. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-Königl (BK) jet model. In particular, we aimed to study the signatures of jet acceleration and magnetic energy conversion.Methods. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components (1749+096, 1055+018, BL Lac, J0132–1654, J0006–0623, CTA 102, and 3C 454.3) and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2–86 GHz range. Combining these data into a multifrequency EHT+ data set, we studied the dependences of the VLBI core component flux density, size, and brightness temperature on the frequency measured in the AGN host frame (and hence on the distance from the central black hole), characterizing them with power law fits. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole.Results. Our observations spanning event horizon to parsec scales indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Only some of the discrepancies may be alleviated by tweaking the model parameters or the jet collimation profile. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations. For our sample, we estimate a general radial dependence of the Doppler factor δ ∝ r≤0.5. This interpretation is consistent with a magnetically accelerated sub-parsec jet. We also estimate a steep decrease of the magnetic field strength with radius B ∝ r−3, hinting at jet acceleration or efficient magnetic energy dissipation.Key words: techniques: interferometric / galaxies: active / galaxies: jets / galaxies: nuclei / quasars: general / quasars: supermassive black holes⋆⋆ Deceased.